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Predictive Modelling of Protest Event Signatures: Analyzing Temporal Dynamics and Digital Activism Discourse Across Global Movements

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Journal of Social Computing 2025年第1期6卷 43-50页

作者： Amila Chethana Nanayakkara B. T. G. Samantha Kumara R. M. Kapila Tharanga Rathnayaka Center for Computer Studies Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Data Science Faculty of Computing Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka Department of Physical Sciences and Technology Faculty of Applied Sciences Sabaragamuwa University of Sri Lanka Belihuloya Sri Lanka

This research introduced a predictive modeling framework to analyze the temporal dynamics of social media discourse during three global movements: the Mahsa Amini Protests (2022), South African Unrest (2021), and the Black Lives Matter Movement (2020). By utilizing Twitter data, the study developed a Protest Social Media Archetype to capture the evolution of tweet activity during key protest periods, identifying common patterns and notable variations in public engagement. Techniques such as LOESS regression and correlation analysis were used to model fluctuations in online activity and assess the impact of social media on public mobilization during socio-political unrest. The findings revealed distinct temporal signatures for each movement, showing similarities in initial engagement and differences in sustained activity across various contexts. These insights underscore the role of digital platforms in protest organization and global solidarity, offering a framework for future studies on digital activism and protest dynamics.

关键词： Analytical models Social computing Fluctuations Social networking (online) Blogs Refining Organizations Predictive models Real-time systems Mathematics

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A 3D Memristive Cubic Map with Dual Discrete Memristors: Design, Implementation, and Application in Image Encryption

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Gao, Suo Iu, Herbert Ho-Ching Erkan, Ugur Simsek, Cemaleddin Toktas, Abdurrahim Cao, Yinghong Wu, Rui Mou, Jun Li, Qi Wang, Chunpeng Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering Perth6009 Australia Faculty of Engineering Electrical and Electronics Engineering Respectively Karamanoglu Mehmetbey University Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Harbin Institute of Technology School of Computer Science and Technology Harbin150001 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system's potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme for image encryption with the *** results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks. © 1991-2012 IEEE.

关键词： Bifurcation (mathematics)

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The Impact of 5G Technology on the Functionality and Performance of Internet of Things (IoT) Devices

The Impact of 5G Technology on the Functionality and Perform...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Prajyot Yesankar Palash Gourshettiwar Pradnyawant Gote Moses Makuei Jiet Ayush Gadkari Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Computer Science & Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The design of wireless mobile devices of the next generation 5G promises to address the demands of complex IOT designs in terms of connectivity technologies. This The study illuminates the architecture, benefits, and needs that5G can satisfy. drawbacks of the 5G network. Numerous sites throughout the world have conducted extensive analyses of IOT devices with 5G capabilities and research on this kind of network. A new era of communication has begun with the introduction of 5G technology, which promises unheard of velocity, low latency, and enormous capacity. The significant effects of 5G on the operation and efficiency of connected Internet of Things (IoT) gadgets are examined in this research. IoT devices stand to gain from improved communication capabilities as 5G networks proliferate, opening the door for creative applications in a variety of industries. The first section of the article gives a summary of the main characteristics of 5G technology, highlighting how it has the potential to completely change how Internet of Things devices interact and communicate. Real-time data transfer is made possible by 5G networks far lower latency and higher capacity, which open up possibilities for faster and smooth interactions between IoT *** report also explores how 5G may affect how much electricity Internet of Things devices use. 5G has the ability to solve a long-standing issue in the IoT ecosystem by extending the life of the batteries of IoT devices with the adoption of cost-effective communication protocols and increased network efficiency. A solution that can efficiently handle enormous volumes of information being transferred at very high capacity is required as the Internet of Things gains popularity One of the main demands or goals that need to be reached in the near future, or the next generation of IOT devices, is more capacity, a faster data transfer speed and less delay.

关键词： Wireless communication Performance evaluation Protocols 5G mobile communication data transfer Real-time systems Mobile handsets Internet of Things Low latency communication Next generation networking

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Supervised Machine Learning Approach to Housing Market

Supervised Machine Learning Approach to Housing Market

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Technological Engineering and its Applications in Sustainable Development (ICTEASD), International Conference for

作者： Izat Hakimi Mohd Ismayatim Hanis Najiah Zakaria Ihsanul Arifin Ramadhani Nurzulaikah Abdullah Fakhitah Ridzuan Wan Azani Mustafa Mohammed Al-Farouni Faculty of Data Science and Computing Universiti Malaysia Kelantan Kota Bharu Malaysia Faculty of Computer Science Universitas Mercu Buana Jakarta Indonesia Faculty of Electrical Engineering & Technology Universiti Malaysia Perlis Arau Perlis Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9798350336474

ISBN: (纸本)9798350336481

Understanding the multifaceted factors influencing housing prices is crucial for facilitating informed decision-making among diverse stakeholders, including homebuyers, sellers, investors, and policymakers. Therefore, the primary objective of this research is to develop a predictive model for housing prices utilizing regression analysis. By leveraging machine learning techniques, such as regression and classification analysis, this study aims to uncover the intricate relationships between various factors and housing prices, ultimately providing valuable insights to stakeholders involved in the real estate market. Through rigorous data analysis and model development, this research seeks to enhance understanding and contribute to more accurate predictions in the realm of housing price dynamics.

