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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kaswan, Kuldeep Singh Malik, Kiran Dhatterwal, Jagjit Singh Naruka, Mahaveer Singh Govardhan, D. School of Computing Science and Engineering Galgotias University UP Greater Noida India Mriem Department of Computer Science and Engineering Rohtak India Koneru Lakshmaiah Education Foundation Department of Artificial Intelligence and Data Science Andhra Pradesh India G L Bajaj Institute of Technology and Management Greater Noida India Institute of Aeronautical Engineering Department of Aeronautical Engineering Hyderabad India

ISBN: (纸本)9798350357769

Deepfakes are artificial intelligence-generated multimedia, primarily videos or audio, that effectively edit or invent information by superimposing one person's likeness or voice onto that of another. They use deep learning techniques such as GAN s to generate hyper-realistic simulations that blur the line between reality and fiction. Deepfakes encompass everything from political satire to possible concerns in the fields of disinformation, privacy invasion, and cybersecurity. Understanding deepfakes is critical in an age when manipulated media may influence public opinion and undermine confidence. The primary objective of this paper is to provide a holistic overview of deepfake technology, shedding light on both its creative potential and inherent risks. Through a systematic exploration of the evolution, applications, techniques, and detection strategies, we aim to equip readers with an informed perspective on this ever-evolving field. By synthesizing existing knowledge and insights, this paper intends to contribute to the ongoing discourse surrounding deepfakes, enabling stakeholders to make informed decisions, develop effective countermeasures, and anticipate the future trajectory of this technology. Ultimately, our work strives to foster awareness and preparedness in an age where discerning the truth has never been more vital. © 2023 IEEE.

关键词： AI CNN Deepfakes GAN Machine Learning RNN

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M3CoT: A Novel Benchmark for Multi-Domain Multi-step Multi-modal Chain-of-Thought

arXiv

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arXiv 2024年

作者： Chen, Qiguang Qin, Libo Zhang, Jin Chen, Zhi Xu, Xiao Che, Wanxiang Research Center for Social Computing and Information Retrieval Harbin Institute of Technology China School of Computer Science and Engineering Central South University China Shanghai AI Laboratory China

Multi-modal Chain-of-Thought (MCoT) requires models to leverage knowledge from both textual and visual modalities for step-by-step reasoning, which gains increasing attention. Nevertheless, the current MCoT benchmark still faces some challenges: (1) absence of visual modal reasoning, (2) single-step visual modal reasoning, and (3) Domain missing, thereby hindering the development of MCoT. Motivated by this, we introduce a novel benchmark (M3CoT) to address the above challenges, advancing the multi-domain, multi-step, and multi-modal CoT. Additionally, we conduct a thorough evaluation involving abundant MCoT approaches on Vision Large Language Models (VLLMs). In addition, we highlight that the current VLLMs still struggle to correctly reason in M3CoT and there remains a large gap between existing VLLMs and human performance in M3CoT, despite their superior results on previous MCoT benchmarks. To our knowledge, we take the first meaningful step toward the multi-domain, multi-step, and multi-modal scenario in MCoT. We hope that M3CoT can serve as a valuable resource, providing a pioneering foundation in multi-domain, multi-step, multi-modal chain-of-thought research. © 2024, CC BY-NC-SA.

关键词： Benchmarking

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Optimized Hybrid CNN-GRU Approach for Accurate Prediction of Cardiovascular Diseases: Integrating Medical Imaging and Clinical Data

Optimized Hybrid CNN-GRU Approach for Accurate Prediction of...

