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arXiv 2022年

作者： Huang, Sheng Tang, Wenhao Huang, Guixin Huangfu, Luwen Yang, Dan Ministry of Education Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing400044 China The School of Big Data and Software Engineering Chongqing University Chonqqing 400044 China Fowler College of Business Center for Human Dynamics in the Mobile Age San Diego State University San DiegoCA92182 United States

Automatic image-based pavement distress detection and recognition are vital for pavement maintenance and management. However, existing deep learning-based methods largely omit the specific characteristics of pavement images, such as high image resolution and low distress area ratio, and are not end-to-end trainable. In this paper, we present a series of simple yet effective end-to-end deep learning approaches named Weakly Supervised Patch Label Inference Networks (WSPLIN) for efficiently addressing these tasks under various application settings. WSPLIN transforms the fully supervised pavement image classification problem into a weakly supervised pavement patch classification problem for solutions. Specifically, WSPLIN first divides the pavement image under different scales into patches with different collection strategies and then employs a Patch Label Inference Network (PLIN) to infer the labels of these patches to fully exploit the resolution and scale information. Notably, we design a patch label sparsity constraint based on the prior knowledge of distress distribution and leverage the Comprehensive Decision Network (CDN) to guide the training of PLIN in a weakly supervised way. Therefore, the patch labels produced by PLIN provide interpretable intermediate information, such as the rough location and the type of distress. We evaluate our method on a large-scale bituminous pavement distress dataset named CQU-BPDD and the augmented Crack500 (Crack500-PDD) dataset, which is a newly constructed pavement distress detection dataset augmented from the Crack500. Extensive results demonstrate the superiority of our method over baselines in both performance and efficiency. The source codes of WSPLIN are released on https://***/DearCaat/wsplin. © 2022, CC BY-NC-SA.

关键词： Image classification

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A Two-phase Prototypical Network Model for Incremental Few-shot Relation Classification 28

A Two-phase Prototypical Network Model for Incremental Few-s...

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28th International Conference on Computational Linguistics, COLING 2020

作者： Ren, Haopeng Cai, Yi Chen, Xiaofeng Wang, Guohua Li, Qing School of Software Engineering South China University of Technology Guangzhou China Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China Department of Computing The Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9781952148279

Relation Classification (RC) plays an important role in natural language processing (NLP). Current conventional supervised and distantly supervised RC models always make a closed-world assumption which ignores the emergence of novel relations in an open environment. To incrementally recognize the novel relations, current two solutions (i.e, re-training and lifelong learning) are designed but suffer from the lack of large-scale labeled data for novel relations. Meanwhile, prototypical network enjoys better performance on both fields of deep supervised learning and few-shot learning. However, it still suffers from the incompatible feature embedding problem when the novel relations come in. Motivated by them, we propose a two-phase prototypical network with prototype attention alignment and triplet loss to dynamically recognize the novel relations with a few support instances meanwhile without catastrophic forgetting. Extensive experiments are conducted to evaluate the effectiveness of our proposed model. © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.

关键词： Deep learning

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3D Virtual Sand Table Display System of Industrial Park Based on Augmented Reality

3D Virtual Sand Table Display System of Industrial Park Base...

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Industrial Application of Artificial Intelligence (IAAI), IEEE International Conference on

作者： Hongyan Lu Jie He Caichun Cen School of Big Data and Software Engineering Wuzhou University Wuzhou China Key Laboratory of Image Processing and Intelligent Information System at Guangxi University Wuzhou University Wuzhou China School of Electronic and Information Engineering Wuzhou University Wuzhou China

