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A Non-normal Warning System for Dam Operation Using Machine Learning

A Non-normal Warning System for Dam Operation Using Machine ...

ACIS International Conference on Software engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD)

作者： Meng-Wei Chang I-Hsien Liu Chuan-Kang Liu Wei-Min Lin Zhi-Yuan Su Jung-Shian Li Dept. of Electrical Engineering Inst. Computer and Communication Engineering National Cheng Kung University Tainan Taiwan dept. of Artificial Intelligence and Computer Engineering National Chin-Yi University of Technology Taichung Taiwan dept. of multimedia and Game Development Chia Nan University of Pharmacy and Science Tainan Taiwan

A country's critical infrastructures are heavily related to the quality of life and safety of the people. As a result, the security protection aspect of critical infrastructure has gained more and more attention nowadays, especially the security of its industrial control system (ICS). To avoid the abnormal condition happening in the critical infrastructure which could put people in great danger, a system that is capable of detecting any abnormal state of the ICS promptly is needed. Fortunately, due to the dramatic growth of the applications of machine learning in recent years, some researchers have already proposed anomaly detection methods with machine learning to provide inst.nt warning and protection for ICS. However, most of the existing anomaly detection research tends to only target one cause that harms the system, such as attacks on the network or physical equipment failures. The ICS will be more comprehensively secured if the anomaly detection system can cover multiple aspects of the ICS. Therefore, we have established a non-normal warning system with the Generative Adversarial Network (GAN) for dam operations in this study, which can detect various types of non-normal operations and notify relevant personnel right away. Note that we use real historical data to make predictions and verify our warning system, and we improve it even more by implementing the visual analysis method, which makes up the indecipherable results often found in unsupervised learning.

关键词： Integrated circuits Training Visualization Dams Machine learning Alarm systems Generative adversarial networks

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Automated Bell Inequality Violation using Uncalibrated Fiber Squeezing Polarization Controllers

Automated Bell Inequality Violation using Uncalibrated Fiber...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Evan Dowling Mark Morris Gerald Baumgartner Rajarshi Roy Thomas E. Murphy Inst. for Research in Electronics & Applied Physics University of Maryland College Park Maryland USA Dept. of Physics University of Maryland College Park Maryland USA Laboratory for Telecommunication Sciences College Park Maryland USA Inst. for Physical Science & Technology University of Maryland College Park Maryland USA Dept. of Electrical & Computer Engineering University of Maryland College Park Maryland USA

We experimentally report on a real-time self-guided method to search for maximal CHSH violations between two observers sharing polarization entangled photon pairs using uncalibrated piezoelectric fiber squeezers as po... 详细信息

ISBN: (纸本)9781957171258

关键词： Photon polarization Polarization Polarization control Quantum communications Quantum key distribution Single photon detectors

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Building Bridge Across the Time: Disruption and Restoration of Murals In the Wild

Building Bridge Across the Time: Disruption and Restoration ...

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International Conference on computer Vision (ICCV)

作者： Huiyang Shao Qianqian Xu Peisong Wen Peifeng Gao Zhiyong Yang Qingming Huang Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS School of Computer Science and Tech. University of Chinese Academy of Sciences State Key Lab of Info. Security Inst. of Info. Engineering CAS School of Cyber Security University of Chinese Academy of Sciences BDKM University of Chinese Academy of Sciences Peng Cheng Laboratory

In this paper, we focus on the mural-restoration task, which aims to detect damaged regions in the mural and repaint them automatically. Different from traditional image restoration tasks like in/out/blind-painting and image renovation, the corrupted mural suffers from more complicated degradation. However, existing mural-restoration methods and datasets still focus on simple degradation like masking. Such a significant gap prevents mural-restoration from being applied to real scenarios. To fill this gap, in this work, we propose a systematic framework to simulate the physical process for damaged murals and provide a new benchmark dataset for mural-restoration. Limited by the simplification of the data synthesis process, the previous mural-restoration methods suffer from poor performance in our proposed dataset. To handle this problem, we propose the Attention Diffusion Framework (ADF) for this challenging task. Within the framework, a damage attention map module is proposed to estimate the damage extent. Facing the diversity of defects, we propose a series of loss functions to choose repair strategies adaptively. Finally, experimental results support the effectiveness of the proposed framework in terms of both mural synthesis and restoration.

