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Preprint ARTICLE | doi:10.20944/preprints202410.0329.v1

Graph Neural Network Based Learning for Facility Location Optimization

Carmen Cvancara

Subject: Computer Science And Mathematics, Computer Science Keywords: Deep Learning; Graph Neural Network; Facility Location; Mixed Integer Programming

Online: 5 October 2024 (16:56:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2053.v4

Note for the P versus NP problem (II)

Frank Vega

Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: Complexity classes; Boolean formula; Graph; Completeness; Polynomial time

Online: 5 October 2024 (16:41:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0342.v1

Blockchain and Internet of Things technologies for Food Traceability in Olive Oil Supply Chains

Vassilios Vitaskos, Konstantinos Demestichas, Sotirios Karetsos, Constantina Costopoulou

Subject: Computer Science And Mathematics, Software Keywords: blockchain, smart contracts, food traceability, Internet of Things (IoT), supply chain, olive oil

Online: 4 October 2024 (16:31:53 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0322.v1

Securing Wireless Networks Against Emerging Threats: An Overview of Protocols and Solutions

Rishit Lakhani, Ram Chandra Sachan

Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: Wireless Security; Emerging Threats; WPA3; Internet of Things (IoT); Cybersecurity Protocols; Blockchain Security; Artificial Intelligence; Threat Detection; 5G Networks; Cryptographic Protocols

Online: 4 October 2024 (12:58:59 CEST)

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As wireless networks have become an integral part of modern communication infrastructure, ensuring their security against a rapidly evolving threat landscape is a critical concern. This research article provides a comprehensive overview of the emerging threats targeting wireless networks, including advanced persistent threats, man-in-the-middle (MitM) attacks, and AI-driven adaptive malware. With the advent of new technologies such as 5G, the Internet of Things (IoT), and artificial intelligence (AI), the attack surface for wireless networks has significantly expanded, demanding more robust and adaptive security protocols.The paper analyzes the efficacy of current wireless security protocols, such as WPA3 and the 802.11i standard, in addressing these emerging vulnerabilities. While these protocols have introduced significant improvements, they are not without limitations. The article further explores innovative solutions such as blockchain-based security frameworks, AI-powered threat detection systems, and the future potential of quantum cryptography in safeguarding wireless communications.Through a critical review of recent case studies and empirical data, the article highlights the key challenges that organizations face in securing wireless networks, particularly in IoT environments where security standards lag behind technological advancements. The research concludes that while existing protocols provide foundational security, they must be continuously updated and augmented with cutting-edge technologies to counter the growing sophistication of cyberattacks.This article aims to provide insights into the state of wireless network security and offer practical recommendations for enhancing security protocols. Future research directions are also discussed, focusing on the integration of AI-driven threat intelligence and the standardization of security protocols across various wireless technologies. The findings underscore the importance of proactive security measures to safeguard wireless networks in an increasingly interconnected world.

Preprint ARTICLE | doi:10.20944/preprints202410.0301.v1

Zero Trust VPN (ZT-VPN): A Cybersecurity Framework for Modern Enterprises to Enhance IT Security and Privacy in Remote Work Environments

Syed Muhammad Zohaib, Syed Muhammad Sajjad, Zafar Iqbal, Muhammad Yousaf, Muhammad Haseeb, Zia Muhammad

Subject: Computer Science And Mathematics, Security Systems Keywords: Zero Trust VPN; Zero Trust Network Access (ZTNA); Enterprise Security Framework; Zero Trust Architecture; Latency and Throughput in VPNs; Secure Remote Access; Zero Trust Security Model; Enterprise VPN Solutions; ZT and VPN Integration

Online: 4 October 2024 (09:47:11 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202408.0776.v2

Using Logs to Mitigate Process Variability and Dependence on Practitioners in Traditional Business Process Automation Software

Thiago Medeiros de Menezes, Ana Carolina Salgado

Subject: Computer Science And Mathematics, Information Systems Keywords: Business Process Automation; Requirements Engineering; requirements specification; software architecture; software design; software development; software engineering

Online: 4 October 2024 (08:15:09 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0276.v1

Beyond ChatGPT: A Hybrid Chatbot Model for Reliable Educational Administration

Kanaan Al-Jaf, Cemil Öz, Tarik A. Rashid

Subject: Computer Science And Mathematics, Computer Science Keywords: Chatbots; ChatGPT; Rule-based; Retrieval-based; Generative-based; Educational Administration; Hybrid model

Online: 4 October 2024 (08:10:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0273.v1

Detection of ECG Waves Using Butterworth Bandpass , Filter and Peak Finding Techniques

Dheiver Francisco Santos

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: ECG; Butterworth filter; peak detection; signal processing; cardiac diagnosis

Online: 4 October 2024 (08:09:12 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0278.v1

Review Non-convex Optimization Method for Machine Learning

Paul Popovich, Greg Fotopoulos, Nicholas Papadopoulos

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Deep Neural Networks; Support Vector Machine; Non-convex Optimization; Gradient Method \and Machine Learning

Online: 4 October 2024 (04:22:32 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0271.v1

Factor-Based Trading Strategy for Index Rebalancing: Predicting Abnormal Returns Using Logistic Classification

Satish Mandavalli

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Index; Predictive Model; Returns; Yield

Online: 4 October 2024 (04:20:56 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0269.v1

Learning A-Share Stock Recommendation From Stock Graph and Historical Price Simultaneously

Hanyang Chen, Tian Wang, Jessada Konpang, Adisorn Sirikham

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Stock Recommendation; Graph Neural Network; Gated Recurrent Network; Self-supervised Learning

Online: 4 October 2024 (00:10:25 CEST)

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Preprint REVIEW | doi:10.20944/preprints202410.0262.v1

Enhancing AI Transparency for Human Understanding: A Comprehensive Review

Malika Ara

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: artificial intelligence; machine learning; blackbox; explainable artificial intelligence

Online: 3 October 2024 (12:14:11 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0226.v1

Exploring and Evaluating Large Language Model Survey Paper Categories

Dylan Gresham

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: machine learning; data science; large language models; graph representation learning

Online: 3 October 2024 (10:57:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0131.v2

Real-time Eye Blink Detection using Computer Vision and Machine Learning

Dheiver Francisco Santos

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Eye Blink Detection; Computer Vision; Machine Learning; MediaPipe; Eye Aspect Ratio; Real-time Processing

