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IEEE Conference on Global Communications (GLOBECOM)

作者： Ji Guo Hongwei Li Wenbo Jiang Guoming Lu Laboratory Of Intelligent Collaborative Computing University of Electronic Science and Technology of China China School of Computer Science and Engineering University of Electronic Science and Technology of China China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Vision Transformers (ViTs) have outperformed traditional Convolutional Neural Networks (CNN) across various computer vision tasks. However, akin to CNN, ViTs are vulnerable to backdoor attacks, where the adversary embeds the backdoor into the victim model, causing it to make wrong predictions about testing samples containing a specific trigger. Existing backdoor attacks against ViTs have the limitation of failing to strike an optimal balance between attack stealthiness and attack *** this work, we propose an Attention Gradient-based Erosion Backdoor (AGEB) targeted at ViTs. Considering the attention mechanism of ViTs, AGEB selectively erodes pixels in areas of maximal attention gradient, embedding a covert backdoor trigger. Unlike previous backdoor attacks against ViTs, AGEB achieves an optimal balance between attack stealthiness and attack effectiveness, ensuring the trigger remains invisible to human detection while preserving the model’s accuracy on clean samples. Extensive experimental evaluations across various ViT architectures and datasets confirm the effectiveness of AGEB, achieving a remarkable Attack Success Rate (ASR) without diminishing Clean Data Accuracy (CDA). Furthermore, the stealthiness of AGEB is rigorously validated, demonstrating minimal visual discrepancies between the clean and the triggered images.

关键词： Computer vision Visualization Accuracy Computer architecture Predictive models Observers Transformers Convolutional neural networks Erosion Testing

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Situation-based Query Generation for Performance Evaluation of Cloud Managed IoT Applications

Situation-based Query Generation for Performance Evaluation ...

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International Conference on Mobile Data Management

作者： Shalmoly Mondal Prem Prakash Jayaraman Alireza Hassani Pari Delir Haghighi Dimitrios Georgakopoulos School of Science Computing and Engineering Technologies Swinburne University of Technology Melbourne Australia Department of Human-Centred Computing Monash University Melbourne Australia

With increased deployment of IoT application on cloud platforms, assessing the performance of such application is an open problem. Currently, approaches are limited to legacy database-based applications and does not cater for the needs of IoT applications. This paper proposes, implements and validates a framework namely, IoTQGen, that can generate situation-based queries to conduct performance evaluation of IoT application hosted by cloud IoT middleware platform. The framework comprises: (i) a model to capture the query requirements of IoT applications; (ii) a data generator to generate IoT data based on specified configuration; and (iii) a set of queries designed to represent data analytic IoT applications. The framework supports different query types that can be typically used to represent and address IoT application scenarios. An important functionality of the framework is its ability to issue queries based on dynamic changes in the state of IoT entities (situations). The framework is evaluated based on two smart city use cases to highlight how the framework can be used to generate complex and dynamic queries tailored for IoT application scenarios.

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Method of data forward generation with partial differential equations for machine learning modeling in fluid mechanics

arXiv

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arXiv 2025年

作者： Chen, Ruilin Jin, Xiaowei Adams, Nikolaus A. Li, Hui School of Civil Engineering Harbin Institute of Technology Harbin150090 China College of Shipbuilding Engineering Harbin Engineering University Harbin150001 China School of Engineering and Design Technical University of Münih Münich85748 Germany Faculty of Computing Harbin Institute of Technology 150001 China

Artificial intelligence (AI) for fluid mechanics has become attractive topic. High-fidelity data is one of most critical issues for the successful applications of AI in fluid mechanics, however, it is expensively obtained or even inaccessible. This study proposes a high-efficient data forward generation method from the partial differential equations (PDEs). Specifically, the solutions of the PDEs are first generated either following a random field (e.g. Gaussian random field, GRF, computational complexity O(NlogN), N is the number of spatial points) or physical laws (e.g. a kind of spectra, computational complexity O(N ‧ M), M is the number of modes), then the source terms, boundary conditions and initial conditions are computed to satisfy PDEs. Thus, the data pairs of source terms, boundary conditions and initial conditions with corresponding solutions of PDEs can be constructed. A Poisson neural network (Poisson-NN) embedded in projection method and a wavelet transform convolutional neuro network (WTCNN) embedded in multigrid numerical simulation for solving incompressible Navier-Stokes equations is respectively proposed. The feasibility of generated data for training Poisson-NN and WTCNN is validated. The results indicate that even without any DNS data, the generated data can train these two models with excellent generalization and accuracy. The data following physical laws can significantly improve the convergence rate, generalization and accuracy than that generated following GRF. Copyright © 2025, The Authors. All rights reserved.

