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Preprint ARTICLE | doi:10.20944/preprints202404.1203.v1

A Sentiment Analysis Approach for Exploring CustomerReviews of Online Food Delivery Services: A Greek Case

Nikolaos Fragkos, Anastasios Liapakis, Maria Ntaliani, Filotheos Ntalianis, Constantina Costopoulou

Subject: Computer Science And Mathematics, Computer Science Keywords: sentiment analysis; customer reviews; online food delivery; food and beverage industry; Greece

Online: 18 April 2024 (08:07:13 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.1217.v1

MIoT-aware Hessian Linear Feature Embedding Based Tucker Congruence Deep Convolutional Network for Epidemic Disease Control with Cloud

Sarah A. Alzakari, Nithya Rekha Sivakumar, Ahmed Zohair Ibrahim, Shekaina Justin, Chandrasekar Chelliah

Subject: Computer Science And Mathematics, Computer Science Keywords: Cloud; Epidemic Disease Control; feature selection; Deep Convolutional Network; Hessian Locally Linear Embedding technique; Manhattan distance measure; Tucker coefficient of congruence regression

Online: 18 April 2024 (07:51:24 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.1018.v1

Discovering Data Domains and Products in Data Meshes Using Semantic Blueprints

Michalis Pingos, Andreas S. Andreou

Subject: Computer Science And Mathematics, Computer Science Keywords: Big Data; Data Lakes; Data Meshes; Data Products; Data Blueprints; Metadata Semantic Enrichment

Online: 16 April 2024 (16:26:06 CEST)

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Preprint REVIEW | doi:10.20944/preprints202404.0911.v1

An Overview on Testing using Selanium

Sibi Mathew

Subject: Computer Science And Mathematics, Computer Science Keywords: selenium testing tool; automation-based testing; test cases; selenium IDE

Online: 15 April 2024 (09:36:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0896.v1

Interpolation Once Binary Search over a Sorted List

Jun-Lin Lin

Subject: Computer Science And Mathematics, Computer Science Keywords: binary search; interpolation search; interpolated binary search

Online: 15 April 2024 (08:44:02 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201908.0037.v14

Note for the P versus NP Problem

Frank Vega

Subject: Computer Science And Mathematics, Computer Science Keywords: Complexity classes; boolean formula; graph; completeness; polynomial time

Online: 12 April 2024 (04:51:11 CEST)

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Preprint REVIEW | doi:10.20944/preprints202401.2194.v2

Cybersecurity & Data Privacy in Fintech

Rajath Karangara, Otilia Manta

Subject: Computer Science And Mathematics, Computer Science Keywords: Fintech; Cybersecurity; Data Privacy; Information Security; Regulatory Compliance

Online: 11 April 2024 (10:40:33 CEST)

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Preprint ARTICLE | doi:10.20944/preprints201908.0037.v13

Note for the P versus NP Problem

Frank Vega

Subject: Computer Science And Mathematics, Computer Science Keywords: Complexity classes; boolean formula; graph; completeness; polynomial time

Online: 8 April 2024 (06:04:04 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0410.v1

Correctness Verification of Mutual Exclusion Algorithms by Model Checking

Libero Nigro, Franco Cicirelli

Subject: Computer Science And Mathematics, Computer Science Keywords: Mutual exclusion algorithms; correctness analysis; automated reasoning; model checking; statistical model checking; Timed Automata; Uppaal

Online: 5 April 2024 (06:41:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202404.0219.v1

Understanding Superlinear Speedup in Current HPC Architectures

Flavio Cesar Cunha Galeazzo, R. Gregor Weiß, Sergey Lesnik, Henrik Rusche, Andreas Ruopp

Subject: Computer Science And Mathematics, Computer Science Keywords: OpenFOAM; CFD; HPC; superlinear speedup

Online: 2 April 2024 (15:14:39 CEST)

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The performance of OpenFOAM in strong scaling tests on HPC systems with AMD EPYC processors exhibits a pronounced superlinear speedup. Simple test cases show superlinear speedups of over 300%, which significantly impacts the efficient use of computing resources. With the last generation of HPC architectures, a superlinear speedup of about 10% to 20% was well expected and accepted by CFD users. The measured superlinear speedup is much more pronounced and withstands the communication overhead to even larger scales. A detailed performance analysis of OpenFOAM follows, employing various High-Performance Computing (HPC) architectures, including AMD, ARM and Intel systems. The performance metric FVOPS (Finite VOlumes solved Per Second) is introduced to compare the performance of Computational Fluid Dynamics (CFD) applications when varying the grid size, as occurs in a strong scaling test. The achievable FVOPS depends on various factors, including the simulation type, boundary conditions, and especially the grid size of a use case. Analysing FVOPS on a single node level with varying grid size shows a significant difference in performance and cache utilization, which explains the large superlinear speedups seen in the strong scaling tests. Furthermore, FVOPS can be used as a simple benchmark to determine the optimal number of grid elements per rank to simulate a given use case at peak efficiency on a given platform, resulting in time, energy, and cost savings. The FVOPS metric also facilitates the direct comparison between different HPC architectures. The tests using AMD, ARM, and Intel processors show a peak in performance when employing around 10,000 grid elements per core. The presence of a large L3 cache on AMD processors is particularly advantageous, as indicated by L3 cache miss rates observed on AMD EPYC processors. Our results suggest that future HPC architectures with larger caches and higher memory bandwidth would benefit the CFD community.

Preprint ARTICLE | doi:10.20944/preprints202404.0154.v1

Towards Improving YARN performance for Frugal Heterogeneous SBC-based Edge Clusters

Basit Qureshi

Subject: Computer Science And Mathematics, Computer Science Keywords: Single Board Computers; Frugal edge computing; Hadoop; YARN; heterogeneous

Online: 2 April 2024 (09:14:06 CEST)

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In the dynamic landscape of sustainable computing, use of edge devices is paramount for reducing the need for large-scale centralized data centers. By processing data locally, edge devices minimize the energy-intensive computing in data centers, improving the overall performance, cost-effectiveness whereas reducing the environmental impact. Edge devices may constitute edge clusters composed of resource frugal Single Board Computers (SBC) such as Raspberry Pi etc. The small form-factor and energy efficiency of these computers makes them ideal for processing large data on the edge. Despite their potential, traditional Hadoop configurations struggle to optimize performance in heterogeneous SBC clusters due to disparities in computing resources. Consequently, we propose modifications to the Yet Another Resource Negotiator (YARN) scheduling mechanism to address these challenges. Our proposed changes include the introduction of a Frugality Index and an adaptiveConfig policy. The Frugality Index categorizes SBC nodes based on their capabilities, enabling intelligent resource allocation. The adaptiveConfig policy dynamically adjusts resource allocation in response to workload and cluster conditions, enhancing system efficiency. Additionally, we introduce a fetch_threshold for reduce tasks to improve task prioritization based on locality and data processing efficiency. We evaluate our approach using a 13-node SBC cluster and conduct experiments with CPU-intensive and IO-intensive Hadoop benchmarks. The results demonstrate significant performance improvements compared to native YARN settings, with execution times 4.7 times faster than the worst_native and 1.9 times faster than the best_native scenarios. Furthermore, the proposed adaptiveConfig policy implementing the frugality index and a fetch_threshold outperforms the native YARN by 5.86 times and 1.79 times in Terasort and wordcount executions respectively. Our findings underscore the effectiveness of our approach in managing the heterogeneous nature of SBC clusters and optimizing performance across various hardware configurations. The adaptive policies prove well-suited to the frugal SBC-cluster context, yielding enhanced outcomes and paving the way for sustainable big data processing initiatives.

