In the present study, we investigate the post-editese phenomenon, i.e., the unique features that set machine translated post-edited texts apart from human-translated texts. We use two literary texts, namely, the English children's novel by Lewis Carroll Alice’s Adventures in Wonderland (AW) and Paula Hawkins' popular book The Girl on the Train (TGOTT) translated from English into Brazilian-Portuguese to investigate whether the post-editese features can be found on the surface of the post-edited (PE) texts. In addition, we examine how the features found in the PE texts differ from the features encountered in the human-translated (HT) and machine translation (MT) versions of the same source text. Results revealed evidence for post-editese for TGOTT only with PE versions being more similar to the MT output than to the HT texts.

Background: Gastric cancer (GC) is one of the leading malignancy diseases worldwide, especially in Asian. CAST is a potential oncogene in GC carcinogenesis process. The character of macrophage infiltration in GC microenvironment was also unaddressed. Methods: We first applied machine searching in gene candidate evaluation of GC. CAST expression was analyzed via the Human Protein Atlas (HPA) and Gene Expression Profiling Interactive Analysis 2 (GEPIA2) database. Protein-protein interaction (PPI) network was downloaded from STRING. We investigated the impact of CAST on clinical prognosis using Kaplan-Meier plotter. The correlations between CAST and Lgr5 and macrophage infiltration in GC was surveyed via TIMER 2.0. Finally, GeneMANIA was also used to evaluate the possible functional linkage between genes. Results: After machine-assisted searching, CAST expression was found signicant difference in the overall survival of GC patients. STRING revealed CAST related proteomics and transcriptomics associations, mainly about CAPN family. Moreover, CAST significantly impacts the prognosis of GC from other datasets validation. Notably, high CAST expression was correlated with worse overall survival in GC patients (hazard ratio = 1.59; logrank P = 9.4 x 10-8). CAST and Lgr5 expressions were both positively correlated with WNT 2 and WNT 2B. Among GC patients in several datasets, CAST and macrophage infiltration evaluated together showed no obvious trend toward poor clinical overall survival. Conclusion: CAST plays an important role in GC clinical prognosis and is associated with WNT 2/WNT 2B/Lgr5. Our study denmostrated that CAST in GC overall survival is regulated by macrophage infiltration.

The color and NIR spectrum are key to build an oil estimation model, thus it requires individual olives clustering before the Sohlext oil extraction method can be applied. The objective was to analyze an OC estimation model of individual olives, based on cluster of similar color and NIR spectrum in different combination of the first and/or the second season. This study was performed with Chilean Arbequina olives in 2016 and 2017. The descriptor of the cluster consisted of the 3 color channels of c1, c2, c3 color model plus 11 reflectance points between 1710 and 1735 nm of each olive, normalized with the Z-score index. Clusters of similar color and NIR spectrum were formed with the k-means++ algorithm, leaving a sufficient amount of olives to be able to perform the Sohlext analysis of OC, as reference value. The estimation models were based on the Support Vector Machine. The test was carried out with the Leave One-Out Cross Validation in different training-testing combinations. The best model predicted the OC with 6% and 13%deviation respect to the real value in one season by itself and when one season tested with another season, respectively. The use of clustering in estimation model is discussed.

Precision agriculture is a technology used by farmers to help food sustainability amidst growing population. One of the tools of precision agriculture is yield monitoring, which helps a farmer manage his production. Yield monitoring is usually done during harvest, however it could also be done early in the growing season. Early prediction of yield, specifically for fruit trees, aids the farmer in the marketing of their product and assists in managing production logistics such as labor requirement and storage needs. In this study, a machine vision system is developed to estimate fruit yield early in the season. The machine vision system uses a color camera to capture images of fruit trees during the full bloom period. An image segmentation algorithm based on an artificial neural network was developed to recognize and count the blossoms on the tree. The artificial neural network segmentation algorithm uses color information and position as input. The resulting correlation between the blossom count and the actual number of fruits on the tree shows the potential of this method to be used for early prediction of fruit yield.

Machine learning (ML) methodologies have been looked upon recently as a potential candidate for mitigating nonlinearity issues in optical communications. In this paper, we experimentally demonstrate a 40-Gb/s 256-quadrature amplitude modulation (QAM) signal-based Radio over Fiber (RoF) system for 50 km of standard single mode fiber length which utilizes support vector machine (SVM) decision method to indicate an effective nonlinearity mitigation. The influence of different impairments in the system is evaluated that includes the influences of Mach-Zehnder Modulator nonlinearities, in-phase and quadrature phase skew of the modulator. By utilizing SVM, the results demonstrated in terms of bit error rate and eye linearity suggest that impairments are significantly reduced and licit input signal power span of 5dBs is enlarged to 15 dBs.

In the fashion field, the use of electroplated small metal parts such as studs, clips and buckles is widespread. The plate is often made of precious metal, such as gold or platinum. Due to the high cost of these materials, it is strategically relevant and of primary importance for manufacturers to avoid any waste by depositing only the strictly necessary amount of material. To this aim, Companies need to be aware of the overall number of items to be electroplated so that it is possible to properly set the parameters driving the galvanic process. Accordingly, the present paper describes a Machine Vision-based method able to automatically count small metal parts arranged on a galvanic frame. The devised method relies on the definition of a proper acquisition system and on the development of image processing-based routines. Such a system is then implemented on a counting machine is meant to be adopted in the galvanic industrial practice to properly define a suitable set or working parameters (such as current, voltage and deposition time) for the electroplating machine and, thereby, to assure the desired plate thickness from one side and to avoid material waste on the other.

In this paper, modification of dual-Halbach permanent magnet (PM) array is investigated to improve performance of tubular linear machine, in terms of flux density and output power. Instead of a classical Halbach array with only radial and axial PMs, proposed model involves insertion of mig-magnets, which have magnetized angle shifted from the reference magnetized angles of axial and radial PMs. This structure leads to elimination of flux leakage and concentration of flux linkage in middle of the coil; therefore, the performance of machine is increased.

Neural microprobes represent an important component of neural prosthetic systems where implanted microprobes record the electro-potentials generated by specific thoughts in the brain and convey the signals to algorithms trained to interpret these thoughts. Here, we present novel elongated multi-site neural probe that can reach depths greater than 10mm. We hypothesize that reaching such depth allows the recording of cognitive signals required to drive cognitive prosthetics. The impedance of the recording sites on the probes was on the order of 500 kΩ at 1 kHz, which is consistent with probes used for neurophysiological recordings. The probes were made porous using Xenon Difluoride (XeF₂) dry etching to improve the biocompatibility and their adherence to the surrounding neural tissue. Numerical studies were performed to determine the reliability of the porous probes. We implanted the elongated probe in rats and show that the elongated probes are capable of simultaneously recording both spikes and local field potentials (LFPs) from various recording sites.

COVID-19 was a terrible worldwide pandemic that caused a global public health crisis, with numerous deaths and a severe economic depression. To suppress the spread of COVID-19 and initiate patient isolation and contact tracing to reduce its effect, early diagnosis was required, with real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test being the most widely used. However, the RT-PCR test was sometimes found to be lengthy and inaccurate and; for that reason, in many cases of severe complications of COVID-19 pneumonia, chest screening with radiography imaging was preferred. In this paper, we carried out a study for COVID-19 detection from computed tomography images, and compared the obtained results using deep learning (DL) and support vector machine (SVM). The findings showed that, despite the excellent results shown by deep learning, the accuracy in predicting COVID-19 using SVM, in the presence of small databases, was slightly higher than DL. The execution time using SVM was also shorter

This study explored the incidence of metaphorical virtual classrooms and interactive learning objects in the interaction of students in online mode. The main objective was to analyze how these digital tools, driven by a set of strategies to promote their use, affect the interaction of students in the virtual classroom system and their derived effects. To this end, the latest version of Moodle was implemented in conjunction with gamification plugins and interactive tools in the higher education institution used as a case study. The methodology consisted of data collection through ordinal instruments applied to the teachers and student performance metrics gathered using a plugin developed to extract accurate metrics of each student's usage and performance through direct queries to the Moodle database and its processing through a neural network. This facilitated the collection of standardized data on the actual metrics of each virtual classroom at the end of the teaching of each subject from both the previous LMS and the newly implemented one. This data was then analyzed using advanced statistical techniques, including Mahalanobis distances, confirmatory factor analysis, and the Wilcoxon signed-rank test. These methods provided a compelling comparison between the old and new systems, revealing significant improvements in the metrics and factors evaluated. The results showed a significant improvement in teachers' perceptions of the usability of the virtual classroom system and an increase in students' academic performance, interaction, progress, and time spent learning in virtual contexts. These results provide solid empirical evidence of the added value of these educational tools as effective strategies for improving student interaction, performance, and motivation in online education.

The advent of deep learning (DL) has revolutionized medical imaging, offering unprecedented avenues for accurate disease classification and diagnosis. DL models have shown remarkable promise for classifying brain tumor from Magnetic Resonance Imaging (MRI) scans. However, despite their impressive performance, the opaque nature of DL models poses challenges in understanding their decision-making processes, particularly crucial in medical contexts where interpretability is essential. This paper explores the intersection of medical image analysis and DL interpretability, aiming to elucidate the decision-making rationale of DL models in brain tumor classification. Leveraging state-of-the-art DL frameworks with transfer learning, we conduct a comprehensive evaluation encompassing both classification accuracy and interpretability. Using state-of-the-art DL frameworks with transfer learning, we conduct a thorough evaluation covering both classification accuracy and interpretability. We employ adaptive path-based techniques to understand the underlying decision-making mechanisms of these models. Grad-CAM and Grad-CAM++ highlight critical image regions where the tumors are located.

In recent years, the viability of employing multi-agent reinforcement learning technology for adaptive traffic signal control has been extensively validated. However, owing to restricted communication among agents and the partial observability of the traffic environment, the process of mapping road network states to actions encounters numerous challenges. To address this problem, this paper proposes a multi-agent deep reinforcement learning model with an emphasis on communication content (CMARL). The model decouples the complex relationships between multi-signal agents through centralized training and decentralized execution. Specifically, we first pass the traffic state through an improved deep neural network to achieve the extraction of high-dimensional semantic information and the learning of the communication matrix. Then the agents selectively interact with each other based on the learned communication matrix and generate the final state features. Finally, the features are inputted to the QMIX network to achieve the final action selection. We compare the CMARL model with 6 other baseline algorithms in real traffic networks. The results show that CMARL can significantly reduce vehicle congestion, and run stably in various scenarios.

This study addressed the need for efficient data collection in micro-mobility using low-end hardware, focusing on developing a system capable of handling large real-time data streams. We proposed a novel data collection system that leverages incremental learning techniques, enabling AI-based services for micro-mobility passengers through smartphones. Our approach utilizes 3-axis acceleration sensors integrated into micro-mobility devices, collecting motion data in real-time. This data is processed through an incremental learning pre-processing server, employing the Broad Learning System algorithm, to ensure robust performance in noisy, real-world environments. The use of incremental learning is pivotal, allowing our system to adapt continuously to new data, enhancing the accuracy and relevance of the AI services provided to passengers. This research not only advances the technical capabilities in micro-mobility data handling but also has the potential to enhance the overall passenger experience through improved AI-based services.

