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5th International Conference on computers and Artificial Intelligence technology, CAIT 2024

作者： Zhou, Wen Xu, Taihua Dai, Lisi Chen, Jianjun Song, Jingjing Jiangsu University of Science and Technology School of Computer Zhenjiang China Chongqing Institute of Engineering Department of Computer and Internet of Things Chongqing China

ISBN: (纸本)9798331530891

Shortest path mining is one of the classic problems in graph theory. How to design an efficient multi-source shortest paths mining algorithm has practical significance. In this article, an efficient multi-source shortest paths algorithm is designed based on rough sets theory. Firstly, within the framework of rough sets theory, investigation has been conducted on the multisource shortest paths, providing a theoretical basis for calculating multi-source shortest paths. Secondly, the R-related set operator is used to search all vertices reachable from each source vertex. Meanwhile, the k-step upper approximation operator is used to search for all vertices that can reach the target vertex. Then, a marking strategy is designed to control the construction of multi-source shortest paths, which can improve the mining efficiency. Finally, a multi-source shortest paths algorithm for unweighted directed graphs (RMSSP) based on rough sets theory is proposed. Experimental results show that the RMSSP algorithm can accurately discover multi-source shortest paths for unweighted directed graphs. Also, it performs better than similar algorithms in terms of computational efficiency. With the expansion of the dataset size, the efficiency advantage of RMSSP algorithm is more obvious. © 2024 IEEE.

关键词： graph theory marking strategy multi-source shortest paths rough sets unweighted directed graphs

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EVA-MVC: Equitable View-weight Allocation for Generic Multi-View Clustering 25

EVA-MVC: Equitable View-weight Allocation for Generic Multi-...

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34th ACM Web Conference, WWW 2025

作者： Fang, Yuan Feng, Xiaofeng Yang, Geping Cai, Ruichu Yang, Yiyang Gong, Zhiguo Hao, Zhifeng School of Computer Science and Technology Guangdong University of Technology Guangzhou China State Key Laboratory of Internet of Things for Smart City University of Macau China College of Engineering Shantou University Shantou China

ISBN: (纸本)9798400712746

Contemporary datasets sourced from the web often adopt a multiview format, collecting data from diverse sources, domains, or modules. Existing methodologies employed to analyze such datasets frequently overlook or inaccurately allocate the view-weights, pivotal metrics reflecting each view’s significance. This work introduces EVA-MVC, a simple yet effective algorithm designed for Equitable View-weight Allocation (EVA) seamlessly integrated with arbitrary Multi-view Clustering (MVC) methods. Within the EVA module, we establish theoretical connections between view supplementarity and Multi-view Subspace Learning (MSL), leading to the partition of views into View Communities (VCs) based on these foundational principles. These VCs exhibit internal supplementarity similarities, facilitating Equitable View-weights Allocation through VC-specific MSL. The proposed EVA process precedes and operates independently of traditional or SOTA MVC approaches, requiring no additional processing or specialized design, making it an ideal preprocessing step for MVC applications. Through comprehensive evaluations across diverse multi-view datasets, our findings reveal that our EVA significantly enhances the effectiveness of mainstream MVC frameworks, resulting in a notable performance improvement. © 2025 Copyright held by the owner/author(s).

关键词： consistency

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Rational 3D object placement based on deep learning based plane detection

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Multimedia Tools and Applications 2023年第28期82卷 44555-44576页

作者： Yang, Wei-Jong Lu, Li-Yu Chan, Din-Yuen Department of Artificial Intelligence and Computer Engineering National Chin-Yi University of Technology Taichung Taiwan Telecommunication Laboratories Chunghwa Telecom Co. Ltd. Internet of Things Laboratory Taoyuan Taiwan Department of Computer Science and Information Engineering National Chiayi University Chiayi Taiwan