Online: 3 October 2024 (10:57:44 CEST)

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Preprint ESSAY | doi:10.20944/preprints202410.0167.v1

The Babel Effect: Analyzing Multilingual Performane Discrepancies in Large Language Models

Basab Jha

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Multilingual Language Models; Large Language Models; Low-resource Languages; Cross-lingualLearning; Natural Language Processing; Tokenization; Data Augmentation

Online: 3 October 2024 (09:00:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0187.v1

Single Solution Based Metaheuristics With Graph Network Based Machine Learning

Mia Cryzan

Subject: Computer Science And Mathematics, Computer Science Keywords: Tabu Search; Simulated Annealing; Variable Neighborhood Search; Graph Convolutional Networks; Graph Neural Networks

Online: 3 October 2024 (08:24:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0198.v1

Survey of Security and Data Attacks on Machine Unlearning In Financial and E-Commerce

Carl Brodzinski

Subject: Computer Science And Mathematics, Computer Science Keywords: Machine Unlearning; Convex Function; Graph Neural Network; Differential privacy; Robust cryptographic mechanisms

Online: 3 October 2024 (08:22:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0192.v1

Review of Meta-heuristics Methods for Machine Learning With Graph Neural Network

Andrea Maldini

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: supervised learning; machine learning; heuristics; graph neural network; variable neighborhood search; genetic algorithm; particle swarm optimization

Online: 3 October 2024 (08:22:07 CEST)

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Preprint REVIEW | doi:10.20944/preprints202410.0213.v1

Applications of Entropy in Data Analysis and Machine Learning: A Review

Salomé A. Sepúlveda-Fontaine, José M. Amigó

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: entropy; entropic measures; data analysis; machine learning; deep learning

Online: 3 October 2024 (08:19:20 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0129.v1

Flight Model: New Explorations into the Fundamental Principles of Intelligence and Consciousness

liu feng, lv benfu, liu ying

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Intelligence; Consciousness; Standard Agent Model; Flight Model; Intelligence Forces

Online: 3 October 2024 (03:56:24 CEST)

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Constructing the fundamental principles of intelligence and consciousness has become a significant scientific challenge. The literature indicates that current theories of intelligence and consciousness have yet to provide comprehensive answers regarding the unified structure, objectives, and significance of the entities generating intelligence and consciousness, the driving forces, the relationship between intelligence and consciousness, and the distinctions between different types of consciousness.This study extends von Neumann architecture to establish Standard Agent Model as an axiom, proposing that any system (agent) possesses five essential capabilities: Information input, output, storage, creation, and control over the use of these capabilities. By taking the maximum and minimum values for each of these five capabilities, two extreme states and two types of intelligence forces of the agent are derived. Inspired by the principles of flight, the above elements are combined to establish the “Flight Model (FM)” of intelligence and consciousness. Intelligence is defined as the agent’s ability to utilize the five capabilities comprehensively to evolve between the two extreme states under the influence of intelligence forces. Consciousness is defined as the agent’s ability to control the use of intelligence under the influence of the two types of intelligence forces. Based on the relationship between control and being controlled, consciousness is further subdivided into self-consciousness, other-consciousness, mix consciousness, and unconsciousness.This study evaluates the intelligence levels and self-awareness of various agents based on the FM theoretical framework through designed experiments. The results show that current artificial intelligence systems exhibit intelligence close to adult human levels but have not yet demonstrated self-consciousness. This study constructs a theoretical system of intelligence and consciousness, providing a theoretical basis for determining the timeline for achieving AGI and addressing AI self-consciousness issues.

Preprint ARTICLE | doi:10.20944/preprints202410.0143.v1

Options for Performing DNN-Based Causal Speech Denoising Using the U-Net Architecture

Hwai-Tsu Hu, Tung-Tsun Lee

Subject: Computer Science And Mathematics, Signal Processing Keywords: speech denoising; causal U-Net; short-time Fourier transform; short-time discrete cosine transform; regression mapping

Online: 3 October 2024 (03:51:59 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0195.v1

Integration of Object Recognition, Color Classification and QR Decoding for the Purposes of Intelligent Robots

Radoslav Vasilev, Nayden Chivarov, Valentina Ivanova

Subject: Computer Science And Mathematics, Robotics Keywords: object recognition; color classification; QR decoding; mobile robots; dynamic environment; perceptual anchoring; artificial intelligence.

Online: 2 October 2024 (15:05:04 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202301.0541.v17

A Solution to the Collatz Conjecture Problem

Baoyuan Duan

Subject: Computer Science And Mathematics, Signal Processing Keywords: Collatz Conjecture; Collatz odd sequence; $(\times 3+2^m-1)\div2^k$ odd sequence; $(\times 3+2^m-1)\div2^k$ odd tree

Online: 2 October 2024 (14:50:39 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0072.v1

Predicting Employee Turnover in the Financial Company: A Comparative Study of CatBoost and XGBoost Models

Ziqing Yin, Baojun Hu, Shuhan Chen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Employee turnover; CatBoost; XGBoost; Machine learning

Online: 2 October 2024 (13:42:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0178.v1

Short Survey in Machine Learning for Soccer Analytics

Pedro Amadu

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: supervised learning; unsupervised learning; reinforcement learning; principal component analysi; soccer analytics

Online: 2 October 2024 (13:21:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0147.v1

The Dark Web And Cybercrime: Identifying Threats And Anticipating Emerging Trends

Sheetal Temara

Subject: Computer Science And Mathematics, Computer Science Keywords: Cybercrime; Dark Web; Cybercriminals; hackers; AI

Online: 2 October 2024 (13:08:14 CEST)

