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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Yanyue Lai, Yilong Wang, Zhenglin Li, Pengfei Zhou, Deyu He, Yulan School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King's College London United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9782493814104

As in the existing opinion summary data set, more than 70% are positive texts, the current opinion summarization approaches are reluctant to generate the negative opinion summary given the input of negative opinions. To address such sentiment bias, two approaches are proposed through two perspectives: model-specific and model-agnostic. For the model-specific approach, a variational autoencoder is proposed to disentangle the input representation into sentiment-relevant and sentiment-irrelevant components through adversarial loss. Therefore, the sentiment information in the input is kept and employed for the following decoding which avoids interference of content information with emotional signals. To further avoid relying on some specific opinion summarization frameworks, a model-agnostic approach based on counterfactual data augmentation is proposed. A dataset with a more balanced emotional polarity distribution is constructed using a large pre-trained language model based on some pairwise and mini-edited principles. Experimental results show that the sentiment consistency of the generated summaries is significantly improved using the proposed approaches, while their semantics quality is unaffected. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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BAT: Mining Binary-API Topic for Multi-service Application Development 26

BAT: Mining Binary-API Topic for Multi-service Application D...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： He, Pengfei Liu, Linlin You, Dianlong Shen, Limin Chen, Zhen Yanshan University School of Information Science and Engineering Qinhuangdao China Chinese Academy of Sciences National Science Libraries Beijing China The Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Qinhuangdao China

ISBN: (纸本)9798350331684

With the growing popularity of API-driven multiservice application (mashup) development, the burgeoning web APIs have left developers drowning in the sea of web API selections. Matching developers with the most appropriate APIs is the key to improving user satisfaction and promoting more popular web applications. As a result, more and more researchers pay attention to web API recommender systems based on collaborative filtering. However, employing collaborative filtering to recommend APIs is challenging due to the severe sparsity of mashup and API interactions. To address this problem, we propose a probabilistic generative model, called the Binary-API Topic model (BAT), to parameterize mashups and APIs. Technically, BAT is equipped with a mechanism to extract binary-APIs and predict unknown pairwise interactions. To improve generality and capture more relevance from a limited number of interactions, we learn binary-API topics by directly modeling the generation of API co-occurrence patterns across the repository (all mashup collections from ***). The main advantage of BAT is that it preserves API co-occurrence patterns in model learning and exploits the rich global relevance. Finally, through extensive experiments, we demonstrate that BAT can achieve the highest performance on the sparse real-world data set. © 2023 IEEE.

关键词： Collaborative filtering

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Cloud-edge-based We-Market:Autonomous Bidding and Peer-to-peer Energy Sharing Among Prosumers

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Journal of Modern Power systems and Clean Energy 2023年第4期11卷 1282-1293页

作者： Fangyuan Si Yinghua Han Qinqin Xu Jinkuan Wang Qiang Zhao College of Information Science and Engineering Northeastern UniversityShenyangChinaand F.Si is also with the State Key Laboratory of Power System and Generation EquipmentDepartment of Electrical EngineeringTsinghua UniversityBeijingChina School of Computer and Communication Engineering Northeastern University at QinhuangdaoQinhuangdaoChina School of Control Engineering Northeastern University at QinhuangdaoQinhuangdaoChina

With the extensive penetration of distributed renewable energy and self-interested prosumers,the emerging power market tends to enable user autonomy by bottom-up control and distributed *** paper is devoted to solving the specific problems of distributed energy management and autonomous bidding and peer-to-peer(P2P)energy sharing among prosumers.A novel cloud-edge-based We-Market is presented,where the prosumers,as edge nodes with independent control,balance the electricity cost and thermal comfort by formulating a dynamic household energy management system(HEMS).Meanwhile,the autonomous bidding is initiated by prosumers via the modified Stone-Geary utility *** the cloud center,a distributed convergence bidding(CB)algorithm based on consistency criterion is developed,which promotes faster and fairer bidding through the interactive iteration with the edge ***,the proposed scheme is built on top of the commercial cloud platform with sufficiently secure and scalable computing *** results show the effectiveness and practicability of the proposed We-Market,which achieves 15%cost reduction with shorter running *** analysis indicates better scalability,which is more suitable for largerscale We-Market implementation.

