Motivated by the #Metoo movement, we explore in this paper people’s perception of female bullying at workplace. We looked at #workplacebullying and found that 1) people were split between identifying the prevalence of workplace bullying against female and the view that such bullying simply does not exist and is a nuisance, 2) The tweets also showed the existence of psychological effects of cyberbullying, and 3) the tweets showed many intervention techniques that can minimize the effects of such bullying. We further explored the top three recurring hashtags mentioned under the #workplacebullying and found that the three top hashtags were #sexism, #feminism and #equality. Our results showed that the above hashtags represent the positive and negative approach to workplace bullying i.e. #feminism hashtag was mostly used by people who denied that workplace bullying against females exist while # sexism was mentioned as the prime cause by people who agree that such bullying exist. #equality overwhelmingly comprises of techniques to minimize workplace bullying against females.

Exploiting the linguistic knowledge of the source language for neural machine translation (NMT) has recently achieved impressive performance on many large-scale language pairs. However, since the Turkish→English machine translation task is low-resource and the source-side Turkish is morphologically-rich, there are limited resources of bilingual corpora and linguistic information available to further improve the NMT performance. Focusing on the above issues, we propose a multi-source NMT approach that models the word feature in parallel to external linguistic features by using two separate encoders to explicitly incorporate linguistic knowledge into the NMT model. We extend the word embedding layer of the knowledge-based encoder to accommodate for each word’s linguistic annotations in the context. Moreover, we share all parameters across encoders to enhance the representation ability of the NMT model on the source language. Experimental results show that our proposed approach achieves substantial improvements of up to 2.4 and 1.1 BLEU scores in Turkish→English and English→Turkish machine translation tasks, respectively, which points to a promising way to utilize the source-side linguistic knowledge for the low-resource NMT.

The known three-component reaction-diffusion system modeling competition and co-existence of different language speakers is under study. A modification of this system is proposed, which is examined by Lie symmetry method; furthermore exact solutions in the form of traveling fronts are constructed and their properties are identified. Plots of the traveling fronts are presented and the relevant interpretation describing the language shift occurred in Ukraine during the Soviet times is suggested

Background and objectives: The language-induced grip force modulation (GFM) can be used to better understand the link between the language and motor functions as an expression of the embodied language. However, the contribution of each brain hemisphere to the language-induced GFM is still unclear. Using six different action verbs as stimuli, this study evaluated the GFM of the left hand in unimanual task to characterize the left- and right-hemisphere contributions. Materials and Methods: The left-hand GFM of 20 healthy consistent right-handers subjects was evaluated using the verbs “to write”, “to hold”, “to pull”(left-lateralized central processing actions), “to draw”, “to tie”, and “to drive” (bi-hemispheric central processing actions) as linguistic stimuli. The time between the word onset and the first interval of statistical significance regarding the baseline (RT) was also measured. Results: The six verbs produced language-induced GFM. The modulation intensity was similar for the six verbs, but the RT was variable. The verbs “to draw”, “to tie”, and “to drive”, whose central processing of the described action is bihemispheric showed a longer Rt compared to the other verbs. Conclusions: The possibility that an action is performed by the left-hand does not interfere with the occurrence of GFM when this action verb is employed as linguistic stimulus. Therefore, the language-induced GFM seems mainly rely on the left hemisphere, and the engagement of the right hemisphere seems to slow down the increase in the GFM intensity.

The violation of privacy, others people or personal, is a very current problem, which concerns not only on the web but also in private life. In the years 1990 it was expected that nowadays, that any routine operation was carried out "manually", and it would be performed through mobile phones or personal computers. The problem pertains the distribution network that allows to share and bring together information and as result the network becomes unsafe, if subjected to attacks. Nowaday we put personal information on web because otherwise we are seen as “weak”. This work aims to measure and analyze how much information are shared by users of a pre-established social network and it is carried out through a set of algorithms techniques of machine learning.

The language we use to describe the past can have a strong influence on the audience’s interpretation of our story. In our experiment, we explore, using 3 different conditions, how the framing and language of an accident report can affect the audience’s proposed solutions to manage the problems found. We find that the approach used to create an accident report can have a powerful influence on the audience’s decision making. Whether we are describing an accident in a similar manner to a crime, using a systems approach or we are accepting of multiple stories which are not linear or coherent, the methods we use to capture and communicate the story have a profound impact on the actions decided upon by the reader.

Humanity, culture, societal development and language issues are mutually reinforcing much as they are intricately interwoven in a non-ceasing dynamic interrelationships within the matrix defined by language standardization or development, acquisition and use that mostly take up central place as unifying and propelling forces in language discourse. Within the confines of globalization which is neither homogenization nor convergence; but the beginning of drawing on the strength of indigenous capabilities to create knowledge ecology that would enliven socio-economic and scientific development of the world in far reaching interconnectivities and relationships across every clime, issues pertaining to language development, acquisition and use become imperative. It is against this background that this paper examines the development/standardization, acquisition and use of Esan language as one of several Nigerian indigenous languages to promote scientific and technological development through knowledge creation, preservation and dissemination. The obsession of using western model and epistemological outlook to achieve appropriate scientific and technological development to the utter neglect of home grown and culturally distilled efforts were also examined. In the light of the dare consequences and the harsh conditions globalization imposes on developing nations, this paper highlights standardization and use of indigenous languages in chatting appropriate trajectory for effectual developmental efforts in fast shrinking contemporary world.

This paper presents an approach to the application of the Model-Based Systems Engineering (MBSE) and Model-Based Systems Architecting (MBSA) principles to develop a Model-Based Pattern Language (MBPL). It takes too long for systems engineers and architects to develop a new system from scratch, particularly new space-based systems derived from the existing space systems architectures. A pattern language is a holistic view of reusable logical model artifacts; many are interdisciplinary and introductory, if at all. The results are mostly a combination of the application-specific logical solution, which further results in the best possible overall solution. The main benefit of the pattern language is reducing the time and validation required to generate a new space-based system architecture; this approach will develop top-level requirements in the initial phase of the system development. The rationale of the methodology proposed by the paper is as follows, collect, and decompose published literature and other open-source information available on space system architectures and system models; develop SysML models for systems, subsystems, products, assembly, subassembly level, and mission-specific requirements using CAMEO SysML software. Arrange these patterns to develop a functional ontology and construct a logical architecture pattern library. This approach created, updated, and managed SysML pattern language, which evaluated the expedited new model construction. Again, our objective is to develop a logical pattern language using public domain information and evaluate patterns by constructing a new space mission concept—for example, planetary surface habitat.

This study presents a strategic approach to developing applications focused on implementing Large Language Models using the Langchain framework in Python. Three language models are highlighted: GPT-3.5 (turbo mode), LLaMA 2, and Vicuna 7B, each with their distinctive features and capabilities. The methodology used is described in detail, including data extraction from medical reports using zero-shot prompting data extraction techniques, interaction with language models, and structured storage of results. The performance of the models in data extraction is evaluated, presenting metrics such as precision, recall, and F1 score. The results demonstrate high model capability in extracting information, although areas for improvement are identified, particularly in data extraction precision. In conclusion, the efficacy of the models in extracting information from medical histories is not considerably acknowledged, with an emphasis on the importance of improving precision and increasing the volume of trained data for future research in healthcare digitalization.

Visualising textual content could be helpful to professionals as well as amateurs across several fields. However, training a text-to-image generator in the mainstream domain requires large amounts of paired text-image and data, which is too expensive to collect since labeling millions of images and videos can be tiresome. GANs like StackGAN and StyleGAN can be considered as solutions to generate images from text. But the images generated may be of low accuracy and resolution, and the entire processing can be highly time-consuming. Moreover, image generation is a notion that is still being researched. Hence, the process of developing a Video Generation model necessitates substantial research. Despite the need for such a model, modern technology has lagged behind the solutions to this problem. This proposal suggests combining two methods, Text modification for Action Definition (TexAD) and SeQuential Image Generation for Video Synthesis (SQIGen). The proposed solution synthesises a sequence of images from textual information feeds and combines these images to create a video. TexAD uses Natural Language Processing and Deep Learning techniques to process, classify and modify text data. SQIGen is an extension of the VQGAN+CLIP neural network architecture that generates a sequence of images from the modified text data

Interactions between proteins and ions are essential for various biological functions like structural stability, metabolism, and signal transport. Given that more than half of all proteins bind to ions, it becomes crucial to identify ion-binding sites. Accurate identification of protein-ion binding sites helps us to understand proteins’ biological functions and plays a significant role in drug discovery. While several computational approaches have been proposed, this remains a challenging problem due to the small size and high versatility of metals and acid radicals. In this study, we propose IonPred, a sequence-based approach that employs ELECTRA (Efficiently Learning an Encoder that Classifies Token Replacements Accurately) to predict ion binding sites using only raw protein sequences. We successfully fine-tuned our pretrained model to predict the binding sites for nine metal ions (Zn2+, Cu2+, Fe2+, Fe3+, Ca2+, Mg2+, Mn2+, Na+, and K+) and four acid radical ion ligands (CO32−, SO42−, PO43−, NO2−). IonPred surpassed six current state-of-the-art tools by over 44.65% and 28.46% respectively in F1 score and MCC when compared on an independent test dataset. Our method is more computationally efficient than existing tools producing prediction results for a hundred sequences for a specific ion in under ten minutes.

A reduced ability to work with information contained in a text is usually registered in low-achieving students. One important internal factor influencing reading comprehension concerns the child’s executive functioning. The current study investigated whether the domain-specific intervention ExeFun-READ targeting language abilities related to reading comprehension and executive functioning in primary school children would be effective in improving their scholastic performance in their reading domain and executive functioning. ExeFun-READ is designed to address the relations between domain-specific versus thinking-skill-oriented (domain-general) instruction. A child’s active learning is focused on semantic, phonetic, morphological and syntactical comprehension of linguistic material. In total, 151 students attending grade four from seven elementary schools took part in the project. The study concerned a pretest-intervention-posttest experimental design with three conditions: the experimental condition, an active, and a passive control group. To assess the children’s level of EF, the Delis–Kaplan Executive Function System test battery was used; to assess children’s language ability in reading domain, the Cognitive Abilities Test (the verbal battery, Thorndike & Hagan, 1986 [1]), was used. In the current study, the intervention led to improved language abilities related to reading comprehension, but in terms of executive functioning the improvement only extended to switching fluency. Regarding the effects of the ExeFun-READ intervention on children’s reading abilities, specified as language abilities related to reading comprehension, significant improvements were found in vocabulary, completion of sentences, and classification of terms in the group of children that received the ExeFun-READ intervention.

The disruptive application of ChatGPT (GPT-3.5, GPT-4) to a variety of domains has become a topic of much discussion in the scientific community and society at large. Large Language Models (LLMs), e.g., BERT, Bard, Generative Pre-trained Transformers (GPTs), LLaMA, etc., have the ability to take instructions, or prompts, from users and generate answers and solutions based on very large volumes of text-based training data. This paper assesses the impact and potential impact of ChatGPT on the field of digital forensics, specifically looking at its latest pre-trained LLM, GPT-4. A series of experiments are conducted to assess its capability across several digital forensic use cases including artefact understanding, evidence searching, code generation, anomaly detection, incident response, and education. Across these topics, its strengths and risks are outlined and a number of general conclusions are drawn. Overall this paper concludes that while there are some potential low-risk applications of ChatGPT within digital forensics, many are either unsuitable at present, since the evidence would need to be uploaded to the service, or they require sufficient knowledge of the topic being asked of the tool to identify incorrect assumptions, inaccuracies, and mistakes. However, to an appropriately knowledgeable user, it could act as a useful supporting tool in some circumstances.

