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arXiv 2025年

作者： Wang, Kai Sun, Zhen Wang, Bailing Fan, Qilin Li, Ming Zhang, Hongke School of Computer Science and Technology Harbin Institute of Technology Weihai China Shandong Key Laboratory of Industrial Network Security China School of Big Data and Software Engineering Chongqing University Chongqing China Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing University Chongqing China Jinan Key Laboratory of Distributed Databases Shandong Inspur Database Technology Co. Ltd Jinan China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China

With the growing interconnection between In-Vehicle Networks (IVNs) and external environments, intelligent vehicles are increasingly vulnerable to sophisticated external network attacks. This paper proposes ATHENA, the first IVN intrusion detection framework that adopts a vehicle-cloud integrated architecture to achieve better security performance for the resource-constrained vehicular environment. Specifically, in the cloud with sufficient resources, ATHENA uses the clustering method of multi-distribution mixture model combined with deep data mining technology to generate the raw Payload Rule Bank of IVN CAN messages, and then improves the rule quality with the help of exploitation on the first-principled physical knowledge of the vehicle system, after which the payload rules are periodically sent to the vehicle terminal. At the vehicle terminal, a simple LSTM component is used to generate the Time Rule Bank representing the long-term time series dependencies and the periodic characteristics of CAN messages, but not for any detection tasks as in traditional usage scenarios, where only the generated time rules are the candidates for further IVN intrusion detection tasks. Based on both the payload and time rules generated from cloud and vehicle terminal, ATHENA can achieve efficient intrusion detection capability by simple rule-base matching operations, rather than using complex black-box reasoning of resource-intensive neural network models, which is in fact only used for rule logic generation phase instead of the actual intrusion detection phase in our framework. Comparative experimental results on the ROAD dataset, which is current the most outstanding real-world in-vehicle CAN dataset covering new instances of sophisticated and stealthy masquerade attacks, demonstrate ATHENA significantly outperforms the state-of-the-art IVN intrusion detection methods in detecting complex attacks. We make the code available at https://***/wangkai-tech23/ATHENA. Copyright

关键词： Network intrusion

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Plug-and-Hide: Provable and Adjustable Diffusion Generative Steganography

arXiv

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arXiv 2024年

作者： Zhu, Jiahao Chen, Zixuan Yang, Lingxiao Xie, Xiaohua Zhou, Yi The School of Computer Science and Engineering Sun Yat-Sen University China The Guangdong Province Key Laboratory of Information Security Technology China The Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Guangzhou510006 China The Zhongshan School of Medicine Sun Yat-Sen University China

Generative Steganography (GS) is a novel technique that utilizes generative models to conceal messages without relying on cover images. Contemporary GS algorithms leverage the powerful generative capabilities of Diffusion Models (DMs) to create high-fidelity stego images. However, these algorithms, while yielding relatively satisfactory generation outcomes and message extraction accuracy, significantly alter modifications to the initial Gaussian noise of DMs, thereby compromising steganographic security. In this paper, we rethink the trade-off among image quality, steganographic security, and message extraction accuracy within Diffusion Generative Steganography (DGS) settings. Our findings reveal that the normality of initial noise of DMs is crucial to these factors and can offer theoretically grounded guidance for DGS design. Based on this insight, we propose a Provable and Adjustable Message Mapping (PA-B2G) approach. It can, on one hand, theoretically guarantee reversible encoding of bit messages from arbitrary distributions into standard Gaussian noise for DMs. On the other hand, its adjustability provides a more natural and fine-grained way to trade off image quality, steganographic security, and message extraction accuracy. By integrating PAB2G with a probability flow ordinary differential equation, we establish an invertible mapping between secret messages and stego images. PA-B2G can be seamlessly incorporated with most mainstream DMs, such as the Stable Diffusion, without necessitating additional training or fine-tuning. Comprehensive experiments corroborate our theoretical insights regarding the trade-off in DGS settings and demonstrate the effectiveness of our DGS algorithm in producing high-quality stego images while preserving desired levels of steganographic security and extraction accuracy. © 2024, CC BY-NC-ND.

