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A Brief Overview of Recent Energy-Saving Researches in Smart Homes 1

18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Sung, Tien-Wen Liang, Qiaoxin Huang, Zeming Hong, Chuanbo Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819906055

ISBN: (纸本)9789819906048

This paper gives a brief overview on the topic of energy saving in smart homes. It provides introduction of some recent researches and developments of energy-saving schemes for smart homes, so that helps readers to know information about the potential issues and to make improvements in the future. A brief summary given in the discussion indicates that not only considering the problem of energy consumption but also paying attention to the comfort and safety for users in the energy-saving solutions is also important. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy conservation

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A Multi-Objective Particle Swarm Optimization Algorithm Based on Decomposition and Multi-Selection Strategy

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Computers, Materials & Continua 2025年第1期82卷 997-1026页

作者： Li Ma Cai Dai Xingsi Xue Cheng Peng School of Computer Science Shaanxi Normal UniversityXi’an710119China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Information Construction and Management Center and Institute of Artificial Intelligence and Educational New Productivity Ningxia Normal UniversityGuyuan756099China

The multi-objective particle swarm optimization algorithm(MOPSO)is widely used to solve multi-objective optimization *** the article,amulti-objective particle swarm optimization algorithmbased on decomposition and multi-selection strategy is proposed to improve the search ***,two update strategies based on decomposition are used to update the evolving population and external archive,***,a multiselection strategy is *** first strategy is for the subspace without a non-dominated *** the neighbor particles,the particle with the smallest penalty-based boundary intersection value is selected as the global optimal solution and the particle far away fromthe search particle and the global optimal solution is selected as the personal optimal solution to enhance global *** second strategy is for the subspace with a non-dominated *** the neighbor particles,two particles are randomly selected,one as the global optimal solution and the other as the personal optimal solution,to enhance local *** third strategy is for Pareto optimal front(PF)discontinuity,which is identified by the cumulative number of iterations of the subspace without non-dominated *** the subsequent iteration,a new probability distribution is used to select from the remaining subspaces to ***,an adaptive inertia weight update strategy based on the dominated degree is designed to further improve the search ***,the proposed algorithmis compared with fivemulti-objective particle swarm optimization algorithms and five multi-objective evolutionary algorithms on 22 test *** results show that the proposed algorithm has better performance.

关键词： Multi-objective optimization multi-objective particle swarm optimization decomposition multi-selection strategy

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Temporal Sentence Grounding with Relevance Feedback in Videos 38

Temporal Sentence Grounding with Relevance Feedback in Video...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Dong, Jianfeng Peng, Xiaoman Liu, Daizong Qu, Xiaoye Yang, Xun Bao, Cuizhu Wang, Meng Zhejiang Gongshang University China Zhejiang Key Laboratory of Big Data and Future E-Commerce Technology China Peking University China Huazhong University of Science and Technology China University of Science and Technology of China China Hefei University of Technology China

As a widely explored multi-modal task, Temporal Sentence Grounding in videos (TSG) endeavors to retrieve a specific video segment matched with a given query text from a video. The traditional paradigm for TSG generally assumes that relevant segments always exist within a given video. However, this assumption is restrictive and unrealistic in real-world applications where the existence of a query-related segment is uncertain, easily resulting in erroneous grounding. Motivated by the research gap and practical application, this paper introduces a new task, named Temporal Sentence Grounding with Relevance Feedback (TSG-RF) in videos, which accommodates the possibility that a video may or may not include a segment related to the query. This task entails localizing precise video segments that semantically align with the query text when such content is present, while delivering definitive feedback on the non-existence of related segments when absent. Moreover, we propose a novel Relation-aware Temporal Sentence Grounding (RaTSG) network for addressing this challenging task. This network first reformulates the TSG-RF task as a foreground-background detection problem by investigating whether the query-related semantics exist in both frame and video levels. Then, a multi-granularity relevance discriminator is exploited to produce precise video-query relevance feedback and a relation-aware segment grounding module is employed to selectively conduct the grounding process, dynamically adapting to the presence or absence of query-related segments in videos. To validate our RaTSG network, we reconstruct two popular TSG datasets, establishing a rigorous benchmark for TSG-RF. Experimental results demonstrate the effectiveness of our proposed RaTSG for the TSG-RF task. Our source code is available at https://***/HuiGuanLab/RaTSG. © 2024 Neural information processing systems foundation. All rights reserved.