关键词： Analytical models Accuracy Linear regression Decision making data preprocessing Machine learning Predictive models

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Smart Energy Guardian: A Hybrid Deep Learning Model for Detecting Fraudulent PV Generation

Smart Energy Guardian: A Hybrid Deep Learning Model for Dete...

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IEEE International Smart Cities Conference (ISC2)

作者： Xiaolu Chen Chenghao Huang Yanru Zhang Hao Wang School of Computer Science and Technology University of Electronic Science and Technology of China Chengdu China Department of Data Science and AI Faculty of Information Technology Monash University Melbourne Australia Shenzhen Institute for Advanced Study Shenzhen China Monash Energy Institute Monash University Melbourne Australia

ISBN: (数字)9798350364316

ISBN: (纸本)9798350364323

With the proliferation of smart grids, smart cities face growing challenges due to cyber-attacks and sophisticated electricity theft behaviors, particularly in residential photovoltaic (PV) generation systems. Traditional Electricity Theft Detection (ETD) methods often struggle to capture complex temporal dependencies and integrating multi-source data, limiting their effectiveness. In this work, we propose an efficient ETD method that accurately identifies fraudulent behaviors in residential PV generation, thus ensuring the supply-demand balance in smart cities. Our hybrid deep learning model, combining multi-scale Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Transformer, excels in capturing both shortterm and long-term temporal dependencies. Additionally, we introduce a data embedding technique that seamlessly integrates time-series data with discrete temperature variables, enhancing detection robustness. Extensive simulation experiments using real-world data validate the effectiveness of our approach, demonstrating significant improvements in the accuracy of detecting sophisticated energy theft activities, thereby contributing to the stability and fairness of energy systems in smart cities.

关键词： Deep learning Temperature distribution Smart cities Power system stability Transformers Stability analysis Robustness Convolutional neural networks Long short term memory Thermal stability

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Predictive Maintenance in IoT Networks Using a Temporal Attention Mechanism Based Federated Convolutional Neural Network 4

Predictive Maintenance in IoT Networks Using a Temporal Atte...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Guduri, Naga Venkata Ramakrishna Charaan, R.M. Dilip Kalaiyarasi, R. Samuthira Pandi, V. Reddy, D.Laxma Selvaprabhu, T. University of Technology and Applied Sciences Faculty of Engineering Al Musannah Oman Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad India Dhanalakshmi College of Engineering Department of Cse Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

In the era of Industry 4.0, edge devices in industrial settings generate vast amounts of data crucial for process optimization and Predictive Maintenance (PM). Traditional cloud-based approaches for handling this data are becoming untenable due to cost, latency, and privacy issues. Federated learning emerges as a viable solution, offering localized and efficient data processing while mitigating these challenges. This paper presents a Federated Convolutional Neural Network with Temporal Attention Mechanism (FedCNN-TAM) for PM tasks in Internet of Things (IoT) networks. This model leverages temporal features in sensor data to enhance predictive accuracy. Evaluated on the CMAPSS dataset, FedCNN-TAM outperforms representative models with a significant performance gain. This model exhibits the potential for broader applications in industrial IoT, setting the stage for future research on its scalability and adaptability to multi-modal data types. © 2024 IEEE.

关键词： Spatio-temporal data

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A Behavioural Network Traffic Novelty Detection for the Internet of Things Infrastructures 1

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11th International Symposium on Parallel Architectures, Algorithms and Programming, PAAP 2020

作者： Hamad, Salma Abdalla Sheng, Quan Z. Tran, Dai Hoang Zhang, Wei Emma Nepal, Surya Department of Computing Faculty of Science and Engineering Macquarie University Sydney Australia School of Computer Science The University of Adelaide Adelaide Australia Data61 CSIRO Sydney Australia

ISBN: (数字)9789811600104

ISBN: (纸本)9789811600098

The Internet of Things (IoT) applied solutions are changing the way the world perceives technology. IoT devices are now being used in a wide range of applications to transfer or share relevant information, hence reducing human interventions. With such widespread IoT solutions, security becomes a significant concern. Many of the IoT devices are vulnerable due to several reasons, including in-secure implementations, poor life cycle management, and inappropriate configurations, leading to an increase in the risk of these devices getting exposed and attacked. However, the current security approaches for detecting compromised IoT devices are inefficient, especially for zero-day attacks. Since no one knows how a new attack would look like, it will be useful to monitor and detect anomalies using accurate detection techniques. This work probes the possibility of detecting IoT network traffic anomalies using novelty detection techniques;thus, it can detect compromised IoT devices. One of this work’s main contributions is developing an IoT anomaly detection system named Behavioural Novelty Detection for IoT Infrastructure (BND-IoT). BND-IoT trains a neural network with novel selected behavioural features extracted from benign traffic only and then uses the novelty techniques to detect any unusual traffic patterns. We show that the presented approach effectively detects anomalies within IoT devices’ network traffic with a robust average F1-score of 96.7% and a low false rejection rate of 7%. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Temporal-Aware Deep Reinforcement Learning for Energy Storage Bidding in Energy and Contingency Reserve Markets