When analyzing user geospatial information through deep learning methods, it is typically necessary to annotate existing geospatial data. Currently, manual annotation methods are commonly employed, suffering from issues related to low efficiency and accuracy. This design is based on the TensorFlow deep learning framework and first realizes the BiLSTM+CRF+AGG deep learning model. Among the models, AGG is the aggregation layer, which is introduced to solve the problem of solid particle size equilibrium. Secondly, based on the characteristics of original data and professional data, an automatic labeling algorithm is proposed. The algorithm first preprocesses the acquired original data set and professional data set, The most valuable unlabeled data set which can make the training model converge quickly is selected from the original data set as the target data set. Sort the target data set based on preset rules and set annotation parameters for the sorted target data set. Based on the set annotation parameters, the corpus is synthesized and used as the annotation result. Thirdly, based on the active learning strategy, a manual annotation auxiliary scheme is proposed, and an Excel generation module that is convenient for manual annotation correction is designed and developed to further improve the efficiency and quality of annotation through iterative processing. The combination of the BiLSTM+CRF+AGG deep learning model and high-quality annotation can accurately identify non-standard, incomplete, or even incorrect geographical information entities. This annotation method has found successful practical application within the context of the research project and has been granted an invention patent.

Abstract:The objects of fine-grained image categories(e.g., bird species) are various subclass under different categories. Because the differences between subclass are very subtle and most of them are concentrated in multiple local areas, the task of fine-grained image recognition is very challenging. At the same time, some fine-grained networks tend to focus on a certain region when judging the target category, resulting in the lack of other auxiliary regional features. To this end, Inception V3 is used as the backbone network, and an enhanced and complementary fine-grained image classification network is designed. While adopting the method of reinforcement learning to obtain more detailed fine grain image features, the complementary network can obtain the complementary discriminant area of the target through the method of attention erasure to increase the network's perception of the overall target. Finally, experiments are conducted on CUB-200-2011, FGVC Aircraft and Stanford dogs three open datasets. The experimental results show that the proposed model has better performance.

A topic of growing interest in urban remote sensing is the automated extraction of geometrical building information for 3D city modeling. Roof geometry information is useful for applications such as urban planning, solar potential estimation and telecommunication installation planning, and wind flow simulations for pollutant diffusion analysis. Recent research has proven that the advance in remote sensing technologies and deep learning methods offer the prospects of deriving the roof structure information accurately and efficiently. In this study, we propose a Vectorized Roof Extractor- method based on Fully Convolutional Networks (FCNs) and advanced polygonization method to extract roof structure from aerial imagery and a normalized Digital Surface Models (nDSM) in a regularized vector format. The roof structure consists of building outlines, external edges of the building roof, inner rooflines, internal intersections of the main roof planes. The methodology is comprised of segmentation, vectorization and post-processing for outer rooflines, external edges of the building roof, and inner rooflines, and internal intersections of the main roof planes. For the comparison, we adapt the Frame field Learning (FFL) method originally designed to extract building polygons [1]. Our experiments are conducted on a custom data set derived for the city of Enschede, The Netherlands, using aerial imagery, nDSM and manually digitized training polygons. The results show that the proposed Vectorized Roof Extractor outperformed adapted FFL on PoLiS distance with values of 3.5 m and 1.2 m for outlines and inner rooflines, respectively. Furthermore, the model surpassed the adapted FFL on PoLiS-thresholded F-score for outlines and inner rooflines, with 0.31 and 0.57, respectively. The Vectorized Roof Extractor produced adequate visual results, with straighter walls and fewer missed inner roofline detections. It can predict buildings with common walls thanks to skeleton graph computation. To summarize, the proposed method is suitable for urban applications and has the potential to be improved further.

"Finding fresh water in the ocean of data." is a challenge that all deep learning domains struggle with, especially in the area of hyperspectral image analysis. As hyperspectral remote sensing technology advances by leaps and bounds, there are increasing amounts of hyperspectral images(HSIs) can be available. Whereas in fact, these unlabeled HSIs are powerless to be used as material to driven a supervised learning task due to the extremely expensive labeling costs and some unknown regions. Although learning-based methods have achieved remarkable performance due to their superior ability to represent features, at the cost, these methods are complex, inflexible and tough to carry out transfer learning. In this paper, we propose the "Instructional Mask AutoEncoder"(IMAE), which is a simple and powerful self-supervised learner for HSI classification that uses a transformer-based mask autoencoder to extract the general features of HSIs through a self-reconstructing agent task. Moreover, we utilize the metric learning to perform an instructor which can direct the model focus on the human interested region of the input so that we can alleviate the defects of transformer-based model such as local attention distraction, lack of inductive bias and tremendous training data requirement. In downstream forward propagation, instead of global average pooling, we employ a learnable aggregation to put the tokens into fullplay. The obtained results illustrate that our method effectively accelerates the convergence rate and promotes the performance in downstream task.

The Kuwaiti dialect is a particular dialect of Arabic spoken in Kuwait; it differs significantly from standard Arabic and the dialects of neighboring countries in the same region. Few research papers with a focus on the Kuwaiti dialect have been published in the field of NLP. In this study, we created Kuwaiti dialect language resources using Q8VaxStance, a vaccine stance labeling system for a large dataset of tweets. This dataset will fill this gap and provide a valuable resource for researchers studying vaccine hesitancy in Kuwait. Furthermore, it will contribute to the Arabic natural language processing field by providing a dataset for developing and evaluating machine learning models for stance detection in the Kuwaiti dialect. The proposed vaccine stance labeling system combines the benefits of weak supervised learning and zero-shot learning; for this purpose, we implemented 52 experiments on 42815 unlabeled tweets extracted between December 2020 and July 2022. The results of the experiments show that using keyword detection in conjunction with zero-shot model labeling functions is significantly better than using only keyword detection labeling functions or just zero-shot model labeling functions. Furthermore, using the Arabic language in both the labels and prompt or a mix of Arabic labels and an English prompt is statistically significant compared to using English in both the labels and prompt for the total number of generated labels evaluation metric. Finally, the best accuracy for Macro-F1 values were found in the experiments KHZSLF-EE4 and KHZSLF-EA1, with values of 0.83 and 0.83, respectively. And, for the total automatically labeled data evaluation metric, experiment KHZSLF-EE4 labeled 42,270 tweets, while experiment KHZSLF-EA1 was able to generate 42,764 labels.

Bone metastasis detection and quantification on bone scintigraphy is challenging and clinical important for treatment and patient’s life quality. To develop a CNN based diagnostic system for automated segmentation on bone metastasis regions is non-trivial, especially for a small dataset. A dataset in house comprising 100 breast cancer patients and 100 prostate cancer patients is utilized for this research. The Double U-Net model is adapted through the integration of background removal, adding negative samples, and transfer learning methods for bone metastasis detection. The performance is investigated via 10-fold cross-validation and computed in pixel-wise scale. The best model we achieved has precision of 63.08%, sensitivity of 70.82%, and F1-score of 66.72%. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

Industrial data scarcity is one of the largest factors holding back the widespread use of machine learning in manufacturing. To overcome this problem, the concept of transfer learning was developed and it achieved high attention in recent industrial research. Our paper focuses on the problem of time series segmentation and presents the first in-depth research about transfer learning for deep-learning based time series segmentation on the example of industrial end-of-line pump testing. In particularly, we investigate if the performance of deep learning models can be increased by pretraining the network with data from other domains. Three different scenarios are analyzed: source and target data being closely related, source and target data being distantly related, and source and target data being non-related. The results demonstrate that transfer learning can enhance the performance of time series segmentation models in respect to accuracy and training speed. The benefit is most clearly seen in scenarios where source and training data are closely related and the number of target training data samples is lowest. However, in the scenario of non-related datasets, cases of negative Transfer Learning were observed as well. Thus, the research emphasizes the potential, but also the challenges of industrial Transfer Learning.

Protein phosphorylation and ADP-ribosylation (ADPr), as two types of post-translational modifications (PTM), are the process of adding phosphate group and ADP-ribose moieties to proteins, respectively. Although both PTM types can occur on many amino acid types, serine is the most common. Serine phosphorylation (pS), serine ADPr (SADPr), and their in situ crosstalks (pSADPr) play essential roles in biological processes. Although in silico classifiers have been developed for predicting pS and SADPr sites, the classifier for predicting pSADPr sites is unavailable. In this study, we developed classifiers to predict pSADPr sites. Specifically, we collected 3250 human pSADPr, 7520 SADPr, 151,227 pS and 80,096 unmodified serine sites. Based on them, we investigated the characteristics of pSADPr sites and constructed three classifiers to predict pSADPr sites from the pS dataset, the SADPr dataset and the protein sequences separately. We built and evaluated five deep-learning classifiers in ten-fold cross-validation and independent test datasets. Three of them (e.g. Convolutional Neural Network with the One-Hot encoding, dubbed CNNOH) performed better than the rest two. For instance, CNNOH had the AUC values of 0.700, 0.914 and 0.954 for recognizing pSADPr sites from the SADPr, pS and unmodified serine sites.Therefore, it is challenging to distinguish pSADPr sites from SADPr sites compared to the other two. It is consistent with our observation that pSADPr's characteristics are more similar to those of SADPr than the rest. Furthermore, we used the classifiers as base classifiers to develop a few stacking-based ensemble classifiers to improve performance. However, none of the ensemble classifiers showed better performances, suggesting that the base classifiers have good enough performances. Finally, we developed an online tool for extensively predicting human pSADPr sites based on the CNNOH classifier, dubbed EdeepSADPr. It is freely available through http://edeepsadpr.bioinfogo.org/.

Coronavirus disease since December 2019 has significantly affected millions of people. Given the effect this disease has on the pulmonary systems of humans, there is a need for chest radiographic imaging (CXR) for monitoring the disease and preventing further deaths. Several studies have been shown that Deep Learning models can achieve promising results for COVID-19 diagnosis towards the CXR perspective. However, this research field is at an initial stage since there is a limited number of large CXR repositories regarding COVID-19. In this study, five deep learning models were analyzed and evaluated with the aim of identifying COVID-19 from chest X-Ray images. More specifically, we utilized the ResNet50, ResNet101, DenseNet121, DenseNet169 and InceptionV3 using Transfer Learning. All models were trained and validated on the largest publicly available repository for COVID-19 CXR images. Furthermore, they were evaluated on unknown data as well, that was not used for training or validation, authenticating their performance, and clarifying their usage in a medical scenario. All models achieved satisfactory performance where ResNet101 was the superior model achieving 96% in Precision, Recall, and Accuracy. Our outcomes show the potential of deep learning models on COVID-19 medical offering a promising way for the deeper understanding of COVID-19.

The keyless systems have replaced the old fashion methods of inserting physical keys in the keyhole to, i.e., unlock the door, because they are inconvenient and easy to be exploited by the threat actors. Keyless systems use the technology of radio frequency (RF) as an interface to transmit signals from the key fob to the vehicle. However, Keyless systems are susceptible to being compromised by a thread actor who intercepts the transmitted signal and performs a reply attack. In this paper, we propose a transfer learning-based model to identify the replay attacks launched against remote keyless controlled vehicles. Specifically, the system makes use of a pre-trained ResNet50 deep neural network to predict the wireless remote signals used to lock or unlock doors of a remote-controlled vehicle system remotely. The signals are finally classified into three classes: real signal, fake signal high gain, and fake signal low gain. We have trained our model with 100 epochs (3800 iterations) on a KeFRA 2022 dataset, a modern dataset. The model has recorded a final validation accuracy of 99.71% and a final validation loss of 0.29% at a low inferencing time of 50 ms for the model-based SGD solver. The experimental evaluation revealed the supremacy of the proposed model.

Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (Predictability, Computability, Stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning (Yu and Kumbier, 2020), to the design of RL algorithms for the digital interventions setting. Further, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We illustrate the use of the PCS framework for designing an RL algorithm for Oralytics, a mobile health study aiming to improve users' tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022.

Regardless of country and age, the importance of history education is always being emphasized. Although the importance of history education is being emphasized in Korea, there are many difficulties in getting students to understand history properly through school classes alone, and it is also difficult to attract students to participate in classes. The effectiveness of education using games has been proven 20 years ago, and the demand for game-based education is gradually increasing in the current education world, which is becoming more open. In this paper, based on the effects proven through research on the existing game-based education, the improvement of historical thinking ability, experiential history learning, and the problems of game-based education introduced in the ESN report and the discomfort of teachers who participated in the education were improved. A plan was suggested to select and use games suitable for basic education. In this thesis, we selected a history game with a clear historical and periodic background and without distortion of history, and experimented with teaching using games focusing on historical thinking and empirical history learning. The learning achievement of textbook-based education was compared.

Regardless of country and age, the importance of history education is always being emphasized. Although the importance of history education is being emphasized in Korea, there are many difficulties in getting students to understand history properly through school classes alone, and it is also difficult to attract students to participate in classes. The effectiveness of education using games has been proven 20 years ago, and the demand for game-based education is gradually increasing in the current education world, which is becoming more open. In this paper, based on the effects proven through research on the existing game-based education, the improvement of historical thinking ability, experiential history learning, and the problems of game-based education introduced in the ESN report and the discomfort of teachers who participated in the education were improved. A plan was suggested to select and use games suitable for basic education. In this thesis, we selected a history game with a clear historical and periodic background and without distortion of history, and experimented with teaching using games focusing on historical thinking and empirical history learning. The learning achievement of textbook-based education was compared.

In software development, developers received bug reports that describe the software bug. Developers find the cause of bug through reviewing the code and reproducing the abnormal behavior that can be considered as tedious and time-consuming processes. The developers need an automated system that incorporates large domain knowledge and recommends a solution for those bugs to ease on developers rather than spending more manual efforts to fixing the bugs or waiting on Q&A websites for other users to reply to them. Stack Overflow is a popular question-answer site that is focusing on programming issues, thus we can benefit knowledge available in this rich platform. This paper, presents a survey covering the methods in the field of mining software repositories. We propose an architecture to build a recommender System using the learning to rank approach. Deep learning is used to construct a model that solve the problem of learning to rank using stack overflow data. Text mining techniques were invested to extract, evaluate and recommend the answers that have the best relevance with the solution of this bug report.

We propose a novel transfer learning method for speech emotion recognition allowing us to obtain promising results when only few training data is available. With as low as 125 examples per emotion class, we were able to reach a higher accuracy than a strong baseline trained on 8 times more data. Our method leverages knowledge contained in pre-trained speech representations extracted from models trained on a more general self-supervised task which doesn’t require human annotations, such as the wav2vec model. We provide detailed insights on the benefits of our approach by varying the training data size, which can help labeling teams to work more efficiently. We compare performance with other popular methods on the IEMOCAP dataset, a well-benchmarked dataset among the Speech Emotion Recognition (SER) research community. Furthermore, we demonstrate that results can be greatly improved by combining acoustic and linguistic knowledge from transfer learning. We align acoustic pre-trained representations with semantic representations from the BERT model through an attention-based recurrent neural network. Performance improves significantly when combining both modalities and scales with the amount of data. When trained on the full IEMOCAP dataset, we reach a new state-of-the-art of 73.9% unweighted accuracy (UA).

Deep learning models are part of the family of artificial neural networks and, as such, it suffers of catastrophic interference when they learn sequentially. In addition, most of these models have a rigid architecture which prevents the incremental learning of new classes. To overcome these drawbacks, in this article we propose the Self-Improving Generative Artificial Neural Network (SIGANN), a type of end-to-end Deep Neural Network system which is able to ease the catastrophic forgetting problem when leaning new classes. In this method, we introduce a novelty detection model to automatically detect samples of new classes, moreover an adversarial auto-encoder is used to produce samples of previous classes. This system consists of three main modules: a classifier module implemented using a Deep Convolutional Neural Network, a generator module based on an adversarial autoencoder; and a novelty detection module, implemented using an OpenMax activation function. Using the EMNIST data set, the model was trained incrementally, starting with a small set of classes. The results of the simulation show that SIGANN is able to retain previous knowledge with a gradual forgetfulness for each learning sequence. Moreover, SIGANN can detect new classes that are hidden in the data and, therefore, proceed with incremental class learning.

In order for robots to attain more general-purpose utility, grasping is a necessary skill to master. Such general-purpose robots may use their perception abilities in order to visually identify grasps for a given object. A grasp describes how a robotic end-effector can be arranged on top of an object to securely grab it between the robotic gripper and successfully lift it without slippage. Traditionally, grasp detection requires expert human knowledge to analytically form the task-specific algorithm, but this is an arduous and time-consuming approach. During the last five years, deep learning methods have enabled significant advancements in robotic vision, natural language processing, and automated driving applications. The successful results of these methods have driven robotics researchers to explore the application of deep learning methods in task generalised robotic applications. This paper reviews the current state-of-the-art in regards to the application of deep learning methods to generalised robotic grasping and discusses how each element of the deep learning approach has improved the overall performance of robotic grasp detection. A number of the most promising approaches are evaluated and the most successful for grasp detection is identified as the one-shot detection method. The availability of suitable volumes of appropriate training data is identified as a major obstacle for effective utilisation of the deep learning approaches, and the use of transfer learning techniques is identified as a potential mechanism to address this. Finally, current trends in the field and future potential research directions are discussed.

The current study characterizes the transient M/M/1 queue manifold info-geometrically, through devising Fisher Information matrix(FIM) and its inverse(IFIM). Additionally, the impact of stability on the existence of IFIM and explore the geodesic equations of motion has been revealed. More potentially, the paper discusses some info-geometric applications to Machine Learning are highlighted. Closing remarks with some challenging open problems combined with the next phase of research are given.

The combination of artificial intelligence algorithms and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. The multi inputs and outputs machine learning can cover small phase interactions or large fluid behavior in industrial domains. This numerical combination can develop the smart multiphase bubble column reactor with the ability of low-cost computational time. It can also decrease case studies for the optimization process when big data is appropriately used during learning. There are still many model parameters that need to be optimized for a very accurate artificial algorithm, including data processing and initialization, the combination of inputs and outputs, number of inputs and model tuning parameters. For this study, we aim to train four inputs big data during learning process by an adaptive neuro-fuzzy inference system or adaptive-network-based fuzzy inference system (ANFIS) method, and we consider the superficial gas velocity as one of the input variables, while for the first time, one of the computational fluid dynamics (CFD) outputs named gas velocity is used as an output of the artificial algorithm. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to , and the number of rules during learning process has a significant effect on the accuracy of this type of modeling. The results also show that propper selection of model parameters results in more accuracy in prediction of the flow characteristics in the column structure.

Recently, there has been, on the one hand, an increase in the complexity of digital device designs and, on the other hand, an increase in the requirements for the development time and the reliability of the designs. One of the directions of solving this problem is developing new techniques for designing digital devices.This paper proposes a new technique for designing digital devices based on finite state machines with datapath (FSMD), when the functioning of the device is described in the form of an algorithm state machine with datapath (ASMD) charts. The new technique is called ASMD-FSMD. Different digital device design techniques are compared to each other using design examples of a synchronous multiplier on field programmable gate array (FPGA). The efficiency of the ASMD-FSMD technique compared to the traditional approach in terms of area and performance was investigated. The ASMD-FSMD technique, compared to the traditional one, reduces the area from 28.6% to 39.7% and increases the speed for some designs to 17.6%. In addition, using the ASMD-FSMD technique significantly reduces design time and increases design reliability. In conclusion, recommendations for using the ASMD-FSMD technique are made.

Robotic systems are crucial in modern manufacturing. Complex assembly tasks require the collaboration of multiple robots. Their orchestration is challenging due to tight tolerances and precision requirements. In this work we set up two Franka Panda robots performing a peg-in-hole insertion task. We structure the control system hierarchically, planning the robots’s trajectories feedback-based with a central policy trained with reinforcement learning. These trajectories are executed by a low-level impedance controller on each robot. To enhance training convergence, we use reverse curriculum learning incorporating domain randomization, varying initial configurations of the task. After training, we test the system in a simulation, studying the impact of curriculum parameters on emerging process characteristics like process time and variance. Finally, we transfer the trained model to a real-world setup, comparing results with simulation as well as classical path planning and control approaches.

Background: Ovarian, cervical, and endometrial cancers stand as fatal health killers for women's mental and physical health, especially affecting female reproductive systems. Exploiting weakly supervised learning and explainable AI techniques are crucial for fast, accurate, and robust automatic marker detection, efficient prevention, and primary treatment of gynecological tumors. Methods: With respect to the PRIMSA methodology, weakly supervised learning schemes and deep learning-based schemes are investigated in the cross-subject fields of clinical diagnostic imaging and explainable AI technology. Related methods, opening research problems, and challenging subjects are explored in the screening and treatment of gynecological tumors and performing cancer image diagnosis in the latest study. Results: Keynote approaches combining ultrasound medicine, explainable AI, and clinical imaging technology are summarized. In combination with learning-based methods with medical imaging, prospective insights are put forward for refreshed concepts on treatments in the area of gynecological oncology, with a feasible range of applications for their corresponding AI techniques. Conclusions: Explainable AI and deep learning-based approaches are capable of performing accurate classification between benign and malignant tumors, yielding a pathway to care for women's health, improving the survival rate of patients, pacing with disease prediction, and matching the strategic goal of "Healthy China 2030".

Hyperspectral small target detection (HSTD) is a promising pixel-level detection task. However, due to the low contrast and imbalance number between the target and background spatially and high dimensions spectrally, it is a challenging one. To address these issues, this work proposes a representation learning-based graph and generative network for hyperspectral small target detection. The model builds a fusion network through frequency representation for HSTD, where the novel architecture incorporates irregular topological data and the spatial-spectral feature to improve its representation ability. Firstly, a graph convolutional network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene’s underlying data structure. The mini-batch-training pattern of the GCN decreases the high computational cost of building an adjacency matrix for high-dimensional data sets. In parallel, the generative model enhances the differentiation reconstruction and the deep feature representation ability with respect to the target spectral signature. Finally, a fusion module compensates for the extracted different types of HS features and integrates their complementary merits for hyperspectral data interpretation while increasing detection and background suppression capabilities. Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.

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.

