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作者： Yao, Haichang Chen, Chen Liu, Shangdong Li, Kui Ji, Yimu Huang, Guangyan Wang, Ruchuan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China School of Computer and Software Nanjing Institute of Industry Technology Nanjing China Industrial Software Engineering Technology Research and Development Center of Jiangsu Education Department Nanjing Institute of Industry Technology Nanjing China Jiangsu HPC and Intelligent Processing Engineer Research Center Nanjing China Institute of High Performance Computing and Bigdata Nanjing University of Posts and Telecommunications Nanjing China School of Information Technology Deakin University Melbourne Australia

In case of sharp road illumination changes, bad weather such as rain, snow or fog, wear or missing of the lane marking, the reflective water stain on the road surface, the shadow obstruction of the tree, and mixed lane markings and other signs, missing detection or wrong detection will occur for the traditional lane marking detection algorithm. In this manuscript, a lane marking detection algorithm based on high-precision map and multisensor fusion is proposed. The basic principle of the algorithm is to use the centimeter-level high-precision positioning combined with high-precision map data to complete the detection of lane markings. In the process of generating high-precision maps or in the uncovered areas of high-precision maps, LIDAR (LIght Detection And Ranging) is used to estimate the curvature of the road to assist in lane marking detection. The experimental results show that the algorithm has lower false detection rate in case of bad road conditions, and the algorithm is robust. © 2020 John Wiley & Sons, Ltd.

关键词： Roads and streets

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A Light Field Image Quality Assessment Method Based on Stereo Vision

A Light Field Image Quality Assessment Method Based on Stere...

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2022 IEEE International Conference on Unmanned Systems, ICUS 2022

作者： Fu, Wei Shen, Xingfa Zhou, Wenhui Zhdanov, Andrei D. Geng, Chuntong Itmo Joint Institute Hangzhou Dianzi University Department of Computer Science Hangzhou China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Itmo University Faculty of Software Engineering and Computer Systems St.Petersburg Russia

ISBN: (数字)9781665484565

ISBN: (纸本)9781665484565

Light field imaging is an important achievement in visual information exploration in recent years, which can capture more abundant visual information from the real world. However, most existing light field image quality assessment (LF-IQA) indicators rely heavily on feature extraction based on high complexity statistics in quality evaluation tasks, which is not comprehensive in future modern applications or power-limited devices. To solve this problem, the research puts forward a light field image quality evaluation method based on stereo vision. By introducing a brand-new light field image coding method and training the neural network, the purpose of image quality evaluation is finally achieved. Some experiments show that our model achieves good results in open source data sets. © 2022 IEEE.

关键词： Stereo vision

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Transfer Learning Model to Indicate Heart Health Status Using Phonocardiogram

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computers, Materials & Continua 2021年第12期69卷 4151-4168页

作者： Vinay Arora Karun Verma Rohan Singh Leekha Kyungroul Lee Chang Choi Takshi Gupta Kashish Bhatia Department of Computer Science and Engineering Thapar Institute of Engineering and TechnologyPatialaPunjabIndia Associate Application ITConcentrixGurugramHaryanaIndia School of Computer Software Daegu Catholic UniversityGyeongsanKorea Department of Computer Engineering Gachon UniversitySeongnam13120Korea Information Security Engineering Soonchunhyang UniversityKorea Department of Computer Engineering University College of EngineeringPunjabi UniversityPatialaPunjabIndia

The early diagnosis of pre-existing coronary disorders helps to control complications such as pulmonary hypertension,irregular cardiac functioning,and heart ***-based learning of heart sound is an efficient technology which can help minimize the workload of manual auscultation by automatically identifying irregular cardiac ***(PCG)and electrocardiogram(ECG)waveforms provide the much-needed information for the diagnosis of these *** this work,the researchers have converted the heart sound signal into its corresponding repeating pattern-based *** 2016 and PASCAL 2011 have been taken as the benchmark datasets to perform *** existing models,***,Xception,Visual Geometry Group(VGG16),ResNet,DenseNet,and InceptionV3 of Transfer Learning have been used for classifying the heart sound signals as normal and *** PhysioNet 2016,DenseNet has outperformed its peer models with an accuracy of 89.04 percent,whereas for PASCAL 2011,VGG has outperformed its peer approaches with an accuracy of 92.96 percent.

