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

作者： Yongqian Sun Yang Guo Minghan Liang Xidao Wen Junhua Kuang Shenglin Zhang Hongbo Li Kaixu Xia Dan Pei Nankai University Tianjin Key Laboratory of Software Experience and Human Computer Interaction BizSeer Haihe Laboratory of Information Technology Application Innovation Tencent Beijing National Research Center for Information Science and Technology Tsinghua University

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

In major tech companies, monitoring server performance data with anomaly detection algorithms is crucial for assessing operational status. Existing models often require separate training or fine-tuning for each server due to generalization limitations, leading to increased storage, memory, and training costs. As the number of servers grows, this approach becomes impractical. To address this, we propose using pretrained language models for time series anomaly detection, leveraging their strong generalization capabilities. Specifically, we employ two pre-trained GPT-2 models as backbones and implement a two-stage fine-tuning strategy to retain learned knowledge while adapting to specific business data characteristics. Our experiments on multiple anomaly detection datasets demonstrate that our method achieves the best average F1-Score, outperforming the leading baseline by 7%.

关键词： Training Adaptation models Time series analysis Production Reliability engineering Data models Software reliability Servers Anomaly detection Monitoring

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Mitigating Misleading Chain-of-Thought Reasoning with Selective Filtering 30

Mitigating Misleading Chain-of-Thought Reasoning with Select...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wu, Yexin Zhang, Zhuosheng Zhao, Hai College of Zhiyuan Shanghai Jiao Tong University China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

ISBN: (纸本)9782493814104

Large language models have manifested remarkable capabilities by leveraging chain-of-thought (CoT) reasoning techniques to solve intricate questions through step-by-step reasoning chains. Despite its success, the efficacy of such reasoning is inherently contingent upon the quality of CoT. However, flawless CoT reasoning cannot be guaranteed due to the presence of indecomposable questions and the potential for erroneous reasoning chains, particularly in the case of small-scale language models. To tackle this challenge, we propose a novel approach called the selective filtering reasoner (SelF-Reasoner) that assesses the entailment relationship between the question and the candidate reasoning chain. Then, we proceed with CoT reasoning when the reasoning chain demonstrates confidence;otherwise, we opt to predict the answer directly. SelF-Reasoner improves the fine-tuned T5 baseline consistently over the scienceQA, ECQA, and LastLetter tasks. Code is available at https://***/LibroWu/SelF-Reasoner. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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Auto-PIP: Real-time Identification of Critical Performance Inflection Points in Software Stress Testing

Auto-PIP: Real-time Identification of Critical Performance I...

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

作者： Shenglin Zhang Xiao Xiong Mengyao Li Yongqian Sun Yongxin Zhao Xia Chen Bowen Deng Dan Pei Nankai University Haihe Laboratory of Information Technology Application Innovation Tianjin Key Laboratory of Software Experience and Human Computer Interaction Huawei Cloud Tsinghua University Beijing National Research Center for Information Science and Technology

ISBN: (数字)9798350367041

ISBN: (纸本)9798350367058

Conducting stress testing is essential to ensure the stability and performance of software systems post-launch. Among its various aspects, the real-time identification of the "Smooth Load Area" (SLA) is crucial for establishing performance benchmarks, identifying bottlenecks, guiding optimization strategies, and strategically allocating resources. However, existing methods are incapable of real-time inflection point detection or require manual intervention. This paper proposes Auto-PIP, an automated identification framework for performance inflection points based on key performance indicators (KPIs) during performance stress tests. Auto-PIP integrates trend testing algorithms with unsupervised anomaly detection algorithms to identify optimal operating points and estimate maximum capacity. Auto-PIP has been deployed in Huawei Cloud, and the evaluation results show that Auto-PIP demonstrated 100% accuracy in optimal inflection point detection, a 41.7% leap over baselines, and 83.9% accuracy in maximum point identification, a 10.7% gain. Additionally, we have released the dataset to the public to promote ongoing research.

关键词： Accuracy Manuals Software systems Market research Real-time systems Software reliability Stress Anomaly detection Optimization Testing

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Saliency Region Detection in Complex Scenes Based on Multi-scale Cascaded Attention 7

Saliency Region Detection in Complex Scenes Based on Multi-s...

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7th IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2021

作者： Wang, Yifeng Huo, Zhenfeng Liu, Aoli Zhao, Lin Wang, Di Xidian University The Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shanxi Province Shanxi Province 710071 China Nanjing University of Science and Technology School of Computer Science and Engineering 210094 China

ISBN: (纸本)9781665441490

This paper proposes a saliency detection method based on multi-scale cascade attention mechanism. It utilizes both channel and spatial weight attention mechanism to effectively learn the salient regions. By generating multi-scale intermediate feature maps, the shallow features are divided into categories of foreground and background. Then, the channel weights are calculated by using the foreground and background feature distribution, and the spatial weights are computed by using the predicted feature map, so that the network is more focused on salient regions and suppresses the interference of background regions. Experimental results show that the model can reliably and accurately detect salient targets and delivers better performance. © 2021 IEEE.

