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23rd International Arab Conference on Information Technology, ACIT 2022

作者： Hassani, Belkis Akram, Khelili Mohamed Okba, Kazar Sihem, Slatnia Harous, Saad Athamena, Belkacem Houhamdi, Zina Smart Computer Science Laboratory Department of Computer Science Biskra Algeria University of Mohamed Khider Biskra Algeria College of Information Technology United Arab Emirate University Department of Information Systems and Security United Arab Emirates College of Computing and Informat Ics Department of Computer Science AL Ain United Arab Emirates University of Sharjah AL Ain United Arab Emirates College of Business Al Ain University Al Ain United Arab Emirates University of Al Ain Al Ain United Arab Emirates

ISBN: (纸本)9798350320244

With the development of ICT and its adoption in various domains, it gained remarkable intention in the healthcare sector which introduce the telemedicine term. The coronavirus pandemic has created several challenges for researchers to develop an accurate and fast detection system. In this paper, we present a new telemedicine application to predict Covid-19 using CNN and Fuzzy set techniques. The evaluation of the system indicates high performance with a 98% F1 score, 99% of recall, 98% for precision, and 97% of accuracy. © 2022 IEEE.

关键词： Telemedicine

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MR2CPPIS: Accurate prediction of protein–protein interaction sites based on multi-scale Res2Net with coordinate attention mechanism

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computers in Biology and Medicine 2024年 176卷 108543-108543页

作者： Gong, Yinyin Li, Rui Liu, Yan Wang, Jilong Cao, Buwen Fu, Xiangzheng Li, Renfa Chen, Danny Z. College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Hunan University Changsha410082 China Peng Cheng Laboratory Shenzhen518066 China College of Information and Electronic Engineering Hunan City University Yiyang413002 China Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States

Proteins play a vital role in various biological processes and achieve their functions through protein–protein interactions (PPIs). Thus, accurate identification of PPI sites is essential. Traditional biological methods for identifying PPIs are costly, labor-intensive, and time-consuming. The development of computational prediction methods for PPI sites offers promising alternatives. Most known deep learning (DL) methods employ layer-wise multi-scale CNNs to extract features from protein sequences. But, these methods usually neglect the spatial positions and hierarchical information embedded within protein sequences, which are actually crucial for PPI site prediction. In this paper, we propose MR2CPPIS, a novel sequence-based DL model that utilizes the multi-scale Res2Net with coordinate attention mechanism to exploit multi-scale features and enhance PPI site prediction capability. We leverage the multi-scale Res2Net to expand the receptive field for each network layer, thus capturing multi-scale information of protein sequences at a granular level. To further explore the local contextual features of each target residue, we employ a coordinate attention block to characterize the precise spatial position information, enabling the network to effectively extract long-range dependencies. We evaluate our MR2CPPIS on three public benchmark datasets (Dset 72, Dset 186, and PDBset 164), achieving state-of-the-art performance. The source codes are available at https://***/YyinGong/MR2CPPIS. © 2024 Elsevier Ltd

关键词： Forecasting

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A ZERO-SHOT METHOD FOR 3D MEDICAL IMAGE SEGMENTATION

A ZERO-SHOT METHOD FOR 3D MEDICAL IMAGE SEGMENTATION

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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Ma, Shiqiang Li, Xuejian Tang, Jijun Guo, Fei School of Computer Science and Technology College of Intelligence and Computing Tianjin University Tianjin 300350 China Department of Computer Science and Engineering University of South Carolina ColumbiaSC29208 United States Key Laboratory of Systems Bioengineering Ministry of Education Tianjin University Tianjin 300072 China