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Live multi-streaming and donation recommendations via coupled donation-response tensor factorization

arXiv

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arXiv 2021年

作者： Lai, Hsu-Chao Huang, Jiun-Long Tsai, Jui-Yi Lee, Wang-Chien Shuai, Hong-Han Yang, De-Nian Dept. of Computer Science National Chiao Tung Univ. Inst. of Information Science Academia Sinica Taiwan Dept. of Computer Science National Chiao Tung Univ. Taiwan Dept. of Electrical and Computer Engineering National Chiao Tung Univ. Inst. of Information Science Academia Sinica Taiwan Dept. of Computer Science and Engineering Pennsylvania State Univ. United States Dept. of Electrical and Computer Engineering National Chiao Tung Univ. Taiwan Inst. of Information Science Research Center Information Technology Innovation Academia Sinica Taiwan

In contrast to traditional online videos, live multi-streaming supports real-time social interactions between multiple streamers and viewers, such as donations. However, donation and multi-streaming channel recommendations are challenging due to complicated streamer and viewer relations, asymmetric communications, and the tradeoff between personal interests and group interactions. In this paper, we introduce Multi-Stream Party (MSP) and formulate a new multi-streaming recommendation problem, called Donation and MSP Recommendation (DAMRec). We propose Multi-stream Party Recommender System (MARS) to extract latent features via socio-temporal coupled donation-response tensor factorization for donation and MSP recommendations. Experimental results on Twitch and Douyu manifest that MARS significantly outperforms existing recommenders by at least 38.8% in terms of hit ratio and mean average precision. Copyright © 2021, The Authors. All rights reserved.

关键词： Factorization

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Real-time Holistic Robot Pose Estimation with Unknown States

arXiv

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arXiv 2024年

作者： Ban, Shikun Fan, Juling Ma, Xiaoxuan Zhu, Wentao Qiao, Yu Wang, Yizhou Center on Frontiers of Computing Studies School of Computer Science Peking University China Inst. for Artificial Intelligence Peking University China Nat’l Eng. Research Center of Visual Technology Nat’l Key Lab of General Artificial Intelligence Department of Automation School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University China

Estimating robot pose from RGB images is a crucial problem in computer vision and robotics. While previous methods have achieved promising performance, most of them presume full knowledge of robot internal states, e.g. ground-truth robot joint angles. However, this assumption is not always valid in practical situations. In real-world applications such as multi-robot collaboration or human-robot interaction, the robot joint states might not be shared or could be unreliable. On the other hand, existing approaches that estimate robot pose without joint state priors suffer from heavy computation burdens and thus cannot support real-time applications. This work introduces an efficient framework for real-time robot pose estimation from RGB images without requiring known robot states. Our method estimates camera-to-robot rotation, robot state parameters, keypoint locations, and root depth, employing a neural network module for each task to facilitate learning and sim-to-real transfer. Notably, it achieves inference in a single feed-forward pass without iterative optimization. Our approach offers a 12× speed increase with state-of-the-art accuracy, enabling real-time holistic robot pose estimation for the first time. Code and models are available at https: //***/Oliverbansk/Holistic-Robot-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

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ScoreHypo: Probabilistic Human Mesh Estimation with Hypothesis Scoring

ScoreHypo: Probabilistic Human Mesh Estimation with Hypothes...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yuan Xu Xiaoxuan Ma Jiajun Su Wentao Zhu Yu Qiao Yizhou Wang Center on Frontiers of Computing Studies School of Computer Science Peking University International Digital Economy Academy (IDEA) School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Inst. for Artificial Intelligence Peking University Nat'1 Eng. Research Center of Visual Technology Nat'1 Key Lab of General Artificial Intelligence

