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arXiv 2025年

作者： Zhao, Yu Huang, Songping Zhou, Dongsheng Ding, Zhaoyun Wang, Fei Nian, Aixin Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology Changsha410000 China National and Local Joint Engineering Laboratory of Computer Aided Design School of Software Engineering Dalian University Dalian116000 China

Obtaining valuable information from massive data efficiently has become our research goal in the era of Big Data. Text summarization technology has been continuously developed to meet this demand. Recent work has also shown that transformer-based pre-trained language models have achieved great success on various tasks in Natural Language Processing (NLP). Aiming at the problem of Chinese news text summary generation and the application of Transformer structure on Chinese, this paper proposes a Chinese news text summarization model (CNsum) based on Transformer structure, and tests it on Chinese datasets such as THUCNews. The results of the conducted experiments show that CNsum achieves better ROUGE score than the baseline models, which verifies the outperformance of the model. © 2025, CC BY.

关键词： Big data

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LED there be DoS: Exploiting variable bitrate IP cameras for network DoS

arXiv

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arXiv 2025年

作者： Goldberg, Emmanuel Brodt, Oleg Elyashar, Aviad Puzis, Rami Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Cyber Labs@BGU Ben-Gurion University of the Negev Israel Department of Computer Science Shamoon College of Engineering Israel

Variable-bitrate video streaming is ubiquitous in video surveillance and CCTV, enabling high-quality video streaming while conserving network bandwidth. However, as the name suggests, variable-bitrate IP cameras can generate sharp traffic spikes depending on the dynamics of the visual input. In this paper, we show that the effectiveness of video compression can be reduced by up to 6× using a simple laser LED pointing at a variable-bitrate IP camera, forcing the camera to generate excessive network traffic. Experiments with IP cameras connected to wired and wireless networks indicate that a laser attack on a single camera can cause significant packet loss in systems sharing the network with the camera and reduce the available bandwidth of a shared network link by 90%. This attack represents a new class of cyber-physical attacks that manipulate variable bitrate devices through changes in the physical environment without a digital presence on the device or on the network. We also analyze the broader view of multidimensional cyberattacks that involve both the physical and digital realms and present a taxonomy that categorizes attacks based on their direction of influence (physical-to-digital or digital-to-physical) and their method of operation (environment-driven or device-driven), highlighting multiple areas for future research. © 2025, CC BY-NC-ND.

关键词： Video streaming

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Stereo Image Super-Resolution via Disparity Estimation and Domain Diffusion 9th

Stereo Image Super-Resolution via Disparity Estimation and D...

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9th International Symposium on Artificial Intelligence and Robotics, ISAIR 2024

作者： Wang, Wanjun Ma, Chunyan Zhu, Hongjun Xu, Kai Han, Huihui School of Computer and Software Engineering Anhui Institute of Information Technology WuHu241199 China School of Artificial Intelligence Department of Information Hohai University NanJing210024 China Platforms of Automotive Intelligent Software and Systems Anhui Institute of Information Technology WuHu241199 China

ISBN: (纸本)9789819629107

Image super-resolution (SR) and disparity estimation are closely related in stereo images, with effective utilization of disparity maps enhancing SR performance. This paper proposes a stereo image super-resolution reconstruction network utilizing disparity estimation and a domain diffusion model. The network integrates disparity estimation with diffusion models for feature summation and domain space construction, enabling interaction between forward and backward diffusion memory units. To further improve reconstructed image quality, we introduce a novel memory unit inspired by long short-term memory concepts. Disparity loss and structural similarity are included in the loss function to achieve more accurate results. Experiments show that our method outperforms existing techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stereo image processing

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M2IST: Multi-Modal Interactive Side-Tuning for Efficient Referring Expression Comprehension

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Liu, Xuyang Liu, Ting Huang, Siteng Xin, Yi Hu, Yue Qin, Long Wang, Donglin Wu, Yuanyuan Chen, Honggang Sichuan University College of Electronics and Information Engineering Chengdu610065 China National University of Defense Technology College of Systems Engineering Changsha410072 China Westlake University School of Engineering Hangzhou310030 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210000 China Chengdu610059 China Yunnan University Yunnan Key Laboratory of Software Engineering Kunming650600 China