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Background/Objective: The Dark Web has played a pivotal role in the progress and sophistication of cybercrime. It provides an incubation network beyond the reach of traditional search engines where cybercriminals create and display exploit kits, offers illicit goods and services, and exchange confidential insider intelligence. Cybercriminals are extremely adept at selecting targets, applying tools to achieve their objectives, and minimizing red tape. The increasing sophistication of cybercriminals and the exponential rise of cybercrime against critical infrastructure underlines the necessity of identifying emerging threats. The objective of this research is to investigate the evolving threats within the Dark Web including crimeware-as-a-service and the integration of AI/ML into cyberattacks to inform risk management strategies and strengthen security measures.Research Problem: The exponential rise in cybercrime against critical infrastructure reflects growing sophisticationpresenting a significant challenge to organizations and society. The motivation behind cybercrime is fundamentally driven by self-greed which has contributed drastically to the magnitude and changes in methods used by cybercriminals to enhance profitability. The impact of cybercrime on business organizations presents an adverse impact on society and carries significant risks for the progress of individuals and the world at large. As cybercriminals adopt new technologies and services such as crimeware-as-a-service, identifying emerging trends becomes crucial to developing proactive strategies for the detection and prevention of cyber threats.Methodology: This research employs a systematic literature review approach to analyze emerging trends in cybercrime originating from the Dark Web. The review includes scholarly articles, news sources, and blog posts sourced from platforms like Google Scholar, IEEE Xplore, and various libraries. The key focus is to answer questions regarding the relationship between the Dark Web and cybercrime, accelerating cybercrime activities, and the benefits and implications of these new trends.Results: Key findings of this paper range from the rise of crimeware-as-a-service attacks and the increasing use of artificial intelligence and/or machine learning capabilities by cybercriminals to automate attacks across various businesses and organizations are also propounded along with information related to entry points and cybercrime attack pathways. The emergence of sophisticated cybercrime techniques, including ransomware-as-a-service, targeted attacks using AI, and exploitation of IoT vulnerabilities, are identified as critical trends. Social engineering, malware, and the rise of remote work have expanded the attack surface for cybercriminals.Discussion: As the use of cybercrime continues to metamorphose, the identification of new threats and extrapolation of emerging trends is critical to investigate the challenges associated with the monitoring and detection of illegitimate activities on the Dark Web as well as for the establishment of proactive risk management strategies and implementation of robust security measures. The research highlights the transformation of cybercrime into a structured and scalable ecosystem driven by technological advancements and service-based attack models. Cybercriminals now leverage AI/ML increasing the sophistication and success of their attacks. The commoditization of cybercrime has enabled less skilled individuals to participate amplifying the volume and diversity of threats faced by organizations.Conclusion: As cybercrime continues to evolve and adopt emerging technologies, organizations must remain vigilant and adaptable. The findings emphasize the need for proactive risk management, continuous monitoring of cybercrime trends, and robust security measures to mitigate the increasing threats originating from the Dark Web. Future research should focus on deeper exploration of AI-driven attacks and the development of more advanced countermeasures to safeguard critical infrastructure.

Preprint ARTICLE | doi:10.20944/preprints202410.0175.v1

Real-Time Heart Rate Estimation from Video Using Face Detection and Signal Processing

Dheiver Francisco Santos

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: heart rate; video analysis; face detection; signal processing

Online: 2 October 2024 (13:05:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0159.v1

Finite Time Hybrid Synchronization Control for Duplex Complex Networks via Intermittent Control

Cheng-jun Xie, Ying Zhang, Qiang Wei

Subject: Computer Science And Mathematics, Other Keywords: finite time synchronization; intermittent control; complex dynamical network

Online: 2 October 2024 (12:13:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0124.v1

Evaluating Success in Digital Transformation of Government Services: Insights and Evidence from Kuwait

Nasser Alshawaaf, Basil Alzougool

Subject: Computer Science And Mathematics, Information Systems Keywords: digitisation; digitalisation; digital transformation; public sector; automatic processing; service process; efficiency

Online: 2 October 2024 (11:59:53 CEST)

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Digital transformation in government services is seen as a progression towards offering more efficient and convenient services. In the wake of the COVID-19 pandemic, many governments worldwide have expedited the digitisation of their services to facilitate social distancing. Digital transformation encompasses the shift from manual, face-to-face services to automated, online-based systems. This process occurs in three stages: digitisation, digitalisation, and digital transformation. However, the varying stages of digital government implementation across countries impact service outcomes differently. Little research has been devoted to focusing on the outcome metrics crucial for the success of digital services, particularly in Kuwait. Moreover, although the existing literature has explored some metrics, specific metrics have been neglected. This study aimed to investigate current trends and preferences in digital government services usage using empirical quantitative data gathered from users of digital services in Kuwait. Speciﬁcally, the aim is fourfold: (i) to identify crucial outcome metrics for the success of digital services, (ii) to assess how users evaluate these services according to these metrics, (iii) to examine significant differences between digital transformation and digitalisation services, and (iv) to develop and validate a model for evaluating the success of digital transformation services. To achieve the study's objectives, 378 users of digital government services were surveyed quantitatively. This study found that users primarily access services through smartphones and applications, underscoring the need for mobile optimisation. There was a marked preference for real-time, automated services, with dissatisfaction for those requiring lengthy approval processes. Six key metrics were identified and grouped into three domains: service-related (customer satisfaction and service quality), convenience-related (accessibility and ease of use), and security-related (perceived security and perceived trust). Digital transformation services outperformed digitalisation services in five key areas: customer satisfaction, service quality, accessibility, ease of use, and perceived security, except perceived trust, which remained consistent across both service types. Several recommendations were proposed to advance government digital transformation services, boost efficiency, and enhance the user experience.

Preprint REVIEW | doi:10.20944/preprints202410.0113.v1

A Systematic Literature Review of Insurance Claims Risk Measurement Using Hidden Markov Model

Hilda Azkiyah Surya, Sukono Sukono, Herlina Napitupulu, Noriszura Ismail

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Insurance claims; risk measurement; SLR; bibliometric analysis; Hidden Markov Model

Online: 2 October 2024 (11:59:44 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0131.v1

Real-time Eye Blink Detection using Computer Vision and Machine Learning

Dheiver Francisco Santos

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Eye Blink Detection; Computer Vision; Machine Learning; MediaPipe; Eye Aspect Ratio; Real-time Processing

Online: 2 October 2024 (11:59:00 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0136.v1

Filtering and Noise Extraction from ECG Signals Using Butterworth Filter

DHEIVER SANTOS

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: ECG; Butterworth filter; noise reduction; signal processing

Online: 2 October 2024 (11:09:53 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0157.v1

School Mathematics in Kazakhstan: a Blueprint for Digital Transformation

Alexei L. Semenov, Alma E. Abylkassymova, Darkhan Akhmed-Zaki

Subject: Computer Science And Mathematics, Other Keywords: Kazakhstan; general education system; math education; problems “not known how to solve”; digital technologies; AI technologies in education; teacher professional development; continuity of math education