ISBN: (纸本)9781665438643

Accurate automatic medical image segmentation technology plays an important role for the diagnosis and treatment of brain tumor. However, existing methods based on outstanding 2.5D and 3D segmentation strategies are time-consumption and hardware-consumption while ensuring high accuracy. In order to reduce the high demand for automatic segmentation of tumor images and avoid the noise interference in a single input image, we propose an end-to-end zero-shot CNN segmentation method. Our method only utilizes two adjacent images, instead of the target image, as the input data of deep neural network to predict the brain tumor area in the target image. Avoiding noise interference in the target image, this method makes full use of the spatial context feature between adjacent slices in order to obtain accurate zero-shot segmentation results. We compare with the state-of-the-art segmentation frameworks on the same benchmark and notice that our method has strong competitiveness. © 2021 IEEE

关键词： Diagnosis

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AN EFFICIENT SPECTRAL TRUST-REGION DEFLATION METHOD FOR MULTIPLE SOLUTIONS

arXiv

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arXiv 2023年

作者： Li, Lin Wang, Li-Lian Li, Huiyuan School of Mathematics and Physics Hunan Key Laboratory for Mathematical Modeling and Scientific Computing University of South China Hengyang421001 China Division of Mathematical Sciences School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore State Key Laboratory of Computer Science Laboratory of Parallel Computing Institute of Software Chinese Academy of Sciences Beijing100190 China

It is quite common that a nonlinear partial differential equation (PDE) admits multiple distinct solutions and each solution may carry a unique physical meaning. One typical approach for finding multiple solutions is to use the Newton method with different initial guesses that ideally fall into the basins of attraction confining the solutions. In this paper, we propose a fast and accurate numerical method for multiple solutions comprised of three ingredients: (i) a well-designed spectral-Galerkin discretization of the underlying PDE leading to a nonlinear algebraic system (NLAS) with multiple solutions;(ii) an effective deflation technique to eliminate a known (founded) solution from the other unknown solutions leading to deflated NLAS;and (iii) a viable nonlinear least-squares and trust-region (LSTR) method for solving the NLAS and the deflated NLAS to find the multiple solutions sequentially one by one. We demonstrate through ample examples of differential equations and comparison with relevant existing approaches that the spectral LSTR-Deflation method has the merits: (i) it is quite flexible in choosing initial values, even starting from the same initial guess for finding all multiple solutions;(ii) it guarantees high-order accuracy;and (iii) it is quite fast to locate multiple distinct solutions and explore new solutions which are not reported in literature. Copyright © 2023, The Authors. All rights reserved.

关键词： Newton-Raphson method

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Generalized Few-shot Semantic Segmentation for LiDAR Point Clouds

Generalized Few-shot Semantic Segmentation for LiDAR Point C...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Pengze Wu Jilin Mei Xijun Zhao Yu Hu Research Center for Intelligent Computing Systems Chinese Academy of Sciences Institute of Computing Technology Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China China North Artificial Intelligence & Innovation Research Institute Collective Intelligence & Collaboration Laboratory (CIC)

Semantic segmentation of LiDAR point clouds can provide assistance for precise perception in autonomous driving, but traditional segmentation methods face challenges such as unbalanced class distribution and insufficient labeling. Generalized few-shot learning has been researched on image data, but these methods are difficult to apply directly to LiDAR point clouds. To tackle these challenges, we propose a generalized few-shot semantic segmentation method based on LiDAR point cloud data, enabling us to predict base and novel classes simultaneously. To improve the performance with limited novel class samples, we integrate semantic vectors and leverage the intrinsic relationship between base and novel class vectors to facilitate learning. We conduct comprehensive comparisons with other methods on the SemanticKITTI and constantly surpass them with higher mIoU, demonstrating the effectiveness of our method.

