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IEEE International Conference on Systems, Man and Cybernetics

作者： Yue Zhou Zhigang Liu Xiaojiang Yu Yajuan Wu Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Computer School of China West Normal University Nanchong Sichuan China

A fast non-negative latent factor (FNLF) model adopts a single latent factor-dependent, non-negative, multiplicative and momentum-incorporated update (SLF-NM 2 U) algorithm, which can ensure fast convergence on a high-dimensional and sparse (HiDS) matrix according to empirical studies in prior researches. However, it is crucial to investigate the theoretical proof regarding the reason why incorporation of a generalized momentum method into an SLF-NM 2 U algorithm can ensure the fast convergence of an FNLF model, which has not been addressed in previous work. Therefore, this paper aims to unveil how a generalized momentum method improves the convergence rate of an FNLF model in the discrete time case by combining physical analysis. The FNLF model is superior to the NLF model in terms of the convergence rate and the prediction accuracy of missing data. This conclusion is obtained by empirically research on the HiDS matrix in industrial applications, which also provides empirical basis for theoretical proof.

关键词： Convergence Analytical models Sparse matrices data models Computational modeling Acceleration Friction

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Cross-modal data fusion and deep learning techniques enabled non-destructive fish vitality recognition during cryogenic waterless live transport

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Biosystems Engineering 2025年 257卷

作者： Yanfei Zhu Wenguan Zhang Yongjun Zhang Xiaoshuan Zhang Mengjie Zhang College of Engineering China Agricultural University Beijing 100083 China School of Information Engineering Shandong Youth University of Political Science Jinan 250103 China New Technology Research and Development Center of Intelligent Information Controlling in Universities of Shandong Jinan 250103 China Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory in Universities of Shandong Jinan 250103 China Sanya Research Institute of China Agricultural University Sanya 572000 China College of Animal Science and Technology China Agricultural University Beijing 100193 China

Cryogenic waterless live transport (CWLT) is widely regarded as a green, efficient and low-carbon transportation strategy, which can ensure a certain degree of fish survival rates, enhance water resource utilisation and reduce transportation costs. This study focuses on the pearl gentian grouper, integrating multi-modal sensors, deep learning and cross-modal data fusion techniques for non-destructive vitality recognition during the live transport process. Changes in fish surface and physiological characteristics were captured based on multi-modal sensors. A cross-modal vitality recognition network (CmVRNet) was developed based on cross-modal data fusion and multi-input deep learning techniques. The enhanced whale optimisation algorithm based on multi-strategy fusion (EWOAm) was developed to optimise hyperparameters of the model (EWOAm-CmVRNet). The results showed that: (1) Cluster analysis classified physiological status of the grouper into four categories: high vitality (HV), medium vitality (MV), weak vitality (WV) and near death (ND), with significant differences between physiological data of fish with different vitality levels but the same size (P < 0.05); (2) EWOAm demonstrated significant performance, successfully addressing shortcomings of the standard WOA and outperforming other comparison algorithms, achieving a balance between global exploration and local search; (3) EWOAm-CmVRNet showed excellent vitality recognition performance. For large groupers, the model achieved the average precision of 93.74 %, average recall of 93.89 %, average F1 score of 93.81 %, average specificity of 97.88 % and average accuracy of 93.69 %; (4) The EWOAm-CmVRNet model, which is constructed based on cross-modal data, outperformed other single-modal vitality recognition models. This study aims to provide technical support to enhance market competitiveness of live aquatic products.

