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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Xiao, Nan Luo, Fei Cao, Liang College of Software Engineering Chengdu University of Information Technology Sichuan Chengdu China Yunnan R&D Institute of Natural Disaster Chengdu University of Information Technology Yunnan Kunming China Automatic Software Generation and Intelligent Service Key Laboratory of Sichuan Province Sichuan Chengdu China CMA - CUIT Meteorological Software Engineering Joint Research Center Sichuan Chengdu China

ISBN: (纸本)9781510680425

The accurate prediction of low-visibility has important application value to the normal operation of navigation, aviation and other transportation vehicles. However, the current low-visibility prediction task mostly uses time series model to extract temporal features, but neglects the spatial correlation between national observation stations. Additionally, those time series model require that the input data must be limited to the Euclidean space. However, due to the discrete distribution of stations, the input data required for multi-station low-visibility prediction does not have a regular spatial structure. To address those limitation, we proposed a weighted location feature convolution based on graph convolution. The construction of the weighted location feature convolution takes into account the distance and pitch angle, reduces the influence caused by the dispersion of national observation stations, and enables the convolution kernel to be shared in different local meteorological spaces. The convolution as a substitute for matrix multiplication within Sequence to Sequence framework, then cascaded the decoder to deal with the spatio-temporal dependence relationship of different levels, leading spatio-temporal fusion model CPGDC. The experimental results show that the CPGDC model achieves has higher accuracy in predicting low-visibility values across multiple national observation stations. © 2024 SPIE.

关键词： Input output programs

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Uncertainty-Aware Physical Simulation of Neural Radiance Fields for Fluids

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Computer Modeling in engineering & Sciences 2024年第7期140卷 1143-1163页

作者： Haojie Lian Jiaqi Wang Leilei Chen Shengze Li Ruochen Cao Qingyuan Hu Peiyun Zhao Key Laboratory of In-Situ Property-Improving Mining of Ministry of Education Taiyuan University of TechnologyTaiyuan030024China Henan International Joint Laboratory of Structural Mechanics and Computational Simulation College of Architectural and Civil EngineeringHuanghuai UniversityZhumadian463000China National Innovation Institute of Defense Technology Academy of Military ScienceBeijing100091China School of Software Taiyuan University of TechnologyJinzhong030600China School of Science Jiangnan UniversityWuxi214122China

This paper presents a novel framework aimed at quantifying uncertainties associated with the 3D reconstruction of smoke *** approach reconstructs color and density fields from 2D images using Neural Radiance Field(NeRF)and improves image quality using frequency *** NeRF model is obtained via joint training ofmultiple artificial neural networks,whereby the expectation and standard deviation of density fields and RGB values can be evaluated for each *** addition,customized physics-informed neural network(PINN)with residual blocks and two-layer activation functions are utilized to input the density fields of the NeRF into Navier-Stokes equations and convection-diffusion equations to reconstruct the velocity *** velocity uncertainties are also evaluated through ensemble *** effectiveness of the proposed algorithm is demonstrated through numerical *** presentmethod is an important step towards downstream tasks such as reliability analysis and robust optimization in engineering design.

关键词： Uncertainty quantification neural radiance field physics-informed neural network frequency regularization twolayer activation function ensemble learning

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An Effective Method for Constructing Knowledge Graph to Search Reusable ROS Nodes 35

An Effective Method for Constructing Knowledge Graph to Sear...

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35th International Conference on software engineering and Knowledge engineering, SEKE 2023

作者： Zhao, Yuxin Mao, Xinjun Bo, Sun Zhang, Tanghaoran Yang, Shuo College of Computer National University of Defense Technology Changsha China Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology Changsha China

Developing robot software is difficult for most software engineers as it requires multi-discipline knowledge such as robotics, AI, and software engineering. Robot Operating Systems (ROS) provides a software development framework and lots of reusable ROS Nodes that encapsulate various robotics functions, which can simplify robot software development in terms of software reuse. However, searching and reusing required ROS Nodes from thousands of ROS Nodes is still challenging due to the scattered distribution of ROS Node information and the need for adequate search methods. In this paper, we present an effective method to construct a ROS Node knowledge graph in support of searching and reusing ROS Nodes. Our method uses multiple data sources, including open-source ROS software in Github and ROS wiki community. We extract two-tuple functional information and task-related noun phrases from the ROS Node description and ROS communication interactions from the ROS Node source code. The constructed ROS Node knowledge graph (RNKG) contains 14,065 entities and 15,767 relations. It provides rich semantic information to comprehensively and precisely describe ROS Nodes, their services, and related interaction topics and messages. © 2023 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Knowledge graph

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FEAT: Towards Fast Environment-Adaptive Task Offloading and Power Allocation in MEC 42