The COVID-19 pandemic measures have negatively impacted education worldwide via travel restrictions, student visas, and physical attendanc,e among many other fac-tors. As one of the first places to impose strict pandemic measures and one of the last places to lift the measures, Mainland China provided a unique venue to investigate the effect of public health pandemic control policies on tertiary education. Considering the large portion of international students in medical schools and the higher susceptibility to the pandemic measures due to the natural synergy with teaching hospitals, medical schools can be an early forefront indicator of the impact of the pandemic on tertiary education. We surveyed 513 participants international medical students from Wen-zhou Medical University’s school of international studies in 2019 and 2022 about their satisfaction and demographic and cultural adaptation parameters. We found that the pandemic restrictions did not generally impact student satisfaction. Further analyzing the associated parameters, we found teaching satisfaction to be independent of age (P=.159), years in China (P=.896) and Chinese Level (P=.577) in the medical university. Teaching satisfaction is associated with the region of origin (P <0.001), acceptance of local social norms (P <0.001), satisfaction with the core values of Chinese culture (P <0.001), acclimatization to life in China (P <0.001), can easily make Chinese friends (P <0.001) among other factors. The findings of this study provide an understanding of the effects of pandemic measures on tertiary education satisfaction and other factors that can aid in international student satisfaction.

This paper presents four key results in the philosophy of language. Firstly, it distinguishes between partial and consistent assertion of a sentence, and introduces the concept of an equivocal sentence, which is both partially asserted and partially denied. Secondly, it proposes a novel definition of truth, stating that a true sentence is one that is consistently asserted. This definition avoids the Liar paradox, does not restrict classical logic, and applies to declarative sentences in the language used by any particular person. Thirdly, the paper introduces an epistemic model of language, called assertional language, which formalizes the definition of truth. Finally, it provides an argument for the falsity of so-called Liar sentences. The proposed definition of truth can be viewed as a formal account of the correspondence theory. The epistemic model is a powerful concept that allows for combining different languages in a meaningful way. This model is uniquely capable of reflecting on epistemic inconsistencies, such as logical paradoxes, in a consistent manner and without resorting to non-classical solutions.

Ion channels are integral membrane proteins that facilitate the movement of ions across cell membranes, playing a key role in a range of biological processes. The high cost and time required for wet lab experiments to characterize ion channels has spurred the development of computational methods for this purpose. In our previous work, we demonstrated the effectiveness of protein language models for ion channel prediction, using a logistic regression classifier to distinguish ion channels from non-ion channels (TooT-BERT-C) and transporters from non-transporters (TooT-BERT-T). In this study, we build upon this approach by using a combination of classical machine learning classifiers and a Convolutional Neural Network (CNN) with fine-tuned representations from ProtBERT, ProtBERT-BFD, and MembraneBERT to discriminate ion channels from non-ion channels. The results of our experiments demonstrate that TooT-BERT-CNN-C, a combination of the representations from ProtBERT-BFD and a CNN, outperforms existing state-of-the-art methods for predicting ion channels, with a Matthews Correlation Coefficient (MCC) of 0.86 and an accuracy of 98.35% on an independent test set.

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7%, 42.0%, and 70.1% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.

Google play store allow the user to download a mobile application (app) and user get inspired by the rating and reviews of the mobile app. A recent study analyzes that user preferences, user opinion for improvement, user sentiment about particular feature and detail with descriptions of experiences are very useful for an application developer. However, many application reviews are very large and difficult to process manually. Star rating is given of the whole application and the developer cannot analyze the single feature. In this research, we have scrapped 282,231 user reviews through different data scraping techniques. We have applied the text classification on these user reviews. We have applied different algorithms and find the precision, accuracy, F1 score and recall. In evaluated results, we have to also find the best algorithm.

With the introduction of the IEC 61850 standard, the path is set for a single communication topology covering all substation levels. The standard has the potential to change the way substations are designed, build, tested and maintained. This means that the key segment of the substation, its protection system, will go through a transition period with the end goal of having a digitalized substation where all information exchange is performed over an Ethernet communication bus. The goal of this paper is to analyse the performance impact of the IEC 61850-9-2LE on the protection system. To do this, a laboratory hardware-in-the-loop test setup is developed representing traditional, hybrid and digital substation topology. This setup served to simulate faults and create transient waveforms in an extended IEEE 123 node test system, which were then used to detect the reaction times of protection relay devices. To verify protection relay results significant number of tests are performed clearly showing benefits which can be gained by distribution system digitalization.

It is broadly acknowledged that children with Developmental Language Disorder (DLD) show verb-related limitations. While most previous studies in Spanish have focused on tense, the mastery of lexical aspect—particularly telicity—has not been the primary focus of much research. Lexical aspect refers to whether an action has a defined endpoint (telic verbs) or not (atelic verbs). This study investigates the effect of telicity on verb recognition in Chilean children with DLD compared to their typically developing (TD) peers using the Event-Related Potential (ERP) technique. The research design is a mixed factorial design with between-group factors of 2 (DLD/TD) and within-group factors of 2 (telic/atelic verbs) and 2 (coherent/incoherent sentences). Participants included 36 school-aged children (18 DLD, 18 TD) aged 7 to 7 years and 11 months. The task required subjects to listen to sentences that either matched or did not match an action in a video, with sentences including telic or atelic verbs. The study found notable differences between groups in how they processed verbs (N400 and post-N400 components) and direct objects (N400 and P600 components). DLD children struggled to differentiate telic and atelic verbs, potentially because they employed overgeneralization strategies consistent with the Event Structural Bootstrapping model.

The aim of this study is to develop a software solution for real-time recognition of sign language words using two arms. This will enable communication between hearing-impaired individuals and those who can hear. Several sign language recognition systems have been developed using different technologies, including cameras, armbands, and gloves. The system developed in this study utilizes surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. Other methods often have drawbacks, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have prevented their practical application. A software has been developed that can run on different operating systems using digital signal processing and machine learning methods specific to the study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The Random Forest algorithm was found to have the highest success rate with 99.875% accuracy, while the Naive Bayes algorithm had the lowest success rate with 87.625% accuracy. Feedback from a test group of 10 people indicated that the system is user-friendly, aesthetically appealing, and practically useful. The new system enables smoother communication for people with hearing disabilities and promises seamless integration into daily life without compromising user comfort or lifestyle quality.

This research presents the Cross-Cultural Intelligent Language Learning System (CILS), a novel approach integrating Artificial Intelligence (AI) into language education to enhance cross-cultural communication. CILS utilizes advanced AI technologies to provide adaptive, personalized learning experiences that cater to the unique linguistic and cultural backgrounds of each learner. By dynamically adjusting content and methodology, CILS significantly improves linguistic proficiency and cultural understanding, essential for effective global interactions. The implementation of CILS in platforms such as Busuu and HelloTalk has demonstrated marked improvements in engagement and communication skills among learners. Empirical studies validate the system's effectiveness in real-world settings, showing enhanced learner performance and increased intercultural competence. Additionally, the Technology Acceptance Model (TAM) applied confirms that the usability and perceived usefulness of AI-driven systems strongly influence learner acceptance and sustained use. This study not only underscores the potential of AI in transforming language education but also highlights the critical role of cultural sensitivity in designing educational technologies.

How many different writers authored the huge number of texts attributed to Galen of Pergamum (129~216 AD), medical doctor and philosopher, a giant in the history of medicine? To find out which texts were his and which ones were written by others is known as the “Galenic Question”. We propose a “solution” to it through a multidisciplinary approach based: (a) on historical research and (b) on a mathematical analysis of the Greek texts. The historical approach has considered historical independent sources and anachronisms. The mathematical approach has been based on a mathematical theory concerning deep–language variables, rarely consciously controlled by any author, therefore capable of giving indications on similarity of texts, with little or no bias. The multidisciplinary approach has convinced us that at least three authors wrote the texts attributed to Galen. The first two very likely were real historical persons: (a) a certain Galen living between the end of the I century BC and the second half of the I century AD; (b) the historical Galen of Pergamum (II–III centuries AD) and (c) several unknown authors hiding under the name Galen, but surely living after Galen of Pergamum’s death.

In recent years, the implementation of artificial intelligence (AI) in accounting has grown in many domains, including auditing. AI permits auditors to evaluate large data sets and rapidly identify discrepancies and sequences in audit. This means auditors can review all of a client's transactions via a low time and effort. This entails auditing firms investing heavily in technological infrastructure as well as training their human resources to use cutting-edge AI technologies. Thus, this research sought to investigate the impact of the use of AI techniques by Jordanian external auditors in fraud risk assessment, the moderating effect of auditor size. The descriptive analytical method was used by distributing 280 questionnaires electronically to auditors employed in auditing firms operating in Jordan, The result reveals that AI techniques are extremely beneficial in fraud risk assessment at all levels, and the relationship between the use of AI techniques and the assessment of fraud risk has improved as auditor size has increased.

Nollywood, based on the idea of Bollywood from India, is a series of outstanding movies that originate from Nigeria. Unfortunately, while the movies are in English, they are hard to understand for many native speakers due to the dialect of English that is spoken. In this article, we accomplish two goals: (1) create a phonetic sub-title model that is able to translate Nigerian English speech to American English and (2) use the most advanced toxicity detectors to discover how toxic the speech is. Our aim is to highlight the text in these videos which is often times ignored for lack of dialectal understanding due the fact that many people in Nigeria speak a native language like Hausa at home.

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN works best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features.

With the widespread Application of Virtual Reality (VR) in education, optimizing foreign language learning in VR has become a focal point of research. This paper introduces a comprehensive theoretical framework (VR-CCL) based on constructivism and cognitive load theory to enhance foreign language learning in VR. Through a literature review, we explore the applications of VR in education, foreign language learning theories, and prior works on technology-assisted language learning. We further detail the three main components of the VR-CCL framework and validate its effectiveness through two case studies: Duolingo VR and Rosetta Stone VR. Finally, we discuss the strengths and limitations of the framework and its implications for educators and developers.

This study investigates how digital alchemy can enhance literature and language teaching through a combination of online and face-to-face learning. It uses constructivist learning theory and the SAMR model to examine how digital tools and resources can transform learner engagement, critical thinking, and creativity. The study gathered data from interviews, focus groups, and document analysis with 30 participants from private HEI schools. The results show that digital alchemy through blended learning allows learners to personalize and collaborate in their exploration of various literary texts. However, the study also identifies some challenges related to resource availability and educator training. The study emphasizes the importance of using digital alchemy in literature and language teaching and suggests some ways for educators, administrators, and policymakers to effectively use digital tools and resources for student-centered teaching methods in the digital era.

The use of artificial intelligence in natural language processing (NLP) has significantly contributed to the advancement of natural language applications such as sentimental analysis, topic modeling, text classification, chatbots, and spam filtering. With a large amount of text generated each day from different sources such as webpages, blogs, emails, social media, and articles, one of the most common tasks in natural language processing is the classification of a text corpus. This is important in many institutions for planning, decision-making, and archives of their projects. Many algorithms exist to automate text classification operations but the most intriguing of them is that which also learns these operations automatically. In this study, we present a new model to infer and learn from data using probabilistic logic and apply it to text classification. This model, called GenCo, is a multi-input single-output (MISO) learning model that uses a collaboration of partial classifications to generate the desired output. It provides a heterogeneity measure to explain its classification results and enables the reduction of the curse of dimensionality in text classification. The classification results are compared with those of conventional text classification models, and it shows that our proposed model has a higher classification performance than conventional models.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96\% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

The practice of nurse health coaching (NHC) draws from the art and science of nursing, behavioral sciences, and evidence-based health coaching methods. This secondary analysis of the audio-recorded natural language of participants during NHC sessions of our recent 8-week RCT evaluates improvement over time in cognitive-behavioral outcomes: Change Talk, Resiliency, Self-Efficacy/Independent Agency, Insight & Pattern Recognition, and Building Towards Sustainability. We developed a measurement tool for coding, Indicators of Health Behavior Change (IHBC), that was designed to allow trained health coach experts to assess the presence and frequency of the indicators in the natural language content of participants. We used a two-step method for randomly selecting the 20-minute audio-recorded session that was analyzed at each time point. Fifty-six participants had high-quality audio recordings of the NHC sessions. Twelve participants were placed in the social determinants of health (SDH) group based on the following: low income (<$20,000/year), early-onset hypertension, and social disadvantages. Our analyses significantly improved Change Talk and the other four factors over time. Our factor analyses indicated two distinct factors at each measurement point of the study, demonstrating the stability of the outcome measures over time. Our newly developed measurement tool, IHBC, proved stable in structure over time and sensitive to change. This NHC program shows promise in improving cognitive-behavioral indicators associated with health behavior change in both non-SDH and SDH individuals.