arXiv

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arXiv 2024年

作者： Li, Jinhao Wang, Changlong Zhang, Yanru Wang, Hao The Department of Data Science and AI Faculty of IT Monash Energy Institute Monash University MelbourneVIC3800 Australia The Department of Civil Engineering Monash University MelbourneVIC3800 Australia The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

The battery energy storage system (BESS) has immense potential for enhancing grid reliability and security through its participation in the electricity market. BESS often seeks various revenue streams by taking part in multiple markets to unlock its full potential, but effective algorithms for joint-market participation under price uncertainties are insufficiently explored in the existing research. To bridge this gap, we develop a novel BESS joint bidding strategy that utilizes deep reinforcement learning (DRL) to bid in the spot and contingency frequency control ancillary services (FCAS) markets. Our approach leverages a transformer-based temporal feature extractor to effectively respond to price fluctuations in seven markets simultaneously and helps DRL learn the best BESS bidding strategy in joint-market participation. Additionally, unlike conventional "black-box" DRL model, our approach is more interpretable and provides valuable insights into the temporal bidding behavior of BESS in the dynamic electricity market. We validate our method using realistic market prices from the Australian National Electricity Market. The results show that our strategy outperforms benchmarks, including both optimization-based and other DRL-based strategies, by substantial margins. Our findings further suggest that effective temporal-aware bidding can significantly increase profits in the spot and contingency FCAS markets compared to individual market participation. © 2024, CC BY.

关键词： Reinforcement learning

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Advanced Machine Learning Approaches to Combat Fake News in the Digital Era

Advanced Machine Learning Approaches to Combat Fake News in ...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Tejinder Kaur Pradosh Kumar Gantayat Swatismita Das Madhusmita Majhi Dept. of MMICTBM Maharishi Markaneshwar (Deemed to be University) Mullana Ambala Haryana India Department of Artificial Intelligence and Data Science Faculty of Science and Technology (IcfaiTech) The ICFAI Foundation for Higher Education Hyderabad Telangana India Raajdhani Engineering College Bhubaneswar Department of Computer Science and Engineering Faculty of Science and Technology (Icfai Tech) The ICFAI Foundation for Higher Education Hyderabad Telangana India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

This paper aims to provide the sustainable design for the user to provide them with ability to check whether the information is fake or real by providing the accuracy of the news using Machine learning via various algorithms with the help of database, which will respond as whether the news is accurate or not. Fake news has become a prevalent issue in today's digital age, causing significant societal impacts by spreading misinformation. This paper explores the application of machine learning techniques for detecting fake news. The paper also addresses challenges such as data scarcity, model interpretability, and the dynamic nature of fake news. We conclude with recommendations for future research directions, emphasizing the need for real-time detection systems and cross-disciplinary collaboration to enhance the effectiveness of fake news detection using machine learning.

关键词： Accuracy Social networking (online) Computational modeling Machine learning Streaming media Transformers Real-time systems data models Fake news Regression tree analysis

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Dynamic Lane Management with IoT for Real-time Lane Configuration and Traffic Flow 2

Dynamic Lane Management with IoT for Real-time Lane Configur...

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2nd International Conference on computer, Communication and Control, IC4 2024

作者： Pasha, Mohammed Rehaman Latha, S. Usha, R. Marin, V. Bini Ishwarya, M.V. Muthulekshmi, M. Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Department of Department of Biotechnology Tamil Nadu Salem India S. A. Engineering College Department of Mathematics Tamil Nadu Chennai India Srm Institute of Science and Technology Faculty of Management Studies Tamil Nadu Ramapuram Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Thazhambur India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350387933

Traffic may be reduced and transportation efficiency improved by properly controlling traffic flow in urban areas. This research provides a new method for managing lanes in real-time by using IoT connectivity. To optimize traffic flow in response to shifting demand patterns, the suggested system prioritizes making lane design changes in real time depending on the current traffic conditions. Using information collected from Internet of Things (IoT) sensors and linked automobiles, the system may dynamically allot lanes in response to variations in traffic volumes and congestion. It describes developing and deploying a dynamic lane control system based on the Internet of Things, stressing the technology's potential to improve traffic flow in congested metropolitan areas. The simulation results show its efficiency in decreasing traffic and increasing throughput. It demonstrates the potential of intelligent transportation systems for creating smarter and more efficient cities by combining IoT, real-time data processing, and traffic control. © 2024 IEEE.

关键词： Internet of things

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