This research introduces a pioneering solution to the challenges posed by gastrointestinal tract (GI) cancer in radiation therapy, focusing on the imperative task of precise organ segmentation for minimizing radiation-induced damage. GI imaging has historically used manual demarcation, which is laborious and uncomfortable for patients. We address this by introducing the ResECA-U-Net deep learning model, a novel combination of the UNet and ResNet34 architectures. Furthermore, we further augment its functionality by incorporating the Efficient Channel Attention (ECA-Net) methodology. By utilizing data from the UW-Madison Carbone Cancer Centre, we carefully investigate several image processing techniques designed to capture critical local characteristics. With its foundation in computer vision concepts, the ResECA-U-Net model is excellent at extracting fine details from GI images. Sophisticated metrics such as intersection over union (IoU) and the dice coefficient are used to evaluate performance. Our study's outcomes demonstrate the effectiveness of the suggested method, yielding an impressive 96.27% Dice coefficient and 91.48% IoU. These results highlight the significant contribution that our strategy has made to the advancement of cancer therapy. Beyond its scientific merits, this work has the potential to significantly enhance cancer patients' quality of life and provide better long-term outcomes. Our work is a significant step towards automating and optimizing the segmentation process, which can potentially change how GI cancer is treated completely.

With the popularity of solar energy in the electricity market, demand arises for data such as precise locations of solar panels for efficient energy planning, management, and distribution. However, this data is not easily accessible and in some cases, information such as precise locations does not exist. Furthermore, existing data sets for training semantic segmentation models of PV installations are limited, and their annotation is time-consuming and labor-intensive. Therefore, for additional remote sensing (RS) data creation, the pix2pix generative adversarial network (GAN) is utilized, enriching the original resampled training data of varying GSDs without compromising its integrity. Experiments done with the DeepLabV3 model, ResNet-50 backbone, and pix2pix GAN architecture were conducted to find the optimal configuration for an accurate RS imagery segmentation model. The result is a fine-tuned solar panel semantic segmentation model, trained using transfer learning and utilizing an optimal amount – 60% of generated RS imagery for additional training data, increasing model accuracy. The findings demonstrate the benefits of using GAN-generated images as additional training data, increasing the size of small data sets, and improving the capabilities of the segmentation model for solar panel detection in RS images.

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 paper presents a two-stage hierarchical neural network using image classification and object detection algorithms as key building blocks for a system that automatically detects a potential design right infringement. This neural network is trained to return the Top-N original design right records that highly resemble the input image of a counterfeit. Design rights specify the unique aesthetic characteristics of a product. Due to the rapid change of trends, new design rights are continuously generated. This work proposes an Ensemble Neural Network (ENN), an artificial neural network model that aims to deal with a large amount of counterfeit data and design right records that are frequently added and deleted. At first, we performed image classification and objection detection learning per design right using the existing models with a proven track record of high accuracy. The distributed models form the backbone of the ENN and yield intermediate results aggregated at a master neural network. This master neural network is a deep residual network paired with a fully connected network. This ensemble layer is trained to determine the sub-models that return the best result for a given input image of a product. In the final stage, the ENN model multiples the inferred similarity coefficients to the weighted input vectors produced by the individual sub-models to assess the similarity between the test input image and the existing product design rights to see any sign of violation. Given 84 design rights and the sample product images taken meticulously under various conditions, our ENN model achieved average Top-1 and Top-3 accuracies of 98.409% and 99.460%, respectively. Upon introducing new design rights data, a partial update of the inference model was done an order of magnitude faster than the single model. ENN maintained a high level of accuracy as it scaled out to handle more design rights. Therefore, the ENN model is expected to offer practical help to the inspectors in the field, such as the customs at the border that deal with a swarm of products.

Agriculture is one of the indispensable fields for the survival of mankind. Potatoes also have a significant role in the field of agriculture. The quality and quantity of potatoes are significantly impacted by several diseases, such as early blight and late blight, and manual interpretation of these leaf diseases is time-consuming and inconvenient. Fortunately, leaf appearance is used to detect diseases in potato plants. Productivity will considerably rise if the infections are detected early. Different types of image processing methods and machine learning methods are used for early recognition of those diseases from leaf images so that the product losses are decreased significantly. For the incredible and marvelous performance of CNN, it is the most popular deep learning method that is used immensely for the recognition of leaf diseases from images. Several pretrained deep learning models, such as VGG16, ResNet50, InceptionV3, MobileNetV2, Xception, and a deep learning model developed using CNN, are employed for potato leaf disease classification and recognition on the same dataset. The transfer learning technique is applied to the pretrained model and the data augmentation technique is applied to the Proposed CNN model for potato disease classification from leaf samples. Compared to pretrained models, the proposed CNN model offers the lowest loss and highest accuracy for potato leaf disease detection while using fewer parameters and layers. It achieves the best performance with a test accuracy of 99.33% compared to other Pretrained models used in the diagnosis of potato leaf disease.

Aiming at the problem of hypersonic morphing vehicle avoiding no-fly zones and reaching the target, an improved predictor-corrector guidance method is proposed. Firstly, the aircraft motion model and the constraint model are established. Then, the basic algorithm is given, the Q-learning method is used to design the attack angle and sweep angle scheme to ensure that the aircraft can fly over the low-altitude zones. The B-spline curve is used to design the location of flight path points and the bank angle scheme is designed according to the predictor-corrector method, so that the aircraft can fly around to avoid high-altitude zones. Next, Monte Carlo reinforcement learning(MCRL) method is used to improve predictor-corrector method and Deep Neural Network(DNN) is used to fit reward function. The improved method can generate trajectory with better performance. Simulation results verify the effectiveness of the proposed algorithm.

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.

Every month teachers face the dilemma of what exercises should their students practice, and what their consequences are on long-term learning. Since teachers prefer to pose their own exercises, this generates a large number of questions, each one attempted by a small number of students. Thus, we couldn’t use models based on big data such as deep learning. Instead, we developed a simple to understand state-space model that predicts end-of-year national test scores. We used 2,386 online fourth-grade mathematic questions designed by teachers and each attempted by some of the 500 students in 24 low socioeconomic schools. We found that the state-space model predictions improved month-by-month and that in most months it outperformed linear regression models. Moreover, the state-space estimator provides for each month a direct mechanism to simulate different practice strategies and compute their impact on the end-of-year standardized national test. We built iso-impact curves based on two critical variables: the number of questions solved correctly in the first attempt and the total number of exercises attempted. This allows the teacher to visualize the trade-off between asking students to do exercises more carefully or doing more exercises. To the best of our knowledge, this model is the first of its kind in education. It is a novel tool that supports teachers drive whole classes to achieve long-term learning targets.

Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson’s Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, it’s detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University.

Language learners often face communication problems when they need to express themselves and do not have this ability. On the other hand, continuous advances in technology create new opportunities to improve second language (L2) acquisition through context-aware ubiquitous learning (CAUL) technology. Since vocabulary is the foundation of all language acquisition, this article presents the ULearnEnglish, an open-source system to allow ubiquitous English learning focused on incidental vocabulary acquisition. To evaluate the proposal, 15 learners used the system developed, and 10 answered a survey based on the Technology Acceptance Model (TAM). Results indicate a favorable response to the use of the learner context to assist them in their learning. The ULearnEnglish achieved an acceptance of 78.66% for the perception of the utility, 96% for the perception of ease of use, 86% for user context assessment, and 88% for ubiquity. This study presented a positive response in using the location of users to assist their learning. Among the main contributions, this study demonstrates an opportunity for ubiquity use in future research in language learning. Also, furthers studies can use the source available to evolve the model and system.

The COVID-19 pandemic forced the transition to emergency online learning without prior preparation or guidelines. This transition has been particularly challenging in developing countries and low-resource contexts and hindered student engagement. We aim to identify the engagement strategies which students, engaging in emergency online learning in low-resource contexts, perceive to be effective. We conducted a sequential mixed-methods study based on Moore’s interaction framework for distance education. First, we conducted a literature review and interviewed ten teachers and ten students to identify a list of engagement strategies. Then, we designed a questionnaire that examines student perceptions of these strategies. We administered the questionnaire to 313 students engaging in emergency online learning in low-resource contexts. Our analysis results showed that student-content engagement strategies, e.g. screen sharing, summaries, and class recordings, are perceived as the most effective, closely followed by student-teacher strategies, e.g. Q&A sessions and reminders. Student-student strategies, e.g. group chat and collaborative work, are perceived as the least effective. The perceived effectiveness of engagement strategies depends on the context and the students’ characteristics, e.g. gender, major, and technology access. To support instructors, instructional designers, and researchers we propose a ten-level guide for engaging students during emergency online classes in low-resource contexts.

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics, in order to provide coherent and high-quality data that enables discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce NoTaMe, an analytical workflow for non-targeted metabolic profiling approaches utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research, and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data, and finally to identify and interpret the compounds that have emerged as interesting.

This study assesses the development of professional skills in university students using serious games (SG), from a sustainability perspective. The Sustainable Development Goals (SDGs) were set by the United Nations’ 2030 Agenda for Sustainable Development. Universities are strategic agents in the transformation process towards sustainability. This way, they should be committed to promoting such sustainable values in the students through curricular sustainability, implementing active methodologies and SG for that purpose. Transversal skills are essential for the development of future graduates. The objective of this study was to assess which professional skills should be developed through the SG called The Island, to improve the degree of student satisfaction with the incorporation of a sustainable curriculum. The data were obtained using a questionnaire, and then analysed using linear regression models, with their inference estimated through the goodness of fit and ANOVA. The first results indicated that the implementation of the SG promoted a strengthening of the students' sustainable curriculum through the development of those skills. It was concluded that the key to success in education for sustainable development is improving the development of strategic thinking, collaborative thinking, and self-awareness, in addition to encouraging systemic, critical, and problem-solving thinking.

Automatic Milking Systems (AMS) are revolutionizing the dairy industry by boosting herd effi-ciency, primarily through increased milk yield per cow and reduced labor costs. The performance of milking machines, whether traditional or automated, can be evaluated using advanced vacuum meters through dynamic testing. This process involves scrutinizing the system and milking rou-tine to identify critical points, utilizing the VaDia™ logger (BioControl AS, Rakkestad, NO). Vacuum recordings were downloaded and analyzed using the VaDia Suite™ software under the guidance of a milking specialist. Access to data from AMS across various manufacturers and herds facilitated a retrospective study aimed at describing and comparing key milk emission parameters for different AMS brands while identifying potential mastitis risk factors. Using proper statistical procedures of SPSS 29.1 (IBM Corp, USA), researchers analyzed data from 4,878 individual quarter milkings from cows in 48 dairy herds. Results indicated significant variability in milking pa-rameters associated to quarter milk yield and AMS brand. Notably, despite AMS standardizing teat preparation and stimulation, the study revealed a surprisingly high frequency of two major mastitis risk factors—bimodality and irregular vacuum fluctuations—occurring more frequently than in conventional milking systems. This study, one of the few comparing different AMS brands and their performance, highlights the crucial role of dynamic testing in evaluating AMS perfor-mance under real-world conditions.

