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International Symposium on Software Reliability Engineering (ISSRE)

作者： Shenglin Zhang Zeyu Che Zhongjie Pan Xiaohui Nie Yongqian Sun Lemeng Pan Dan Pei Nankai University Haihe Laboratory of Information Technology Application Innovation Computer Network Information Center Chinese Academy of Sciences Tianjin Key Laboratory of Software Experience and Human Computer Interaction Huawei Beijing National Research Center for Information Science and Technology Tsinghua University

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

Trace data is crucial for system observability and maintainability within microservices architectures, and many operation algorithms depend heavily on trace data, including anomaly detection, root cause analysis, etc. However, the actual performance of these algorithms might be unsatisfactory due to the absence of high-quality labeled datasets for effective training and evaluation. Since billions of traces could be generated daily for large-scale microservices, labeling overhead is the main hurdle to obtaining high-quality trace *** this paper, we propose labelEase, a novel semi-automatic trace labeling tool, which uses active learning techniques to achieve efficient and accurate trace labeling. For anomaly trace labeling, labelEase clusters similar traces with a graph-based trace representation technique and selects a few representative traces for human labeling, avoiding labeling most of the traces. For root cause labeling, labelEase aggregates the labeled anomalous traces and identifies the service’s failures for operators to label. Our systematic experiments on two large-scale datasets show that labelEase achieves over 0.98 F 1 -score in anomaly trace labeling and 0.89 precision of failure detection in root cause labeling, labelEase can reduce operators’ labeling overhead by more than 99.9%. To the best of our knowledge, we are the first to propose a semi-automatic trace labeling tool capable of achieving efficient and accurate trace labeling.

关键词： Training Location awareness Accuracy Costs Transfer learning Microservice architectures Manuals Data models labeling Anomaly detection

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Automatic Gait Motion Capture with Missing-Marker Fillings

Automatic Gait Motion Capture with Missing-Marker Fillings

引用

International Conference on Pattern Recognition

作者： Xiaoming Deng Shihong Xia Wenzhong Wang Zhaoqi Wang Liang Chang Hongan Wang Beiiinz Key Lab of Human-Computer Interaction Chinese Academy of Sciences China Institute of Computing Technology Chinese Academy of Sciences China Department of Computer Science and Technology Anhui University China College of Information Science and Technology Beijing Normal University China

ISBN: (纸本)9781479952106

Although marker-based optical motion capture has been a useful method for computer animation during the past decades, automatic and robust motion tracking from multiple video sequences is still very challenging. Several critical issues in practical implementations are not adequately addressed. For example, how to track and identify the reconstructed 3D points after image matching process? How to handle the heavy occlusion problem? This paper gives a careful investigation of the above issues. In particular, we propose a novel way to track and identify proper markers, and a new method of filling missing markers by taking account of the human model constraints. Experiments are presented to show its accuracy and robustness.

关键词： Three-dimensional displays Tracking Motion segmentation labeling Cameras Image reconstruction Filling

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SpeechMirror: A Multimodal Visual Analytics System for Personalized Reflection of Online Public Speaking Effectiveness

arXiv

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arXiv 2023年

作者： Huang, Zeyuan He, Qiang Maher, Kevin Deng, Xiaoming Lai, Yu-Kun Ma, Cuixia Qin, Sheng-Feng Liu, Yong-Jin Wang, Hongan The Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences and also with the School of Computer Science and Technology University of Chinese Academy of Sciences China Diatom Design Limited Liability Company United States The School of Computer Science and Informatics Cardiff University United Kingdom The School of Design Northumbria University United Kingdom The Department of Computer Science and Technology MOE-Key Laboratory of Pervasive Computing Tsinghua University China

As communications are increasingly taking place virtually, the ability to present well online is becoming an indispensable skill. Online speakers are facing unique challenges in engaging with remote audiences. However, there has been a lack of evidence-based analytical systems for people to comprehensively evaluate online speeches and further discover possibilities for improvement. This paper introduces SpeechMirror, a visual analytics system facilitating reflection on a speech based on insights from a collection of online speeches. The system estimates the impact of different speech techniques on effectiveness and applies them to a speech to give users awareness of the performance of speech techniques. A similarity recommendation approach based on speech factors or script content supports guided exploration to expand knowledge of presentation evidence and accelerate the discovery of speech delivery possibilities. SpeechMirror provides intuitive visualizations and interactions for users to understand speech factors. Among them, SpeechTwin, a novel multimodal visual summary of speech, supports rapid understanding of critical speech factors and comparison of different speech samples, and SpeechPlayer augments the speech video by integrating visualization of the speaker's body language with interaction, for focused analysis. The system utilizes visualizations suited to the distinct nature of different speech factors for user comprehension. The proposed system and visualization techniques were evaluated with domain experts and amateurs, demonstrating usability for users with low visualization literacy and its efficacy in assisting users to develop insights for potential improvement. Copyright © 2023, The Authors. All rights reserved.