Preprint ARTICLE | doi:10.20944/preprints201908.0037.v12

Note for the P versus NP Problem

Frank Vega

Subject: Computer Science And Mathematics, Computer Science Keywords: Complexity classes; boolean formula; graph; completeness; polynomial time

Online: 1 April 2024 (17:14:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202403.1569.v1

Highly-Sensitive Measure of Complexity Captures Boolean Networks Regimes and Temporal Order More Optimally

Manuel de J. Luevano, Alejandro Puga

Subject: Computer Science And Mathematics, Computer Science Keywords: Random boolean ; networks; ; entropy; ; algorithmic ; complexity; compressibility

Online: 26 March 2024 (10:07:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1310.v1

An Improved Deadlock Detection and Resolution Algorithm for Distributed Computing Systems

Tarek Helmy

Subject: Computer Science And Mathematics, Computer Science Keywords: Distributed Computing Systems; Deadlock Detection

Online: 26 March 2024 (03:12:27 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1357.v1

Towards A Distributed Digital Twin Framework for Predictive Maintenance in Manufacturing Systems

Ibrahim Abdullahi, Stefano Longo, Mohammad Samie

Subject: Computer Science And Mathematics, Computer Science Keywords: Digital Twins; Predictive Maintenance; Wind Turbines; Fog Computing; Machine Learning; Internet of Things

Online: 22 March 2024 (09:21:10 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1311.v1

Research on Weed and Crop Identification System Using Pixel-Wise Segmentation

Xin-Zhi Hu, Wang-su Jeon, Sang-Yong Rhee

Subject: Computer Science And Mathematics, Computer Science Keywords: Semantic Segmentation; Weed Recognition; Deep Learning; Image Recognition; Machine Vision

Online: 22 March 2024 (08:17:47 CET)

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Preprint ESSAY | doi:10.20944/preprints202403.1119.v1

Black Swan Events: Mitigating Disruption to a Supply Chain Using Blockchain and Artificial Intelligence

Gordon Bowen, Hamid Jahankhani, Imad Yasir Nawaz

Subject: Computer Science And Mathematics, Computer Science Keywords: artificial intelligence; bblockchain; sustainable supply chain system; black swan events

Online: 22 March 2024 (07:40:26 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1227.v1

DM-YOLOv8: Improved Cucumber Disease and Insect Detection Model Based on YOLOV8

Ji-Yuan Ding, Wang-su Jeon, Sang-Yong Rhee, Chang-Man Zou

Subject: Computer Science And Mathematics, Computer Science Keywords: component; yolov8s; MultiCat; C2fe; ADC2f; Pest detection

Online: 20 March 2024 (12:31:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1167.v1

Secure IoT communication: Implementing a One-Time-Pad Protocol with True Random Numbers and Secure Multiparty Sums

Julio Fenner, Patricio Galeas, Francisco Escobar, Rail Neira

Subject: Computer Science And Mathematics, Computer Science Keywords: One time pad cryptography; Internet of Things; Secure multiparty computation

Online: 19 March 2024 (16:13:18 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.1030.v1

Effectiveness of AI-Based Resource Optimization in Cloud Environments: An Empirical Study Focused on Comparative Analysis between MSA and Rehost Systems

Do Jin Yoo, Cheong Jeong Seo

Subject: Computer Science And Mathematics, Computer Science Keywords: Cloud Resourece Optimization; AI-Based Automation in Cloud Computing; Performance Management in Cloud Environments; Cost Efficiency in Cloud Services; Turbonomic ARM

Online: 18 March 2024 (11:37:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints201908.0037.v11

Note for the P versus NP Problem

Frank Vega

Subject: Computer Science And Mathematics, Computer Science Keywords: Complexity classes; boolean formula; graph; completeness; polynomial time

Online: 18 March 2024 (08:29:48 CET)

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Preprint ARTICLE | doi:10.20944/preprints201908.0037.v10

Note for the P versus NP Problem

Frank Vega

Subject: Computer Science And Mathematics, Computer Science Keywords: Complexity classes; boolean formula; graph; completeness; polynomial time

Online: 14 March 2024 (10:11:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0679.v1

Predicting and Analyzing Rainfall Patterns in Ethiopia Using Linear Regression Modeling

Moges Tsegaw Melesse, Gezahegn Mulusew Delele

Subject: Computer Science And Mathematics, Computer Science Keywords: Rainfall patterns; Ethiopia; Linear regression modeling; R-squared score; Mean squared error

Online: 14 March 2024 (05:28:48 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0600.v1

Medical Support Vehicle Location and Deployment at Mass Casualty Incidents, Case Study: Mexico City

Miguel Medina-Pérez, Giovanni Guzmán, Magdalena Saldana-Pérez, Valeria Karina Legaria-Santiago

Subject: Computer Science And Mathematics, Computer Science Keywords: multiobjective optimization; mass casualty incidents; geographic information systems

Online: 11 March 2024 (21:14:51 CET)

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Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival in these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors such as insufficient prevention education, limited disaster assets, and inadequate coordination between public and private emergency services may complicate these situations. In large-scale emergencies, multiple hotspots are usually observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as an optimization problem, focusing on the location and allocation of resources in mass casualty incidents. The proposed case study is Mexico City in a post-earthquake scenario, using voluntary geographic information, open government data, and historical data from the September 19, 2017 earthquake. Additionally, it is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

Preprint ARTICLE | doi:10.20944/preprints202403.0654.v1

Design and Development of A Blockchain-Based Financial Aid Distribution System

Md. Raisul Hasan Shahrukh, Sifat Momen, Nafees Mansoor

Subject: Computer Science And Mathematics, Computer Science Keywords: Blockchain; Financial Distribution; Process Automation; Smart Contracts; Hyperledger

Online: 11 March 2024 (15:19:42 CET)

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Preprint ARTICLE | doi:10.20944/preprints201908.0037.v9

Note for the P versus NP Problem

Frank Vega

Subject: Computer Science And Mathematics, Computer Science Keywords: Complexity classes; boolean formula; graph; completeness; polynomial time

Online: 8 March 2024 (11:06:11 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0307.v1

Performance Comparison of CFD Microbenchmarks on Diverse HPC Architectures

Flavio C. C. Galeazzo, Marta Garcia-Gasulla, Elisabetta Boella, Josep Pocurull, Sergey Lesnik, Henrik Rusche, Simone Bnà, Matteo Cerminara, Federico Brogi, Filippo Marchetti, Daniele Gregori, R. Gregor Weiß, Andreas Ruopp

Subject: Computer Science And Mathematics, Computer Science Keywords: CFD; OpenFOAM; benchmark

Online: 6 March 2024 (10:12:17 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0265.v1

Security and Ownership in User Defined Data Meshes

Michalis Pingos, Panayiotis Christodoulou, Andreas S. Andreou

Subject: Computer Science And Mathematics, Computer Science Keywords: Big Data; Smart Data Processing; Systems of Deep Insight; Data Meshes; Data Lakes; Data Products; Blockchain; NFT; Data Blueprints

Online: 5 March 2024 (15:04:49 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0262.v1

Environmental Constraints for Intelligent Internet of Deep-Sea/Underwater Things Relying on Enterprise Architecture Approach

Charbel Geryes AOUN, Noura MANSOUR, Fadi DORNAIKA, Loic LAGADEC

Subject: Computer Science And Mathematics, Computer Science Keywords: IIoUT; Smart Sensors; Smart Fusion Servers; Domain Specific Modeling Languages; ArchiMate; Enterprise Architecture; Marine Observatories; NS-3; IMS.

