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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xingyun Li Lin Lu Xinyu Yi Hao Wang Yunshao Zheng Yanhong Yu Qingxiang Wang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 Shandong Mental Health Center Jinan China Shandong University of Traditional Chinese Medicine Jinan China

With the rise in societal pressures, depression and anxiety have increasingly become prominent mental health conditions impacting people’s lives. To enhance the efficacy of automatic detection for these disorders, we have developed an experimental framework called the Voluntary Facial Expression Mimicry(VFEM). This framework led to the creation of the VFEM dataset, which supports related research endeavors. Subsequently, we introduce the LI-FPN designed specifically for the automatic identification of depression and anxiety disorders. The LI-FPN comprises two core components: the Learning and Imitation Module(LIM) and the Spatio-temporal Feature Pyramid network(STFPN). Within the LIM, we leverage sequence features to facilitate comprehensive feature extraction through learning and imitation steps. The STFPN is designed to focus on outliers in multi-scale features for further screening. Compared with traditional attention methods, LI-FPN is more suitable for processing sequence data features and small sample datasets. Upon training using the VFEM dataset, the LI-FPN achieves impressive accuracies: 0.850 for depression detection, 0.835 for anxiety detection, and 0.786 for co-occurrence detection of depression and anxiety. Meanwhile, LI-FPN also achieves SOAT results on AVEC2014 dataset. The source code for LI-FPN is accessible at https://***/muzixingyun/LI-FPN

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Multi-Channel Hypergraph Convolution Group Recommendation with Member Information Enhancement

Multi-Channel Hypergraph Convolution Group Recommendation wi...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Tianhao Chen Qian Gao Jun Fan Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan Shandong China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Shandong China China Telecom Digital Intelligence Techonology Co Ltd Jinan Shandong China

Group recommendation involves comprehensively considering various aspects, including members and items, to predict the overall interests of a group and recommend suitable items through a recommendation system. With the rapid evolution of the Internet, online group activities have become increasingly prevalent, making group recommendation a highly discussed topic within the realm of recommendation systems. However, current research in group recommendation still confronts the following challenges. Firstly, a predominant portion of group recommendation research focuses solely on aggregating group-level information, neglecting the valuable contribution of higher-order member-level insights to group recommendation. Moreover, some aggregation strategies overly prioritize fairness, thereby disregarding common real-world patterns. Secondly, previous studies have primarily relied on aggregating individual member interests to establish group preferences, lacking a holistic consideration of group dynamics. To tackle these challenges, this study introduces an innovative approach by implementing multi-channel hypergraph convolution within the member perspective. This approach aims to effectively extract higher-order insights from members by utilizing a member information enhancement module. By establishing group interconnections based on similarity and employing an adaptive fusion network to amalgamate multiple viewpoints, a final representation is derived. Experimental results demonstrate that our proposed model surpasses baseline models by 3% to 4% in terms of hit rate and accuracy, validating the efficacy of our approach.

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Research on Image Caption Based on Multiple Word Embedding Representations 3

Research on Image Caption Based on Multiple Word Embedding R...

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3rd International Conference on Natural Language Processing, ICNLP 2021

作者： Lin, Zhen-Xian Qu, Jia-Xin Yang, Xiao-Bao Hou, Zhi-Qiang Ding, Chen Xi'An University of Posts and Telecommunications School of Science Xi'an China Xi'An University of Posts and Telecommunications School of Telecommunication and Information Engineering Xi'an China Northwestern Polytechnical University School of Computer Science and Technology Xi'an China Xi'An University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi'an China

ISBN: (纸本)9781665414111

Word embedding representation has been a research hot spot in the field of computer vision and natural language processing. The semantic quality of representation severely affects the effects of machine translation and image caption. The key factors affecting the quality of word embedding representation are capture of contextual semantics and solution of the problem of polysemy. In this paper, the principles of the current mainstream word embedding representation methods were explored, such as sparse representation (One-Hot), static representation (Word2Vec, GloVe), dynamic representation (ELMo, BERT) representation and based on position information (***-Position Embedding). The impact of those word embedding representations on image caption task was researched attentively. And those representations methods were compared experimentally using the MSCOCO data based on the same image caption model. The pros and cons of these methods were judged according to the accuracy and richness of the extracted label data. Experimental results show that the BERT model representation with dynamic bidirectional semantics has the best performance in image caption tasks, but it requires longer training time. The *** representation has the second best image semantic description effect, and its training speed is the fastest. © 2021 IEEE.

