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International Symposium on Software Reliability Engineering (ISSRE)

作者： Ke Zhang Chenxi Zhang Xin Peng Chaofeng Sha School of Computer Science Fudan University China Shanghai Key Laboratory of Data Science Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China

ISBN: (数字)9781665451321

ISBN: (纸本)9781665451338

Distributed tracing has been an important part of microservice infrastructure and learning-based trace analysis has been used to detect anomalies in microservice systems. Existing learning-based trace anomaly detection approaches ei-ther assume that trace patterns can be learned from normal execution or rely on fault injection to produce labeled traces (i.e., normal/anomalous ones). However, in practice it is often difficult to ensure that the normal execution does not involve anomalous traces or obtain a large variety of normal and anomalous traces through fault injection. In this paper, we propose PUTraceAD, a trace anomaly detection approach that can alleviate the above problems. PUTraceAD represents a trace as a span causal graph with node features such as operation name, response code, duration time. Based on the graph representation, PUTraceAD trains a GNN- and PU learning-based trace anomaly detection model. During the process, PU (Positive and Unlabeled) learning optimizes model parameters through estimating the data distribution. Therefore, PUTraceAD can train the model based on a small set of labeled anomalous traces and a large set of unlabeled traces. Our evaluation shows that PUTraceAD outperforms existing unsupervised trace anomaly detection approaches and only slightly underperforms a supervised learning-based approach that takes full advantage of labeled traces.

关键词： Codes Microservice architectures Data models Software reliability Time factors Anomaly detection

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From Failure to Insight: Analyzing Disk Breakdowns in Large-Scale HPC Environments

From Failure to Insight: Analyzing Disk Breakdowns in Large-...

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2024 Workshops of the International Conference for High Performance computing, Networking, Storage and Analysis, SC Workshops 2024

作者： George, Anjus Wang, Meng Hanley, Jesse Ransom, Garrett Wilson Bent, John Zimmer, Christopher Oak Ridge National Laboratory National Center for Computational Sciences Oak RidgeTN United States University of Chicago Department of Computer Science ChicagoIL United States Los Alamos National Laboratory HPC Infrastructure Los AlamosNM United States Los Alamos National Laboratory High Performance Computing Los AlamosNM United States

ISBN: (纸本)9798350355543

Disk failure data provides valuable insights for preventing failures, enhancing storage robustness, guiding system design and deployment, and ensuring reliable operations at data centers. This paper introduces two disk failure datasets collected from large-scale HPC production environments over the past five years, comprising over 5,000 failure records from more than 40,000 disks. We analyzed these datasets across multiple dimensions, including temporal, spatial, and relational trends, and performed a comprehensive reliability assessment. Our analysis yielded numerous observations and insights that influence various operational aspects of HPC storage systems. We believe this study offers a holistic understanding of disk failure trends likely to interest the HPC storage community. © 2024 IEEE.

关键词： Cause effect analysis Failure data analysis Hard disk drives HPC storage Reliability Summit Supercomputer

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Real-Time 3D-Aware Portrait Video Relighting

Real-Time 3D-Aware Portrait Video Relighting

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Ziqi Cai Kaiwen Jiang Shu-Yu Chen Yu-Kun Lai Hongbo Fu Boxin Shi Lin Gao Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences Beijing Jiaotong University University of California San Diego Cardiff University City University of Hong Kong The Hong Kong University of Science and Technology National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University University of Chinese Academy of Sciences

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Synthesizing realistic videos of talking faces under custom lighting conditions and viewing angles benefits various downstream applications like video conferencing. However, most existing relighting methods are either time-consuming or unable to adjust the viewpoints. In this paper, we present the first real-time 3D-aware method for relighting in-the-wild videos of talking faces based on Neural Radiance Fields (NeRF). Given an input portrait video, our method can synthesize talking faces under both novel views and novel lighting conditions with a photo-realistic and disentangled 3D representation. Specifically, we infer an albedo tri-plane, as well as a shading tri-plane based on a desired lighting condition for each video frame with fast dual-encoders. We also leverage a temporal consistency network to ensure smooth transitions and reduce flickering artifacts. Our method runs at 32.98 fps on consumer-level hardware and achieves state-of-the-art results in terms of reconstruction quality, lighting error, lighting instability, temporal consistency and inference speed. We demonstrate the effectiveness and interactivity of our method on various portrait videos with diverse lighting and viewing conditions.

