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Business Analytics for Technology and Security (ICBATS), International Conference on

作者： Samar Billi Noaman Amer Ibrahim Liaqat Ali M. W. Iqbal Asma Ashraf Usama Haseeb Salman Muneer Rasah Almajed K. Hamid Faculty of Management Canadian University Dubai Dubai UAE College of Computer and Information Technology American University in the Emirates Dubai United Arab Emirates College of Engineering and Technology University of Science and Technology of Fujairah Fujairah UAE Department of Software Engineering Superior University Lahore Pakistan Department of Software Engineering Superior University Lahore Pakistan Department of Computer Science Superior University Lahore Pakistan School of Computer Science NCBA&E Lahore Pakistan Applied Science Research Center Applied Science Private University Amman Jordan College of Computer and Information Technology American University in the Dubai United Arab Emirates

COVID-19 affected our lives intensly. That state of affairs made humans helpless. They had been depressed and experienced loneliness. At that time many human beings were determined to play video games just to loosen up their minds. Many games changed into an additional source of revenue wherein during COVID-19 they were playing while earning money. With these advantages, there were also some poor effects was accrued. A number of players remained playing video games post COVID-19. The carried out survey is based on the social video games results on players’ well-being and additionally on the effects of gamers’ health and their sensible lifestyles. We are going to investigate the behavior of gamers engaged with video games during the COVID-19 lockdown and the video games affects on their well-being, the time they served in playing video games, and the consequential effect on their behavior and social and mental well-being. The results provide a start line for empirically grounded discussions on video games at some stage in the pandemic, their use, and potential outcomes. Different agegroups of players have been investigated. Most players are between 18 and 30 years. A number of the gamers during lock down played a few hours but most of players were males who spent most of their day playing video games. However, now the ratio of playing video games is reduced as examined with past circumstances. Roughly we can say that the condition as a whole is better, the reason why players enforced video games in their post COVID-19 practical life 1 .

关键词： COVID-19 Video games Pandemics Games Machine learning Behavioral sciences Security

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Validating IP Reputation in Cloud Firewall Systems Using Machine Learning Driven Signature Generation and Detection Techniques

Validating IP Reputation in Cloud Firewall Systems Using Mac...

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IEEE Industrial Electronics and Applications Conference (IEACon)

作者： NW Chanka Lasantha Valmik Tilwari MWP Maduranga Ruvan Abeysekara Nilotpal Chakraborty Debashree Sharma Faculty of Graduate Studies IIC University of Technology Phnom Penh Cambodia Department of Electronics & Communication Engineering Indian Institute of Information Technology Guwahati India Department of Software Engineering Sri Lanka Technology Campus Padukka Sri Lanka Department of Computer Science Engineering Indian Institute of Information Technology Guwahati India

ISBN: (数字)9798350382976

ISBN: (纸本)9798350382983

This paper offers a new approach to validation for IP address reputation (IPR) using dynamic signatures achieved through the application of machine learning (ML) to conduct an accurate detection of malicious IPs. The proposed solution connects to the Amazon Web Services (AWS) Web Application Firewall (WAF) to improve security by deploying artificial intelligence (AI) models and natural language (NL) processing for automation of information extraction from IP-related information saved in the AWS object storages. All these lead to minimization of the number of false positives, real-time feedback, and optimization of IP reputation checking. The experimental outcome proves the capability of the proposed system to mitigate the IP address of the attacker and improve the protection of web applications against cybercrimes. This new approach is much advanced and a major leap ahead of other conventional IP reputation techniques, which offer a good protection shield against spam, DDoS, and other such evils.

