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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Yu, Shenbao Pan, Yinghui Zeng, Yifeng Doshi, Prashant Liu, Guoquan Poh, Kim-Leng Lin, Mingwei College of Computer and Cyber Security Fujian Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University United Kingdom Intelligent Thought and Action Lab School of Computing University of Georgia United States Financial Technology Research Institute Fudan University China College of Design and Engineering National University of Singapore Singapore

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. Data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models. © 2024 Neural information processing systems foundation. All rights reserved.

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Tracking Anti-Vax Social Movement Using AI-Based Social Media Monitoring

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第4期3卷 290-299页

作者： Sufi, Fahim K. Razzak, Imran Khalil, Ibrahim Federal Government Solution Design and Architecture MelbourneVIC3000 Australia University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia Rmit University School of Computing Technologies MelbourneVIC3000 Australia

Anti-Vax social movement possesses a dire threat to governments' aim of mass vaccination. Moreover, by propagating serious misconceptions and misinformation about COVID-19, Anti-Vaxxers directly and indirectly cause harm to the overall health and wellbeing of the society. In addition, the ongoing clashes between Anti-Vaxxers and Pro-Vaxxers have created great social conflicts in recent times. This article proposes an artificial intelligence (AI)-based solution to identify and monitor social groups, such as Anti-Vax and Pro-Vax, in ethical manner. The proposed solution uses AI-based sentiment analysis and named entity recognition (NER) to advice political scientist, social scientists, and policy makers to assess the influence and impact of social groups. The proposed solution was deployed via iOS, Android, and Windows App on a range of platforms integrating publicly available 40 857 Twitter data related to COVID-19 in 55 different languages from 15 June 2021 till 31 December 2021. Our system demonstrated that Anti-Vax and Pro-Vax social movements posted 72% and 65% more negative contents compared to average negative sentiments of global COVID-19 related posts during the monitored period. Moreover, Anti-Vax-related posts with 'Hoax' keyword were found to have the highest level of social impact with 38 849 retweets and highest level of negativity (i.e., sentiment score of 0.87). We found out that Pro-Vax community engages in social conflict with Anti-Vaxxers referring them as ignorant (i.e., average sentiment score 0.91), stupid (i.e., average sentiment score 0.89), and confused (i.e., average sentiment score 0.88). Most importantly, the social conflict detection system reveals possible locations of conflict. © 2020 IEEE.

关键词： COVID-19

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Twitter sentiment analysis using conditional generative adversarial network

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International Journal of Cognitive computing in engineering 2024年第1期5卷 161-169页

作者： Mahalakshmi, V. Shenbagavalli, P. Raguvaran, S. Rajakumareswaran, V. Sivaraman, E. Department of Information Technology Sengunthar College of Engineering Tiruchengode Tiruchengode637205 India Assistant Professor Karunya Institute of Technology and Sciences Coimbatore India Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu 603203 India Assistant Professor Department of Computer Science and Design Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India Department of Electrical and Computer Engineering Curtin University Miri Malaysia

Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %. © 2024

关键词： Sentiment analysis

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Community Energy Storage System for Cost Benefits

Community Energy Storage System for Cost Benefits

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2023 IEEE Jordan International Joint Conference on Electrical engineering and Information Technology, JEEIT 2023

作者： Ikpehai, Augustine Sunkanmi, Olajide Anoh, Kelvin Sheffield Hallam University Dept. of Engineering & Mathematics SheffieldS1 1WB United Kingdom School of Engineering Computing & Design University of Chichester Bognor RegisPO21 1HR United Kingdom

ISBN: (纸本)9798350324051

Future low-carbon energy systems will be people-centred. However, optimal utilisation of renewable-based distributed generation in neighbourhood energy market (NEM) remains a limiting factor. This paper investigates a NEM and evaluates the benefits of central energy storage system (ESS) in maximising collective self-consumption and savings, using Sheffield city centre, UK as a case study. The results show that the central ESS has a significant impact on utilization of renewable resources, reduces cost of energy and offers flexibility of the grid through peak reduction. In particular, use of central ESS reduces peak demand by up to 55 % while the community's self-consumption and self-sufficiency increased by 28.8% and 32 % respectively. © 2023 IEEE.