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Monocular 3D human mesh estimation is an ill-posed problem, characterized by inherent ambiguity and occlusion. While recent probabilistic methods propose generating multiple solutions, little attention is paid to obtaining high-quality estimates from them. To address this limitation, we introduce ScoreHypo, a versatile framework by first leveraging our novel HypoNet to generate multiple hy-potheses, followed by employing a meticulously designed scorer, ScoreNet, to evaluate and select high-quality esti-mates. ScoreHypo formulates the estimation process as a re-verse denoising process, where HypoNet produces a diverse set of plausible estimates that effectively align with the im-age cues. Subsequently, ScoreNet is employed to rigorously evaluate and rank these estimates based on their quality and finally identify superior ones. Experimental results demon-strate that HypoNet outperforms existing state-of-the-art probabilistic methods as a multi-hypothesis mesh estimator. Moreover, the estimates selected by ScoreNet significantly outperform random generation or simple averaging. Notably, the trained ScoreNet exhibits generalizability, as it can effectively score existing methods and significantly reduce their errors by more than 15%. Code and models are available at ht tps: / /xy02- 05. gi thub. io/ScoreHypo.

关键词： computer vision Three-dimensional displays Accuracy Codes Noise reduction Estimation Benchmark testing

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Asymptotically unbiased inst.nce-wise regularized partial AUC optimization: theory and algorithm 22

Asymptotically unbiased instance-wise regularized partial AU...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Huiyang Shao Qianqian Xu Zhiyong Yang Shilong Bao Qingming Huang Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS School of Computer Science and Tech. University of Chinese Academy of Sciences State Key Lab of Info. Security Inst. of Info. Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and BDKM University of Chinese Academy of Sciences and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

The Partial Area Under the ROC Curve (PAUC), typically including One-way Partial AUC (OPAUC) and Two-way Partial AUC (TPAUC), measures the average performance of a binary classifier within a specific false positive rate and/or true positive rate interval, which is a widely adopted measure when decision constraints must be considered. Consequentiy, PAUC optimization has naturally attracted increasing attention in the machine learning community within the last few years. Nonetheless, most of the existing methods could only optimize PAUC approximately, leading to inevitable biases that are not controllable. Fortunately, a recent work presents an unbiased formulation of the PAUC optimization problem via distributional robust optimization. However, it is based on the pair-wise formulation of AUC, which suffers from the limited scalability w.r.t. sample size and a slow convergence rate, especially for TPAUC. To address this issue, we present a simpler reformulation of the problem in an asymptotically unbiased and inst.nce-wise manner. For both OPAUC and TPAUC, we come to a nonconvex strongly concave minimax regularized problem of inst.nce-wise functions. On top of this, we employ an efficient solver enjoys a linear per-iteration computational complexity w.r.t. the sample size and a time-complexity of O(ε-1/3) to reach a ε stationary point. Furthermore, we find that the minimax reformulation also facilitates the theoretical analysis of generalization error as a byproduct. Compared with the existing results, we present new error bounds that are much easier to prove and could deal with hypotheses with real-valued outputs. Finally, extensive experiments on several benchmark datasets demonstrate the effectiveness of our method.

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Condition Monitoring & Fault Detection in Photovoltaic Modules Using Machine Learning

Condition Monitoring & Fault Detection in Photovoltaic Modul...

