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2nd International Conference on Robotics, Automation and Intelligent Control, ICRAIC 2022

作者： Li, He Jia, Huimei Zhang, Rui Yang, Shuaipeng Qi, Qinglei Liu, Tianyang Henan Costar Group Co. Ltd Nanyang China College of Information Engineering Henan University of Science and Technology Luoyang China Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China Sydney Business School Shanghai University Shanghai China

For the detection of objects floating in the river, most of the traditional intelligent video monitoring methods are used to monitor through manual viewing. During the traditional video data transmission and processing, the massive amount of data will put pressure on the background such as network and computing power. The new effective method is to crop and optimize YOLOV5s, add a specific image pre-processing module, and deploy it by edge computing, embed a SOC (System on Chip) chip in the web camera for real-time processing of video data. Simulation experiments prove that the above method can improve the accuracy and speed of the target detection network. © Published under licence by IOP Publishing Ltd.

关键词： Edge computing

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Maverick: Personalized Edge-Assisted Federated Learning with Contrastive Training 25

Maverick: Personalized Edge-Assisted Federated Learning with...

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34th ACM Web Conference, WWW 2025

作者： Wang, Kaibin He, Qiang Dong, Zeqian Chen, Rui He, Chuan Chua, Caslon Chen, Feifei Yang, Yun Swinburne University of Technology Melbourne Australia Huazhong University of Science and Technology Wuhan China Deakin University Melbourne Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798400712746

In an edge-assisted federated learning (FL) system, edge servers aggregate the local models from the clients within their coverage areas to produce intermediate models for the production of the global model. This significantly reduces the communication overhead incurred during the FL process. To accelerate model convergence, FedEdge, the state-of-the-art edge-assisted FL system, trains clients’ models in local federations when they wait for the global model in each training round. However, our investigation reveals that it drives the global model towards clients with excessive local training, causing model drifts that undermine model performance for other clients. To tackle this problem, this paper presents Maverick, a new edge-assisted FL system that mitigates model drifts by training personalized local models for clients through contrastive local training. It introduces a model-contrastive loss to facilitate personalized local federated training by driving clients’ local models away from the global model and close to their corresponding intermediate models. In addition, Maverick includes anomalous models in contrastive local training as negative samples to accelerate the convergence of clients’ local models. Extensive experiments are conducted on three widely-used models trained on three datasets to comprehensively evaluate the performance of Maverick. Compared to state-of-the-art edge-assisted FL systems, Maverick accelerates model convergence by up to 16.2x and improves model accuracy by up to 12.7%. © 2025 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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ADGRL:A Deep Graph Reinforcement Learning Model with Attention for Online Judge Exercise Recommendation

ADGRL:A Deep Graph Reinforcement Learning Model with Attenti...

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International Conference on computer Supported Cooperative Work in Design

作者： Bo Cui Dou Hao Engineering Research Center of Ecological Big Data Ministry of Education College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Online Judge (OJ) systems provide a valuable platform for independent programming skill improvement. However, the sheer number of exercises available often overwhelms users when selecting practice problems. To address this issue, we present the Attention-based Deep Graph Reinforcement Learning algorithm (ADGRL) for exercise recommendation. ADGRL employs graph convolutional neural networks to effectively model user-problem relationships and incorporates an attention mechanism for enhanced personalized recommendations. The model is trained using reinforcement learning to better understand user behavior and provide precise suggestions. Our model is evaluated and trained on a dataset with 14,818 users, 18,171 problems, and 2,887,011 interactions. Comparative experiments with recent graph-based recommendation models demonstrate that ADGRL outperforms them in terms of recall and normalized discounted cumulative gain.

关键词： Attention mechanisms Federated learning Computational modeling Reinforcement learning Programming Convolutional neural networks

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Progress in research on ultrasound radiomics for predicting the prognosis of breast cancer

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Cancer Innovation 2023年第4期2卷 283-289页

作者： Xuantong Gong Xuefeng Liu Xiaozheng Xie Yong Wang Department of Ultrasound National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBejjing Advanced Innovation Center for Big Data and Brain Computing(BDBC)Beihang UniversityBeijingChina School of Computer and Communication Engi neering.University of Science and Technology Beijing BeijingChina

Breast cancer is the most common malignant tumor and the leading cause of cancer-related deaths in women *** means of predicting the prognosis of breast cancer are very helpful in guiding treatment and improving patients'*** extracted by radiomics reflect the genetic and molecular characteristics of a tumor and are related to its biological behavior and the patient's ***,radiomics provides a new approach to noninvasive assessment of breast cancer *** is one of the commonest clinical means of examining breast *** recent years,some results of research into ultrasound radiomics for diagnosing breast cancer,predicting lymph node status,treatment response,recurrence and survival times,and other aspects,have been *** this article,we review the current research status and technical challenges of ultrasound radiomics for predicting breast cancer *** aim to provide a reference for radiomics researchers,promote the development of ultrasound radiomics,and advance its clinical application.

