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3rd Vision-Based Remote Physiological Signal Sensing Challenge and Workshop, RePSS 2024

作者： Hu, Yi Liu, Xuenan Rao, Xinlong Li, Bojing School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei230601 China School of Software Hefei University of Technology Anhui Hefei230601 China

The Video Photoplethysmography (VPPG) technique, while increasingly popular due to its convenience and cost-effectiveness, faces challenges in handling continuous head movements and vigorous motion interferences encountered in real-life scenarios. In this paper, we present a Transformer-based approach aimed at enhancing the robustness of heart rate estimation from facial videos. Leveraging the selfattention mechanism inherent in Transformers, our method adeptly captures both temporal dependencies and spatial information, thereby elevating the accuracy and resilience of heart rate estimation, even in challenging conditions. Through extensive experiments conducted on real-world face video datasets, we illustrate the effectiveness of our approach. Our results demonstrate significant improvements over existing methods in mitigating motion artifacts and enhancing the reliability of non-contact heart rate estimation in practical environments. © 2024 Copyright for this paper by its authors.

关键词： heart rate detection non-contact type Transformer Video Photoplethysmography

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Disentangled Hypergraph Collaborative Filtering for Social Recommendation

Disentangled Hypergraph Collaborative Filtering for Social R...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Liu, Xiao Meng, Shunmei Li, Qianmu Xu, Xiaolong Qi, Lianyong Dou, Wanchun Zhang, Jing Zhang, Xuyun Nanjing University of Science and Technology China Nanjing University State Key Lab. for Novel Software Technology China Nanjing University of Information Science and Technology School of Computer and Software China College of Computer Science and Technology China Southeast University School of Cyber Science and Engineering China Macquarie University School of Computing Australia

ISBN: (纸本)9798350304855

In the current era of information overload, service recommendations have emerged as a valuable tool for enhancing the user experience. Among them, social recommendation models have shown promising results by incorporating social relationships to improve representation learning. However, most of these models lack fine-grained modeling of social user behavior, leading to a unified representation of users and a loss of expressiveness in user representations. To address this issue, we propose DisenHGCF, a new social recommendation approach based on disentangled hypergraph collaborative filtering, to disentangle the representations of users and items at the granularity of social users' intents. This approach aims to provide a more nuanced understanding of the user, leading to more accurate and personalized recommendations. To be specific, DisenHGCF leverages hypergraphs to represent the complex relationships among users, friends, and items. By using the hypergraph disentangling module based on attention, it is able to disentangle the user's intents and generate users representations in cooperating their intents for recommendation tasks. Additionally, a contrastive learning task based on intent-weight perturbation is designed to enhance representational learning. The experimental results obtained from the BeiBei and Beidian datasets demonstrate the superiority of our proposed approach in comparison to previous baseline methods, as evidenced by higher Recall and NDCG scores. © 2023 IEEE.

关键词： Collaborative filtering

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CARE-EMRs: Efficient and Privacy-Preserving Data Sharing Framework for Electronic Medical Records

CARE-EMRs: Efficient and Privacy-Preserving Data Sharing Fra...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Tao, Yu Zhou, Lu Zhu, Yi Li, Yuhang Ge, Chunpeng Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China Jiangsu University College of Computer Science and Communication Engineering Zhenjiang China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China and Software School Jinan China

ISBN: (纸本)9798350386226

Cross-institutional Electronic Medical Records (EMRs) sharing significantly improves healthcare quality and reduces costs. Current EMRs are often stored on cloud servers and protected by Attribute-based Encryption (ABE) for access control. Cross-institutional sharing allows doctors to request EMRs access from target Healthcare Institutions (HIs) through attribute-based collaboration, enabling flexible and secure access. However, existing collaboration in practical EMRs sharing scenarios suffers from inefficiency, hindering timely access and treatment. This paper proposes an efficient and privacy-preserving data sharing framework for EMRs, called CARE-EMRs. CARE-EMRs enables efficient EMRs data access for doctors across different HIs through an outsourcing mechanism. Furthermore, we designed an improved cryptographic primitive PPD-CP-ABE to serve as the security foundation for CARE-EMRs framework, ensuring privacy of the outsourced process. We prove the security of CARE-EMRs. Performance analysis indicates that it significantly reduces the computation and communication overhead. © 2024 IEEE.

