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An unsupervised anomaly detection framework for detecting anomalies in real time through network system’s log files analysis

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High-Confidence Computing 2021年第2期1卷 1-6页

作者： Vannel Zeufack Donghyun Kim Daehee Seo Ahyoung Lee Department of Computer Science Kennesaw State UniversityMariettaGA 30060USA Department of Computer Science Georgia State UniversityAtlantaGA 30303USA Faculty of Artificial Intelligence and Data Engineering Sangmyung UniversitySeoul 03016South Korea

Nowadays,in almost every computer system,log files are used to keep records of occurring *** log files are then used for analyzing and debugging system *** to this important utility,researchers have worked on finding fast and efficient ways to detect anomalies in a computer system by analyzing its log *** in log-based anomaly detection can be divided into two main categories:batch log-based anomaly detection and streaming log-based anomaly *** log-based anomaly detection is computationally heavy and does not allow us to instantaneously detect *** the other hand,streaming anomaly detection allows for immediate ***,current streaming approaches are mainly *** this work,we propose a fully unsupervised framework which can detect anomalies in real *** test our framework on hdfs log files and successfully detect anomalies with an F-1 score of 83%.

关键词： Anomaly detection Unsupervised machine learning Clustering OPTICS Log analysis

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3D Reconstruction and Estimation from Single-view 2D Image by Deep Learning – A Survey

3D Reconstruction and Estimation from Single-view 2D Image b...

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Artificial Intelligence (CAI), IEEE Conference on

作者： Yongfeng Shan Christy Jie Liang Min Xu School of Computer Science Faculty of Engineering and IT University of Technology Sydney Sydney Australia School of Electrical and Data Faculty of Engineering and IT University of Technology Sydney Sydney Australia

ISBN: (数字)9798350354096

ISBN: (纸本)9798350354102

3D Object Reconstruction from a single-view 2D image has become a promising research field. However, it remains a crucial and unsolved core issue in AI and computer Vision research. Many scholars think it is the future of Artificial Intelligence and is well deserved at the irreplaceable heart of future AI research. In this paper, the research history of 3D object reconstruction is introduced, and the current state-of-the-art research methods and most novel results are investigated and discussed. A prediction for the best research methods and reconstruction model for this field is made. This paper also provided the essential clues and trends in 2D images or scenes inverse to 3D sceneries by 3D reconstruction. This interdisciplinary research area requires the researchers to have rich knowledge in but not limited to computer graphics (Such as OpenGL), deep learning, computer vision (like OpenCV), and the neurocognitive logics and principles of the cerebral cortex.

关键词： Surveys Deep learning computer vision Three-dimensional displays Shape Estimation Market research

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Relative magnetic field and slipping effect on Casson dusty fluid of two phase fluctuating flow over inclined parallel plate

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South African Journal of Chemical Engineering 2023年 44卷 135-146页

作者： khan, Dolat Kumam, Poom Kumam, Wiyada Suttiarporn, Panawan Rehman, Ali Fixed Point Research Laboratory Fixed Point Theory and Applications Research Group Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Center of Excellence in Theoretical and Computational Science (TaCS-CoE) Faculty of Science King Mongkut's University of Technology Thonburi (KMUTT) 126 Pracha Uthit Rd. Bang Mod Bangkok Thung Khru 10140 Thailand Applied Mathematics for Science and Engineering Research Unit (AMSERU) Program in Applied Statistics Department of Mathematics and Computer Science Faculty of Science and Technology Rajamangala University of Technology Thanyaburi (RMUTT) Pathum Thani 12110 Thailand Faculty of Science Energy and Environment King Mongkut's University of Technology North Bangkok Rayong Campus Rayong 21120 Thailand Special Interest Group for Modelling and Data Analytics Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus 21030 Malaysia

Due to its significant applications in magnetic devices for cell separation, magnetic drugs for cancer tumor treatment, blood flow adjustment during surgery, magnetic endoscopy, and fluid pumping in industrial and engineering processes, magnetohydrodynamics (MHD) has a wide range of applications. Therefore, the aim of this study to investigate the Relative Magnetic Field and slipping effect on Casson dusty fluid of two-phase fluctuating flow over inclined parallel plate. More exactly, the relative magnetic phenomena are fixed relatively to the plate (MFFRP) or fixed relatively to the fluid (MFFRF). The mathematical modeling was established as the set of partial differential equations for dusty Casson fluid. Buckingham's pi theorem is used to find out the dimensionless variables, to make our system dimensionless. The solution in perturb form is to find out by incorporating Poincare–Lighthill perturbation technique (PLPT). To know in-depth different parameters the graphical results for both velocities are plotted with the help of software Mathcad- 15. It is clearly detected that the relative magnetic field performs a vital part in the fluid as well as particle motion. Moreover, the relative magnetic field play a vital role in the decreasing of skin friction of both plates. © 2023