关键词： Energy storage

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Smart Traffic Management System Using RFID System

Smart Traffic Management System Using RFID System

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2024 MIT Art, design and Technology school of computing International Conference, MITADTSoCiCon 2024

作者： Deshumkh, Shrikala Gayakwad, Milind More, Nitin S. Jadhav, Rohini Kadam, Kalyani Magar, Hanmant Amity University Mumbai India College of Engineering Pune India MIT Art Design and Technology University School of Computing Department of CSE Pune India Symbiosis Institute of Technology Pune India Pune India Vishwakarma Institute of Information Technology BRACT's India

ISBN: (纸本)9798350362879

Traffic management is an essential aspect of administration. In the 21st century since everything is going smart therefore our traffic management should also be smart. Congestion caused by heavy traffic is a major problem around the world. Accidents, increased trip time, poor law and order management, etc., are caused by improper management of the traffic. As the population is increasing day by day heavy traffic is becoming a greater problem. Due to these problems, the financial situations of states are heavily affected. Many so-called countries have smart management of their traffic, but this problem is greater in third-world countries or developing countries. In this paper, we are going to discuss a methodology that is used around the world to handle such problems and this method is the RFID system which is a radio frequency identification system. © 2024 IEEE.

关键词： Developing countries

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Enhancing 5G and 6G Communication with Tripartite Perfect W-States and LOCC Approach

Enhancing 5G and 6G Communication with Tripartite Perfect W-...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Mansoor Ali Khan Muhammad Naveed Aman Biplab Sikdar Department of Electrical and Computer Engineering College of Design and Engineering National University of Singapore Singapore School of Computing College of Engineering University of Nebraska-Lincoln Lincoln NE United States

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

The rapid evolution of communication networks to 5G/6G has introduced significant challenges, particularly in ensuring data security, low latency, high throughput, efficient energy consumption, dynamic access to multiple connection types, and managing the influx of connected devices. Quantum communication, leveraging entangled states like the perfect W-state, offers a promising solution with its high degree of entanglement, secure information transmission, and resilience against decoherence. However, practical applications and experimental validations remain limited, especially regarding the integration with Local Operations and Classical Communication (LOCC) protocols. Additionally, optimizing perfect W-state-based communication protocols for the splitting and sharing of quantum information has been scarcely explored. This article presents a Quantum Information Sharing and Splitting (QISS) protocol that integrates perfect W-states with LOCC to enhance 5G and 6G communication. Using the Eagle r3 processor based on superconducting qubits, our experiments demonstrated a fidelity of 0.82 ± 0.02 for the perfect W-state circuit and 0.55 ± 0.03 for the integrated W-state + LOCC in the QISS communication prototype. These findings, quantified through Quantum State Tomography, significantly improve communication security, network densification, and effectiveness. Furthermore, our research addresses existing gaps in quantum communication implementation, paving the way for scalable quantum networks and advanced encryption methods. This work marks a substantial step towards secure and efficient data transmission in next-generation communication systems.

关键词： 6G mobile communication Protocols 5G mobile communication Qubit Information sharing Tomography Quantum state Encryption Quantum networks Next generation networking

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Memory-Efficient Spike-Time-Dependent Plasticity for Future Driverless Car Park Infrastructure

Memory-Efficient Spike-Time-Dependent Plasticity for Future ...

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Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Kennedy Chinedu Okafor Bamidele Adebisi Kelvin Anoh School of Engineering Manchester Metropolitan University Manchester United Kingdom School of Engineering Computing and Design University of Chichester Bognor Regis UK

In addition to localization and mapping, current challenges faced by driverless (autonomous) car parks encompass computational complexity, resulting in elevated CPU utilisation and substantial memory consumption. This paper presents a memory-efficient Spike-Time-Dependent Plasticity (STDP) approach for future driverless car park infrastructure (DCPI). The proposed method utilizes self-organized mapping (SOM) classification and memory optimization techniques to analyze traffic occupancy trends. Results from geospatial data mining show optimised memory usage for vacant slots and trajectory mining clusters. Additionally, the impact of lateral suppression on STDP is demonstrated, highlighting the usefulness of STDP in managing complex memory scenarios in DCPI. Results show that STDP provides a robust neuromorphic scheme for lightweight memory utilisation particularly for tasks requiring precise timing and coordination.

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Toward Democratized Generative AI in Next-Generation Mobile Edge Networks

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IEEE Network 2025年

作者： Zhang, Ruichen He, Jiayi Luo, Xiaofeng Niyato, Dusit Kang, Jiawen Xiong, Zehui Li, Yonghui Sikdar, Biplab Nanyang Technological University College of Computing and Data Science Singapore Guangdong University of Technology School of Automation Guangzhou510006 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore University of Sydney School of Electrical and Information Engineering SydneyNSW2006 Australia National University of Singapore College of Design and Engineering Department of Electrical and Computer Engineering Singapore