关键词： Navier Stokes equations

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Energy Generation from Plastic Composites: A Systematic Review of Sustainable Practices and Technologies

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Circular Economy and Sustainability 2025年第2期5卷 1307-1343页

作者： Ametefe, Divine Senanu Ametefe, George Dzorgbenya John, Dah Aliu, Abdulmalik Adozuka Owen, Macaulay M. Shuib, Solehuddin Hamid, Aisha Wireless Communication Technology Group (WiCOT) College of Engineering School of Electrical Engineering Universiti Teknologi MARA (UiTM) Selangor Shah Alam 40450 Malaysia Department of Biotechnology College of Science Engineering and Technology Osun State University Osogbo Nigeria College of Computing Informatics and Mathematics Universiti Teknologi MARA (UiTM) Selangor Puncak Perdana 40150 Malaysia College of Built Environment Universiti Teknologi MARA (UiTM) Selangor Shah Alam 40450 Malaysia Institute of Energy Infrastructure IEI Universiti Tenaga Nasional UNITEN Jalan IKRAM-UNITEN Selangor Kajang 43000 Malaysia School of Mechanical Engineering College of Engineering Universiti Teknologi MARA UiTM Shah Alam 40450 Malaysia School of Chemistry and Environment Faculty of Applied Sciences Universiti Teknologi MARA Selangor Shah Alam 40450 Malaysia

Energy generation from plastic composites offers a viable solution to the dual challenges of plastic waste management and renewable energy production. This study explores the potential of plastic composites for energy generation through key waste-to-energy technologies, including incineration, pyrolysis, gasification, and anaerobic digestion. The study employs a Systematic Literature Review (SLR) methodology, following the PRISMA guidelines, and draws from two primary databases: ScienceDirect and Web of Science. Each method is assessed in terms of energy efficiency, environmental impact, economic feasibility, and scalability, based on studies published between 2020 and the second quarter of 2024. Pyrolysis and gasification emerge as particularly promising technologies due to their flexibility and higher energy recovery potential. Incineration, although widely used and well-established, presents challenges related to emissions, despite improvements in emissions control technologies. Anaerobic digestion, though limited in its application to conventional plastics, provides sustainability benefits when used with biodegradable materials. The analysis reveals regional disparities in research focus, the influence of policy frameworks, and the need for technological innovation to optimize these processes. Ultimately, the findings highlight the importance of integrating energy recovery from plastic waste within circular economy models to achieve sustainable waste management and renewable energy goals. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Circular Economy Plastic Recycling Renewable Energy Production Sustainable Waste Management Waste-to-Energy

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Evaluation of Wireless Sensor Networks Module using IoT Approach 3

Evaluation of Wireless Sensor Networks Module using IoT Appr...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Anand, L. Padmalal, S. Seetha, J. Juliana, R. Naveen Kumar, P.S. Parasa, Gayatri Srm Institute of Science and Technology School of Computing Department of Networking and Communications Kattanguluthur India Mangalam College of Engg Department of Computer Science and Engineering Kerala India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India Loyola-ICAM College of Engineering and Technology Department of Information Technology Chennai India St. Ann's College of Engineering and Technology Department of CSE-DATA Science AP Chirala India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Guntur India

ISBN: (纸本)9781665462167

Microcomputers and medical devices with signal transceivers that operate on a specific radio display constitute the backbone of wireless sensor networks (WS Ns) that monitor environmental conditions (temperature, pressure, light, vibration levels, location). It is widely used in WAN sensor networks because of its flexible design and low setup fees. The u200b touch network allows for the connection of up to 65,000 devices, while the Intelligent sensors on other wireless networks are used to transfer data ports and assign wireless networks. Since the price of wireless solutions has been decreasing, and their functional capabilities have been growing, they are gradually replacing wired ones in telemetry data gathering systems and long- distance detecting communication. A deep learning model was used in this investigation to prevent the sensor nodes from manipulating data. Sensor nodes include a lot of parameters and estimations. If these projected data values are altered, network performance will suffer, and the node's lifetime will be reduced. Data security became a priority when the sensor nodes were distributed. This new method is 98.82% more efficient than the previous one. © 2023 IEEE.