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International Conference on Sustainable Technology for Power and Energy Systems (STPES)

作者： Shivam Tyagi Bhavya Dhingra Anuradha Tomar Department of Computer Science Netaji Subhas University of Technology Delhi India Department of Electrical Engineering Netaji Subhas University of Technology Delhi India Department of Inst. and Control Engg. Netaji Subhas University of Technology Delhi India

ISBN: (纸本)9781665459167

There has been a significant increase in the use of renewable energy solutions such as Photovoltaic (PV) power plant projects to decrease the dependency on fossil fuels while still meeting the global energy demands. However, these technologies come with their own set of problems. Condition monitoring and maintenance of PV modules are one of the challenges that need to be tackled to make these new energy solutions even more efficient. The cleaning of PV modules and the maintenance of hardware components used in a power plant are significantly important to the total energy yield and efficiency of the project. This paper provides a detailed look into the application of machine learning algorithms for condition monitoring and fault detection in photovoltaic modules. This paper employs various regression models to predict the power output of the PV modules accurately. The model takes inputs from the inverter connected to an array mesh of PV modules and applies a trained prediction model that can work in real-time. The system is based on comparing linear, Bayesian and non-linear regression models on their ability to explain the variance of the yield by PV modules.

关键词： Photovoltaic systems Condition monitoring Renewable energy sources Machine learning algorithms Fault detection Machine learning Predictive models

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InTrust-IoT: Intelligent Ecosystem based on Power Profiling of Trusted device(s) in IoT for Hardware Trojan Detection 21

InTrust-IoT: Intelligent Ecosystem based on Power Profiling ...

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Workshop on Hardware and Architectural Support for Security and Privacy

作者： Hawzhin Mohammed Faiq Khalid Paul Sawyer Gabriella Cataloni Syed Rafay Hasan Information Science and Technology Penn State University United States Inst. of Computer Eng. Technische Universitat Wien Austria School of Engineering Liberty University United States Dept. of Elect. and Comp. Eng. University of Mount Union United States Electrical and Computer Engineering Tennessee Tech University United States

ISBN: (纸本)9781450396141

Modern Resource-Constrained (RC) Internet of Things (IoT) devices are subject to several types of attacks, including hardware-level attacks. Most of the existing state-of-the-art solutions are invasive, require expensive design time interventions, or need dataset generation from non-trusted RC-IoT devices or both. We argue that the health of modern RC-IoT devices requires a final line of defense against.possible hardware attacks that go undetected during the IC design and test process. Hence, in this paper, we propose a defense methodology against.non-zero-day and zero-day attacks, leveraging machine learning techniques trained on the dataset obtained without design time intervention and using ‘only’ trusted IoT devices. In the process, a complete eco-system is developed where data is generated through a trusted group of devices, and machine learning is done on these trusted datasets. Next, this trusted trained model is deployed in regular IoT systems that contain untrusted devices, where the attack on untrusted devices can be detected in real-time. Our results indicate that for non-zero-day attacks, the proposed technique can concurrently detect DoS and power depletion attacks with an accuracy of about 80%. Similarly, zero-day attack experiments are able to detect the attack without fail as well.

关键词： Decision Tree IoT Power Profiling Hardware Trojan Machine Learning Internet-of-Things Multi-level Perceptron

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Society-Level Software Governance: A Challenging Scenario 42

Society-Level Software Governance: A Challenging Scenario

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42nd IEEE/ACM International Conference on Software engineering Workshops, ICSEW 2020

作者： Musil, Juergen Musil, Angelika Weyns, Danny Biffl, Stefan CDL-SQI Inst. Inf. Systems Engineering TU Wien Austria Dep. of Computer Science KU Leuven Belgium Linnaeus University Växjö Sweden

ISBN: (纸本)9781450379632

The technology-driven transformation process continues to spawn novel, growth-oriented digital application domains and platforms. The user base of these society-level software systems consists of a larger proportion of the community and that involve a large set of stakeholder groups. In case of an incident there is a public demand from a variety of stakeholders for multilateral intervention in order to correct the behavior of the software system. For software engineering as a technical discipline that has been fostered and matured in corporate and organizational context, this is a major challenge because it has to deal with a multitude of multidisciplinary stakeholders and their concerns. In order to stimulate further discussions, we discuss software governance on societal level and identify future research challenges of this increasingly relevant topic. © 2020 ACM.

关键词： computer software

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