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International Conference on Computer Science & Education (ICCSE)

作者： Zhehao Zhou Shenbao Yu Jiewen Guan Bilian Chen Langcai Cao Department of Automation Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Xiamen China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

We investigate the problem of user preferences changing over time for a collaborative rating prediction recommendation system. The rating data of a time-aware collaborative recommendation can be represented as a 3rd order tensor, each of which denotes users, items and time. We mainly focus on establishing a user preference transfer model according to the time latent factor matrice, which is computed by tensor decomposition. Subsequently, a novel nonnegative tensor decomposition with temporal similarity (TS-NTD) is put forward to predict rating values. The proposed temporal similarity measure succeeds in describing various changes in user preferences. We evaluate the TS-NTD on two real world datasets, and experimental results show that our model has higher accuracy than state-of-the-art models.

关键词： Matrix decomposition Tensile stress Collaboration Optimization Computational modeling Predictive models Mathematical model

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Asynchronous Activity Detection for Cell-Free Massive MIMO: From Centralized to Distributed Algorithms

arXiv

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arXiv 2022年

作者： Li, Yang Lin, Qingfeng Liu, Ya-Feng Ai, Bo Wu, Yik-Chung Shenzhen Research Institute of Big Data Shenzhen518172 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China

Device activity detection in the emerging cell-free massive multiple-input multiple-output (MIMO) systems has been recognized as a crucial task in machine-type communications, in which multiple access points (APs) jointly identify the active devices from a large number of potential devices based on the received signals. Most of the existing works addressing this problem rely on the impractical assumption that different active devices transmit signals synchronously. However, in practice, synchronization cannot be guaranteed due to the low-cost oscillators, which brings additional discontinuous and nonconvex constraints to the detection problem. To address this challenge, this paper reveals an equivalent reformulation to the asynchronous activity detection problem, which facilitates the development of a centralized algorithm and a distributed algorithm that satisfy the highly nonconvex constraints in a gentle fashion as the iteration number increases, so that the sequence generated by the proposed algorithms can get around bad stationary points. To reduce the capacity requirements of the fronthauls, we further design a communication-efficient accelerated distributed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms outperform state-of-the-art approaches, and the proposed accelerated distributed algorithm achieves a close detection performance to that of the centralized algorithm but with a much smaller number of bits to be transmitted on the fronthaul links. Copyright © 2022, The Authors. All rights reserved.

关键词： Internet of things

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An intelligent Resource Scheduling Method with Edge Channel Deployment for BPM

An Intelligent Resource Scheduling Method with Edge Channel ...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Bowen Liu Wanchun Dou Xiaokang Zhou Xuyun Zhang Lianyong Qi Fei Dai Chaochao Chen State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Faculty of Data Science Shiga University Hikone Japan Department of Computing Macquarie University Sydney Australia College of Computer and Software China University of Petroleum (East China) Qingdao China College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China College of Computer Science and Technology Zhejiang University Hangzhou China

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users’ status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method.

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Meet Changes with Constancy: Learning Invariance in Multi-Source Translation 28

Meet Changes with Constancy: Learning Invariance in Multi-So...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Liu, Jianfeng Luo, Ling Ao, Xiang Song, Yan Xu, Haoran Ye, Jian Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences China Beijing Key Laboratory of Mobile Computing and Pervasive Device China Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences China The Chinese University of HongKong Shenzhen China Shenzhen Research Institute of Big Data China

ISBN: (纸本)9781952148279

Multi-source neural machine translation aims to translate from parallel sources of information (e.g. languages, images, etc.) to a single target language, which has shown better performance than most one-to-one systems. Despite the remarkable success of existing models, they usually neglect the fact that multiple source inputs may have inconsistencies. Such differences might bring noise to the task and limit the performance of existing multi-source NMT approaches due to their indiscriminate usage of input sources for target word predictions. In this paper, we attempt to leverage the potential complementary information among distinct sources and alleviate the occasional conflicts of them. To accomplish that, we propose a source invariance network to learn the invariant information of parallel sources. Such network can be easily integrated with multi-encoder based multi-source NMT methods (e.g. multi-encoder RNN and transformer) to enhance the translation results. Extensive experiments on two multi-source translation tasks demonstrate that the proposed approach not only achieves clear gains in translation quality but also captures implicit invariance between different sources. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Signal encoding

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Non-invasive estimation of pulmonary hypertension and clinical deterioration risk in pediatric congenital heart disease:Development and validation of predictive tools

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Chinese Medical Journal 2024年第11期137卷 1384-1386页