关键词： Sensor nodes

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IoT Security Frameworks: A Comparative Review with a Focus on Privacy 2

IoT Security Frameworks: A Comparative Review with a Focus o...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Hasnawi, Abduljaleel Niu, Yitong Tawfeq, Jamal F. Ranjan Pradhan, Manas Salahat, Mohammed Ghazal, Taher M. Ministry Of Higher Education And Scientific Research Baghdad Iraq College Of Information Technology Imam Ja'afar Al-Sadiq University Department Of Communication Technology Engineering Baghdad Iraq School Of Aeronautical Engineering AnYang University Anyang China Medical Technical College Al-Farahidi University Department Of Medical Instrumentation Technical Engineering Baghdad10070 Iraq School Of Computing Skyline University College University City Sharjah Sharjah United Arab Emirates College Of Engineering And Technology University Of Science And Technology Of Fujairah Fujairah United Arab Emirates Khalifa University Centre For Cyber Physical Systems Computer Science Department United Arab Emirates Center For Cyber Security Faculty Of Information Science And Technology Selangor Bangi43600 Malaysia

ISBN: (纸本)9798350394962

The privacy of sensitive data within the Internet of Things (IoT) is at risk due to potential privacy violations. These violations can occur through illegal access by external entities, as well as inadvertent disclosures or intentional attacks by those within the system who exceed their authorized access privileges. Furthermore, there needs to be more control experienced by data owners regarding their sensitive data as it transitions from their domain to other entities within the Internet of Things (IoT) ecosystem. Numerous methods have been presented in the academic literature over the past few years to effectively tackle the privacy concerns associated with the Internet of Things (IoT). This paper aims to classify existing literature by organizing it according to the specific technology or technique employed to safeguard privacy. The classification encompasses four primary areas of privacy solutions, specifically, solutions based on encryption, solutions based on identity management, solutions based on policy enforcement, and solutions focused on self-protection. The paper examines various state-of-the-art solutions within each category. It begins by providing a broad comparison between these solutions and our work. Subsequently, it focuses on a specific comparison of the privacy objectives of the offered PEFM solutions. © 2024 IEEE.

关键词： Internet of things

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Enhancing Literary Exploration: A Machine Learning Approach to Book Recommendations

Enhancing Literary Exploration: A Machine Learning Approach ...

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International Conference on Contemporary computing and Informatics (IC3I)

作者： Kritika Singh Sumita Gupta Saru Dhir Sapna Gambhir Department of Computer Science & Engineering Amity School of Engineering & Technology Amity University Noida Uttar Pradesh India Department of Information Technology Amity School of Engineering & Technology Amity University Noida Uttar Pradesh India Department of Computer Science College of Engineering and Computing George Mason University USA

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

In today’s era of rapid internet expansion, the need for efficient information retrieval tools is paramount. Recommendation systems serve as indispensable instruments, aiding users in navigating vast datasets swiftly and accessing relevant information. This paper presents a novel approach to book recommendation systems, offering readers a quick and intuitive method to discover their next read. Implementations encompass three algorithms: Popularity-based, Contentbased, and Collaborative-based recommendation systems. The primary focus lies in proposing a swift book idea system, facilitating users in promptly discovering fitting reading materials. By harnessing the power of these algorithms, readers can explore a diverse range of book options tailored to their preferences, enhancing their overall reading experience and satisfaction. Through this study, we aim to contribute to the advancement of recommendation systems, providing valuable insights and practical solutions for optimizing the book discovery process in the digital age.

关键词： Navigation Instruments Fitting Machine learning Information retrieval Information age Informatics Recommender systems

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Secured Wireless Energy Transfer for the Internet of Everything in Ambient Intelligent Environments

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IEEE Internet of Things Magazine 2022年第1期5卷 62-66页

作者： Sah, Dinesh Kumar Poongodi, M. Donta, Praveen Kumar Hamdi, Mounir Cengiz, Korhan Kamruzzaman, M.M. Rauf, Hafiz Tayyab Indian Institute of Technology Indian School of Mines India College of Science and Engineering Hamad Bin Khalifa University Division of Information and Computing Technology Doha Qatar Tu Wien Austria Trakya University Turkey Jouf University Sakaka Saudi Arabia University of Bradford United Kingdom