关键词： Gaussian noise (electronic)

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Health care counselling via voicebot using multinomial naive bayes algorithm 5

Health care counselling via voicebot using multinomial naive...

引用

5th International Conference on Communication and Electronics Systems, ICCES 2020

作者： Revathy, S. Niranjani, R. Roslin Kanushya, J. School of Computing Sathyabama Institute of Science And Technology Chennai Tamil Nadu India Department of Information Technology Sathyabama Institute of Science And Technology Chennai Tamil Nadu India

ISBN: (纸本)9781728153711

Voicebot bestow the user, the ability of receiving retort within minutes, no matter their geographic location, or the apparatus, they are using to transmission. Data related to the health issues are gathered from medical experts, and all of them are stored on the server side of the system. The software furnished by the system offers answers and solutions for audit and treating patients aftercombining descriptions of the illness and the symptoms, diagnostics, predictions and prescription. This software will be greatly useful to people to protect them from complication, in this proposed system the Voicebot is used to solve the difficulties facing by people at the time of illness, which influencethe healthof the person. With all these factors, the Voicebot updates its knowledge base and allows patients to benefit from using it. This designed bot is commonly used for general Health Care. Here the input is given by the user in format of text. This text will be analyzed by Multinomial Algorithm, by which the data are fetched from the Data Base and generates voice response. When examine all these bots, the oddity of the proposed system can get response by both sensory and textual methods. This graces the untaught customers to use proficiently. It bears the whole system in customer's cordial manner. The needof the proposed system is minimum time and energy and can clarify our doubts at any time. © 2020 IEEE.

关键词： Health care

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More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference

arXiv

引用

arXiv 2022年

作者： Lan, Zixun Hong, Binjie Ma, Ye Ma, Fei Department of Applied Mathematics School of Science China Department of Information and Computing Science School of Advanced Technology China Department of Financial and Actuarial Mathematics School of Science Xi’an Jiaotong-Liverpool University SIP Suzhou215123 China

Graph similarity measurement, which computes the distance/similarity between two graphs, arises in various graph-related tasks. Recent learning-based methods lack interpretability, as they directly transform interaction information between two graphs into one hidden vector and then map it to similarity. To cope with this problem, this study proposes a more interpretable end-to-end paradigm for graph similarity learning, named Similarity Computation via Maximum Common Subgraph Inference (INFMCS). Our critical insight into INFMCS is the strong correlation between similarity score and Maximum Common Subgraph (MCS). We implicitly infer MCS to obtain the normalized MCS size, with the supervision information being only the similarity score during training. To capture more global information, we also stack some vanilla transformer encoder layers with graph convolution layers and propose a novel permutation-invariant node Positional Encoding. The entire model is quite simple yet effective. Comprehensive experiments demonstrate that INFMCS consistently outperforms state-of-the-art baselines for graph-graph classification and regression tasks. Ablation experiments verify the effectiveness of the proposed computation paradigm and other components. Also, visualization and statistics of results reveal the interpretability of INFMCS. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Joint Sparsification and Quantization for Wireless Federated Learning under Communication Constraints

Joint Sparsification and Quantization for Wireless Federated...

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IEEE Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

作者： Junshen Su Xijun Wang Xiang Chen Ray-Guang Cheng School of Electronics and Information Technology Sun Yat-Sen University Guangzhou China Guangdong Provincial Key Laboratory of Big Data Computing The Chinese University of Hong Kong Shenzhen China Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan

Communication efficiency is one of the key bottlenecks in Federated Learning (FL). Compression techniques, such as sparsification and quantization, are used to reduce communication overhead. However, joint designs of these techniques under communication constraints are not well-explored. This paper investigates the joint uplink compression problem in communication-constrained FL systems. We propose a Block-TopK sparsification scheme to reduce the proportion of bits used for locating entries of a sparsified vector. Considering the communication constraints, an optimization formulation is proposed to minimize the compression error. By solving the optimization problem, our joint compression method provides a better trade-off between sparsity budget and bit width. Numerical results demonstrate that our approach achieves 99.96% of baseline accuracy with only 1.56% of the baseline communication overhead when training ResNet-18 on CIFAR-10.