Effective acquisition of 3D planar features from a 2D image for immersive AR applications is challenging without any 3D depth information. In this paper, we present a novel planar object placement (POP) system for rational insertion of 3D objects on plane surfaces and fitting their plane orientations in a given 2D image. The POP system is composed of an adaptive edge extractor (AEE), a vanishing point detector (VPD), a rational fitting transformer (RFT) and a plane normal detection network (PNDnet), which performs the estimation of planar features. With the inputs of the color image and the guided edge map retrieved by the AEE, the PNDnet based on a gating weighted U-Net, whose gating weights are controlled by global cross-level connection (GCC) blocks, can properly extract 3D information including planar pixel-wise normal map, semantic plane-segmented map and plane-wise normal vectors. Simultaneously, with plane-wise normal vectors and the VPD-estimated vanishing points, the RFT can easily induce the best placement position for projecting the AR object on a destination plane with better coherent 3D perspectives. Besides, the POP system allows the users to make the subtle tunes of 3D orientation to the inserted object for personal preferences. Experimental results show that the proposed POP system, which successfully guides the 3D-inserted objects to match up the surfaces and orientations of the planes in the target image, will become an effective AR tool for the insertion of 3D objects in target images and videos in the future. © 2023, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Semantic Segmentation

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RML++: Regroup Median Loss for Combating Label Noise

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International Journal of computer Vision 2025年 1-22页

作者： Li, Fengpeng Li, Kemou Wang, Qizhou Han, Bo Tian, Jinyu Zhou, Jiantao State Key Laboratory of Internet of Things for Smart City and Department of Computer and Information Science University of Macau Macao TMLR Group Department of Computer Science Hong Kong Baptist University Kowloon Hong Kong School of Computer Science and Engineering Macau University of Science and Technology Wai Long Road Taipa Macao

Training deep neural networks (DNNs) typically necessitates large-scale, high-quality annotated datasets. However, due to the inherent challenges of precisely annotating vast numbers of training samples, label noise—characterized by potentially erroneous annotations—is common yet detrimental in practice. Currently, to combat the negative impacts of label noise, mainstream studies follow a pipeline that begins with data sampling and is followed by loss correction. Data sampling aims to partition the original training dataset into clean and noisy subsets, but it often suffers from biased sampling that can mislead models. Additionally, loss correction typically requires knowledge of the noise rate as a priori information, of which the precise estimation can be challenging. To this end, we propose a novel method, Regroup Median Loss Plus Plus (RML++), that addresses both of the previous drawbacks. Specifically, the training dataset is partitioned into clean and noisy subsets using a newly designed separation approach, which synergistically combines prediction consistency with an adaptive threshold to ensure a reliable sampling. Moreover, to ensure the noisy subsets can be robustly learned by models, we suggest to estimate the losses of noisy training samples by utilizing the same-class samples from the clean subset. Subsequently, the proposed method corrects the labels of noisy samples based on the model predictions with the regularization of RML++. Compared to state-of-the-art (SOTA) methods, RML++ achieves significant improvements on both synthetic and challenging real-world datasets. The source code is available at https://***/Feng-peng-Li/RML-Extension. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Label noise Robust estimation Weakly supervised learning

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Mixed Deep Reinforcement Learning Considering Discrete-continuous Hybrid Action Space for Smart Home Energy Management

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Journal of Modern Power Systems and Clean Energy 2022年第3期10卷 743-754页

作者： Chao Huang Hongcai Zhang Long Wang Xiong Luo Yonghua Song IEEE State Key Laboratory of Internet of Things for Smart City University of MacaoMacao S.A.R.China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Shunde Graduate School University of Science and Technology BeijingFoshan 528399China State Key Laboratory of Internet of Things for Smart City and Department of Electrical and Computer Engineering University of MacaoMacao S.A.R.China