关键词： PCG signals transfer learning repeating pattern-based spectrogram biomedical signals internet of things(IoT)

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What Makes Good In-Context Demonstrations for Code Intelligence Tasks with LLMs?

What Makes Good In-Context Demonstrations for Code Intellige...

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IEEE International Conference on Automated software engineering (ASE)

作者： Shuzheng Gao Xin-Cheng Wen Cuiyun Gao Wenxuan Wang Hongyu Zhang Michael R. Lyu School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer Science and Engineering The Chinese University of Hong Kong China School of Big Data and Software Engineering Chongqing University China

Pre-trained models of source code have gained widespread popularity in many code intelligence tasks. Recently, with the scaling of the model and corpus size, large language models have shown the ability of in-context learning (ICL). ICL employs task instructions and a few examples as demonstrations, and then inputs the demonstrations to the language models for making predictions. This new learning paradigm is training-free and has shown impressive performance in various natural language processing and code intelligence tasks. However, the performance of ICL heavily relies on the quality of demonstrations, e.g., the selected examples. It is important to systematically investigate how to construct a good demonstration for code-related tasks. In this paper, we empirically explore the impact of three key factors on the performance of ICL in code intelligence tasks: the selection, order, and number of demonstration examples. We conduct extensive experiments on three code intelligence tasks including code summarization, bug fixing, and program synthesis. Our experimental results demonstrate that all the above three factors dramatically impact the performance of ICL in code intelligence tasks. Additionally, we summarize our findings and provide takeaway suggestions on how to construct effective demonstrations, taking into account these three perspectives. We also show that a carefully-designed demonstration based on our findings can lead to substantial improvements over widely-used demonstration construction methods, e.g., improving BLEU-4, EM, and EM by at least 9.90%, 175.96%, and 50.81% on code summarization, bug fixing, and program synthesis, respectively.

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Joint Channel and Multi-User Detection Empowered with Machine Learning

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computers, Materials & Continua 2022年第1期70卷 109-121页

作者： Mohammad Sh.Daoud Areej Fatima Waseem Ahmad Khan Muhammad Adnan Khan Sagheer Abbas Baha Ihnaini Munir Ahmad Muhammad Sheraz Javeid Shabib Aftab College of Engineering Al Ain UniversityAbu Dhabi112612UAE Department of Computer Science Lahore Garrison UniversityLahore54792Pakistan School of Computer Science National College of Business Administration and EconomicsLahore54000Pakistan Riphah School of Computing and Innovation Faculty of ComputingRiphah International UniversityLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of Software EngineeringGachon UniversitySeongnam13557South Korea Department of Computer Science College of Science and TechnologyWenzhou Kean University325060USA Department of Computer Science Hameeda Rasheed Institute of Science and TechnologyMultan66000Pakistan

The numbers of multimedia applications and their users increase with each passing *** multi-carrier systems have been developed along with varying techniques of space-time coding to address the demand of the future generation of network *** this article,a fuzzy logic empowered adaptive backpropagation neural network(FLeABPNN)algorithm is proposed for joint channel and multi-user detection(CMD).FLeABPNN has two *** first stage estimates the channel parameters,and the second performsmulti-user *** proposed approach capitalizes on a neuro-fuzzy hybrid systemthat combines the competencies of both fuzzy logic and neural *** study analyzes the results of using FLeABPNN based on a multiple-input andmultiple-output(MIMO)receiver with conventional partial oppositemutant particle swarmoptimization(POMPSO),total-OMPSO(TOMPSO),fuzzy logic empowered POMPSO(FL-POMPSO),and FL-TOMPSO-based MIMO *** FLeABPNN-based receiver renders better results than other techniques in terms of minimum mean square error,minimum mean channel error,and bit error rate.

