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32nd ACM International Conference on Multimedia, MM 2024

作者： Qia, Nwei Li, Kun Guo, Dan Hu, Bin Wang, Meng School of Computer Science and Information Engineering School of Artificial Intelligence Hefei University of Technology Hefei China CCAI Zhejiang University Hangzhou China Hefei Comprehensive National Science Center Hefei China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China

ISBN: (纸本)9798400706868

Remote photoplethysmography (rPPG) measurement aims to estimate physiological signals by analyzing subtle skin color changes induced by heartbeats in facial videos. Existing methods primarily rely on the fundamental video frame features or vanilla facial ROI (region of interest) features. Recognizing the varying light absorption and reactions of different facial regions over time, we adopt a new perspective to conduct a more fine-grained exploration of the key clues present in different facial regions within each frame and across temporal frames. Concretely, we propose a novel clustering-driven remote physiological measurement framework called Cluster-Phys, which employs a facial ROI prototypical clustering module to adaptively cluster the representative facial ROI features as facial prototypes and then update facial prototypes with highly semantic correlated base ROI features. In this way, our approach can mine facial clues from a more compact and informative prototype level rather than the conventional video/ROI level. Furthermore, we also propose a spatial-temporal prototype interaction module to learn facial prototype correlation from both spatial (across prototypes) and temporal (within prototype) perspectives. Extensive experiments are conducted on both intra-dataset and cross-dataset tests. The results show that our Cluster-Phys achieves significant performance improvement with less computation consumption. The source code will be available at https://***/VUT-HFUT/ClusterPhys. © 2024 ACM.

关键词： Photoplethysmography

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Research on Pedestrian Detection Based on Improved YOLOv3 7

Research on Pedestrian Detection Based on Improved YOLOv3

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2022 7th International Conference on Electronic technology and Information science, ICETIS 2022

作者： Wang, Hansong Liang, Quan Xiong, Neng Hu, Jinjing School of Computer Science and Mathematics Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Ming and Application Fuzhou China

ISBN: (纸本)9783800757794

The YOLOv3 algorithm is an anchor-based algorithm, which is difficult to cover all shapes of targets. It is often necessary to perform cluster analysis to determine the optimal anchors before training. These anchors have weak generalization performance and increase the complexity of detection. Cause the detection speed to slow down. Aiming at the different poses of pedestrians in pedestrian detection, the paper proposes an improved YOLOv3 algorithm for pedestrian detection. The anchor free method is used to cover all shapes of pedestrian poses, and the HSV data enhancement method is used to process the training images to improve the accuracy and speed of pedestrian detection. The experimental results show that compared with the original algorithm, in the INRIA pedestrian dataset and the Caltech pedestrian dataset, the improved algorithm in the article has increased accuracy by 9.23% and 5.01%. The speed has increased by 0.021 s and 0.025 s, respectively. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Anchors

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Short-term power load forecasting based on BP neural network optimized by genetic algorithm 7

Short-term power load forecasting based on BP neural network...

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2022 7th International Conference on Electronic technology and Information science, ICETIS 2022

作者： Xiong, Neng Liang, Quan Xiong, Neng Yu, Wenze Hong, Chuanbo Wang, Hansong School of Computer Science and Mathematics Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fuzhou China

ISBN: (纸本)9783800757794

Accurate short-term power load forecasting is of great significance to the safe operation of power systems. Aiming at the defects of traditional BP neural network that it is easy to fall into local optimization and low prediction accuracy, Proposed to optimize the BP neural network with genetic algorithm, To improve the initial weights and thresholds of the network, increase the convergence speed, And then establish the genetic algorithm to optimize the prediction model of the BP neural network. The model uses one input, output layer, double hidden layers, Analyze and forecast the power load data of a company. Experiment simulation through matlab platform, The results show that the BP neural network model optimized by genetic algorithm has higher prediction accuracy, better stability, and more practical value. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Genetic algorithms

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A Verifiable Privacy-Preserving Outsourced Prediction Scheme Based on Blockchain in Smart Healthcare

A Verifiable Privacy-Preserving Outsourced Prediction Scheme...

