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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Hongxing Li Guochu Shou Yaqiong Liu Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

The need for precise timing is increasing with the development of the smart city, and intelligent transport is a key application that often relies on the distribution of accurate timing. Existing time synchronization solutions cannot adequately meet the precise timing needs for practical applications in intelligent transportation. Global Navigation Satellite Systems (GNSS) signals, for instance, can be obstructed, weakened, or deflected in urban canyon environments. While certain sectors like telecommunications and smart grids have successfully adopted IEEE 1588 for high-precision time synchronization, its dependence on standalone networks and limited application within local area networks poses challenges when catering to the wide-ranging and mobile demands of intelligent transportation applications. This paper categorizes the time synchronization requirements of intelligent transport and proposes a time synchronization scheme over converged networks, which separates time transmission and precision compensation based on software-defined networking (SDN) principles, and makes it possible to provide precise timing for intelligent transport devices, including on-board units (OBUs) and roadside units (RSUs), over wired and wireless communication networks. Experimental results indicate that the proposed scheme achieves a relative time error of less than 1.2 microseconds over the converged networks.

关键词： Wireless communication Global navigation satellite system Accuracy Smart cities Transportation Telecommunications Smart grids Synchronization software defined networking Local area networks

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Exploring Leximin Principle for Fair Core-Selecting Combinatorial Auctions: Payment Rule Design and Implementation

arXiv

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arXiv 2023年

作者： Cheng, Hao Kong, Shufeng Deng, Yanchen Liu, Caihua Wu, Xiaohu An, Bo Wang, Chongjun State Key Laboratory for Novel Software Technology Nanjing University China School of Software Engineering Sun Yat-Sen University China School of Computer Science and Engineering Nanyang Technological University Singapore School of Artificial Intelligence Guilin University of Electronic Technology China National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications China

Core-selecting combinatorial auctions (CAs) restrict the auction result in the core such that no coalitions could improve their utilities by engaging in collusion. The minimum-revenue-core (MRC) rule is a widely used core-selecting payment rule to maximize the total utilities of all bidders. However, the MRC rule can suffer from severe unfairness since it ignores individuals’ utilities. To address this limitation, we propose to explore the leximin principle to achieve fairness in core-selecting CAs since the leximin principle prefers to maximize the utility of the worst-off;the resulting bidder-leximin-optimal (BLO) payment rule is then theoretically analyzed and an effective algorithm is further provided to compute the BLO outcome. Moreover, we conduct extensive experiments to show that our algorithm returns fairer utility distributions and is faster than existing algorithms of core-selecting payment rules. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Three-Dimensional Numerical Study on the Effect of Radius of the Equal-Area Mixing Chamber on Auxiliary Entraining of the Two-Stage Ejector

SSRN

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SSRN 2023年

作者： Yan, Jia Gao, Haiping Wang, Zheng School of Civil Engineering and Architecture Southwest University of Science and Technology Mianyang City621010 China Tianfu Engineering Numerical Simulation and Software Innovation Center School of Computer Sciences Sichuan University Chengdu City610041 China

In this study, for the two-stage ejector under three inlet conditions of air-conditioning, refrigerating and freezing, a three-dimensional simulation is used to study the impact of the mixing chamber radius of the first and second stage on the minimum pressure in the ejector without auxiliary entraining first, and then, the impact of real auxiliary entraining effects on ejector performance for two-stage ejector with varied equal-area mixing chamber radius is evaluated accordingly. Results show that: (1) as the second stage equal-area mixing chamber radius (Ram2) increases, the minimum first stage ejector pressure (Pmin1) decreases and the minimum second stage ejector pressure (Pmin2) increases;(2) with the increase of radius of the first-stage equal-area mixing chamber (Ram1), the decreasing effect of Pmin1 and the increasing effect of Pmin2 are both weakened as Ram2 increases from 3.0 mm to 4.6 mm;(3) the minimum pressures of the two-stage have a great influence on the auxiliary entraining effect. © 2023, The Authors. All rights reserved.

关键词： Ejectors (pumps)

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Extracting Spatial High Utility Co-location Patterns Based on Fuzzy Feature Clusters 9th

Extracting Spatial High Utility Co-location Patterns Based o...

