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7th International Conference on Computer Information Science and Application Technology, CISAT 2024

作者： Xu, Wanchao Zhang, Junchao Zhang, Xinyu Li, Fengxia Tao, Jinyi Zhao, Rongli Taiyuan University of Technology College of Electrical and Power Engineering Shanxi Taiyuan030024 China Shanxi Provincial Engineering Research Center of Electric Drive and Internet of Things Shanxi Taiyuan030024 China Nanjing University College of Software Jiangsu Nanjing210093 China National Intelligent Lighting and Smart Wearable Technology Innovation Center Shanxi Province Taiyuan030024 China Eng. Technol. Res. Ctr. of Smart City Lighting Data Sharing and Pub. Serv. Platform Based on Big Data Shanxi Province Taiyuan030024 China Research Institute of New Materials and Chemical Engineering Shanxi Zhejiang University Shanxi Taiyuan030032 China

ISBN: (纸本)9798350375107

In view of the risk of fall for the elderly in the home care environment, a surveillance video display fall detection method of the improved YOLOv5 algorithm is improved, and the Transformer module that increases the human attention mechanism is combined with the CSPDarknet structure of the backbone part of the YOLOv5 algorithm. The system collects video data through the camera, trains and verifies the video data, and then judges the action and posture of the target person. It connects the WIFI module of Orange School to send the action and posture information to the guardian's mobile phone to remind the guardian to timely treat the person who fell down, so as to improve the rescue efficiency. On the basis of improving the algorithm structure, the loss function was improved and conducted repeated training. The experimental results showed that the improved algorithm mAP improved by 2.8%, the F1 score by 2.2%, the number of processing frames per second by 10% during inference, and achieved the fall detection accuracy of 98.6%. © 2024 IEEE.

关键词： Fall detection

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Fed2PKD: Bridging Model Diversity in Federated Learning via Two-Pronged Knowledge Distillation

Fed2PKD: Bridging Model Diversity in Federated Learning via ...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Zaipeng Xie Han Xu Xing Gao Junchen Jiang Ruiqian Han Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China College of Computer Science and Software Engineering Hohai University Nanjing China The Hong Kong University of Science and Technology (Guangzhou) Guangzhou China

ISBN: (数字)9798350368536

ISBN: (纸本)9798350368543

Heterogeneous federated learning (HFL) enables collaborative learning across clients with diverse model architectures and data distributions while preserving privacy. However, existing HFL approaches often struggle to effectively address the challenges posed by model diversity, leading to suboptimal performance and limited generalization ability. This paper pro-poses Fed2PKD, a novel HFL framework that tackles these challenges through a two-pronged knowledge distillation approach. Fed2PKD combines prototypical contrastive knowledge distillation to align client embeddings with global class prototypes and semi-supervised global knowledge distillation to capture global data characteristics. Experimental results on three benchmarks (MNIST, CIFAR10, and CIFAR100) demonstrate that Fed2PKD significantly outperforms existing state-of-the-art HFL methods, achieving average improvements of up to 30.53%, 13.89%, and 5.80 % in global model accuracy, respectively. Furthermore, Fed2PKD enables personalized models for each client, adapting to their specific data distributions and model architectures while benefiting from global knowledge sharing. Theoretical analysis provides convergence guarantees for Fed2PKD under realistic assumptions. Fed2PKD represents a significant step forward in HFL, unlocking the potential for privacy-preserving collaborative learning in real-world scenarios with model and data diversity.

关键词： Knowledge engineering Adaptation models Cloud computing data privacy Federated learning Computational modeling Prototypes

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COMET: "Cone of experience" enhanced large multimodal model for mathematical problem generation

arXiv

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arXiv 2024年

作者： Liu, Sannyuya Feng, Jintian Yang, Zongkai Luo, Yawei Wan, Qian Shen, Xiaoxuan Sun, Jianwen National Engineering Research Center of Educational Big Data Central China Normal University Wuhan China School of Software Technology Zhejiang University Hangzhou China Faculty of Artificial Intelligence in Education Central China Normal University Wuhan China

The automatic generation of high-quality mathematical problems is practically valuable in many educational scenarios. Large multimodal model provides a novel technical approach for the mathematical problem generation because of its wide success in cross-modal data scenarios. However, the traditional method of separating problem solving from problem generation and the mainstream fine-tuning framework of monotonous data structure with homogeneous training objectives limit the application of large multimodal model in mathematical problem generation. Addressing these challenges, this paper proposes COMET, a "Cone of Experience" enhanced large multimodal model for mathematical problem generation. Firstly, from the perspective of mutual ability promotion and application logic, we unify stem generation and problem solving into mathematical problem generation. Secondly, a three-stage fine-turning framework guided by the "Cone of Experience" is proposed. The framework divides the fine-tuning data into symbolic experience, iconic experience, and direct experience to draw parallels with experiences in the career growth of teachers. Several fine-grained data construction and injection methods are designed in this framework. Finally, we construct a Chinese multimodal mathematical problem dataset to fill the vacancy of Chinese multimodal data in this field. Combined with objective and subjective indicators, experiments on multiple datasets fully verify the effectiveness of the proposed framework and model. Copyright © 2024, The Authors. All rights reserved.