Online: 2 October 2024 (10:49:48 CEST)

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Preprint REVIEW | doi:10.20944/preprints202410.0098.v1

An Overview of Empirical Evaluation of Explainable AI (Xai): A Comprehensive Guideline to User-Centered Evaluation in Xai

Sidra Naveed, Gunnar Stevens, Dean-Robin Kern

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: AI transparency; Explainable artificial intelligence (XAI); XAI evaluation procedure; User-centered evaluation

Online: 2 October 2024 (09:46:02 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0091.v1

Solvability of a Boundary Value Problem for One-Dimensional Motion of a Granular Matter

Irina Akhmerova, Alexander Papin, Artyom Pravdivtsev, Alla Ustyuzhanova

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: granular temperature; density; granular material; solvability

Online: 2 October 2024 (08:08:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0078.v1

Linguistic Secret Sharing via Ambiguous Token Selection for IoT Security

Kai Gao, Ji-Hwei Horng, Ching-Chun Chang, Chin-Chen Chang

Subject: Computer Science And Mathematics, Security Systems Keywords: Ambiguous token selection; Galois field; linguistic secret sharing; language modeling

Online: 2 October 2024 (08:07:05 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0088.v1

Some Notes on the Gini Index and New Inequality Measures: The nth Gini Index

José Manuel Gavilan Ruiz, África Ruiz-Gándara, Francisco Javier Ortega Irizo, Luis Gonzalez Abril

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: inequality measures; mean difference; order statistics; rank-dependent inequality measurement

Online: 2 October 2024 (07:10:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2413.v1

SafeMD:Ownership-based Safe Memory Deallocation for C Programs

Xiaohua Yin, Zhiqiu Huang, Shuanglong Kan, Guohua Shen

Subject: Computer Science And Mathematics, Software Keywords: C; memory leaks; memory deallocation; Rust; ownership

Online: 2 October 2024 (04:56:20 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0099.v1

AI-Driven Multimodal Analysis for Innovations in Additive Friction Stir Deposition Techniques

Akshansh Mishra

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Additive Friction Stir Deposition; Artificial Intelligence; Multimodal; RAG; Additive Manufacturing

Online: 1 October 2024 (16:59:09 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0095.v1

Multi-Phase Adaptive Recoding: An Analogue of Partial Retransmission in Batched Network Coding

Hoover H. F. Yin, Mehrdad Tahernia, Hugo Wai Leung Mak

Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: Random Linear Network Coding (RLNC); Batched Network Coding (BNC); Adaptive recoding; Effective information transmission; Hop-by-hop feedback

Online: 1 October 2024 (16:38:07 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0073.v1

Mixed Perturbation: Generating Directionally Diverse Perturbations for Adversarial Training

Changhun Hyun, Hyeyoung Park

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: adversarial robustness; adversarial training; adversarial perturbations, evasion attack, multi-task learning

Online: 1 October 2024 (14:00:01 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0002.v1

Security Proof of SSSD Protocols Built on Top of SMC Protocol

Mohammad Anagreh, Peeter Laud

Subject: Computer Science And Mathematics, Security Systems Keywords: Secure multiparty computation; Arithmetic Black Box; Universal Composability; Privacy-preserving computation; Single-source shortest distances; SIMD parallel; Bellman-Ford; Sharemind

Online: 1 October 2024 (11:54:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0010.v1

Strategies for Professors on How to Mitigate the Negative Effects of ChatGPT in Higher Education

Thiago Maciel

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Negative Effects of Chatgpt; Strategies for Teachers; Mitigating the Impacts of AI; Pedagogical Strategies; Preventing Academic Plagiarism

Online: 1 October 2024 (11:52:10 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202410.0046.v1

Sports Analytics with Graph Neural Networks and Graph Convolutional Networks

Tim Todd Drexler

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Graph Neural Networks; Graph Convolutional Networks

Online: 1 October 2024 (11:15:39 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2449.v1

How Much Does the Dynamic F0 Curve Affect the Expression of Emotion in Utterances?

Tae-Jin Yoon

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: emotional speech recognition; fundamental frequency (F0); pitch contours; generalized additive mixed models (GAMMs); non-linear dynamics; speech processing

Online: 1 October 2024 (08:43:37 CEST)

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The modulation of vocal elements such as pitch, loudness, and duration plays a crucial role in conveying both linguistic information and the speaker’s emotional state. While acoustic features like fundamental frequency (F0) variability have been widely studied in emotional speech analysis, challenges remain in accurately classifying emotions due to the complex and dynamic nature of vocal expressions. Traditional analytical methods often oversimplify these dynamics, potentially overlooking intricate patterns indicative of specific emotions. This study aims to enhance emotion classification in speech by directly incorporating dynamic F0 contours into the analytical framework using Generalized Additive Mixed Models (GAMMs). We utilized the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), focusing on eight distinct emotional states expressed by 24 professional actors. Sonorant segments were extracted, and F0 measurements were converted into semitones relative to a 100 Hz baseline to standardize pitch variations. By employing GAMMs, we modeled non-linear trajectories of F0 contours over time, accounting for both fixed effects (emotions) and random effects (individual speaker variability). Our analysis revealed that incorporating emotion-specific non-linear time effects and individual speaker differences significantly improved the model’s explanatory power, ultimately explaining up to 66.5% of the variance in F0. The inclusion of random smooths for time within speakers captured individual temporal modulation patterns, providing a more accurate representation of emotional speech dynamics. The results demonstrate that dynamic modeling of F0 contours using GAMMs enhances the accuracy of emotion classification in speech. This approach captures the nuanced pitch patterns associated with different emotions and accounts for individual variability among speakers. The findings contribute to a deeper understanding of the vocal expression of emotions and offer valuable insights for advancing speech emotion recognition systems.

