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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhou, Siyuan Wang, Ziqing Dong, Yunquan Tang, Bin Tan, Guoping Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing China Hohai University College of Information Science and Engineering Nanjing China Nanjing University of Information Science and Technology School of Electronic and Information Engineering Nanjing China

ISBN: (纸本)9798350351255

The promptness of decision-making is of paramount importance in coordinate decision-making communication systems. In this paper, we envision a system in which a sender transmits information to a receiver for decision-making. The decision-making process encompasses the extraction of decision-related information followed by subsequent computation. To assess the impact of communication delays, randomness of decision-making and computational time on the immediacy of decisions, we introduce a novel performance metric, namely the age of decision (AoD). Specifically, AoD is defined as the time elapsed since the generation of the update upon which the most recent decision is based. Considering the stochastic nature of decision-making, we analyze the average peak AoD in systems where the arrival and service processes follow general processes or Poisson processes. To facilitate our derivations, we introduce the concept of effective decisions to denote those decisions that can lead to the system's peak AoD. Simulation results confirm the validity of our derivations and illustrate that appropriately increasing the rate of decision-making can significantly reduce the system's average peak AoD. © 2024 IEEE.

关键词： Stochastic systems

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DSM: Question Generation over knowledge Base via Modeling Diverse Subgraphs with Meta-learner

DSM: Question Generation over Knowledge Base via Modeling Di...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Guo, Shasha Zhang, Jing Wang, Yanling Zhang, Qianyi Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education China

Existing methods on knowledge base question generation (KBQG) learn a one-size-fits-all model by training together all subgraphs without distinguishing the diverse semantics of subgraphs. In this work, we show that making use of the past experience on semantically similar subgraphs can reduce the learning difficulty and promote the performance of KBQG models. To achieve this, we propose a novel approach to model diverse subgraphs with meta-learner (DSM). Specifically, we devise a graph contrastive learning-based retriever to identify semantically similar subgraphs, so that we can construct the semantics-aware learning tasks for the meta-learner to learn semantics-specific and semantics-agnostic knowledge on and across these tasks. Extensive experiments on two widely-adopted benchmarks for KBQG show that DSM derives new state-of-the-art performance and benefits the question answering tasks as a means of data augmentation. Codes and datasets are available online. © 2022 Association for Computational Linguistics.

关键词： Semantics

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Deployment optimization for target perpetual coverage in energy harvesting wireless sensor network

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Digital Communications and Networks 2024年第2期10卷 498-508页

作者： Zhenkun Jin Yixuan Geng Chenlu Zhu Yunzhi Xia Xianjun Deng Lingzhi Yi Xianlan Wang Wuhan Business University 430056China Wuhan Research Institute of Post and Telecommunication Wuhan430074China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data SecuritySchool of Cyber Science and EngineeringHuazhong University of Science and TechnologyWuhan430074China Hubei Chutian Expressway Digital Technology Co.Ltd Wuhan430074China School of Information and Safety Engineering Zhongnan University of Economics and LawWuhan430073China

Energy limitation of traditional Wireless Sensor Networks(WSNs)greatly confines the network lifetime due to generating and processing massive sensing data with a limited *** energy harvesting WSN is a novel network architecture to address the limitation of traditional ***,existing coverage and deployment schemes neglect the environmental correlation of sensor nodes and external energy with respect to physical *** considering the spatial correlation of the environment and the uneven distribution of energy in energy harvesting WSN,we investigate how to deploy a collection of sensor nodes to save the deployment cost while ensuring the target perpetual *** Confident information Coverage(CIC)model is adopted to formulate the CIC Minimum Deployment Cost Target Perpetual Coverage(CICMTP)problem to minimize the deployed sensor *** the CICMTP is NP-hard,we devise two approximation algorithms named Local Greedy Threshold Algorithm based on CIC(LGTA-CIC)and Overall Greedy Search Algorithm based on CIC(OGSA-CIC).The LGTA-CIC has a low time complexity and the OGSA-CIC has a better approximation *** simulation results demonstrate that the OGSA-CIC is able to achieve lower deployment cost and the performance of the proposed algorithms outperforms GRNP,TPNP and EENP algorithms.

