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Source-Free Domain Adaptation for Millimeter Wave Radar Based Human Activity Recognition

Source-Free Domain Adaptation for Millimeter Wave Radar Base...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jin Liu Dejiao Zeng Ludi Li Hanhe Lin Xu Tian Hunan Provincial Key Laboratory on Bioinformatics School of Computer Science and Engineering Central South University Changsha China School of Software Xinjiang University Urumqi China School of Science and Engineering University of Dundee United Kingdom

Human activity recognition based on millimeter-wave radar is dedicated to monitor people’s daily activities and detect specific dangerous actions. Although existing methods achieve some improvement, they rarely consider the challenges of domain difference, such as ages and environments. To address this challenge, we propose a source-free domain adaptation method for millimeter wave radar based human activity recognition, which achieves knowledge transfer from the source domain to the target domain. Firstly, we propose balanced clustering to obtain cluster centers of source domain as the prior-knowledge through the pre-trained model. Then, in order to perform domain adaptation, the model is fine-tuned by the integration of domain adaptation and self-supervision of the target domain. Experiment results on several transfer tasks show that our proposed method is effective in human activity recognition and outperforms some other advanced transfer learning methods.

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SCOTCluster: Deep Clustering with Optimal Transport for Large-scale Single-cell RNA-seq Data

SCOTCluster: Deep Clustering with Optimal Transport for Larg...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Faning Long Xiaojun Ding Xiaoqing Peng Jianxin Wang Xiaoshu Zhu School of Computer Science and Engineering Yulin Normal University Yulin China Center for Medical Genetics School of Life Sciences Central South University Changsha China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (纸本)9781665429825

Single-cell RNA sequencing (scRNA-seq) presents cell heterogeneity in a high resolution to explore cell development. The high dimension and the high noise in scRNAseq data bring some computational challenges. By introducing optimal transmission regularization, we proposed a novel deep clustering method, called SCOTCluster, which accurately learned low-dimensional representations. In SCOTCluster, a joint training strategy was designed by integrating AutoEncoder and soft k-means. Notably, to improve simultaneously the accuracy and robustness, the optimal transmission was introduced in the objective function of soft k-means, and entropy regularization and Sinkhorn iterative algorithm were performed to constraint the cluster size. To test the performance, we compared SCOTCluster with five state-of the-art methods on 16 real large-scale scRNA-seq datasets. The experimental results showed that SCOTCluster improved the training stability and clustering performance.

关键词： Training Sequential analysis RNA Conferences Clustering methods Linear programming Robustness

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UniSleepPos: Sleep Posture Identification System Utilizing Millimeter-wave Radar

UniSleepPos: Sleep Posture Identification System Utilizing M...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Kun Liu Min Li Chu He Xu Tian Junbin Mao Min Zeng Jin Liu Xinjiang Engineering Research Center of Big Data and Intelligent Software School of Software Xinjiang University Urumqi China Hunan Provincial Key Lab on Bioinformatics School of Computer Science and Engineering Central South University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Sleep posture identification is crucial for accurately assessing sleep quality and diagnosing related diseases. In the realm of non-intrusive sleep monitoring, non-contact technologies are becoming increasingly mainstream. Millimeter-wave radar is frequently utilized in sleep posture identification due to its high resolution, strong penetration, and excellent sensitivity. However, traditional radar-based methods for sleep posture identification often struggle with reliability when dealing with diverse individuals and complex sleep environments. To address these challenges, we propose UniSleepPos, which designs a novel dual-view fusion mechanism to integrate depression and elevation angle signals obtained from radar, thus accurately capturing the posture information of the monitored subject in three-dimensional space. Furthermore, we combine sleep posture identification with individual characteristics, utilizing existing individual labels as prior knowledge to assist in sleep posture identification. The integration of prior knowledge provides a valuable information source for the model, helping to enhance its understanding of the data and improve its performance. We collected sleep posture data from eight volunteers using millimeter-wave radar devices under various environmental conditions. Leave-one-subject-out experiments were conducted to validate the effectiveness of UniSleepPos. The results indicated that UniSleepPos significantly outperforms existing methods, demonstrating its potential for practical applications.

关键词： Performance evaluation Sensitivity Spaceborne radar Millimeter wave radar Depression Data models Reliability Monitoring Signal resolution Diseases

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Predicting helix boundaries of α-helix transmembrane protein with feedback conditional random fields 11th

Predicting helix boundaries of α-helix transmembrane protei...