关键词： Channel and multi-user detection minimum mean square error multiple-input and multiple-output minimum mean channel error bit error rate

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Hierarchical Crowdsourcing for Data Labeling with Heterogeneous Crowd 39

Hierarchical Crowdsourcing for Data Labeling with Heterogene...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Zhang, Haodi Huang, Wenxi Su, Zhenhan Chen, Junyang Jiang, Di Fan, Lixin Zhang, Chen Lian, Defu Wu, Kaishun Shenzhen University College of Computer Science and Software Engineering China Shenzhen University WeBank Institute of Financial Technology China Webank Co. Ltd. China Hong Kong Polytechnic University Department of Computing Hong Kong University of Science and Technology of China School of Data Science China

ISBN: (纸本)9798350322279

With the rapid and continuous development of data-driven technologies such as supervised learning, high-quality labeled data sets are commonly required by many applications. Due to the easiness of crowdsourcing small tasks with low cost, a straightforward solution for label quality improvement is to collect multiple labels from a crowd, and then aggregate the answers. The aggregation strategies include majority voting and its many variants, EM-based approaches, Graph Neural Nets and so on. However, due to the uncertainty information loss and commonly existing task correlations, the aggregated labels usually contain errors and may damnify the downstream model *** address the above problem, we propose a hierarchical crowdsourcing framework1 for data labeling with noisy answers about correlated data. We make use of the heterogeneity of the labeling crowd and form an initialization-checking-update loop to improve the quality of labeled data. We formalize and successfully solve the core optimization problem, namely, selecting a proper set of checking tasks for each round. We prove that maximizing the expected quality improvement is equivalent to minimizing the conditional entropy of the observations given the crowdsourced answer families for the selected task set, which is NP-hard to solve. Therefore, we design an efficient approximation algorithm and conduct a series of experiments on real data. The experimental results show that the proposed method effectively improves the quality of the labeled data sets as well as the SOTA performance, yet without extra human labor costs. © 2023 IEEE.

关键词： Approximation algorithms

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A Survey of Recommender Systems Based on Hypergraph Neural Networks 1

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Liu, Canwei He, Tingqin Zhu, Hangyu Li, Yanlu Xie, Songyou Hosam, Osama School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Guangdong Financial High-tech Zone "Blockchain +" Fintech Research Institute Guangzhou510623 China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan411201 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Higher Colleges of Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

Unlike highly purposeful search, a recommender system tends to uncover the user’s potential interests and is a personalized information filtering system. Recently, the performance of hypergraph neural networks in classification tasks has attracted much attention. Compared with traditional recommender systems, hypergraph neural network-based recommender systems have better mining higher-order associations, accurate modeling of multivariate relationships, handling of multimodal and heterogeneous data, and clustering advantages. This fact drives the development of recommendation algorithms based on hypergraph neural networks. To this end, we 1) define generic links of recommender systems, and systematically analyze the challenges of hypergraph neural network-based recommender systems in different research directions. 2) present some new perspectives on existing weaknesses and future developments. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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Q2oE: Balancing QoE Fairness and Preference for Video Streaming from Cooperative Edge Servers

Q2oE: Balancing QoE Fairness and Preference for Video Stream...