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A New Free-Standing Aqueous Zinc-Ion Capacitor Based on MnO2–CNTs Cathode and MXene Anode

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Nano-Micro Letters 2019年第4期11卷 227-238页

作者： Siliang Wang Qiang Wang Wei Zeng Min Wang Limin Ruan Yanan Ma Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityNo.3 Feixi RoadHefei 230039Anhui ProvincePeople’s Republic of China National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information EngineeringAnhui UniversityNo.111 Jiulong RoadHefei 230601Anhui ProvincePeople’s Republic of China School of Sciences Hubei University of Automotive TechnologyNo.167 Checheng West RoadShiyan 442002Hubei ProvincePeople’s Republic of China

Restricted by their energy storage mechanism,current energy storage devices have certain drawbacks,such as low power density for batteries and low energy density for ***,the nearest ion capacitors,such as lithium-ion and sodium-ion capacitors containing battery-type and capacitor-type electrodes,may allow achieving both high energy and power *** the inspiration,a new zinc-ion capacitor(ZIC)has been designed and realized by assembling the free-standing manganese dioxide-carbon nanotubes(MnO2-CNTs)battery-type cathode and MXene(Ti3C2Tx)capacitortype anode in an aqueous *** ZIC can avoid the insecurity issues that frequently occurred in lithium-ion and sodium-ion capacitors in organic *** expected,the ZIC in an aqueous liquid electrolyte exhibits excellent electrochemical performance(based on the total weight of cathode and anode),such as a high specific capacitance of 115.1 F g?1(1 mV s?1),high energy density of 98.6 Wh kg?1(77.5 W kg?1),high power density of 2480.6 W kg?1(29.7 Wh kg?1),and high capacitance retention of^83.6%of its initial capacitance(15,000 cycles).Even in an aqueous gel electrolyte,the ZIC also exhibits excellent *** work provides an essential strategy for designing next-generation high-performance energy storage devices.

关键词： Energy storage Zinc-ion capacitor Battery-type and capacitor-type electrodes MXene Electrochemical performance

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FedLGMatch: Federated semi-supervised learning via joint local and global pseudo labeling

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Knowledge-Based Systems 2025年 320卷

作者： Zhao, Qing Chu, Jielei Li, Zhaoyu Huang, Wei Luo, Zhipeng Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China China Railway Engineering Group Limited Beijing100039 China China Railway Eryuan Engineering Group Co. Ltd. Sichuan Chengdu610031 China College of Computer and Data Science Fuzhou University China

The bulk of existing Federated Learning (FL) algorithms pay attention to supervised setting and assume that clients have fully labeled data. However, it may be impractical for all clients to obtain plenty of labels due to high annotation costs. Hence, the Federated Semi-Supervised Learning (FSSL) as a promising paradigm has better prospect in many realistic applications (e.g. medical scenario). Under Labels-at-Server scenario, most pseudo labeling based FSSL approaches use only the global model to generate pseudo-labels for unlabeled data, while the local models are ignored. When the local data distribution is much more different from the central server (e.g., Non-IID setting), the generated pseudo-labels may contain much noise, thus, resulting in more serious confirmation bias. To tackle the crucial issue, a novel Federated Semi-Supervised Learning via Joint Local and Global Pseudo Labeling (FedLGMatch) framework is proposed in this paper. The prominent advantage of the proposed FedLGMatch is that it allows local models trained in the last communication round to assist global model in generating pseudo-labels, which successfully emphasizes more clean pseudo-label learning at each client. Experimental results also show that FedLGMatch achieves significant performance improvements than other state-of-the-art models on the standard benchmark datasets. © 2025 Elsevier B.V.

关键词： Semi-supervised learning

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Recommending more suitable music based on users’ real context 14th

Recommending more suitable music based on users’ real conte...

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14th EAI International Conference on Collaborative computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Yang, Qing Zhan, Le Han, Li Zhang, Jingwei Bi, Zhongqin School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China College of Computer Science and Technology Shanghai University of Electronic Power Shanghai200090 China

