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IEEE Transactions on Vehicular Technology 2025年

作者： Xie, Xinke Lu, Yang Chi, Chong-Yung Chen, Wei Ai, Bo Niyato, Dusit Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China Beijing Jiaotong University School of Computer Science and Technology Beijing100044 China National Tsing Hua University Institute of Communications Engineering Department of Electrical Engineering Hsinchu30013 Taiwan Beijing Jiaotong University School of Electronics and Information Engineering Beijing100044 China Nanyang Technological University College of Computing and Data Science 639798 Singapore

This paper proposes an unsupervised deep-learning (DL) approach by integrating Transformer and Kolmogorov-Arnold networks (KAN) termed KANsformer to realize scalable beamforming for mobile communication systems. Specifically, we consider a classic multi-input single-output energy efficiency maximization problem subject to the total power budget. The proposed KANsformer first extracts hidden features via a multi-head self-attention mechanism and then reads out the desired beamforming design via KAN. Numerical results are provided to evaluate the KANsformer in terms of generalization performance, transfer learning and ablation experiment. Overall, the KANsformer outperforms the existing benchmark DL approaches, and is adaptable to the variation in the number of mobile users with real-time and near-optimal inference. © 1967-2012 IEEE.

关键词： Budget control

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Explainable Adversarial Attacks on Coarse-to-Fine Classifiers

Explainable Adversarial Attacks on Coarse-to-Fine Classifier...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Akram Heidarizadeh Connor Hatfield Lorenzo Lazzarotto HanQin Cai George Atia Dept. of Electrical and Computer Engineering University of Central Florida Orlando FL USA School of Technology Pontifícia Universidade Católica do Rio Grande do Sul Porto Alegre Brazil Dept. of Statistics and Data Science Dept. of Computer Science University of Central Florida Orlando FL USA Dept. of Electrical and Computer Engineering Dept. of Computer Science University of Central Florida Orlando FL USA

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Traditional adversarial attacks typically aim to alter the predicted labels of input images by generating perturbations that are imperceptible to the human eye. However, these approaches often lack explainability. Moreover, most existing work on adversarial attacks focuses on single-stage classifiers, but multi-stage classifiers are largely unexplored. In this paper, we introduce instance-based adversarial attacks for multi-stage classifiers, leveraging Layer-wise Relevance Propagation (LRP), which assigns relevance scores to pixels based on their influence on classification outcomes. Our approach generates explainable adversarial perturbations by utilizing LRP to identify and target key features critical for both coarse and fine-grained classifications. Unlike conventional attacks, our method not only induces misclassification but also enhances the interpretability of the model’s behavior across classification stages, as demonstrated by experimental results.

关键词： Perturbation methods Decision making Signal processing Acoustics Speech processing

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Explainable Adversarial Attacks on Coarse-to-Fine Classifiers

arXiv

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arXiv 2025年

作者： Heidarizadeh, Akram Hatfield, Connor Lazzarotto, Lorenzo Cai, HanQin Atia, George Dept. of Electrical and Computer Engineering University of Central Florida OrlandoFL United States School of Technology Pontifícia Universidade Católica do Rio Grande Sul Porto Alegre Brazil Dept. of Statistics and Data Science Dept. of Computer Science University of Central Florida OrlandoFL United States Dept. of Electrical and Computer Engineering Dept. of Computer Science University of Central Florida OrlandoFL United States

关键词： Generative adversarial networks

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Research on the Change of the SMB of the Medical Sector before and after the Epidemic 6

Research on the Change of the SMB of the Medical Sector befo...

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6th International Conference on E-Business and Internet, ICEBI 2022

作者： Wu, Yue Zhang, Kaiqi Huang, Jiehong Wang, Qisong School of Data Sciences Zhejiang University of Finance and Economics Zhejiang Province Hangzhou City China School of Logistics Management and Engineering Zhuhai College of Jilin University Guangdong Province Zhuhai City China School of International Economy and Trade Nanjing Institute of Technology Jiangsu Province Nanjing City China School of Computer Science East China University of Science and Technology Shanghai City China

ISBN: (纸本)9781450398640

This study confirms that the applicability of the Fama-French five-factor model in the Chinese market is related to the characteristics of the Chinese stock market, studies the factor differences between GEM and STB in China market and uses random forest algorithms to make predictions in different types of stock markets. It is concluded that SMB is the most significant in the Chinese market, GEM and STB has no significant difference. In addition, this work examines the change in returns of the pharmaceutical sector before and after the epidemic, perform time series analysis and independent sample mean test on the SMB size factor, then analyzes the policy reasons for the trend of the pharmaceutical sector and draw the conclusions that the epidemic has a negative effect on the SMB and the time trend has a positive effect on the SMB factor under the influence of controlling the epidemic. © 2022 ACM.