Online: 5 March 2024 (12:29:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints202403.0012.v1

Flexible Techniques to Detect Typical Hidden Errors in Large Longitudinal Datasets

Renato Bruni, Cinzia Daraio, Simone Di Leo

Subject: Computer Science And Mathematics, Computer Science Keywords: big data; information processing; information reconstruction; data quality: longitudinal data sequences

Online: 1 March 2024 (10:33:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.1161.v1

Unveiling Influence in Networks: A Novel Centrality Metric and Comparative Analysis through Graph-Based Models

Nada BENDAHMAN, Dounia LOTFI

Subject: Computer Science And Mathematics, Computer Science Keywords: Social network; graph; influential actor; centrality measure.

Online: 20 February 2024 (14:55:56 CET)

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Preprint REVIEW | doi:10.20944/preprints202402.1052.v1

Industrial Metaverse: A Comprehensive Review, Environmental Impact and Challenges

Sindiso Nleya, Mthulisi Velempini

Subject: Computer Science And Mathematics, Computer Science Keywords: Industrial; Metaverse; Digital twin'; deployment; Security; Environmental impact

Online: 20 February 2024 (04:35:16 CET)

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The industrial metaverse paradigm is generally a virtual environment that integrates various technologies such as augmented reality (AR) and mixed reality (MR) to enhance business operations and processes. It aims to streamline workflows, reduce error rates, improve efficiency, and provide a more engaging experience for employees. The promise of the industrial Metaverse to drive sustainability and resource efficiency is compelling. Utilizing advanced technologies like the Industrial Metaverse is essential for staying competitive in today’s rapidly evolving business environment. However, the environmental impact of the technologies underpinning the industrial metaverse, like data centres and network infrastructure, should not be overlooked. The ecological footprint of these technologies must be considered in the sustainability equation. Researchers have warned that by 2025, without sustainable AI practices, AI will consume more energy than the human workforce, significantly offsetting carbon-zero gains. As the metaverse persists in evolving and gaining momentum, it will be necessary for companies to prioritize sustainability and explore new ways to balance technological advancements with environmental stewardship. However, recent studies have conjectured that the Metaverse holds the potential to reduce carbon emissions, as digital replacements for physical goods become more prevalent and physical activities like mobility and construction are reduced. However, the specific extent to which this substitution can alleviate environmental concerns remains understudied, presenting a knowledge gap in understanding the real-world impact of digital replacements. Thus, the objective of this paper is to provide a comprehensive review of the industrial metaverse as well as explore the environmental impact of the industrial metaverse. The methodological approach is that of integrative literature review design wherein multiple sources from web science and databases such as ACM library, IEEE Library, and Google Scholar, were analysed to provide a comprehensive understanding of the developments in the industrial metaverse. Specifically, firstly by considering the Industrial metaverse architecture we elucidate the concept of the "industrial metaverse" and the associated enabling technologies. Secondly, an exploration through discussion, of the prevalent use cases and deployment of the emerging industrial metaverse. Thirdly, exploration of the impact of the industrial metaverse on the environment. In the fourth instance, we address novel security and privacy risks as well as upcoming research challenges, keeping in mind that the industrial metaverse is based on a strong data fabric. Results point to the Industrial Metaverse as having both positive and negative environmental effects by energy consumption, e-waste, and pollution. Overall research, however, indicates that most industrial metaverse applications have a positive environmental impact and subsequently trend toward sustainability. Ultimately for sustainability in the industrial metaverse, enterprises may consider utilizing renewable energy sources and cloud services. Furthermore, examining the effects of products on the environment as well as creating a circular economy.

Preprint ARTICLE | doi:10.20944/preprints202402.0187.v2

Optimization and Performance Analysis of CAT Method for DNA Sequence Similarity Searching and Alignment

Veska Gancheva, Hristo Stoev

Subject: Computer Science And Mathematics, Computer Science Keywords: bioinformatics; biological data sequences; DNA sequences; metadada; performance analysis; similarity searching; sequence alignment

Online: 19 February 2024 (16:12:31 CET)

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Bioinformatics is a rapidly developing field enabling scientific experiments through computer models and simulations. Considering the vast databases of biological data available, it is ex-tremely important to develop efficient methods and algorithms for their processing. Sequence comparison is the best method for studying the evolutionary interaction between genes. It is based on alignment – the process of arranging two or more sequences to achieve the maximum level of identity and degree of similarity. The paper presents a new version of the algorithm for pairwise DNA sequences alignment, based on a new method called CAT, where a dependency with a previous match and the closes neighbor are taken in consideration to increase uniqueness of the CAT profile and to reduce possible collisions, i.e. two or more sequence having same CAT profiles. This makes proposed algorithm suitable for finding exact match of a concrete DNA se-quence in a big set of DNA data faster. The generation of CAT profiles is made once before data has been uploaded to the database, allowing the profiles to be used as metadata for the sequenc-es. It consists of an algorithm to calculate a CAT profile against the selected reference sequences and an algorithm to compare two sequences based on the calculated CAT profiles. Improve-ments in generation of the CAT profiles, are detailed described in the paper. Block scheme, pseudo code tables and figures were updated according to the proposed new version and ex-perimental results. Experiments have been carried out with the new version of the method and different datasets to align DNA sequences based on the CAT method. New experimental results in terms of collisions, speed, and efficiency of the proposed solutions are presented. Experiments related to the performance comparison with Needleman-Wunsch were re-executed with the new version of the algorithm to confirm that we have the same performance. An analysis of the per-formance of the proposed CAT based algorithm against Knuth–Morris–Pratt algorithm, which has a complexity of O(n) and is one of the most widely used for searching biological data, was performed. The impact of prior matching dependencies on uniqueness for generated CAT pro-files is investigated. The analysis of the experimental results obtained by sequence alignment shows a small deviation of the proposed algorithm based on the CAT method, which can be ig-nored if this deviation is acceptable at the expense of performance. The time efficiency of the CAT algorithm remains constant, regardless of the length of the sequences. Therefore, the ad-vantage of the proposed method is its fast processing in the alignment of large sequences, for which the execution of the exact algorithms takes a long time. The example code realization of the CAT Method, under the protection of the GNU General Public License v3.0, can be accessed on GitHub at: https://github.com/HristoS/CATSequenceAnalysis.