Hypertension is a major risk factor for cardiovascular and cerebrovascular diseases. Previous studies have indicated that the presence of heavy metals in the air may be a risk factor for hypertension. This study examined the combined effects of heavy metals on blood pressure considering, sex differences. A health impact survey was conducted with 561 residents near waste incineration facilities in Buki-myeon, Cheongju, Korea. Blood samples were analyzed for heavy metal concentrations, and the blood pressure was measured. Multiple linear and Bayesian kernel machine regressions (BKMR) were used to assess the association between heavy metal exposure and blood pressure. Men had higher blood mercury and lead concentrations, while women had higher cadmium levels. The multiple linear regression analysis revealed no significant associations between heavy metals and systolic blood pressure. However, cadmium was significantly associated with increased diastolic blood pressure in the total group and women, whereas lead was associated with the same in men. In the BKMR analysis, the joint effect of the three metals was significantly associated with diastolic blood pressure below the 45th percentile and above the 55th percentile only in men. These findings underscore the importance of considering sex differences in environmental health studies and public health strategies.

The paper concerns with the identification of current communication technologies and software functionalities from the field of digitalization and Industry 4.0, from the point of view of benefits and applicability for the machine manufacturer. In order to identify the relevant technologies and functionalities, both a descriptive and statistical literature research was carried out. As a result, both the currently most widespread technologies and eleven IT functionalities relevant to mechanical engineers were identified and described in terms of their application and implementation. In addition, a knowledge gap in the area of industry transfer in the field of Industry 4.0 / Digitalization was identified

The importance of performance excellence and operator’s safety is fundamental not only when operators perform repetitive and controlled industrial tasks, but also in case of abrupt gestures due to inattention and unexpected circumstances. Since optical systems work at too low frequencies and they are not able to detect gestures as early as possible, combining the use of wearable magneto-inertial measurement units (MIMUs) with the adoption of deep learning techniques can be useful to instruct the machine about human motion. To improve the initial training phase of neural networks for high classification performance, gestures repeatability over time has to be verified. Since test-retest approach has been poorly applied based on MIMUs signals in a context of human-machine interaction, the aim of this work was to evaluate the repeatability of pick-and-place gestures composed of both normal and abrupt movements. Overall, results demonstrated an excellent test-retest repeatability for normal movements and a fair-to-good test-retest repeatability for abrupt movements. Moreover whereas the reduction of time between test and retest sessions increased repeatability indices.. In detail, results of the test suggested how to improve the reinforcement learning for the identification of gestures onset, whereas results of the retest gave important information about the time necessary to retrain the network.

Water pollution is a common problem for dams situated within an urban or agricultural catchment. This can negatively affect the hydro ecosystem, drinking, recreational and other uses of water. In this study, the drinking water quality class of the Roodeplaat Dam, South Africa which faces pollution problems was modeled using machine learning algorisms in Python Jupyter Notebook 6.0.0. Eleven monthly water quality parameters recorded at five sampling stations from January 1981 to September 2017 were used for training and testing the model. Five machine learning classifiers: Gaussian Naïve Bayes (GNB), K-nearest neighbors (KNN), Decision Tree (DT), Support Vector Machines (SVM), and Linear Regression (LR) at a test size of 20%, 25%, 30%, and 40% were used to classify water into five classes (Excellent to Very bad). It was investigated that the dam water has only three classes good, medium, and bad. The prediction accuracies of machine learning algorithms from the highest to the lowest were 96.39%, 96.17%, 92.25%, 90.20, and 54.19% for KNN, DT, SVM, GNB, and LR, respectively. Therefore, KNN at a test size of 30% was recommended to classify the water quality of Roodeplat Dam accurately. Hence, machine learning algorithms can be used to identify the class of water quality before the water is treated and distributed for drinking use.

Multimodal user interfaces promise a natural and intuitive human machine interactions. But is the extra effort for the development of a complex multi-sensor system justified, or can users also be satisfied with one input modality already? This study investigates interactions in an industrial weld inspection workstation. Three unimodal interfaces, including spatial interaction with buttons augmented on a workpiece or a worktable, and speech commands, were tested individually and in a multimodal combination. Within the unimodal conditions, users preferred the augmented worktable, but overall, the interindividual usage of all input technologies in the multimodal condition was ranked best. Our findings indicate that the implementation and use of multiple input modalities is valuable, and that it is difficult to predict the usability of individual input modalities for complex systems.

We consider the problem of scheduling tasks on a single machine subject to an uncertain breakdown to minimize the flowtime. Assume the machine is unavailable during the breakdown; the starting time of the breakdown is a random variable s with distribution function D(s) and the terminating time of the breakdown has no any other information; jobs are non-resumable. Under the assumptions, starting from the perspective of statistical optimization, we first define the expected competitive ratio of an algorithm to find the optimized solution with the considered problem. Further, we propose and prove a certain result on the expected competitive ratio of the SPT rule. In particular, we show that under a quite loose condition the expected competitive ratio of the SPT rule is no more than 5/4. Meanwhile, we also make some discussions about our studies. What we have done will rich and improve the studying results on the area of scheduling to minimize flowtime and advance the development of online optimization and statistical optimization.

The electrical stimulation of the visual cortices has the potential to restore vision to blind individuals. Until now, the results of visual cortical prosthetics has been limited as no prosthesis has restored a full working vision but the field has shown a renewed interest these last years thanks to wireless and technological advances. However, several scientific and technical challenges are still open in order to achieve the therapeutic benefit expected by these new devices. One of the main challenges is the electrical stimulation of the brain itself. In this review, we analyze the results in electrode-based visual cortical prosthetics from the electrical point of view. We first briefly describe what is known about the electrode-tissue interface and safety of electrical stimulation. Then we focus on the psychophysics of prosthetic vision and the state-of-the-art on the interplay between the electrical stimulation of the visual cortex and phosphene perception. Lastly, we discuss the challenges and perspectives of visual cortex electrical stimulation and electrode array design to develop the new generation implantable cortical visual prostheses.

Cloud computations are based on the computer networks such as Internet which presents a new pattern to provide, consume and deliver services such as infrastructure, software, ground and other resources using network. The inappropriate timing of assigning loads to the virtual machines in the computational space could lead to unbalance in the system. One of the challenging planning problems. In the cloud data centers is considering both assigning and migration (transfer) of the virtual machines with the ability of reconfiguration and the integrated features of the hosting physical machines. In this article, we introduce an integrated and dynamic timing algorithm based on the Genetic evolution algorithm. The suggested method was evaluated based on these factors and different inputs. Our suggested method is done using Java programming language and cloud-SME simulation. The results show that the execution time and the response time were improved by 12 and 1 percent respectively.

In current efforts to digitize manufacturing and move it into the fourth stage of the industrial revolution, a wide range of integration solutions is being considered to enable manufacturing to adapt to change. In transforming a factory into a self-organized, autonomous factory, companies are currently struggling with rapidly changing requirements and production factors, among other things. This is a particular problem for the human being as an actor within the factory, as the amount of new technologies and protocols increases the training effort. Proprietary interfaces of the control providers, a wide range of different communication protocols, complicate the understanding of the production processes, the evaluation and testability of new use cases and increase the danger of creating silos of knowledge as well as building collaboration barriers. As a solution to these problems, we propose an open software platform and define a way to model use case driven domain specific asset representation (DSA) that focuses on the human being and his needs for representing the factory in a way that it meets his requirements for the current production needs. We therefore conducted research on google scholar on human factors in industry 4.0 and used technologies as well as already existing platforms and their architecture.

Recognition of the human interaction on the unconstrained videos taken from cameras and remote sensing platforms like a drone is a challenging problem. This study presents a method to resolve issues of motion blur, poor quality of videos, occlusions, the difference in body structure or size, and high computation or memory requirement. This study contributes to the improvement of recognition of human interaction during disasters such as an earthquake and flood utilizing drone videos for rescue and emergency management. We used Support Vector Machine (SVM) to classify the high-level and stationary features obtained from Convolutional Neural Network (CNN) in key-frames from videos. We extracted conceptual features by employing CNN to recognize objects from first and last images from a video. The proposed method demonstrated the context of a scene, which is significant in determining the behaviour of human in the videos. In this method, we do not require person detection, tracking, and many instances of images. The proposed method was tested for the University of Central Florida (UCF Sports Action), Olympic Sports videos. These videos were taken from the ground platform. Besides, camera drone video was captured from Southwest Jiaotong University (SWJTU) Sports Centre and incorporated to test the developed method in this study. This study accomplished an acceptable performance with an accuracy of 90.42%, which has indicated improvement of more than 4.92% as compared to the existing methods.

Dynamic measurement error correction is an effective method to improve the sensor precision. Dynamic measurement error prediction is an important part of error correction, support vector machine (SVM) is often used to predicting the dynamic measurement error of sensors. Traditionally, the parameters of SVM were always set by manual, which can not ensure the model’s performance. In this paper, a method of SVM based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement error of sensors. Natural selection and Simulated annealing are added in PSO to raise the ability to avoid local optimum. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM’s parameters, they are the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absoluter percentage error are employed to evaluate the prediction models’ performances. The experiment results show that the NAPSO-SVM has a better prediction precision and a less prediction errors among the three algorithms, and it is an effective method in predicting dynamic measurement errors of sensors.

A novel method based on a hybrid incremental modeling approach has been designed and applied to imbalance detection in ultra-high precision rotating machines. The model is obtained by a two-step iterative process that combines an overall model (least-squares fitting) with a local model (fuzzy k-nearest-neighbour) to take advantage of their complementary capacities. Three normalization strategies of evaluating the effect on accuracy are analyzed. A comparative study demonstrates that the hybrid incremental model provides better error-based performance indices for detecting imbalance than a nonlinear regression model and an adaptive neural-fuzzy inference system model. The suitability of Mahanolobis normalization for hybrid incremental modeling is also demonstrated in this case study. The proposed strategy for imbalance detection is simple, fast, and non-intrusive, reducing the deterioration in the performance of ultra-high precision rotating machines due to vibrations.

In this paper, a new approach to retrieve semantic images based on shape and geometric features of image in conjunction with multi-class support vector machine is proposed. Zernike moment as shape feature is to verify the invariance of objects for silhouette image. In addition, a set of geometrical features is to explore the objects shape using two features of rectangularity and circularity. Then the extracted features are normalized and employed for multi-class support vector machine either for learning or retrieving processes. The retrieving process relies on three main tasks which namely Query Engine, Matching Module and Ontology Manger, respectively. Query Engine is to build the input text or image query using SPARQL language. The matching module extracts the shape and geometric features of image’s objects and employ them to Ontology Manger which in turn inserts them in ontology knowledge base. Benchmark mammals have been conducted to empirically conclude the outcome of proposed approach. Our experiment on text and image retrieval yields efficient results to problematic phenomena than previously reported.

One of the tools for optimal crop production is regular monitoring and assessment of crops. During the growing season of fruit trees, the bloom period has increased photosynthetic rates that correlate with the fruiting process. This paper presents the development of an image processing algorithm to detect peach blossoms on trees. Images of an experimental peach orchard were acquired from the Parma Research and Extension Center of the University of Idaho using an off-the-shelf unmanned aerial system (UAS), equipped with a multispectral camera (Near-infrared, Green, Blue). The orchard has different stone fruit varieties and different plant training system. Individual tree images (high-resolution) and arrays of trees images (low-resolution) were acquired to evaluate the detection capability. The image processing algorithm was based on different vegetation indices. Initial results showed that the image processing algorithm could detect peach blossoms and demonstrate good potential as a monitoring tool for orchard management.