A Twi-English parallel corpus is certainly an important resource for Machine Translation of Twi (ISO 639-3), a Low-Resource African Language (LRAL) which is mainly spoken in Ghana and Ivory Coast. Currently large-scale multi-domain Twi-English parallel corpus is still unavailable partly due to the difficulties and the arduous efforts required in its design. In this paper, we present TWIENG: a large-scale multi-domain Twi-English parallel corpus. We crawled the sentences from the web using web crawlers, translated, aligned, tokenized and compiled to create the corpus. We crawled English sentences from Ghanaian indigenous electronic news portals, Ghanaian Parliamentary Hansards, Twi Bible and crowdsourcing via google forms. The sentences were translated by professional translators and linguists, they were then aligned, tokenized and compiled. The corpus was curated using the sketch engine, a corpus manager and analysis software developed by Lexical Computing Limited. The corpus was manually evaluated by Twi professional linguists. The Corpus has 5,419 parallel sentences.

The deep neural network - BERT model (Bidirectional Encoder Representations from Transformers) and the stocks cumulative abnormal return is used in this article to analyze the sentiment of financial texts. The proposed approach, unlike those used so far, does not require the creation of dictionaries, takes into account the broad context of words and their meaning in financial texts, eliminates the problem of ambiguity of words in various contexts, does not require manual labelling of data and is free from the subjective assessment of the researcher. The sentiment of financial texts in the meaning presented in this paper is directly related to the market reaction to the information contained in these texts. For texts belonging to one of the two classes (positive or negative) with the highest probability the BERT model gives the results of predictions with a precision level of 62.38% for the positive class and 55% for the negative class. The results at this level can be used in event study, market efficiency research, investment strategy development or support of investment analysts using fundamental analysis.

Now when the whole world is still under COVID-19 pandemic, many schools have transferred the teaching from physical classroom to online platforms. It is highly important for schools and online learning platforms to investigate the feedback to get valuable insights about online teaching process so that both platforms and teachers are able to learn which aspect they can improve to achieve better teaching performance. But handling reviews expressed by students would be a pretty laborious work if they were handled manually as well as it is unrealistic to handle large-scale feedback from e-learning platform. In order to address this problem, both machine learning algorithms and deep learning models are used in recent research to automatically process students' review getting the opinion, sentiment and attitudes expressed by the students. Such studies may play a crucial role in improving various interactive online learning platforms by incorporating automatic analysis of feedback. Therefore, we conduct an overview study of sentiment analysis in educational field presented in recent research, to help people grasp an overall understanding of the sentiment analysis research. Besides, according to the literature review, we identify three future directions that researchers can focus on in automatically feedback processing: high-level entity extraction, multi-lingual sentiment analysis, and handling of figurative language.

With the proliferation of locating devices, more and more raw spatial trajectories are formed, and many works enrich these raw trajectories with semantics, and mine patterns from both raw and semantic trajectories, but access control of spatial trajectories is not considered yet. We present a multi-policy secure model for semantic spatial trajectories. In our model, Mandatory Access Control, Role Based Access Control and Discretionary Access control are all enforced, separately and combined, and we represent the model semi-formally in Ontology Web Language.

Abstract: Hand gestures are an essential kind of nonverbal communication that let people engage and be expressive with one another. As technology has progressed, hand gesture recognition systems have become an important field of study, providing a link between human activities and artificial intelligence. An overview of the field of hand gesture recognition systems is given in this abstract, along with an explanation of its importance, difficulties, and possible uses. This abstract begins by examining the role that hand gestures have in communication. It then goes on to show the cultural significance and wide range of variations in gestural expressions that occur in many contexts and societies. Artificial intelligence systems face a unique problem in comprehending and interpreting these motions, which calls for the combination of computer vision, machine learning, and pattern recognition approaches. The abstract goes on to address why people develop hand motions. The purpose behind creating systems to recognize hand gestures is also covered in the abstract, with a focus on how these systems could improve human-computer interaction in several fields, including robotics, virtual reality, healthcare, and assistive technologies. These systems have the potential to enhance accessibility, efficiency, and user experience by allowing people to engage with devices and interfaces naturally and intuitively. The abstract also discusses environmental elements including backdrop clutter and lighting, as well as the technological difficulties in identifying and understanding hand gestures, which include variations in hand forms, orientations, and movements. To tackle these obstacles, strong algorithms that can record and interpret complex hand movements in real-time must be developed.

The article suggests a Value Proposition (VP) framework that enables the analysis of the beneficial impact of Artificial Intelligence (AI) resources and capabilities on specific VP development activities. To develop such a framework, we examined existing management research publications to identify and extract assertions that could be used as a source of actionable insights for new growth-oriented companies. The extracted assertions were assembled into a corpus of texts that were subjected to topic modelling analysis – a machine learning approach to natural language processing that is used to identify latent themes in large corpora of text documents. The topic modeling resulted in the identification of seven topics. Each topic is defined by a set of most frequent words co-occurring in a distinctive subset of texts that could be interpreted in terms of activities constituting the core elements of the VP framework. We then examined each activity in terms of its potential to be enhanced by employing AI resources and capabilities. The interpretation of the topic modeling results led to the identification of seven topics: 1) Value created; 2) Stakeholder value propositions; 3) Foreign market entry; 4) Customer base; 5) Continuous improvement; 6) cross-border operations; and 7) Company image. The uniqueness of the adopted topic modeling approach consists in the quality of the assertions and the interpretation of the seven topics as an activity framework, i.e. in its capacity to generate actionable insights for practitioners. The additional analysis suggests that there is a potential for AI to enhance emerging the four core elements of the VP framework: Value created, Stakeholder value propositions, Foreign market entry, and Customer base. More importantly, we found that the greatest number of assertions related to activities that could be enhanced by AI are part of the Customer base topic, i.e., the topic that is most directly related to the growth potential of the companies. In addition, the VP framework suggests that a company’s customer base growth is continuously enhanced through a positive loop enabled by activities focused on the Continuous improvement of the activities and the amount of Value created, the alignment of Stakeholder value propositions, and companies’ Foreign market entry. Thus, Foreign market entry appears as a factor that helps in understanding the beneficial impact of AI on the VP development of new growth-oriented companies.

Deep learning is based on matrix computing with a large amount of hidden parameters that is not visible outside the computing module. Deep learning is a non-linear system and a linear approach is not possible. It is natural for people to visualize the algorithm and to follow some hidden parameters. In this paper, we propose a simple graphical programming of a nonlinear system based on drawing the simplest unit, such as a single neuron model. A more complex scheme of a classical neural network is obtained by commands copy-move-place of one neuron. The number of neurons and layers can be chosen arbitrarily. Once the scheme is complete, the implementation code is evaluated with symbolic parameters and nonlinear activation functions. This cannot be done manually. With the symbolic expression of outputs in terms of inputs and symbolic parameters, including symbolic activation pure functions, some other properties can be derived in closed form. This unique original approach can help scientists and developers and design numerical algorithms for machine learning and to understand how deep learning algorithms work.

This study employs a qualitative research approach to investigate the intrinsic and extrinsic motivations that influence college students' engagement, learning experiences, and long-term commitment to language and literature studies within the context of blended learning modality. Drawing on the theoretical frameworks of Self-Determination Theory and Achievement Goal Theory, the research explores the complex interplay between intrinsic factors (e.g., passion, self-improvement, personal growth) and extrinsic factors (e.g., external rewards, recognition). The study is conducted at SMC among the 30 participant and utilizes interviews and classroom observations, in conjunction with a comprehensive review of relevant literature, to gain insights into the impact of these motivations on academic performance and learning outcomes, particularly in blended learning environments. The study acknowledges the limitations inherent in its locale-specific scope and the challenges associated with ensuring the validity and reliability of data collected through online surveys and virtual classroom observations. The findings of this study have significant implications for educators and policymakers, highlighting the importance of nurturing intrinsic motivation while leveraging extrinsic factors to establish supportive learning environments that foster student autonomy, incorporate student interests, and provide timely feedback. By comprehending the motivations that drive student engagement and long-term commitment, educators can enhance learning experiences, elevate academic performance, and sustain students' engagement with language and literature studies amidst the evolving educational landscape of blended learning modality.

Purpose: Hearing assistive technology (HAT) has been shown to be a viable solution to the speech-in-noise perception (SPIN) issue in children with autism spectrum disorder (ASD); however, little is known about its efficacy in tonal language speakers. This study compared sentence-level SPIN performance between Chinese children with ASD and neurotypical (NT) children and evaluated HAT use in improving SPIN performance and easing SPIN difficulty. Methods: Children with ASD (n=26) and NT children (n=19) aged 6-12 performed two adaptive tests in steady-state noise and three fixed-level tests in quiet and steady-state noise with and without using HAT. Speech recognition thresholds (SRT) and accuracy rates were assessed using adaptive and fixed-level tests, respectively. Parents or teachers of the ASD group completed a questionnaire regarding children’s listening difficulty under six circumstances before and after a ten-day trial period of HAT use. Results: Although the two groups of children had comparable SRTs, the ASD group showed a significantly lower SPIN accuracy rate than the NT group. Also, a significant impact of noise was found in the ASD group’s accuracy rate, but not in the NT group’s. There was a general improvement in the ASD group’s SPIN performance with HAT and a decrease in their listening difficulty ratings across all conditions after the device trial. Conclusion: The findings indicated inadequate SPIN in the ASD group using a relatively sensitive measure to gauge SPIN performance among children. The markedly increased accuracy rate in noise during HAT-on sessions for the ASD group confirmed the feasibility of HAT for improving SPIN performance in controlled laboratory settings, and the reduced post-use ratings of listening difficulty further confirmed the benefits of HAT use in daily scenarios.

Colonoscopy is used for colorectal cancer (CRC) screening. Extracting details of the colonoscopy findings from free text in electronic health records (EHRs) can be used to determine patient risk for CRC and colorectal screening strategies. In this study, we developed and evaluated the accuracy of a deep learning model framework to extract information for the clinical decision support system to analyze relevant free-text colonoscopy reports, including indications, pathology, and findings notes. The Bio-Bi-LSTM-CRF framework was developed using Bidirectional Long Short-term Memory (Bi-LSTM) and Conditional Random Fields (CRF) to extract several clinical features from these free-text reports, including indications for the colonoscopy, findings during the colonoscopy, and pathology of the resected material. We then trained the Bio-Bi-LSTM-CRF and existing Bi-LSTM-CRF models on 80% of 4,000 manually annotated notes obtained from the colonoscopy reports of 3,867 patients. The clinical notes were from a group of patients aged over 40 years old enrolled in four Veterans Affairs Medical Centers. A total of 10% of the remaining annotated notes were used to train hyperparameter, while the remaining 10% were used to evaluate the accuracy of our model (Bio-Bi-LSTM-CRF) and to compare the results with the outcomes obtained using Bi-LSTM-CRF. The results of our experiments showed that the bidirectional encoder representations by integrating dictionary function vector from Bio-Bi-LSTM-CRF and strategies character sequence embedding approach is an effective way to identify colonoscopy features from EHR-extracted clinical notes. Therefore, the Bio-Bi-LSTM-CRF model is concluded to be capable of creating new opportunities to identify patients at risk for colon cancer and to study their health outcomes.

The notion of information and complexity are important concepts in many scientific fields such as molecular biology, evolutionary theory, and exobiology. Many measures of these quantities, either rely on a statistical notion of information, are difficult to compute, or can only be applied to strings. Based on assembly theory, we propose the notion of a \textit{ladderpath}, which describes how an object can be decomposed into a hierarchical structure using repetitive elements. From the ladderpath two measures naturally emerge: the ladderpath-index and the order-index, which represent two axes of complexity. We show how the ladderpath approach can be applied to both strings and spatial patterns and argue that all systems that undergo evolution can be described as ladderpaths. Further, we discuss possible applications to human language and the origins of life. The ladderpath approach provides a novel characterization of the information that is contained in a single object (or a system) and could aid in our understanding of evolving systems and the origin of life in particular.