关键词： computer vision Three-dimensional displays Lighting Neural radiance field Real-time systems Hardware Pattern recognition

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How Augmented Reality Works: Technical Issues, User Issues, Content Development, and Curriculum Adaptations as Challenges Through Bibliometric Analysis

How Augmented Reality Works: Technical Issues, User Issues, ...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Gamal Kusuma Zamahsari Mufidah Nur Amalia Lailatul Rifah Frihandhika Permana Communication Science Department Digital Language Learning Center Faculty of Humanities Bina Nusantara University Jakarta Indonesia Computer Science Department Digital Language Learning Center Faculty of Humanities Bina Nusantara University Jakarta Indonesia Digital Language Learning Center Visual Communication Design Faculty of Humanities Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Augmented reality has been effectively used in the domain of education. Nevertheless, the development of technology is not without its problems, particularly when it comes to aligning it with existing demands and prevailing trends. The primary objective of this research endeavor is to provide a comprehensive account and identify pertinent concerns pertaining to the challenges encountered in the use of augmented reality (AR), particularly within the realm of education. A bibliometric study was performed using analytical tools available on the *** website, and the Vos viewer application was used for the viewing of bibliometric data. The study's results indicate that there are many concerns that stakeholders should take into consideration while using augmented reality in educational settings. There are four distinct hurdles in the adaptation of Augmented Reality (AR) technology to meet the demands of learning environments. These challenges include technical issues, user-related concerns, the production of AR content, and the adaptation of curricula to effectively incorporate AR into educational settings.

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JMNet: A joint matting network for automatic human matting

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Computational visual Media 2020年第2期6卷 215-224页

作者： Xian Wu Xiao-Nan Fang Tao Chen Fang-Lue Zhang Department of Computer Science and Technology Tsinghua UniversityBeijing100084China AI Center at Visual China Group BurlingameCA94010USA School of Engineering and Computer Science Victoria University of WellingtonWellingtonNew Zealand

We propose a novel end-to-end deep learning framework, the Joint Matting Network(JMNet), to automatically generate alpha mattes for human *** utilize the intrinsic structures of the human body as seen in images by introducing a pose estimation module,which can provide both global structural guidance and a local attention focus for the matting task. Our network model includes a pose network, a trimap network, a matting network, and a shared encoder to extract features for the above three networks. We also append a trimap refinement module and utilize gradient loss to provide a sharper alpha matte. Extensive experiments have shown that our method outperforms state-of-theart human matting techniques;the shared encoder leads to better performance and lower memory *** model can process real images downloaded from the Internet for use in composition applications.

关键词： alpha matting human images deep learning pose estimation

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ParaMac: A General Unsupervised Paraphrase Generation Framework Leveraging Semantic Constraints and Diversifying Mechanisms

ParaMac: A General Unsupervised Paraphrase Generation Framew...

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2022 Findings of the Association for Computational Linguistics: EMNLP 2022

作者： Liu, Jinxin Shi, Jiaxin Qi, Ji Hou, Lei Li, Juanzi Tian, Qi Department of Computer Science and Technology BNRist China THU - Siemens Ltd. China Joint Research Center for Industrial Intelligence and IoT Tsinghua University Beijing China Huawei Cloud Computing Technologies Co. Ltd

Paraphrase generation reflects the ability to understand the meaning from the language surface form and rephrase it to other expressions. Recent paraphrase generation works have paid attention to unsupervised approaches based on Pre-trained Language Models (PLMs) to avoid heavy reliance on parallel data by utilizing PLMs' generation ability. However, the generated pairs of existing unsupervised methods are usually weak either in semantic equivalence or expression diversity. In this paper, we present a novel unsupervised paraphrase generation framework called Paraphrase Machine. By employing multi-aspect equivalence constraints and multi-granularity diversifying mechanisms, Paraphrase Machine is able to achieve good semantic equivalence and expressive diversity, producing a high-quality unsupervised paraphrase dataset. Based on this dataset, we train a general paraphrase model, which can be directly applied to rewrite the input sentence of various domains without any fine-tuning, and achieves substantial gains of 9.1% and 3.3% absolutely in BLEU score over previous SOTA on Quora and MSCOCO. By further fine-tuning our model with domain-specific training sets, the improvement can be increased to even 18.0% and 4.6%. Most importantly, by applying it to language understanding and generation tasks under the low-resource setting, we demonstrate that our model can serve as a universal data augmentor to boost the few-shot performance (e.g., average 2.0% gain on GLUE). Code and data can be found at https://***/Matthewlliu/UnsupervisedParaphrase. © 2022 Association for Computational Linguistics.