关键词： breast cancer deeplearning prognosisprediction radiomics ultrasound

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The NP-Completeness of (1,0)-Regular (k,s)-SAT 14

The NP-Completeness of (1,0)-Regular (k,s)-SAT

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14th International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC 2022

作者： Fu, Zufeng Wang, Haiying Li, He Liu, Jinjiang Nanyang Normal University School of Computer Science and Technology Nanyang China Nanyang Normal University Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image Nanyang China

ISBN: (数字)9781665461696

ISBN: (纸本)9781665461696

Dealing with uncertainty in optimization is increasingly recognized as a key necessity for tackling real-world problems. A (1,0)-super solution has better robust, and can protect against default risk of variable assignment. When flipped a value of any one of variables, the (1,0)-super solution still is a solution. In this work, we study the completeness of (1,0)-regular (k,s)-SAT. (1,0)-regular (k,s)-SAT is a problem to decide whether a regular (k,s)-CNF formula has a (1,0)-super solution. We give a reduction method that transform from (k,s)-SAT to (1,0)-regular (k+1,s)-SAT if an unsatisfiable (k,s)-CNF formula exists, and a structure method of forced-regular (k,s)-CNF formula. This implies that (1,0)-regular (k +1,s)-SAT problem has a sudden jump from triviality to NP-completeness for every k > 3. © 2022 IEEE.

关键词： Uncertainty Transforms Proposals Optimization Man-machine systems Cybernetics

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High-Precision Fast Direction-of-Arrival Estimation Method for Planar Array

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Space(science & Technology) 2023年第1期3卷 150-153页

作者： Shuai Li Lei Li Bo Liu Yihao Song Ming Li Jie Ren Wenting Jiang Institute of Telecommunication and Navigation Satellites China Academy of Space TechnologyBeijing 100094China Innovation Center of Satellite Communication System CNSABeijing 100094China School of Science and Engineering The Chinese University of Hong KongShenzhenand Shenzhen Research Institute of Big DataShenzhen 518172China

The multiple signal classification method for direction-of-arrival estimation is widely applied in practical ***,the multiple signal classification method with planar array requires 2-dimensional on-grid spectrum searches,which would lead to the grid mismatch and high computational ***,a high-precision fast direction-of-arrival estimation method for planar array is *** the proposed method,a 2-stage grid search approach over the 2-dimensional spectrum is firstly applied to obtain a quick coarse estimation of direction of ***,the estimation of higher precision is achieved via a quadratic surface fitting *** results verified the effectiveness of the proposed method.

关键词： complexity estimation planar

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Transformer-Inspired Deep Adaptive CSI Sensing With Learnable Quantizer

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IEEE Transactions on Vehicular Technology 2025年

作者： Chen, Qian Li, Lei Luo, Xiaodong Chang, Tsung-Hui The Chinese University of HongKong School of Science and Engineering Shenzhen China Shenzhen Research Institute of Big Data China Shenzhen Research Institute of Big Data Shenzhen International Center For Industrial And Applied Mathematics China Chinese University of HongKong School of Artificial Intelligence Shenzhen China

As a key enabler for 5 G and next-generation communication, multi-antenna techniques usually require accurate channel state information (CSI) to release their potential. However, the corresponding CSI sensing is challenging, especially in frequency division duplex systems under the codebook-based feedback mechanism. While this is conventionally accomplished by solving a phase retrieval (PR) problem, existing methods based on PR usually utilize heuristic sensing precoders that can hardly capture the temporal dependencies among feedbacks effectively and sense adaptively. Inspired by Transformer, in this work, we propose a CSI sensing deep neural network, leveraging multi-head attention to learn the environment knowledge from the feedbacks and adjust the precoders adaptively. Moreover, we further consider the impact of feedback quantization, develop a learnable computation-efficient quantizer, and embed it into our proposed adaptive sensing. Numerical results show that our proposed adaptive sensing greatly outperforms existing approaches, and the learnable quantizer can effectively alleviate the performance loss due to feedback quantization. © 1967-2012 IEEE.