Preprint ARTICLE | doi:10.20944/preprints202402.0946.v1

Finding Negative Associations from Medical Data Streams Based on Frequent and Regular Patterns

RajaRao Budaraju, Sastry Kodanda Rama Jammalamadaka

Subject: Computer Science And Mathematics, Computer Science Keywords: Data streams; Negative associations; Adverse effects; side reactions; Frequent and Regular patterns

Online: 19 February 2024 (11:35:26 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0869.v1

Performance Comparison of VVC, AV1, HEVC and AVC for High Resolutions

Miroslav Uhrina, Lukas Sevcik, Juraj Bienik, Lenka Smatanova

Subject: Computer Science And Mathematics, Computer Science Keywords: H.264/AVC; H.265/HEVC; H.266/VVC; AV1; QoE; Objective Assessment; PSNR; SSIM; VMAF; FHD; UHD; 8K

Online: 16 February 2024 (11:34:06 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0867.v1

UHD Database Focus on Smart Cities and Smart Transport

Lukas Sevcik, Miroslav Uhrina, Jaroslav Frnda

Subject: Computer Science And Mathematics, Computer Science Keywords: UHD database; smart cities; smart transport; subjective evaluation; VMAF; QoE

Online: 15 February 2024 (15:52:42 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0047.v2

Survey on Simulation and Vulnerability Testing in Smart Grid

Shampa Banik, Trapa Banik

Subject: Computer Science And Mathematics, Computer Science Keywords: Smart grid; vulnerability; cyber-attack; taxonomy; simulation

Online: 12 February 2024 (10:40:12 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0441.v1

Creating an Immersive Virtual-Reality Space for Multiuser Synchronous Co-Located Collaboration: Design Considerations and Influencing Factors

Qinna Feng, Heng Luo, Zijian Li, Jiarong Liang, Gege Li, Yan Yi

Subject: Computer Science And Mathematics, Computer Science Keywords: immersive virtual reality; synchronous co-located collaboration; collaboration experience; design consideration

Online: 7 February 2024 (11:52:19 CET)

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Preprint REVIEW | doi:10.20944/preprints202402.0320.v1

Research Progress on the Application of Crop Yield Calculation based on Image Analysis Technology

Feng Yu, Ming Wang, Jun Xiao, Qian Zhang, Jinmeng Zhang, Xin Liu, Yang Ping, Rupeng Luan

Subject: Computer Science And Mathematics, Computer Science Keywords: remote sensing images; visible light images; machine learning; deep learning; biomass; yield calculation

Online: 6 February 2024 (03:52:45 CET)

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Preprint REVIEW | doi:10.20944/preprints202402.0227.v1

Developing Real-Time Alert Systems With SpringBoot: Outline the Process of Creating an Application to Monitor and Alert Discrepancies in Trade Inquiries

Dharika Kapil

Subject: Computer Science And Mathematics, Computer Science Keywords: Financial Trading Systems; Real-Time Alert Systems; SpringBoot; Microservices Architecture; Machine Learning in Finance; DataProcessing; Apache Kafka; Blockchain in Financial Systems; Cloud Computing; User Interface Design

Online: 5 February 2024 (06:21:37 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0170.v1

Cloud Computing Transformed IT Organizations With Its On-Demand Model, Notably Infrastructure as a Service (IaaS). Cloud Providers Manage Extensive Physical Devices, Consuming Substantial Energy and Bandwidth for Data Traffic During Virtual Machine D

Ketema Deresa Biru

Subject: Computer Science And Mathematics, Computer Science Keywords: cloud computing; data center; energy efficient virtual machine placement; physical machine

Online: 5 February 2024 (05:32:07 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0187.v1

Optimization and Performance Analysis of CAT Method for DNA Sequence Similarity Searching and Alignment

Veska Gancheva, Hristo Stoev

Subject: Computer Science And Mathematics, Computer Science Keywords: bioinformatics; biological data sequences; DNA sequences; sequence alignment

Online: 4 February 2024 (17:15:55 CET)

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Preprint ARTICLE | doi:10.20944/preprints202402.0116.v1

HyPedSim: A Multi-level Crowd Simulation Framework – Methodology, Calibration, and Validation

Huu-Tu Dang, Benoit Gaudou, Nicolas Verstaevel

Subject: Computer Science And Mathematics, Computer Science Keywords: Agent-based architecture; Multi-level behaviour; Pedestrian modeling; Multi-scale simulation

Online: 2 February 2024 (09:26:03 CET)

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Preprint REVIEW | doi:10.20944/preprints202402.0047.v1

Survey on Simulation and Vulnerability Testing in Smart Grid

Shampa Banik, Trapa Banik

Subject: Computer Science And Mathematics, Computer Science Keywords: Smart grid; vulnerability; cyber-attack; taxonomy; simulation

Online: 1 February 2024 (11:19:28 CET)

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Preprint REVIEW | doi:10.20944/preprints202401.2194.v1

Cybersecurity & Data Privacy in Fintech

Rajath Karangara, Otilia Manta

Subject: Computer Science And Mathematics, Computer Science Keywords: Fintech; Cybersecurity; Data Privacy; Information Security; Regulatory Compliance

Online: 31 January 2024 (09:43:05 CET)

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Preprint COMMUNICATION | doi:10.20944/preprints202401.2023.v1

Investigating the Global Fear associated with COVID-19 using Subjectivity Analysis and Deep Learning

Nirmalya Thakur, Kesha A. Patel, Audrey Poon, Rishika Shah, Nazif Azizi, Changhee Han

Subject: Computer Science And Mathematics, Computer Science Keywords: COVID-19; Big Data; Data Analysis; Machine Learning; Subjectivity Analysis; Data Science; Deep Learning; Mental Health

Online: 29 January 2024 (15:42:52 CET)

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The work presented in this paper makes multiple scientific contributions related to the investigation of the global fear associated with COVID-19 by performing a comprehensive analysis of a dataset comprising survey responses of participants from 40 countries. First, the results of subjectivity analysis of responses where participants indicated their biggest concern related to COVID-19 showed that the average subjectivity in responses by the age group of 41-50 decreased from April 2020 to June 2020, the average subjectivity in responses by the age group of 71-80 drastically increased from May 2020, and the age group of 11-20 indicated the least level of subjectivity in their responses between June 2020 to August 2020. Second, subjectivity analysis also revealed the percentage of highly opinionated, neutral opinionated, and least opinionated responses per age-group where the analyzed age groups were 11-20, 21-30, 31-40, 41-50, 51-60, 61-70, 71-80, and 81-90. For instance, the percentage of highly opinionated, neutral opinionated, and least opinionated responses by the age group of 11-20 were 17.92%, 16.24%, and 65.84%, respectively. Third, data analysis of responses from different age groups showed that the highest percentage of responses indicating that they were very worried about COVID-19 came from individuals in the age group of 21-30. Fourth, data analysis of the survey responses also revealed that in the context of taking precautions to prevent contracting COVID-19, the percentage of individuals in the age group of 31-40 taking precautions was higher as compared to the percentages of individuals from the age groups of 41-50, 51-60, 61-70, 71-80, and 81-90. Finally, a deep learning model was developed to detect if the survey respondents were seeing or planning to see a psychologist or psychiatrist for any mental health issues related to COVID-19. The deep learning model used the responses to multiple questions in the context of fear, preparedness, and response related to COVID-19 from the dataset and achieved an overall performance accuracy of 91.62% after 500 epochs.

