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32nd International Conference on Concurrency Theory, CONCUR 2021

作者： Glabbeek, Rob Van Höfner, Peter Wang, Weiyou Data61 Csiro Sydney Australia University of New South Wales Computer Science and Engineering Sydney Australia School of Computing Australian National University Canberra Australia

ISBN: (纸本)9783959772037

Most fairness assumptions used for verifying liveness properties are criticised for being too strong or unrealistic. On the other hand, justness, arguably the minimal fairness assumption required for the verification of liveness properties, is not preserved by classical semantic equivalences, such as strong bisimilarity. To overcome this deficiency, we introduce a finer alternative to strong bisimilarity, called enabling preserving bisimilarity. We prove that this equivalence is justness-preserving and a congruence for all standard operators, including parallel composition. © Rob van Glabbeek, Peter Höfner, and Weiyou Wang;.

关键词： Semantics

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Layered Structure Aware Dependent Microservice Placement Toward Cost Efficient Edge Clouds

Layered Structure Aware Dependent Microservice Placement Tow...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Deze Zeng Hongmin Geng Lin Gu Zhexiong Li School of Computer Science China University of Geosciences Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Although the containers are featured by light-weightness, it is still resource-consuming to pull and startup a large container image, especially in relatively resource-constrained edge cloud. Fortunately, Docker, as the most widely used container, provides a unique layered architecture that allows the same layer to be shared between microservices so as to lower the deployment cost. Meanwhile, it is highly desirable to deploy dependent microservices of an application together to lower the operation cost. Therefore, the balancing of microservice deployment cost and the operation cost should be considered comprehensively to achieve minimal overall cost of an on-demand application. In this paper, we first formulate this problem into a Quadratic Integer Programming form (QIP) and prove it as a NP-hard problem. We further propose a Randomized Rounding-based Microservice Deployment and Layer Pulling (RR-MDLP) algorithm with low computation complexity and guaranteed approximation ratio. Through extensive experiments, we verify the high efficiency of our algorithm by the fact that it significantly outperforms existing state-of-the-art microservice deployment strategies.

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A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks

arXiv

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arXiv 2023年

作者： Chen, Xinyi Yang, Qu Wu, Jibin Li, Haizhou Tan, Kay Chen Department of Computing The Hong Kong Polytechnic University Hong Kong Department of Electrical and Computer Engineering National University of Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen [CUHK- Shenzhen China

Recently, brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks. However, these SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation. Given that each neural coding scheme possesses its own merits and drawbacks, these SNNs encounter challenges in achieving optimal performance such as accuracy, response time, efficiency, and robustness, all of which are crucial for practical applications. In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes. As an initial exploration in this direction, we propose a hybrid neural coding and learning framework, which encompasses a neural coding zoo with diverse neural coding schemes discovered in neuroscience. Additionally, it incorporates a flexible neural coding assignment strategy to accommodate task-specific requirements, along with novel layer-wise learning methods to effectively implement hybrid coding SNNs. We demonstrate the superiority of the proposed framework on image classification and sound localization tasks. Specifically, the proposed hybrid coding SNNs achieve comparable accuracy to state-of-the-art SNNs, while exhibiting significantly reduced inference latency and energy consumption, as well as high noise robustness. This study yields valuable insights into hybrid neural coding designs, paving the way for developing high-performance neuromorphic systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Pattern recognition

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Tl-Snn: Event-Driven Visual-Tactile Learning with Temporal and Location Spiking Neurons

SSRN

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SSRN 2024年

作者： Jing, Yang Yin, Baofan Li, Shaobo Su, Zhidong Zhang, Zhaohu School of Mechanical Engineering Guizhou University Jiaxiu South Road Guizhou Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Jiaxiu South Road Guizhou Guiyang550025 China School of Electrical and Computer Engineering Oklahoma State University Stillwater OK OklahomaOK74078 United States

With the continuous development of neuromorphic sensors and spiking neural networks, there is increasing attention on event-driven perception learning in both vision and tactile domains. However, due to the limited information representation capability of existing spiking neurons and the high spatio-temporal complexity of event-driven visual and tactile data, we focus on exploring the application potential of visual and tactile perception in event-driven datasets. We propose an innovative spiking neural network method for integrating visual and tactile perception, aiming to significantly enhance the perceptual and information integration capabilities of the fusion network. Our approach enables the extraction of features from both time and position dimensions, thereby more effectively capturing the spatio-temporal dependencies in event data. Additionally, we introduce a weighted spike loss function to optimize model performance and meet specific task requirements. Experimental results demonstrate that the proposed visual and tactile fusion spiking neural network achieves superior performance compared to baseline algorithms on object recognition, container detection, and slip detection datasets. © 2024, The Authors. All rights reserved.