关键词： Energy harvesting WSN Deployment optimization Confident information coverage(CIC) Target perpetual coverage

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Statistical Model on CRAFT

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Chinese Journal of Electronics 2022年第4期31卷 698-712页

作者： WANG Caibing GUO Hao YE Dingfeng WANG Ping State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Tianjin Aerospace Zhongwei Data System Technology Co. Ltd.

Many cryptanalytic techniques for symmetric-key primitives rely on specific statistical analysis to extract some secrete key information from a large number of known or chosen plaintext-ciphertext pairs. For example, there is a standard statistical model for differential cryptanalysis that determines the success probability and complexity of the attack given some predefined configurations of the attack. In this work, we investigate the differential attack proposed by Guo et al. at Fast Software Encryption Conference 2020 and find that in this attack, the statistical behavior of the counters for key candidates deviate from standard scenarios, where both the correct key and the correct key xor specific difference are expected to receive the largest number of votes. Based on this bimodal behavior, we give three different statistical models for truncated differential distinguisher on CRAFT(a cryptographic algorithm name) for bimodal phenomena. Then, we provide the formulas about the success probability and data complexity for different models under the condition of a fixed threshold value. Also, we verify the validity of our models for bimodal phenomena by experiments on round-reduced of the versions distinguishers on CRAFT. We find that the success probability of theory and experiment are close when we fix the data complexity and threshold value. Finally, we compare the three models using the mathematical tool Matlab and conclude that Model 3 has better performance.

关键词： Differential cryptanalysis Statistical models Success probability data complexity Bimodal behavior CRAFT Differential fault analysis (DFA) attacks

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Differentially Private and Heterogeneity-Robust Federated Learning With Theoretical Guarantee

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6369-6384页

作者： Wang, Xiuhua Wang, Shuai Li, Yiwei Fan, Fengrui Li, Shikang Lin, Xiaodong Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu611731 China Xiamen University of Technology Fujian Key Laboratory of Communication Network and Information Processing Xiamen361024 China University of Guelph School of Computer Science GuelphONN1G 2W1 Canada

Federated learning (FL) is a popular distributed paradigm where enormous clients collaboratively train a machine learning (ML) model under the orchestration of a central server without knowing the clients' private raw data. The development of effective FL algorithms faces multiple practical challenges including data heterogeneity and clients' privacy protection. Despite that numerous attempts have been made to deal with data heterogeneity or rigorous privacy protection, none have effectively tackled both issues simultaneously. In this article, we propose a differentially private and heterogeneity-robust FL algorithm, named DP-FedCVR to mitigate the data heterogeneity by following the client-variance-reduction strategy. Besides, it adopts a sophisticated differential privacy (DP) mechanism where the privacy-amplified strategy is applied, to achieve a rigorous privacy protection guarantee. We show that the proposed DP-FedCVR algorithm maintains its heterogeneity-robustness though DP noises are incorporated, while achieving a sublinear convergence rate for a nonconvex FL problem. Numerical experiments based on image classification tasks are presented to demonstrate that DP-FedCVR provides superior performance over the benchmark algorithms in the presence of data heterogeneity and various DP privacy budgets. © 2020 IEEE.

关键词： Federated learning

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Decoupled Prototype Learning for Reliable Test-Time Adaptation

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IEEE Transactions on Multimedia 2025年 27卷 3585-3597页

作者： Wang, Guowei Ding, Changxing Tan, Wentao Tan, Mingkui South China University of Technology School of Electronic and Information Engineering 381 Wushan Road Tianhe District Guangzhou510000 China Pazhou Lab Guangzhou510330 China South China University of Technology School of Future Technology 381 Wushan Road Tianhe District Guangzhou510000 China South China University of Technology School of Software Engineering Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou510006 China

Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the target domain during inference. One popular approach involves fine-tuning model with cross-entropy loss according to estimated pseudo-labels. However, its performance is significantly affected by noisy pseudo-labels. This study reveals that minimizing the classification error of each sample causes the cross-entropy loss's vulnerability to label noise. To address this issue, we propose a novel Decoupled Prototype Learning (DPL) method that features prototype-centric loss computation. First, we decouple the optimization of class prototypes. For each class prototype, we reduce its distance with positive samples and enlarge its distance with negative samples in a contrastive manner. This strategy prevents the model from overfitting to noisy pseudo-labels. Second, we propose a memory-based strategy to enhance DPL's robustness for the small batch sizes often encountered in TTA. We update each class's pseudo-feature from a memory in a momentum manner and insert an additional DPL loss. Finally, we introduce a consistency regularization-based approach to leverage samples with unconfident pseudo-labels. This approach transfers feature styles of samples with unconfident pseudo-labels to those with confident pseudo-labels. Thus, more reliable samples for TTA are created. The experimental results demonstrate that our methods achieve state-of-the-art performance on domain generalization benchmarks, and reliably improve the performance of self-training-based methods on image corruption benchmarks. The code will be released. © 1999-2012 IEEE.

关键词： Benchmarking

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Quantum multiparameter estimation with multimode photon catalysis entangled squeezed state

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Frontiers of physics 2023年第4期18卷 265-276页

作者： Huan Zhang Wei Ye Shoukang Chang Ying Xia Liyun Hu Zeyang Liao School of Physics Sun Yat-sen UniversityGuangzhou 510275China School of Information Engineering Nanchang Hangkong UniversityNanchang 330063China MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter Shaanxi Province Key Laboratory of Quantum Information and Quantum Optoelectronic DevicesSchool of PhysicsXi’an Jiaotong UniversityXi’an 710049China Center for Quantum Science and Technology Jiangxi Normal UniversityNanchang 330022China

We propose a method to generate the multi-mode entangled catalysis squeezed vacuum states(MECSVS)by embedding the cross-Kerr nonlinear medium into the Mach–Zehnder *** method realizes the exchange of quantum states between different modes based on Fredkin *** addition,we study the MECSVS as the probe state of multi-arm optical interferometer to realize multi-phase simultaneous *** results show that the quantum Cramer–Rao bound(QCRB)of phase estimation can be improved by increasing the number of catalytic photons or decreasing the transmissivity of the optical beam splitter using for photon *** addition,we also show that even if there is photon loss,the QCRB of our photon catalysis scheme is lower than that of the ideal entangled squeezed vacuum states(ESVS),which shows that by performing the photon catalytic operation is more robust against photon loss than that without the catalytic *** results here can find applications in quantum metrology for multiparatmeter estimation.

关键词： quantum metrology squeezing vacuum quantum catalysis multi-parameter estimation

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Two-Stage Guided Constraint Differential Evolution Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第4期8卷 1109-1133页

作者： Sung, Tien-Wen Liang, Qiaoxin Hong, Chuanbo Huang, Zeming Li, Wei Nguyen, Trinh-Dong College of Computer Science and Mathematics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China School of Electronic Electrical Engineering and Physics Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Department of Software Engineering University of Information Technology Ho Chi Minh City Viet Nam

The development of population intelligence has shown a great trend of a linear surge in recent years, and a large number of intelligent algorithms inspired by biology have been studied. Among them, differential evolution (DE) algorithms have attracted the attention of many researchers because of their simplicity, high efficiency, powerful search capability, etc. DE has strong exploitability and can approach the global optimum faster, but it cannot avoid the phenomenon of search stagnation and premature convergence. To address the drawbacks of DE, the two-stage bootstrap constrained differential evolution algorithm (TSGCDE) is proposed in this paper. In TSGCDE, instead of the traditional one population, the population is divided into two as a way to increase the diversity of the population. Divided into two stages, each stage searches for optimal solutions in different directions in different populations, and the optimal solutions and the bootstrap solutions generated by the populations in the two stages are bounded by each other’s bootstrap, which makes the DE have a more powerful optimal-seeking ability and easier to jump out of the local optimum. To validate the performance of TSGCDE, we conducted experiments on CEC2013 and CEC2014 benchmarks with some well-known algorithm variants, and the experimental results show that our proposed algorithm is competitive. © 2023, Taiwan Ubiquitous information CO LTD. All rights reserved.