Breast cancer is the most prevalent type of disease among women. It has become one of the foremost causes of death among women globally. Early detection plays a significant role in administering personalized treatment and improving patient outcomes. Mammography procedures are often used to detect early-stage cancer cells. This traditional method of mammography while valuable has limitations of potential for false positives and negatives, patient discomfort, and radiation exposure. Therefore, there is a probe for more accurate techniques required in detecting breast cancer, leading to exploring the potential of machine learning in the classification of diagnostic images due to its efficiency and accuracy. This study conducted a comparative analysis of pre-trained CNNs (ResNet50 and VGG16) and Vision Transformers (ViT-Base and SWIN Transformer) with the inclusion of ViT-Base trained from scratch model architectures to effectively classify breast cancer mammographic images into benign and malignant cases. The Swin transformer exhibits superior performance with 99.8% accuracy and a precision of 99.8%. These findings demonstrate the efficiency of deep learning to accurately classify breast cancer mammographic images for the diagnosis of breast cancer, leading to improvement in patient outcomes.

The advancement of autonomous driving technology is becoming increasingly vital in the modern technological landscape, promising notable enhancements in safety, efficiency, traffic management, and energy use. Despite these benefits, conventional deep reinforcement learning algorithms often struggle to navigate complex driving environments effectively. To tackle this challenge, we propose a novel network called DynamicNoise, designed to significantly boost algorithmic performance by introducing noise into the Deep Q-Network (DQN) and Double Deep Q-Network (DDQN). Drawing inspiration from the NoiseNet architecture, DynamicNoise uses stochastic perturbations to improve the exploration capabilities of these models, leading to more robust learning outcomes. Our experiments demonstrate a 57.25% improvement in navigation effectiveness within a 2D experimental setting. Moreover, by integrating noise into the action selection and fully connected layers of the Soft Actor-Critic (SAC) model in the more complex 3D CARLA simulation environment, our approach achieved an 18.9% performance gain, substantially surpassing traditional methods. These results confirm that the DynamicNoise network significantly enhances the performance of autonomous driving systems across various simulated environments, regardless of their dimensionality and complexity, by improving their exploration capabilities rather than just their efficiency.

Magnetic resonance imaging (MRI) is crucial for its superior soft tissue contrast and high spatial resolution. Integrating deep learning algorithms into MRI reconstruction enhances image quality and efficiency, but a comprehensive review of optimization-based deep learning models for MRI reconstruction has been missing. This study fills that gap by examining the latest optimization-based algorithms in deep learning for MRI reconstruction, including gradient descent algorithms, proximal gradient descent algorithms, ADMM, PDHG, and diffusion models combined with gradient descent. Learnable optimization algorithms (LOA) are highlighted for their ability to map optimization processes to structured neural networks, improving model interpretability and performance. The study demonstrates significant advancements in MRI reconstruction through deep learning, with successful clinical applications. These findings provide valuable insights and resources for researchers aiming to advance medical imaging using innovative deep learning techniques.

This study presents the Distilled MixUp Squeeze ResNet (DMSResNet), an optimized variant of the Residual Network architecture designed for high accuracy and computational efficiency. By integrating Squeeze-and-Excitation blocks, MixUp data augmentation, and knowledge distillation, it achieves competitive performance within strict parameter constraints and limited training resources. Evaluated on the CIFAR-10 dataset, the model attains a 96.56% accuracy with only 4.3 million total parameters. The results demonstrate that optimization of established architectures can yield performance comparable to newer models, especially in resource-limited scenarios. This work contributes to the development of efficient deep learning models for applications in constrained computational environments.

This survey provides a comprehensive overview of in-context learning (ICL) in large language models (LLMs), a phenomenon where models can adapt to new tasks without parameter updates by leveraging task-relevant information within the input context. We explore the definition and mechanisms of ICL, investigate the factors contributing to its emergence, and discuss strategies for optimizing and effectively utilizing ICL in various applications. Through a systematic review of recent literature, we first clarify what ICL is, distinguishing it from traditional fine-tuning approaches and highlighting its unique characteristics. We then delve into the underlying causes of ICL, examining theories ranging from implicit meta-learning during pre-training to the emergence of task vectors in LLMs. The survey also covers various approaches to enhance ICL performance, including prompt engineering techniques, demonstration selection strategies, and methods for improving generalization across diverse tasks. Additionally, we discuss the limitations and challenges of ICL, such as its sensitivity to demonstration ordering and potential biases. By synthesizing findings from numerous studies, we aim to provide researchers and practitioners with a clear understanding of the current state of ICL research, its practical implications, and promising directions for future investigation. This survey serves as a valuable resource for those seeking to leverage ICL capabilities in LLMs and contributes to the ongoing discourse on the remarkable adaptability of these models.

Market crises pose significant challenges for businesses, emphasizing the importance of effective crisis management strategies. Central to these strategies is the ability to identify and retain loyal customers, who often serve as the bedrock of stability during tumultuous times. This paper investigates the application of deep meta-learning analysis to predict loyal customers as a cornerstone of market crisis management. Drawing upon an extensive literature review, the study explores previous research on market crises, customer loyalty, and the evolution of deep learning and meta-learning in crisis management contexts. Methodologically, the paper outlines data collection, model development, and evaluation procedures tailored for deep meta-learning-based customer prediction. In results, the overall accuracy of model 85%, a precision 0.88, recall 0.82, and F1-score 0.85 were obtained. The analysis demonstrates the effectiveness of deep meta-learning models in accurately identifying loyal customers during market crises, offering insights into their performance and applicability compared to traditional methods. Practical implications include potential applications in crisis management for businesses and considerations for real-world implementation.

In the age of digital revolution technology is being used more and more in education around the world to improve student learning. By enabling ongoing skill development and knowledge acquisition through digital platforms e-learning has become a crucial tool. Through E-Learning online learning is becoming increasingly popular and effective in addition to traditional methods of instruction as highlighted by the latest statistics from Badan Pusat Statistik Indonesia (BPS). In order to address the issues of scalability, interactivity and security in e-learning platforms. This study introduces a web-based learning environment that is focused on teaching programming. Leveraging the MERN stack, while also integrating TypeScript for improved code reliability, Next.js for accelerated React.js functionality, Tailwind CSS for responsive design. Backend features include media management with Cloudinary, secure video streaming with VdoCipher, data storage with MongoDB, and caching with Redis. Iterative development will be following agile methodology. In order to create a vibrant and encouraging learning environment the platform includes interactive course pages multimedia content real-time feedback mechanisms and community-building tools. Despite some early difficulties the implementation phase effectively achieved development goals. To further support international online learning initiatives further efforts will be directed toward improving platform security and functionality.

Divulsuperbac (DSB) is a Service Learning project targeted at the educational community. It started in 2019 and has engaged Biology degree students in a Microbiology-focused initiative in the Valencian Community (Spain), for four academic years. The DSB project comprises various outreach activities intending to raise awareness of the superbugs' threat to pre-university students. We created an exhibition that contained 14 infographics on the topic of antimicrobial resistance which was presented every week in high schools. The infographics were presented by university students, who explained the problem to the pre-university students and subsequently assessed their comprehension of the topic. The surveys conducted yielded satisfactory results, which motivated us to extend this project to other regions of the world.

Deforestation through land-use conversion, illegal logging, and timber trafficking is believed to cause ~10% of the annual human carbon dioxide emissions at the global level. Given the large contribution, local and national policies have been set in place in the effort to reduce deforestation and support reforestation. However, accurate assessment of forest carbon stock is expensive and challenging in remote areas and on large geographical scales. To improve carbon stock monitoring and evaluation of fine-scale forest loss, we developed a rapid, automatic, scalable, and cost-efficient generalized deep learning framework that uses diverse remote sensing data and satellite imagery to derive aboveground carbon density from accurate estimates of tree canopy heights at fine-grained resolution (30x30 meters) in remote tropical rainforests. The remote sensing data is composed of Landsat-8, Sentinel-1, land cover, digital elevation model, and NASA CMS airborne LiDAR, as well as vegetation indices, texture metrics, and climatic data. Data sources were compiled into a data pipeline which produced >300 features and 2 million observations over forests in Indonesian Borneo. Using LiDAR swath data on canopy forest high for ~100,000 hectares in Indonesian Borneo to create a training and validation datasets, our neural network model produced aboveground carbon density estimates with R2 of 0.82, which is a significant improvement from comparable works by using Random Forest (R2 of 0.3-0.5). This deep learning framework can be used to facilitate further carbon stock modeling in other forest regions (e.g., Brazil) as well as for the general purpose of climate change mitigation.

In this study, a comprehensive review on production scheduling based on reinforcement learning (RL) techniques using bibliometric analysis has been carried out. The aim of this work is, among other things, to point out the growing interest in this domain and to outline the influence of RL as a type of machine learning on production scheduling. To achieve this, the paper explores production scheduling using RL by investigating the descriptive metadata of all pertinent publications contained in the Web of Science database. The study focuses on a wide spectrum of publications spanning the years between 1998 and 2023. The findings and recommendations of this study can serve as new insights for future research endeavors in the realm of production scheduling using RL techniques.

Emotion detection holds significant importance in facilitating human-computer interaction, enhancing the depth of engagement. By integrating this capability, we pave the way for forthcoming AI technologies to possess a blend of cognitive and emotional understanding, bridging the divide between machine functionality and human emotional complexity. This progress has the potential to reshape how machines perceive and respond to human emotions, ushering in an era of empathetic and intuitive artificial systems. This paper introduces a novel approach to multimodal emotion recognition, seamlessly integrating speech and text modalities to accurately infer emotional states. Employing CNNs, we meticulously analyze speech using Mel spectrograms, while a BERT-based model processes the textual component, leveraging its bidirectional layers for profound semantic comprehension. The outputs from both modalities are combined using an attention-based fusion mechanism that optimally weighs their contributions. The proposed method undergoes meticulous testing on two distinct datasets: Carnegie Mellon University’s Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) dataset and the Multimodal Emotion Lines Dataset (MELD). The results demonstrate superior efficacy compared to existing frameworks, achieving an accuracy of 88.4% and an F1-score of 87.9% on the CMU-MOSEI dataset, and a notable weighted accuracy (WA) of 67.81% and a weighted F1 (WF1) score of 66.32% on the MELD dataset. This comprehensive system offers precise emotion detection and introduces several significant advancements in the field.

The purpose of this paper is to propose a novel transfer learning regularization method based on knowledge distillation. Recently, transfer learning methods have been used in various fields. However, the problems such as knowledge loss still occur during the process of transfer learning to a new target dataset. To solve these problems, there are various regularization methods based on knowledge distillation techniques. In this paper, we propose a transfer learning regularization method based on feature map alignment used in the field of knowledge distillation. The proposed method is composed of two attention based submodules: self-pixel attention (SPA) and global channel attention (GCA). The self-pixel attention submodule utilizes both the feature maps of the source and target models, so that it provides an opportunity to jointly consider the features of the target and the knowledge of the source. The global channel attention submodule determines the importance of channels through all layers, unlike the existing methods that calculate these only within a single layer. Accordingly, transfer learning regularization is performed by considering both the internal of each single layer and the depth of the entire layer. Consequently, the proposed method using both of these submodules showed overall improved classification accuracy than the existing methods in classification experiments on commonly used dataset.