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Terminal Sliding Modes-Based PLLs for Three-Phase Grid-Connected Inverters

Terminal Sliding Modes-Based PLLs for Three-Phase Grid-Conne...

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Annual Conference of Industrial Electronics Society

作者： Pooyan Alinaghi Hosseinabadi Mostefa Kermadi Hemanshu Pota Saad Mekhilef Ali Soltani Sharif Abadi The School of Engineering and Information Technology The University of New South Wales Canberra ACT Australia The Power Electronics and Renewable Energy Research Laboratory (PEARL) University of Malaya Kuala Lumpur Malaysia The School of Science Computing and Engineering Technologies Swinburne University of Technology Hawthorn VIC Australia Faculty of Mechatronics Institute of Automatic Control and Robotics Warsaw University of Technology Warsaw Poland

This paper proposes and experimentally implements new phase-locked loop algorithms using non-adaptive and adaptive terminal sliding modes for the three-phase grid-connected inverters. The proposed algorithms aim to extract the grid voltage phase and frequency and provide them for the inverter controller. The proposed sliding mode-based PLL algorithms provide faster, more robust, and more accurate detection of the grid voltage information compared to the conventional PI-PLL. This leads to better current controller performance and subsequently, improves the quality of the injected currents into the grid. The finite-time stability notion is incorporated with robust sliding modes to ensure a fast convergence rate. An adaptive law is proposed to determine a proper value for the switching gain which leads to a better overall performance of the PLL compared with a non-adaptive one. The Lyapunov stability theory is used to obtain the stability proofs of the closed-loop systems under both designed sliding modes-based PLLs. The proposed PLL algorithms are validated by simulation and experimental results for the three-phase grid-connected inverter under normal and various abnormal grid conditions.

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T-PickSeer: Visual Analysis of Taxi Pick-up Point Selection Behavior

arXiv

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arXiv 2023年

作者： Gu, Shuxian Dai, Yemo Feng, Zezheng Wang, Yong Zeng, Haipeng School of Intelligent Systems Engineering Sun Yat-sen University China Huawei Technologies Co. Ltd China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong School of Computing and Information Systems Singapore Management University Singapore

Taxi drivers often take much time to navigate the streets to look for passengers, which leads to high vacancy rates and wasted resources. Empty taxi cruising remains a big concern for taxi companies. Analyzing the pick-up point selection behavior can solve this problem effectively, providing suggestions for taxi management and dispatch. Many studies have been devoted to analyzing and recommending hot-spot regions of pick-up points, which can make it easier for drivers to pick up passengers. However, the selection of pick-up points is complex and affected by multiple factors, such as convenience and traffic management. Most existing approaches cannot produce satisfactory results in real-world applications because of the changing travel demands and the lack of interpretability. In this paper, we introduce a visual analytics system, T-PickSeer, for taxi company analysts to better explore and understand the pick-up point selection behavior of passengers. We explore massive taxi GPS data and employ an overview-to-detail approach to enable effective analysis of pick-up point selection. Our system provides coordinated views to compare different regularities and characteristics in different regions. Also, our system assists in identifying potential pick-up points and checking the performance of each pick-up point. Three case studies based on a real-world dataset and interviews with experts have demonstrated the effectiveness of our system. Copyright © 2023, The Authors. All rights reserved.