Preprint ARTICLE | doi:10.20944/preprints202401.1955.v1

Bluetooth Device Identification Using RF Fingerprinting and Jensen-Shannon Divergence

Rene Francisco Santana-Cruz, Martin Moreno-Guzman, César Enrique Rojas-López, Ricardo Vázquez-Morán, Rubén Vázquez-Medina

Subject: Computer Science And Mathematics, Computer Science Keywords: Identification systems; Radio Frequency Fingerprints (RFF); IoT device identification; Cybersecurity; Wireless Communication

Online: 29 January 2024 (10:49:09 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1608.v1

New Nodes for Node‐RED Library within OpenBCI Category for EEG‐Based Brain‐Machine Interface Design and Integration in IoT

Adelina Kremenska, Anna Lekova

Subject: Computer Science And Mathematics, Computer Science Keywords: BCI Software Platforms; Node-RED; BrainFlow; IoT; OpenBCI technology

Online: 23 January 2024 (07:30:51 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1432.v1

On Move with Interleaving (MwI) Implementation

Borut Žalik, Aljaž Jeromel, Ivana Kolingerová, Niko Lukač, Blaž Repnik

Subject: Computer Science And Mathematics, Computer Science Keywords: string transformation; lookup table; efficiency; Java; C++

Online: 19 January 2024 (06:04:45 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.1431.v1

Research on Wheat Image Encryption Based on Different Chaotic Systems

Yi Shao, Huiru Zhu, Xuefeng Deng, Hua Yang

Subject: Computer Science And Mathematics, Computer Science Keywords: chaotic mapping; image encryption; wheat image

Online: 18 January 2024 (14:53:16 CET)

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Preprint CONCEPT PAPER | doi:10.20944/preprints202401.1343.v1

Gamification in Engineering Education after the COVID-19 pandemic

Serafeim Triantafyllou, Andreas Oikonomou

Subject: Computer Science And Mathematics, Computer Science Keywords: gamification; motivational theories; engineering education

Online: 17 January 2024 (16:19:36 CET)

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The world of education has changed. And for all the trials and challenges of managing education during a pandemic, we have also learned a few things that can make our teaching and learning experiences even better in the future.The pandemic imposed constant balance exercises in all aspects of our daily life, including education. Suddenly the living room of the house turned into a work space, the kitchen replaced the canteen, the children's office became a student seat. There was a new area of unknown, irregular and unpredictable forced experimentation. Now what is right, beneficial and effective is an unanswerable question in the absence of previous experience, and the only way to answer it is by trial and error.Universities, in the context of "emergency" remote teaching, also had to experiment. Professors and students found themselves inside virtual classrooms, facing digital boards, closed cameras and microphones.How could a professor not be frustrated by low levels of interaction during the lesson, and how could a student understand a problem without disrupting the fragile digital teaching flow? What was the learning outcome after each two-hour e-teaching? Was the learner's attention maintained, the learning objective achieved, knowledge transferred, new skills acquired? The answers had to be given through experimentation, creativity and innovation.To stop digital teaching from being monotonous and boring, we tried to turn it into a game in order to engage students and lead to better learning outcomes. Gamification is the use of game mechanics in non-game situations. It involves the use of video game elements, such as leaderboards, levels and badges in non-game activities aimed at improving the user experience and increasing user engagement. We find examples of gamification in various fields, e.g. in physical exercise and health (Pereira et al., 2014; Johnson et al., 2016), in the promotion of products and services (Huotari, & Hamari, 2012) and, of course, in education (Triantafyllou & Georgiadis, 2022). For example, airlines enable travelers to earn points with each trip that they can later redeem, or sports equipment companies have developed apps on smart phones and watches so that users can celebrate their performance with digital trophies, compete and challenge their running friends.In the learning process, gamification includes direct feedback mechanisms for learners, point systems, ranking tables, prizes, badges, progress bars and avatars among others, with the main objectives of mobilizing, encouraging and guiding the participant to achieve learning progress. Game-based learning makes the most of these mechanisms with an a priori architecture, which clearly defines the goal of the game, its basic functions and scenario, the rules of progression, competition and interaction with the application and teammates (Triantafyllou & Sapounidis, 2023).The reason why gamification is of particular interest is that research shows an increased degree of involvement and interest of participants when an environment or activity incorporates elements of gamification (Triantafyllou & Georgiadis, 2022). Game-based learning therefore activates not only cognitive functions, such as attention or perception, but also feelings of excitement, surprise, joy, sadness or even anger. This experience results in the achievement of meaningful learning objectives, such as deepening knowledge and developing intrapersonal (e.g. project scheduling) and interpersonal skills (e.g. collaboration).

Preprint ARTICLE | doi:10.20944/preprints202401.0811.v1

Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways

Chuanxiang Song, Seong-Yoon Shin, Kwang-Seong Shin

Subject: Computer Science And Mathematics, Computer Science Keywords: Machine Learning; Personalized Education; Adaptive Learning Systems; Online Learning Platforms; Educational Data Analysis

Online: 10 January 2024 (09:25:52 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0208.v1

Cross-Feature Transfer Learning For Efficient Tensor Program Generation

Gaurav Verma, Siddhisanket Raskar, Murali Emani, Barbara Chapman

Subject: Computer Science And Mathematics, Computer Science Keywords: auto-tuning; deep learning compilers; heterogeneous transfer learning; tensor program generation

Online: 3 January 2024 (08:38:03 CET)

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Preprint REVIEW | doi:10.20944/preprints202312.1990.v2

Multilingual Sentiment Analysis Using Deep Learning: Survey

Almas Bibi, Uswa Ihsan, Humaira Ashraf, NZ Jhanjhi

Subject: Computer Science And Mathematics, Computer Science Keywords: sentiment; deep learning; multilingual

Online: 3 January 2024 (03:13:11 CET)

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Preprint ARTICLE | doi:10.20944/preprints202401.0046.v1

Blasting fun with Gamification in Corporate Training

Serafeim Triantafyllou

Subject: Computer Science And Mathematics, Computer Science Keywords: gamification technologies; gamification in corporate training; gamified learning ;

Online: 2 January 2024 (07:24:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.2330.v1

Exploring Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps

J. Alberto Conejero, Andrei Velichko, Òscar Garibo-i-Orts, Yuriy Izotov, Viet-Thanh Pham

Subject: Computer Science And Mathematics, Computer Science Keywords: chaotic maps; NNetEn; neural network entropy; horizontal visibility graphs; fuzzy entropy; classification; entropy global efficiency; GEFMCC

Online: 29 December 2023 (16:34:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.2149.v1

Leveraging Medical Discourse to Answer Complex Questions

Boris Galitsky

Subject: Computer Science And Mathematics, Computer Science Keywords: Question answering system in health; computational medical discourse; large language models

Online: 29 December 2023 (01:11:27 CET)

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Preprint REVIEW | doi:10.20944/preprints202312.1672.v2

Improved E-learning Based Recommender Systems: A Survey

Oswa Maryam, Humaira Ashraf, Tehmina Amjad, Noor Jhanjhi

Subject: Computer Science And Mathematics, Computer Science Keywords: E-learning; Learning Analytics; Machine Learning; ; MOOCs; Recommender Systems

Online: 28 December 2023 (15:19:04 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.2043.v1

Passenger Routing Algorithm for COVID-19 Spreading Prevention by Minimising Overcrowding

Dimitrios Tolikas, Evangelos Spyrou, Vasileios Kappatos

Subject: Computer Science And Mathematics, Computer Science Keywords: routing; weight; COVID-19; overcrowding; virus; passenger

Online: 27 December 2023 (10:09:16 CET)

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Preprint REVIEW | doi:10.20944/preprints202312.1995.v1