关键词： Time series analysis

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Transductive – Long Short-Term Memory Network for the Fake News Detection

Transductive – Long Short-Term Memory Network for the Fake ...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE International Conference on

作者： Sanjaikanth E Vadakkethil Somanathan Pillai Rajiv Avacharmal R. Archana Reddy Piyush Kumar Pareek Pankaj Zanke School of Electrical Engineering and Computer Science University of North Dakota Grand Forks USA Center for Data Science and Artificial intelligence New York Life Insurance Company Farmington Hills Michigan USA School of Computer Science and Engineering SR University Warangal Telangana India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Sapiens International Corporation USA

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

Recently, fake news and rumors are distributing majorly and rapidly in all over world. That circumstance causes the production and propagation of incorrect news articles. As well as mis data and fake news are maximized through user without correct verification. In this manuscript, proposed a Transductive - Long Short-Term Memory (T-LSTM) method for detecting fake news. The Buzzfeed and ISOT datasets are used in this manuscript for fake news detection and it is pre-processed by tokenization, removal of stop word and stemming. Then, the reduction of feature is performed by using Probability Principal Component Analysis (PPCA) and it is classified by using T-LSTM method. The proposed T-LSTM method attained accuracy 96.51 %, precision 94.64 %, recall 92.33 % and f1-score 93.57% on Buzzfeed dataset. The proposed T-LSTM method attained accuracy 98.43%, precision 97.76%, recall 97.02% and f1-score 97.35% on ISOT dataset. The proposed T-LSTM method performed well than other existing methods like LSTM -Levy Flight (LSTM-LF) and OptNet.

关键词： Circuits Production Feature extraction Tokenization Fake news Distributed computing Long short term memory

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Enhancing Kidney Stone Detection in CT scans with Image Processing and the DCNN-RELM Model

Enhancing Kidney Stone Detection in CT scans with Image Proc...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： Manjula Prabakaran Jesmin Zakaria S Kannan Santhiya P Rajasekar P M V Rathnamma Department of CSE- (Data Science) Madanapalle Institute of Technology & Science Andhra Pradesh India Department of Electronics & Communication Engineering (ECE) NSHM Knowledge Campus Durgapur West Bengal India Department of Computer Science and Engineering School of Engineering and Technology Dhanalakshmi Srinivasan University Trichy Tamilnadu India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamilnadu India Department of CSE KLM College of Engineering for Women Kadapa Andhra Pradesh India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

CT of the urinary system, abdomen, and kidneys appears to improve kidney stone screening and identification. Segmentation and categorization are essential for kidney stone identification and management. This work developed a semi-automatic kidney stone detection system using geometric concepts and image processing. The main steps include model training, feature extraction, segmentation, embossing, and preprocessing. Preprocessing involves trimming the input image to remove unnecessary kidney regions to focus on critical CT scan locations. Relief embossing enhanced image details. Segmentation accurately classified veins and blood arteries to define kidney stone boundaries. CNN-RELM was our training model. Compared to CNN and ELM models, the recommended system performed better. Its 93.87% kidney stone detection and localization accuracy prove its reliability. The semi-automatic system's reliable kidney stone detection and segmentation method may improve clinical decision-making. Combining CNN-RELM modeling and enhanced image processing could lead to medical imaging applications.

关键词： Training Image segmentation Accuracy Three-dimensional displays Computed tomography Embossing data preprocessing Reliability Kidney Biomedical imaging

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Neural Network Panning: Screening the Optimal Sparse Network Before Training

arXiv

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arXiv 2022年

作者： Kang, Xiatao Li, Ping Yao, Jiayi Li, Chengxi School of Computer and Communication Engineering Changsha University of Science and Technology China School of Computer Xidian University China Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transp China

Pruning on neural networks before training not only compresses the original models, but also accelerates the network training phase, which has substantial application value. The current work focuses on fine-grained pruning, which uses metrics to calculate weight scores for weight screening, and extends from the initial single-order pruning to iterative pruning. Through these works, we argue that network pruning can be summarized as an expressive force transfer process of weights, where the reserved weights will take on the expressive force from the removed ones for the purpose of maintaining the performance of original networks. In order to achieve optimal expressive force scheduling, we propose a pruning scheme before training called Neural Network Panning which guides expressive force transfer through multi-index and multi-process steps, and designs a kind of panning agent based on reinforcement learning to automate processes. Experimental results show that Panning performs better than various available pruning before training methods. Our code is made public at: https://***/kangxiatao/RLPanning. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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A data-Centric Accelerator for High-Performance Hypergraph Processing 22

A Data-Centric Accelerator for High-Performance Hypergraph P...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Qinggang Wang Long Zheng Ao Hu Yu Huang Pengcheng Yao Chuangyi Gui Xiaofei Liao Hai Jin Jingling Xue 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 and Zhejiang Lab Hangzhou 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 School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (纸本)9781665462723