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BAT: A Versatile Bipartite Attention-based Approach for Comprehensive Truth Inference in Mobile Crowdsourcing

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IEEE Transactions on Mobile computing 2025年

作者： Liu, Jiacheng Tang, Feilong Liu, Hao Chen, Long Yu, Yichuan Zhu, Yanmin Yu, Jiadi Hou, Xiaofeng Heng, Pheng-Ann The Chinese University of Hong Kong Department of Computer Science and Engineering 999077 Hong Kong Shanghai Jiao Tong University School of Computer Science Shanghai200240 China East China Normal University School of Data Science and Engineering China Simon Fraser University School of Computing Science Canada Huawei Technologies Co. Ltd China Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Shenzhen518055 China

The proliferation of smart mobile devices has catalyzed the growth of Mobile CrowdSourcing (MCS) as a distributed problem-solving paradigm. MCS platforms heavily rely on advanced truth inference techniques to extract reliable information from diverse and potentially noisy crowd-contributed data. Existing truth inference models often made simplified assumptions about workers or tasks, employing complex Bayesian models or stringent data aggregation methods. These approaches tend to be task-specific, primarily limited to categorical labeling, making adaptations to other mobile computing scenarios labor-intensive. To address these limitations, we introduce the Bipartite Attention-driven Truth (BAT), a versatile approach tailored for mobile computing environments. BAT utilizes an Attributed Bipartite Graph (ABG) to holistically model the MCS process, with workers and tasks as nodes connected by edges representing answer-specific attributes. The approach employs a bipartite graph neural network with an innovative attention mechanism to assess the importance of different answers. BAT extends beyond categorical tasks to support numerical ones by incorporating novel feature representations and model extensions. Theoretical analyses clarify the link between answer similarity and worker expertise. Extensive experiments using diverse real-world datasets demonstrate BAT's superior performance compared to state-of-the-art categorical and numerical truth inference models, highlighting its effectiveness in mobile computing scenarios. © 2002-2012 IEEE.

关键词： Graph neural networks

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Heuristic Algorithms for Placing Geomagnetically Induced Current Blocking Devices

arXiv

引用

arXiv 2023年

作者： Ryu, Minseok Attia, Ahmed Barnes, Arthur Bent, Russell Leyffer, Sven Mate, Adam School of Computing and Augmented Intelligence Arizona State University TempeAZ United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Information Systems and Modeling Group United States Applied Mathematics and Plasma Physics Group Los Alamos National Laboratory Los AlamosNM United States

We propose a new heuristic approach for solving the challenge of determining optimal placements for geomagnetically induced current blocking devices on electrical grids. Traditionally, these determinations are approached by formulating the problem as mixed-integer nonlinear programming models and solving them using optimization solvers based on the spatial branch-and-bound algorithm. However, computing an optimal solution using the solvers often demands substantial computational time due to their inability to leverage the inherent problem structure. Therefore, in this work we propose a new heuristic approach based on a three-block alternating direction method of multipliers algorithm, and we compare it with an existing stochastic learning algorithm. Both heuristics exploit the structure of the problem of interest. We test these heuristic approaches through extensive numerical experiments conducted on the EPRI-21 and UIUC-150 test systems. The outcomes showcase the superior performance of our methodologies in terms of both solution quality and computational speed when compared with conventional solvers. Copyright © 2023, The Authors. All rights reserved.

关键词： Heuristic algorithms

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Current-Constrained Finite-Time Control Scheme for Speed Regulation of PMSM systems With Unmatched Disturbances

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IEEE Transactions on Automation science and Engineering 2025年 22卷 16877-16887页

作者： Huiming Wang Zhize Zhang Yingjie Luo Junxiao Wang Jiankun Sun Yunda Yan Xianlun Tang Chongqing Key Laboratory of Complex Systems and Autonomous Control Chongqing University of Posts and Telecommunications Chongqing China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China College of Information Engineering Zhejiang University of Technology Hangzhou China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Wuhan China Department of Computer Science University College London London U.K.