The rapid development of generative artificial intelligence (AI) technologies, including large language models (LLMs), has brought transformative changes to various fields. However, deploying such advanced models on mobile and edge devices remains challenging due to their high computational, memory, communication, and energy requirements. To address these challenges, we propose a model-centric framework for democratizing generative AI deployment on mobile and edge networks. First, we comprehensively review key compact model strategies, such as quantization, model pruning, and knowledge distillation, and present key performance metrics to optimize generative AI for mobile deployment. Next, we provide a focused review of mobile and edge networks, emphasizing the specific challenges and requirements of these environments. We further conduct a case study demonstrating the effectiveness of these strategies by deploying LLMs on real mobile edge devices. Experimental results highlight the practicality of democratized LLMs, with significant improvements in generalization accuracy, hallucination rate, accessibility, and resource consumption. Finally, we discuss potential research directions to further advance the deployment of generative AI in resource-constrained environments. © 1986-2012 IEEE.

关键词： Generative adversarial networks

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Defending Against Vishing Attacks: A Comprehensive Review for Prevention and Mitigation Techniques

Defending Against Vishing Attacks: A Comprehensive Review fo...

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International Conference on Recent Developments in Cyber Security, ReDCySec 2023

作者： Ashfaq, Shaikh Chandre, Pankaj Pathan, Shafi Mande, Uday Nimbalkar, Madhukar Mahalle, Parikshit Information Technology Department M H Saboo Siddik College of Engineering Mumbai India Computer Science and Engineering Department MIT School of Computing MIT Art Design and Technology University Loni Kalbhor Pune India Artificial Intelligence and Data Science Department Vishwakarma Institute of Information Technology Kondhwa Pune India

ISBN: (纸本)9789819998104

Vishing attacks, or voice phishing attacks, are a type of social engineering attack in which attackers use voice communication channels, such as phone calls, to trick victims into divulging sensitive information or performing actions that compromise their security. Vishing attacks have become increasingly common in recent years and can have severe consequences for individuals, businesses, and organizations. In this review, we examine various prevention and mitigation techniques that can be used to defend against vishing attacks. We start by discussing the common tactics used by attackers and how they exploit vulnerabilities in human behaviour to achieve their goals. We then present an overview of different prevention and mitigation techniques, including education and awareness campaigns, technical solutions such as authentication mechanisms and policy-based approaches. We also evaluate the effectiveness of different techniques, highlighting their strengths and weaknesses, and provide guidance on how to implement a comprehensive vishing defence strategy. Finally, we discuss emerging trends and challenges in vishing attacks and defence, such as the use of artificial intelligence and deepfake technology, and suggest directions for future research and development. Overall, this review provides a comprehensive and up-to-date overview of the current state of vishing attacks and defence, and should be of interest to researchers, practitioners, and individuals who want to enhance their knowledge and understanding of this important topic. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Artificial intelligence

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Strengths,weaknesses,opportunities,and threats(SWOT)analysis of supercapacitors:A review

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Journal of Energy Chemistry 2023年第4期79卷 611-638页

作者： Pragati A.Shinde Qaisar Abbas Nilesh R.Chodankar Katsuhiko Ariga Mohammad Ali Abdelkareem Abdul Ghani Olabi Sustainable Energy&Power Systems Research Centre RISEUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates International Center for Materials Nanoarchitectonics(WPI-MANA) National Institute for Materials Science(NIMS)1-1 NamikiTsukuba 305-0044Japan Department of Energy and Material Engineering Dongguk University-SeoulSeoul 04620Republic of Korea Graduate School of Frontier Sciences The University of Tokyo5-1-5 KashiwanohaKashiwaChiba 277-8561Japan Mechanical Engineering and Design School of Engineering and Applied ScienceAston UniversityAston TriangleBirmingham B47ETUK School of Computing Engineering and Physical SciencesUniversity of The West of ScotlandGlasgowPA12BEUK

The development of clean and sustainable energy sources has received widespread interest in the past few decades due to the rolling energy demands while extenuating the rising tiers of greenhouse gases and environmental *** to their intermittent nature,these green and sustainable sources require appropriate energy storage *** different energy storage technologies,electrochemical energy storage devices,particularly supercapacitors(SCs),have fascinated global attention for their utilization in electric vehicles,power supports,portable electronics,and many others application requiring electric energy devices for their ***,the growth of SCs in the commercial market has squeezed requirements,and further developments are obligatory for their effective *** the meantime,SCs also face technical complications and contests for their introduction in industrial settings because of their low energy density and high Levelized *** present study combines core strengths,weaknesses,opportunities,and threats(SWOT)analysis of SCs with new perspectives and recent *** challenges and the future progressive prospects of SCs are also presented in *** review will afford consistent direction and new superhighways for the further development of SCs as standalone and complementary energy storage systems.

关键词： Supercapacitors Energy storage Strengths Opportunities

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