Referring expression comprehension (REC) is a vision-language task to locate a target object in an image based on a language expression. Fully fine-tuning general-purpose pre-trained vision-language foundation models for REC yields impressive performance but becomes increasingly costly. Parameter-efficient transfer learning (PETL) methods have shown strong performance with fewer tunable parameters. However, directly applying PETL to REC faces two challenges: (1) insufficient multi-modal interaction between pre-trained vision-language foundation models, and (2) high GPU memory usage due to gradients passing through the heavy vision-language foundation models. To this end, we present M2IST: Multi-Modal Interactive Side-Tuning with M3ISAs: Mixture of Multi-Modal Interactive Side-Adapters. During fine-tuning, we fix the pre-trained uni-modal encoders and update M3ISAs to enable efficient vision-language alignment for REC. Empirical results reveal that M2IST achieves better performance-efficiency trade-off than full fine-tuning and other PETL methods, requiring only 2.11% tunable parameters, 39.61% GPU memory, and 63.46% training time while maintaining competitive performance. Our code is released at https://***/xuyang-liu16/M2IST. © 1991-2012 IEEE.

关键词： Visual languages

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CoEdge: A Collaborative Architecture for Efficient Task Offloading among Multiple Edge Service Providers

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IEEE Internet of Things Journal 2025年

作者： Xie, Xingrui Cheng, Geyao Liu, Han Luo, Lailong Ren, Bangbang Guo, Deke National University of Defense Technology National Key Laboratory of Information Systems Engineering Hunan Changsha410073 China National University of Defense Technology State Key Laboratory of Complex and Critical Software Environment College of Information and Communication Hubei Wuhan430019 China National University of Defense Technology National Key Laboratory of Information Systems Engineering National Laboratory for Parallel and Distributed Processing Hunan Changsha410073 China

Edge computing is an emerging paradigm poised to process a substantial portion of latency-sensitive and computation-intensive tasks through edge service providers (ESPs). However, these ESPs typically operate independently and locally to serve their registered users. When processing burst tasks, the ESPs have to either scale up their respective capacities by introducing additional hardware or compromise user experience by rejecting some user requests, leading to high commercial investment or service degradation. Inspired by the promise of the win-win situation for ESPs and users, we envision a novel task offloading strategy that realizes the following rationales simultaneously: 1) collaborative service, 2) rapid response, and 3) sustainable profitability, while the existing methods fail to achieve them at one shot. To this end, we report CoEdge, a collaborative architecture for efficient task offloading among multiple ESPs in the edge network, aiming at simultaneously minimizing service delay for users and enhancing service profit for ESPs. To achieve this, CoEdge employs a central optimizer to implement a two-stage strategy that determines the task scheduling and service pricing hierarchically. We then formulate these problems and prove their NP-hardness. Additionally, we also propose efficient approximate algorithms to accommodate large-scale computing scenarios with low complexity. Experimental results using real-world datasets demonstrate that our CoEdge can significantly reduce service delay by 2.87x to 4.15x for users and considerably increase service profit by 32% for ESPs. © 2014 IEEE.

关键词： NP-hard

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Transparent AI for Adaptive Fraud Detection 19

Transparent AI for Adaptive Fraud Detection

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19th International Conference on Ubiquitous information Management and Communication, IMCOM 2025

作者： Agomuo, Okechukwu Clement Uzoma, Agomuo Kingsley Khan, Zohaib Otuomasirichi, Agomuo Ijeoma Muzamal, Junaid Hussain Department of Operations and Information Systems University of Utah Salt Lake City United States Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Dept. of Software Engineering FAST NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Fast School of Computing Fast NUCES Lahore Pakistan