关键词： Embeddings

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Long Text Classification Based on BERT 5

Long Text Classification Based on BERT

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5th IEEE Information technology, networking, Electronic and Automation Control Conference, ITNEC 2021

作者： Weijie, DIng Yunyi, Li Jing, Zhang Xuchen, Shen Big-data and Network Security Research Institute Zhejiang Police College Hangzhou310053 China College of Information Engineering Zhejiang University of Technology Hangzhou310023 China Key Laboratory of Public Security Information Application Based on Big-data Architecture Ministry of Public Security Hangzhou310053 China Zhejiang Police College Department of Investigation Hangzhou310053 China Zhejiang Police College Department of Computer and Information Security Hangzhou310053 China Science and Technology Management Section Hangzhou311203 China

ISBN: (纸本)9781665415989

Existing text classification algorithms generally have limitations in terms of text length and yield poor classification results for long texts. To address this problem, we propose a BERT-based long text classification method. First, we slice the long text and use BERT to encode the sliced clauses to obtain the local semantic information. Second, we use BiLSTM to fuse the local semantic information and adopt the attention mechanism to increase the weight of important clauses in the long text, so as to obtain the global semantic information. Finally, the global semantic information is input to the softmax layer for classification. Experimental results show that the proposed method achieves higher accuracy than commonly used models. © 2021 IEEE.

关键词： Classification (of information)

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Efficient Multi-Authority Attribute-based Signcryption with Constant-Size Ciphertext

Efficient Multi-Authority Attribute-based Signcryption with ...

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IEEE Conference on Dependable and Secure Computing

作者： Yang Zhao Ankang Ruan Guohang Dan Jicheng Huang Yi Ding Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu Sichuan Institute of Electronic and Information Engineering of UESTC in Guangdong Guangdong China

ISBN: (数字)9781728175348

ISBN: (纸本)9781728175355

Recently, efficient fine-grained access mechanism has been studied as a main concern in cloud storage area for several years. Attribute-based signcryption (ABSC) which is logical combination of attribute-based encryption(ABE) and attribute-based signature(ABS), can provide confidentiality, authenticity for sensitive data and anonymous authentication. At the same time it is more efficient than previous “encrypt-then-sign” and “sign-then-encrypt” patterns. However, most of the existing ABSC schemes fail to serve for real scenario of multiple authorities and have heavy communication overhead and computing overhead. Hence, we construct a novel ABSC scheme realizing multi-authority access control and constant-size ciphertext that does not depend on the number of attributes or authorities. Furthermore, our scheme provides public verifiability of the ciphertext and privacy protection for the signcryptor. Specially, it is proven to be secure in the standard model, including ciphertext indistinguishability under adaptive chosen ciphertext attacks and existential unforgeability under adaptive chosen message attack.

关键词： Privacy Cloud computing Adaptation models Outsourcing Encryption Servers Standards

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DAMBLO: Improving Arrhythmia Classification with Plug-and-Play Dual Attention-Based Multiscale Feature Learning Block

SSRN

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SSRN 2023年

作者： Zhuang, Tianming Qin, Zhiguang You, Li Deng, Erqiang Ding, Yi Cao, Mingsheng Guo, Yingkun Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu610054 China Department of Molecular Genetics Erasmus University Medical Center Rotterdam Netherlands Department of Radiology Key Laboratory of Obstetric and Gynecologic and Pediatric Diseases and Birth Defects Ministry of Education West China Second University Hospital Sichuan University Sichuan Chengdu610072 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China

The electrocardiogram diagnosis played an important role in early arrhythmia prevention and cardiovascular disease detection. How to analysis and detect the electrocardiogram automatically becomes a challenging task in clinical practice. Convolutional neural network based approaches have been widely applied in the field of automatic arrhythmia detection and analysis. However, the existing methods fails to preserve the abundant and essential latent representatives from multiple dimensions. To address these problems, a dual spatial-channel aggregation module is proposed, which expects to concentrate on the prominent target information along spatial and channel dimension of electrocardiogram signals. Moreover, a multi-scale enhancement module with channel-wise gating mechanism is also proposed to enlarge the receptive resolution and further to improve the learning ability of long-range dependencies from the global information. Furthermore, these two proposed modules are integrated as a "plug-and-play" block that can be easily adopted as a drop-in replacement for any 3×3 convolution layers in convolutional neural networ based architectures. In order to evaluate the effectiveness and generalization of the proposed block, it is plugged in a series of convolutional neural network based frameworks and are evaluated on two benchmark datasets, MIT-BIH and PTB. The extensive experimental results show that the proposed "plug-and-play" block can significantly improve the arrhythmia classification performance. Moreover, compared to other state-of-the-art methods, the framework plugged with the proposed block can achieve a competitive classification performance, proving the feasibility and advancement of the proposed dual attention-based multiscale feature learning block. © 2023, The Authors. All rights reserved.

关键词： Electrocardiograms

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yan Zeng ChengChuang Huang YiJie Ni ChunBao Zhou JiLin Zhang Jue Wang MingYao Zhou MeiTing Xue YunQuan Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou China Data Security Governance Zhejiang Engineering Research Center Hangzhou China School of ITMO Joint Institute Hangzhou Dianzi University Hangzhou China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing China HuaWei State Key Laboratory of Computer Architecture Institute of Computing Technology of the Chinese Academy of Sciences Beijing China

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network’s dataflow graph and device cluster’s topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method.