关键词： Electronic health record

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DUAL ATTENTION-BASED MULTI-SCALE FEATURE FUSION APPROACH FOR DYNAMIC MUSIC EMOTION RECOGNITION 24

DUAL ATTENTION-BASED MULTI-SCALE FEATURE FUSION APPROACH FOR...

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24th International Society for Music Information Retrieval Conference, ISMIR 2023

作者： Zhang, Liyue Yang, Xinyu Zhang, Yichi Luo, Jing The School of Software Xi'An Jiaotong University China The School of Computer Science and Technology Xi'An Jiaotong University China

ISBN: (纸本)9781732729933

Music Emotion Recognition (MER) refers to automatically extracting emotional information from music and predicting its perceived emotions, and it has social and psychological applications. This paper proposes a Dual Attentionbased Multi-scale Feature Fusion (DAMFF) method and a newly developed dataset named MER1101 for Dynamic Music Emotion Recognition (DMER). Specifically, multi-scale features are first extracted from the log Mel-spectrogram by multiple parallel convolutional blocks. Then, a Dual Attention Feature Fusion (DAFF) module is utilized to achieve multi-scale context fusion and capture emotion-critical features in both spatial and channel dimensions. Finally, a BiLSTM-based sequence learning model is employed for dynamic music emotion prediction. To enrich existing music emotion datasets, we developed a high-quality dataset, MER1101, which has a balanced emotional distribution, covering over 10 genres, at least four languages, and more than a thousand song snippets. We demonstrate the effectiveness of our proposed DAMFF approach on both the developed MER1101 dataset, as well as on the established DEAM2015 dataset. Compared with other models, our model achieves a higher Consistency Correlation Coefficient (CCC), and has strong predictive power in arousal with comparable results in valence. © L. Zhang, X. Yang, Y. Zhang, J. Luo.

关键词： Emotion Recognition

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Multi-Scale Fusion and Decomposition Network for Single Image Deraining

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IEEE Transactions on Image Processing 2024年 33卷 191-204页

作者： Wang, Qiong Jiang, Kui Wang, Zheng Ren, Wenqi Zhang, Jianhui Lin, Chia-Wen Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Sun Yat-sen University School of Cyber Science and Technology Guangzhou510275 China Hangzhou Dianzi University College of Computer Science and Technology Hangzhou310018 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu30013 Taiwan

Convolutional neural networks (CNNs) and self-attention (SA) have demonstrated remarkable success in low-level vision tasks, such as image super-resolution, deraining, and dehazing. The former excels in acquiring local connections with translation equivariance, while the latter is better at capturing long-range dependencies. However, both CNNs and Transformers suffer from individual limitations, such as limited receptive field and weak diversity representation of CNNs during low efficiency and weak local relation learning of SA. To this end, we propose a multi-scale fusion and decomposition network (MFDNet) for rain perturbation removal, which unifies the merits of these two architectures while maintaining both effectiveness and efficiency. To achieve the decomposition and association of rain and rain-free features, we introduce an asymmetrical scheme designed as a dual-path mutual representation network that enables iterative refinement. Additionally, we incorporate high-efficiency convolutions throughout the network and use resolution rescaling to balance computational complexity with performance. Comprehensive evaluations show that the proposed approach outperforms most of the latest SOTA deraining methods and is versatile and robust in various image restoration tasks, including underwater image enhancement, image dehazing, and low-light image enhancement. The source codes and pretrained models are available at https://***/qwangg/MFDNet. © 1992-2012 IEEE.