关键词： Swarm intelligence

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Impacts of aggregation method on the simulation of multispectral broadband reflectance 4

Impacts of aggregation method on the simulation of multispec...

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4th International Conference on Geology, Mapping, and Remote Sensing, ICGMRS 2023

作者： Chen, Feng Yu, Guanghao Gao, Zhibin Song, Yuejun College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen361024 China Jiangxi Key Laboratory of Soil Erosion and Prevention Jiangxi Academy of Water Science and Engineering Nanchang330029 China

ISBN: (纸本)9781510672741

Simulation of multispectral broadband data from hyperspectral profiles is applicable for many purposes, mainly including sensor assessment and cross-calibration. For the simulation process, several aggregation methods have been proposed. In this paper, three aggregation methods were implemented, to investigate the impacts associated with method on the simulation of multispectral broadband. Comparative analyses were conducted in terms of channel reflectance and several derived vegetation indices, while mainly taking the wide field of view (WFV) sensor onboard Gaofen-6 for case study. Furthermore, impacts on the between-sensor comparability with the Landsat 8 Operational Land Imager (OLI) were discussed. Differences among aggregation methods in reflectance of the Gaofen-6 WFV varied along with channels, and obvious differences were observed in channels over near-infrared (770-890 nm) and red-edge (690-730 nm). Between-sensor difference relative to the Landsat 8 OLI was observed mainly over near-infrared, while the variations among aggregation methods were about 0.4%-2.0%. © 2024 COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Reflection

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Dr_ OrangeApp: Revolutionizing Real-Time Pest Detection in Citrus Orchards 24

Dr_ OrangeApp: Revolutionizing Real-Time Pest Detection in C...

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4th Asia-Pacific Artificial Intelligence and Big data Forum, AIBDF 2024

作者： Moshayedi, Ata Jahangir Jawadi, Mohammad Jalil Roy, Atanu Shuvam Rostami, Elahe Wang, Zhonghua Yang, Liqin Xu, Qun School of Information Engineering Jiangxi University of Science and Technology Jiangxi Ganzhou China Department of Computer Science and Engineering Indian Institute of Technology Uttar Pradesh Kanpur India Institute of Quality Standards & Testing Technology for Agro-products Fujian Provincial Key Laboratory of Quality and Safety of Agricultural Products Fujian Academy of Agricultural Sciences Fujian Fuzhou China Jiangxi Environmental Engineering Vocational College Jiangxi Ganzhou China

ISBN: (纸本)9798400710865

Orange pests pose a significant threat to citrus crops, leading to reduced yield and quality, making effective control essential for healthy orchards and agricultural productivity. In this paper the novel idea of The development of the Dr_ Orange application which is facilitates real-time monitoring of pest infestations, enabling timely farmer interventions that improve crop health, reduce pesticide usage, and enhance productivity. To make the Application the Personal image classifier along the interface used to make the model. the have some features like Chart, report and QR generating which can help the farmers to detect the pests faster and also follow up the process of curiness. To test the app two type of test investigated and the The app has been rigorously tested on 313 images, over the five repeatable pests yielding strong overall performance with an average accuracy and recall of 0.75, perfect precision of 1.00, and an F1 score of 0.856. As the final test the application was evaluated in an orange orchard, successfully identifying and reporting the occurrence of Citrus Leafminer pests in 10 trees. Although the app demonstrates high precision, challenges such as variations in lighting conditions, the angle of the photographs, and data quality can affect result accuracy. Repeatedly taking pictures can help mitigate these effects and improve the interface’s impact on detection accuracy. based on obtained results, the proposed App enhances pest management efficiency for orange crops while also promoting improved agricultural productivity. © 2024 Copyright held by the owner/author(s).

关键词： Citrus fruits

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