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Extended Abstracts of the 2023 CHI Conference on human Factors in Computing Systems, CHI EA 2023

作者： Long, Duri Roberts, Jessica Magerko, Brian Holstein, Kenneth Dipaola, Daniella Martin, Fred Department of Communication Studies Northwestern University United States School of Interactive Computing Georgia Tech United States School of Literature Media and Communication Georgia Tech United States Human-Computer Interaction Institute Carnegie Mellon University United States Personal Robots Group Mit Media Lab United States Computer Science University of Massachusetts Lowell United States

ISBN: (纸本)9781450394222

Fostering public AI literacy has been a growing area of interest at CHI for several years, and a substantial community is forming around issues such as teaching children how to build and program AI systems, designing learning experiences to broaden public understanding of AI, developing explainable AI systems, understanding how novices make sense of AI, and exploring the relationship between public policy, ethics, and AI literacy. Previous workshops related to AI literacy have been held at other conferences (e.g., SIGCSE, AAAI) that have been mostly focused on bringing together researchers and educators interested in AI education in K-12 classroom environments, an important subfield of this area. Our workshop seeks to cast a wider net that encompasses both HCI research related to introducing AI in K-12 education and also HCI research that is concerned with issues of AI literacy more broadly, including adult education, interactions with AI in the workplace, understanding how users make sense of and learn about AI systems, research on developing explainable AI (XAI) for non-expert users, and public policy issues related to AI literacy. © 2023 Owner/Author.

关键词： Public policy

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SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera Video Tracking and Recognition

SIVD: Dataset of Iranian Vehicles for Real-Time Multi-Camera...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Farbod Siahkali Seyed Amirmahdi Alavi Mehdi Tale Masouleh Human and Robot Interaction Laboratory School of Electrical and Computer Engineering University of Tehran Tehran Iran Human and Robot Interaction Laboratory School of Computer Science Islamic Azad University Central Tehran Branch Tehran Iran

ISBN: (纸本)9781665476249

In this paper, a new publicly available 1 web-Scraped Iranian Vehicle Dataset (SIVD) for simultaneous real-time vehicle tracking and recognition is proposed. The datasets provided for Iranian cars in the literature have two fundamental problems. First, the lack of images from different angles, and second, the small number of classes compared to the dispersion of car models in the real world. Therefore, for the purposes of this paper, Iranian vehicle images from car sales websites are collected, and the SIVD dataset is proposed which contains 29 classes and 36,705 images. This paper aims at developing a classification network for Iranian vehicle recognition and implement a real-time tracking system using the YOLOv5 network to perform real-time vehicle model recognition and tracking tasks simultaneously. The ResNet50 achieved an accuracy of 99.29%, the highest among the investigated classification networks. 1 https://***/Farbod-Siahkali/Iranian-Vehicle-Tracking-and-Recognition

关键词： Representation learning Image recognition Video tracking Machine learning algorithms Neural networks Real-time systems Automobiles

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A Neural Architecture Search Method with Multi-Dimensional Correlation Representation for Fault Diagnosis

A Neural Architecture Search Method with Multi-Dimensional C...

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Smart World Congress (SWC), IEEE

作者： Yumeng Liu Tingqi Wang Xu Zheng Ling Tian Hongan Wang Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

As deep learning has become more widely used for fault diagnosis, the shortcomings of model transferability and human model design costs are growing increasingly evident. The current work has tackled each of these two issues. Domain adaptation methods can provide transportability by mapping data to a common domain. For the consumption of manually constructed networks, Neural Architecture Search (NAS) is an intelligent solution that automatically select the ideal network structure. Unfortunately, there is little work to connect these two methodologies in fault diagnosis. Furthermore, these approaches are incapable in extracting internal correlations from instrumental signal data. In this paper, we propose a transferrable NAS fault diagnosis method, which maximizes savings in manual costs. We also integrate graph neural networks (GNN) in a stepwise manner into the NAS and domain adaptation to provide a multi-dimension correlation representation. Firstly, we structure the signal data into graph based on temporal relationships. Then, NAS designs automatically for selecting the optimal network structure and useful features. In NAS, graph encoder of GNN is used to establish internal relationships. Finally, domain adaptation provides transferability. At the same time, GNN’s weights in domain adaptation help to further mine data internal associations. We tested our method on two publicly available datasets and performed comparisons. The experimental results show the proposed method has superior performance.

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A Dual-view Semi-supervised Learning Framework for Combinatorial Few-shot Fault Diagnosis

A Dual-view Semi-supervised Learning Framework for Combinato...

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IEEE International Conference on Performance, Computing and Communications (IPCCC)

作者： Yumeng Liu Wenzhang Zhong Ke Yan Ling Tian Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

The accurate and comprehensive fault diagnosis is pivotal for the maintenance of mechanical equipment, which may directly impact the safety in production and avoid unnecessary losses of equipment. With the pervasively adopted sensing components, sensing data can be collected from mechanical equipment and analyzed for fault diagnosis. Previous solutions apply sophisticated deep learning models for such fault diagnosis tasks and have achieved impressive performance. However, these methods usually ignore the existences of diverse characteristics of faults, and formulate the fault diagnosis as a simple classification problem. Considering necessity of comprehensive and multi-view fault diagnosis, this work proposes a novel dual-view learning framework for combinatorial mechanical fault classification. The framework includes dual encoders to simultaneously learn the feature representation tied with the position and size of faults on equipment. Then these features are mutually mixed for better representation and the mixed features are merged and feed into deep neural networks for further representation learning. The query samples are classified through metric-based comparison to search for the closest type of composite fault. Moreover, the proposed framework also applies typical metric-based meta learning method to handle the issue of small training datasets, and adopts semi-supervised learning method to make use of both labeled and unlabeled samples. Finally, the experimental results on public datasets show that the proposed method can outperform previous solutions and achieve state-of-the-art performance.

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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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Multivariate Time Series Anomaly Detection based on Pre-trained Models with Dual-Attention Mechanism

Multivariate Time Series Anomaly Detection based on Pre-trai...

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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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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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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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