Preprint ARTICLE | doi:10.20944/preprints202409.2461.v1

Evaluating the Impact of Windowing Techniques on Fourier Transform Preprocessed Signals for Deep Learning-Based ECG Classification

Niken Prasasti Martono, Hayato Ohwada

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: arrhythmia; electrocardiogram (ECG); ECG classification; signal processing; deep learning; convolutional neural network (CNN)

Online: 1 October 2024 (08:40:05 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2460.v1

Enhancing Player Experience in an FPS with Dynamic Audio Cue Adjustment Based on Gaussian Progress Regression

Xiaoxu Li, Yi Xia, Mustafa Can Gursesli, Xiao You, Siyuan Chen, Ruck Thawonmas

Subject: Computer Science And Mathematics, Computer Science Keywords: First-person shooter; Gaussian progress regression; Dynamic difficulty adjustment; Audio cue

Online: 1 October 2024 (03:37:40 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2462.v1

A Reference Architecture for Smart Car Parking Management Systems

Mert Ozkaya

Subject: Computer Science And Mathematics, Software Keywords: Smart car parking; reference architecture; software architecture; multiple viewpoints

Online: 1 October 2024 (03:36:11 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2463.v1

On a Convergence of Quasi-Periodic Interpolations Exact for the Polyharmonic-Neumann Eigenfunctions

Arnak Poghosyan, Lusine Poghosyan, Rafayel Barkhudaryan

Subject: Computer Science And Mathematics, Mathematics Keywords: Truncated Fourier series; convergence acceleration; quasi-periodic interpolation; polyharmonic-Neumann eigenfunctions; modified Fourier basis

Online: 30 September 2024 (17:44:26 CEST)

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Preprint REVIEW | doi:10.20944/preprints202409.2451.v1

Harnessing AI and IoT for the Future of Healthcare: A Comprehensive Review on Chronic Disease Management and Pandemic Response

Dang Anh Tuan, Dang Tri Thanh

Subject: Computer Science And Mathematics, Computer Science Keywords: AI in healthcare, IoT in healthcare, chronic disease management, smart inhalers, continuous glucose monitoring, AI predictive models, COVID-19 pandemic response, healthcare automation, remote patient monitoring, AI ethics in healthcare.

Online: 30 September 2024 (17:12:53 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2053.v3

Note for the P versus NP problem (II)

Frank Vega

Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: Complexity classes; Boolean formula; Graph; Completeness; Polynomial time

Online: 30 September 2024 (15:17:45 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2417.v1

Harnessing Multimodal Data and Mult-Recall Strategies for Enhanced Product Recommendation in E-Commerce

Siyue Li

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: product recommendation; mult-recall; LightGBM ranker; feature engineering; machine learning

Online: 30 September 2024 (14:37:58 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2415.v1

Accelerating and Compressing Transformer-based PLMs for Enhanced Comprehension of Computer Terminology

Jian Peng, Kai Zhong

Subject: Computer Science And Mathematics, Computer Science Keywords: Pre-training models; parallelism; mixed compression method; computer-specialized terms

Online: 30 September 2024 (14:37:06 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2425.v1

Biomathematics in Cancer Research: Looking into How Mathematical Models Are Used to Understand Tumor Growth and the Effectiveness of Different Treatment Strategies

Akinbusola Olushola, Victoria Alao

Subject: Computer Science And Mathematics, Applied Mathematics Keywords: Biomathematics, Tumor growth, Mathematical models, Cancer treatment optimization, Stochastic models, Personalized medicine

Online: 30 September 2024 (14:29:55 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2373.v1

Modeling SOS traffic using a CTMC

Ahmad Hani EL FAWAL, Ali Mansour, Hussein El Ghor, Nuha A. Ismail, Sally Shamaa

Subject: Computer Science And Mathematics, Computer Networks And Communications Keywords: M2M; H2H; IoT; CTMC; Markov chains; Emergency

Online: 30 September 2024 (12:44:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2350.v1

Effects of Generative AI in Tourism Industry

Galina Ilieva, Tania Yankova, Stanislava Klisarova-Belcheva

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: generative artificial intelligence; dynamic itinerary planning; AI-driven travel suggestions; automated review summarization; interactive travel assistance; tourism industry; smart travel planning; AI powered customer service

Online: 30 September 2024 (11:57:59 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2399.v1

Identification and Analysis of Anticancer Therapeutic Targets from the Polysaccharides PSK and PSP Using Inverse Docking

Carlos Iván López-Gil, Alejandro Téllez-Jurado, Marco Antonio Velasco-Velázquez, Miguel Angel Anducho-Reyes

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: PSK; PSP; inverse docking; carcinogenic proteins; Trametes versicolor; bioinformatics; immunostimulant

Online: 30 September 2024 (10:42:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2267.v1

Investigating the Riemann Hypothesis with HQF ASA and Modular Surfaces - a Detailed Description

Hime Oliveira

Subject: Computer Science And Mathematics, Mathematics Keywords: Riemann Hypothesis; Riemann Zeta Function; Artificial Inference; HQF ASA; Global Optimization.

Online: 30 September 2024 (09:28:49 CEST)

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Preprint REVIEW | doi:10.20944/preprints202409.2336.v1

Esports Training, Periodization, and Software – a Scoping Review

Andrzej Białecki, Bartłomiej Michalak, Jan Gajewski

Subject: Computer Science And Mathematics, Other Keywords: esports; sport science; periodization; training; gaming; competitive gaming; review; human-computer interaction

Online: 30 September 2024 (09:12:09 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2367.v1

BOANN: Bayesian-Optimized Attentive Neural Network for Classification

Luoyao He, Xingqi Wang, Yuzhen Lin, Xinjin Li, Yu Ma, Zhenglin Li

Subject: Computer Science And Mathematics, Computer Science Keywords: ; deep learning; Image classification; Convolutional neural network; Bayesian optimization ;

Online: 30 September 2024 (08:57:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2215.v1

Probabilities on a Heyting Algebra

Jean-Pascal Laedermann

Subject: Computer Science And Mathematics, Logic Keywords: probability; Heyting algebra; projector; quantum physics

Online: 30 September 2024 (08:53:43 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2362.v1

Survey for Exploring Blockchain with Graph Neural Network

Amanda Hasebe

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Graph Neural Network; Graph Convolutional Networks; Graph Attention Networks; blockchain; cryptocurrency

Online: 30 September 2024 (07:47:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2285.v1

A Dual-Module System for Copyright-Free Image Recommendation and Infringement Detection in Educational Materials

Yeongha Kim, Soyeon Kim, Seonghyun Min, Youngung Han, Ohyoung Lee, Wongyum Kim

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Copyright protection; Copyright-free; Image retrieval; Infringement detection