关键词： Radio frequency Accuracy Firewalls (computing) Web services Natural languages Real-time systems IP networks Protection computer crime Optimization

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G-Bot: Revealing Government Programs with Smarted Assistance

G-Bot: Revealing Government Programs with Smarted Assistance

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Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), International Conference on

作者： A. Ramathilagam Kavitha Gunasekaran Elamparithi Pandian M. Satheesh Kumar D. David Neels Ponkumar R. Saravanakumar Department of Computer Science and Engineering P.S.R. Engineering College Sivakasi Tamil Nadu India Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

Residents have had trouble getting accurate state program information for years. G-bot's innovative technology employs NLP to link customers to state-level policies and activities to bridge this knowledge gap. G-bot's core NLP module is taught to deliver reliable and trustworthy information to help people make healthy and progressing decisions. G-bot provides real-time, reliable data to promote public awareness, transparency, and involvement in government processes. Users may explore the enormous state program and regulatory information using G-bot's simple interface. G-bot's Flask integration helps users locate vital information. G-bot enhances user-centric apps beyond information retrieval. AI-powered language processing ushers in a new era, letting individuals shape their communities. G-bot uses NER to identify government entities. This tool simplifies identifying organizations, programs, laws, agencies, and people, improving search results and customizing insights for better decision-making.

关键词： Accuracy Shape Government Natural languages Decision making Market research Information retrieval Real-time systems Microelectronics Reliability

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Autonomous Max-Flow Interplanetary Laser Link Scheduling for Martian Exploration

Autonomous Max-Flow Interplanetary Laser Link Scheduling for...

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IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)

作者： Jason Gerard Andre Ibrahim Juan A. Fraire Sandra Céspedes Department of Computer Science & Software Engineering Concordia University Montreal Canada INSA Lyon CITI UR3720 Inria France CONICET - Universidad Nacional de Córdoba Córdoba Argentina

ISBN: (数字)9798350351118

ISBN: (纸本)9798350351125

New communication technologies like delay and disruption-tolerant networking (DTN) and free-space laser communications are being developed to handle massive planetary telemetry data. However, existing infrastructure cannot meet the scalability and autonomy demands of future space missions, resulting in 95% of data left on the mission planet. Manual scheduling of deep-space laser transmissions by operators is inefficient and error-prone, limiting mission parallelization, while autonomous scheduling, though promising, may limit optimal network routes reducing the network capacity. We hypothesize that autonomously scheduling contact opportunities will enable acquisition, tracking, and pointing (ATP) for free-space lasers and increase the capacity of interplanetary backhaul networks. In this work, we propose the Laser Link Scheduler (LLS), which combines a novel max-flow network capacity model and a decision-making algorithm for contact selection. We define a series of deep-space communication scenarios where data is collected from orbiters around Mars and backhauled to Earth. LLS is evaluated in simulations using contact plans generated based on realistic conditions and orbital dynamics. Our results confirm that autonomous contact scheduling increases network capacity by 1.5-2x, reducing operational costs and enabling highly parallel space exploration missions.

关键词： Earth Wireless communication Space missions Laser modes Delays Topology Planetary orbits Telemetry Deep-space communications Backhaul networks

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Stop-and-go wave super-resolution reconstruction via iterative refinement

arXiv

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arXiv 2024年

作者： Ji, Junyi Richardson, Alex Gloudemans, Derek Zachár, Gergely Nice, Matthew Barbour, William Sprinkle, Jonathan Piccoli, Benedetto Work, Daniel B. Department of Civil and Environmental Engineering Vanderbilt University United States Institute for Software Integrated Systems Vanderbilt University United States Department of Computer Science Vanderbilt University United States Department of Mathematical Sciences Rutgers University Camden United States

Stop-and-go waves are a fundamental phenomenon in freeway traffic flow, contributing to inefficiencies, crashes, and emissions. Recent advancements in high-fidelity sensor technologies have improved the ability to capture detailed traffic dynamics, yet such systems remain scarce and costly. In contrast, conventional traffic sensors are widely deployed but suffer from relatively coarse-grain data resolution, potentially impeding accurate analysis of stop-and-go waves. This article explores whether generative AI models can enhance the resolution of conventional traffic sensor to approximate the quality of high-fidelity observations. We present a novel approach using a conditional diffusion denoising model, designed to reconstruct fine-grained traffic speed field from radar-based conventional sensors via iterative refinement. We introduce a new dataset, WaveX, comprising 132 hours of data from both low and high-fidelity sensor systems, totaling over 2 million vehicle miles traveled. Our approach leverages this dataset to formulate the traffic measurement refinement problem as a spatio-temporal super-resolution task. We demonstrate that our model can effectively reproduce the patterns of stop-and-go waves, achieving high accuracy in capturing these critical traffic dynamics. Our results show promising advancements in traffic data refinement, offering a cost-effective way to leverage existing low spatio-temporal resolution sensor networks for improved traffic analysis and management. We also open-source our dataset, trained model and code to enable further research and applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Highway administration