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Cross-Modality Comprehensive Feature Aggregation Network for Road Scene Segmentation 8

Cross-Modality Comprehensive Feature Aggregation Network for...

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8th International Symposium on Computer science and Intelligent Control, ISCSIC 2024

作者： Yang, Yale Zhang, Zihao Hou, Huifang Zhang, Wenwen Zhuang, Chunsheng Henan University of Technology College of Information Science and Engineering Zhengzhou China Henan University of Technology Key Laboratory of Grain Information Processing and Control Ministry of Education Henan Key Laboratory of Grain Photoelectric Detection and Control School of Artificial Intelligence and Big Data Zhengzhou China Data Henan University of Technology School of Artificial Intelligence and Big Zhengzhou China Henan Academy of Sciences Zhengzhou China

ISBN: (纸本)9798350380286

RGB-thermal semantic segmentation is a potential solution for reliable semantic scene understanding under adverse weather and complex lighting conditions. However, past studies have primarily focused on the design of fusion paradigms. Unfortunately, these methods have not fully considered the modality differences caused by different imaging mechanisms, thereby reducing the performance of pixel-level semantic analysis. In response to this issue, this paper proposes an innovative Cross-Modality Comprehensive Feature Aggregation Network (CFANet). This method first designs a Cross-Modality Feature Rectification Module (CFRM) to align modality differences;then, it constructs a Global-Local Fusion Module (GLF) to sharpen object edges and refine the segmentation map. Results on two urban scene understanding datasets demonstrate that CFANet can effectively aggregate multi-layer deep features and outperform existing advanced deep learning methods in scene understanding performance. © 2024 IEEE.

关键词： Semantic Segmentation

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An Overview of Recent Research on IoT-Based Energy Management System in Smart Homes 1

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Sung, Tien-Wen Huang, Zeming Liang, Qiaoxin Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and Mathematics Fujian University of Technology Fuzhou350118 China

ISBN: (数字)9789819906055

ISBN: (纸本)9789819906048

Increasing load demand and energy crisis issues are becoming more and more concerned. Nowadays, the demand for electricity in household life is rising sharply and the structure of electricity consumption is gradually becoming complex. With the development of the Internet of Things technology, it provides a better platform for the improvement of the energy management system. The home energy management system (HEMS) based on the Internet of Things comes into being, which can integrate the management of all home power loads and distributed energy, realize the optimal configuration of energy, and help users save electricity. This paper firstly describes the basic concept of IoT smart home energy management system, then describes the framework of HEMS, and finally reviews the current research status in this field from three modules, namely, data collection module, communication module, and management and optimization module, and discusses its technical challenges and future research directions. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy management systems

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CPAKE: Dynamic Batch Authenticated key Exchange with Conditional Privacy 24th

CPAKE: Dynamic Batch Authenticated Key Exchange with Condit...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Xiang, Axin Tian, Youliang Xiong, Jinbo Ying, Zuobin Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology Guiyang550025 China Industrial School of Joint Innovation Quanzhou Vocational and Technical University Quanzhou362268 China Faculty of Data Science City University of Macau 999078 China

ISBN: (纸本)9789819615247

Authenticated key exchange (AKE) needs to be designed for realizing point-to-multipoint secure communications in blockchain networks (BNet). However, since BNet is open, untrusted and decentralized, traditional certificateless AKE schemes are difficult to overcome two technical bottlenecks, i.e., private distribution of partial keys and accurate identification of anonymous traitors. This paper proposes a conditionally private and dynamic batch AKE scheme (CPAKE) in BNet. We first develop a privacy-preserving certificateless key generation algorithm to prevent the disclosure of nodes’ privacy during partial key distribution. Then, we design a simple anonymous traitor tracing mechanism against Byzantine adversaries to accurately identify and trace the traitor’s identity. Finally, we build a dynamic batch authentication protocol with decentralized node management to ensure the final validity of honest nodes. Moreover, performance analysis indicates that our scheme reduces the signing and batch-verifying time by approximately 193.10 ms and 10.93 ms respectively over state-of-the-art. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Current Status of Objectification of Four Diagnostic Methods on Constitution Recognition of Chinese Medicine