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11th International Conference on Intelligent Computing, ICIC 2015

作者： Wang, Kun Wu, Hongjie Lu, Weizhong Fu, Baochuan Lü, Qiang Huang, Xu School of Electronic and Information Engineering Suzhou University of Science and Technology Suzhou215009 China School of Computer Science and Technology Soochow University Suzhou215006 China Jiangsu Provincial Key Lab for Information Processing Technologies Suzhou215006 China School of Information Engineering Huzhou University Huzhou313000 China

ISBN: (纸本)9783319221793

Transmembrane proteins play an important role in cellular energy production, signal transmission, metabolism. Existing machine learning methods are difficult to model the global correlation of the membrane protein sequence, and they also can not improve the quality of the model from sophisticated sequence features. To address these problems, in this paper we proposed a novel method by a feedback conditional random fields (FCRF) to predict helix boundaries of α-helix transmembrane protein. A feedback mechanism was introduced into multi-level conditional random fields. The results of lower level model were used to calculate new feedback features to enhance the ability of basic conditional random fields. One wide-used dataset DB1 was used to validate the performance of the method. The method achieved 95% on helix location accuracy. Compared with the other predictors, FCRF ranks first on the accuracy of helix location. © Springer International Publishing Switzerland 2015.

关键词： Learning systems

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Privacy preserving for network coding in smart grid 15th

Privacy preserving for network coding in smart grid

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15th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2015

作者： He, Shiming Zeng, Weini Xie, Kun Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer and Communication Engineering Hunan Province Engineering Research Center of Electric Transportation and Smart Distributed Network Changsha University of Science and Technology Changsha410114 China The 716th Research Institute China Shipbuilding Industry Corporation Lianyungang222061 China College of Computer Science and Electronics Engineering Hunan University Changsha410082 China Department of Electrical and Computer Engineering State University of New York at Stony Brook New York United States

ISBN: (纸本)9783319271361

In smart grid, privacy implications to individuals and their family is an important issue, due to the fine-grained usage data collection. Wireless communications are considered by many utility companies to obtain information. Network coding is exploited in smart grids, to enhance network performance in terms of throughput, delay, robustness, and energy consumption. However, Network Coding introduces new challenge for privacy preserving due to the encoding of packet in forwarder nodes. We propose a distributed privacy preserving scheme for network coding in smart grid, which considers the converged flows character of smart grid and exploits a homomorphic encryption function to decrease the complex in forwarder node. The message content of packet is encrypted and the tag of packet is encrypted by homomorphic encryption function. Then the forwarder node linear random codes the encrypted message contents and directly processes the tags cryptotext based on the homomorphism feature. It offers message content confidentiality privacy preserving feature, which can efficiently thwart traffic analysis. Extensive security analysis and performance evaluations demonstrate the validity and efficiency of the proposed scheme. © Springer International Publishing Switzerland 2015.

关键词： Smart power grids

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Interactive Web API Recommendation via Exploring Mashup-API Interactions and Functional Description

Interactive Web API Recommendation via Exploring Mashup-API ...

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International Conference on computer Supported Cooperative Work in Design

作者： Wanjun Chen Qiqi Chen Jiexun Shen Guosheng Kang Jianxun Liu Buqing Cao Lihong Zhang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the advance of service computing technology, the number of Web APIs has risen dramatically over the Internet. Users tend to use Web APIs to achieve their business needs. However, it is difficult for users to find and select the desirable ones due to the plethora of Web APIs. To address this problem, some collaborative filtering-based Web API recommendation methods have been proposed even though their performance is still far from satisfaction, since they only rely on Mashup-API interactions and feature interactions are not considered in the recommendation model. To further improve the recommendation performance, this paper proposes an interactive Web API recommendation method via exploring both Mashup-API interactions and functional description documents of Mashups and Web APIs. Specifically, LightGCN is employed to derive the node representations for the Mashup-API interaction graph, and BERT model is used for the text representations of functional description documents. Furthermore, the two presentations of both the Mashup and Web API are concatenated as the input of ANFM (Attentional Neural Factorization Machine) model, in which low and high-order feature interactions are fully modeled and the weights of feature interactions are trained by attention mechanism. Solid experiments are conducted over a real-world dataset and the experimental results indicate that the proposed method outperforms the baseline methods.

关键词： Attention mechanisms Federated learning Mashups Computational modeling Semantics Neural networks Service computing

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Multi-view Hypergraph-based Self-supervised Learning Model for Web API Recommendation

Multi-view Hypergraph-based Self-supervised Learning Model f...

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International Symposium on Parallel and Distributed Processing with Applications, ISPA

作者： Jiexun Shen Dongfan Li Wanjun Chen Yong Xiao Guosheng Kang Jianxun Liu Zhenlian Peng School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the rapid development of service computing technology, how to recommend the desirable Web APIs to developers from the large number of APIs is a challenge. The traditional methods based on collaborative filtering are limited by data sparsity. With the support of multi-dimensional relational feature modeling, graph neural network-based methods are proposed to mitigate data sparsity, but their convolution nature may amplify the noise effect. Therefore, how to simultaneously reduce the impact of sparsity and noisy data has been an open question in the field of API recommendation. To address the problem, this paper proposes a self-supervised learning method based on multi-view hypergraph for Web API recommendation. First, the Mashup-API interaction graph is transformed into a hypergraph, and the hyperedges are used as intermediate hubs to transfer messages between nodes, maintaining the global collaboration effect between Mashup and API nodes. Then, a multi-view strategy is adopted to generate embeddings of Mashups and APIs through information fusion, by which recommendation probability is derived by dot product between the embeddings of Mashups and Web APIs. To train the model parameters effectively, a self-supervised learning method is used to reduce the effect of noisy data to improve the embeddings. Extensive experiments are conducted on a real-world dataset, and the experimental results show that the proposed model outperforms the baselines.