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2023 IEEE International Conference on E-Health Networking, Application and Services, Healthcom 2023

作者： Li, Ta Tian, Youliang Xiong, Jinbo Wang, Linjie College of Computer Science and Technology Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou350117 China School of Data Science Tongren University Tongren554300 China

ISBN: (纸本)9798350302301

The swift progression of the Internet of Things and the extensive integration of machine learning have spurred the growth of intelligent healthcare. Many intelligent healthcare devices, limited by their own computing and storage resources, require outsourcing data analysis tasks to cloud platforms for efficient and accurate results. Unfortunately, malicious cloud services lead to privacy breaches in outsourced data and untrustworthiness in learning models. To address these challenges, this paper proposes a verifiable privacy-preserving outsourced prediction scheme based on blockchain in smart healthcare (VPOL). Specifically, by incorporating blockchain technology into VPOL, we build a robust and scalable framework to prevent falsification of outsourced data and learning models in a decentralized and transparent manner. Then, we design a training committee approach to ensure the reliability of outsourced prediction and employ homomorphic encryption and commitment scheme to protect the privacy and integrity of the data. Finally, theoretical analysis proves the effectiveness and security of VPOL. Sufficient experiments demonstrate that VPOL achieves the approximate accuracy of the plaintext. © 2023 IEEE.

关键词： Blockchain

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HUNTER: Releasing Persistent Memory Write Performance with A Novel PM-DRAM Collaboration Architecture 23

HUNTER: Releasing Persistent Memory Write Performance with A...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Yanqi Pan Yifeng Zhang Wen Xia Xiangyu Zou Cai Deng School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China and Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies

ISBN: (纸本)9798350323481

We present HUNTER, a POSIX-compliant persistent memory (PM) file system that fully releases PM's write performance. Compared to state-of-the-art ones, HUNTER proposes a novel PM-DRAM collaboration architecture to significantly eliminate/reduce software overheads in the write path. Expensive in-PM metadata are updated asynchronously to hide their performance penalties. Furthermore, in-PM metadata/data are laid out separately for locality awareness, enabling collaboration with asynchronous architecture. HUNTER also adopts several lightweight in-DRAM allocators/indexes to manage PM *** results suggest that HUNTER achieves 2.0--3.4× write bandwidth compared to state-of-the-art PM file systems in write-intensive workloads and shows similar write bandwidth compared to bare PM.

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Functional Connectivity Patterns Learning for EEG-based Emotion Recognition

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IEEE Transactions on Cognitive and Developmental Systems 2024年第3期17卷 480-494页

作者： Shi, Chongxing Chen, C. L. Philip Li, Shuzhen Zhang, Tong South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Guangzhou510335 China Eng. Res. Ctr. of the Min. of Educ. on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

Neuroscience research reveals that different emotions are associated with different functional connectivity structures of brain regions. However, many existing EEG-based emotion recognition methods use these connectivity patterns broadly without distinguishing between specific emotions. Additionally, the non-stationarity of EEG signals often results in high variations across different periods, leading models to extract time-specific features instead of emotional features. This paper proposes a Functional Connectivity Patterns Learning network (FCPL) for EEG-based emotion recognition to address these challenges. FCPL includes a coefficient branch, a graph construction module, and a period domain adversarial module. These components capture individual characteristics and specific emotional connectivity patterns and reduce period-related variations, respectively. FCPL achieves state-of-the-art results: 42.04%/28.81% for seven-class subject-dependent/independent experiments on the MPED dataset, 97.45%/89.88% for subject-dependent/independent experiments on the SEED dataset, and 95.98%/96.19% for valence/arousal subject-dependent experiments and 67.90%/65.60% for valence/arousal subject-independent experiments on the DREAMER dataset. This work advances the exploration of functional connectivity structures in EEG signals from coarse-grained emotion-related patterns to fine-grained emotional distinctions, promoting neuroscience and EEG-based emotion recognition technologies. © 2016 IEEE.

关键词： Emotion Recognition

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DRPTM: A Decoupled Read-Efficient High-Scalable Persistent Transactional Memory 23

DRPTM: A Decoupled Read-Efficient High-Scalable Persistent T...

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Wenkai Liang Hao Hu Xiangyu Zou Wen Xia Yanqi Pan School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China and Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies

ISBN: (纸本)9798350323481

Persistent transactional memory (PTM) exploits transactions to provide an easy crash-consistent interface for persistent memory (PM). However, because of the substantial reader-side overhead brought on by the low bandwidth and long persistence latency of PM, present PTM research cannot scale effectively. This paper proposes a highly scalable PTM system, DRPTM, which allows nearly non-overhead reads without lowering the isolation level. DRPTM decouples persistence latency from concurrency control and traces the read-only copy maintained in logs as a lightweight read set. The evaluation shows that DRPTM significantly outperforms the state-of-the-art PTM systems for various workloads and achieves near-linear scalability.

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Multi-Scale Feature Extraction with Supervised Contrastive Learning for Vulnerability Detection 36

Multi-Scale Feature Extraction with Supervised Contrastive L...