Prediction for Pakistan Super League Using Machine Learning Techniques: A Survey

Noor ul Ain, Humaira Ashraf, NZ Jhanjhi

Subject: Computer Science And Mathematics, Computer Science Keywords: Prediction; Machine Learning; Cricinfo

Online: 26 December 2023 (14:20:34 CET)

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Preprint REVIEW | doi:10.20944/preprints202312.1990.v1

Multilingual Sentiment Analysis Using Deep Learning: Survey

Uswa Ihsan, Humaira Ashraf, NZ Jhanjhi

Subject: Computer Science And Mathematics, Computer Science Keywords: sentiment; deep learning; multilingual ;

Online: 26 December 2023 (11:19:03 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1962.v1

Reputation and Quality Aware Incentive Mechanism for Mobile Crowd Sensing Using Smart Devices

Irum Shahzadi, Humaira Ashraf, Uswa Ihsan, NZ Jhanjhi

Subject: Computer Science And Mathematics, Computer Science Keywords: Internet of Things (IoT); mobile crowd sensing (MCS); Complexity Level; Feedback

Online: 26 December 2023 (09:47:59 CET)

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The Internet of Things (also known as IoT), a revolution, has enhanced the relevance of mobile employees' job efficiency in mobile crowd sensing (MCS). Incentives based on reputation for mobile workers (MW) play an important role in increasing service utilization, motivating mobile employees, and developing confidence in the service. The cell phone is the most exploited entity in mobile crowd-sensing MCS and cloud computing. In the underlying investigation, we observed that they had not evaluated the complexity level of a task, resulting in a strong reputation for executing multiple basic tasks. A person who completes a tough task may have a lower reputation score. Complexity levels of tasks (CLT) were developed for reputation evaluation on a crowd-sensing network, which would be utilized to evaluate the MW reputation. We offered simple and complex levels of challenges to assess reputation scores. Tasks receive incentives based on their reputation. By measuring reputation on (CLT) complexity level of task, this research will help the system to Maintain the reputation of the mobile worker so that entities of the system get the maximum benefit out of it, by hiring well-reputed mobile workers and MW receive an incentive on it. Furthermore, we conduct a comparative analysis between our scheme and various machine learning algorithms to identify the algorithm that best performs in evaluating task complexity. Considering both the simple and complex reputation scores, the comparison suggests that after analyzing the data, it is clear that the CLT (proposed) scheme and the Linear regression scheme not only outperformed the Neural Network scheme in terms of Complex Reputation Scores but also in terms of Simple Reputation Scores. We also apply statistical tests, such as p-tests and t-tests, to determine the significance of the results obtained from different algorithms. Considering MW 308, MW 345, and MW 1045's efforts and abilities, the MCS system may offer incentives and awards to further motivate and consider their continuing contributions. This reputation-based incentivization technique not only encourages strong competition among mobile workers, but also makes sure the delivery of high-quality services inside the MCS system.

Preprint ARTICLE | doi:10.20944/preprints202312.1891.v1

Sensing Data Concealment in NFTs: A Steganographic Model for Confidential Cross-Border Information Exchange

Ghassan Al-Sumaidaee, Zeljko Zilic

Subject: Computer Science And Mathematics, Computer Science Keywords: NFTs; Blockchain; Steganography; Sensors

Online: 26 December 2023 (07:48:06 CET)

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Preprint REVIEW | doi:10.20944/preprints202312.1739.v1

DEEP LEARNING BASED QUESTION ANSWERING SYSTEM (SURVEY)

Nayyab Saeed, humaira ashraf, NZ Jhanjhi

Subject: Computer Science And Mathematics, Computer Science Keywords: BERT, RNN, Depp Learning

Online: 22 December 2023 (11:48:43 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1684.v1

Domain Specific Dictionary between Human and Machine Languages

Md Saiful Islam, Fei Liu

Subject: Computer Science And Mathematics, Computer Science Keywords: Knowledge Graph; Medicine Dictionary; Structured Triples; Information Extraction; Question Answering; Artificial Intelligence

Online: 22 December 2023 (08:09:11 CET)

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Preprint REVIEW | doi:10.20944/preprints202312.1682.v1

Online Reviews Classification and Summarization: A Comprehensive Analysis

Saima Abdullah, Humaira Ashraf, NZ Jhanjhi

Subject: Computer Science And Mathematics, Computer Science Keywords: online reviews, classification, summarization, machine learning, systematic literature ;
review.

Online: 22 December 2023 (07:01:39 CET)

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Preprint REVIEW | doi:10.20944/preprints202312.1672.v1

Improved E-learning Based Recommender Systems: A Survey

Oswa Maryam, Humaira Ashraf, Noor Jhanjhi

Subject: Computer Science And Mathematics, Computer Science Keywords: E-learning; Learning Analytics; Machine Learning; ; MOOCs; Recommender Systems

Online: 21 December 2023 (16:04:51 CET)

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Preprint REVIEW | doi:10.20944/preprints202312.1667.v1

Blockchain Based Information Retrieval System: A Survey

Sidrah Kaleem, Humaira Ashraf, NZ Jhanjhi

Subject: Computer Science And Mathematics, Computer Science Keywords: Blockchain, IPFS, Cloud Computing

Online: 21 December 2023 (14:58:25 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1641.v1

Secure Logistics Monitoring System Based on Wireless Sensor Network

Sidra Hameed, humaira ashraf, NZ Jhanjhi

Subject: Computer Science And Mathematics, Computer Science Keywords: SCM; logistics; wireless network

Online: 21 December 2023 (14:02:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1589.v1

Moving Object Detection Using Deep Learning Algorithms

Fareeha Mumtaz, Humaira Ashraf, NZ Jhanjhi

Subject: Computer Science And Mathematics, Computer Science Keywords: moving object detection; deep learning algorithms; YOLO; optical flow; object tracking; fusion techniques; performance evaluation ;

Online: 21 December 2023 (13:03:22 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1656.v1

Investigating Misinformation about COVID-19 on YouTube using Topic Modeling, Sentiment Analysis, and Language Analysis

Nirmalya Thakur, Shuqi Cui, Victoria Knieling, Karam Khanna, Mingchen Shao

Subject: Computer Science And Mathematics, Computer Science Keywords: COVID-19; YouTube; Misinformation; Big Data; Data Analysis; Topic Modeling; Sentiment Analysis; Correlation Analysis

Online: 21 December 2023 (11:52:17 CET)

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The work presented in this paper makes multiple scientific contributions with a specific focus on the analysis of misinformation about COVID-19 on YouTube. First, the results of topic modeling performed on the video descriptions of YouTube videos containing misinformation about COVID-19 revealed four distinct themes or focus areas - Promotion and Outreach Efforts, Treatment for COVID-19, Conspiracy Theories regarding COVID-19, and COVID-19 and Politics. Second, the results of topic-specific sentiment analysis revealed the sentiment associated with each of these themes. For the videos belonging to the theme of Promotion and Outreach Efforts, 45.8% were neutral, 39.8% were positive, and 14.4% were negative, for the videos belonging to the theme of Treatment for COVID-19, 38.113% were positive, 31.343% were neutral, and 30.544% were negative, for the videos belonging to the theme of Conspiracy Theories regarding COVID-19, 46.9% were positive, 31.0% were neutral, and 22.1% were negative, and for the videos belonging to the theme of COVID-19 and Politics, 35.70% were positive, 32.86% were negative, and 31.44% were negative. Third, topic-specific language analysis was performed to detect the various languages in which the video descriptions per topic were published on YouTube. This analysis revealed multiple novel insights. For instance, for all the themes, English and Spanish were the most widely used and second-most widely used languages, respectively. Fourth, the patterns of sharing these videos on other social media channels such as Facebook and Twitter were also investigated. The results revealed that videos containing video descriptions in English were shared the highest number of times on Facebook and Twitter. Finally, correlation analysis was performed by taking into account multiple characteristics of these videos. The results revealed that the correlation between the length of the video title and the number of Tweets as well as the correlation between the length of the video title and the number of Facebook posts was statistically significant.