With the rapid development of science and technology, many new concepts and terms appear, especially in English. Other languages try to express these concepts with words from their own vocabulary. In the specific case of Arabic, there are many ways to find a counterpart for a particular new concept, such as using an existing word to denote the new concept, derivation, and blending. When these methods fail, the new concepts are simply phonetically transliterated. This has the disadvantage that most of the transliterated terms do not conform to the rules of the Arabic language and lead to a distortion of the language. Some modern linguists call for using the generation strategy to translate the new terms into Arabic by using the unused Arabic roots. Therefore, it is necessary to provide a resource that contains all Arabic roots with a categorization of what is used, what is available for use, and what is rejected according to the phonetic system. This work provides a comprehensive lexicon that contains all possible Arabic triliteral roots, determines the status of each root in terms of usage and acceptability, and provides a mechanism for giving preference to roots when there is more than one root that indicates the desired meaning.

Social media platforms have been entirely an undeniable part of the lifestyle for the past decade. Analyzing the information being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and risen user satisfaction. For deriving any further conclusion, first, it is necessary to know how to compare users. In this paper, a hybrid model has been proposed to measure Twitter profiles’ similarity and quantifies the likeness degree of profiles by calculating features considering users’ behavioral habits. For this, first, the timeline of each profile has been extracted using the official TwitterAPI. Then, in parallel, three aspects of a profile are deliberated. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized to compare the behavioral ratios of two profiles. Next, the audience network is extracted for each user, and for estimating the similarity of two sets, Jaccard similarity is used. Finally, for the Content similarity measurement, the tweets are preprocessed respecting the feature extraction method; TF-IDF and DistilBERT for feature extraction are employed and then compared using the cosine similarity method. Results have shown that TF-IDF has slightly better performance; therefore, the more straightforward solution is selected for the model. Similarity level of different profiles. As in the case study, a Random Forest classification model was trained on almost 20000 users revealed a 97.24% accuracy. This comparison enables us to find duplicate profiles with nearly the same behavior and content.

This article propose a didactic, through games, tool based on information and communication technologies, in order to eliminate possible communication barriers and to promote the inclusion of students with hearing impairment in Physical Education classes. To this end, a dossier of traditional games has been developed. These are structured in turn into objectives, materials, organisation, graphic description and a QR code for each game. These codes are linked to different videos hosted on the YouTube platform, in which the explanation of the games, mentioned above, can be visualised graphically using sign language. The whole creative process is described in the article, as well as possible pedagogical applications of the use of the tools created for this purpose in other educational contexts.

A more sustainable society and economy also implies a more sustainable behavior in the consumption of financial products. A possible change in focus can come from the demand side, so that more sustainable consumption of financial products have to go hand to hand with financial literacy. However, financial literacy, potential favoring of this sustainable behavior, is far from reaching an international consensus about its definition, object and scope. Two objectives are analyzed; the different interpretations of financial literacy in the academic literature, as well as its evolution and how, in what context and with what other concepts the term is used in social networks. Scientometric techniques and content analysis have been used to carry out a systematic review of literature and also NLP to analyze the comments on Social Networks. Critical moments are identified in the definition of financial literacy. Also ten sentiments are analyzed in social networks in wich positivity, trust, and anticipation predominate. Greater attention to this issue is necessary both from the private initiative and from public policies, so that financial literacy is an effective tool for a more sustainable behavior by consumers. Finally, a new definition is proposed based on our findings.

Bayesian formulations of learning imply that whenever the evidence for a correlation between events in an animal’s habitat is sufficient, the correlation is learned. This implies that regularities can be learnt rapidly, from small numbers of learning examples. This speed of learning gives maximum possible fitness, and no faster learning is possible. There is evidence in many domains that animals and people can learn at nearly Bayesian optimal speeds. These domains include associative conditioning, and the more complex domains of navigation and language. There are computational models of learning which learn at near-Bayesian speeds in complex domains, and which can scale well – to learn thousands of pieces of knowledge (i.e., relations and associations). These are not neural net models. They can be defined in computational terms, as algorithms and data structures at David Marr’s [1] Level Two. Their key data structures are composite feature structures, which are graphs of multiple linked nodes. This leads to the hypothesis that animal learning results not from deep neural nets (which typically require thousands of training exam-ples), but from neural implementations of the Level Two models of fast learning; and that neu-rons provide the facilities needed to implement those models at Marr’s Level Three. The required facilities include feature structures, dynamic binding, one-shot memory for many feature struc-tures, pattern-based associative retrieval, unification and generalization of feature structures. These may be supported by multiplexing of data and metadata in the same neural fibres.

The aim of this study was to build a tool to analyze, using artificial intelligence, the sentiment perception of users who answered two questions from the CSQ – 8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satis-faction level of the participants involved, with a view to establishing strategies to improve fu-ture experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks and transfer learning, so as to classify the inputs into the following 3 categories: negative, neutral and positive. Due to the lim-ited amount of data available - 86 registers for the first and 68 for the second - transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02 % and 90.53 % respectively based on ground truth labeled by 3 experts. Finally, we proposed a complementary analysis, using com-puter graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages

The recent advancements of the Semantic Web and Linked Data have changed the working of the traditional web. There is significant adoption of the Resource Description Framework (RDF) format for saving of web-based data. This massive adoption has paved the way for the development of various centralized and distributed RDF processing engines. These engines employ various mechanisms to implement critical components of the query processing engines such as data storage, indexing, language support, and query execution. All these components govern how queries are executed and can have a substantial effect on the query runtime. For example, the storage of RDF data in various ways significantly affects the data storage space required and the query runtime performance. The type of indexing approach used in RDF engines is critical for fast data lookup. The type of the underlying querying language (e.g., SPARQL or SQL) used for query execution is a crucial optimization component of the RDF storage solutions. Finally, query execution involving different join orders significantly affects the query response time. This paper provides a comprehensive review of centralized and distributed RDF engines in terms of storage, indexing, language support, and query execution.

This study examines the balancing between urban transportation security and supply chain efficiency amid global disruptions like the COVID-19 pandemic, attacks on maritime lanes, and geopolitical conflicts. The authors combine bibliometric network analysis and natural language processing (NLP) to review scholarly literature from 2017 to 2023. This approach identifies and visualizes evolving thematic trends. Findings reveal a shift towards integrating advanced technologies such as blockchain, AI, and IoT to enhance security without compromising efficiency. This study highlights the significance of integrating urban planning principles and sustainable practices to enhance transportation security and supply chain efficiency within urban environments. Actionable insights for policymakers and supply chain managers emphasize the adoption of integrated, technology-driven security solutions to foster resilient and sustainable supply chains. This research contributes to academic discourse by refining the understanding of evolving priorities in transportation security and supply chain management, offering both theoretical and practical implications for secure and efficient global supply chains.

Product attribute extraction is an growing field in e- commerce business, with several applications including product ranking, product recommendation, future assortment planning and improving online shopping customer experiences. Under- standing the customer needs is critical part of online business, specifically fashion products. Retailers uses assortment planning to determine the mix of products to offer in each store and channel, stay responsive to market dynamics and to manage inventory and catalogs. The goal is to offer the right styles, in the right sizes and colors, through the right channels. When shoppers find products that meet their needs and desires, they are more likely to return for future purchases, fostering customer loyalty. Product attributes are a key factor in assortment planning. In this paper we present PAE, a product attribute extraction algorithm for future trend reports consisting text and images in PDF format. Most existing methods focus on attribute extraction from titles or product descriptions or utilize visual information from existing product images. Compared to the prior works, our work focuses on attribute extraction from PDF files where upcoming fashion trends are explained. This work proposes a more comprehensive framework that fully utilizes the different modalities for attribute extraction and help retailers to plan the assortment in advance. Our contributions are three-fold: (a) We develop PAE, an efficient framework to extract attributes from unstructured data (text and images); (b) We provide catalog matching methodology based on BERT representations to discover the existing attributes using upcoming attribute values; (c) We conduct extensive experiments with several baselines and show that PAE is an effective, flexible and on par or superior (avg 92.5% F1-Score) framework to existing state-of-the-art for attribute value extraction task.

In the dynamic field of mental health care, the nuanced application of Artificial Intelligence (AI) through Natural Language Processing (NLP) and Automatic Speech Recognition (ASR) is a pivotal innovation in patient empowerment and service optimization. This study introduces a distinctive online therapy platform that capitalizes on the synergy of NLP and ASR to offer unprecedented levels of interactive and personalized therapeutic interventions. The architecture of our system is meticulously detailed, featuring an ASR component with an impressive Word Error Rate (WER) of 14% when trained on the diverse French subsets of the Mozilla Common Voice dataset, complemented by a high-precision NLP framework skilled in processing and responding to user inputs. The evaluation of our system highlights its efficacy in enhancing therapy sessions and user satisfaction, with an emphasis on the qualitative aspects of user feedback. The paper addresses challenges such as dataset representativeness and language model refinement and articulates the strategic solutions employed to overcome them. The paper concludes with forward-looking perspectives on AI's role in mental health services, advocating for the creation of sophisticated, language-specific datasets and models to satisfy the increasing demands of online therapy, reflecting a growing commitment of patients in the management of their therapy. This research underscores the transformative impact of AI in advancing mental health care into the digital age, representing a significant evolution over existing methodologies.

Leveraging the open-world understanding capacity of large-scale visual-language pre-trained models has become a hot-spot in point cloud classification. Recent approaches rely on transferable visual-language pre-trained models, classifying point clouds by projecting them into 2D images and evaluating consistency with textual prompts. These methods benefit from the robust open-world understanding capabilities of visual-language pre-trained models and require no additional training. However, they face several challenges summarized as prompt ambiguity, image domain gap, view weights confusion, and feature deviation. In response to these challenges, we propose PointBLIP, a zero-training point cloud classification network based on the recently introduced BLIP-2 visual-language model. PointBLIP is adept at processing similarities between multi-images and multi-prompts. We separately introduce a novel method for point cloud zero-shot and few-shot classification, which involves comparing multiple features to achieve effective classification. Simultaneously, we enhance the input data quality for both the image and text sides of PointBLIP. In point cloud zero-shot classification tasks, we outperform state-of-the-art methods on three benchmark datasets. For few-shot classification tasks, to the best of our knowledge, we present the first zero-training few-shot point cloud method, surpassing previous works under the same conditions and showcasing comparable performance to full-training methods.

The work of this paper presents multiple novel findings from a comprehensive analysis of about 150,000 tweets about exoskeletons posted between May 2017 and May 2023. First, findings from content analysis and temporal analysis of these tweets reveal the specific months per year when a significantly higher volume of Tweets was posted and the time windows when the highest number of tweets, the lowest number of tweets, tweets with the highest number of hashtags, and tweets with the highest number of user mentions were posted. Second, the paper shows that there are statistically significant correlations between the number of tweets posted per hour and different characteristics of these tweets. Third, the paper presents a multiple linear regression model to predict the number of tweets posted per hour in terms of these characteristics of tweets. The R2 score of this model was observed to be 0.9540. Fourth, the paper reports that the 10 most popular hashtags were #exoskeleton, #robotics, #iot, #technology, #tech #innovation, #ai, #sci, #construction and #news. Fifth, sentiment analysis of these tweets was performed using VADER and the DistilRoBERTa-base library. The results show that the percentage of positive, neutral, and negative tweets were 46.8%, 33.1%, and 20.1%, respectively. The results also show that in the tweets that did not express a neutral sentiment, the sentiment of surprise was the most common sentiment. It was followed by the sentiments of joy, disgust, sadness, fear, and anger. Furthermore, analysis of hashtag-specific sentiments revealed several novel insights, for instance, for almost all the months in 2022, the usage of #ai in tweets about exoskeletons was mainly associated with a positive sentiment. Sixth, text processing-based approaches were used to detect possibly sarcastic tweets and tweets that contained news. Finally, a comparison of positive tweets, negative tweets, neutral tweets, possibly sarcastic tweets, and tweets that contained news, in terms of different characteristic properties of these tweets are presented. The findings reveal multiple novel insights, for instance, the average number of hashtags used in tweets that contained news has considerably increased since January 2022.