ISBN: (纸本)9798331507817

The rapid evolution of digital financial services has significantly increased the vulnerability of financial systems to fraudulent activities. Traditional machine learning models have offered substantial progress in detecting fraudulent transactions;however, they often lack adaptability and transparency, critical for dynamic financial environments and regulatory compliance. This paper introduces a novel approach that integrates Deep Reinforcement Learning (DRL) with Explainable Artificial Intelligence (XAI) to enhance fraud detection capabilities in cloud-based financial platforms. Our model, tested on the synthetic PaySim mobile money transaction dataset, leverages the adaptive nature of DRL to continuously learn and update its fraud detection strategies based on evolving transaction patterns. Concurrently, the integration of XAI provides clear, interpretable insights into the decision-making process, thus ensuring transparency and building trust. The proposed model demonstrates superior performance with an achieved accuracy of 99.5% and an F1-score of 99.0%, significantly outperforming traditional fraud detection systems. These results not only underscore the efficacy of our model in improving the accuracy of fraud detection but also highlight its capability in offering significant improvements in understanding the underlying reasons for each decision, thereby aiding compliance with financial regulations. The practical implications of this research highlight its potential to provide a robust, scalable, and transparent solution for real-time fraud detection, which is essential for safeguarding the integrity of modern financial systems. © 2025 IEEE.

关键词： Decentralized finance

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Data-Driven Decision Management: Multi-Modal Analytics in a Health Case Study 19

Data-Driven Decision Management: Multi-Modal Analytics in a ...

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19th International Conference on Ubiquitous information Management and Communication, IMCOM 2025

作者： Agomuo, Okechukwu Clement Khan, Zohaib Otuomasirichi, Agomuo Ijeoma Uzoma, Agomuo Kingsley Muzamal, Junaid Hussain Department of Operations and Information Systems University of Utah Salt Lake City United States Dept. of Software Engineering FAST NUCES Lahore Pakistan Department of Agricultural Economics University of Nigeria Enugu Nigeria Center for Environmental Management and Green Energy University of Nigeria Enugu Nigeria Fast School of Computing Fast NUCES Lahore Pakistan

ISBN: (纸本)9798331507817

In an era where data is abundant yet underutilized, organizations across various sectors face challenges in extracting actionable insights from complex, multi-modal datasets. To address these challenges, this study proposes a versatile data analytics framework designed to enhance predictive accuracy and support informed decision-making. The framework integrates structured and unstructured data, leveraging advanced Transformer architectures to capture temporal dynamics and complex interactions within the data. In a healthcare use case, the framework demonstrated its effectiveness by achieving an Accuracy of 0.854, an F1-Score of 0.895, and an AUC-ROC of 0.930, significantly outperforming traditional models. By incorporating SHAP for interpretability, the framework ensures that its predictions are both accurate and transparent, making it a valuable tool for decision-makers. While healthcare is one application, the framework’s flexibility allows it to be adapted to a wide range of industries. Future work will focus on validating the framework across different sectors and integrating it into operational workflows to optimize decision-making and outcomes. © 2025 IEEE.

关键词： Electronic health record

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Brain Function Assessment of EEG in Mild Cognitive Impairment using Timelag Analysis

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, information and systems 2025年第4期145卷 467-473页

作者： Tanabe, Akifumi Harachi, Kento Yamamoto, Yusuke Muramatsu, Ayumi Nagahara, Hajime Takemura, Noriko Shimojo, Shinji Furihata, Daisuke Mizuno-Matsumoto, Yuko Graduate School of Information Science University of Hyogo 7-1-28 Minatojima Minami Chuo-ku Hyogo Kobe650-0047 Japan Department of Medical Engineering Aino University 4-5-4 Higashi-Ohda Ibaraki Osaka567-0012 Japan D3 Center The University of Osaka 2-8 Yamadaoka Osaka Suita565-0871 Japan D3 Center The University of Osaka 5-1 Mihogaoka Ibaraki Osaka567-0047 Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology 680-4 Kawazu Fukuoka Iizuka820-8502 Japan Faculty of Software and Information Technology Aomori University 2-3-1 Shachihata Aomori-shi Aomori030-0943 Japan