关键词： Image reconstruction

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Collaborative learning-based inter-dependent task dispatching and co-location in an integrated edge computing system

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Digital Communications and Networks 2024年第6期10卷 1837-1850页

作者： Uchechukwu Awada Jiankang Zhang Sheng Chen Shuangzhi Li Shouyi Yang School of Software Henan Institute of Science and TechnologyXinxiang 453003China School of Electrical and Information Engineering Zhengzhou UniversityZhengzhou 450001China Department of Computing and Informatics Bournemouth UniversityPoole BH125BBUK School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Faculty of Information Science and Engineering Ocean University of ChinaQingdao 266100China

Recently,several edge deployment types,such as on-premise edge clusters,Unmanned Aerial Vehicles(UAV)-attached edge devices,telecommunication base stations installed with edge clusters,etc.,are being deployed to enable faster response time for latency-sensitive *** fundamental problem is where and how to offload and schedule multi-dependent tasks so as to minimize their collective execution time and to achieve high resource *** approaches randomly dispatch tasks naively to available edge nodes without considering the resource demands of tasks,inter-dependencies of tasks and edge resource *** approaches can result in the longer waiting time for tasks due to insufficient resource availability or dependency support,as well as provider ***,we present Edge Colla,which is based on the integration of edge resources running across multi-edge *** Colla leverages learning techniques to intelligently dispatch multidependent tasks,and a variant bin-packing optimization method to co-locate these tasks firmly on available nodes to optimally utilize *** experiments on real-world datasets from Alibaba on task dependencies show that our approach can achieve optimal performance than the baseline schemes.

关键词： Edge computing Collaborative learning Resource utilization Execution time Edge federation Gang scheduling

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An Efficient Local Search Solver for Mixed Integer Programming 30

An Efficient Local Search Solver for Mixed Integer Programmi...

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30th International Conference on Principles and Practice of Constraint Programming, CP 2024

作者： Lin, Peng Zou, Mengchuan Cai, Shaowei State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9783959773362

Mixed integer programming (MIP) is a fundamental model in operations research. Local search is a powerful method for solving hard problems, but the development of local search solvers for MIP still needs to be explored. This work develops an efficient local search solver for solving MIP, called Local-MIP. We propose two new operators for MIP to adaptively modify variables for optimizing the objective function and satisfying constraints, respectively. Furthermore, we design a new weighting scheme to dynamically balance the priority between the objective function and each constraint, and propose a two-level scoring function structure to hierarchically guide the search for high-quality feasible solutions. Experiments are conducted on seven public benchmarks to compare Local-MIP with state-of-the-art MIP solvers, which demonstrate that Local-MIP significantly outperforms CPLEX, HiGHS, SCIP and Feasibility Jump, and is competitive with the most powerful commercial solver Gurobi. Moreover, Local-MIP establishes 4 new records for MIPLIB open instances. © Peng Lin, Mengchuan Zou, and Shaowei Cai.

关键词： Mixed-integer linear programming

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FlyTracker: Motion Tracking and Obstacle Detection for Drones Using Event Cameras 42

FlyTracker: Motion Tracking and Obstacle Detection for Drone...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Wu, Yue Xu, Jingao Li, Danyang Xie, Yadong Cao, Hao Li, Fan Yang, Zheng Tsinghua University School of Software Beijing China Beijing Institute of Technology School of Computer Science Beijing China