Online: 30 September 2024 (07:29:11 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2376.v1

Graph Neural Network for Daily Supply Chain Problems

Fabian Raul Gonzalez

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: supply chain; graph neural network; machine learning

Online: 30 September 2024 (06:07:57 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2355.v1

Existence of Two Solutions for $(p(x), Q(x))$-Laplacian

Rahmani Mostafa, Belhadj Karim, Zerouali Abdellah, Lakhdi Abdessamad

Subject: Computer Science And Mathematics, Computational Mathematics Keywords: Ekeland's variational principle; Elliptic equations; $(p(x); q(x))$-Laplacian; Mountain pass theorem; Steklov eigenvalue problem

Online: 30 September 2024 (05:35:59 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2331.v1

Advancing Subgroup Discovery in Classification: A Novel Random PRIM-Based Classifier Compared with well-established Algorithms

Rym Nassih, Abdelaziz Berrado

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Classification; Subgroup Discovery; Patient Rule Induction Method; Metarules; Interpretability

Online: 30 September 2024 (04:10:40 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2330.v1

Situational Awareness Classification Based on EEG Signals and Spiking Neural Network

Yakir Hadad, Moshe Bensimon, Yehuda Ben-Shimol, Shlomo Greenberg

Subject: Computer Science And Mathematics, Computer Science Keywords: EEG classification; Neuromorphic computing; Spiking neural network; Spike-continuous-time-neuron; Features extraction; Frequency analysis

Online: 29 September 2024 (16:24:32 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2228.v1

Acceptance Sampling Plans Based on Conformance Probability—Inspection of Lots and Processes by Attributes

Steffen Uhlig, Bertrand Colson, Roger Kissling, Sam Ellis, Mike Hicks, John Vandenbemden, Francesca Pennecchi, Rainer Göb, Petra Gowik

Subject: Computer Science And Mathematics, Probability And Statistics Keywords: acceptance sampling plan; conformity assessment; Bayesian statistics; producer's risk; consumer's risk; conformance probability; lot inspection by attributes

Online: 29 September 2024 (11:22:19 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2235.v1

Proof of Goldbach's Conjecture

Shaosheng Hou, Yue Huang, Lin-Jun Ma

Subject: Computer Science And Mathematics, Mathematics Keywords: Goldbach Conjecture; Prime Numbers; Mathematical Proof; Number Theory; Odd Prime Numbers; Composite Numbers

Online: 29 September 2024 (11:16:11 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2053.v2

Note for the P versus NP problem (II)

Frank Vega

Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: Complexity classes; Boolean formula; Graph; Completeness; Polynomial time

Online: 29 September 2024 (11:14:57 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2295.v1

AI-Assisted Pilot Fatigue Risk Assessment: Integrating Facial Recognition and Physiological Signal Analysis

Weibo Jin, Haoxing Liu, Fangzhou Shen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Generative AI; Inertial Measurement Units (IMUs); Fatigue Risk Assessment; Facial Recognition

Online: 29 September 2024 (11:02:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2292.v1

FL-APB: Balancing Privacy Protection and Performance Optimization for Adversarial Training in Federated Learning

Teng Liu, Hao Wu, Xidong Sun, Chaojie Niu, Hao Yin

Subject: Computer Science And Mathematics, Security Systems Keywords: federated learning; adversarial training; privacy protection; reinforcement learning

Online: 29 September 2024 (07:32:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2259.v1

Exploring the Role of Genetic and Environmental Features in Colorectal Cancer Development: An Agent-Based Approach

Marco Ledda, Alessandro Pluchino, Marco Ragusa

Subject: Computer Science And Mathematics, Mathematical And Computational Biology Keywords: Agent-based Models; Colorectal cancer (CRC); Biological evolution

Online: 29 September 2024 (05:58:46 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2258.v1

On Geometry, Arithmetics and Chaos

Lars Andersen

Subject: Computer Science And Mathematics, Geometry And Topology Keywords: Geometry; Singularities

Online: 29 September 2024 (05:55:06 CEST)

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Preprint CONCEPT PAPER | doi:10.20944/preprints202409.2256.v1

Optimizing Exotic Option Pricing: Monte Carlo Simulation and Variance Reduction Techniques

Srinivas Raju Gottimukkala

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Asian; Barrier; Black-Scholes; Financial; Monte Carlo

Online: 29 September 2024 (05:53:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2217.v1

An Empirical Study on ERP Travel, Trade, and Tourism Systems

Huma Pervez, Muhammad Mohsin, Abid Ali

Subject: Computer Science And Mathematics, Software Keywords: Enterprise Resource Planning; Survey; Travel; Tourism; Lighthouse; Jmeter

Online: 29 September 2024 (02:54:34 CEST)

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In this paper, we present findings from a recently completed research about the use of enterprise resource planning (ERP) in travel, trade, and tourism businesses. Initially, we selected twelve existing travel, trade, and tourism ERP systems and preform practical analysis and testing of these systems via JMETER and Google chrome Lighthouse tools. In addition to that, an online survey was conducted (with an e-mail invitation and telephone call reminder) to collect data from three Pakistani Travel, Trade, and Tourism organizations. Furthermore, we adopted qualitative research practices by going through various research papers and research studies from journals, books, and internet, etc. This study is constructed on only secondary statistics. The goal of this article is to look at ERP systems in terms of their concept and use, as well as how they affect the modern travel business. An ERP system is a business system that integrates several corporate processes into a single database, such as supply chain management, manufacturing, financial management, and project management. ERP systems are utilized in a variety of businesses around the world, including in the manufacturing and service industries. Across a range of industries, service firms have recently spent significant resources on the implementation of Enterprise Resource Planning (ERP) systems to improve the efficiency, cost-effectiveness, and quality of service operations. To assist them to build their businesses, the travel, trade, and tourist industries all rely on an integrated information network. The work focuses on only one industry i-e Travel, Trade, and Tourism. This may limit the applicability of its broad conclusions to other industries or industries. The findings of this study compare and contrast the ERP implementation challenges faced by existing organizations of travel, trade, and tourism companies. This is the first empirical study on the difficulties surrounding ERP installation in travel, trade, and tourism companies. It is also the first study to compare ERP adoption in multiple companies in the areas of travel, trade, and tourism.