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SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban Substations

SHEDAD: SNN-Enhanced District Heating Anomaly Detection for ...

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International Conference on Fog and Mobile Edge Computing (FMEC)

作者： Jonne Van Dreven Abbas Cheddad Sadi Alawadi Ahmad Nauman Ghazi Jad Al Koussa Dirk Vanhoudt Department of Computer Science Blekinge Institute of Technology Karlskrona Sweden Unit Energy Technology Flemish Institute for Technological Research (VITO) Mol Belgium EnergyVille Genk Belgium Department of Software Engineering Blekinge Institute of Technology Karlskrona Sweden

ISBN: (数字)9798350366488

ISBN: (纸本)9798350366495

District Heating (DH) systems are essential for energy-efficient urban heating. However, despite the advancements in automated fault detection and diagnosis (FDD), DH still faces challenges in operational faults that impact efficiency. This study introduces the Shared Nearest Neighbor Enhanced District Heating Anomaly Detection (SHEDAD) approach, designed to approximate the DH network topology and allow for local anomaly detection without disclosing sensitive information, such as substation locations. The approach leverages a multi-adaptive k-Nearest Neighbor (k-NN) graph to improve the initial neighborhood creation. Moreover, it introduces a merging technique that reduces noise and eliminates trivial edges. We use the Median Absolute Deviation (MAD) and modified z-scores to flag anomalous substations. The results reveal that SHEDAD outperforms traditional clustering methods, achieving significantly lower intra-cluster variance and distance. Additionally, SHEDAD effectively isolates and identifies two distinct categories of anomalies: supply temperatures and substation performance. We identified 30 anomalous substations and reached a sensitivity of approximately 65% and specificity of approximately 97%. By focusing on this subset of poor-performing substations in the network, SHEDAD enables more targeted and effective maintenance interventions, which can reduce energy usage while optimizing network performance.

关键词： Temperature sensors Substations Sensitivity Multi-access edge computing Network topology District heating Noise Merging Nearest neighbor methods Anomaly detection

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Disjoint Compatibility via graph classes

arXiv

引用

arXiv 2024年

作者： Aichholzer, Oswin Obmann, Julia Paták, Pavel Perz, Daniel Tkadlec, Josef Vogtenhuber, Birgit Graz University of Technology Institute of Software Technology Austria Czech Technical University in Prague Department of Applied Mathematics Czech Republic University of Perugia Department of Engineering Italy Charles University Prague Computer Science Institute Czech Republic

Two plane drawings of graphs on the same set of points are called disjoint compatible if their union is plane and they do not have an edge in common. Let S be a convex point set of 2n ≥ 10 points and let H be a family of plane drawings on S. Two plane perfect matchings M1 and M2 on S (which do not need to be disjoint nor compatible) are disjoint H-compatible if there exists a drawing in H which is disjoint compatible to both M1 and M2. In this work, we consider the graph which has all plane perfect matchings as vertices and where two vertices are connected by an edge if the matchings are disjoint H-compatible. We study the diameter of this graph when H is the family of all plane spanning trees, caterpillars or paths. We show that in the first two cases the graph is connected with constant and linear diameter, respectively, while in the third case it is disconnected. © 2024, CC BY-NC-ND.

关键词： Graphic methods

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Predicting Students’ Performance using Machine Learning Classifiers

Predicting Students’ Performance using Machine Learning Cla...