ISBN: (纸本)9798350334142

Location awareness in environments is one of the key parts for drones' applications and have been explored through various visual sensors. However, standard cameras easily suffer from motion blur under high moving speeds and low-quality image under poor illumination, which brings challenges for drones to perform motion tracking. Recently, a kind of bio-inspired sensors called event cameras emerge, offering advantages like high temporal resolution, high dynamic range and low latency, which motivate us to explore their potential to perform motion tracking in limited scenarios. In this paper, we propose FlyTracker, aiming at developing visual sensing ability for drones of both individual and circumambient location-relevant contextual, by using a monocular event camera. In FlyTracker, background-subtraction-based method is proposed to distinguish moving objects from background and fusion-based photometric features are carefully designed to obtain motion information. Through multilevel fusion of events and images, which are heterogeneous visual data, FlyTracker can effectively and reliably track the 6-DoF pose of the drone as well as monitor relative positions of moving obstacles. We evaluate performance of FlyTracker in different environments and the results show that FlyTracker is more accurate than the state-of-the-art baselines. © 2023 IEEE.

关键词： Drones

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Exploring Large Language Models Text Style Transfer Capabilities 27

Exploring Large Language Models Text Style Transfer Capabili...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Li, Weijie Gu, Zhentao Fan, Xiaochao Deng, Wenjun Yang, Yong Zhao, Xinyuan Diao, Yufeng Yang, Liang College of Computer Science and Technology Xinjiang Normal University China School of Software Xinjiang University China College of Computer Science and Technology Inner Mongolia Minzu University China School of Computer Science and Technology Dalian University of Technology China Key Laboratory of Social Computing and Cognitive Intelligence Ministry of Education Dalian China

ISBN: (纸本)9781643685489

The emergence of Large Language Models (LLMs) provides a new solution to text generation tasks that involve high complexity, such as text style transfer (TST) tasks. However, previous studies have not fully explored the TST capabilities of different LLMs, and have faced issues with a lack of uniform standards in the human evaluation stage. This makes the results of human evaluation difficult to reproduce and less credible. To address this, this paper designs a prompt template to guide the cutting-edge LLMs to perform effective text style transfer and carries out an in-depth comparative analysis of various small-scale language models. In the stage of human evaluation, this paper eschews the conventional rating system, opting instead for a comparative human assessment methodology, which we refer to as duel-ranking. This method determines the relative ranking of models through mutual comparison, serving as an alternative to direct scoring. Detailed evaluation instructions are provided herein, to enhance the reproducibility of this method and ensure consistency throughout the evaluation process. This manual evaluation process reveals that GPT-3.5 and GPT-4 exhibit excellent performance in the TST tasks. © 2024 The Authors.

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Detection of Edge-blurred Small Targets of Steel Surface Defects Based on SAM-YOLO 2

Detection of Edge-blurred Small Targets of Steel Surface Def...

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2nd IEEE International Conference on Electrical, Automation and computer Engineering, ICEACE 2024

作者： Wu, Yanxu Liu, Kai Zhou, Xianghao Zhang, Xiumei University of Science and Technology Liaoning School of Electronics and Information Engineering Anshan China South-Central Minzu University School of Computer Science Wuhan China Beihang University School of Automation Science and Electrical Engineering Beijing China University of Science and Technology Liaoning School of Computer and Software Engineering Anshan China

ISBN: (纸本)9798350368208

To address the issue of challenging detection tasks for tiny and medium-sized objects because of backdrop confusion and inadequate feature representation of steel surface flaws, this paper proposes an efficient feature-enhanced, feature-fused SAM-YOLO detector based on the YOLOv8 model. It adds a new feature fusion module MSFF based on the original YOLOv8 model and replaces the bottleneck module in the C2f module with a feature enhancement module SACM. These two modules improve the enhancement and detection of defect edge features, local area sensing, and the network capability of multi-scale feature fusion, and the effect of SAM-YOLO is verified through several experiments on the NEU-DET steel surface defect dataset. The mAP50 of SAM-YOLO reaches 77.9%. mAP50:95 reaches 49.1%, exceeding the original YOLOv8 baseline model by 6.9% and surpassing several other benchmark models. To confirm the legitimacy of each SAM-YOLO model module, ablation tests were conducted on the model in the interim. © 2024 IEEE.

关键词： Target tracking

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