关键词： Modal analysis

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Deep Reinforcement Learning for Joint Service Placement and Request Scheduling in Mobile Edge Computing Networks

Deep Reinforcement Learning for Joint Service Placement and ...

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IEEE Symposium on Computers and Communications (ISCC)

作者： Yuxuan Deng Xiuhua Li Chuan Sun Jinlong Hao Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China Chongqing Changan Automobile Software Technology Co. Ltd Chongqing China TKLAN College of Intelligence & Computing Tianjin University Tianjin China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

Mobile edge computing aims to provide cloud-like services on edge servers located near Mobile Devices (MDs) with higher Quality of Service (QoS). However, the mobility of MDs makes it difficult to find a global optimal solution for the coupled service placement and request scheduling problem. To address these issues, we consider a three-tier MEC network with vertical and horizontal cooperation. Then we formulate the joint service placement and request scheduling problem in a mobile scenario with heterogeneous services and resource limits, and convert it into two Markov decision processes to decouple decisions across successive time slots. We propose a Cyclic Deep Q-network-based Service placement and Request scheduling (CDSR) framework to find a long-term optimal solution despite future information unavailability. Specifically, to solve the issue of enormous action space, we decompose the system agent and train them cyclically. Evaluation results demonstrates the effectiveness of our proposed CDSR on user-perceived QoS.

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Path Planning for Intelligent Robots Based on Deep Q-learning With Experience Replay and Heuristic Knowledge

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 1179-1189页

作者： Lan Jiang Hongyun Huang Zuohua Ding the Laboratory of Intelligent Computing and Software Engineering Zhejiang Sci-Tech UniversityHangzhou 310018China the Center of Multi-Media Big Data of Library Zhejiang Sci-Tech UniversityHangzhou 310018China

Path planning and obstacle avoidance are two challenging problems in the study of intelligent robots. In this paper, we develop a new method to alleviate these problems based on deep Q-learning with experience replay and heuristic knowledge. In this method, a neural network has been used to resolve the "curse of dimensionality" issue of the Q-table in reinforcement learning. When a robot is walking in an unknown environment, it collects experience data which is used for training a neural network;such a process is called experience *** knowledge helps the robot avoid blind exploration and provides more effective data for training the neural network. The simulation results show that in comparison with the existing methods, our method can converge to an optimal action strategy with less time and can explore a path in an unknown environment with fewer steps and larger average reward.

关键词： Deep Q-learning(DQL) experience replay(ER) heuristic knowledge(HK) path planning

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Joint Beamforming and Power Allocation for RIS Aided Full-Duplex Integrated Sensing and Uplink Communication System

arXiv

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arXiv 2023年

作者： Guo, Yuan Liu, Yang Wu, Qingqing Li, Xiaoyang Shi, Qingjiang The School of Information and Communication Engineering Dalian University of Technology Dalian China Electronic Engineering Shanghai Jiao Tong University Shanghai China The Shenzhen Research Institute of Big Data Shenzhen China The School of Software Engineering Tongji University Shanghai China

Integrated sensing and communication (ISAC) capability is envisioned as one key feature for future cellular networks. Classical half-duplex (HD) radar sensing is conducted in a "first-emit-then-listen" manner. One challenge to realize HD ISAC lies in the discrepancy of the two systems’ time scheduling for transmitting and receiving. This difficulty can be overcome by full-duplex (FD) transceivers. Besides, ISAC generally has to comprise its communication rate due to realizing sensing functionality. This loss can be compensated by the emerging reconfigurable intelligent surface (RIS) technology. This paper considers the joint design of beamforming, power allocation and signal processing in a FD uplink communication system aided by RIS, which is a highly nonconvex problem. To resolve this challenge, via leveraging the cutting-the-edge majorization-minimization (MM) and penalty-dual-decomposition (PDD) methods, we develop an iterative solution that optimizes all variables via using convex optimization techniques. Besides, by wisely exploiting alternative direction method of multipliers (ADMM) and optimality analysis, we further develop a low complexity solution that updates all variables analytically and runs highly efficiently. Numerical results are provided to verify the effectiveness and efficiency of our proposed algorithms and demonstrate the significant performance boosting by employing RIS in the FD ISAC system. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching 39

DAMPER: A Dual-Stage Medical Report Generation Framework wit...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Xiaofei Chen, Wenting Liu, Jie Lu, Qisheng Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)157735897X

Medical report generation is crucial for clinical diagnosis and patient management, summarizing diagnoses and recommendations based on medical imaging. However, existing work often overlook the clinical pipeline involved in report writing, where physicians typically conduct an initial quick review followed by a detailed examination. Moreover, current alignment methods may lead to misaligned relationships. To address these issues, we propose DAMPER, a dual-stage framework for medical report generation that mimics the clinical pipeline of report writing in two stages. In the first stage, a MeSH-Guided Coarse-Grained Alignment (MCG) stage that aligns chest X-ray (CXR) image features with medical subject headings (MeSH) features to generate a rough keyphrase representation of the overall impression. In the second stage, a Hypergraph-Enhanced Fine-Grained Alignment (HFG) stage that constructs hypergraphs for image patches and report annotations, modeling high-order relationships within each modality and performing hypergraph matching to capture semantic correlations between image regions and textual phrases. Finally,the coarse-grained visual features, generated MeSH representations, and visual hypergraph features are fed into a report decoder to produce the final medical report. Extensive experiments on public datasets demonstrate the effectiveness of DAMPER in generating comprehensive and accurate medical reports, outperforming state-of-the-art methods across various evaluation metrics. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Diagnosis

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PC-CSP: An Efficient Partial Convolution in Cross Stage Partial Network for Small Targets Detection

PC-CSP: An Efficient Partial Convolution in Cross Stage Part...