This research aimed to reveal the information process in the brain of the patients who suffered from mild cognitive impairment (MCI) using the power spectra and timelag analysis in electroencephalogram (EEG). The patients with MCI have memory or thinking problems, and are at a great risk of developing Alzheimer's disease or a related dementia. Ten patients with MCI and 13 healthy adults (non-MCI) were examined. The EEGs (122.88 seconds/subject) during a state of relaxation with the eyes closed, were analyzed using the Fourier analysis (power spectrum) and the timelag analysis. The results showed that the mean value of the power spectra in MCI was significantly higher than the mean value in non-MCI in the theta band. The mean value of the absolute timelag values (ATL) in MCI was significantly lower than the mean value in non-MCI in the frontal and left temporal areas. Our findings suggest that the features from EEG of MCI were abstracted in the theta band, and the propagation of information related to memory in brain would becoming fast in MCI. © 2025 The Institute of Electrical Engineers of Japan.

关键词： Electroencephalography

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Highly Accurate Crow Search Optimized CNN Classifier for Cyclone Prediction

Highly Accurate Crow Search Optimized CNN Classifier for Cyc...

引用

Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Ramya Kavin K. S R. Anuja A. T. R. Krishna Priya D. Karthikeyan Kiruba Thangam Raja Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Tamilnadu India Department of Electrical and Electronics Engineering Government College of Engineering Tirunelveli Tamilnadu India Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari Tamilnadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) VIT University Vellore

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Accurate and timely prediction of cyclone events is essential for effective disaster management and risk mitigation. This study introduces a highly precise cyclone prediction model that combines Convolutional Neural Networks (CNNs) with the optimization capabilities of the Crow Search Algorithm (CSA). The resulting model, called Crow Search Optimized CNN (CSO-CNN), harnesses the feature extraction and classification strengths of CNNs, while the CSA is used to optimize the hyperparameters of the CNN architecture for optimal performance. The model is trained and validated on a comprehensive dataset that includes satellite imagery and meteorological data from a wide range of cyclone events. The CSO-CNN achieves remarkable classification accuracy, surpassing traditional CNN models and other leading techniques. Its ability to quickly and accurately assess the likelihood of cyclone formation provides crucial decision support for disaster management authorities, enabling timely and effective response strategies. By integrating the CSA optimization technique with the CNN framework, this study presents a novel approach to cyclone prediction. It highlights the potential of combining advanced Machine Learning (ML) algorithms to tackle complex environmental challenges and deepen our understanding of intricate atmospheric phenomena.

关键词： Accuracy Atmospheric modeling Disaster management Predictive models Prediction algorithms Feature extraction Cyclones Satellite images Convolutional neural networks Optimization

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Enhancing Intrusion Detection systems Using RNN, LSTM, and Hybrid RNN-LSTM Models

Enhancing Intrusion Detection Systems Using RNN, LSTM, and H...

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Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： Sahar Ebadinezhad Nooshin Nooraei Nia Nasratullah Shirzad Nwabueze Kenneth Osemeha Department of Computer Information Systems Computer Information Systems Research and Technology Center (CISRTC) Near East University Nicosia Turkey Artificial Intelligence Near East University Nicosia North Cyprus Software Engineering Department Near East University Nicosia North Cyprus

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Due to The lack of comparison studies and practical applications of RNN, LSTM, and hybrid RNN-LSTM models for intrusion detection systems, especially when managing class imbalances in complex network datasets, represents a gap in the literature. This study investigates the application of deep learning techniques to improve the detection capabilities of IDS. For this study, we applied and assessed Recurrent Neural Network, Long Short-Term Memory, and Hybrid RNN-LSTM models on a UNSW-NB15 dataset, and to redress class imbalance we trained and validated our models using synthetic minority over-sampling. The RNN-LSTM model was shown to perform the best with a 9 4. 0 0 % accuracy in comparison to other models. These experimental results signify the possibility of enhancing IDS performance and a dependable method of detecting various network intrusions with the use of hybrid models comprised of RNN & LSTM.

关键词： Deep learning Recurrent neural networks Accuracy Computational modeling Intrusion detection Computer architecture Network security Hyperparameter optimization Network intrusion Long short term memory

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