关键词： Semantics

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Editorial:Special Topic on Federated Learning for IoT and Edge computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database

arXiv

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arXiv 2025年

作者： Hu, Yan Gong, Mingdao Qiu, Zhongxi Liu, Jiabao Shen, Hongli Yuan, Mingzhen Zhang, Xiaoqing Li, Heng Lu, Hai Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Beijing China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database1. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

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Towards Efficient Discrete Controller Synthesis: Semantics-Aware Stepwise Policy Design via LLM

Towards Efficient Discrete Controller Synthesis: Semantics-A...

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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Yusei Ishimizu Jialong Li Takuto Yamauchi Sinan Chen Jinyu Cai Takanori Hirano Kenji Tei School of Computing Tokyo Institute of Technology Tokyo Japan Department of Computer Science and Engineering Waseda University Tokyo Japan Center of Mathematical and Data Sciences Kobe University Kobe Japan

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

Discrete Controller Synthesis (DCS) is an automated generation technique to generate controllers from environment models and requirements. The time and space complexities of DCS are critical because they directly govern the feasibility of using DCS for real-time generation of controllers to cope with environmental changes. Stepwise DCS is considered a promising approach to prevent the construction of ineffective state spaces in the subsequent analysis of the full controller by analyzing sub-controllers that meet part of the requirements at an early stage. However, constructing a stepwise policy is a challenge, as the current methods often require domain experts to manually build it by understanding and reasoning the semantics of each sub-environment model and sub-requirement. To reduce the effort of manual construction, this paper introduces a method that leverages large language models (LLMs) to automate the generation of stepwise policies by utilizing the LLM's ability to understand and reason about the semantics of state machines. The evaluation using GPT-4 on a drone control scenario demonstrates the effectiveness of our method, with an average reduction of 82.2% in states and 91.2% in transitions.

关键词： Large language models Semantics Manuals Aerospace electronics Real-time systems Cognition Complexity theory Drones

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CMU-Flownet: Exploring Point Cloud Scene Flow Estimation in Occluded Scenario

arXiv

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arXiv 2024年

作者： Chen, Jingze Yao, Junfeng Lin, Qiqin Li, Lei Center for Digital Media Computing School of Film School of Informatics Xiamen University China Institute of Artificial Intelligence Xiamen University China Department of Computer Science University of Copenhagen Denmark

Occlusions hinder point cloud frame alignment in LiDAR data, a challenge inadequately addressed by scene flow models tested mainly on occlusion-free datasets. Attempts to integrate occlusion handling within networks often suffer accuracy issues due to two main limitations: a) the inadequate use of occlusion information, often merging it with flow estimation without an effective integration strategy, and b) reliance on distance-weighted upsampling that falls short in correcting occlusion-related errors. To address these challenges, we introduce the Correlation Matrix Upsampling Flownet (CMU-Flownet), incorporating an occlusion estimation module within its cost volume layer, alongside an Occlusion-aware Cost Volume (OCV) mechanism. Specifically, we propose an enhanced upsampling approach that expands the sensory field of the sampling process which integrates a Correlation Matrix designed to evaluate point-level similarity. Meanwhile, our model robustly integrates occlusion data within the context of scene flow, deploying this information strategically during the refinement phase of the flow estimation. The efficacy of this approach is demonstrated through subsequent experimental validation. Empirical assessments reveal that CMU-Flownet establishes state-of-the-art performance within the realms of occluded Flyingthings3D and KITTY datasets, surpassing previous methodologies across a majority of evaluated metrics. Copyright © 2024, The Authors. All rights reserved.

关键词： Signal sampling

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