The article investigates the speed regulation of permanent magnet synchronous motor (PMSM) systems. Existing control methods of the non-cascade structure suffer from the drawbacks of unsatisfactory anti-disturbance performance and slow convergence rate when the system is affected by disturbances, especially unmatched disturbances. Meanwhile, the requirements of current constraint and fast dynamics cannot be effectively balanced in the single-loop structure of speed and current using traditional control methods such as the PID controller. Because large transient currents induced by fast dynamics may damage the hardware of the system. Therefore, a current-constrained finite-time control approach is proposed. Specifically, a robust finite-time control scheme is developed with the assistance of the improved finite-time observer technique. The proposed method is capable of actively suppressing both matched and unmatched disturbances in non-cascade control systems. Simultaneously, an effective penalty mechanism is established to incorporate a specific gain function into the designed controller. This approach restricts the q-axis current to a predefined safe range without solving an optimization problem. Finally, comparative experiment results indicate that the newly proposed finite-time control method outperforms the baseline control methods in terms of disturbance rejection, convergence rate, and current constraint. Note to Practitioners—This paper addresses practical challenges in controlling permanent magnet synchronous motor (PMSM) systems, such as ensuring robust disturbance rejection while limiting transient currents to protect the hardware. Traditional methods often fail to balance fast dynamics with current constraints, leading to inefficiencies and potential damage. To tackle these issues, the proposed finite-time control approach introduces a practical solution that achieves robust disturbance rejection while ensuring that transient currents remain within safe o

关键词： Convergence Observers Control systems Automation Transient analysis Asymptotic stability Stability analysis Regulation Permanent magnet motors Optimization

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Learning-rate-free Momentum SGD with Reshuffling Converges in Nonsmooth Nonconvex Optimization

arXiv

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arXiv 2024年

作者： Hu, Xiaoyin Xiao, Nachuan Liu, Xin Toh, Kim-Chuan School of Computer and Computing Science Hangzhou City University Hangzhou310015 China Institute of Operational Research and Analytics National University of Singapore Singapore State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences China Department of Mathematics Institute of Operations Research and Analytics National University of Singapore Singapore119076 Singapore

In this paper, we propose a generalized framework for developing learning-rate-free momentum stochastic gradient descent (SGD) methods in the minimization of nonsmooth nonconvex functions, especially in training nonsmooth neural networks. Our framework adaptively generates learning rates based on the historical data of stochastic subgradients and iterates. Under mild conditions, we prove that our proposed framework enjoys global convergence to the stationary points of the objective function in the sense of the conservative field, hence providing convergence guarantees for training nonsmooth neural networks. Based on our proposed framework, we propose a novel learning-rate-free momentum SGD method (LFM). Preliminary numerical experiments reveal that LFM performs comparably to the state-of-the-art learning-rate-free methods (which have not been shown theoretically to be convergence) across well-known neural network training benchmarks. Copyright © 2024, The Authors. All rights reserved.

关键词： Momentum

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Pseudo complementary measurement for traditional single-pixel cameras

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Chinese Physics B 2020年第11期29卷 315-322页

作者： Qian Dong Xu-Ri Yao Xin Liu Bing Liu Guang-Jie Zhai State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Key Laboratory of Electronics and Information Technology for Space Systems National Space Science CenterChinese Academy of SciencesBeijing 100190China State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

A traditional single-pixel camera needs a large number of measurements to reconstruct the object with compressive sensing *** with the 1/0 matrices in classical measurement,the 1/-1 matrices in the complementary measurement has better property for reconstruction computation and returns better reconstruction ***,each row of the 1/-1 matrices needs two measurements with the traditional single-pixel camera which results into double measurements compared with the 1/0 *** this paper,we consider the pseudo complementary measurement which only takes the same amount of measurements with the row number of some properly designed 1/0 matrix to compute the total luminous flux of the objective and derives the measurement data of the corresponding 1/-1 matrix in a mathematical *** numerical simulation and experimental result show that the pseudo complementary measurement is an efficient tool for the traditional single-pixel camera imaging under low measurement rate,which can combine the advantages of the classical and complementary measurements and significantly improve the peak signal-to-noise ratio.

关键词： computational imaging single-pixel camera

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