FEAT: Towards Fast Environment-Adaptive Task Offloading and ...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Ren, Tao Hu, Zheyuan He, Hang Niu, Jianwei Liu, Xuefeng Institute of Software Chinese Academy of Sciences Laboratory for Internet Software Technologies Beijing China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Mobile edge computing (MEC) has been proposed to provide mobile devices with both satisfactory computing resources and latency. Key issues in MEC include task offloading and power allocation (TOPA), for which deep reinforcement learning (DRL) is becoming a popular methodology. However, most DRL-based TOPA approaches are typically developed in a certain environment, lacking the adaptability to unseen environments. Motivated by this, this paper proposes a Fast Environment-Adaptive TOPA (FEAT) approach that could adapt to unseen environments with little fine-tuning. Specifically, we first split MEC states into the internal state and environmental state. Then, based on these two types of states, we develop two main components of FEAT - a group of internal state-dependent TOPA meta-policies and an environmental state-embedded steerer. Meta-policies learn TOPA skills within the internal state space (allowing reusing meta-policies in different environments), while the steerer learns to choose appropriate meta-policies according to embedded environmental states. When encountering an unseen environment with the same internal state space, FEAT only needs to fine-tune the steerer using the newly embedded environmental state with few internal state explorations. Extensive experimental results on simulation and testbed demonstrate that FEAT outperforms the state-of-the-art by more than 16.4% in terms of fine-tuning speeds. © 2023 IEEE.

关键词： Mobile edge computing

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Deep Semantic and Attentive Network for Unsupervised Video Summarization

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ACM Transactions on Multimedia Computing, Communications and Applications 2022年第2期18卷 1-21页

作者： Zhong, Sheng-Hua Lin, Jingxu Lu, Jianglin Fares, Ahmed Ren, Tongwei College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical Engineering Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Cairo Egypt State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performance, they require frame-level annotated labels. Such an annotation process is time-consuming and tedious. In this article, we propose a novel deep summarization framework named Deep Semantic and Attentive Network for Video Summarization (DSAVS) that can select the most semantically representative summary by minimizing the distance between video representation and text representation without any frame-level labels. Another challenge associated with video summarization tasks mainly originates from the difficulty of considering temporal information over a long time. Long Short-Term Memory (LSTM) performs well for temporal dependencies modeling but does not work well with long video clips. Therefore, we introduce a self-attention mechanism into our summarization framework to capture the long-range temporal dependencies among the frames. Extensive experiments on two popular benchmark datasets, i.e., SumMe and TVSum, show that our proposed framework outperforms other state-of-the-art unsupervised approaches and even most supervised methods. © 2022 Association for Computing Machinery.

关键词： Semantics

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Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing 42

Enabling Communication-Efficient Federated Learning via Dist...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Guan, Yixuan Liu, Xuefeng Ren, Tao Niu, Jianwei Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Chinese Academy of Sciences Laboratory for Internet Software Technologies Institute of Software Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Federated learning (FL) trains a shared global model by periodically aggregating gradients from local devices. Communication overhead becomes a principal bottleneck in FL since participating devices usually suffer from limited bandwidth and unreliable connections in uplink transmission. To address this problem, the gradient compression methods based on compressed sensing (CS) theory have been put forward recently. However, most existing CS-based works compress gradients independently, ignoring the gradient correlations between participants or adjacent communication rounds, which constrains the achievement of higher compression rates. In view of the above observation, we propose a novel gradient compression scheme named FedDCS, guided by distributed compressed sensing (DCS) theory. Following the design philosophy of separate encoding and joint decoding in DCS, FedDCS compresses gradients for participants in each round separately while reconstructing them at the central server jointly via fully exploiting correlated gradients from the previous round, which are known as side information (SI). Benefiting from this design, reconstruction performance is significantly improved with fewer decoding errors also iterations under the identical compression rate, and the total uploading bits to achieve model convergence are considerably reduced. Theoretical analysis and extensive experiments conducted on MNIST and Fashion-MNIST both verify the effectiveness of our approach. © 2023 IEEE.

关键词： Compressed sensing

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FBI-Net: Frequency Band Integration Network for Infrared Small Target Segmentation

FBI-Net: Frequency Band Integration Network for Infrared Sma...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Xin, Biqiao Li, Qiang Mao, Qianchen Wang, Jinbao Wang, Bingshu School of Software Northwestern Polytechnical University Xi'an China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

ISBN: (纸本)9798350368741

Small targets in infrared imagery exhibit challenging characteristics due to their minimal semantic information and the extremely imbalanced distribution between the targets and the background. In this paper, we propose a frequency band integration network to extract salient features of infrared small targets in both the spatial and frequency domains. To excavate the high-frequency features of the small targets, we propose a frequency decoupling-fusion module. To decrease the semantic loss that occurs in deep networks, we propose a semantic injection mechanism to assist in retaining critical information from shallow layers. Experimental results show that our proposed method reaches higher prediction accuracy and robustness in the infrared small target segmentation task compared with other state-of-the-art approaches. © 2025 IEEE.