作者： Ting Wang Dansha Zhou Yuqin Chen Suhua Kuang Yue Xing Qijian Yi Zhengxia Pan Weibin Xu Jiao Rao Yunqi Liu Guoliang Lu Ziying Lin Xiang Li Yi Xie Yulong Wu Peng An Xiaoxiao Deng Jiayue He Jiayi Xie Chenxi Li Gang Geng Daiyin Tian Enmei Liu Jingsi Huang Zhou Fu Jian Wang Department of Respiratory Chongqing Higher Institution Engineering Research Center of Children’s Medical Big Data Intelligent ApplicationNational Clinical Research Center for Child Health and DisordersMinistry of Education Key Laboratory of Child Development and DisordersChildren’s Hospital of Chongqing Medical UniversityChongqing 400014China State Key Laboratory of Respiratory Diseases National Center for Respiratory MedicineGuangdong Key Laboratory of Vascular DiseasesNational Clinical Research Center for Respiratory DiseasesGuangzhou Institute of Respiratory Healththe First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou Medical UniversityGuangzhouGuangdong 510120China Department of Cardiac Surgery The First Affiliated Hospital of Guangzhou Medical UniversityGuangzhou Medical UniversityGuangzhouGuangdong 510120China Department of Cardiovascular Medicine Children’s Hospital of Chongqing Medical University National Clinical Research Center for Child Health and DisordersMinistry of Education Key Laboratory of Child Development and DisordersChildren’s Hospital of Chongqing Medical UniversityChongqing 400014China Department of Thoracic and Cardiac Surgery Children’s Hospital of Chongqing Medical University National Clinical Research Center for Child Health and DisordersMinistry of Education Key Laboratory of Child Development and DisordersChildren’s Hospital of Chongqing Medical UniversityChongqing 400014China Department of Cardiac Center of Guangdong Women and Children Hospital Guangzhou GuangzhouGuangdong 511400China Department of Guangzhou Laboratory Guangzhou International Bio IslandGuangzhouGuangdong 510005China

To the Editor:Owing to the heterogeneity of congenital heart disease-associated pulmonary hypertension(CHDPH)disease and the development of the pulmonary vascular system in pediatric patients,the management of CHD-PH in children is signicantly different from that in adults.[1]Early identication and intervention of CHD-PH are of great signicance for disease management in children with CHD,and non-invasive,accurate clinical tools are urgently needed to predict PH risk in children with ***,the applicability of the current risk-scoring system to pediatric CHD-PH remains ***,there are presently no user-friendly,non-invasive online tools available for predicting CHD-PH,and there is a scarcity of related studies in Asian *** study aimed to develop and validate the prediction tools for estimating the risk of pulmonary hypertension and clinical deterioration in children with congenital heart disease.

关键词： pediatric congenital hypertension

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Stepdown SLOPE for Controlled Feature Selection

arXiv

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arXiv 2023年

作者： Liang, Jingxuan Zhang, Xuelin Chen, Hong Li, Weifu Tang, Xin College of Science Huazhong Agricultural University Wuhan430070 China College of Informatics Huazhong Agricultural University Wuhan430070 China Ping An Property & Casualty Insurance Company Shenzhen China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Wuhan430070 China Key Laboratory of Smart Farming for Agricultural Animals Wuhan430070 China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan430070 China

Sorted L-One Penalized Estimation (SLOPE) has shown the nice theoretical property as well as empirical behavior recently on the false discovery rate (FDR) control of high-dimensional feature selection by adaptively imposing the non-increasing sequence of tuning parameters on the sorted 1 penalties. This paper goes beyond the previous concern limited to the FDR control by considering the stepdown-based SLOPE to control the probability of k or more false rejections (k-FWER) and the false discovery proportion (FDP). Two new SLOPEs, called k-SLOPE and F-SLOPE, are proposed to realize k-FWER and FDP control respectively, where the stepdown procedure is injected into the SLOPE scheme. For the proposed stepdown SLOPEs, we establish their theoretical guarantees on controlling k-FWER and FDP under the orthogonal design setting, and also provide an intuitive guideline for the choice of regularization parameter sequence in much general setting. Empirical evaluations on simulated data validate the effectiveness of our approaches on controlled feature selection and support our theoretical findings. Copyright © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Exploring A CAM - Based Approach for Weakly Supervised Fire Detection Task

Exploring A CAM - Based Approach for Weakly Supervised Fire ...

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IEEE International Conference on e-Business Engineering (ICEBE)

作者： Lvlong Lai Jian Chen Huichou Huang Qingyao Wu School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot Ministry of Education City University of Hong Kong Hong Kong China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665444194

Most existing works in fire detection literature use available detectors like Faster RCNN, SSD, YOLO, etc. to localize the fire in images. These approaches work well but require object-level annotation for training, which is created manually and is very expensive. In this paper, we explore the weakly supervised fire detection task (WSFD) in which only the image-level annotation is given. We propose an approach based on class activation map (CAM). The CAM-based approach firstly trains a deep neural network as the classifier for identifying fire and non-fire images. For a fire image in the inference stage, it uses the classifier to create a CAM and then further generates the bounding boxes according to the CAM. To evaluate the effectiveness of our approach, we collect and construct a benchmark dataset named WS-FireNet and conduct comprehensive experiments on it. The experiment results show that in a way the performance of our approach is satisfactory.

关键词： Training Deep learning Annotations IEEE Sections Conferences Neural networks Detectors

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Dual attention mechanism object tracking algorithm based on Fully-convolutional Siamese network

Dual attention mechanism object tracking algorithm based on ...