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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Xinwei Huang Yin Xu Jinbo Cai He Sun Jie Wu Mingjun Xiao School of Software Engineering University of Science and Technology of China China Suzhou Institute for Advanced Study University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China Department of Computer and Information Sciences Temple University USA

ISBN: (数字)9798350363999

ISBN: (纸本)9798350364002

Quality of Experience (QoE) is a pivotal factor that defines the overall quality within the realm of video streaming services. To improve users' QoE indicators, existing methods have proposed many QoE models and Adaptive Bitrate (ABR) algorithms. However, these advanced methods do not account for the tradeoff between users' personalized preferences and fairness, leading to resource wastage on the edge server and an inefficient bitrate allocation. Given resource constraints, non-cooperative edge servers frequently request the cloud to enhance user QoE, consequently resulting in high-latency transmissions. In this paper, we address this issue by introducing collaborative edge servers, which alleviate the burden of video chunk transcoding and caching on individual servers. Specifically, we design a novel QoE model named Q 2 0E that incorporates user-level QoE preference and QoE fairness in this scenario. Furthermore, we propose Q2D3, a Q 2 oE-aware reinforcement learning algorithm based on a video chunk transcoding strategy for mobility-aware vehicular networks, to improve Q 2 oE model performance.

关键词： Measurement Simulation Bit rate Transcoding Reinforcement learning Streaming media Smart systems Quality of experience Servers Resource management

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Write Your Own Code Checker: An Automated Test-Driven Checker Development Approach with LLMs

arXiv

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arXiv 2024年

作者： Xie, Yuanyuan Liu, Jun Yan, Jiwei Huang, Jinhao Yan, Jun Zhang, Jian State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences Beijing China China School of Advanced Interdisciplinary Science UCAS United Kingdom

With the rising demand for code quality assurance, developers are not only utilizing existing static code checkers but also seeking custom checkers to satisfy their specific needs. Nowadays, various code-checking frameworks provide extensive checker customization interfaces to meet this need. However, both the abstract checking logic and the complex API usage of large-scale checker frameworks make this task challenging. To this end, automated code checker generation is anticipated to ease the burden of checker development. In this paper, we propose AutoChecker, an innovative LLM-powered approach that can write code checkers automatically based on only a rule description and a test suite. To achieve comprehensive checking logic, AutoChecker incrementally updates the checker’s logic by focusing on solving one selected case each time. To obtain precise API knowledge, during each iteration, it leverages fine-grained logic-guided API-context retrieval, where it first decomposes the checking logic into a series of sub-operations and then retrieves checker-related API-contexts for each sub-operation. For evaluation, we apply AutoChecker, five baselines, and three ablation methods using multiple LLMs to generate checkers for 20 randomly selected PMD rules. Experimental results show that AutoChecker significantly outperforms others across all effectiveness metrics, with an average test pass rate of 82.28%. Additionally, the checkers generated by AutoChecker can be successfully applied to real-world projects, matching the performance of official checkers. © 2024, CC BY.

关键词： Automatic test pattern generation

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Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge

Generalizing 6-DoF Grasp Detection via Domain Prior Knowledg...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Haoxiang Ma Modi Shi Boyang Gao Di Huang State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Harbin Institute of Technology Harbin China Geometry Robotics

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We focus on the generalization ability of the 6-DoF grasp detection method in this paper. While learning-based grasp detection methods can predict grasp poses for unseen ob-jects using the grasp distribution learned from the training set, they often exhibit a significant performance drop when encountering objects with diverse shapes and struc-tures. To enhance the grasp detection methods' general-ization ability, we incorporate domain prior knowledge of robotic grasping, enabling better adaptation to objects with significant shape and structure differences. More specifi-cally, we employ the physical constraint regularization during the training phase to guide the model towards predicting grasps that comply with the physical rule on grasping. For the unstable grasp poses predicted on novel objects, we design a contact-score joint optimization using the pro-jection contact map to refine these poses in cluttered sce-narios. Extensive experiments conducted on the GraspNet-1 billion benchmark demonstrate a substantial performance gain on the novel object set and the real-world grasping experiments also demonstrate the effectiveness of our gen-eralizing 6-DoF grasp detection method. Code is available at https://***/mahaoxiang822/Generalizing-Grasp.

关键词： Training computer vision Shape Grasping Benchmark testing Predictive models Performance gain

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