关键词： Taxicabs

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Dynamic AI-Driven Network Slicing with O-RAN for Continuous Connectivity in Connected Vehicles and Onboard Consumer Electronics

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IEEE Transactions on Consumer Electronics 2025年

作者： Shah, Syed Danial Ali Bashir, Ali Kashif Al-Otaibi, Yasser D. Dabel, Maryam M. Al Ali, Farman University of Leeds School of Electronic and Electrical Engineering LeedsLS2 9JT United Kingdom Manchester Metropolitan University Department of Computing and Mathematics M15 6BH United Kingdom Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India King Abdulaziz University Faculty of Computing and Information Technology in Rabigh Department of Information Systems Jeddah21589 Saudi Arabia University of Hafr Al Department of Computer Science and Engineering Batin Saudi Arabia Sungkyunkwan University School of Convergence Seoul Korea Republic of

The rise of connected and autonomous vehicles signifies an era of intelligent transportation systems, where robust and continued network connectivity is essential for critical applications and enhanced in-vehicle Consumer Electronics (CE) experiences. Slicing at the network's edge offers tailored and dedicated logical networks for diverse and low-latency vehicular demands, including Advanced Driver Assistance Systems (ADAS) and in-car infotainment. However, seamless migration of network slices as vehicles traverse coverage areas of different network operators presents formidable challenges, such as ensuring continuous connectivity and uninterrupted service for both safety-critical systems and consumer-oriented services. In this paper, we introduced dynamic network slicing for continuous connectivity in connected vehicles and onboard CE using the Open Radio Access Network (O-RAN) framework in a highly dynamic and mobile environment. We implemented an xAPP within O-RAN that enables Deep Reinforcement Learning (DRL) agent to learn optimal policies through interaction with the network, guiding intelligent decisions on slice migration, resource allocation, and handover optimization. We conducted simulations and evaluations to demonstrate the effectiveness of the proposed xAPP in maintaining optimal Quality of Service (QoS), ensuring efficient RAN resource utilization, minimizing service interruptions, and prioritizing safety-critical slices, all while supporting seamless operation of CE within vehicles during mobility. © 1975-2011 IEEE.

关键词： Advanced driver assistance systems

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An Extended Direction-Oriented Interpolation Method via Spatial Direction Vector Validity Measure

An Extended Direction-Oriented Interpolation Method via Spat...

引用

Frontiers technology of information and Computer (ICFTIC), IEEE International Conference on

作者： Yuhui Lin Zubin Ye Wenbin Kuang School of Computing and Information Science Fuzhou Institute of Technology Fuzhou China Hunan *** Interactive Entertainment Media Co. Ltd. Changsha China

In this paper, an extended and improved direction-oriented interpolation method is proposed for de-interlacing. The proposed method records the minimum square error and max square error while block-matching, and uses these square errors to measure the validity of detected spatial direction vectors. When the spatial direction vector is ignorable, the proposed method will revise it to avoid staircase effect. The performance of the proposed method is analyzed by de-interlacing some popular video sequences and compared with the original DOI method and the YADIF method.

关键词： Interpolation Measurement uncertainty Video sequences Computational efficiency

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Rough Set Model and Approximations in Fuzzy Formal Contexts

Rough Set Model and Approximations in Fuzzy Formal Contexts

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Machine Learning and computing (ACMLC), Asia Conference on

作者： Yu-Ru Syau En-Bing Lin Department of Information Management National Formosa University Huwei Yunlin Taiwan School of Computing and Data Science Wentworth Institute of Technology Boston MA USA

Given a set of objects G with a set of attributes M and a binary relation I, a formal context (G, M, I) corresponds to a Boolean-valued information table in rough set theory, and the kernel relation of the function from G to M which maps each object of G to its afterset. This coincides with the indiscernibility relation of the Boolean-valued information table. Rough set model and Variable-Precision model (VP-model) under a formal context can be formulated based on the associated kernel relation on the objects set. $\alpha$-fuzzified rough set model and VP-model under a fuzzy formal context are formulated analogously. We present an example to demonstrate the proposed $\alpha$-fuzzified rough set model and VP-model under a fuzzy formal context.

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