ISBN: (纸本)9783030129804

Music recommendation is an popular function for personalized services and smart applications since it focuses on discovering users’ leisure preference. The traditional music recommendation strategy captured users’ music preference by analyzing their historical behaviors to conduct personalized recommendation. However, users’ current states, such as in busy working or in a leisure travel, etc., have an important influence on their music enjoyment. Usually, those existing methods only focus on pushing their favorite music to users, which may be not the most suitable for current scenarios. Users’ current states should be taken into account to make more perfect music recommendation. Considering the above problem, this paper proposes a music recommendation method by considering both users’ current states and their historical behaviors. First, a feature selection process based on ReliefF method is applied to discover the optimal features for the following recommendation. Second, we construct different feature groups according to the feature weights and introduce Naive Bayes model and Adaboost algorithm to train these feature groups, which will output a base classifier for each feature group. Finally, a majority voting strategy decides the optimal music type and each user will be recommended more suitable music based on their current context. The experiments on the real datasets show the effectiveness of the proposed method. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Adaptive boosting

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Quantum Walks–Based Adaptive Distribution Generation with Efficient CUDA-Q Acceleration

arXiv

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arXiv 2025年

作者： Chang, Yen Jui Wang, Wei-Ting Liu, Chen-Yu Wang, Yun-Yuan Chang, Ching-Ray Master Program in Intelligent Computing and Big Data Chung Yuan Christian University No. 200 Zhongbei Rd. Zhongli Dist Taoyuan City320314 Taiwan Quantum Information Center Chung Yuan Christian University No. 200 Zhongbei Rd. Zhongli Dist Taoyuan City320314 Taiwan Department of Physics National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan Graduate Institute of Applied Physics National Taiwan University No. 1 Sec. 4 Roosevelt Rd. Taipei106319 Taiwan NVIDIA AI Technology Center NVIDIA Corp Taipei610101 Taiwan

We present a novel Adaptive Distribution Generator (ADG) that leverages a quantum walks–based approach to generate high precision and efficiency of target probability distributions. Our method integrates variational quantum circuits with discrete-time quantum walks(DTQWs)—specifically, split-step quantum walks(SSQWs) and their entangled extensions—to dynamically tune coin parameters and drive the evolution of quantum states towards desired distributions. This enables accurate one-dimensional probability modeling for applications such as financial simulation and structured two-dimensional pattern generation exemplified by digit representations (0–9). Implemented within the CUDA-Q framework, our approach exploits GPU acceleration to significantly reduce computational overhead and improve scalability relative to conventional methods. Extensive benchmarks demonstrate that our Quantum Walk–Based Adaptive Distribution Generator (QWs-based ADG) achieves high simulation fidelity and bridges the gap between theoretical quantum algorithms and practical high-performance computation. © 2025, CC BY.

关键词： Qubits

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Poisoning attack against estimating from pairwise comparisons

arXiv

引用

arXiv 2021年

作者： Ma, Ke Xu, Qianqian Zeng, Jinshan Cao, Xiaochun Huang, Qingming The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen518055 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang330022 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China The School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Big Data Mining and Knowledge Management The School of Economics and Management University of Chinese Academy of Sciences Beijing100049 China

As pairwise ranking becomes broadly employed for elections, sports competitions, recommendation, information retrieval and so on, attackers have strong motivation and incentives to manipulate or disrupt the ranking list. They could inject malicious comparisons into the training data to fool the target ranking algorithm. Such a technique is called "poisoning attack" in regression and classification tasks. In this paper, to the best of our knowledge, we initiate the first systematic investigation of data poisoning attack on the pairwise ranking algorithms, which can be generally formalized as the dynamic and static games between the ranker and the attacker, and can be modeled as certain kinds of integer programming problems mathematically. To break the computational hurdle of the underlying integer programming problems, we reformulate them into the distributionally robust optimization (DRO) problems, which are computational tractable. Based on such DRO formulations, we propose two efficient poisoning attack algorithms and establish the associated theoretical guarantees including the existence of Nash equilibrium and the generalization ability bounds. The effectiveness of the suggested poisoning attack strategies is demonstrated by a series of toy simulations and several real data experiments. These experimental results show that the proposed methods can significantly reduce the performance of the ranker in the sense that the correlation between the true ranking list and the aggregated results with toxic data can be decreased dramatically. © 2021, CC BY-NC-ND.