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Chinese Journal of Integrative Medicine 2022年第12期28卷 1137-1146页

作者： LI Cong-cong YAN Xin-sheng LIU Ming-hao TENG Gui-fa School of Information Science and Technology Hebei Agricultural UniversityBaodingHebei Province 071001China Hebei Key Laboratory of Agricultural Big Data BaodingHebei Province 071001China

Chinese medicine(CM)has thousands of years of experience in prevention of *** for CM,people's constitution is closely related to their health status,thus recognition of CM constitution is the fundamental and core contentof research on constitution *** development of technologies such as sensors,arificial intelligence and big data,objectification of the four diagnostic methods of CM has gradually matured,bringing changes in the mindset and innovations in technical means for recognition of CM *** paper presents a systematic review of the latest research trends in constitution recognition based on objectification of diagnostic methods in CM.

关键词： Chinese medicine constitution recognition four diagnostic methods objectification machine learning deep learning

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Instance by instance: An iterative framework for multi-instance 3d registration

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1117-1128页

作者： Yang, Jiaqi Cao, Xinyue Zhang, Xiyu Cheng, Yuxin Qi, Zhaoshuai Quan, Siwen National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi'an China School of Electronic and Control Engineering Chang'an University Xi'an MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area Shenzhen University Shenzhen China

Multi-instance registration is a challenging problem in computer vision and robotics, where multiple instances of an object need to be registered in a standard coordinate system. Pioneers followed a non-extensible one-shot framework, which prioritizes the registration of simple and isolated instances, often struggling to accurately register challenging or occluded instances. To address these challenges, we propose the first iterative framework for multi-instance 3D registration (MI-3DReg) in this work, termed instance-by-instance (IBI). It successively registers instances while systematically reducing outliers, starting from the easiest and progressing to more challenging ones. This enhances the likelihood of effectively registering instances that may have been initially overlooked, allowing for successful registration in subsequent iterations. Under the IBI framework, we further propose a sparse-to-dense correspondence-based multi-instance registration method (IBI-S2DC) to enhance the robustness of MI-3DReg. Experiments on both synthetic and real datasets have demonstrated the effectiveness of IBI and suggested the new state-of-the-art performance with IBI-S2DC, e.g., our mean registration F1 score is 12.02%/12.35% higher than the existing state-of-the-art on the synthetic/real datasets. The source codes are availableonline at https://***/caoxy01/IBI. © 2014 Chinese Association of Automation.

关键词： 3D modeling

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Multi-layer network embedding on scc-based network with motif

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Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and Computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

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Unified Uncertainty Estimation for Cognitive Diagnosis Models 24

Unified Uncertainty Estimation for Cognitive Diagnosis Model...

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33rd ACM Web Conference, WWW 2024

作者： Wang, Fei Liu, Qi Chen, Enhong Liu, Chuanren Huang, Zhenya Wu, Jinze Wang, Shijin School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Hefei China Business Analytics and Statistics The University of Tennessee Knoxville United States IFLYTEK Research State Key Laboratory of Cognitive Intelligence Hefei China State Key Laboratory of Cognitive Intelligence Hefei China

ISBN: (纸本)9798400701719

Cognitive diagnosis models have been widely used in different areas, especially intelligent education, to measure users' proficiency levels on knowledge concepts, based on which users can get personalized instructions. As the measurement is not always reliable due to the weak links of the models and data, the uncertainty of measurement also offers important information for decisions. However, the research on the uncertainty estimation lags behind that on advanced model structures for cognitive diagnosis. Existing approaches have limited efficiency and leave an academic blank for sophisticated models which have interaction function parameters (e.g., deep learning-based models). To address these problems, we propose a unified uncertainty estimation approach for a wide range of cognitive diagnosis models. Specifically, based on the idea of estimating the posterior distributions of cognitive diagnosis model parameters, we first provide a unified objective function for mini-batch based optimization that can be more efficiently applied to a wide range of models and large datasets. Then, we modify the reparameterization approach in order to adapt to parameters defined on different domains. Furthermore, we decompose the uncertainty of diagnostic parameters into data aspect and model aspect, which better explains the source of uncertainty. Extensive experiments demonstrate that our method is effective and can provide useful insights into the uncertainty of cognitive diagnosis. © 2024 ACM.

关键词： Large datasets

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