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Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： Junmin Xue Yunbo Rao Jiangang Wu Mei Huang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Operation Data Center of Postal Savings Bank of China Beijing P. R. China Beijing China Chengdu Aerospace Science and industry big data Research Institute Corporation Limited Chengdu China

ISBN: (数字)9798350350890

ISBN: (纸本)9798350350906

The common approach for small object detection does not work well in complex scenes with objects of multiple sizes. This is because the detection of small objects and objects of other sizes often difficult to take into account at the same time. To overcome these limitations, we introduce an efficient Partial Convolution in Cross Stage Partial (PC-CSP) network for detecting small targets. First, the introduction of the DCNv3 module improves the feature extraction for detection, slimneck is utilized to enhance the attention mechanism suitable for objects of varying sizes. Then, PC-CSP is employed to streamline the detection backbone network. Last, the loss function is refined by incorporating the concept of Inner. Through extensive experiments, our method demonstrates a significant increase in accuracy of 6.8%, and mAP@0.5 by 8.97% on the standard dataset compared to the original model. Overall, the proposed method exhibits excellent performance for small objects, such as: mobile phones, food, and faces.

关键词： Visualization Convolution Semantics Refining Object detection Feature extraction Mobile handsets Pattern recognition Object recognition Standards

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Traffic Aware Power Saving Communication Assisted By Double-Faced Active RIS

Traffic Aware Power Saving Communication Assisted By Double-...

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IEEE International Conference on Communications (ICC)

作者： Yuyan Zhou Yang Liu Qingqing Wu Qingjiang Shi Jun Zhao School of Information and Communication Engineering Dalian University of Technology Department of Electronic Engineering Shanghai Jiao Tong University School of Software Engineering Tongji University and Shenzhen Research Institute of Big Data Shenzhen China School of Computer Science and Engineering Nanyang Technological University

Despite its high energy and hardware efficiency, some defects of the reconfigurable intelligence surface (RIS) technology have come to be realized, including the severe fading loss and restricted-to-half-space coverage. This paper proposes a novel double-faced-active (DFA)-RIS structure to overcome these defects. Besides, we utilize this novel DFA-RIS to improve power saving of the communication system. Unlike traditional power saving literature, we aim at fulfilling queueing stability and long-term power minimization in a downlink system assisted by the DFA-RIS, with a realistic data arriving process taken into consideration. Enlightened by Lyapunov control theory, we propose an online optimization strategy that adaptively adjusts DFA-RIS configuration. Each online problem can be efficiently solved by leveraging alternative directional method of multipliers (ADMM) method. Numerical results demonstrate the effectiveness of our proposed Lyapunov-guided strategy and DFA-RIS’ superiority over the classical passive RIS.

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A Lightening Fine-grained Access Control Scheme Supporting Full Policy Hiding in Cloud Storage

A Lightening Fine-grained Access Control Scheme Supporting F...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Jiaoli Shi Chao Hu Shimao Yao Zhuolin Mei Bin Wu Hui Li School of Computer and Big Data Jiujiang Univ. Jiujiang China School of Software IoT Engineering Jiangxi Univ. of Finance and Economics Nanchang China Jiujiang Key Lab. of Network and Information Security Jiujiang China School of Computer and Big Data Jiujiang University Jiujiang China School of Elec. and Comp. Engineering Peking University Shenzhen China

Efficiency, security, and flexibility are difficult to balance in a full policy hiding CP-ABE scheme. This paper creatively converts the privacy matching problem of access policy and a user’s private key to the privacy three-party set computation problem. Then the new privacy set computation problem is resolved by a message driven key generation method. Additionally, the ROBDD (Reduced Ordered Binary Decision Diagrams) is utilized to rich access structure expression and convert access policies into feasible path sets. Each feasible path is composed of attributes and these attributes are bound to the ciphertext using CP-ABE. Based on the innovative ideas mentioned above, we present a Lighten Fine-grained Access Control scheme supporting Full Policy Hidden (LFAC-FPH) that integrates data confidentiality protection, access policy rich expression but complete hiding, fine-grained access control, and lightweight terminal computing. Our scheme has three advantages: 1) High Efficiency. The proposed scheme can achieve fast privacy matching of access policies during user decryption. 2) Full Policy Hiding. The access policy bound by an owner on the ciphertext does not leak any information. Moreover, a user cannot know attributes they do not own. 3) High Flexibility. The decryption process does not require online from owners or KGC, and system attributes can be added at any time.

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