The Internet of Everything (IoE), as the primary driving force in our society's digital transformation, constitutes resource-constrained physical objects connected as a network. Energy provisioning is one of the bottlenecks to these resource-constrained devices because they operate using limited power batteries. Wireless energy transfer (WET) is a promising technology to address the issue because it can transfer power without any physical connection between the energy-constrained devices and the power sources. WET is equipped with an unmanned aerial vehicle (UAV), and it can travel in an ambient intelligence environment (AIE) to recharge the batteries of IoE. There are several challenges imposed while scheduling the UAVs for charging and discharging in the AIE. In this context, this article discusses various challenges imposed while scheduling the WET-equipped UAVs in the network, including privacy and security vulnerabilities. This article also compares the performance variance while using different energy sources for WET, along with the future scope. © 2018 IEEE.

关键词： Energy transfer

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SoulMate: Short-Text Author Linking Through Multi-Aspect Temporal-Textual Embedding (Extended Abstract)

SoulMate: Short-Text Author Linking Through Multi-Aspect Tem...

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International Conference on Data engineering

作者： Saeed Najafipour Saeid Hosseini Wen Hua Mohammad Reza Kangavari Xiaofang Zhou School of Information Technology and Electrical Engineering Iran University of Science and Technology Tehran Iran Faculty of Computing and Information Technology Sohar University Sohar Oman Department of Computing Hong Kong Polytechnic University Hong Kong Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

We devise a neural network-based temporal-textual framework that generates subgraphs with highly correlated authors from short-text contents. Our approach computes the relevance score (edge weight) between authors by considering a portmanteau of contents and concepts. It then employs a stack-wise graph-cutting algorithm to extract communities of related authors. Experimental results show that our multi-aspect vector space model can gain higher performance than other knowledge-centered competitors in linking short-text authors.

关键词： Accuracy Computational modeling Data engineering Vectors Inference algorithms Data models

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Learning Multitask Commonness and Uniqueness for Multimodal Sarcasm Detection and Sentiment Analysis in Conversation

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 1349-1361页

作者： Zhang, Yazhou Yu, Yang Zhao, Dongming Li, Zuhe Wang, Bo Hou, Yuexian Tiwari, Prayag Qin, Jing Centre for Smart Health School of Nursing The Hong Kong Polytechnic University Hong Kong Artificial Intelligence Laboratory China Mobile Communication Group Tianjin Company Ltd. Tianjin300020 China College of Intelligence and Computing Tianjin University Tianjin300072 China Zhengzhou University of Light Industry Zhengzhou450002 China Software Engineering College Zhengzhou University of Light Industry Zhengzhou450002 China College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou450002 China School of Information Technology Halmstad University Halmstad301 18 Sweden

Sarcasm is a form of figurative language device to express human inner feelings, where the author writes the positive sentence on surface form, while he/she actually expresses negative sentiment, vice versa. Sentiment, thus, comes into sight, and is closely related with sarcasm, leading to the recent popularity of multimodal sarcasm and sentiment joint detection in conversation (dialogue). The key challenges involve multimodal fusion and multitask interaction. Most of the existing studies have focused on building multimodal fused representation, while the commonness and uniqueness across related tasks has not received attention. To fill this gap, we propose a multimodal multitask interaction learning framework, termed MIL, for joint detection of sarcasm and sentiment. Specifically, a cross-modal target attention mechanism is proposed to automatically learn the alignment between texts and images/speeches. In addition, a multimodal interaction learning paradigm consisting of a dual-gating network, three separate fully connected layers that simultaneously capture the commonness and uniqueness. Comprehensive experiments on two benchmarking datasets (i.e., Memotion and MUStARD) show the effectiveness of the proposedmodel over state-of-the-art baselines with a significant improvement of 1.9%, 2.4% in terms of F1. Impact Statement-Human language is multi-affective in nature, where sentiment and sarcasm are often coupled with each *** propose a state-of-the-art multi-task learning (MTL) paradigm to jointly study them, which is of great significance for deepening the understanding of multi-affective analysis and emotional intelligence. The recent works still adopted the hard parameter sharing based MTL paradigm, which mainly focused on the commonness across tasks. Our work is quite different from them, where we make the first attempt to design a soft parameter sharing MTL paradigm, aiming to take the uniqueness of individual task into *** argue that both th

关键词： Sentiment analysis

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