关键词： Channel state information

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Finding the Missing data: A BERT-Inspired Approach Against Package Loss in Wireless Sensing

Finding the Missing Data: A BERT-Inspired Approach Against P...

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IEEE Conference on computer Communications Workshops, INFOCOM Wksps

作者： Zijian Zhao Tingwei Chen Fanyi Meng Hang Li Xiaoyang Li Guangxu Zhu Shenzhen Research Institute of Big Data School of Computer Science and Engineering Sun Yat-sen University School of Science and Engineering The Chinese University of Hong Kong Shenzhen

ISBN: (数字)9798350384475

ISBN: (纸本)9798350384482

Despite the development of various deep learning methods for Wi-Fi sensing, package loss often results in noncontinuous estimation of the Channel State Information (CSI), which negatively impacts the performance of the learning models. To overcome this challenge, we propose a deep learning model based on Bidirectional Encoder Representations from Transformers (BERT) for CSI recovery, named CSI-BERT. CSI-BERT can be trained in an self-supervised manner on the target dataset without the need for additional data. Furthermore, unlike traditional interpolation methods that focus on one subcarrier at a time, CSI-BERT captures the sequential relationships across different subcarriers. Experimental results demonstrate that CSI-BERT achieves lower error rates and faster speed compared to traditional interpolation methods, even when facing with high loss rates. Moreover, by harnessing the recovered CSI obtained from CSI-BERT, other deep learning models like Residual Network and Recurrent Neural Network can achieve an average increase in accuracy of approximately 15% in Wi-Fi sensing tasks. The collected dataset WiGesture and code for our model are publicly available at https://***/RS2002/CSI-BERT.

关键词： Deep learning Wireless communication Interpolation Wireless sensor networks Transformers data models Sensors

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Ponzi Scheme Detection Based on CNN and bigRU combined with Attention Mechanism

Ponzi Scheme Detection Based on CNN and BiGRU combined with ...

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International Conference on computer Supported Cooperative Work in Design

作者： Bo Cui Guoqing Wang Engineering Research Center of Ecological Big Data Ministry of Education College of Computer Science Inner Mongolia University Hohhot China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The anonymity of blockchain and its inadequate supervision make it difficult to investigate criminal activities on the blockchain. In recent years, criminals have profited from deploying Ponzi schemes on the Ethereum blockchain through smart contracts, resulting in substantial economic losses and adverse impacts, seriously impeding the development of the blockchain community and technology. However, despite some research on identifying Ponzi schemes on Ethereum, existing methods face certain difficulties in data acquisition, complex feature construction, and insufficient exploration of opcode data features. To address these issues, this paper proposes a method that only relies on smart contract opcodes to verify whether a contract is a Ponzi scheme. Specifically, Word2vec word embedding technology is first used to train the data, obtaining opcode word vector representations through the training process. Subsequently, by passing the word vectors into the Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit Network (bigRU) models, spatial and semantic features are extracted to better capture semantic information at different levels of smart contract opcodes. The attention mechanism allocates different weights to various features, highlighting key attributes. The results of the experiment show that the proposed method displays a strong detection performance, indicating specific improvements in precision, recall, and F1 score in contrast to previous methods.

关键词： Attention mechanisms Biological system modeling Smart contracts Semantics Feature extraction Vectors Blockchains

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Differentially Private Deep Learning with Iterative Gradient Descent Optimization

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ACM/IMS Transactions on data science 2021年第4期2卷 1–27页

作者： Ding, Xiaofeng Chen, Lin Zhou, Pan Jiang, Wenbin Jin, Hai National Engineering Research Center for Big Data Technology and System Lab Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

Deep learning has achieved great success in various areas and its success is closely linked to the availability of massive data. But in general, a large dataset could include sensitive data and therefore the model should have the capability to avoid privacy leakage. To achieve this aim, many works apply the famous privacy framework named differential privacy into deep learning to preserve privacy. In this article, we propose a novel perturbed iterative gradient descent optimization (PIGDO) algorithm and prove that this algorithm satisfies the differential privacy. Besides, we propose a modified moments accountant (MMA) method to conduct the privacy analysis and obtain a tighter bound of privacy loss compared with the original moments accountant method. A number of experiments demonstrate that our optimization algorithm can not only improve the model accuracy and training speed, but also achieve better privacy guarantees over the state-of-the-art algorithm while reaching the equivalent accuracy. We provide codes for all of our experiments in https://***/CGCL-codes/***. © 2022 Association for Computing Machinery.

关键词： Large dataset

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