关键词： Attention mechanism Channel weight attention Saliency detection Spatial weight attention

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Instance Regularization for Discriminative Language Model Pre-training

Instance Regularization for Discriminative Language Model Pr...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Zhang, Zhuosheng Zhao, Hai Zhou, Ming Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Langboat Technology

Discriminative pre-trained language models (PrLMs) can be generalized as denoising auto-encoders that work with two procedures, ennoising and denoising. First, an ennoising process corrupts texts with arbitrary noising functions to construct training instances. Then, a denoising language model is trained to restore the corrupted tokens. Existing studies have made progress by optimizing independent strategies of either ennoising or denosing. They treat training instances equally throughout the training process, with little attention on the individual contribution of those instances. To model explicit signals of instance contribution, this work proposes to estimate the complexity of restoring the original sentences from corrupted ones in language model pretraining. The estimations involve the corruption degree in the ennoising data construction process and the prediction confidence in the denoising counterpart. Experimental results on natural language understanding and reading comprehension benchmarks show that our approach improves pre-training efficiency, effectiveness, and robustness. Code is publicly available at https://***/cooelf/InstanceReg. © 2022 Association for Computational Linguistics.

关键词： Computational linguistics

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What makes IM users (un)responsive: An empirical investigation for understanding IM responsiveness

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International Journal of human computer Studies 2023年 172卷

作者： Lee, Hao-Ping [Hank] Chiang, Yi-Shyuan Chou, Yu-Ling Lin, Kung-Pai Chang, Yung-Ju Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Human–Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Department of Computer Science University of Illinois Urbana-Champaign UrbanaIL United States

Instant messaging (IM) communication has been widely studied due to its prevalence in our everyday communication. Numerous factors that contribute to (un)responsiveness have been identified. Yet an integrated view of the factors that influence IM responsiveness remains absent. This paper reports qualitative findings from interviews with 46 IM users, and identifies five main elements underlying IM users’ response decisions: response habits, need fulfillment, perceived obligation, perceived readiness/suitability, and pace/rhythm coordination. To the best of our knowledge, this is the first integrated view of the key elements underlying IM responsiveness. Among these elements, we particularly highlight that regarding the influence of contextual factors on IM responsiveness, what may matters more is IM users’ perceptions of their readiness and suitability of the contexts, rather than the contexts’ objective properties. We also uncovered pace/rhythm coordination as a crucial factor behind IM responsiveness, which has been little discussed in the literature. Responsiveness, as our findings show, was often not a consequence, but a manifestation, of users’ pursuit of specific responsiveness-related aims such as ongoing shaping and sustaining of a dyad's pace and rhythm. © 2022 Elsevier Ltd

关键词： Message passing

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Learning a Subspace and Clustering Simultaneously with Manifold Regularized Nonnegative Matrix Factorization

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Guidance, Navigation and Control 2024年第3期4卷 147-165页

作者： Feiping Nie Huimin Chen Heng Huang Chris H.Q.Ding Xuelong Li School of Computer Science Northwestern Polytechnical University Xi'an 710072 P.R.China School of Artificial Intelligence Optics and Electronics(iOPEN)Northwestern Polytechnical University Xi'an 710072 P.R.China Key Laboratory of Intelligent Interaction and Applications(Northwestern Polytechnical University)Ministry of Industry and Information Technology Xi'an 710072 P.R.China Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA 15260USA Department of Computer Science and Engineering The Chinese University of Hong Kong Shenzhen 518172 P.R.China Institute of Artificial Intelligence(TeleAI)China Telecom Corporation Limited 31 Jinrong Street Beijing 100033P.R.China

With the incredible growth of high-dimensional data such as microarray gene expression data and web blogs from internet, the researchers are desirable to develop new clustering techniques to address the critical problem created by irrelevant dimensions. Properties of Nonnegative Matrix Factorization(NMF) as a clustering method were studied by relating its formulation to other methods such as K-means clustering. In this paper, by introducing clustering indicator constraints on NMF and incorporating manifold regularization to preserve geometric structures,we propose a novel manifold regularized NMF method that can simultaneously learn subspace and do clustering. As a result, our clustering results can directly assign cluster label to data points. Extensive experimental results show that our method outperforms related other methods.