Background: Although electronic health records (EHR) provide useful insights into disease patterns and patient treatment optimisation, their reliance on unstructured data presents a difficulty. Because of their narrative structure, echocardiography reports, which provide extensive pathology information for cardiovascular patients, are particularly challenging to extract and analyse. Although natural language processing (NLP) has been utilised successfully in a variety of medical fields, it is not commonly used in echocardiography analysis. Objectives: To develop an NLP-based approach for extracting and categorizing data from echocardiography reports by accurately converting continuous (e.g. LVOT VTI, AV VTI, and TR Vmax) and discrete (e.g. Regurgitation severity) outcomes in semi-structured narrative format into structured and categorized format, allowing for future research or clinical use. Methods: 135,062 Trans-Thoracic Echocardiogram (TTE) reports were derived from 146967 baseline Echocardiogram reports and split into three cohorts: Training and Validation (n = 1075), Test Dataset (n = 98) and Application Dataset (n = 133,889). The NLP system was developed and iteratively refined using medical expert knowledge. The system was used to curate a moderate-fidelity database from extractions of 133,889 reports. A hold-out validation set of 98 reports was blindly annotated and extracted by two clinicians for comparison with the NLP extraction. Agreement, discrimination, accuracy and calibration of outcome measure extractions were evaluated. Results: Continuous outcomes including LVOT VTI, AV VTI, and TR Vmax exhibited perfect inter-rater reliability using intra-class correlation scores (ICC=1.00, P< 0.05) alongside high R2 values, demonstrating an ideal alignment between the NLP system and clinicians. Good level (ICC =0.75-0.9, P<0.05) of inter-rater reliability were observed for outcomes such as LVOT Diam, Lateral MAPSE, Peak E Velocity, Lateral E' Velocity, PV Vmax, Sinuses of Valsalva, and Ascending Aorta diameters. Furthermore, the accuracy rate for discrete outcome measures was 91.38% in the confusion matrix analysis, indicating effective performance. Conclusions: The NLP-based technique yielded good results when it came to extracting and categorising data from echocardiography reports. The system demonstrated a high degree of agreement and concordance with clinician extractions. This study contributes to the effective use of semi-structured data by providing a useful tool for converting semi-structured text to structured echo report that can be used for data management. Additional validation and implementation in healthcare settings can improve data availability and support research and clinical decision-making.

A topical challenge for algorithms in general and for automatic image categorization and generation in particular is presented with the aim of highlighting strengths and deficiencies of current Artificial Intelligence approaches while sketching a way forward. A general lack of encompassing symbol-embedding and (not only) -grounding in some bodily basis is made responsible for current deficiencies. A concomitant dearth of hierarchical organization of concepts follows suite. As a remedy for these shortcomings, it is proposed to take a wide step back and to newly incorporate aspects of cybernetics and analog control processes. It is claimed that a promising overarching perspective is provided by the Ouroboros Model with a valid and versatile algorithmic backbone for general cognition at all accessible levels of abstraction and capabilities.

(1) Background: Artificial intelligence (AI) has existed in some form for decades, but recent rapid advances in a subset called machine learning (ML) — and more specifically deep learning (DL), a neural network-based approach — have made headlines for the potential to revolutionize and automate multiple large sectors of society, including scientific research and the healthcare field. Furthermore, large language models (LLMs) — which are built on DL — could lead to a more seamless, natural interaction between humans and computers. (2) Methods: We reviewed numerous publications on this subject from recent years. (3) Results: We found these studies collectively show that AI is positively disrupting both biomedical research and medical practice, such as optical imaging in surgery guidance. (4) Conclusions: However, we recommend caution in over-reliance on AI in the laboratory or the clinic due to anticipated risks and current limitations.

As time continues to advance, the need for robust security threat mitigation has become increasingly vital in software. However, ensuring early effective security threat mitigation requires optimal test data and consistent test case design. It is a constant struggle to maximize test coverage through test data optimization. We conducted explanatory research to maximize test coverage of security requirements as modeled in Structured Misuse Case Description (SMCD) i.e., structured specification of misuse case, so as to improve consistency in optimal test data generation. We specified constraints upon Mal activity in Object Constraint Language (OCL) in order to minimize human dependency and improve consistency in optimal test data generation. It was evident through results that MC/DC generated optimal test data of security threats through SMCD in comparison to the Decision Coverage method thus resulting in designing a significantly lower number of test cases and yet maximizing test coverage of security threats. MC/DC generated test data with n+1, while Decision Coverage generated test data with〖 2〗^n, we, therefore, conclude that MC/DC maximizes test coverage through optimal test data from SMCD in comparison to Decision Coverage.

The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use-cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times of its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today's living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset, by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 tweets about exoskeletons that were posted in a 5-year period from May 21, 2017, to May 21, 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset.

Context: Anomaly detection in a data center is a challenging task, having to consider different services on various resources. Current literature shows the application of artificial intelligence techniques to either log files or monitoring data: the former created by services at run time, while the latter produced by specific sensors directly on the physical or virtual machine. Objectives: We propose a model that exploits information both in log files and monitoring data to identify patterns and detect anomalies over time. Methods: The key idea is to use on one side natural language processing solutions to detect problems at service level, extracting words that represent anomalies. Clustering and topic modeling techniques have been used to identify patterns and group them with respect to topics. On the other side time series anomaly detection technique has been applied to sensors data in order to combine problems found in the log files with problems stored in the monitoring data. Results: We have tested our approach on a real data center equipped with log files and monitoring data that can characterize the behaviour of physical and virtual resources in production. We have observed a correspondence between anomalies in log files and monitoring data, e.g. an increase in memory usage or machine load. The results are extremely promising. Conclusion: Our model requires to integrate site administrators' expertise in order to consider all critical scenario in the data center and understand results properly.

With the rapid development of science and technology, many new concepts and terms appear, especially in English. Other languages try to express these concepts with words from their own vocabulary. In the specific case of Arabic, there are many ways to find a counterpart for a particular new concept, such as using an existing word to denote the new concept, derivation, and blending. When these methods fail, the new concepts are simply phonetically transliterated. This has the disadvantage that most of the transliterated terms do not conform to the rules of the Arabic language and lead to a distortion of the language. Some modern linguists call for using the generation strategy to translate the new terms into Arabic by using the unused Arabic roots. Therefore, it is necessary to provide a resource that contains all Arabic roots with a categorization of what is used, what is available for use, and what is rejected according to the phonetic system. This work provides a comprehensive lexicon that contains all possible Arabic triliteral roots, determines the status of each root in terms of usage and acceptability, and provides a mechanism for giving preference to roots when there is more than one root that indicates the desired meaning.

We live in a complex world characterised by complex people, complex times, and complex social, technological, and ecological environments. There is clear evidence that governments are failing at most public matters. The recent COVID-19 pandemic is a high example of global governance failure both at preventing such pandemics and managing the COVID-19 pandemic. It is time that all of us take responsibility and look into ways of collaboratively improving the governance of public matters, our matters. While there are many reasons for government failures, we believe the lack of information availability is a fundamental reason that limits the government’s ability to act smartly and allows the lack of transparency to creep into policy and action leading to corruption and failure. To this end, this paper introduces the concept of deep journalism, a data-driven deep learning-based approach for discovering multi-perspective parameters related to a topic of interest. We build three datasets (a newspaper, a technology magazine, and a Web of Science dataset) and discover the academic, industrial, public, governance, and political parameters for the transportation sector as a case study to introduce deep journalism and our tool DeepJournal (Version 1.0) that implements our proposed approach. We elaborate on 89 transportation parameters and hundreds of dimensions reviewing 400 technical, academic, and news articles. The findings related to the multi-perspective view of transportation reported in this paper show that there are many important problems seen by the public that industry and academia seem to not place their focus on. On the other hand, academia produces much broader and deeper knowledge on the subject such as a wide range of pollutions affecting the people and planet do not get to reach the public eye. Our deep journalism approach could find the gaps and highlight them to the public and other stakeholders.

Predatory research field in science is when an emerging scientific topic destroys current topics and characterizes a main scientific change. Predatory research field can be a basic driver of scientific and technological change that generates a 'creative destruction' in science and society in contexts of knowledge-based competition and rapid changes. The prediction of proposed theory of predatory research fields is that it destroys with a fast growth other research fields. The theoretical approach is tested here in research fields of large language models (LLM) by analyzing the transformers (a deep learning architecture based on the multi-head attention mechanism) proposed in 2017 and from November 2022 started main applications in generative artificial intelligence with innovations of BERT, ChatGPT, Microsoft Copilot (launched on February 7, 2023) and other natural language processing tools driven by AI technology for engaging conversations, gain insights, automate tasks, etc., etc. Statistical evidence suggests that growth rate of transformer technologies is 80.58%, a high level compared to all other research fields in machine learning (having a growth rate of 13.83%). Moreover, predatory research field of transformers has a destructive power such that all other domains in LLM from 2021 to 2023 have a general reduction of scientific growth. The impact of transformers is much more drastic of previous radical technologies such as CNN having a temporal growth rate of 0.16%, lower than 0.38% by transformers, ceteris paribus. These analysis reveals that transformers have characteristics to generate a radical scientific and technological change in a not-too-distant future. Overall, then, the study suggests that predatory research field on emerging topics and technologies can generate path-breaking innovations and the examination here can clarify the essential elements of the science dynamics for a better theory of scientific and technological change, providing also main implications for knowledge policy to support promising research fields and technologies to guide economic and social change.

Digital recruitment systems have revolutionized the hiring paradigm, imparting exceptional efficiencies and extending the reach for both employers and job seekers. This investigation scrutinizes the efficacy of classical machine learning methodologies alongside advanced large language models (LLMs) in aligning resumes with job categories. Traditional matching techniques, such as Logistic Regression, Decision Trees, Naïve Bayes, and Support Vector Machines, are constrained by the necessity of manual feature extraction, limited feature representation, and performance degradation, particularly as dataset size escalates, rendering them less suitable for large-scale applications. Conversely, LLMs like GPT-4, GPT-3, and LLAMA, adeptly process unstructured textual content, capturing nuanced language and context with greater precision. We evaluate these methodologies utilizing two datasets comprising resumes and job descriptions to ascertain accuracy, efficiency, and scalability. Our results reveal that while conventional models excel with structured data, LLMs significantly enhance the interpretation and matching of intricate textual information. This study highlights the transformative potential of LLMs in recruitment, offering insights into their application and future research avenues.

This research presents a novel approach to film analysis, leveraging word vectors to objectively evaluate movie critiques. Focusing on Director Chanwook Park’s award-winning film, ’Decision to Leave’, the study employs word vectors derived from the movie’s script of Korean text. Traditional critiques often emphasize contrasting elements and themes, but their subjective nature poses challenges in objective validation. To address this, we trained a language model using LSTM on the film’s script, obtaining word vectors that capture the essence of the narrative. These vectors were then used to perform various text analyses, including similarity and analogy operation for the keywords suggested by the critiques. By comparing the semantic relationships in the critiques with those derived from the word vectors, we could objectively validate the critiques’ assertions. Furthermore, we visualized the word vectors in a two-dimensional space, confirming the spatial relationships of key terms highlighted in critiques. The study underscores the potential of word vectors in providing a more objective lens for film analysis, bridging the gap between traditional film criticism and data-driven insights.