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Communication, Control and Intelligent Systems (CCIS)

作者： Prasanna V Suresh Kumar N Divyaprabha P R Sundaram Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Chennai India Department of Information Technology Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Sri Ramakrishna Engineering College Coimbatore India Department of Computer Science and Engineering Ganesh College of Engineering Salem India

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

As the primary cause of death globally, cardiovascular diseases (CVDs) demand precise and timely prediction to enhance patient outcomes. Other examples of conventional approaches for CVD prediction include statistical models as well as machine learning models such as a single model that often struggle to address complex and comprehensive data from clinical records and Medical Imaging. To address these issues, the concept proposed here is to integrate the medical imaging data with clinical data as new apart from utilizing the hybrid CNN–GRU model to predict cardiovascular diseases with higher accuracy and interpretability. While the GRU module deals with the time sequences of clinical data including past history and other test results, the CNN module is to extract the spatial features from the medical images. Thus, by integrating these modules, it is more accurate to predict the data of the patient as a synergy of spatial and temporal arrangements. Cross-validation techniques and performance indices are used for model training and testing on a diverse data set containing imaging and other clinical-related data. Experimental results depict that the suggested hybrid CNN-GRU model performs much better than the traditional approaches and has higher levels of predicted accuracy and robustness. It is also feasible to generalize this integrated strategy to other medical disorder where multi-modal data analysis is crucial, besides enhancing the capacity to estimate CVDs. The findings underline the importance of integrating doctors’ information with radiological images and applying new patterns of neural networks to augmenting clinical judgment. The suggested work is implemented using python.

关键词： Analytical models Accuracy Data analysis Computational modeling Predictive models Feature extraction Data models Convolutional neural networks Cardiovascular diseases Biomedical imaging

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KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks

arXiv

引用

arXiv 2023年

作者： Yu, Zhizhi Jin, Di Huo, Cuiying Wang, Zhiqiang Liu, Xiulong Qi, Heng Wu, Jia Wu, Lingfei College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Technology Dalian University of Technology Liaoning China School of Computing Macquarie University Sydney Australia Content and Knowledge Graph Pinterest New York United States

Social Internet of Things (SIoT), a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things), paving the way for the next generation of Internet of Things. However, due to the risks and uncertainty, a crucial and urgent problem to be settled is establishing reliable relationships within SIoT, that is, trust evaluation. Graph neural networks for trust evaluation typically adopt a straightforward way such as one-hot or node2vec to comprehend node characteristics, which ignores the valuable semantic knowledge attached to nodes. Moreover, the underlying structure of SIoT is usually complex, including both the heterogeneous graph structure and pairwise trust relationships, which renders hard to preserve the properties of SIoT trust during information propagation. To address these aforementioned problems, we propose a novel knowledge-enhanced graph neural network (KGTrust) for better trust evaluation in SIoT. Specifically, we first extract useful knowledge from users' comment behaviors and external structured triples related to object descriptions, in order to gain a deeper insight into the semantics of users and objects. Furthermore, we introduce a discriminative convolutional layer that utilizes heterogeneous graph structure, node semantics, and augmented trust relationships to learn node embeddings from the perspective of a user as a trustor or a trustee, effectively capturing multi-aspect properties of SIoT trust during information propagation. Finally, a trust prediction layer is developed to estimate the trust relationships between pairwise nodes. Extensive experiments on three public datasets illustrate the superior performance of KGTrust over state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Transfer Learning-based Fruit Image Recognition

Transfer Learning-based Fruit Image Recognition

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IEEE Conference on Systems, Process & Control (ICSPC)

作者： Ting-Feng Low Isaac Jit-Yan Lim Yong-Xuan Tan Kian-Ming Lim Chin-Poo Lee Pa-Pa-Min Faculty of Information Science & Technology (FIST) Multimedia University Melaka Malaysia School of Computer Science University of Nottingham Ningbo China Ningbo China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University Jiangsu Province China

ISBN: (数字)9798350391398

ISBN: (纸本)9798350391404

This research explores fruit image recognition using deep learning techniques, aiming to develop a robust and efficient model for accurately classifying diverse fruits. By leveraging transfer learning, the study applies pre-trained models with a particular focus on the EfficientNetV2-S architecture. The Fruits 360 dataset serves as the benchmark for evaluating the model’s performance. The research follows a systematic evaluation protocol, with accuracy as the key metric. Techniques such as fine-tuning are employed to enhance performance, and the study also considers the impact of model efficiency. The proposed method, based on the EfficientNetV2-S model, strikes a balance between accuracy and computational efficiency, providing valuable insights into the practical applications of deep learning for fruit image recognition.