关键词： We-Market bidding energy sharing prosumer peer-to-peer cloud-edge

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Optimizing Acoustic Echo Cancellation with Variable Step Size in Adaptive Filtering

Optimizing Acoustic Echo Cancellation with Variable Step Siz...

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2024 International Conference on advanced Technologies for Communications, ATC 2024

作者： Chen, Yen-Yi Wang, Jian-Hong Chan, Po-Cheng Liang, Kai-Wen Wang, Ze-Yu Wang, Jia-Ching School of Computer Science and Technology Shandong University of Technology Shandong Zibo China National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan Advanced Technology Laboratory Chunghwa Telecom Laboratories Taiwan

ISBN: (纸本)9798350353983

This paper presents an innovative approach to acoustic echo cancellation (AEC) by applying Variable Step Size (VSS) separately to each adaptive filtering technique: Normalized Least Mean Square (NLMS), and Proportionate Normalized Least Mean Square (PNLMS). This application is tailored to overcome the challenges of step size adjustment and signal sparsity, significantly enhancing system performance. We demonstrate that utilizing VSS with each algorithm independently allows for optimized control over convergence rates-larger step sizes quicken convergence while smaller sizes ensure smoother transitions. This method improves the robustness and efficiency of echo cancellation systems, removing the need for manual step size adjustments and proving highly effective in real-time applications. © 2024 IEEE.

关键词： Echo suppression

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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for advanced Control Applications

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Non-Line-of-Sight Multipath Classification Method for BDS Using Convolutional Sparse Autoencoder with LSTM

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Tsinghua science and Technology 2025年第1期30卷 68-86页

作者： Yahang Qin Zhenni Li Shengli Xie Bo Li Ming Liu Victor Kuzin School of Automation Guangdong University of Technologyand also with Guangdong-Hong Kong-Macao Joint Laboratory for Smart Discrete ManufacturingGuangzhou 510006China School of Automation Guangdong University of TechnologyGuangdong Key Laboratory of IoT Information TechnologyGuangzhou 510006China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Key Laboratory of Intelligent Information Processing and System Integration of IoTMinistry of EducationGuangzhou 510006China Department of Electronic and Computer Engineering Hong Kong University of Science and TechnologHong Kong 999077China Academician of Russian Engineering Academy Moscow Moscow 125009Russia

Multipath signal recognition is crucial to the ability to provide high-precision absolute-position services by the BeiDou Navigation Satellite system(BDS).However,most existing approaches to this issue involve supervised machine learning(ML)methods,and it is difficult to move to unsupervised multipath signal recognition because of the limitations in signal *** by an autoencoder with powerful unsupervised feature extraction,we propose a new deep learning(DL)model for BDS signal recognition that places a long short-term memory(LSTM)module in series with a convolutional sparse autoencoder to create a new autoencoder ***,we propose to capture the temporal correlations in long-duration BeiDou satellite time-series signals by using the LSTM module to mine the temporal change patterns in the time ***,we develop a convolutional sparse autoencoder method that learns a compressed representation of the input data,which then enables downscaled and unsupervised feature extraction from long-duration BeiDou satellite series ***,we add an l_(1/2) regularizer to the objective function of our DL model to remove redundant neurons from the neural network while ensuring recognition *** tested our proposed approach on a real urban canyon dataset,and the results demonstrated that our algorithm could achieve better classification performance than two ML-based methods(e.g.,11%better than a support vector machine)and two existing DL-based methods(e.g.,7.26%better than convolutional neural networks).

关键词： convolutional sparse autoencoder BeiDou Navigation Satellite system(BDS) long short-term memory(LSTM) multipath classification

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BHJO: A Novel Hybrid Metaheuristic Algorithm Combining the Beluga Whale, Honey Badger, and Jellyfish Search Optimizers for Solving engineering Design Problems

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computer Modeling in engineering & sciences 2024年第10期141卷 219-265页

作者： Farouq Zitouni Saad Harous Abdulaziz S.Almazyad Ali Wagdy Mohamed Guojiang Xiong Fatima Zohra Khechiba Khadidja Â Kherchouche Department of Computer Science and Information Technology Kasdi Merbah UniversityP.O.Box 30000OuarglaAlgeria Department of Computer Science College of Computing and InformaticsUniversity of SharjahP.O.Box 27272SharjahUnited Arab Emirates Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11937Jordan Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China

Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization *** approach aims to leverage the strengths of multiple algorithms,enhancing solution quality,convergence speed,and robustness,thereby offering a more versatile and efficient means of solving intricate real-world optimization *** this paper,we introduce a hybrid algorithm that amalgamates three distinct metaheuristics:the Beluga Whale Optimization(BWO),the Honey Badger Algorithm(HBA),and the Jellyfish Search(JS)*** proposed hybrid algorithm will be referred to as *** this fusion,the BHJO algorithm aims to leverage the strengths of each *** this hybridization,we thoroughly examined the exploration and exploitation capabilities of the BWO,HBA,and JS metaheuristics,as well as their ability to strike a balance between exploration and *** meticulous analysis allowed us to identify the pros and cons of each algorithm,enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization *** addition,the BHJO algorithm incorporates Opposition-Based Learning(OBL)to harness the advantages offered by this technique,leveraging its diverse exploration,accelerated convergence,and improved solution quality to enhance the overall performance and effectiveness of the hybrid ***,the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems,providing a comprehensive assessment of its efficacy and applicability in diverse problem ***,the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms,where mean and standard deviation values were utilized as evaluation *** rigorous comparison aimed to assess the performance of the BHJOalgorithmabout its counterparts,shedd

关键词： Global optimization hybridization of metaheuristics beluga whale optimization honey badger algorithm jellyfish search optimizer chaotic maps opposition-based learning

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Association Rule Guided Web API Complementary Function Recommendation for Mashup Creation: An Explainable Perspective 17th

Association Rule Guided Web API Complementary Function Recom...

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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： He, Pengfei Qi, Wenchao Liu, Xiaowei Liu, Linlin You, Dianlong Shen, Limin Chen, Zhen School of Information Science and Engineering Yanshan University Qinhuangdao China National Science Libraries Chinese Academy of Sciences Beijing China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China

ISBN: (纸本)9789819923557

As more and more service providers encapsulate and publish their data and services on the internet in the form of Web APIs, the number of Web APIs is ever-increasing. For this reason, Web API recommendation is gaining momentum and has achieved high performance in accuracy. However, few studies paid attention to function recommendation. As an indispensable entity in the mashup-API ecosystem, function is not only the in-facto basis of API taxonomy, but also determines the compatibility and the internal construction pattern of mashup composition. Considering that users adopt multiple function invocations during a development cycle and the number of functions is also increasing, we propose the complementary function recommendation(CFR), a function-to-function problem. To solve the CFR, we regard each mashup as a transaction set for frequent pattern mining and propose an association rule-based complementary function recommendation(ARCFR) system, which provides function recommendation and corresponding probability explanation. Our experiments show that ARCFR can recommend complementary function effectively, and we give two applicable scenarios to demonstrate the practical value of our method in more aspects. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Association rules

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Opnet: Deep Occlusion Perception Network with Boundary Awareness for Amodal Instance Segmentation

Opnet: Deep Occlusion Perception Network with Boundary Aware...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shihui Zhang Ziteng Xue Yuhong Jiang Houlin Wang School of Information Science and Engineering Yanshan University Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province

The Amodal Instance Segmentation (AIS) task aims to infer the visible and occluded regions of an object instance. Existing AIS methods typically focus on directly predicting visible and occluded regions or leveraging prior knowledge to guide predictions. However, these methods often ignore the perception of occluded views, leading to inaccurate results. To address this issue and achieve high-quality AIS, we propose a boundary-aware Occlusion Perception Network (OPNet). OPNet consists of three main components: the Dynamic Feature Augmentation Pyramid (DFAP), the Dual-path Boundary Aware Module (DBAM), and the Shape-guide Refinement Module (SRM). Specifically, DBAM employs an occlusion-perception strategy to learn discriminative features with boundary information, enabling it to distinguish occlusion from multiple views. Additionally, DFAP and SRM optimize the results by enhancing feature aggregation and imposing geometric constraints. Experiments on the D2SA, KINS, and CWALT datasets show that OPNet significantly outperforms state-of-the-art AIS methods that without prior knowledge. Code is available at https://***/ZitengXue/OPNet.

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