ISBN: (纸本)9781665465816

To improve the interactivity of 3D virtual sand table displays, an industrial park 3D virtual sand table display system based on augmented reality is designed. The system consists of multiple modules. The design of the animation browsing module is based on the roaming walking model environment. The walking and roaming module can enable participants to experience the real visual and tactile virtual space in the process of input browsing. In the surrounding environment and traffic condition module, the surrounding environment and traffic condition display are created by means of static pictures and picture buttons. The VRML scene space module is responsible for creating objects such as VRML viewpoints. Scenario module transformation completes event transmission through routing. The virtual sand table VRML optimization modeling module is responsible for constructing the virtual sand table VRML model. The function of the multiperson collaboration module is to provide collaboration capability for plotting and discussion functions. In the multiuser detection module, yolov2 is used for multiuser detection. The augmented reality hardware module is mainly composed of computers and hololens. Through the combination of hardware and software, a three-dimensional virtual sand table display of an industrial park is realized. The test results show that the performance of the designed system is good and can meet the design requirements.

关键词： Multiuser detection Legged locomotion Visualization Three-dimensional displays Computational modeling Display systems Collaboration

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AIServiceX: A Knowledge Graph-Based Intelligent Question-Answering System for Personal Services 1

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16th World Congress on Services, SERVICES 2020, held as part of the Services Conference Federation, SCF 2020

作者： Sun, Yao Gui, Wenming Han, Cheng Zhang, Yan Zhang, Shudong School of Software Engineering Jinling Institute of Technology Nanjing211169 China Nanjing Institute of Big Data Jinling Institute of Technology Nanjing211169 China School of Computer Engineering Jinling Institute of Technology Nanjing211169 China Information Engineering College Capital Normal University Beijing100048 China

ISBN: (数字)9783030595951

ISBN: (纸本)9783030595944

Knowledge graph-based question-answering systems are widely used in e-commerce enterprises. They can reduce the costs of customer services and improve service capabilities. The description of questions is often ambiguous, and the knowledge graphs update in online personal services always has a high overhead. To address the above issues, by augmenting domain semantics, this paper proposes a knowledge graph-based intelligent question-answering system called as AIServiceX. It employs a gate recurrent unit model to identify entities and assertions, and then gets the most related semantic augmentation contents from existing external domain knowledge via topic comparison. Then, it ranks all the candidates to get optimal answers by discovering several heuristic rules. Periodically, it augments the global knowledge graph with minimized updating costs with an Integer linear programming resolving model. This mechanism can recognize question entities precisely, and map domain knowledge to the KG automatically, which achieves a high answering precision with a low overhead. Experiments with a production e-commerce data show that AIServiceX can improve the precision. © 2020, Springer Nature Switzerland AG.

关键词： Knowledge graph

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HGeoHashBase: an optimized storage model of spatial objects for location-based services

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Frontiers of Computer Science 2020年第1期14卷 208-218页

作者： Jingwei ZHANG Chao YANG Qing YANG Yuming LIN Yanchun ZHANG Guangxi Key Laboratory of Trusted Software Guilin University of Electronic TechnologyGuilin 541004China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic TechnologyGuilin 541004China Guangxi Key Laboratory of Automatic Measurement Technology and Instrument Guilin University of Electronic TechnologyGuilin 541004China Centre for Applied Informatics College of Engineering and ScienceVictoria UniversityMelbourne 8001Australia

Many location-based services need to query objects existing in a specific space,such as location-based tourism resource *** a large number of spatial objects and the real-time object access requirements of location-based services pose a big challenge for spatial object storage and query *** this paper,we propose HGeoHashBase,an improved storage model by integrating GeoHash with key-value structure,to organize spatial objects for efficient range *** is responsible for spatial encoding and key-value structure as underlying data *** the similarity of the encodings for objects in the close geographical locations and the multi-version data mechanism are blended into the proposed model *** the tradeoff between encoding precision and query performance,a theoretical proof is *** experiments are designed and conducted,whose results show that the proposed model can gain significant performance improvement.