关键词： Deep learning Training Adaptation models Image recognition Accuracy Systematics Protocols Computational modeling Transfer learning Data models

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Owl: Differential-Based Side-Channel Leakage Detection for CUDA Applications

Owl: Differential-Based Side-Channel Leakage Detection for C...

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International Conference on Dependable Systems and Networks (DSN)

作者： Yu Zhao Wenjie Xue Weijie Chen Weizhong Qiang Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350341058

ISBN: (纸本)9798350341065

Over the past decade, various methods for detecting side-channel leakage have been proposed and proven to be effective against CPU side-channel attacks. These methods are valuable in assisting developers to identify and patch side-channel vulnerabilities. Nevertheless, recent research has revealed the feasibility of exploiting side-channel vulnerabilities to steal sensitive information from GPU applications, which are beyond the reach of previous side-channel detection methods. Therefore, in this paper, we conduct an in-depth examination of various GPU features and present Owl, a novel side-channel detection tool targeting CUDA applications on NVIDIA GPUs. Owl is designed to detect and locate side-channel leakage in various types of CUDA applications. When tracking the execution of CUDA applications, we design a hierarchical tracing scheme and extend the A-DCFG (Attributed Dynamic Control Flow Graph) to address the massively parallel execution in CUDA, ensuring Owl's detection scalability. After completing the initial assessment and filtering, we conduct statistical tests on the differences in program traces to determine whether they are indeed caused by input variations, subsequently facilitating the positioning of side-channel leaks. We evaluate Owl's capability to detect side-channel leaks by testing it on Libgpucrypto, PyTorch, and nvJPEG. Meanwhile, we verify that our solution effectively handles a large number of threads. Owl has successfully identified hundreds of leaks within these applications. To the best of our knowledge, we are the first to implement side-channel leakage detection for general CUDA applications.

关键词： Target tracking Statistical analysis Filtering Scalability Graphics processing units Side-channel attacks Feature extraction

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Extremal maximal entanglement

引用

Physical Review A 2025年第5期111卷 052410-052410页

作者： Wanchen Zhang Yu Ning Fei Shi Xiande Zhang School of Mathematical Sciences University of Science and Technology of China Hefei 230026 China Hefei National Laboratory University of Science and Technology of China Hefei 230088 China Department of Computer Science School of Computing and Data Science University of Hong Kong Hong Kong 999077 China

A pure multipartite quantum state is called absolutely maximally entangled if all reductions of no more than half of the parties are maximally mixed. However, an n-qubit absolutely maximally entangled state exists only when n equals 2, 3, 5, and 6. A natural question arises when it does not exist: Which n-qubit pure state has the largest number of maximally mixed ⌊n/2⌋-party reductions? We denote this number by Qex(n). It was shown that Qex(4)=4 by Higuchi and Sudbery [Phys. Lett. A 273, 213 (2000)] and Qex(7)=32 by Huber et al. [Phys. Rev. Lett. 118, 200502 (2017)]. In this paper, we give a general upper bound of Qex(n) by linking the well-known Turán problem in graph theory and provide lower bounds using constructive and probabilistic methods. In particular, we show that Qex(8)=56, which is the third known value for this problem.

关键词： Quantum entanglement

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Large Scale Finite-Temperature Real-time Time Dependent Density Functional Theory Calculation with Hybrid Functional on ARM and GPU Systems

arXiv

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arXiv 2025年

作者： Liu, Rongrong Guo, Zhuoqiang Sha, Qiuchen Zhao, Tong Li, Haibo Hu, Wei Liu, Lijun Tan, Guangming Jia, Weile State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Mathematics and Statistics The University of Melbourne Melbourne Australia School of Computer Science and Technology University of Science and Technology of China Hefei China Department of Mechanical Engineering Graduate School of Engineering Osaka University Osaka Japan