The problem addressed in this article consists of the motion control of a quadrotor, comprising model disturbances and uncertainties. In order to tackle model uncertainty, adaptive control based on reinforcement learning is used. The distinctive feature of this article, in comparison with other works on quadrotor control using reinforcement learning is the exploration of the underlying optimal control problem in which a quadratic cost and a linear dynamics allow an algorithm that runs in real time. Instead of identifying a plant model, adaptation is obtained by approximating the performance index given by the Q-function using directional forgetting recursive least squares that rely on a linear regressor build from quadratic functions of input/output data. The adaptive algorithm proposed is tested in simulation in a cascade control structure that drives a quadrotor. Simulations show the improvement in performance that results when the proposed algorithm is turned on.

With the increased emphasis on competition in academic settings, anxiety is becoming more common, which inevitably has some impact on students’ learning process and result. This study aimed to explore how competition-induced anxiety influences the subjective cognitive load (SCL), attention level and test score. Meanwhile, we investigated the mediating role of the behavioral inhibition system/behavioral activation system (BIS/BAS) in those processes. A total of 101 college students were recruited in Study 1 to learn five micro-lectures from massive open online courses (MOOC) under with and without competitive conditions. The results showed that participants’ state anxiety (SA) scores were higher after the experiment, participants in the competition condition had higher test scores, and the relationship between SA/ trait anxiety (TA) and SCL could be mediated by the BIS. To obtain more objective data on learning processes (attention levels), we conducted Study 2, which collected behavioral and EEG data from 42 college students during the online learning. The results showed that the competition group had higher SA, lower attention levels, and worse test scores, and the relationship between SA/TA and attention levels could be mediated through the BIS. The present study not only expands previous research by finding that BIS functioning plays an important role in the effects of anxiety on cognitive load and attention, but also offers implications for using competitive strategies to motivate students according to their aptitude.

In recent years the field of computer science has experienced great changes, due to the incredible advances in the field of artificial intelligence. Deep Learning models are responsible for most of them since the biggest milestones occurred in 2012 when AlexNet won the image classification challenge called ImageNet. These models have demonstrated great performances in different types of complex tasks like image restoration, medical diagnosis or object recognition. Their disadvantages are related to their high data dependency, which forces experts in the field to follow a precise methodology to obtain accurate models. In this paper, we describe a complete workflow that begins with the management of the raw data until the in-depth interpretation of the performance of the models. This should be taken as a high-level consultation document describing good practices that should be applied. Apart from the step-by-step methodology, we present different use cases that correspond to the two main problems of the field: classification and regression.

Neurodegenerative illnesses, such as Parkinson’s disease (PD), have a substantial impact on the overall well-being of those who are affected. This study investigates and contrasts the capabilities of convolutional neural networks (CNN) in detecting Parkinson’s disease (PD) by utilising hand-drawn images alongside wave and spiral images as input data. This study employs pre-trained CNN models, specifically MobileNet, ResNet50, EfficientNet-B1, and InceptionV3, to classify Parkinson’s disease (PD). The findings demonstrate that MobileNet surpasses other architectural designs, as evidenced by the F1-Score of the four classes: Spiral Normal (0.87), Spiral Parkinson (0.86), Wave Normal (0.97), and Wave Parkinson (0.97). MobileNet has also shown a remarkable accuracy of 0.92 in diagnosing Parkinson’s disease. The result demonstrates the efficacy of MobileNet in extracting features from images. The results of this study enhance the application of deep learning methods in the early detection of PD, as well as help indicate the effectiveness of patient therapy and exercise, promising better patient outcomes through timely intervention and treatment.

In federated learning (FL), clients train models locally without sharing raw data, ensuring data privacy. In particular, federated distillation transfers knowledge to clients regardless of the model architecture. However, when groups of clients with different data distributions exist, sharing the same knowledge among all clients becomes impractical. To address this issue, this paper presents an approach that clusters clients based on the output of a client model trained using their own data. The clients are clustered based on the predictions of their models for each label on a public dataset. Prior knowledge of the number of groups is not required. Evaluations on the MNIST dataset showed that our method accurately identified these group structures and improved accuracy by 15–75% compared with traditional federated distillation algorithms when distinct group structures were present. Additionally, we observed significant performance improvements in smaller client groups, bringing us closer to fair FL.

A remarkable strengths of deep neural networks lies in their ability to discover patterns and representations from complex data. Deep learning models can learn and refine features at different levels of abstraction, enabling them to handle complex tasks in computer vision recognition, natural language processing, and speech recognition. The ability to generalize from examples, coupled with the capacity to process vast amounts of data, empowers deep learning to achieve state-of-the-art results across a wide spectrum of applications. An argument for this ability is based on similarities between deep neural networks and the nervous system. In this report, we argue that despite the remarkable performance of deep learning, there are still gaps between deep learning and the nervous system that needs to be closed to enable deep learning doing tasks that currently only the nervous system can perform

Voice spoofing attempts to break into a specific automatic speaker verification (ASV) system by forging the user’s voice, and can be used through methods, such as text-to-speech (TTS), voice conversion (VC), and replay attacks. Recently, deep learning-based voice spoofing countermeasures have been developed. however, the problem with replay is that it is difficult to construct a large number of datasets because it requires a physical recording process. To overcome these problems, this study proposes a pre-training framework based on multi-order acoustic simulation for replay voice spoofing detection. Multi-order acoustic simulation utilizes existing clean signal and room impulse response (RIR) datasets to generate audios, which simulate the various acoustic configurations of the original and replayed audios. The acoustic configuration refers to factors, such as the microphone type, reverberation, time delay, and noise that may occur between a speaker and microphone during the recording process. We assume that a deep learning model trained on an audio that simulates the various acoustic configurations of the original and replayed audios can classify the acoustic configurations of the original and replay audios well. To validate this, we performed pre-training to classify the audio generated by the multi-order acoustic simulation into 3 classes: clean signal, audio simulating the acoustic configuration of the original audio, and audio simulating the acoustic configuration of the replay audio. We also set the weights of the pre-training model to the initial weights of the replay voice spoofing detection model using the existing replay voice spoofing dataset and then performed fine-tuning. To validate the effectiveness of the proposed method, we evaluated the performance of the conventional method without pre-training and proposed method using an objective metric, i.e., the accuracy. As a result, the conventional method achieved 92.94% accuracy and proposed method achieved 98.16% accuracy.

Estimating precipitation is of critical importance to climate systems and decision-making processes. This paper presents Espresso, a deep learning model designed for estimating precipitation from satellite observations on a global scale. Conventional methods, like ground-based radars, are limited in terms of spatial coverage. Satellite observations, on the other hand, allow global coverage. Combined with deep learning methods these observations offer the opportunity to address the challenge of estimating precicpation on a global scale. This research paper presents the development of a deep learning model using geostationary satellite data as input and generating instantaneous rainfall rates, calibrated using data from the Global Precipitation Measurement Core Observatory (GPMCO). The performance impact of various input data configurations on Espresso was investigated. These configurations include a sequence of four images from geostationary satellites and the optimal selection of channels. Additional descriptive features were explored to enhance the model’s robustness for global aplications. When evaluated against the GPMCO test set, Espresso demonstrated highly accurate precipitation estimation, especially within equatorial regions. A comparison against six other operational products using multiple metrics indicated its competitive performance. The model’s superior storm localization and intensity estimation were further confirmed through visual comparisons in case studies. Espresso has been incorporated as an operational product at Météo-France, delivering high-quality, real-time global precipitation estimates every 30 minutes.

In Taiwan, peanuts are classified by hand. The kernel quality is judged based on the appearance of peanuts. The identification takes considerable time and labor. Fatigue can induce misrecogni-tion or grade cognition instability due. Peanut defects are generally divided into health, underde-velopment, insect bite, and rupture. This study employed two imaging methods push-boom FX10 and Snapshot. Deep learning was used to detect peanut defects. A push-broom instrument was used for testing. There were 1,560 peanuts, including 240 good and 240 bad ones. There were a total of 1,080 good peanuts and bad peanuts in the test set data, the band selection was used, and a CNN model was built. Based on the results from three methods, 3DCNN could classify with 97%accuracy. The Snapshot was used to achieve the real-time design of a lightweight CNN model. Finally, five bands were selected using PCA, and the screening could be accurate and efficient. The maximum overall accuracy was about 98.5%. Kappa was 97.3% with real-time recognition. This will be a major advantage in the future practical application and commercialization processes. This technique can reduce labor costs, achieve intelligent detection and intelligent grading, and bring breakthroughs to smart agriculture for other crops as well.

In the study, it was aimed to develop a valid and reliable scale in order to determine the self-efficacy perceptions of classroom teachers in the field of mathematics learning difficulties. In the development process of the scale, the basic resources and the scale development studies in the literature were examined. First, a pool of substances was created. Content validity was ensured by taking the opinions of 21 expert participants in the 24 question areas determined in the item pool. Content validity index was used in content validity. In this analysis, two questions were removed from the scale. Preliminary application was made with 44 participating teachers who are experts in their fields and exploratory factor analysis was started. For exploratory factor analysis, 359 people consisting of classroom teachers in Sakarya province were studied. In the explanatory factor analysis, 5 items were removed from the scale by looking at the factor load. The factor loadings for the 17 items in the scale vary between 0.508 and 0.976. Among the comparative fit indices obtained as a result of confirmatory factor analysis, RMSEA=0.067 and Cmin/DF=1.568 were calculated. The Cronbach alpha reliability coefficient of the measurements is 0.970. The data obtained were analyzed with statistical methods using SPSS 25, AMOS 24 and Microsoft Excel programs. The validity and reliability criteria of the scale developed within the scope of the study were met.

Plant-based protein sources derived from microalgae offer promise as low-carbon emission and nutrient-dense sources for human consumption. The current management of microalgae cultivation relies heavily on manual microscopic examination in order to identify desired and competing species, as well as predators. In this study, we trained and tested a transfer learning model modified from EfficientNetV2 B3 on 434 and 161 prospectively acquired images of the preferred Nannochloropsis sp microalgae and competitor Spirulina, respectively, and achieved >98% classification for both species on tenfold cross-validation. The model was further enhanced with gradient-weighted class activation mapping, which allowed visualization of regions of the input images that were relevant to the classification, thereby improving its explainability. In this paper, we demonstrate that a simple deep transfer learning model can be used to identify microalgae species. The application addresses the practical need for robust automated monitoring of microalgae populations on industrial microalgae farms.

Intestinal parasitic infections pose a grave threat to human health, particularly in tropical and subtropical regions. The traditional manual microscopy system of intestinal parasites detection remains the golden standard procedure for the diagnosis of parasites cyst or eggs, but this approach is costly time-consuming (30min/sample), highly tedious, and also required specialist. However, computer vision based on deep learning has made great stride in recent time. Despite the significant advances in deep convolutional neural network-based architectures, little research has been conducted to explore the potential of these techniques in the field of parasitology, specifically for intestinal parasites. Therefore, the goal of this research is to evaluate the performance of proposed state-of-the-art transfer learning architecture for detecting and classifying intestinal parasite eggs from images. We would ensure that patients receive prompt treatment while also relieving experts of extra work if we used such an architecture. Here, in stage first, we applied image pre-processing and augmentation to the dataset, and in stage second, we utilized the YOLOv5 algorithms for detection and classification and then compared their performance based on different parameters. Our algorithms achieved a mean average precision of 97% approximatiely and 8.5 ms detection time per sample for 5,393 intestinal parasite images. Thus, this approach may form a solid theoretical basis for real-time detection and classification in routine clinical examinations while accelerating the process to satisfy increasing demand.