The work presented in this paper presents several novel findings from a comprehensive analysis of about 50,000 Tweets about online learning during COVID-19, posted on Twitter between November 9, 2021, and July 13, 2022. First, the results of sentiment analysis from VADER, Afinn, and TextBlob show that a higher percentage of these tweets were positive. The results of gender-specific sentiment analysis indicate that for positive tweets, negative tweets, and neutral tweets, between males and females, males posted a higher percentage of the tweets. Second, the results from subjectivity analysis show that the percentage of least opinionated, neutral opinionated, and highly opinionated tweets were 56.568%, 30.898%, and 12.534%, respectively. The gender-specific results for subjectivity analysis indicate that for each subjectivity class, males posted a higher percentage of tweets as compared to females. Third, toxicity detection was performed on the tweets to detect different categories of toxic content - toxicity, obscene, identity attack, insult, threat, and sexually explicit. The gender-specific analysis of the percentage of tweets posted by each gender in each of these categories revealed several novel insights. For instance, for the sexually explicit category, females posted a higher percentage of tweets as compared to males. Fourth, gender-specific tweeting patterns for each of these categories of toxic content were analyzed to understand the trends of the same. The results unraveled multiple paradigms of tweeting behavior, for instance, the intensity of obscene content in tweets about online learning by males and females has decreased since May 2022. Fifth, the average activity of males and females per month was calculated. The findings indicate that the average activity of females has been higher in all months as compared to males other than March 2022. Finally, country-specific tweeting patterns of males and females were also performed which presented multiple novel insights, for instance, in India a higher percentage of the tweets about online learning during COVID-19 were posted by males as compared to females.

Since the outbreak of COVID-19, social media platforms, such as Twitter, have experienced a tremendous increase in conversations related to Long COVID. The term “Long COVID” describes the persistence of symptoms of COVID-19 for several weeks or even months following the initial infection. Recent works in this field have focused on sentiment analysis of Tweets related to COVID-19 to unveil the multifaceted spectrum of emotions, viewpoints, and perspectives held by the Twitter community. However, most of these works did not focus on Long COVID, and the few works that focused on Long COVID have limitations. Furthermore, no prior work in this field has investigated Tweets where individuals self-reported experiencing Long COVID on Twitter. The work presented in this paper aims to address these research challenges by presenting multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between May 25, 2020, and January 31, 2023. First, the analysis shows that the average number of Tweets per month where individuals self-reported Long COVID on Twitter, has been considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings of sentiment analysis using VADER show that the percentage of Tweets with positive, negative, and neutral sentiment were 43.12%, 42.65%, and 14.22%, respectively. Third, the analysis of sentiments associated with these Tweets also shows that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.

Hate Speech Detection in Arabic presents a multifaceted challenge due to the broad and diverse linguistic terrain. With its multiple dialects and rich cultural subtleties, Arabic requires particular measures to address hate speech online successfully. To address this issue, academics and developers have used natural language processing (NLP) methods and machine learning algorithms adapted to the complexities of Arabic text. However, many proposed methods were hampered by a lack of a comprehensive dataset/corpus of Arabic hate speech. In this research, we propose a novel multi-class public Arabic dataset comprised of 403,688 annotated tweets categorized as extremely positive, positive, neutral, or negative based on the presence of hate speech. Using our developed dataset, we additionally characterize the performance of multiple machine learning models for Hate speech identification in Arabic Jordanian dialect tweets. Specifically, the Word2Vec, TF-IDF, and AraBert text representation models have been applied to produce word vectors. With the help of these models, we can provide classification models with vectors representing text. After that, seven Machine learning classifiers have been evaluated: Support Vector Machine (SVM), Logistic Regression (LR), Naive Bays (NB), Random Forest (RF), AdaBoost (Ada), XGBoost (XGB), and CatBoost (CatB). In light of this, the experimental evaluation revealed that, in this challenging and unstructured setting, our gathered and annotated datasets were rather efficient and generated encouraging assessment outcomes. This will enable academics to delve further into this crucial field of study.

Detecting suicidal ideation in adults with major depression is crucial for timely intervention and prevention of self-harm. As suicide is influenced by various biological, socio-cultural and psychological factors, traditional screening methods have accuracy and efficiency limitations. In certain cultures, societal stigma and marginalization can compel individuals with depression to conceal their suffering. Such individuals often turn to online social media platforms and share their experiences with peers under the protection of anonymity. Our research explored the potential of machine learning detection of suicidal ideation among Romanian adults with major depression that contributed to a web-based depression support forum. A trained algorithm (C4.5 decision tree) analyzed 125 posts fed to on a free access online support forum over 5 years (2014 – 2018) and classified them based on suicidal ideation content. 32 texts (25%) were identified as having a high probability of suicidal ideation content. 65% of the authors were male, with a mean age of 36.7±10.3 years and an average duration of illness of 3.4±1.4 years. Texts indicating positive suicidal ideation were generally shorter and elicited more general responses but fewer professional responses compared to those without suicidal ideation content. The study's main limitations include the relatively small number of classified texts, the absence of prospective information and the lack of qualitative evaluation of the excerpts' content. As socio-demographic and linguistic actuarial results were comparable to data reported by real life studies, we may consider basic text mining techniques as a screening tool that is able to detect suicidal ideation in texts written in unstructured Romanian language.

We introduce the Open Research Knowledge Graph Agriculture Named Entity Recognition (the ORKG Agri-NER) corpus and service for contribution-centric scientific entity extraction and classification. The ORKG Agri-NER corpus is a seminal benchmark for the evaluation of contribution-centric scientific entity extraction and classification in the agricultural domain. It comprises titles of scholarly papers that are available as Open Access articles on a major publishing platform. We describe the creation of this corpus and highlight the obtained findings in terms of the following features: 1) a generic conceptual formalism focused on capturing scientific entities in agriculture that reflect the direct contribution of a work; 2) a performance benchmark for named entity recognition of scientific entities in the agricultural domain by empirically evaluating various state-of-the-art sequence labeling neural architectures and transformer models; and 3) a delineated 3-step automatic entity resolution procedure for the resolution of the scientific entities to an authoritative ontology, specifically AGROVOC that is released in the Linked Open Vocabularies cloud. With this work we aim to provide a strong foundation for future work on the automatic discovery of scientific entities in the scholarly literature of the agricultural domain.

The critical period (CP) phenomenon in language development ranks among the 125 conundrums facing scientists in the 21st century, according to Science. While the phenomenon itself has been noncontroversial in first language acquisition, it still warrants an adequate explanation. Predicated on language acquisition as a complex process, questions among the first to be raised include: How do children accomplish this remarkable feat in such a short amount of time? And how do nature and nurture come together to influence language learning? In second language acquisition, however, both the notion of CP, albeit popular, and its empirical evidence have remained contested to this date - among the questions, whether the observed evidence counts as CP-specific and/or whether or not it warrants an isomorphic attribution to maturational constraints. Entwined in this debate are two well-established facets of inter-learner differential attainment. The first is that there exists a stark difference in ultimate attainment between younger and older learners. A second facet is the vast differences in ultimate attainment among older learners. In this article, adopting a social physics approach, we mathematically establish both the relationship between nature and nurture contributions and the presence of a critical period, and, at once, tender a parsimonious and probable theory for the twin phenomena of inter-learner differential attainment.

The enormous growth in the penetration of electric vehicles (EVs), has laid the path to advancements in the charging infrastructure. Connectivity between charging stations is an essential prerequisite for future EV adoption to alleviate users’ “range anxiety”. The existing charging stations fail to adopt power provision allocation and scheduling management. To improve the existing charging infrastructure data based on real-time information and availability of reserves at charging stations could be uploaded to the users to help them locate the nearest charging station for an EV. This research article focuses on an a interactive user application developed through SQL and PHP platform to allocate the charging slots based on estimated battery parameters, which uses data communication with charging stations to receive the slot availability information. The proposed server-based real-time forecast charging infrastructure avoids waiting times and its scheduling management efficiently prevents the EV from halting on road due to battery drain out. The proposed model is implemented using a low-cost microcontroller and the system etiquette tested.

This study explores the impact of multi-user interaction, group work, and simulations within virtual worlds on language learning and the development of intergroup empathy. Two distinct research projects were conducted, involving a total of 241 participants aged 19-45. The language learning study engaged 116 participants in diverse interactive experiences, while the intercultural study had 125 participants collaborating in multicultural groups and participating in perspective-taking simulations. Both studies employed qualitative data collection methods, including surveys, interviews, and observations. The findings suggest that the combination of networking strategies, collaborative learning, and simulations within virtual worlds contributes to improvements in learners' language proficiency, confidence, and empathy towards diverse social groups. Participants reported increased motivation and engagement, attributed to the immersive and interactive nature of the virtual environments. The studies highlight the importance of collaboration and reflection in facilitating language acquisition and intercultural understanding. Technical challenges were identified as potential barriers to implementation. The results demonstrate the potential of virtual worlds to enhance language education and foster empathy in diverse societies, offering valuable insights for educators and researchers. However, the findings may be limited by the specific contexts and sample sizes of the studies, warranting further research to explore the generalizability and long-term impact of virtual world interventions. not exaggerate the main conclusions.

As digitalization expands across all sectors, the economic toll of software defects on the U.S. economy reaches up to $2.41 trillion annually. High-profile incidents like the Boeing 787-Max 8 crash have shown the devastating potential of these defects, highlighting the critical importance of software testing within quality assurance frameworks. However, due to its complexity and resource intensity, the exhaustive nature of comprehensive testing often surpasses budget constraints. This research utilizes a machine learning (ML) model to enhance software testing decisions by pinpointing areas most susceptible to defects and optimizing scarce resource allocation. Previous studies have shown promising results using cost-sensitive training to refine ML models, improving predictive accuracy by reducing false negatives through addressing class imbalances in defect prediction datasets. This approach facilitates more targeted and effective testing efforts. Nevertheless, the generalizability of these models across different projects (cross-project) and programming languages (cross-language) remained untested. This study validates the model's applicability across diverse development environments by integrating various datasets from distinct projects into a unified, using a more interpretable ML approach. The results demonstrate that ML can support software testing decisions, enabling teams to identify up to seven times more defective modules with the same testing effort as a benchmark.

This paper proposes a new dynamic analysis of reasoning. It makes the following contributions. First, the current states of the psychology of reasoning are critically reviewed from theoretical and empirical perspectives. The long-standing controversies between logicians and psychologists are clarified by introducing the gauge theoretic structure. The debates between the mental logic theory and the mental models theory are carefully reviewed by identifying the problem types used in the empirical studies. Certain mental fluctuations are found and a quantum theoretic solution is examined. Second, it solves the measurement problem within the Hamiltonian framework. The modeling structure underlying reasoning dynamics is the electrodynamics. They are both the single-charge dynamic systems sharing the U(1) symmetry. The mental logic theory and the mental models theory are now characterized as wavefunctions in a unified account of the dynamic analysis with the yes/no measurements. The stochastic sampling method is introduced to solve the measurement problem, which has a wide range of applications in social sciences. Third, a novel metatheoretic framework of reasoning dynamics within Lagrangian framework is proposed. It solves the “Man vs. Men” problem via gauge theoretic modeling, by making the distinction between the global level and the local level. At each level, the gauge transformations are applied to achieve the global symmetry and the local gauge symmetry. It explains the meanings of the gauge principle and the Nether theorem. Fourth, the special theory of relativity is applied to address the “Man vs. Men” problem. It explains how to draw the global language cone and the individualized local language cones. Finally, it points out that borrowing modeling methods from physics has a long tradition in psychology. The present work utilizes the conceptual and modeling methods found in modern theoretical physics.

This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had the ‘neutral’ emotion. The other emotions - ‘bad’, ‘good’, ‘terrible’, and ‘great’ were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domains embedded in the tweets were found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other URLs. Finally, to support similar research and development in this field centered around the analysis of tweets, we have developed an open-access Twitter dataset that comprises tweets about the SARS-CoV-2 omicron variant since the first detected case of this variant on November 24, 2021.