关键词： location-based services distributed storage model storage and access optimization

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Robust Adaptive Beamforming via Worst-Case SINR Maximization with Nonconvex Uncertainty Sets

arXiv

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arXiv 2022年

作者： Huang, Yongwei Fu, Hao Vorobyov, Sergiy A. Luo, Zhi-Quan School of Information Engineering Guangdong University of Technology University Town Guangdong Guangzhou510006 China Huawei Technologies Software Avenue Yuhuatai District Jiangsu Nanjing China The Department of Signal Processing and Acoustics Aalto University Espoo02150 Finland School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen Research Institute of Big Data Guangdong518172 China

This paper considers a formulation of the robust adaptive beamforming (RAB) problem based on worst-case signal-to-interference-plus-noise ratio (SINR) maximization with a nonconvex uncertainty set for the steering vectors. The uncertainty set consists of a similarity constraint and a (nonconvex) double-sided ball constraint. The worst-case SINR maximization problem is turned into a quadratic matrix inequality (QMI) problem using the strong duality of semidefinite programming. Then a linear matrix inequality (LMI) relaxation for the QMI problem is proposed, with an additional valid linear constraint. Necessary and sufficient conditions for the tightened LMI relaxation problem to have a rank-one solution are established. When the tightened LMI relaxation problem still has a high-rank solution, the LMI relaxation problem is further restricted to become a bilinear matrix inequality (BLMI) problem. We then propose an iterative algorithm to solve the BLMI problem that finds an optimal/suboptimal solution for the original RAB problem by solving the BLMI formulations. To validate our results, simulation examples are presented to demonstrate the improved array output SINR of the proposed robust beamformer. © 2022, CC BY-NC-SA.

关键词： Linear matrix inequalities

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A Vision-based Lane Departure Warning Framework

A Vision-based Lane Departure Warning Framework

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IEEE International Conference on e-Business engineering (ICEBE)

作者： Jiaju Wu Pengshuai Yin Xin Shu Huichou Huang Fei Liu Qingyao Wu School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education City University of Hong Kong Hong Kong China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665444194

Lane departure warning is an essential function of driver assistance systems. In this paper, we propose a vision-based lane departure warning framework, which can determine whether the vehicle deviates from the lane only with images as input. Our framework combines the deep learning method and traditional line detection algorithm. Specifically, the framework consists of a Lane Boundaries Localization module, a Lane Boundaries Generator module, and a Departure Warning module. The Lane Boundaries Localization module is a deep neural network for finding the area containing lane Boundaries. Then the Lane Boundaries Generator module performs backbone detection and line detection to generate lane boundaries on the located area. After identifying the lane boundaries, the Departure Warning module can quickly determine whether the vehicle deviates from the lane. Additionally, we implemented and deployed our framework to an Android platform and tested its running speed.

关键词： Location awareness Deep learning Conferences Neural networks Transforms Generators Convolutional neural networks

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Physical Layer Security for Wireless-Powered Ambient Backscatter Cooperative Communication Networks

TechRxiv

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TechRxiv 2023年

作者： Li, Xingwang Jiang, Junjie Wang, Hao Chen, Gaojie Du, Jianhe Hu, Chunqiang Mumtaz, Shahid The School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo454000 China University of Surrey GuildfordGU2 7XH United Kingdom The State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China The School of Big Data & Software Engineering Chongqing University Chongqing400030 China The Institute of Telecommunications Aveiro3810078 Portugal The ARIES Research Center Universidad Antonio de Nebrija MadridE-28040 Spain

Low power consumption and high spectrum efficiency as the great challenges for multi-device access to Internet-of-Things (IoT) have put forward stringent requirements on the future intelligent network. Ambient backscatter communication (ABcom) is regarded as a promising technology to cope with the two challenges, where backscatter device (BD) can reflect ambient radio frequency (RF) signals without additional bandwidth. However, minimalist structural design of BD makes ABcom security vulnerable in wireless propagation environments. By virtue of this fact, this paper considers the physical layer security (PLS) of a wireless-powered ambient backscatter cooperative communication network threatened by an eavesdropper, where a BD with nonlinear energy harvesting model cooperates with decode-and-forward (DF) relay for secure communication. The PLS performance is investigated by deriving the secrecy outage probability (SOP) and secure energy efficiency (SEE). Specifically, the closed-form and asymptotic expressions of SOP are derived as well as the secrecy diversity order for the first time. As an energy-constrained device, balancing power consumption and security is major concern for BD, thus the SEE of the proposed network is studied. The results from numerical analysis show that the performance improvement of SOP and SEE is impacted by system parameters, including transmission power, secrecy rate threshold, reflection efficiency and distance between the source and BD, which provide guidance on balancing security and energy efficiency in ambient backscatter cooperative relay networks. © 2023, CC BY.