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9th China National Conference on Big Data and Social Computing, BDSC 2024

作者： Jin, Peijie Wang, Xiaoxuan Xiong, Wen Wang, Lizhen Gao, Song School of Information Science and Technology Yunnan Normal University Kunming650500 China Engineering Research Center of Computer Vision and Intelligent Control Technology Department of Education of Yunnan Province Kunming650500 China Dianchi College Yunnan University Kunming650500 China School of Software Yunnan University Kunming650500 China

ISBN: (纸本)9789819758029

Sets of spatial features whose instances frequently appear together in nearby areas are regarded as spatial co-location patterns (SCPs). Spatial high utility co-location pattern(SHUCP), which is an extended research of SCPs, can better reflect the high quality aggregation phenomenon among spatial features. Utility participation index (UPI) is a commonly used adaptive utility measurement in most of the existing methods. Unfortunately, UPI does not take into account the impacts of fuzziness and overlap of proximity relationships on the calculation of utility. Additionally, UPI mainly relies on the computation of the instance layer and does not satisfy the downward closure property. In this paper, we combines fuzzy set theory to establish fuzzy neighbor relationships and discuss the issue of instance overlap between neighbor relationships. Then, a new calculation method for the utility index between spatial features has been design with considering fuzzy contribution score of instances. Using Fuzzy Chameleon Clustering algorithm (FCC) based on fuzzy utility index, this paper further extracts SHUCPs from different high utility fuzzy feature clusters. Experiments are conducted on three synthetic data sets and two real data sets to prove the rationality and effectiveness of the proposed algorithm. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Fuzzy set theory

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Understanding Heterophily for Graph Neural Networks

arXiv

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arXiv 2024年

作者： Wang, Junfu Guo, Yuanfang Yang, Liang Wang, Yunhong State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Shen Yuan Honors College Beihang University Beijing China School of Artificial Intelligence Hebei University of Technology Tianjin China

Graphs with heterophily have been regarded as challenging scenarios for Graph Neural Networks (GNNs), where nodes are connected with dissimilar neighbors through various patterns. In this paper, we present theoretical understandings of heterophily for GNNs by incorporating the graph convolution (GC) operations into fully connected networks via the proposed Heterophilous Stochastic Block Models (HSBM), a general random graph model that can accommodate diverse heterophily patterns. Our theoretical investigation comprehensively analyze the impact of heterophily from three critical aspects. Firstly, for the impact of different heterophily patterns, we show that the separability gains are determined by two factors, i.e., the Euclidean distance of the neighborhood distributions and pE [deg], where E [deg] is the averaged node degree. Secondly, we show that the neighborhood inconsistency has a detrimental impact on separability, which is similar to degrading E [deg] by a specific factor. Finally, for the impact of stacking multiple layers, we show that the separability gains are determined by the normalized distance of the lpowered neighborhood distributions, indicating that nodes still possess separability in various regimes, even when over-smoothing occurs. Extensive experiments on both synthetic and real-world data verify the effectiveness of our theory. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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DeltaFS: Pursuing Zero Update Overhead via Metadata-Enabled Delta Compression for Log-structured File System on Mobile Devices

arXiv

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arXiv 2022年

作者： Wu, Chao Ji, Cheng Yuan, Geng Pan, Riwei Guo, Weichao Yu, Chao Zhu, Zongwei Wang, Yanzhi Department of Electrical and Computer Engineering Northeastern University United States School of Computer Science Nanjing University of Science and Technology China Department of Computer Science City University of Hong Kong Hong Kong Guangdong Oppo Mobile Telecommunications Corp. Ltd China School of Software Engineering University of Science and Technology of Chinese China

Data compression has been widely adopted to release mobile devices from intensive write pressure. Delta compression is particularly promising for its high compression efficacy over conventional compression methods. However, this method suffers from non-trivial system overheads incurred by delta maintenance and read penalty, which prevents its applicability on mobile devices. To this end, this paper proposes DeltaFS, a metadata-enabled Delta compression on log-structured File System for mobile devices, to achieve utmost compressing efficiency and zero hardware costs. DeltaFS smartly exploits the out-of-place updating ability of Log-structured File System (LFS) to alleviate the problems of write amplification, which is the key bottleneck for delta compression implementation. Specifically, DeltaFS utilizes the inline area in file inodes for delta maintenance with zero hardware cost, and integrates an inline area manage strategy to improve the utilization of constrained inline area. Moreover, a complimentary delta maintenance strategy is incorporated, which selectively maintains delta chunks in the main data area to break through the limitation of constrained inline area. Experimental results show that DeltaFS substantially reduces write traffics by up to 64.8%, and improves the I/O performance by up to 37.3%.1 © 2022, CC BY.

关键词： Metadata

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Intelligent Systems for Early Malaria Disease Detection in Patient Cells Using Transfer Learning Approaches

Intelligent Systems for Early Malaria Disease Detection in P...