Objective: The Acute Cystitis Symptom Score (ACSS) was developed and validated as a self-reporting questionnaire for diagnosing and monitoring acute, uncomplicated cystitis (AC) in female patients. The study aims at the translation of the ACSS into Greek from original Russian as a source and American English as a new master version and at its linguistic validation. Material and Methods: Three independent professional native Greek translators, two of them experts in Russian and one in English, translated the ACSS from Russian and American English into Greek. The second group of three translators translated each of the three versions back into the original language to detect or correct any important discrepancies. These three Greek versions were then used for linguistic validation. Results: The English to Greek translation reflected more the spoken language, the two Russian to Greek translations more the written, formal language. A total of 60 randomly selected females and 30 healthcare professionals was asked about their preferences and to comment on each of the three translations. Considering all comments the scientific committee (SC) developed the Greek consensus version. For cognitive assessment additional 30 healthcare professionals and 30 females were asked to comment on the intelligibility of all items. Considering their comments the linguistically validated Greek study version was established by the SC. Conclusion: The linguistically validated Greek version of the ACSS can now be used for the clinical validation study.

This study aims to put forth a new method to study the socio-spatial boundaries by using georeferenced community-authored reviews for restaurants. In this study, we show that food choice, drink choice, and restaurant ambience can be good indicators of socio-economic status of the ambient population in different neighborhoods. To this end, we use Yelp user reviews to distinguish different neighborhoods in terms of their food purchases and identify resultant boundaries in 10 North American metropolitan areas. This data-set includes restaurant reviews as well as a limited number of user check-ins and rating in those cities. We use Natural Language Processing (NLP) techniques to select a set of potential features pertaining to food, drink and ambience from Yelp user comments for each geolocated restaurant. We then select those features which determine one’s choice of restaurant and the rating that he/she provides for that restaurant. After identifying these features, we identify neighborhoods where similar taste is practiced. We show that neighborhoods identified through our method show statistically significant differences based on demographic factors such as income, racial composition, and education. We suggest that this method helps urban planners to understand the social dynamics of contemporary cities in absence of information on service-oriented cultural characteristics of urban communities.

“You should know the words by the company they keep!” has been one of the most famous slogans attributed to John Rubert Firth, 1957. This has ignited a whole school in linguistic research known as the British empiricist contextualism. Sixty years later, many un- or semi-supervised machine learning algorithms have been successfully designed and implemented aiming at extracting word meaning from within the context of a text corpus. These algorithms treat words, more or less, as vectors of real numbers representing frequencies of word occurrences within context and word meaning as positions of words in a high-dimensional vector space model. Word associations, in turn, are treated as calculated distances among them. With the rise of Deep Learning (DL) and other artificial neural networks based architectures, learning the positioning of words and extracting word associations as measured by their distances has further improved. In this paper, however, we revisited the main stream of algorithmic approaches and set the stage for a partly cross-disciplinary evaluation framework to judge about the nature of the extracted word associations by state-of-the-art machine learning algorithms. Our preliminary results are based on word associations extracted from the application of DL framework on a Google News text corpus, as well as on comparisons with human created word association lists such as word collocation dictionaries and psycholinguistic experiments. The results and conclusions provide some insights into the inherited limitations in interpreting the type of word associations and underpinning relations between words with inevitable consequences in other areas, such as extraction of knowledge graphs or image understanding.

Undoubtedly, there is a potential impact of large language models like ChatGPT in revolutionizing information retrieval and knowledge discovery, particularly in the context of the vast amount of electronic material available. On another strong note, the current work offers a first time ever mathematical approach to accurately fine tune perplexity AI, which sets a giant step ahead towards accurate next generations AI. Having started this world leading discovery, the upper and lower bounds of perplexity AI, namely , , respectively, are obtained for the first-time ever. Concluding remarks combined with emerging open problems and next research phase are provided.

This paper proposes a novel concept within Quantum Natural Language Processing (QNLP) to encode the interpersonal metafunction of Systemic Functional Linguistics (SFL), particularly tenor, into quantum circuits by treating Japanese honorifics seriously. Because of English language bias in the literature, the incorporation of nuanced aspects of interpersonal communication evident in languages, such as the aforesaid Japanese, remains under-developed and overlooked. Utilizing lambeq, monoidal categories, and string diagrams, this study extends lambeq’s quantum computing framework to capture not only grammar but also social context—specifically the roles and relationships that define interpersonal interactions in quantum circuits. This approach represents a significant step toward that end simply by defining the honorific type h in lambeq. This innovative strategy exposes the capability of quantum circuits to model the complex structures of language, moving beyond grammar and even semantics—the horizontal, textual dimension of language—to embrace the vertical, hierarchical dimension of human communication and social interaction. Through this lens, the paper underscores the potential of QNLP to transcend traditional linguistic analysis, advocating for a broader and more nuanced understanding of language that includes not only what is said but also the social persona of the interlocutors. An algorithm for parsing Japanese grammar into pregroup diagrams containing the h type is introduced along with a codebase for other researchers to use and to which to contribute. Lastly, a toy experiment is performed to demonstrate that the circuits generated from these pregroup diagrams are suitable for Quantum Machine Learning (QML) applications.

The recent past has witnessed a growing interest in technologies for creating chatbots. Advances in Large Language Models for natural language processing are underpinning rapid progress in chatbot development, and experts predict revolutionary changes in the labour market as many manual tasks are replaced by virtual assistants in a range of business functions. As the new technology becomes more accessible and advanced, more companies are exploring the possibilities of implementing virtual assistants to automate routine tasks and improve service. This article reports on qualitative inductive research undertaken within a chatbot development team operating in a major international enterprise. The findings identify critical success factors for chatbot projects, and, based on the in-depth case study, a model is put forward to aid the manageability of chatbot implementation. The presented model can serve as an exemplar guide for researchers and practitioners working on the design, development and implementation of virtual assistants. It is flexible and applicable in a wide range of business contexts, linking strategic business goals with execution steps. It is particularly applicable for teams with no experience of chatbot implementation, reducing uncertainty and managing decisions and risks throughout the project lifecycle, thereby increasing the likelihood of project success.

To proactively manage information security, enterprises often employ information security risk assessment techniques. Asset value - which is used to calculate the financial impact of possible threats - is one of the key parameters of information security risk. However, assets in a system are rarely independent, and their values are typically interdependent. Asset owners and IT teams may hold different views as regards these values, and there is thus the need to reduce subjectivity in a qualitative risk assessment. The research entails the development of a conceptual framework derived from the literature to minimize subjectivity, and the design of a system based on those concepts. The study uses the Unified Modeling Language as a design tool and puts forward an object-oriented model for defining asset values, in which the relationships between assets, vulnerabilities and related threats are identified. A “segregation of duties” approach is integrated into the risk management system to mitigate against subjectivity and better determine asset values. Survey responses from 16 practitioners working in the private and public sectors confirm the validity of the approach, but suggest it may be more workable in larger organisations where resources allow dedicated risk professionals to operate.

When animals evolve sufficient intelligence and dexterity to be able to learn to fabricate utility products (UPs) like tools, the UP's they produce become part of an induced-reproduction system that intrinsically shares many life-like traits with biological organisms, including genome-like fabrication and operation information that is physically-encoded in the animal fabricator’s neural networks. When this set of life- like traits includes a sufficient capacity for system-improving cultural evolution (UP-evolvability), the UPs become ‘para-alive’, i.e., nearly alive, or a form of non-biological UP-paralife that is equivalent to the life- status of biological viruses, plasmids, and transposons. In the companion paper I focus on the evolution of UP-paralife in the context of modern, language-capable humans and its predicted evolution going forward in time (Rice 2022). Here I look backward in time and focus on the origin of UP-paralife and its subsequent coevolution with human intelligence. I begin by determining the pathways leading to the evolution of large brains in the rare lineages of biological life that have sufficient intelligence to learn to fabricate tools –a critical first step in the evolution of UP-paralife. The simplest forms of these learning- based UPs, made by species like chimpanzees and New Caledonian crows, represent only proto-UP- paralife because they lack sufficient UP-evolvability. Expanded UP-evolvability required a combination of three attributes that enabled continuous niche-expansion of the animal fabricator via a new and advanced form of UP-mediated teamwork (TW): i) self-domestication that facilitated TW among low-related individuals, ii) learned volitional words (protolanguage) that represent ephemeral UPs that coordinate TW, and iii) learned fabrication of simple flaked-stone tools with cutting and chopping capabilities (a UP to make other structural UPs) that expanded teammate phenotypes and TW capabilities. This specific triad of attributes is synergistic because each one acts as a TW-enhancer that can gradually erode different components of the three major constraints on TW operation and expansion: too much selfishness, insufficient coordination signals, and insufficient physical traits of teammates. The increase in UP- evolvability was transformative and marked the origin of UP-paralife and the initiation of coevolution between UP-paralife (cultural evolution) and the intelligence of its hominin/human symbiont (genetic evolution) that fostered 2.5 million years of: i) continuous brain size increase and niche-expansion within the genus Homo, and ii) parallel advances in the diversity, complexity and uses of UP-paralife. This coevolution also fostered evolutionary expansion of word-based communication, and eventually language, that acted in a catalyst-like manner to facilitate the evolution of increasingly complex forms of imagination, reasoning, mentalizing, and UP-generating technology. I next focus on the evolution of creativity in the human lineage –in the form of divergent thinking and creative imagination. I conclude that the evolution of this advanced cognitive feature required a preadaptation of sufficient intelligence and is the component of human cognition that was the major causal factor generating the greatly expanded diversity and complexity of UP-paralife currently associated with modern humans. Lastly, I apply my findings to the issue of the prevalence of extraterrestrial intelligent life. I conclude that any exoplanets with detected chemical life will very rarely (e.g., probability ~10-5 for a planet closely matching Earth’s characteristics) have evolved intelligence equalling or exceeding that of humans.

We live in the information age and, ironically, meeting the core function of journalism – i.e., to provide people access to unbiased information – has never been more difficult. This paper explores deep journalism, our data-driven Artificial Intelligence (AI) based journalism approach to study how the LinkedIn media could be useful for journalism. Specifically, we apply our deep journalism approach to LinkedIn to automatically extract and analyse big data to provide the public with information about labour markets, people’s skills and education, and businesses and industries from multi-generational perspectives. The Great Resignation and Quiet Quitting phenomena coupled with rapidly changing generational attitudes are bringing unprecedented and uncertain changes to labour markets and our economies and societies, and hence the need for journalistic investigations into these topics is highly significant. We combine big data and machine learning to create a whole machine learning pipeline and a software tool for journalism that allows discovering parameters for age dynamics in labour markets using LinkedIn data. We collect a total of 57,000 posts from LinkedIn and use it to discover 15 parameters by Latent Dirichlet Allocation algorithm (LDA) and group them into five macro-parameters, namely Generations-Specific Issues, Skills & Qualifications, Employment Sectors, Consumer Industries, and Employment Issues. The journalism approach used in this paper can automatically discover and make objective, cross-sectional, and multi-perspective information available to all. It can bring rigour to journalism by making it easy to generate information using machine learning and can make tools and information available so that anyone can uncover information about matters of public importance. This work is novel since none of the earlier works have reported such an approach and tool and leveraged it to use LinkedIn media for journalism and to discover multigenerational perspectives (parameters) for age dynamics in labour markets. The approach could be extended with additional AI tools and other media.

Ongoing COVID-19 Pandemic has resulted into massive damage to various platforms of global economy which has caused disruption to human livelihood. Natural Language Processing has been extensively used in different organizations to categorize sentiments, perform recommendation, summarizing information and topic modelling. This research aims to understand the non-medical impact of COVID-19 on global economy by leveraging the natural language processing methodology. This methodology comprises of text classification which includes topic modelling on unstructured COVID-19 media articles dataset provided by Anacode. Like other Natural Language Processing algorithms, Latent Dirichlet allocation (LDA) and Non-negative matrix factorization (NMF) has been proposed to classify the media articles dataset in order to analyze COVID-19 pandemic impacts in the different sectors of global economy. Model Accuracy was examined based on the coherence and perplexity score which came out to be 0.51 and -10.90 using LDA algorithm. Both the LDA and NMF algorithm identified similar prevalent topics that was impacted by COVID-19 pandemic in multiple sectors of economy. Through intertopic distance map visualization produced by LDA algorithm, it can be reciprocated that general industries which includes children schooling, parental care, and family gatherings had the major impact followed by business sector and the financial industry.