关键词： Feature Fusion Infrared Small Target Segmentation Semantic Injection Wavelet Transformation

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Security and Privacy in Solar Insecticidal Lamps Internet of Things:Requirements and Challenges

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 58-73页

作者： Qingsong Zhao Lei Shu Kailiang Li Mohamed Amine Ferrag Ximeng Liu Yanbin Li the College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210031 the State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023 the State Key Laboratory of Cryptology Beijing 100878China IEEE the School of Engineering University of LincolnLincoln LN67TSUK the Artificial Intelligence and Digital Science Research Center Technology Innovation InstituteMasdar City 9639UAE the College of Computer and Data Science Fuzhou UniversityFuzhou 350108China

Solar insecticidal lamps(SIL) can effectively control pests and reduce the use of pesticides. Combining SIL and Internet of Things(IoT) has formed a new type of agricultural IoT,known as SIL-IoT, which can improve the effectiveness of migratory phototropic pest control. However, since the SIL is connected to the Internet, it is vulnerable to various security *** issues can lead to serious consequences, such as tampering with the parameters of SIL, illegally starting and stopping SIL,etc. In this paper, we describe the overall security requirements of SIL-IoT and present an extensive survey of security and privacy solutions for SIL-IoT. We investigate the background and logical architecture of SIL-IoT, discuss SIL-IoT security scenarios, and analyze potential attacks. Starting from the security requirements of SIL-IoT we divide them into six categories, namely privacy, authentication, confidentiality, access control, availability,and integrity. Next, we describe the SIL-IoT privacy and security solutions, as well as the blockchain-based solutions. Based on the current survey, we finally discuss the challenges and future research directions of SIL-IoT.

关键词： Challenges Internet of Things(IoT) privacy and security security requirements solar insecticidal lamps(SIL)

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Efficient Multi-User for Task Offloading and Server Allocation in Mobile Edge Computing Systems

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China Communications 2022年第7期19卷 226-238页

作者： Qiuming Liu Jing Li Jianming Wei Ruoxuan Zhou Zheng Chai Shumin Liu School of Software Engineering Jiangxi University of Science and TechnologyNanchang 330013China Nanchang Key laboratory of Virtual Digital Factory and Cultural Communications Nanchang 330013China

Mobile edge computing has emerged as a new paradigm to enhance computing capabilities by offloading complicated tasks to nearby cloud *** conserve energy as well as maintain quality of service,low time complexity algorithm is proposed to complete task offloading and server *** this paper,a multi-user with multiple tasks and single server scenario is considered for small network,taking full account of factors including data size,bandwidth,channel state ***,we consider a multi-server scenario for bigger network,where the influence of task priority is taken into *** jointly minimize delay and energy cost,we propose a distributed unsupervised learning-based offloading framework for task offloading and server *** exploit a memory pool to store input data and corresponding decisions as key-value pairs for model to learn to solve optimization *** further reduce time cost and achieve near-optimal performance,we use convolutional neural networks to process mass data based on fully connected *** results show that the proposed algorithm performs better than other offloading schemes,which can generate near-optimal offloading decision timely.

关键词： distributed unsupervised learning energy efficiency mobile edge computing task offloading

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Research on global register allocation for code containing array-unit dual-usage register names

Research on global register allocation for code containing a...

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作者： Hu, Yonghua Zhang, Xin Wang, Shuying Liang, Wei Li, Kuan-Ching School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China

Array-unit dual-usage register is a kind of register resource that can be read or written as a whole or individually. It is mainly configured in processors with SIMD processing units and provides register-level speed data transfer between the scalar and vector processing units. To improve the efficiency of algorithms by using an array-unit dual-usage register, we investigate in this article the problem of adapting register allocation to code containing array-unit dual-usage register names. We propose a corresponding global register allocation method by combining the allocation of regular registers with array-unit dual-usage register, ensuring that the names of array-unit dual-usage register can be used in the input code of register allocation. Moreover, we present the processing framework of this method and the specific algorithms of some related vital aspects and demonstrate the working principles of the algorithms by an example. Experimental studies were conducted on a platform based on the FT-M7002 DSP core, and showing that our register allocation method can effectively handle codes containing array-unit dual-usage register names and support relevant application algorithms to improve their data transfer scheme. For some typical algorithms with input matrix, substantial performance improvements of twofold or higher are achieved. © 2022 John Wiley & Sons, Ltd.

关键词： Data transfer

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