Preprint REVIEW | doi:10.20944/preprints202409.2252.v1

Phase Error Correction in Magnetic Resonance: A Review of Models, Optimization Functions, and Optimizers in Traditional Statistics and Neural Networks

Aixiang Jiang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: phase error correction; statistical models; neural networks; optimization functions; optimizers

Online: 27 September 2024 (16:42:20 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2180.v1

Optimizing E-Commerce with Multi-Objective Recommendations Using Ensemble Learning

Jiaxin Lu

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: E-commerce; Multi-objective recommendation system; User behavior prediction; Ensemble learning; Sample imbalance

Online: 27 September 2024 (12:31:31 CEST)

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Preprint REVIEW | doi:10.20944/preprints202409.1974.v2

Bridging the Gap Between Black Box AI and Clinical Practice: Advancing Explainable AI for Trust, Ethics, and Personalized Healthcare Diagnostics

Dang Anh Tuan

Subject: Computer Science And Mathematics, Computer Science Keywords: Explainable AI (XAI); Healthcare diagnostics; Grad-CAM; SHAP; LIME; Trust in AI; Ethical AI; Bias mitigation; Regulatory compliance; Personalized care; AI interpretability; Clinical decision-making

Online: 27 September 2024 (12:31:01 CEST)

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Preprint REVIEW | doi:10.20944/preprints202409.2164.v1

Mathematical Problems of Artificial Intelligence and Their Solution Through the Universality of the Zeta Function

Asset Durmagambetov

Subject: Computer Science And Mathematics, Other Keywords: artificial intelligence; Riemann zeta function; universality; optimization; interpretability

Online: 27 September 2024 (11:46:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2208.v1

Artificial Intelligence in Flight Safety: Fatigue Monitoring and Risk Mitigation Technologies

Weibo Jin, Haoxing Liu, Fangzhou Shen

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: ; Artificial intelligence; physiological fatigue; pilots; flight safety

Online: 27 September 2024 (11:39:24 CEST)

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With the improvement of computer, artificial intelligence, information technology and other technical levels, the relationship between man-machine environment systems is more complicated and diversified. The optimization, iteration and development of the new generation of intelligent equipment system and human-computer interaction interface put forward higher requirements for ensuring the safety of personnel, improving the efficiency of human-computer interaction and improving the efficiency of the system. Such as intelligent cabin adaptive cognitive decision aid system, how to adopt intelligent information display and human-computer interaction, optimize information processing, strengthen situational awareness; How to effectively present information and improve the efficiency of human-computer interaction, so that the system has good security, applicability and maximize its effectiveness; How to deal with man-machine matching and man-machine collaboration problems, so as to improve the efficiency of man-machine/unmanned collaborative work. Human factors throughout the life cycle of equipment systems must be fully considered. The human factor is considered in the system design, so that people, machines and the environment can work together and adapt to each other, so as to achieve benign interaction and feedback between people and equipment and interface and complete the full transmission and communication of human-machine intelligent interaction information. The development of new aircraft human-computer interaction systems combined with new technological methods has also gradually changed the role of pilots and staff. From the system operator gradually into the monitor and decision maker, especially with the improvement of the degree of intelligent flight, information technology, advanced complex airborne equipment is increasing, the amount of information that operators need to deal with is also increasing, and the allowed time for judgment and decision is very short, and the mental resources that pilots bear are gradually rising. As the mental load is a key factor affecting the allocation of cognitive tasks, when encountering emergency situations, the mental load overload caused by the increase of information processing tasks often occurs, which seriously affects the task performance of operators, physical and psychological comfort and flight safety, and thus affects the efficiency and safety of the entire aircraft man-machine system. This requires us to conduct real-time analysis of human-computer interaction situational awareness, especially the individual cognitive state as an uncontrollable factor.

Preprint ARTICLE | doi:10.20944/preprints202409.2174.v1

Cyber Edge: Current State of Cyber Security in Aotearoa-New Zealand, Opportunities, and Challenges

Md. Rajib Hasan, Nurul I. Sarkar, Noor H. S. Alani, Raymond Lutui

Subject: Computer Science And Mathematics, Information Systems Keywords: Cyber Edge; Cultural Integration; Cybersecurity; Aotearoa-New Zealand; Cybersecurity Resilience; Diversity; Cultural Competency; Inclusive Strategies ;

Online: 27 September 2024 (07:13:38 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2135.v1

Enhancing Stress Identification Using Machine Learning: Revealing Key Factors with SHAP- Driven Explainable AI

Akhil Chintalapati, Ramanathan Annamalai, Khashbat Enkhbat, Fatih Ozaydin, Karthikeyan Sivashanmugam

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Stress Detection; Machine Learning; Explainable AI

Online: 27 September 2024 (00:12:46 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2061.v1

Exact Unlearning with Convex and Non-Convex Functions

Cassandra Lindstrom

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: exact unlearning; machine unlearning; convex function; non-convex function

Online: 26 September 2024 (16:59:14 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2066.v1

Fusion of Visual and Textual Data for Enhanced Semantic Representations

Lyra Sterling, Kairos Vale, Ava Martinez

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Multimodal Integration; Semantic Embeddings; Representation Learning; Transfer Learning

Online: 26 September 2024 (16:55:15 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2117.v1

Graph Neural Network and Increment Learning in Blockchain

Chang Qi

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Graph Neural Networks; Incremental Learning; Blockchain

Online: 26 September 2024 (14:47:35 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2121.v1

Optimizing Unsupervised Feature Engineering and Predictive Models for Thyroid Cancer Recurrence Prediction

Emmanuel Onah, Uche Jude Eze, Abdullahi Salahudeen Abdulraheem, Ugochukwu Gabriel Ezigbo, Kosisochi Chinwendu Amorha

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Well-differentiated thyroid cancer; Recurrence prediction; Unsupervised Data Engineering Methods; Dimensionality Reduction Techniques; Machine learning; Clustering; Principal Component Analysis; Truncated Singular Value Decomposition; Logistic Regression

Online: 26 September 2024 (14:45:33 CEST)