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International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)

作者： Gul Zaman Khan Ibrar Ali Shah Farhatullah Muhammad Abul Hassan Hazrat Junaid Fouzia Sardar Department of Computer software Engineering UET Mardan Pakistan School of Automation China University of Geosciences Wuhan China Department of Information Engineering and Computer Science University of Trento Italy Department of Computer Science and Information Technology University of Malakand Pakistan Department of Zoology University f Malakand Dir Lower Pakistan

Malaria is an infectious disease spread by mosquitoes that effect humans and other animals. It is a massive threat to humanity, with instances growing each year. It is essential to prevent and diagnose malaria immediately and efficiently. For the time being, conventional methods are used for diagnosing malaria in which the patient's blood sample is examined by microscope or by using malaria RTD kits. This approach has several limitations because it requires medical expertise, is expensive, takes a long time, and the results are unsatisfactory. Artificial intelligence-based systems can prevent and help in diagnose of this infectious disease. Because of these limitations, the proposed work has proposed an AI-based diagnosis system that can detect malaria parasites immediately and efficiently. In the proposed experiment, we have applied four different pre-trained deep learning models on the image dataset with some preprocessing and optimization techniques for malaria parasite detection. After investigations, evaluation matrices such as precision, Recall, F1-score, sensitivity, and specificity are used to measure the performance of the proposed models. The Inception-Resnet outperformed by achieving 95% accuracy, VGG16 achieved 92% accuracy, inception achieved 93% accuracy, and VGG19 achieved 91% accuracy. The positive outcomes of this study show that this approach performs much better than the approaches currently used. Furthermore, the proposed method is relevant to health experts for screening purposes.

关键词： Deep learning Humanities Infectious diseases Microscopy Transfer learning Sensitivity and specificity Intelligent systems

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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AUGRMIXAT: A DATA PROCESSING AND TRAINING METHOD FOR IMPROVING MULTIPLE ROBUSTNESS AND GENERALIZATION PERFORMANCE

arXiv

引用

arXiv 2022年

作者： Liu, Xiaoliang Shen, Furao Zhao, Jian Nie, Changhai National Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Electronic Science and Engineering Nanjing University China

Deep neural networks are powerful, but they also have shortcomings such as their sensitivity to adversarial examples, noise, blur, occlusion, etc. Moreover, ensuring the reliability and robustness of deep neural network models is crucial for their application in safety-critical areas. Much previous work has been proposed to improve specific robustness. However, we find that the specific robustness is often improved at the sacrifice of the additional robustness or generalization ability of the neural network model. In particular, adversarial training methods significantly hurt the generalization performance on unperturbed data when improving adversarial robustness. In this paper, we propose a new data processing and training method, called AugRmixAT, which can simultaneously improve the generalization ability and multiple robustness of neural network models. Finally, we validate the effectiveness of AugRmixAT on the CIFAR-10/100 and Tiny-ImageNet datasets. The experiments demonstrate that AugRmixAT can improve the model's generalization performance while enhancing the white-box robustness, black-box robustness, common corruption robustness, and partial occlusion robustness. Copyright © 2022, The Authors. All rights reserved.

关键词： Neural network models

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How Mature is Requirements Engineering for AI-based Systems? A Systematic Mapping Study on Practices, Challenges, and Future Research Directions

arXiv

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arXiv 2024年

作者： Umm-E-Habiba Haug, Markus Bogner, Justus Wagner, Stefan Institute of Software Engineering University of Stuttgart Stuttgart70569 Germany Department of Software Engineering University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Department of Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands TUM School of Communication Information and Technology Technical University of Munich Heilbronn Germany

Artificial intelligence (AI) permeates all fields of life, which resulted in new challenges in requirements engineering for artificial intelligence (RE4AI), e.g., the difficulty in specifying and validating requirements for AI or considering new quality requirements due to emerging ethical implications. It is currently unclear if existing RE methods are sufficient or if new ones are needed to address these challenges. Therefore, our goal is to provide a comprehensive overview of RE4AI to researchers and practitioners. What has been achieved so far, i.e., what practices are available, and what research gaps and challenges still need to be addressed? To achieve this, we conducted a systematic mapping study combining query string search and extensive snowballing. The extracted data was aggregated, and results were synthesized using thematic analysis. Our selection process led to the inclusion of 126 primary studies. Existing RE4AI research focuses mainly on requirements analysis and elicitation, with most practices applied in these areas. Furthermore, we identified requirements specification, explainability, and the gap between machine learning engineers and end-users as the most prevalent challenges, along with a few others. Additionally, we proposed seven potential research directions to address these challenges. Practitioners can use our results to identify and select suitable RE methods for working on their AI-based systems, while researchers can build on the identified gaps and research directions to push the field forward. ∗ © 2024, CC BY.

关键词： Requirements engineering

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