关键词： Subspace learning clustering nonnegative matrix factorization manifold learning

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Surface defect identification using Bayesian filtering on a 3D mesh

Surface defect identification using Bayesian filtering on a ...

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作者： Dalle Vedove, Matteo Bonetto, Matteo Lamon, Edoardo Palopoli, Luigi Saveriano, Matteo Fontanelli, Daniele Department of Industrial Engineering Universitá di Trento Trento Italy DRIM Ph.D. of National Interest in Robotics and Intelligent Machines Italy Department of Information Engineering and Computer Science Universitá di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

This paper presents a CAD-based approach for automated surface defect detection. We leverage the a-priori knowledge embedded in a CAD model and integrate it with point cloud data acquired from commercially available stereo and depth cameras. The proposed method first transforms the CAD model into a high-density polygonal mesh, where each vertex represents a state variable in 3D space. Subsequently, a weighted least squares algorithm is employed to iteratively estimate the state of the scanned workpiece based on the captured point cloud measurements. This framework offers the potential to incorporate information from diverse sensors into the CAD domain, facilitating a more comprehensive analysis. Preliminary results demonstrate promising performance, with the algorithm achieving convergence to a sub-millimeter standard deviation in the region of interest using only approximately 50 point cloud samples. This highlights the potential of utilising commercially available stereo cameras for high-precision quality control applications. © 2025

关键词： Mesh generation

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Opportunities for Serious Game Technologies to Engage Children with Autism in a Pakistani Sociocultural and Institutional Context: An investigation of the design space for serious game technologies to enhance engagement of children with autism and to facilitate external support provided 22

Opportunities for Serious Game Technologies to Engage Childr...

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Proceedings of the 34th Australian Conference on human-computer interaction

作者： Sara Muneeb Laurianne Sitbon Faizan Ahmad Research Center for Human Computer Interaction COMSATS University Islamabad Lahore Campus Pakistan and School of Computer Science Queensland University of Technology (QUT) Australia School of Computer Science Queensland University of Technology (QUT) Australia Department of Computer Science School of Technologies Cardiff Metropolitan University United Kingdom

ISBN: (纸本)9798400700248

Usability and design of serious game technologies have been widely investigated as academic interventions tools for children with autism. However, besides the technology, the sociocultural and institutional context also impacts the gameplay activities of children with autism as well as the external support provided by instructors. Therefore, this paper investigates i) the impact of sociocultural and institutional context over the engagement of children with autism with serious game technologies, ii) the types of external support children with autism are offered or require during gameplay session, and iii) the type of rewards and encouragers that can act as unbiased support to increase the engagement of the children with autism during *** paper investigates these factors with an inductive analysis of educational gameplay activities of 23 children with autism in Lahore, Pakistan. Our study found that sociocultural and institutional context has substantial impact over the gameplay activities of children with autism. Children required both physical and verbal support during the gameplay activities, and institutional context influences the behavior of instructors as well. Finally, dynamic animations, rewards and encouragers can keep the children engaged for longer in the gameplay.

关键词： serious game technologies children with autism Game-based learning support context and encouragers rewards

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Body Politics: Unpacking Tensions and Future Perspectives for Body-Centric Design Research in HCI

Body Politics: Unpacking Tensions and Future Perspectives fo...

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Tsaknaki, Vasiliki Alaoui, Sarah Fdili Homewood, Sarah Fritsch, Jonas Brynskov, Anna Núñez-Pacheco, Claudia Carlson, Kristin Spiel, Katta Gillies, Marco Fyfe Pietro Harrington, Christina IT University of Copenhagen Copenhagen Denmark Creative Computing Institute University of the Arts London London United Kingdom Department of Computer Science University of Copenhagen Copenhagen Denmark Department of Computer Science and Media Technology Malmö University Malmö Sweden Creative Technologies Program Illinois State University NormalIL United States Crip Collective Human-Computer Interaction Group TU Wien Vienna Austria Computing Goldsmiths University of London London United Kingdom HCI Institute Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9798400713958

human bodies are deeply political as they carry historical and social meanings, including race, gender, sexuality, ethnicity, class, and abilities. The expanding body-centric research in HCI can be traced in the plurality of methods, theories and domains that take bodies as a central point of departure, when designing or studying interaction with technologies. This one-day workshop will bring together researchers and practitioners within the CHI community to discuss, map, and unpack emerging tensions and challenges on the topic of body politics for HCI. Interested participants are invited to submit examples from their own research, which, in the workshop, will be used as a point of departure to critically reflect on and expand body-centric methods, theories and domains through the lens of body politics. Workshop outcomes will include charting future directions for body-centric research to address challenges and opportunities of acknowledging that bodies are always political in design research. © 2025 Copyright held by the owner/author(s).

关键词： Bodies design research politics

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