关键词： Modal analysis

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Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Model Based on Large Language Models

Enhanced Fine-Tuning of Lightweight Domain-Specific Q&A Mode...

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IEEE International Conference on Software Reliability Engineering Workshops (ISSRE Wksp)

作者： Shenglin Zhang Pengtian Zhu Minghua Ma Jiagang Wang Yongqian Sun Dongwen Li Jingyu Wang Qianying Guo Xiaolei Hua Lin Zhu Dan Pei Nankai University Haihe Laboratory of Information Technology Application Innovation Microsoft Tsinghua University Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Mobile Research Institute Beijing National Research Center for Information Science and Technology

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

Large language models (LLMs) excel at general question-answering (Q&A) but often fall short in specialized domains due to a lack of domain-specific knowledge. Commercial companies face the dual challenges of privacy protection and resource constraints when involving LLMs for fine-tuning. This paper propose a novel framework, Self-Evolution, designed to address these issues by leveraging lightweight open-source LLMs through multiple iterative fine-tuning rounds. To enhance the efficiency of iterative fine-tuning, Self-Evolution employ a strategy that filters and reinforces the knowledge with higher value during the iterative process. We employed Self-Evolution on Qwen1.5-7B-Chat using 4,000 documents containing rich domain knowledge from China Mobile, achieving a performance score 174% higher on domain-specific question-answering evaluations than Qwen1.5-7B-Chat and even 22% higher than Qwen1.5-72B-Chat. Self-Evolution has been deployed in China Mobile’s daily operation and maintenance for 117 days, and it improves the efficiency of locating alarms, fixing problems, and finding related reports, with an average efficiency improvement of over 18.6%. In addition, we release Self-Evolution framework code in https://***/Zero-Pointer/Self-Evolution.

关键词： Privacy Filters Large language models Conferences Software reliability Maintenance Iterative methods Data mining Protection Faces

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Impairments in Spatial Representations of Place Cells and Neural rhythms In APP knock-in rat model 46

Impairments in Spatial Representations of Place Cells and Ne...

引用

46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Xueling Wang, Yimeng Pang, Keliang Lu, Bai Tian, Yutao Zheng, Chenguang Fudan University Aging and Longevity Institute Institute of Biological Science Zhongshan Hospital Shanghai China Tsinghua University-Peking University Joint Center for Life Sciences Tsinghua University School of Pharmaceutical Sciences IDG/McGovern Institute for Brain Research Beijing China Tianjin University Academy of Medical Engineering and Translational Medicine Medical College Tianjin China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (纸本)9798350371499

Alzheimer's disease (AD) is a neurodegenerative disease that severely impacts spatial memory. Place cells in the hippocampus play an important role in spatial memory. However, neuronal activity in the hippocampus is difficult to monitor and study in human AD patients. Thus, it is critical to have an animal model that resembles closely human AD. The newly developed APPNL-G-F rat model, which is a knock-in rat line of the amyloid precursor protein (APP) with Swedish-Beyreuther/Iberian-Arctic mutations, presents a unique opportunity. In this study, we investigated how hippocampal place cells and neural rhythms were impaired in APP rats. Place cells of APP rats showed a less defined spatial tunning and disrupted remapping. In addition, APP rats exhibited decreased power of theta rhythm and slow gamma rhythm. These results suggest that the impairment of spatial memory in APP rats may be caused by the damage of place cells' ability to represent spatial location and abnormal neural rhythm in the hippocampus. © 2024 IEEE.