关键词： Energy harvesting

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Decentralized linear MMSE equalizer under colored noise for massive MIMO systems

arXiv

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arXiv 2021年

作者： Zhao, Xiaotong Guan, Xin Li, Mian Shi, Qingjiang School of Software Engineering Tongji University Shanghai China School of Elec. Info. and Comm. Huazhong University of Science and Technology Wuhan China Shenzhen Research Institute of Big Data Shenzhen China

Conventional uplink equalization in massive MIMO systems relies on a centralized baseband processing architecture. However, as the number of base station antenna increases, centralized baseband processing architectures encounter two bottlenecks, i.e., the tremendous data interconnection and the highdimensional computation. To tackle these obstacles, decentralized baseband processing was proposed for uplink equalization, but only applicable to the scenarios with unpractical white Gaussian noise assumption. This paper presents an uplink linear minimum mean-square error (L-MMSE) equalization method in the daisy chain decentralized baseband processing architecture under colored noise assumption. The optimized L-MMSE equalizer is derived by exploiting the block coordinate descent method, which shows near-optimal performance both in theoretical and simulation while significantly mitigating the bottlenecks. Copyright © 2021, The Authors. All rights reserved.

关键词： White noise

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Peculiar: Smart Contract Vulnerability Detection Based on Crucial data Flow Graph and Pre-training Techniques

Peculiar: Smart Contract Vulnerability Detection Based on Cr...

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International Symposium on software Reliability engineering (ISSRE)

作者： Hongjun Wu Zhuo Zhang Shangwen Wang Yan Lei Bo Lin Yihao Qin Haoyu Zhang Xiaoguang Mao National University of Defense Technology Changsha China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin China School of Big Data & Software Engineering Chongqing University Chongqing China Defense Innovation Institute Academy of Military Sciences Beijing China

ISBN: (纸本)9781665425889

Smart contracts with natural economic attributes have been widely and rapidly developed in various fields. However, the bugs and vulnerabilities in smart contracts have brought huge economic losses, which has strengthened people's attention to the security issues of smart contracts. The immutability of smart contracts makes people more willing to conduct security checks before deploying smart contracts. Nonetheless, existing smart contract vulnerability detection techniques are far away from enough: static analysis approaches rely heavily on manually crafted heuristics which is difficult to reuse across different types of vulnerabilities while deep learning based approaches also have unique limitations. In this study, we propose a novel approach, Peculiar, which uses Pre-training technique for detection of smart contract vulnerabilities based on crucial data flow graph. Compared against the traditional data flow graph which is already utilized in existing approach, crucial data flow graph is less complex and does not bring an unnecessarily deep hierarchy, which makes the model easy to focus on the critical features. Moreover, we also involve pre-training technique in our model due to the dramatic improvements it has achieved on a variety of NLP tasks. Our empirical results show that Peculiar can achieve 91.80 % precision and 92.40 % recall in detecting reentrancy vulnerability, one of the most severe and common smart contract vulnerabilities, on 40,932 smart contract files, which is significantly better than the state-of-the-art methods (e.g., Smartcheck achieves 79.37% precision and 70.50% recall). Meanwhile, another experiment shows that Peculiar is more discerning to reentrancy vulnerability than existing approaches. The ablation experiment reveals that both crucial data flow graph and pre-trained model contribute significantly to the performances of Peculiar.

关键词： Economics Deep learning Smart contracts Neural networks Static analysis data models software reliability

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