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Background/Objectives: Thyroid cancer, particularly well-differentiated thyroid cancer, is one of the most prevalent endocrine malignancies, with a rising incidence. Although it generally has a favorable prognosis, recurrence is common. Accurate prediction of recurrence is crucial for optimizing treatment plans and improving patient outcomes. This study aimed to advance the state-of-the-art in thyroid cancer recurrence prediction by refining feature engineering techniques and exploring a diverse ensemble of machine learning algorithms and an artificial neural network, using the differentiated thyroid cancer dataset from the UCI Machine Learning Repository; Methods: Various unsupervised data engineering methods, such as dimensionality reduction and clustering, were employed to enhance feature quality and mitigate noise, using stratified 10-fold cross-validation. The best-performing dimensionality reduction techniques were used to build classification model pipelines employing each of several machine learning models and an artificial neural network. The performance of these classification pipelines were assessed using metrics sensitive to class imbalance; Results: Principal Component Analysis and Truncated Singular Value Decomposition achieved superior clustering performance and moderate variances in their first principal components. Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbors, and Feedforward Neural Network models all achieved high performance, with Logistic Regression pipelines demonstrating balanced accuracy, F1 score, AUC, sensitivity, specificity, and precision—all greater than 0.93 on the test set and slightly lower in 10-fold cross-validation. Gradient Boosting classification pipelines performed the lowest, though still with respectable metrics; Conclusions: This study shows that employing feature engineering techniques like Principal Component Analysis or Truncated Singular Value Decomposition in Logistic Regression, Random Forest, Feedforward Neural Networks, Support Vector Machine, and K-Nearest Neighbors classification pipelines can improve thyroid cancer recurrence prediction accuracy and reliability, supporting more personalized treatment strategies in post-treatment patients.

Preprint ARTICLE | doi:10.20944/preprints202409.2107.v1

Exploring Round Dataset for Domain Generalization in Autonomous Vehicle Trajectory Prediction

Zikai Zhang

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Machine Learning; Domain Generalization; Driving Behavior; Motion Forecasting; Trajectory Prediction

Online: 26 September 2024 (14:44:48 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2129.v1

Approaches of Critical Infrastructure Companies to Recover from Cyber-Attack: Insights from Internal Specialists and External In-Formation Security Auditors

Iryna Leroy, Iryna Zolotaryova, Serhii Semenov

Subject: Computer Science And Mathematics, Security Systems Keywords: information security; information security assessment; digital; reputation management; cyber autonomy; cyber resilience

Online: 26 September 2024 (14:43:49 CEST)

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Preprint CONCEPT PAPER | doi:10.20944/preprints202409.2136.v1

Degrade to Upgrade: Harnessing Programmed Degradation for Sustainable, Evolving Interactive Devices

Qiuyu Lu

Subject: Computer Science And Mathematics, Other Keywords: Sustainability; Degradation; Unmaking; Device

Online: 26 September 2024 (14:03:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2125.v1

Empirical Evaluation and Analysis of YOLO Models in Smart Transportation

Lan Anh Nguyen, Yongseok Son, Manh Dat Tran

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Convolution neural network (CNN); real-time object detection; road transportation; smart transportation; You Only Look Once (YOLO)

Online: 26 September 2024 (13:27:42 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2128.v1

Variational Color Shift and Auto‐Encoder based on Large Separable Kernel Attention for Enhanced Text CAPTCHA Vulnerability Assessment

Xing Wan, Juliana Johari, Fazlina Ahmat Ruslan

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: CAPTCHA recognition; color shift; auto encoder; attention mechanism; large kernel

Online: 26 September 2024 (13:25:59 CEST)

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Preprint COMMUNICATION | doi:10.20944/preprints202409.2099.v1

SOAMC: A Semi-Supervised Open Set RecognitionAlgorithm for Automatic Modulation Classification

Chengliang Di, Jinwei Ji, Chao Sun, Linlin Liang

Subject: Computer Science And Mathematics, Computer Science Keywords: semi-supervised learning; automatic modulationclassification; open set recognition

Online: 26 September 2024 (12:48:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2118.v1

High Order Difference Schemes for Space Riesz Variable-Order Nonlinear Fractional Diffusion Equations

Qiuya Wang, Furong Lin

Subject: Computer Science And Mathematics, Computational Mathematics Keywords: VO fractional derivative; 4WSGD; stability; convergence; PGMRES method; spectral analysis

Online: 26 September 2024 (12:21:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202109.0480.v9

A Note on Fermat’s Last Theorem

Frank Vega

Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Fermat’ s equation; prime numbers; coprime integers; Fermat’ s little theorem

Online: 26 September 2024 (12:15:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2051.v1

Enhanced Chaotic Pseudorandom Number Generation Using Multiple Bernoulli Maps with FPGA Optimizations

LEONARDO PALACIOS-LUENGAS, REYNA CAROLINA MEDINA-RAMÍREZ, RICARDO MARCELÍN-JIMÉNEZ, ENRIQUE RODRIGUEZ-COLINA, Francisco R. Castillo-Soria, RUBEN VAZQUEZ-MEDINA

Subject: Computer Science And Mathematics, Security Systems Keywords: Chaotic pseudorandom number generator; Chaos theory; Multiple Bernoulli chaotic maps; Robust chaotic map; Statistical analysis; FPGA implementation

Online: 26 September 2024 (09:59:41 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2068.v1

Threat Models to Machine Unlearning

Larry Milner

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: threat model; machine unlearning; data-driven model; privacy model

Online: 26 September 2024 (09:55:31 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2043.v1

About Stability of One System of Stochastic Difference Equations with Exponential Nonlinearity

Leonid Shaikhet

Subject: Computer Science And Mathematics, Mathematics Keywords: nonlinear difference equations; positive equilibrium; stochastic perturbations; asymptotic mean square stability; stability in probability; linear matrix inequality (LMI); numerical simulations; MATLAB

Online: 26 September 2024 (09:12:14 CEST)

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Preprint COMMUNICATION | doi:10.20944/preprints202409.2037.v1

Decoding by Factual Prompts and Hallucination Prompts Improves Factuality in Large Language Models

Bojie Lv, Ao Feng, Chenlong Xie

Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: large language models; hallucinations; prompt; contrastive decoding

Online: 26 September 2024 (09:08:12 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202409.2053.v1

Note for the P versus NP problem (II)

Frank Vega

Subject: Computer Science And Mathematics, Data Structures, Algorithms And Complexity Keywords: Complexity classes; Boolean formula; Graph; Completeness; Polynomial time

Online: 26 September 2024 (07:44:40 CEST)

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Computer Science and Mathematics