Ultra-fast electronic phenomena originating from finite temperature, such as nonlinear optical excitation, can be simulated with high fidelity via real-time time dependent density functional theory (rt-TDDFT) calculations with hybrid functional. However, previous rt-TDDFT simulations of real materials using the optimal gauge-known as the parallel transport gauge-have been limited to low-temperature systems with band gaps. In this paper, we introduce the parallel transport-implicit midpoint (PT-IM) method, which significantly accelerates finite-temperature rt-TDDFT calculations of real materials with hybrid function. We first implement PT-IM with hybrid functional in our plane wave code PWDFT, and optimized it on both GPU and ARM platforms to build a solid baseline code. Next, we propose a diagonalization method to reduce computation and communication complexity, and then, we employ adaptively compressed exchange (ACE) method to reduce the frequency of the most expensive Fock exchange operator. Finally, we adopt the ring_based method and the shared memory mechanism to overlap computation and communication and alleviate memory consumption respectively. Numerical results show that our optimized code can reach 3072 atoms for rt-TDDFT simulation with hybrid functional at finite temperature on 192 computing nodes, the time-to-solution for one time step is 429.3s, which is 41.4 times faster compared to the baseline. © 2025, CC BY-NC-SA.

关键词： Graphics processing unit

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Machine-Learning-Based Spam Mail Detector

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SN computer science 2023年第6期4卷 858页

作者： Charanarur, Panem Jain, Harsh Rao, G. Srinivasa Samanta, Debabrata Sengar, Sandeep Singh Hewage, Chaminda Thushara Department of Cyber Security and Digital Forensics National Forensic Sciences University Tripura campus Tripura Agartala India School of Cyber Security and Digital Forensics Goa Ponda India Department of Computer Science Government Degree College Sitaphalmandi Hyderabad India Department of Computing and Information Technologies Rochester Institute of Technology Kosovo Germia Campus Prishtina Kosovo 10000 Department of Computer Science Cardiff Metropolitan University Cardiff CF5 2YB United Kingdom

The proliferation of spam emails, a predominant form of online harassment, has elevated the significance of email in daily life. As a consequence, a substantial portion of individuals remain vulnerable to fraudulent activities. Despite Gmail’s “spam mail filtration system,” its effectiveness is not absolute. It occasionally misclassifies legitimate messages, leading to their confinement in the spam folder, or overlooks potentially harmful spam emails. This results in the occurrence of false positives. This research scrutinizes the historical data, cookies, caches, Session Restores, flash artifacts, and super cookies of Internet Explorer, Firefox, and Chrome on the Windows 10 platform. Data was collected through Google, Firefox, and Internet Explorer, operating within the Windows 10 environment. It has been observed that browsers store user behavior data on the host computer’s hard drive. The implications of this study hold substantial value for computer forensics researchers, law enforcement professionals, and digital forensics experts. The study leverages Python, alongside pertinent libraries such as pandas, Numpy, Matplotlib, scikit-learn, and flask, to facilitate its investigation. The experiment result and analysis show KN and NB algorithms have the best accuracy and precision score compared to other Algorithms. © 2023, Crown.

关键词： Cookies E-mail Inbox Spam mail Trap Trash folder

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When the Future Becomes the Past: Taming Temporal Correspondence for Self-supervised Video Representation Learning

arXiv

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arXiv 2025年

作者： Liu, Yang Xu, Qianqian Wen, Peisong Dai, Siran Huang, Qingming School of Computer Science and Technology University of Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Sciences China Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China

The past decade has witnessed notable achievements in self-supervised learning for video tasks. Recent efforts typically adopt the Masked Video Modeling (MVM) paradigm, leading to significant progress on multiple video tasks. However, two critical challenges remain: 1) Without human annotations, the random temporal sampling introduces uncertainty, increasing the difficulty of model training. 2) Previous MVM methods primarily recover the masked patches in the pixel space, leading to insufficient information compression for downstream tasks. To address these challenges jointly, we propose a self-supervised framework that leverages Temporal Correspondence for video Representation learning (T-CoRe). For challenge 1), we propose a sandwich sampling strategy that selects two auxiliary frames to reduce reconstruction uncertainty in a two-side-squeezing manner. Addressing challenge 2), we introduce an auxiliary branch into a self-distillation architecture to restore representations in the latent space, generating high-level semantic representations enriched with temporal information. Experiments of T-CoRe consistently present superior performance across several downstream tasks, demonstrating its effectiveness for video representation learning. The code is available at https://***/yafeng19/T-CORE. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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