This paper develops deep reinforcement learning(DRL)algorithms for optimizing the operation of home energy system which consists of photovoltaic(PV)panels,battery energy storage system,and household ***-free DRL algorithms can efficiently handle the difficulty of energy system modeling and uncertainty of PV ***,discretecontinuous hybrid action space of the considered home energy system challenges existing DRL algorithms for either discrete actions or continuous ***,a mixed deep reinforcement learning(MDRL)algorithm is proposed,which integrates deep Q-learning(DQL)algorithm and deep deterministic policy gradient(DDPG)*** DQL algorithm deals with discrete actions,while the DDPG algorithm handles continuous *** MDRL algorithm learns optimal strategy by trialand-error interactions with the ***,unsafe actions,which violate system constraints,can give rise to great *** handle such problem,a safe-MDRL algorithm is further *** studies demonstrate that the proposed MDRL algorithm can efficiently handle the challenge from discrete-continuous hybrid action space for home energy *** proposed MDRL algorithm reduces the operation cost while maintaining the human thermal comfort by comparing with benchmark algorithms on the test ***,the safe-MDRL algorithm greatly reduces the loss of thermal comfort in the learning stage by the proposed MDRL algorithm.

关键词： Demand response deep reinforcement learning discrete-continuous action space home energy management safe reinforcement learning

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Research on the Application of Agile Testing in Satellite Ground Control System 24

Research on the Application of Agile Testing in Satellite Gr...

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2024 International Conference on Machine Learning, Pattern Recognition and Automation engineering, MLPRAE 2024

作者： Chen, Zhanhua Li, Guoyong Cao, Yanun Du, Chenxin The 54th Research Institute of China Electronics Technology Group Corporation China College of Computer Internet of Things Engineering Nanjing University of Information Science and Technology Binjiang College China

ISBN: (纸本)9798400709876

Traditional software testing has gradually exposed many drawbacks in multi-project development, through a multi-dimensional comparative analysis of the limitations of traditional testing and the characteristics of agile testing, based on the characteristics of the satellite ground control system and the development mode, a targeted and operable agile software testing model is designed to increase the cyclic iteration of the test, test-driven development and exploratory testing are introduced into the testing methodology, and the test implementation enhances and clarifies interface automation testing and accurate regression testing based on impact factors, utilizes integration testing based on micro-service groups of business domains and script testing of system operation scenarios, and provides a feasible method for solving the testing of large-scale information systems. The test results show that the test method alleviates the pressure of the centralized outbreak of traditional testing, digs out the hidden dangers of the system early and effectively, improves the testing efficiency, ensures the product quality, and enhances the users' confidence in the software products. © 2024 ACM.

关键词： Integration testing

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Machine Learning-driven Antenna Selection Strategies for Fading MIMO Systems 2

Machine Learning-driven Antenna Selection Strategies for Fad...

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2nd IEEE International Conference on Advancements and Key Challenges in Green Energy and Computing, AKGEC 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India Madhav Institute of Technology & Science Deemed University Centre for Internet of Things Gwalior India

ISBN: (纸本)9798350390179

In this study, we investigate better performing antenna selection strategies in wireless communication systems based on machine learning algorithms. Reinforcement learning as well as neural networks among others are analysed for different metrics such a bit error rate (BER), signal-to-noise ratio (SNR) and data throughput. These results show that these methods are working well against the dynamic changing of channel conditions to provide reliable and efficient. The computational complexity analysis suggests affordable overhead, allowing the feasibility of real-time implementation. Although this method can simulate the scenario in a real environment, it is not scalable due to simulation fidelity and antenna array. On the one hand, future research will refine and expand upon these ML models with real-time adaptation capabilities - On the other hand significant field trials are needed to put this work into practice for specific applications making sure that we develop robust adaptive wireless communication systems in practice. © 2024 IEEE.

关键词： Adversarial machine learning

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Modelling and Optimal Allocation of CCHP-Integrated-Energy-System Considering Low-Carbon and Economic Benefits: Methods and Vision 6

Modelling and Optimal Allocation of CCHP-Integrated-Energy-S...