关键词： Rats

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Stackelberg-Game Computation Offloading Scheme for Parked Vehicle-Assisted VEC and Experiment Analysis

IEEE Transactions on Intelligent Vehicles

引用

IEEE Transactions on Intelligent Vehicles 2024年 1-12页

作者： Liu, Chunhong Zhao, Mengdi Wang, Huaichen Cheng, Bo Liu, Jialei Yuan, Peiyan Engineering Lab of Intelligence Business & Internet of Things and School of Computer and Information Engineering Henan Normal University Xinxiang China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle and School of Computer Engineering Hubei University of Arts and Science Xiangyang China

Vehicle edge computing (VEC) offers users low-latency and high-reliability services by using computational resources at the network's edge. Nevertheless, because of inadequate infrastructure and limited resources, computation-intensive and delay-sensitive vehicle applications cannot be performed efficiently at the edge. Therefore, several studies have used the idle resources of parked vehicles to assist in computation offloading. In this paper, we propose a parked vehicle-assisted vehicle edge computing architecture considering multi-agent collaboration, including intelligent vehicles and edge servers. Additionally, we propose a framework for a parallel Internet of Vehicles (IoV) utilizing computational experiment. The service provider is assigned the role of owning VEC resources and recruiting parking vehicle resources. The model was constructed by using the resource consumption-service relationship of both offloading parties to ensure service quality. First, a Stackelberg game model was constructed based on the interaction between requesting vehicles and a service provider. The latter was the leader, and the requesting vehicles were the followers. The Nash equilibrium for optimal pricing and offloading allocations was attained and verified, and a distributed gradient-based equilibrium algorithm was designed to solve the Stackelberg game model and obtain the final decision through mutual communication. The method also protects the privacy of participants and respects the willingness of requesting vehicles to offload. Finally, the simulation experiments confirmed that the proposed algorithm can achieve game equilibrium. Furthermore, it outperformed state-of-the-art algorithms in improving the service provider's utility. IEEE

关键词： Vehicles

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Congenital hydrocephalus:a review of recent advances in genetic etiology and molecular mechanisms

引用

Military Medical Research 2024年第6期11卷 942-958页

作者： Xiu-Yun Liu Xin Song Marek Czosnyka Chiara Robba Zofia Czosnyka Jennifer Lee Summers Hui-Jie Yu Guo-Yi Gao Peter Smielewski Fang Guo Mei-Jun Pang Dong Ming Medical School Tianjin UniversityTianjin 300072China State Key Laboratory of Advanced Medical Materials and Devices Tianjin UniversityTianjin 300072China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin 300380China School of Pharmaceutical Science and Technology Tianjin University300072 TianjinChina Department of Clinical Neurosciences Addenbrooke’s HospitalUniversity of CambridgeCambridge CB20QQUK San Martino Policlinico Hospital IRCCS for Oncology and Neuroscience16132 GenoaItaly Department of Anesthesiology and Critical Care Medicine Johns Hopkins UniversityBaltimoreMD 21287USA Department of Neurosurgery Tianjin Medical University General HospitalTianjin 300052China Department of Neurosurgery Beijing Tiantan HospitalCapital Medical UniversityBeijing 100070China Department of Neurosurgery Tianjin Huanhu HospitalTianjin 300350China

The global prevalence rate for congenital hydrocephalus(CH)is approximately one out of every five hundred births with multifaceted predisposing factors at *** influences stand as a major contributor to CH pathogenesis,and epidemiological evidence suggests their involvement in up to 40%of all cases observed *** about an individual’s genetic susceptibility can significantly improve prognostic precision while aiding clinical decision-making ***,the precise genetic etiology has only been pinpointed in fewer than 5%of human *** occurrences of CH cases are required for comprehensive gene sequencing aimed at uncovering additional potential genetic loci.A deeper comprehension of its underlying genetics may offer invaluable insights into the molecular and cellular basis of this brain *** review provides a summary of pertinent genes identified through gene sequencing technologies in humans,in addition to the 4 genes currently associated with CH(2 X-linked genes L1CAM and AP1S2,2 autosomal recessive MPDZ and CCDC88C).Others predominantly participate in aqueduct abnormalities,ciliary movement,and nervous system *** prospective CH-related genes revealed through animal model gene-editing techniques are further outlined,focusing mainly on 4 pathways,namely cilia synthesis and movement,ion channels and transportation,Reissner’s fiber(RF)synthesis,cell apoptosis,and ***,the proper functioning of motile cilia provides significant impulsion for cerebrospinal fluid(CSF)circulation within the brain ventricles while mutations in cilia-related genes constitute a primary cause underlying this *** far,only a limited number of CH-associated genes have been identified in *** integration of genotype and phenotype for disease diagnosis represents a new trend in the medical *** models provide insights into the pathogenesis of CH and contribute to our understanding of its association w