Hypergraph processing has emerged as a powerful approach for analyzing complex multilateral relationships among multiple entities. Past research on building hypergraph systems suggests that changing the scheduling order of bipartite edge tasks can improve the overlap-induced data locality in hypergraph processing. However, due to the complex intertwined connections between vertices and hyperedges, it is almost impossible to find a locality-optimal scheduling order. Thus, these task-centric hypergraph systems often suffer from substantial off-chip *** this paper, we first propose a novel data-centric Load-Trigger-Reduce (LTR) execution model to exploit fully the locality in hypergraph processing. Unlike a task-centric model that loads the required data along with a task, our LTR model invokes tasks as per the data used. Specifically, once the hypergraph data is loaded into the on-chip memory, all of its relevant computation tasks will be triggered simultaneously to output intermediate results, which are finally reduced to update the final results. Our LTR model enables all hypergraph data to be accessed once in each iteration. To fully exploit the LTR performance potential, we further architect an LTR-driven hypergraph accelerator, XuLin, which features with an adaptive data loading mechanism to minimize the loading cost via chunk merging at runtime. XuLin is also equipped with a priority-based differential data reduction scheme to reduce the impact of conflicting updates on performance. We have implemented XuLin both on a Xilinx Alveo U250 FPGA card and using a cycle-accurate simulator. The results show that XuLin outperforms the state-of-the-art hypergraph processing solutions Hygra and ChGraph by 20.47× and 8.77× on average, respectively.

关键词： hypergraph data-centric accelerator

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CCLNet: Causal and Contrastive Learning Framework for Enhanced Pulmonary Embolism Detection

CCLNet: Causal and Contrastive Learning Framework for Enhanc...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Zhipeng Wang Ruiquan Ge Jianxun Yu Feiwei Qin Yuan Tian Nannan Li Wenwen Min Ahmed Elazab Changmiao Wang HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou China School of Computer Science Hangzhou Dianzi University Hangzhou China School of Management Dalian Polytechnic University Dalian China Second Affiliated Hospital Chinese University of Hong Kong Shenzhen China School of Science and Technology Macau University Macau China School of Information Science and Engineering Yunnan University Yunnan China School of Biomedical Engineering Shenzhen University Shenzhen China Medical Big Data Lab Shenzhen Research Institute of Big Data Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The fusion of multimodal medical data is crucial for helping doctors make accurate treatment decisions. For example, combining Computed Tomography Pulmonary Angiography (CTPA) with Electronic Health Records (EHR) can significantly improve the accuracy of Pulmonary Embolism (PE) detection, thereby increasing patient survival rates. Although multimodal learning has advantages in PE diagnosis, the heterogeneity of multimodal data poses a significant challenge to accurate diagnosis. The natural semantic and structural differences between data modalities make it difficult to effectively integrate their information. In addition, within a single modality, the existence of redundant and irrelevant information introduces unnecessary variability, making the data more complex, and making stable diagnosis challenging. To address these issues, we propose a new framework called CCLNet, which includes a contrastive learning component for addressing inter-modality heterogeneity and a causal learning component for handling intra-modality heterogeneity. Specifically, we achieve precise alignment between visual and tabular modalities by using global-level information to soften labels during contrastive learning. In addition, by using causal intervention methods to eliminate the influence of heterogeneous factors within the modality, we can accurately reveal the causal relationship between features and targets, thereby improving the accuracy and stability of the model. Experimental results demonstrate that our method performs excellently, achieving the best results. Our code is available at https://***/LeavingStarW/CLPE.

关键词： Visualization Accuracy Smoothing methods Hospitals Pulmonary diseases Lungs Semantics Contrastive learning Stability analysis Medical diagnostic imaging

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Temporal Based Instance-Level Fusion for Video Object Detection 17th

Temporal Based Instance-Level Fusion for Video Object Detect...

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17th Chinese Intelligent Systems Conference, CISC 2021

作者： Cai, Qiang Kang, Nan Li, Haisheng Cao, Jian Liu, Wenqing Wan, Ruyi School of Computer Science and Engineering Beijing Technology and Business University Beijing100048 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing China National Engineering Laboratory For Agri-product Quality Traceability Beijing China Beijing zhiyunda Technology Co. Ltd. Beijing100085 China

ISBN: (纸本)9789811663277

Static image object detection is naively applied to videos in a frame-by-frame approach, and the prediction is often temporally inconsistent. Enhancement of every selected frame becomes a typical solution by aggregating temporal nearby frames features. But the solutions above just use features in pixel-level. In this work, we emphasize the importance of the temporal sequential relationship, which considers calibrating instance-level features on time line, and combining the pixel-level features with the calibrated instance-level features finally. In order to prevent mismatch in video boundary, pixel-level re-fusion is remained to amend above issue. Although the instance-level fusion gains more global motion cues for the pixel-level calibration’s sake of robustness to occlusion, using temporal information can further improve the robustness. Experiments on the large-scale ImageNet VID dataset state the improvement of the our work for object detection in videos. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Object detection

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