关键词： Toys

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Parallel Implementation of W-projection Wide-Field Imaging

arXiv

引用

arXiv 2019年

作者： Baoqiang, L.A.O. Tao, A.N. Ang, Y.U. Zhang, Wenhui Wang, Junyi Quan, G.U.O. Shaoguang, G.U.O. Xiaocong, W.U. Shanghai Astronomical Observatory Key Laboratory of Radio Astronomy Chinese Academy of Sciences Shanghai200030 China Guangxi Cooperative Innovation Center of cloud computing and Big Data Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges Universities Key Laboratory of cloud computing and complex systems Guilin University of Electronic Technology Guilin541004 China

W-projection is a wide-field imaging technique that is widely used in radio synthesis arrays. Processing the wide-field big data generated by the future Square Kilometre Array (SKA) will require significant updates to current methods to significantly reduce the time consumed on data processing. data loading and gridding are found to be two major time-consuming tasks in w-projection. In this paper, we investigate two parallel methods of accelerating w-projection processing on multiple nodes: they are the hybrid Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) method based on multicore Central Processing Units (CPUs) and the hybrid MPI and Compute Unified Device Architecture (CUDA) method based on Graphics Processing Units (GPUs). Both methods are successfully employed and operated in various computational environments, confirming their robustness. The experimental results show that the total runtime of both MPI+OpenMP and MPI+CUDA methods is significantly shorter than that of single-thread processing. MPI+CUDA generally shows faster performance when running on multiple nodes than MPI+OpenMP, especially on large numbers of nodes. The single-precision GPU-based processing yields faster computation than the double-precision processing;while the single- and double-precision CPU-based processing shows consistent computational performance. The gridding time remarkably increases when the support size of the convolution kernel is larger than 8 and the image size is larger than 2048 pixels. The present research offers useful guidance for developing SKA imaging pipelines. Copyright © 2019, The Authors. All rights reserved.

关键词： Message passing

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Security analysis oriented physical components modeling in quantum key distribution 1

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1st International Symposium on Mobile Internet Security, MobiSec 2016

作者： Mao, Xilong Li, Yan Peng, Yan Zhao, Baokang College of Computer National University of Defense Technology Changsha410073 China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China

ISBN: (数字)9789811078507

ISBN: (纸本)9789811078491

Quantum Key Distribution (QKD), based on fundamental principles of quantum mechanics, plays an irreplaceable role in national defense, financial and government affairs. Security analysis of QKD system is of great importance. However, existing studies on modeling QKD system are theory analysis based. In this paper, we propose a Simulation System of Physical Components (SSPC) in QKD system which modeling the three key modules: single photon source, quantum channel and single photon detector, it could generate the simulated key resemble to real QKD physical system and its parameters of the physical components are configurable. Therefore, solution can be deployed in different QKD physical systems. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Quantum cryptography

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Research on SDN Multi Controller Deployment based on K-means++

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Journal of Physics: Conference Series 2020年第1期1606卷

作者： Li Yue Chen Junyan Liang Chuxin Lei Xiaochun School of computer and information security Guilin University of Electronic Technology Guilin 541004 China Guangxi cloud computing and big data collaborative innovation center Guilin 541001 China Guangxi key laboratory of image and graphic intelligent processing Guilin 541004 China

In large-scale SDN network, a single centralized controller can not meet the demand, and multiple controllers are needed to deal with the problem, which leads to the problem of multi control balanced deployment. In this paper, the topology of SDN switches and links is known. The main research contents are as follows: the mathematical model of SDN multi controller deployment is established, and the appropriate network topology is selected and the multi controller deployment problem is solved. In the research, the number of controllers needed in the network and the switches managed by each controller is determined by the algorithm results, and the mapping relationship between controllers and switches is established. By analyzing the deployment results of the same network topology under different algorithms, the influence of different clustering algorithms on the experimental results is obtained. At the same time, the better deployment experiment results are simulated.

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