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6th International Conference on Energy Systems and Electrical Power, ICESEP 2024

作者： Li, Maodong Lao, Kengweng Lu, Wentao University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering China School of Information Science and Technology Fudan University Shanghai China

ISBN: (纸本)9798350353563

Along with the development of smart energy, we take typical integrated energy systems (IES) into research considering various coupling modes and constraints, building a model of the IES for combined cooling, heating and power system, and propose the using of gas micro-turbine as a power supply. On this basis, the three dimension particle swarm optimization algorithm is applied to conduct operational and management research in one typical day, which provides a reference for the economic and low-carbon planning and operation of the IES, moreover demonstrates the superiority and effectiveness of the integrated energy systems compared to the traditional discrete energy systems. Furthermore, the paper gives the effective steps of allocation and optimization methods and vision of integrated energy systems. © 2024 IEEE.

关键词： Energy development

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Energy Efficient Transmitter Creation by Consuming Free Energy in Molecular Communication

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IEEE Transactions on Molecular, Biological, and Multi-Scale Communications 2025年第2期11卷 292-303页

作者： Jing, Dongliang Li, Linjuan Cheng, Zhen Lin, Lin Eckford, Andrew W. Northwest A&F University College of Mechanical and Electronic Engineering Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs Yangling712100 China Northwest A&F University College of Mechanical and Electronic Engineering Yangling712100 China Zhejiang University of Technology School of Computer Science and Technology Hangzhou310023 China Tongji University College of Electronics and Information Engineering Shanghai200092 China York University Department of Electrical Engineering and Computer Science TorontoONM3J 1P3 Canada

Information molecules play a crucial role in molecular communication (MC), acting as carriers for information transfer. A common approach to get information molecules in MC involves harvesting them from the environment;however, the harvested molecules are often a mixture of various environmental molecules, and the initial concentration ratios in the reservoirs are identical, which hampers high-fidelity transmission techniques such as molecular shift keying (MoSK). This paper presents a transmitter design that harvests molecules from the surrounding environment and stores them in two reservoirs. To separate the mixed molecules, energy is consumed to transfer them between reservoirs. Given limited energy resources, this work explores energy-efficient strategies to optimize transmitter performance. Through theoretical analysis and simulations, we investigate different methods for moving molecules between reservoirs. The results demonstrate that transferring higher initial concentration molecules enhances transmitter performance, while using fewer molecules per transfer further improves efficiency. These findings provide valuable insights for optimizing MC systems through energy-efficient molecule transfer techniques. © 2015 IEEE.

关键词： Transmitters

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Evolution and Advancement of Quantum Computing in the Era of Networking and Cryptography

Evolution and Advancement of Quantum Computing in the Era of...

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2023 International Conference on Advances in Computation, Communication and Information technology, ICAICCIT 2023

作者： Moazzam, Javed Pawar, Rahul Khare, Mayank Deep Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science and Information Technology Bangalore India Noida Institute of Engineering and Technology Department of Internet of Things Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350344387

Over the past decade, we've witnessed a remarkable and rapid surge in the realm of high-performance computing. The entire computing landscape, encompassing cloud and fog computing, has seen an unprecedented surge in popularity. Cloud computing, a system interconnecting myriad components, delivers application, data, and storage services over the internet. Quantum computing, on the other hand, harnesses the remarkable phenomena of quantum mechanics like superposition and entanglement for computational purposes. This paper serves as an expansive introduction to the fundamental principles, historical evolution, and breakthroughs in quantum computing, shedding light on its applications in network technology and cryptography. Moreover, it delves into the exciting potential for future game development empowered by quantum technology. In particular, we'll uncover the latest strides in cognitive networking and cryptography, drawing attention to the cutting-edge developments. Finally, we'll scrutinize the on-going research endeavours and lingering questions demanding further exploration within the dynamic realm of quantum computing. © 2023 IEEE.

关键词： Cryptography high performance computing and fog computing Network Quantum Computing

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