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UJN-Land: A Large-Scale High-Resolution Parcel of Land of Multi-temporal dataset with CNN Based Semantic Segmentation Analysis 7th

UJN-Land: A Large-Scale High-Resolution Parcel of Land of Mu...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2021

作者： Shen, Yuan Wang, Yulin Yang, Shiyong Li, Yan Han, Shiyuan Liu, Zhen Xu, Tao School of Information Science and Engineering University of Jinan Jinan250022 China Highresolution Earth Observation System Shandong Center of Data and Application University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of Jinan Jinan250022 China Shandong Institutes of Industrial Technonlogy Jinan250102 China Land Spatial Data and Remote Sensing Technology Institute of Shandong Province Jinan250002 China

ISBN: (纸本)9789811672125

Semantic segmentation of land in remote sensing images plays an important role in urban management and rural planning, and can provide intelligent analysis for urban development. Convolutional neural network (CNN) based methods have achieved good performance in object detection in computer vision, which benefits from the popularity of large-scale datasets. However, most of segmentation of land dataset were constructed based on various satellite images and samples that lack the number and diversity of existing datasets. These problems have brought challenges to the improvement and application of CNN based land parcel segmentation methods. This paper presented a large-scale high-resolution land dataset based on CNN analysis. More than 200,000 images of 256*256 pixels were obtained from Gaofen satellite images with a spatial resolution of 0.8 m, in which four category samples were manually annotated. In the experiment, three CNN based semantic segmentation methods were used as experimental baseline methods. The experimental results showed that the availability of the UJN-Land dataset is beneficial to the planning and management of cultivated land. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Semantic Segmentation

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FedAL: Black-Box Federated Knowledge Distillation Enabled by Adversarial Learning

arXiv

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arXiv 2023年

作者： Han, Pengchao Shi, Xingyan Huang, Jianwei The School of Information Engineering Guangdong University of Technology Guangzhou510006 China The School of Data Science The Chinese University of Hong Kong Shenzhen China The School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of Hong Kong Guangdong Shenzhen518172 China

Knowledge distillation (KD) can enable collaborative learning among distributed clients that have different model architectures and do not share their local data and model parameters with others. Each client updates its local model using the average model output/feature of all client models as the target, known as federated KD. However, existing federated KD methods often do not perform well when clients’ local models are trained with heterogeneous local datasets. In this paper, we propose Federated knowledge distillation enabled by Adversarial Learning (FedAL) to address the data heterogeneity among clients. First, to alleviate the local model output divergence across clients caused by data heterogeneity, the server acts as a discriminator to guide clients’ local model training to achieve consensus model outputs among clients through a min-max game between clients and the discriminator. Moreover, catastrophic forgetting may happen during the clients’ local training and global knowledge transfer due to clients’ heterogeneous local data. Towards this challenge, we design the less-forgetting regularization for both local training and global knowledge transfer to guarantee clients’ ability to transfer/learn knowledge to/from others. Experimental results show that FedAL and its variants achieve higher accuracy than other federated KD baselines. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Model-Based Reinforcement Learning for Quantized Federated Learning Performance Optimization

Model-Based Reinforcement Learning for Quantized Federated L...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Nuocheng Yang Sihua Wang Mingzhe Chen Christopher G. Brinton Changchuan Yin Walid Saad Shuguang Cui Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China State Key Laboratory Of Networking And Switching Technology Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Bradley Department of Electrical and Computer Engineering Virginia Tech Arlington VA USA Shenzhen Research Institute of Big Data (SRIBD) and the Future Network of Intelligence Institute (FNii) Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781665435413

This paper considers improving wireless communication and computation efficiency in federated learning (FL) via model quantization. In the proposed bitwidth FL scheme, edge devices train and transmit quantized versions of their local FL model parameters to a coordinating server, which, in turn, aggregates them into a quantized global model and synchronizes the devices. With the goal of jointly determining the set of participating devices in each training iteration and the bitwidths employed at the devices, we pose an optimization problem for minimizing the training loss of quantized FL under a device sampling budget and delay requirement. Our analytical results show that the improvement of FL training loss between two consecutive iterations depends on not only the device selection and quantization scheme, but also on several parameters inherent to the model being learned. As a result, we propose, a model-based reinforcement learning (RL) method to optimize action selection over iterations. Compared to model-free RL, the proposed approach leverages the derived mathematical characterization of the FL training process to discover an effective device selection and quantization scheme without imposing additional device communication overhead. Numerical evaluations show that the proposed FL framework can achieve the same classification performance while reducing the number of training iterations needed for convergence by 20% compared to model-free RL-based FL.

关键词： Training Performance evaluation Wireless communication Quantization (signal) Computational modeling Reinforcement learning Mathematical models

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