Preprint REVIEW | doi:10.20944/preprints202312.1600.v1

Machine Learning-based Aircraft Conflict Prediction: A Systematic Literature Review

Maryam Jamal, humaira ashraf, NZ Jhanjhi

Subject: Computer Science And Mathematics, Computer Science Keywords: aircraft conflict prediction; machine learning; safety; air traffic control

Online: 21 December 2023 (11:16:59 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1609.v1

Efficient Cryptographic Technique for Data Protection of Wireless Body Area Network

Saddiqa Javaid, Humaira Ashraf, NZ Jhanjhi

Subject: Computer Science And Mathematics, Computer Science Keywords: wireless body area networks; data security; internet; man-in-the-middle attack; efficient cryptographic technique; cryptography; DNA; ECC

Online: 21 December 2023 (06:32:09 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1556.v1

An Efficient Simulated Annealing Algorithm for Short Addition Sequence

Hazem M. Bahig, Mohamed A.G. Hazber, Hatem M. Bahig

Subject: Computer Science And Mathematics, Computer Science Keywords: addition sequence; addition chain; simulated annealing; heuristic algorithm

Online: 20 December 2023 (12:59:14 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1391.v1

Data-Driven ICS Network Simulation for Synthetic Data Generation

Minseo Kim, Seungho Jeon, Jake Cho, Seonghyeon Gong

Subject: Computer Science And Mathematics, Computer Science Keywords: Industrial Control Systems (ICS); Synthetic Data Generation; Data-Driven Simulation; Machine Learning; Cybersecurity

Online: 19 December 2023 (06:01:34 CET)

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Industrial Control Systems (ICS) are integral to managing and optimizing processes in various industries, including manufacturing, power generation, and more. However, the scarcity of widely adopted ICS datasets hampers research efforts in areas like optimization and security. This scarcity arises due to the substantial cost and technical expertise required to create physical ICS environments. In response to these challenges, this paper presents a groundbreaking approach to generating synthetic ICS data through a data-driven ICS network simulation. We circumvent the need for expensive hardware by recreating the entire ICS environment in software. Moreover, rather than manually replicating the control logic of ICS components, we leverage existing data to autonomously generate control logic. The core of our method involves the stochastic setting of setpoints, which introduces randomness into the generated data. Setpoints serve as target values for controlling the operation of the ICS process. This approach enables us to augment existing ICS datasets and cater to the data requirements of machine learning-based ICS intrusion detection systems and other data-driven applications. Our simulated ICS environment employs virtualized containers to mimic the behavior of real-world PLCs and SCADA systems, while control logic is deduced from publicly available ICS datasets. Setpoints are generated probabilistically to ensure data diversity. Experimental results validate the fidelity of our synthetic data, emphasizing its ability to closely replicate temporal and statistical characteristics of real-world ICS networks. In conclusion, this innovative data-driven ICS network simulation offers a cost-effective and scalable solution for generating synthetic ICS data. It empowers researchers in the field of ICS optimization and security with diverse, realistic datasets, furthering advancements in this critical domain. Future work may involve refining the simulation model and exploring additional applications for synthetic ICS data.

Preprint ARTICLE | doi:10.20944/preprints202312.1356.v1

Impact of Flooding Attack on Wireless Sensor Networks: Framework and Toolkit

Rakan AlGhofaili, Hussah Albinali, Farag Ahmed Azzedin

Subject: Computer Science And Mathematics, Computer Science Keywords: RPL, Tool, Flooding attack, DoS, WSN, IoT

Online: 18 December 2023 (15:13:32 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.1201.v1

A Survey on Dependency of Parallel Clustering Platforms on Clustering Algorithms with Their Clustering Criteria for Big Data

Maradana Durga Venkata Prasad, Srikanth Thota

Subject: Computer Science And Mathematics, Computer Science Keywords: Clustering; Machine learning; Clustering Algorithms; Clustering Criteria; Clustering Platform; Clustering Criteria; Big Data; horizontal scaling platforms; MapReduce; Spark; P2P; vertical scaling platforms; Multi-cores; GPU; FPGA

Online: 15 December 2023 (15:03:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.0955.v1

A Clustering-Based Approach to Identifying Potential Output Anomalies with Combinatorial Testing for Web Applications

Karen Wei, Shin-Jie Lee

Subject: Computer Science And Mathematics, Computer Science Keywords: combinatorial testing; pairwise testing; hierarchical clustering; test oracle

Online: 13 December 2023 (08:04:18 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.0948.v1

Applying Moving Target Defense Against Data Theft Ransomware on Windows OS

Shishi Liu, Xin Chen

Subject: Computer Science And Mathematics, Computer Science Keywords: Cybersecurity; Data Theft; Moving Target Defense; Ransomware; Risk Mitigation; Windows OS

Online: 13 December 2023 (05:24:07 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.0410.v1

Development of Self-generated and LPWA-based Crop Growth Environment Monitoring and Bigdata Analysis System

YeonJae Oh

Subject: Computer Science And Mathematics, Computer Science Keywords: smart farm; field crop growth environment monitoring; bigdata analysis; low power wide area(LPWA); one machine to machine(oneM2M),

Online: 6 December 2023 (10:43:27 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.0198.v1

Gamification and new approaches in Engineering Education

Serafeim Triantafyllou

Subject: Computer Science And Mathematics, Computer Science Keywords: gamification; motivational theories; engineering education; micro-credentials &; engineering

Online: 4 December 2023 (11:29:39 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.0158.v1

Analyzing Data Theft Ransomware Traffic Patterns Using BERT

Gabriela Almeida, Felipe Vasconcelos

Subject: Computer Science And Mathematics, Computer Science Keywords: Ransomware Evolution; Data Theft; Network Traffic Analysis; BERT Model; Cybersecurity Adaptation

Online: 4 December 2023 (06:58:59 CET)

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Preprint ARTICLE | doi:10.20944/preprints202312.0025.v1

A Data-Driven Approach to Team Formation in Software Engineering Based on Personality Traits

Jan Vasiljević, Dejan Lavbič

Subject: Computer Science And Mathematics, Computer Science Keywords: team formation; personality traits; software engineering; data-driven approach; simulated annealing

Online: 1 December 2023 (08:10:35 CET)

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Preprint REVIEW | doi:10.20944/preprints202311.1607.v1

Rational Vaccine Design for SARS-CoV-2 Virus—A Systematic Review

Aritra Ghosh, Mirjana Pavlovic, Maria M. Larrondo-Petrie

Subject: Computer Science And Mathematics, Computer Science Keywords: COVID-19; SARS-COV2; rational vaccine design; coronavirus; epitope prediction; antibodies; RNA vaccines

Online: 27 November 2023 (07:20:43 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1385.v1