关键词： Congenital hydrocephalus Genetic disease Central system Cilia Ventricle

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Programming by Example Made Easy

arXiv

引用

arXiv 2023年

作者： Wu, Jiarong Wei, Lili Jiang, Yanyan Cheung, Shing-Chi Ren, Luyao Xu, Chang Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Electrical and Computer Engineering McGill University Montreal Canada State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Key Lab of High Confidence Software Technologies Ministry of Education Department of Computer Science and Technology EECS Peking University Beijing China

Programming by example (PBE) is an emerging programming paradigm that automatically synthesizes programs specified by user-provided input-output examples. Despite the convenience for end-users, implementing PBE tools often requires strong expertise in programming language and synthesis algorithms. Such a level of knowledge is uncommon among software developers. It greatly limits the broad adoption of PBE by the industry. To facilitate the adoption of PBE techniques, we propose a PBE framework called Bee, which leverages an "entity-action" model based on relational tables to ease PBE development for a wide but restrained range of domains. Implementing PBE tools with Bee only requires adapting domain-specific data entities and user actions to tables, with no need to design a domain-specific language or an efficient synthesis algorithm. The synthesis algorithm of Bee exploits bidirectional searching and constraint-solving techniques to address the challenge of value computation nested in table transformation. We evaluated Bee’s effectiveness on 64 PBE tasks from three different domains and usability with a human study of 12 participants. Evaluation results show that Bee is easier to learn and use than the state-of-the-art PBE framework, and the bidirectional algorithm achieves comparable performance to domain-specifically optimized synthesizers. Copyright © 2023, The Authors. All rights reserved.

关键词： Problem oriented languages

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Scenario Design Tool Based on Hybrid Input

引用

Ruan Jian Xue Bao/Journal of Software 2019年 30卷 48-61页

作者： Tong, Qing-Shan Zhang, Zong-Qi Huang, Jin Tian, Feng Liu, Jie Dai, Guo-Zhong Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100190 China

Pen-based user interface relying on touch technology is one of the Post-WIMP interfaces. It discards the physical keyboard and mouse, which changed the method of human-computer interaction to some extent. Though sketch drawing software and recognition software are constantly emerging, there is no mature pen-based interface design development tool. Based on the PGIS interaction paradigm and scenario design method, this paper develops a tool named with SDT that allows hybrid input of graphics and sketchs based on pen-based interaction primitives. Firstly, based on the principle of high-cohesion and low-coupling in the field of software engineering, the "Separation-Fusion" design method is proposed. Accordingly, the overall architecture of the system is put forward. Secondly, the essential technologies are elaborated from three aspects: user interface description language, pen-based interactive primitive and mono-case, hybrid input. Thirdly, an example of a complete application is built by the SDT, which makes the availability and feasibility of the system more convincible. Finally, the advantage and effectiveness of the tool are verified by two evaluation experiments. © Copyright 2019, Institute of Software, the Chinese Academy of sciences. All rights reserved.

关键词： human computer interaction

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Revisiting metric learning for few-shot image classification

arXiv

引用

arXiv 2019年

作者： Li, Xiaomeng Yu, Lequan Fu, Chi-Wing Fang, Meng Heng, Pheng-Ann Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Tencent AI Lab United States

The goal of few-shot learning is to recognize new visual concepts with just a few amount of labeled samples in each class. Recent effective metric-based few-shot approaches employ neural networks to learn a feature similarity comparison between query and support examples. However, the importance of feature embedding, i.e., exploring the relationship among training samples, is neglected. In this work, we present a simple yet powerful baseline for few-shot classification by emphasizing the importance of feature embedding. Specifically, we revisit the classical triplet network from deep metric learning, and extend it into a deep K-tuplet network for few-shot learning, utilizing the relationship among the input samples to learn a general representation learning via episode-training. Once trained, our network is able to extract discriminative features for unseen novel categories and can be seamlessly incorporated with a non-linear distance metric function to facilitate the few-shot classification. Our result on the miniImageNet benchmark outperforms other metric-based few-shot classification methods. More importantly, when evaluated on completely different datasets (Caltech-101, CUB-200, Stanford Dogs and Cars) using the model trained with miniImageNet, our method significantly outperforms prior methods, demonstrating its superior capability to generalize to unseen classes. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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