Objectives: Studies evaluating machine learning (ML) algorithms on cross-lingual RadLex® mappings for developing context-sensitive radiological reporting tools are lacking. Therefore, we investigated whether ML-based approaches can be utilized to assist radiologists in providing key imaging biomarkers – such as The Alberta stroke programme early CT score (APECTS). Material and Methods: A stratified random sample (age, gender, year) of CT reports (n=206) with suspected ischemic stroke was generated out of 3997 reports signed off between 2015-2019. Three independent, blinded readers assessed these reports and manually annotated clinico-radiologically relevant key features. The primary outcome was whether ASPECTS should have been provided (yes/no: 154/52). For all reports, both the findings and impressions underwent cross-lingual (German to English) RadLex®-mappings using natural language processing. Well-established ML-algorithms including classification trees, random forests, elastic net, support vector machines (SVMs) and boosted trees were evaluated in a 5 x 5-fold nested cross-validation framework. Further, a linear classifier (fastText) was directly fitted on the German reports. Ensemble learning was used to provide robust importance rankings of these ML-algorithms. Performance was evaluated using derivates of the confusion matrix and metrics of calibration including AUC, brier score and log loss as well as visually by calibration plots. Results: On this imbalanced classification task SVMs showed the highest accuracies both on human-extracted- (87%) and fully automated RadLex® features (findings: 82.5%; impressions: 85.4%). FastText without pre-trained language model showed the highest accuracy (89.3%) and AUC (92%) on the impressions. Ensemble learner revealed that boosted trees, fastText and SVMs are the most important ML-classifiers. Boosted trees fitted on the findings showed the best overall calibration curve. Conclusions: Contextual ML-based assistance suggesting ASPECTS while reporting neuroradiological emergencies is feasible, even if ML-models are restricted to be developed on limited and highly imbalanced data sets.

In this paper, we present a novel approach for managing the file system in Linux using a voice assistant. Our system allows users to perform file system operations such as creating directories, renaming files, and deleting files by issuing voice commands. We develop a voice assistant using Python libraries and integrate it with the file system in Linux. The voice assistant is capable of understanding natural language and executing commands based on the user's voice inputs. We conduct experiments to evaluate the performance of the system and demonstrate that our approach is effective and efficient in managing the file system using voice commands. Our system can enhance the accessibility and usability of the file system in Linux for individuals with disabilities or those who prefer a hands-free approach to file management.

In recent times, using image-based deep learning techniques has been very useful for classifying fruits. However, mango is a particular challenge because its shelf life is directly related to the degree of maturity at the time of harvest, which is determined mainly through the visual assessment of the operators when sorting them, doing it incorrectly, coupled with the blows and compressions exposed during storage and transport, trigger the deterioration in their quality, hence the importance of this operation. In this sense, the purpose of this study focuses on designing a computational model based on Deep Learning that uses a convolutional neural network (CNN) parameterized to select mangoes according to their color tone based on their maturity stage. A dataset was established from 201 labeled images, then a CNN architecture was built, obtaining a model on which 3 criteria were evaluated: the number of convolution and grouping layers, the resolution of the input images and the number of training epochs required. As a result, an input image resolution of 32 x 32 pixels was achieved, the process included a convolution layer followed by pooling that fed another layer that processed the results and ended in an output layer with 2 neurons under the One Hot Encoding representation. The model required 20 epochs to perfect its training, achieving 96.04 % accuracy in mango image selection. This study supports the potential of CNNs in quality analysis through mango color selection, even in uncertainty scenarios.

We investigated how Artificial Intelligence (AI) reveals factors shaping COVID-19 vaccine hesitancy among healthcare providers by examining their open-text comments. We conducted a longitudinal survey starting in spring of 2020 with 38,788 current and former female nurses in three national cohorts to assess how the pandemic has affected their livelihood. In January and March-April 2021 surveys, participants were invited to contribute open-text comments and answer specific questions about COVID-19 vaccine uptake. A closed-ended question in the survey identified vaccine-hesitant (VH) participants who either had no intention or were unsure of receiving a COVID-19 vaccine. We collected 1,970 comments from VH participants and trained two Machine Learning (ML) algorithms to identify behavioral factors related to VH. The first predictive model classified each comment into one of three Health Belief Model (HBM) constructs (barriers, severity, and susceptibility) related to adopting disease prevention activities. The second predictive model used the words in January comments to predict the vaccine status of VH in March-April 2021; vaccine status was correctly predicted 89% of the time. Our results showed that 35% of VH participants cited barriers, 17% severity, and 7% susceptibility to receiving a COVID-19 vaccine. Out of the HBM constructs, the VH participants citing a barrier, such as allergic reactions and side effects, had the most associated change in vaccine status from VH to later receiving a vaccine.

The public release of ChatGPT, a generative artificial intelligence language model, caused wide-spread public interest in its abilities but also concern about the implications of the application on academia, depending on whether it was deemed benevolent (e.g., supporting analysis and simplification of tasks) or malevolent (e.g., assignment writing and academic misconduct). While ChatGPT has been shown to provide answers of sufficient quality to pass some university exams, its capacity to write essays that require an exploration of value concepts is unknown. This paper presents the results of a study where ChatGPT 4 (release May 2023) was tasked with writing a 1500-word essay to discuss the nature of values used in the assessment of cultural heritage significance. Based on an analysis of 36 iterations, ChatGPT writes essays of limited length of about 50% of the stipulated word count which are primarily descriptive without any depth or complexity. The concepts, which are often flawed and suffer from inverted logic, are presented in an arbitrary sequence with limited coherence and without any defined line of argument. Given that it is a generative language model, ChatGPT often splits concepts and uses one or more words to develop tangential arguments. While ChatGPT provides references as tasked, many are fictitious, albeit with plausible authors and titles. At present ChatGPT has the ability to critique its own work but seems unable to incorporate that critique in a meaningful way to improve a previous draft. Setting aside conceptual flaws such as inverted logic, several of the essays could possibly pass as a junior school assignment, but fall far short of what would be expected in senior school, let alone at a college and university level.

Long unpunctuated texts containing complex linguistic sentences are a stumbling block to processing any low-resource languages. Thus, approaches that attempt to segment lengthy texts with no proper punctuation into simple candidate sentences are a vitally important preprocessing task in many hard-to-solve NLP applications. In this paper, we propose (PDTS) a punctuation detection approach for segmenting Arabic text, built on top of a multilingual BERT-based model and some generic linguistic rules. Furthermore, we showcase how PDTS can be effectively employed as a text tokenizer for unpunctuated documents (i.e., mimicking the transcribed audio-to-text documents). Experimental findings across two evaluation protocols (involving an ablation study and a human-based judgment) demonstrate that PDTS is practically effective in both performance quality and computational cost.

Artificial intelligence (AI) techniques have grown rapidly in recent years in the context of computing with smart mobile phones that typically allows the devices to function in an intelligent manner. Popular AI techniques include machine learning and deep learning methods, natural language processing, as well as knowledge representation and expert systems, can be used to make the target mobile applications intelligent and more effective. In this paper, we present a comprehensive view on mobile data science and intelligent apps in terms of concepts and AI-based modeling that can be used to design and develop intelligent mobile applications for the betterment of human life in their diverse day-to-day situation. This study also includes the concepts and insights of various AI-powered intelligent apps in several application domains, ranging from personalized recommendation to healthcare services, including COVID-19 pandemic management in recent days. Finally, we highlight several research issues and future directions relevant to our analysis in the area of mobile data science and intelligent apps. Overall, this paper aims to serve as a reference point and guidelines for the mobile application developers as well as the researchers in this domain, particularly from the technical point of view.

Understanding brain function is one of the most important open problems in science today. At present, there is no concrete theory for how the brain works. Here, a theory is presented that provides a detailed mechanistic biological account of the brain’s capacities. Brain function is managed by a response (R) process that is structurally similar to the immune response, and shows anatomical and molecular specificity. Different R process stages utilize different cortical layers, hippocampus fields, basal ganglia paths, GABAergic interneurons, cerebellum paths, and molecular agents such as dopamine, serotonin and opioids. We show how the R process supports hierarchical action sequences, language and thought. The theory is supported by a large body of experimental evidence in many modalities, and accounts for virtually all of the major facts known about the brain at the system level.

As digital communication becomes increasingly integral to personal and corporate activities, phishing attacks have emerged as a prevalent threat, ingeniously mimicking legitimate sources to illicitly acquire sensitive information. This research paper details the development of a sophisticated phishing detection application utilizing the DistilBERT-based model, finetuned on a diverse array of email datasets. The application significantly enhances the precision of phishing detection mechanisms, adeptly reducing the incidence of successful phishing attacks. Initial tests have demonstrated a precision rate of over 95% in detecting phishing emails, outperforming traditional rule-based filters substantially. The application exhibits robust defences against zero-day phishing attacks through its advanced machine learning framework, which dynamically adapts to emerging phishing strategies. This paper explores the methodology of developing the DistilBERT model, evaluates its efficacy against existing solutions, and discusses its implications for future cybersecurity practices. The study’s findings underscore the potential of AI-driven tools in transforming cybersecurity measures, offering a proactive approach to thwarting phishing attempts and safeguarding sensitive data.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

Multilingual classrooms are becoming more common worldwide due to increasing mobility. Teaching foreign languages using the mother tongue in multilingual classrooms has become problematic; some of the significant concerns in Japanese language acquisition – such as the high contextuality of the language, vocabulary and kanji learning – make the direct teaching method confusing for many students, especially at the beginner and elementary levels. This article provides the preliminary results of a study carried out as a component of a more extensive empirical research project on instructional languages in classrooms teaching Japanese as a foreign language. It aims to gather, analyse and synthesise data from a survey conducted at a supranational level. The results of the first two sections of a questionnaire containing closed and open-ended questions were used for a quantitative analysis of the profile of Japanese language instructors with a focus on finding possible connections. The wide geographic distribution of the respondents (274 teachers from fifty-seven countries and regions) made it possible to collect data from a more representative sample and draw meaningful conclusions about the spread of multilingual classrooms that would be impossible in smaller-scale research. The study’s primary results show the proportion of multilingual classrooms among countries in different regions and across different educational stages, the distribution of Japanese language instructors in various educational stages depending on the highest level of formal education attained, and the proportion of part-time instructors working in different academic institutions.

Human-to-human communication via the computer is mainly done using a keyboard or microphone. In the field of Virtual Reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g. silent commands during task force training) or simply not possible (e.g. if the user has a hearing loss). Data gloves help to increase immersion within the VR as they correspond to our natural interaction. At the same time, they offer the possibility to accurately capture hand shapes, such as those used in non-verbal communication (e.g. thumbs up, okay gesture, ...) and in sign language. In this paper, we present a hand shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an Outlier Detection and a Feature Selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial Data Augmentation, i.e., we create new artificial data from the recorded and filtered data to augment the training dataset. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. Voting Meta-Classifier (VL2) has proven to be the most accurate, albeit slowest, classifier. A good alternative is Random Forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. Outlier Detection has proven to be a effective approach, especially in improving classification time. Overall, we have shown that our hand shape recognition system using data gloves is suitable for communication within VR.

The rapid proliferation of information in the digital era underscores the importance of accurate historical representation and interpretation. While artificial intelligence (AI) has shown promise in various fields, its potential for historical fact-checking and gap-filling remains largely untapped. This study evaluates the performance of three large language models (LLMs)—GPT-3.5, GPT-4, and Google- BARD—in the context of predicting and verifying historical events based on given data. A novel metric, "Distance to Reality" (DTR), is introduced to assess the models' outputs against established historical facts. The results reveal a substantial potential for AI in historical studies, with GPT-4 demonstrating superior performance. This paper underscores the need for further research into AI's role in enriching our understanding of the past and bridging historical knowledge gaps.