关键词： Neural networks

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CasCenter: A Cascaded Center Network for Visual Tracking

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IEEE Transactions on Consumer Electronics 2025年

作者： Li, Qun Zhang, Haijun Yang, Kai Shi, Yong-Guo Zeng, Deqiang Yap, Wun-She Xili University Town Department of Computer Science Harbin Institute of Technology Shenzhen518055 China WuHan Textile University School of Computer Science and Artificial Intelligence Wuhan430200 China Neijiang Normal University School of Mathematics and Information Science Data Recovery Key Laboratory of Sichuan Province Neijiang641100 China Universiti Tunku Abdul Rahman Lee Kong Chian Faculty of Engineering and Science Sungai Long43000 Malaysia

Object tracking has advanced significantly with Transformer-based architectures in recent years. However, replacing convolutional layers with global cross-attention in the tracking head of these architectures results in a loss of object-centric inductive bias. Consequently, existing Transformer-based methods often struggle with complex real-life scenarios, such as low resolution, background clutter, and scale variation. To address this issue, we propose a new Vision Transformer-based anchor-free tracking framework named CasCenter. Specifically, the framework features a cascade attention module in the decoder that propagates tracking cues from the previous tracking head to refine object features in a coarse-to-fine manner, enabling the tracker to focus more effectively on the target. Additionally, to further improve tracking stability and accuracy, we incorporate SIoU loss, a multi-scale tracking head, and a Gaussian mask-constrained cross-attention mechanism that emphasizes target regions while suppressing background interference. Extensive experiments demonstrate the superiority of our proposed CasCenter. © 1975-2011 IEEE.

关键词： Object detection

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Aspect-oriented requirement analysis based on formal method

Aspect-oriented requirement analysis based on formal method

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2021 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2021

作者： Qu, Can Zhang, Xuhui Chen, Haihua Zhang, Lichen School of Information City College of Huizhou Huizhou China School of Electrical and Computer Engineering Nanfang College Guangzhou Guangzhou China School of Data Science Guangzhou Huashang College Guangzhou China School of Computers Guangdong University of Technology Guangzhou China

Aspect-Oriented Programming (AOP) can effectively solve the code-tangling and code-scattering which are caused by crosscutting attributes, However, much work is presented on the programming and the implementation phase, and few works have addressed the early model stages. In this paper, we do research on the requirements engineering phase of software by using AOP technology. First in the requirements analysis phase, we identified the functional requirements and non-functional requirements by the separation of concerns, then to realize the functional requirements with components and the non-functional requirements with aspects. Then we represent the components and aspects by combining with the formal language Aspect-Z which have characteristics of accurate description;As actions accompany with conflicts while two or more aspects effect on the same joint point synchronously, we propose a method to solve by defining aspects priority level. © Published under licence by IOP Publishing Ltd.

关键词： Formal verification

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Real-Time Traffic Flow Optimization using Adaptive IoT and data Analytics: A Novel DeepStreamNet Model

Real-Time Traffic Flow Optimization using Adaptive IoT and D...

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Sustainable Expert Systems (ICSES), International Conference on

作者： V.S. Saranya Garimidi Subbarao Dega Balakotaiah Maloth Bhavsingh K. Suresh Babu Srinivasa Rao Dhanikonda Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chennai Department o Computer Science and Engineering (AIDS) Pace Institute of Technology and Sciences Department of Computer Science and Engineering Vignan's Founadation for Science Technology & Research (Deemed to be University) Department of Computer Science and Engineering Ashok: Womens Engineering College Kurnool Andhra Pradesh Department of Computer Science and Engineering Narasaraopeta Engineering College(Autonomous) Narasaraopet A.P Department of Artificial Intelligence and Data Science Faculty of Science and Technology (IcfaiTech) ICFAI Foundation for Higher Education (Deemed to Be University) Hyderabad Telangana India

ISBN: (数字)9798331540364

ISBN: (纸本)9798331540371

Thisstudy introduces the DeepStreamNet model, an advanced framework for enhancing real-time traffic management in urban environments using adaptive IoT and sophisticated big data analytics. Central to our approach is the integration of convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) units, which synergize with IoT devices to facilitate dynamic adaptation to fluctuating traffic conditions. The model excels in operational performance, achieving a notable accuracy of 94.7% in congestion prediction with an exceptionally low latency of only 45 ms per decision cycle and the capability to process up to 50,000 data points per second. These technical achievements have marked significant improvements over traditional traffic management systems, enhancing traffic flow efficiency and reducing congestion effectively. The results underscore the transformative potential of the DeepStreamNet model in urban traffic management and public safety, offering actionable insights for intelligent transportation systems and setting the stage for more informed urban planning. Looking forward, we suggest extending the application of our model to areas such as logistics and broader smart city initiatives, highlighting its adaptability and broad utility in various urban contexts

关键词： Adaptation models Analytical models Accuracy Smart cities Urban planning data models Real-time systems Internet of Things Optimization Context modeling

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A review of research on traction load models and modeling methods for electrified railways

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Renewable and Sustainable Energy Reviews 2025年 219卷