关键词： Knowledge engineering Mashups Noise Neural networks Collaboration Service computing Self-supervised learning Feature extraction Data models Noise measurement

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Increment adaptive correlation filter for visual tracking 11

Increment adaptive correlation filter for visual tracking

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11th International Symposium on Multispectral Image Processing and Pattern Recognition: Pattern Recognition and computer Vision, MIPPR 2019

作者： Chen, Gang Fang, Zhiwen Yue, Zhou Liu, Bo Xiao, Yang Li, Yanan Department of Information Hunan University of Humanities Science and Technology Loudi Hunan China Department of Energy and Electromechanical Engineering Hunan University of Humanities Science and Technology Loudi Hunan China School of Biomedical Engineering Southern Medical University Guangzhou Guangdong China National Key Lab of Sci. and Tech. on Multispectral Information Processing School of Automation Huazhong Univ. of Sci. and Tech. Wuhan Hubei China School of Computer Science and Engineering Wuhan Institute of Technology Wuhan Hubei China

ISBN: (纸本)9781510636378

Currently, the correlation filter is widely used in visual tracking because of its effectiveness and efficiency. To adapt the representation to changing target appearances, a linear interpolation is used to update tracking models according to a manually designed learning rate. However, The limitation of manually tricks make methods only apply to some special scenes because the threshold parameters are sensitive to different response maps in complex scenes. In this paper, to overcome this problem, an adaptive increment correlation filter based tracker is proposed. Different from traditional linear interpolation depending on a manual learning rate, the increment is learned by linear regression based on the history tracking model and the current training samples. Experimentally, we show that our algorithm can outperform state-of-the-art key point-based trackers. © SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Adaptive filtering

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JMF-SS: Joint Matrix Factorization for Web API Recommendation with Mashup-Mashup Similarities and API-API Similarities

JMF-SS: Joint Matrix Factorization for Web API Recommendatio...

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International Conference on computer Supported Cooperative Work in Design

作者： Lihong Zhang Jing He Yamei Nie Xiaokang Zhou Qiqi Chen Guosheng Kang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the development of service computing technology, the number of publicly available Web APIs online has increased dramatically. Developers tend to use Web APIs to implement their software development requirements. However, due to the large number of Web APIs, how to select the appropriate Web API from the huge resource library for Mashup development has become a challenge. To solve this problem, some researchers have proposed Web API recommendation methods based on collaborative filtering or matrix factorization. However, the Web API recommendation performance is still limited due to the reliance on Mashup- API interaction and the ignorance of Mashup and Web API description documents. Moreover, the Mashup-API interaction matrix is extremely sparse, resulting in low accuracy in matrix factorization and collaborative filtering. To further improve the recommendation performance, we propose a novel joint matrix factorization method for the Mashup-API interaction matrix by incorporating the Mashup-Mashup similarity matrix and the API-API similarity matrix. A set of experiments are conducted on a real-world dataset, and the experimental results show that the proposed method outperforms the baselines.

关键词： Measurement Accuracy Federated learning Mashups Collaborative filtering Service computing Libraries

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Diversified QoS-Centric Service Recommendation for Uncertain QoS Preferences

Diversified QoS-Centric Service Recommendation for Uncertain...

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IEEE International Conference on Services Computing (SCC)

作者： Guosheng Kang Jianxun Liu Buqing Cao Yong Xiao Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9781728187891

ISBN: (纸本)9781728187907

With the wide adoption of SOA (Service Oriented Architecture), a massive amount of Web services emerge on the Internet. Finding the desired services becomes a challenge. Thus, service recommendation has become of paramount research to relieve users' difficulty in service selection. Existing Web service recommendation approaches employ utility functions or skyline techniques with the assumption that users can provide numerical QoS (Quality of Service) preferences. However, in practice, it is hard for users, even for professional users, to provide specific QoS preferences in their service requests. Thus, how to effectively recommend services to users when their QoS preferences are uncertain is a challenging issue. To solve the problem, this paper focuses on the characteristics of user requirements, and proposes a diversified QoS-centric service recommendation approach for uncertain QoS preferences, by which a list of services with desired QoS fulfillment and diversity are produced. Extensive experiments are conducted on a real-world dataset to demonstrate the effectiveness and efficiency of our approach.

关键词： Uncertainty Conferences Service computing Quality of service Euclidean distance Service-oriented architecture

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