In this work we introduce an end-to-end multi-modal neural network to segment the Gross Tumor Volume (GTV) from 3D-CBCT’s during radiotherapy. We improve the tumor segmentation by using a U-net which takes additional information such as the tumor mask generated at the planning phase along with the CBCT volume. The mask contains relevant information about the tumor’s location and can guide the model to use this knowledge appropriately to give a better prediction. This technique could become an alternative to produce segmentation masks of GTV in CBCT automatically during radiotherapy as in the traditional RT-pipeline, they are not segmented. We have evaluated our model on a dataset of 82 patients who have undergone radiotherapy. We compare the results of registered target volumes from planning CT as mask seed with 2 different types of multi-modal architectures. Our model shows a DSC of 0.706±0.002 with Late Fusion and 0.702±0.015 with Early Fusion using the GTV Mask. The performance of the two models on this mask is similar, so we perform further experiments with different types of masks which suggest that Late Fusion model produces a better segmentation of the tumor than the Early Fusion model. We also provide an ablation study consisting of a single modality U-net and a metric based on the Planning CT mask registration. It indicates a clear advantage of using our model to produce segmentation for this type of imaging.

Federated Distillation (FD) has recently attracted increasing attention for its efficiency in aggregating multiple diverse local models trained from statistically heterogeneous data of distributed clients. Existing FD methods generally treat these models equally by merely computing the average of their output soft predictions for some given input distillation sample, which does not take the diversity across all local models into account, thus leading to degraded performance of the aggregated model, especially when some local models learn little knowledge about the sample. In this paper, we propose a new perspective that treats the local data in each client as a specific domain and design a novel domain knowledge aware federated distillation method, dubbed DaFKD, that can discern the importance of each model to the distillation sample, and thus is able to optimize the ensemble of soft predictions from diverse models. Specifically, we employ a domain discriminator for each client, which is trained to identify the correlation factor between the sample and the corresponding domain. Then, to facilitate the training of the domain discriminator while saving communication costs, we propose sharing its partial parameters with the classification model. Extensive experiments on various datasets and settings show that the proposed method can improve the model accuracy by up to $6.02\%$ compared to state-of-the-art baselines.

Objective: To investigate commonalities and variations in the learning outcomes, curriculum content, assessment methods and competencies in undergraduate orthodontic curricula globally. Methods: This scoping review followed the updated methodological guidance proposed by the Joanna Briggs Institute and reported in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). A search on electronic databases PubMed, Scopus and Embase was conducted for the last 25 years. Google Scholar was used to identify eligible unpublished and grey literature. Results: The total number of reports identified was 231. After removal of 62 duplicates, 169 reports were included in the title and abstract screening. Finally, 17 studies were included in the review which included 13 cross-sectional surveys, three expert panel proceedings and one discussion paper. Marked variations were reported in undergraduate orthodontic curricula and competency assessments at the level of individual countries, regionally as well as globally. The challenges of imparting competency in orthodontic treatment during undergraduate dental education are also acknowledged. Conclusion: Lack of consistency in undergraduate orthodontic education is evidenced by several Delphi studies to develop consensus on orthodontic teaching in undergraduate programs. A common message emanating from the available studies on undergraduate orthodontic education seems to emphasize a focus on assessment and diagnosis of orthodontic treatment needs of patients and a basic understanding of contemporary treatment options to facilitate patient referral.

The emergence of unmanned aerial vehicles (UAVs) raised multiple concerns, given their potential for malicious misuse in unlawful acts Vision-based counter-UAV applications offer a reliable solution compared to acoustic and radio frequency-based solutions because of their high detection accuracy in diverse weather conditions. The existing solutions work well on trained datasets, but their accuracy is relatively low for real-time detection. In this paper, we model deep learning-empowered solutions to improve the multi-class UAV's classification performance using single-shot object detection algorithms YOLOv5 and YOLOv7. The transfer learning is employed for performance improvement and rapid training with improved results. We customized a multi-class dataset containing multi-rotor, fixed-wing, and single-rotor UAVs in challenging weather conditions. Experiments show that the integration of transfer learning has achieved good results, with an overall best average-classification precision of 94\%, an average recall of 93.1\%, a mAP$@$0.5 average of 95.3\%, and an average F1 score of 92.33\%. The dataset and code are available as an open source: https://github.com/ZeeshanKaleem/YOLOV5-Large-vs-YOLOV7.git

Hand gesture recognition has many valuable applications in engineering and health care. This study proposes a novel model which can accurately distinguish hand gestures using forearm muscles' surface electromyogram (sEMG) signals. A deep learning algorithm with hyper parameters impacting the final model’s accuracy and a convolutional neural network (CNN) were employed in the recognition stage. The number of convolutional layers, kernels per layer, and neurons in the dense layer were selected for optimization, while the remaining parameters, such as the learning rate, batch size, and number of epochs, were chosen based on trial and error and prior knowledge. The optimal values for the selected hyperparameters were obtained using a genetic algorithm to achieve maximum recognition accuracy. The UC2018 Dual-Myo database was used for training and testing the model based on EMG signals characterizing the activity of eight different hand gestures. The final structure of the model consisted of two convolutional layers with 131 and 28 kernels, a dense layer with 111 neurons, and a softmax layer with eight neurons. Upon optimizing the hyperparameters using the genetic algorithm, the accuracy of the proposed model increased from 91.86% to 96.4% at best and 95.3% on average in real-time applications and 99.6% in an offline mode. Future work is warranted towards improving the architecture and the computational cost.

This article addresses a challenge modeling novices face in model conceptualization: the recognition of behavior modes. Despite behavior modes being at the heart of system dynamics modeling, there is no unifying taxonomy for them, making them harder to learn for beginners. The article proposes criteria a taxonomy should satisfy, critically reviews previous taxonomies in the literature, and then introduces a new consistent taxonomy based on slope and curvature with mode names that refer only to visual cues and free of references to prior mathematical or domain-specific knowledge. Evidence from an exploratory experiment suggests that novices actually have difficulties classifying curves when using previous taxonomies. Links from these elementary modes to analogous terms in other taxonomies in diverse disciplines allow this taxonomy to be related to other ones. The article concludes by mentioning relevant limitations and future steps.

To solve two key problems in the identification of people who are infected with COVID-19: the first problem is that the identification accuracy is not high enough. The second problem is that present identification method such as nucleic acid testing is expensive in many countries. Methods: So, I decided to design a fast identification method for COVID-19 patients which is based on deep learning. After the model (CoughNet) learns more than 6,000 cough spectrograms of both COVID-19 patients and normal people, the accuracy rate of identification of COVID-19 patients and normal people is higher than 99% in the test set. Structure: This paper is mainly divided into three parts: the first part introduces the main background and research status of the research; The second part introduces the research methods; The third part introduces the specific process of the experiment.

The pervasiveness of cardiovascular disease and physician misdiagnosis creates the urgent need for artificial intelligence models to improve diagnosis accuracy. The first objective of this study was to train machine learning models on publicly available data sets containing simple medical information of patients to diagnose cardiovascular disease. The Multilayer Perceptron (MLP) assembled for this task performed optimally with an F1 score of 0.8968. This prompted the creation of an open-source, automated cardiovascular disease diagnosis tool, powered by the MLP. The second objective of this study was to employ a meta-learning methodology called Local Interpretable Model-Agnostic Explanations (LIME) to understand the impact of different features on the model's diagnosis in the form of marginal probabilities. K-Means Clustering was employed to segment the data into ten clusters, after which each data example was passed through LIME. The resulting histograms depict the complex relationship between feature, cluster, and impact on diagnosis. A series of P-values with contrasting orders of magnitude shows the nuances in the MLP's understanding of patients from different clusters. The results of meta-learning analysis reveal that the most important features for cardiovascular disease diagnosis are fasting blood sugar, type of chest pain, and slope of the ST segment on an electrocardiogram. Future experiments should replicate the novel methodology introduced in this study on data sets containing more specialized medical features in order to gain practical medical insights about different types of cardiovascular disease represented by each cluster. Finally, feature engineering pathways should be explored with consideration of these results to create versatile diagnosis models not only for cardiovascular disease, but adaptable to other diseases as well.

A hybrid smart process and material design system for nanoimprinting is proposed, which combines with a learning system based on experimental and numerical simulation results. Instead of carrying out extensive learning experiments for various condition, the simulation learning results are partially complimented where the results can be theoretically predicted by numerical simulation. In other word, the lacking data in experimental learning are complimented by simulation-based learning results. Therefore, the prediction of nanoimprint results under various conditions without experimental learning could be realized even for unknown materials. In this study, material and process design for a low-temperature nanoimprint process using glycerol-containing polyvinyl alcohol are demonstrated. Experimental results under limited conditions are learned to investigate optimum Glycerol concentrations and process temperatures. On the other hand, simulation-based learning is used to predict the dependence on press pressure and shape parameters. The prediction results for unknown Glycerol concentrations agreed well with the follow-up experiments.

The Covid-19 also known as the Coronavirus is a viral disease from the SARS-CoV-2 family of virus, as at December 2019 the first case of this virus infection was identified at Wuhan, China, this seemingly isolated case soon became a global pandemic, whose effect was felt globally which also had colossal effects on both health, economic and politics . As at the time of this research about 4.5 million people have died of the Coronavirus and over 215 million people already infected by it. This pandemic stood out not just for its scale but for how social media was a major contribution to its spread as well as to curbing it. The power of social media was used to spread misinformation as well as to spread awareness on the subject, with both having massive impact on the people. In this paper we will be running a sentimental analysis on twitter under the keyword “Covid-19 and Coronavirus”, twitter is a powerful social media tool that is known for its ability to keep trends in the form of tweets, we will be drawing correlations between the peaks of tweet with the peak of infection. We will also be analyzing to know what the impact of these tweets are having on the rate of the infection and vice versa. We will also be analyzing what people are tweeting most about, what are the talking points, comparing both real time and past tweets with real time infection and death rates using deep different learning methods to access what information can be derived from it.

Daytime napping offers benefits for motor memory learning and is used as habitual countermeasure to improve daytime functioning. A single napping is shown to ameliorate motor memory learning, although the effect of consecutive napping on motor memory consolidation remains unclear. This study aimed to explore the effect of daytime napping over multiple days on motor memory learning. Twenty university students were divided into the napping group and no-nap (awake) group. Napping group performed motor adaption tasks before and after napping for three consecutive days, whereas no-nap group performed the task on the similar time schedule as the napping group. In addition, a subsequent retest was conducted one week after the end of the intervention. The speed to complete the task of the napping group was significantly shorter than that of the awake group in the retention test. No significant difference was confirmed for trajectory length to complete the task. Thus, consecutive napping may facilitate motor learning and motor memory consolidation in the long term.