作者： Che, Yulong Wang, Xiaoru Ge, Leijiao Lin, Hongjian Lyu, Xiaoqin Su, Hongsheng Wang, Hao College of Automation and Electrical Engineering Lanzhou Jiaotong University Gansu Province Lanzhou730070 China School of Electrical Engineering Southwest Jiaotong University Sichuan Province Chengdu611756 China School of Electrical and Information Engineering Tianjin University Tianjin300072 China Department of Electrical and Computer Engineering University of Alberta EdmontonT6G 1K7 Canada Department of Data Science and AI Faculty of Information Technology Monash University MelbourneVIC3800 Australia

As the electrified railways, especially high-speed railways, continue to expand, the proportion of traction load in overall power load is increasing significantly. Establishing a traction load model is crucial for the design and operation of traction power supply system (TPSS) and the evaluation of impact on power systems. The paper provides an overview of key issues related to traction loads in electrified railways. It systematically summarizes research trends and achievements on traction load characteristics, models, and modeling methods, as well as their comparability and practicality. Firstly, we analyze the electrical and spatiotemporal characteristics of traction loads. Secondly, based on the modeling requirements and application scenarios of traction loads, this paper classifies and compares existing traction load models. Then, we review the current research status of traction load modeling methods, with a focus on deterministic and uncertainty modeling methods for traction loads. The advantages and disadvantages of various traction load modeling methods are analyzed and compared. Finally, it highlights trends in traction load modeling, including traction load forecasting, multi random variable modeling, data-driven approaches, and integration of AI and IoT. This paper presents a comprehensive survey on traction load modeling, current practices, and future outlook, while providing guidance for selecting appropriate traction load models. © 2025 Elsevier Ltd

关键词： Subways

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Intelligent Reflecting Surface-Aided Radar Spoofing

arXiv

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arXiv 2024年

作者： Wang, Haozhe Zheng, Beixiong Shao, Xiaodan Zhang, Rui Shenzhen Research Institute of Big Data School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China School of Microelectronics South China University of Technology Guangzhou511442 China Shenzhen Research Institute of Big Data Shenzhen518172 China The Institute for Digital Communications Friedrich-Alexander-University Erlangen-Nuremberg Erlangen91054 Germany The Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

Electronic countermeasure (ECM) technology plays a critical role in modern electronic warfare, which can interfere with enemy radar detection systems by noise or deceptive signals. However, the conventional active jamming strategy incurs additional hardware and power costs and has the potential threat of exposing the target itself. To tackle the above challenges, we propose a new intelligent reflecting surface (IRS)-aided radar spoofing strategy in this letter, where IRS is deployed on the surface of a target to help eliminate the signals reflected towards the hostile radar to shield the target, while simultaneously redirecting its reflected signal towards a surrounding clutter to generate deceptive angle-of-arrival (AoA) sensing information for the radar. We optimize the IRS’s reflection to maximize the received signal power at the radar from the direction of the selected clutter subject to the constraint that its received power from the direction of the target is lower than a given detection threshold. We first solve this non-convex optimization problem using the semidefinite relaxation (SDR) method and further propose a lower-complexity solution for real-time implementation. Simulation results validate the efficacy of our proposed IRS-aided spoofing system as compared to various benchmark schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Direction of arrival

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A spectrum-enhanced attention model for semantic segmentation of remote sensing images

A spectrum-enhanced attention model for semantic segmentatio...

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

作者： Xin Li Feng Xu Feifei Tao Yao Tong Xin Lyu Jianyi Zhong André Kaup College of Computer Science and Software Engineering Hohai University Nanjing China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Nanjing China School of Computer Engineering Jiangsu Ocean University Lianyungang China School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Chair of Multimedia Communications and Signal Processing Friedrich-Alexander University Erlangen-Nuremberg Erlangen Germany

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Semantic segmentation of remote sensing images (RSIs) is essential for applications such as environmental monitoring, urban planning, and disaster management. Convolutional Neural Networks (CNNs) and their variants struggle to capture comprehensive spectral context for learning discriminative representations. In this paper, we propose a Spectrum-Enhanced Network (SPENet) that leverages the Frequency Transformer Block (FTB) to capture rich spectral context. FTB integrates Spectrum-Enhanced Attention (SEA) with Multi-Head Frequency Self-Attention (MH-FSA), incorporating more informative contextual cues. Specifically, SEA aggregates spectral statistics through covariance matrix normalization before applying channel-wise attention. By projecting feature maps onto the frequency domain, MH-FSA provides the network with a broader context, extending beyond the low-frequency focus of standard self-attention mechanisms. Extensive experiments on the ISPRS Potsdam and LoveDA datasets show that SPENet significantly outperforms state-of-the-art methods. Besides, the proposed SEA module notably rises average F1-score/overall accuracy/mean insert over union wiht more than 2.5/2.6%/2.3%, as demonstrated by ablation study.

关键词： Semantic segmentation Frequency-domain analysis Urban planning Semantics Speech enhancement Signal processing Transformers Sensors Remote sensing Standards

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