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Energy, Power and Electrical Engineering (EPEE), International Conference on

作者： Yiqi Cheng Yanru Zhong College of Computer and Information Security Guilin University of Electronic Technology Guilin Guangxi China Guangxi Key Laboratory of Image And Graphic Intelligent Processing Guilin University of Electronic Technology Guilin Guangxi China

ISBN: (数字)9798331518066

ISBN: (纸本)9798331518073

Non-intrusive load monitoring (NILM) estimates the energy usage of individual devices by analyzing the total power meter data from a household. To address the issue of decreased monitoring accuracy with an increasing number of electricity meters, this paper proposes a solution for single appliance load monitoring (Tra-CNN). The solution uses the UK-DALE dataset, which contains high and low frequencies electricity consumption data of multiple households in the UK and the electricity consumption data of each household appliance, and uses the active power of the appliance as the input of the algorithm. The first part of the model consists of a convolutional layer, a normalization layer, and a dropout layer, with the primary task of capturing local dependencies in the data. The second part consists of a Transformer, whose self-attention mechanism associates features at different positions in the sequence to achieve modeling of long-term dependencies. The third part introduces a convolutional neural network, which uses a sliding window for convolution operations to extract local features from different positions and further extract features from sequence data. The final decoder consists of a transposed convolutional layer and a normalization layer, designed to revert the learned features to their original dimensions. Through experimental verification, the effectiveness and superiority of Tra-CNN are shown, which provides important ideas and methods for the further development of NILM technology.

关键词： Meters Home appliances Load monitoring Electricity Computational modeling Transformers Feature extraction Convolutional neural networks Data mining Load modeling

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Self-Training with Contrastive Learning for Adversarial Domain Adaptation

Self-Training with Contrastive Learning for Adversarial Doma...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shanshan Wang Minbin Hu Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China School of Computer Science and Technology Anhui University Hefei China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Unsupervised domain adaptation (UDA) aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. Traditional methods often focus on minimizing the distribution gap between feature spaces of the two domains to achieve domain-invariant representations. However, this approach may not fully leverage the inherent class-specific information and could adversely affect decision boundaries in the target domain. In this paper, we introduce an adversarial contrastive self-training framework. The model integrates both intra-class and inter-class domain differences for better alignment between the source and target domains. Initially, we augment target domain samples using Fourier transformations, obtaining reliable samples and pseudo-labels through self-training. We then employ intra-domain contrastive learning on the reliable samples and pseudo-labels, alongside labeled source domain samples, to achieve intra-domain compactness. Finally, we utilize adversarial learning to achieve global alignment. This comprehensive approach ensures improved adaptation performance while also addressing the limitations of previous methods.

关键词： Adaptation models Neural networks Contrastive learning Adversarial machine learning Reliability

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Corrigendum to "Hyperledger sawtooth based supplychain traceability system for counterfeit drugs" [Comput. Ind. Eng. 190 (2024) 110021] (computers & Industrial Engineering (2024) 190, (S0360835224001426), (10.1016/***.2024.110021))

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computers and Industrial Engineering 2025年 204卷

作者： Nawaz, Anum Wang, Liguan Irfan, Muhammad Westerlund, Tomi Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Turku Intelligent and Embedded Robotic Systems Lab Faculty of Technology University of Turku Finland

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Overview of Task Offloading of Wireless Sensor Network in Edge Computing Environment

Overview of Task Offloading of Wireless Sensor Network in Ed...

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IEEE International Conference on Electronic information and Communication technology (ICEICT)

作者： XiQiang Hou YiFan Hu Fuqiang Wang School of information and control engineering Qingdao University of Technology Qingdao China Shandong Computer Science Center and Shandong Provincial Key Laboratory of Computer Networks Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Computer Science Center and Shandong Provincial Key Laboratory of Computer Networks Qilu University of Technology (Shandong Academy of Sciences) China

This review article introduces the concepts, server architecture and application scenarios of Mobile Edge Computing (MEC) and Wireless Sensor Network (WSN). By differentiating between rechargeable and non-rechargeable systems, it presents the research status of task offloading in WSN in an edge computing environment. Various strategies are analyzed and compared in terms of their characteristics and application domains. The existing methods can reduce offloading latency and energy consumption, but still face some issues. Based on this, the research hotspots of edge computing task offloading for WSN are summarized, and its research and development trends are pointed out.