Give Me A Sign: Using Data Gloves For Static Hand Shape Recognition

Philipp Achenbach, Sebastian Laux, Dennis Purdack, Philipp Niklas Müller, Stefan Peter Göbel

Subject: Computer Science And Mathematics, Computer Science Keywords: machine learning: classification; support vector machines; random forest classifier; outlier detection; feature selection; data augmentation; hand shape recognition; sign language; virtual reality

Online: 22 November 2023 (05:51:51 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1147.v1

Enhanced Ransomware Identification via Feature Extraction with Class Feature Weighting

Fang Wang

Subject: Computer Science And Mathematics, Computer Science Keywords: ransomware detection; machine learning; dynamic analysis; n-grams; Class Feature Weighting (CFW)

Online: 20 November 2023 (11:06:27 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.1141.v1

An Improved Ensemble Learning-Based Approach for Retail Product Recognition

Huei-Yung Lin, Po-Yu Hsieh, Sen-Yih Chou, Chin-Chen Chang

Subject: Computer Science And Mathematics, Computer Science Keywords: ensemble learning; retail product recognition; convolutional neural network; unmanned store

Online: 17 November 2023 (07:32:36 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.0995.v1

Exploring the Potential of Artificial Intelligence in Primary Care: Insights From Stakeholders’ Perspectives

Teresa Sides, Dhouha Kbaier, Tracie Farrell, Aisling Third

Subject: Computer Science And Mathematics, Computer Science Keywords: artificial intelligence; primary care; AI acceptance; patient-centered care; eXplainable AI

Online: 15 November 2023 (09:57:14 CET)

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Preprint REVIEW | doi:10.20944/preprints202311.0841.v1

From On-Premises to Cloud: Crafting Your Pathway for Migration Success

Jayden Samuel Adesh Koshy, Sam Wen Ping, Chua Ying Hui, Tai Qiao Hui, Saira Muzafar

Subject: Computer Science And Mathematics, Computer Science Keywords: Cloud Computing; IaaS; Cloud Services; Cloud Providers; Velostrata; ; AWS; SDN-enabled cloud computing

Online: 14 November 2023 (16:39:26 CET)

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Preprint REVIEW | doi:10.20944/preprints202311.0850.v1

Cloud Computing Migration: A Thoughtful Decision

Yap Ming Xuan, Malcolm Tan Yu Xuen, Saira Muzafar

Subject: Computer Science And Mathematics, Computer Science Keywords: Cloud Computing; Amazon Web Services (AWS); Software Defined Networks (SDN)

Online: 14 November 2023 (11:32:06 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.0905.v1

Mining Negative Associations From Medical Databases Considering Frequent, Regular, Closed and Maximal Patterns

Sastry Kodanda Rama Jammalamadaka, Raja Rao Budaraju

Subject: Computer Science And Mathematics, Computer Science Keywords: data mining; databases; closed item sets; maximal item sets; regular patterns; frequent patterns; negative associations; maximal patterns; frequent patterns; static

Online: 14 November 2023 (10:12:27 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.0818.v1

Transformer Text Classification Model for Arabic Dialects that Utilizes Inductive Transfer

Laith H. Baniata, Sangwoo Kang

Subject: Computer Science And Mathematics, Computer Science Keywords: transformer; inductive transfer; text classification; Arabic dialects; positional encoding; 5-polarity

Online: 13 November 2023 (12:11:04 CET)

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Preprint REVIEW | doi:10.20944/preprints202311.0798.v1

A Survey on Ransomware Threats: Contrasting Static and Dynamic Analysis Methods

Qian Kang, Yuanyuan Gu

Subject: Computer Science And Mathematics, Computer Science Keywords: ransomware detection; static analysis; dynamic analysis; cybersecurity; threat mitigation

Online: 13 November 2023 (10:09:49 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.0671.v1

Semantic Modelling Approach for Safety-Related Traffic Information using DATEX II

J. Javier Samper-Zapater, Julian Gutiérrez-Moret, Jose Macario Rocha, Juan José Martinez-Durá, Vicente R. Tomás

Subject: Computer Science And Mathematics, Computer Science Keywords: Safety Road Traffic; Semantic web; LOD

Online: 10 November 2023 (07:31:57 CET)

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Preprint REVIEW | doi:10.20944/preprints202311.0664.v1

Wireless Technology Security and Privacy: A Comprehensive Study

Hamza Azam, Micheal Tan, Lim Tik Pin, Muhammad Afiq Syahmi, Antony Liew Wen Qian, Huang Jingyan, Muhammad Fuhad Uddin, Siva Raja Sindiramutty

Subject: Computer Science And Mathematics, Computer Science Keywords: Intrusion Prevention System (IPS); Intrusion Detection System (IDS); Intrusion Detection and Presentation System (IDPS); Deep Packet Inspection (DPI); Network-Based Prevention System (NIPS)

Online: 9 November 2023 (16:13:31 CET)

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Preprint REVIEW | doi:10.20944/preprints202311.0623.v1

Defending the Digital Frontier: IDPS and the Battle Against Cyber Threat

Hamza Azam, Mohammad Irfan Dulloo, Muhammad Hassan Majeed, Janelle Phang Hui Wan, Lee Tong Xin, Muhammed Ahnaf Tajwar, Siva Raja Sindiramutty

Subject: Computer Science And Mathematics, Computer Science Keywords: Intrusion Prevention System (IPS); Intrusion Detection System (IDS); Intrusion Detection and Presentation System (IDPS); Deep Packet Inspection (DPI); Network-Based Prevention System (NIPS)

Online: 9 November 2023 (10:59:26 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.0399.v1

Name Disambiguation Scheme Based on Heterogeneous Academic Sites

Dojin Choi, Junhyeok Jang, Sangho Song, Hyeonbyeong Lee, HyeonbyeongJongtae Lim, Kyoungsoo Bok, Jaesoo Yoo

Subject: Computer Science And Mathematics, Computer Science Keywords: name disambiguation; author name disambiguation; deep-learning; multiclass classification; HAC

Online: 7 November 2023 (10:39:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.0089.v1

How Close Is Existing C/C++ Code to a Safe Subset?

Christian DeLozier

Subject: Computer Science And Mathematics, Computer Science Keywords: programming languages; safe subset; memory safety; C++

Online: 2 November 2023 (06:57:58 CET)

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Preprint ARTICLE | doi:10.20944/preprints202311.0065.v1

An Approach to Implementing High-Performance Computing for Problem Solving in Workflow-based Energy Infrastructure Resilience Studies

Alexander Feoktistov, Alexei Edelev, Andrei Tchernykh, Sergey Gorsky, Olga Basharina, Evgeniy Fereferov

Subject: Computer Science And Mathematics, Computer Science Keywords: energy systems; resilience; vulnerability; high-performance computing; IMDG; scientific workflows

Online: 2 November 2023 (00:23:09 CET)

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Preprint ARTICLE | doi:10.20944/preprints202310.2066.v1

Random Walks Based Node Centralities to Attack Complex Networks

Massimiliano Turchetto, Michele Bellingeri, Roberto Alfieri, Ngoc-Kim-Khanh Khan, Quang Nguyen, Davide Cassi

Subject: Computer Science And Mathematics, Computer Science Keywords: real-world networks; node centrality; random walk processes; network robustness; network random walks

Online: 1 November 2023 (03:09:43 CET)

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Computer Science and Mathematics