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36th International Conference on Software Engineering and Knowledge Engineering, SEKE 2024

作者： Zhao, Yahui Lu, Lu Liang, Zhihong Suo, Siliang School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Pengcheng Laboratory Shenzhen China Electric Power Research Institute CSG Guangzhou China Guangdong Provincial Key Laboratory of Power System Network Security Guangzhou China

ISBN: (纸本)1891706594

The capacity of Deep Learning to automatically learn features from source code has facilitated its extensive utilization in detecting software vulnerabilities. However, existing pre-trained models regard code snippets as token sequences, neglecting the inherent structure of the code. The utilization of graph-based code representations is constrained by the limitations of Graph Neural networks, particularly the difficulty in capturing long-range dependencies, which renders it challenging to learn grammatical and semantic information from complex code graph representations. This paper proposes a novel multi-scale software vulnerability detection method based on supervised contrastive learning. The method integrates local features obtained from code paths with structural features extracted from the Code Property Graph by GraphTrans, thereby achieving a multi-scale feature representation of the source code. Additionally, a supervised contrastive loss function is employed during training in order to fully utilize label information and address the class imbalance problem. The experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods, achieving the highest accuracy, precision, and F1 on the real-world benchmark dataset CodeXGLUE for software vulnerability detection. © 2024 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Contrastive Learning

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Cost-Effective Data Caching Optimization with Distance-aware in Edge Computing 2

Cost-Effective Data Caching Optimization with Distance-aware...

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2022 2nd Conference on High Performance Computing and Communication Engineering, HPCCE 2022

作者： Wang, Futian Han, Yinchao Wu, Zhimin Zhang, Cheng Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China

ISBN: (纸本)9781510663374

In recent years, edge data caching has been concerned by more and more research scholars. Caching data, especially popular data, on edge servers can significantly reduce users’ latency, while also greatly reducing direct data transmission between users and remote clouds, and slowing down the congestion of the backbone network. Most of the existing research on edge data caching is mainly aim at minimizing users’ latency, saving devices’ energy, and improving the hit rate of data caching, etc. From the perspective of an app vendor, how to formulate an edge data caching solution that can bring him the most profit while complying with a given budget constraint and considering the experience requirements of different users is a very realistic and challenging problem. In this paper, we formulate the Profit-Maximizing Edge Data Caching(PMEDC) as a constrained optimization problem. For PMEDC in small scale scenarios, we propose an optimization model named PMEDC-IP that can derive the optimal caching strategy. For PMEDC in large scale scenarios, we propose an approximation algorithm named MPF(Maximum Profit First) that can find an approximate optimal caching scheme in a reasonable time. We conduct extensive experiments on a real-world dataset, and based on the final experimental results, PMEDC-IP and MPF proposed by us significantly outperform the other three representative methods. © 2023 SPIE.

关键词： Profitability

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Cross-Store Next-Basket Recommendation 24

Cross-Store Next-Basket Recommendation

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Ma, Liang-Chen Li, Ya Mai, Zi-Feng Liang, Fei-Yao Wang, Chang-Dong Chen, Min Guizani, Mohsen School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering South China University of Technology Guangzhou China Pazhou Laboratory Guangzhou China Machine Learning Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798331506681

Next-basket recommendation (NBR) infers a set of items that a user will interact with in the next basket. Existing methods often struggle with the data sparsity problem, particularly when the number of baskets is significantly large due to diverse user behaviors. Cross-domain recommendation (CDR) can effectively alleviate this problem in NBR by transferring knowledge across different domains. Nevertheless, these methods often rely on the similarities of overlapping users, which leads to the negative transfer problem and ignores the overlapping items that are general in real-world scenarios like chain stores. In this paper, we provide a clear symbolic definition of cross-store recommendation (CSR) and distinguish it from CDR. We also propose a novel CSNBR model for cross-store next-basket recommendation task. To fully model the transferable collaborative information between two stores, we learn the embeddings of users, baskets, and items by two intra-store bipartite graphs, and use an inter-store unified bipartite graph to transfer the previously learned knowledge. Furthermore, to alleviate the negative transfer problem, we propose to reconstruct the inter-store unified bipartite graph by utilizing user embeddings obtained from the transfer layer and the disentanglement layer. We also employ two sequence encoders to model the historical sequential information at basket-level and item-level. Extensive experiments conducted on real-world datasets demonstrate the effectiveness of the CSNBR model. © 2024 IEEE.

关键词： Graph embeddings

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