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Pseudo-3D CNN with inter-slice attention for glioma grading 3

Pseudo-3D CNN with inter-slice attention for glioma grading

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3rd International Conference on computer Science and Communication technology, ICCSCT 2022

作者： Du, Shanshan Cao, Zhuoqun Zhao, Rui-Wei Zhang, Xiaobo Feng, Rui School of Information Science and Technology Fudan University Shanghai China School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Children's Hospital Fudan University Shanghai China

ISBN: (纸本)9781510661240

Gliomas comprise around 80 percent of all malignant brain tumors which can be further classified into the low-grade glioma and high-grade glioma categories. Compared to low-grade gliomas, high-grade gliomas show more malignant behaviour since they usually grow rapidly and frequently destroy healthy brain tissue. Therefore, it is important to determine the malignancy of gliomas for initial treatment plan. Most existing methods are based on radiomics or transfer learning which extract features from single-slice without considering that 3D features between adjacent slices can provide stronger discriminative power. In this work, we propose to incorporate the attention mechanism and pseudo-3d module into a deep convolutional network architecture, which can reduce model size and produce fine features for glioma grading. Our work focuses on the inter-slice relationship and propose an attention unit, named "Inter-Slice Attention Module", which adaptively refines intermediate feature maps by modelling dependencies between adjacent slices. We evaluate our method on an open dataset of gliomas, achieving mean accuracy of 89.47%. © 2022 SPIE.

关键词： Grading

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NeRSP: Neural 3D Reconstruction for Reflective Objects with Sparse Polarized Images

NeRSP: Neural 3D Reconstruction for Reflective Objects with ...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yufei Han Heng Guo Koki Fukai Hiroaki Santo Boxin Shi Fumio Okura Zhanyu Ma Yunpeng Jia Beijing University of Posts and Telecommunications Graduate School of Information Science and Technology Osaka University National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We present NeRSP, a Neural 3D reconstruction technique for Reflective surfaces with Sparse Polarized images. Reflective surface reconstruction is extremely challenging as specular reflections are view-dependent and thus violate the multiview consistency for multiview stereo. On the other hand, sparse image inputs, as a practical capture setting, commonly cause incomplete or distorted results due to the lack of correspondence matching. This paper jointly han-dles the challenges from sparse inputs and reflective surfaces by leveraging polarized images. We derive photomet-ric and geometric cues from the polarimetric image formation model and multiview azimuth consistency, which jointly optimize the surface geometry modeled via implicit neural representation. Based on the experiments on our synthetic and real datasets, we achieve the state-of-the-art surface reconstruction results with only 6 views as input.

关键词： Reflectivity Geometry Surface reconstruction computer vision Three-dimensional displays Shape Focusing

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PoseConv3D-TAFI:Skeleton Action Recognition Using Frame Interrelations in Conv3D

PoseConv3D-TAFI:Skeleton Action Recognition Using Frame Inte...

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International Conference on computer Supported Cooperative Work in Design

作者： Zhixin Li Xiaoming Wu Chunlin Zhao Yu Song Xiangzhi Liu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

In skeleton-based action recognition, accurately recognizing human actions is challenging due to complex movements, keypoints noise and action similarity. This paper introduces the PoseConv3D-Tafimodel, which combines PoseConv3D with the new TSCA, IFA modules and an integrated learning strategy. The TSCA module improves the temporal modeling of joints and the recognition of complex actions. The IFA module exploits inter-frame differences to distinguish similar actions better. The integrated learning strategy improves the robustness and generalization of the model. Experiments show that PoseConv3D-TAFI improves the top-1 accuracy by 1.4% and 2.02% on the UCF101 and HMDB51 datasets, respectively, and outperforms existing methods in handling complex and similar actions. The model shows strong potential for practical applications of skeleton-based action recognition.