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International Conference on Networking and Network Applications (NaNA)

作者： Sugang Ma Zixian Zhang Lei Zhang Yanping Chen Xiaobao Yang Lei Pu Zhiqiang Hou School of Computer Science and Technology Xi’an University of Posts and Telecommunications Xi’an Shaanxi China School of Information Engineering Chang’an University Xi’an Shaanxi China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an Key Laboratory of Big Data and Intelligent Computing Xi’an University of Posts and Telecommunications Xi’an Shaanxi China School of Information and Navigation Air Force Engineering University Xi’an Shaanxi China

In an effort to the problem of insufficient tracking performance of the Fully-convolutional Siamese network (SiamFC) in complex scenarios, a dual attention mechanism object tracking algorithm based on the Fully-convolutional Siamese network is proposed to improve the generalization capability of the tracker by ameliorating the robustness of the template characteristics. Firstly, a global context attention module is appended after the backbone network of SiamFC to ameliorate the power of original feature extraction from two dimensions of spatial and channel. Then, a coordinate attention module is introduced to augment the capability of feature extraction in the channel dimension. Finally, the model of the proposed algorithm is trained on the Got-10k dataset. Five related algorithms are tested on the OTB2015 dataset, the results of experiments manifest that our algorithm outperforms the baseline trackers, the success and precision rate of the proposed algorithm are improved by 3.3% and 6.3%. The average tracking speed is 145FPS, which can demand the requirement of real-time tracking.

关键词： Target tracking Feature extraction Real-time systems Robustness Object tracking Task analysis

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Improving Incremental Learning: A Closer Look at the Softmax Function

SSRN

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SSRN 2024年

作者： Zhai, Zheng Zhang, Jiali Wang, Haiyu Wu, Mingxin Yang, Keshun Qiao, Xiaoyan Sun, Qiang Beijing Normal University No.18 Jinfeng Road Guangdong Zhuhai519087 China Shandong Technology and Business University Shandong Yantai China Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Shandong China Immersion Technology and Evaluation Shandong Engineering Research Center Shandong China School of Mathematics Sichuan University Chengdu China College of Liberal Arts and Sciences University of Illinois Urbana-Champaign IL United States Department of Statistical Sciences University of Toronto ON Canada Department of Computer Science University of Toronto ON Canada Department of Statistics and Data Science MBZUAI Abu Dhabi United Arab Emirates

This paper investigates the limitations of the widely adopted softmax cross-entropy loss in incremental learning problems. Specifically, we highlight how the shift-invariant property of this loss function can lead to multiple optimal solutions and imbalanced weights across different tasks, which exacerbates catastrophic forgetting. To address these issues, we propose two key modifications to the objective function: (1) regularizing or replacing the original joint distillation loss with a summation of task-specific distillation losses, and/or (2) utilizing a shift-sensitive prediction loss function, such as the binary cross-entropy loss. Numerical experiments validate the effectiveness of these proposals, demonstrating improved prediction accuracy and a significant reduction in forgetting. © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Neural mechanisms of top-down divided and selective spatial attention in visual and auditory perception

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Brain Science Advances 2023年第2期9卷 95-113页

作者： Zhongtian Guan Meng Lin Qiong Wu Jinglong Wu Kewei Chen Hongbin Han Dehua Chui Xu Zhang Chunlin Li School of Biomedical Engineering Capital Medical UniversityBeijing 100069China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Capital Medical UniversityBeijing 100069China Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical UniversityBeijing 100069China Peking University First Hospital Beijing 100034China Biomedical Engineering Laboratory Graduate School of Natural Science and TechnologyOkayama University3-1-1 Tsushima-nakaOkayamaJapan Key Laboratory of Biomimetic Robots and Systems Ministry of EducationBeijing Institute of TechnologyBeijing 100081China Computational Image Analysis Banner Alzheimer’s Institute and Banner Good Samaritan Medical CentrePET CentrePhoenixAZ 85006USA Radiology Peking University Third HospitalBeijing 100191China Neuroscience Research Institute/Peking University Third Hospital Beijing 100191China

Top-down attention mechanisms require the selection of specificobjects or locations;however,the brain mechanism involved when attention is allocated across different modalities is not well *** aim of this study was to use functional magnetic resonance imaging to define the neural mechanisms underlyingdivided and selective spatial attention.A concurrent audiovisual stimulus was used,and subjects were prompted to focus on a visual,auditory and audiovisual stimulus in a Posner *** results confirmed the better performance of selectiveattention compared to devided *** found differences in the activation level of the frontoparietal network,visual/auditorycortex,the putamen and the salience network under differentattention *** further used Granger causality(GC)toexplore effective connectivity differences between *** in GC connectivity between visual and auditory selective tasksreflected the visual dominance effect under spatial *** addition,our results supported the role of the putamen inredistributing attention and the functional separation of the *** summary,we explored the audiovisual top-down allocation of attention and observed the differences in neuralmechanisms under endogenous attention modes,which revealedthe differences in cross-modal expression in visual and auditory attention under attentional modulation.

关键词： top-down mechanism divided spatial attention selective spatial attention fMRI Granger causality

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