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Information Technology (ACIT)

作者： Gheed M. Alsalem Noor Sarhan Mustafa Hammad Bayan Zawaideh Counseling and Special Education Department University of Jordan Amman Jordan Department of Computer Science University of Bahrain Sakheer Bahrain Department of Software Engineering Mutah University Alkarak Jordan Foundation and Support Programs Luminus Technical College University Amman Jordan

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

This paper presents students’ performance prediction model based on Machine Learning (ML) classifiers. The used dataset provides three groups of features that may affect students’ performance. Features selection approaches have been applied to the dataset. Several well-known ML classifiers including Neural Network (ANN), Bayes Network (Bayes Net), and Decision Tree (DT). Along with the original approach, hyper approach is applied using boosting, bagging, and voting classifiers. The performance of these algorithms is examined and compared using a confusion matrix. Results show that behavioral features have a major impact on students’ performance and ML algorithms can be used effectively to build students’ performance prediction model.

关键词： Hands Machine learning algorithms Neural networks Predictive models Prediction algorithms Feature extraction Classification algorithms Bayes methods Information technology Bagging

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Hierarchical State Abstraction Based on Structural Information Principles

arXiv

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arXiv 2023年

作者： Zeng, Xianghua Peng, Hao Li, Angsheng Liu, Chunyang He, Lifang Yu, Philip S. State Key Laboratory of Software Development Environment Beihang University China Zhongguancun Laboratory China Didi Chuxing China Computer Science & Engineering Lehigh University United States Department of Computer Science University of Illinois at Chicago United States

State abstraction optimizes decision-making by ignoring irrelevant environmental information in reinforcement learning with rich observations. Nevertheless, recent approaches focus on adequate representational capacities resulting in essential information loss, affecting their performances on challenging tasks. In this article, we propose a novel mathematical Structural Information principles-based State Abstraction framework, namely SISA, from the information-theoretic perspective. Specifically, an unsupervised, adaptive hierarchical state clustering method without requiring manual assistance is presented, and meanwhile, an optimal encoding tree is generated. On each non-root tree node, a new aggregation function and condition structural entropy are designed to achieve hierarchical state abstraction and compensate for sampling-induced essential information loss in state abstraction. Empirical evaluations on a visual gridworld domain and six continuous control benchmarks demonstrate that, compared with five SOTA state abstraction approaches, SISA significantly improves mean episode reward and sample efficiency up to 18.98 and 44.44%, respectively. Besides, we experimentally show that SISA is a general framework that can be flexibly integrated with different representation-learning objectives to improve their performances further. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Analyzing Digital Image Anti-Forensics Techniques and Countermeasures

Analyzing Digital Image Anti-Forensics Techniques and Counte...

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International Conference on Advanced Communication Technology (ICACT)

作者： Saurabh Agarwal Rajeev Kumar Abha Devi Ki-Hyun Jung Department of Information and Communication Engineering Yeungnam University Gyeongsan Republic of Korea Department of Computer Science and Engineering Blockchain Technology Research Lab Delhi Technological University New Delhi India Department of Software Convergence Andong National University Gyeongbuk Republic of Korea Department of English JS(PG) College Sikandrabad Uttar Pradesh India

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

With the increasing use of digital media, digital forensics has become a crucial method for ascertaining authenticity and detecting manipulations in images. However, intentional anti-forensic manipulations to hide forensic clues have made verification more difficult. The efforts to develop countermeasures against anti-forensics have contributed to the evolution toward robust techniques in the field of anti-forensics; hence, the development of anti-forensic methods and their respective countermeasures will remain continuous. This work provides a scientometric analysis, a powerful tool for the study of the growth of research output in accordance with time and citation, author, organizational, and keyword co-occurrence analysis. It enhances state-of-the-art analysis by underlining highly influential authors, organizations, and works and stipulates knowledge gaps. This would consequently provide a platform for building a community where insights and inclusiveness among researchers, industry practitioners, and policymakers would all come together to guide their strategies and decisions for innovation in this dynamic field.

关键词： Industries Technological innovation Privacy Image forensics Digital forensics Organizations Media Market research Security Research and development

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