关键词： Human computer interaction Accuracy Attention mechanisms Federated learning Surveillance Noise Pose estimation Skeleton Robustness

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MAML MOT: Multiple Object Tracking based on Meta-Learning

arXiv

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arXiv 2024年

作者： Chen, Jiayi Deng, Chunhua School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China

With the advancement of video analysis technology, the multi-object tracking (MOT) problem in complex scenes involving pedestrians is gaining increasing importance. This challenge primarily involves two key tasks: pedestrian detection and re-identification. While significant progress has been achieved in pedestrian detection tasks in recent years, enhancing the effectiveness of re-identification tasks remains a persistent challenge. This difficulty arises from the large total number of pedestrian samples in multi-object tracking datasets and the scarcity of individual instance samples. Motivated by recent rapid advancements in meta-learning techniques, we introduce MAML MOT, a meta-learning-based training approach for multi-object tracking. This approach leverages the rapid learning capability of meta-learning to tackle the issue of sample scarcity in pedestrian re-identification tasks, aiming to improve the model’s generalization performance and robustness. Experimental results demonstrate that the proposed method achieves high accuracy on mainstream datasets in the MOT Challenge. This offers new perspectives and solutions for research in the field of pedestrian multi-object tracking. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Research and Implementation of Medical information Protection and Sharing Based on Blockchain 7th

Research and Implementation of Medical Information Protectio...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Fang, Wei Jia, Xuelei Jia, Wei School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China Information Center of the Second People’s Hospital of Huai’an Huai’an223002 China

ISBN: (纸本)9783030786113

Medical information data is showing massive growth. How to make good use of these medical data and realize data sharing and protection is a hot topic now. Based on the research of traditional medical information system and information security technology, this paper proposes a strategy of combining IPFS, Blockchain and Asymmetric Encryption to realize the protection and sharing of medical information. Since the data on the Blockchain and IPFS are immutable, the operation is traceable, and the encryption operation of Asymmetric Encryption will greatly protect the security of medical information and avoid malicious tampering and malicious denial of medical information problem. For the sharing of medical information, this paper proposes the use of ECC algorithm based on Asymmetric Encryption to realize the sharing of medical records. Through the experiment and test of protection and sharing strategy, we found that the transformation of the traditional medical information system by using Blockchain technology can well realize the security problem of medical information in the process of sharing, and ensure that the information after sharing is safe, credible and not tampered. © 2021, Springer Nature Switzerland AG.

关键词： Blockchain

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A English Pronunciation Error Detection and Quality Evaluation Model Based on English Text

A English Pronunciation Error Detection and Quality Evaluati...

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computer Engineering and Intelligent Communications (ISCEIC), International Symposium on

作者： Qingkai Guo Guimin Huang Jianxing Lin Ya Zhou School of Computer Science and Information Security Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

In English learning, speaking practice is crucial, but traditional classroom teaching can hardly meet the needs of most learners. In this paper, we investigate and improve the two essential techniques of pronunciation error detection and pronunciation quality evaluation in the computer Assisted Language Learning (CALL) system. In terms of pronunciation error detection, We extracted phoneme pairs’ discriminative acoustic and phonological features. We used Support Vector Machine (SVM) for pronunciation error detection, and the accuracy was improved to 81.26%. For pronunciation quality evaluation, we added two new dimensions of pronunciation fluency and intonation, innovatively designed the word duration ratio, and introduced the Support Vector Regression (SVR) algorithm to achieve the effective integration of different dimensional features. The experiments show that the improved pronunciation quality evaluation model has higher performance and practical value.

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