关键词： Brinkman fluid Dust particles Fluctuating vertical plate Inclined relative magnetic field PLPT

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Evaluation of China's Digital Economy: A Case Study using Entropy and TOPSIS 11

Evaluation of China's Digital Economy: A Case Study using En...

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11th International Conference on Information Technology and Quantitative Management, ITQM 2024

作者： Liu, Junjie Wang, Boya Xue, Jiayu Qu, Yi Shi, Yong Sino-Danish College University of Chinese Academy of Sciences Beijing100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China Faculty of Science and Engineering University of Nottingham Ningbo China Zhejiang Ningbo315100 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States

Recently, digital economy in social science has become increasingly significant. A well-constructed evaluation structure could generally and promptly describe and reveal its overall social digitalization development. Building on existent researches, this paper improves the evaluation system, which comprises four first-level indicators and thirty-one second-level indicators. Notably, this paper innovatively incorporates higher education into digital innovation of the evaluation. The Entropy method is utilized to assign weights to various indicators in an objective way, with employing the TOPSIS approach to prioritize the development of digital economy in places based on the closeness to the ideal solution. By analysing related data related from 31 provinces in China between 2017 to 2021, this paper greatly finds: Firstly, the development of digital economy vary significantly across provinces, with most provinces at a relatively poor level, indicating considerable potential for overall growth;Secondly, Guangdong, Jiangsu, Zhejiang, and Shandong consistently ranked in the top four from 2017 to 2021, reflecting their specific development attributes and their emphasis on digital economy;Thirdly, significant disparities exist both between regions and among provinces. Consequently, this paper suggests establishing a comprehensive national digital economy development plan, formulating region-specific digital economy policies tailored to local conditions, and encouraging leading provinces to actively share experiences with other provinces. © 2024 The Authors.

关键词： Digital economy Evaluation Multi-source information TOPSIS

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Exploring Automated Feature Engineering for Energy Consumption Forecasting with AutoML

Exploring Automated Feature Engineering for Energy Consumpti...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Nasser Alkhulaifi Alexander L. Bowler Direnc Pekaslan Isaac Triguero Nicholas J. Watson Computational Optimisation and Learning (COL) Lab School of Computer Science University of Nottingham Nottingham UK Faculty of Environment School of Food Science and Nutrition University of Leeds Leeds UK Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham Nottingham UK Department of Computer Science and Artificial Intelligence University of Granada Spain DaSCI Andalusian Institute in Data Science and Computational Intelligence Granada Spain

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Machine learning methods are widely used to predict energy consumption, aiming to enhance efficiency and support environmental goals. However, developing these models is traditionally time-consuming and expert-dependent. While Automated Machine Learning (AutoML) has emerged as a valuable approach to streamlining machine learning pipelines, including appropriate preprocessing and learning algorithms, it may still require human experts. These experts might be needed to generate new, interpretable features that could significantly improve model performance. This is particularly relevant in complex settings such as the energy domain, where deep learning's automatic feature extraction lacks interpretability. To address this challenge, this exploratory work introduces an automated feature engineering method tailored for energy forecasting problems. It involves generating a comprehensive set of features that can be fed into AutoML, thereby reducing the need for domain knowledge in feature engineering. The proposed method has been validated using eleven datasets from various energy domains, including residential buildings, renewable energy, and regional energy consumption, with state-of-the-art AutoML methods, namely H20, TPOT, AutoGluon, and FLAML. The results demonstrate a noticeable reduction in prediction errors across all the examined datasets.

关键词： Knowledge engineering Deep learning Energy consumption Renewable energy sources Machine learning algorithms Pipelines Automated machine learning Prediction algorithms Feature extraction Forecasting

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The Frontier of data Erasure: Machine Unlearning for Large Language Models

arXiv

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arXiv 2024年

作者： Qu, Youyang Ding, Ming Sun, Nan Kanchana Zhu, Tianqing Niyato, Dusit Data61 Commonwealth Scientific and Industrial Research Organization Australia School of Systems & Computing University of New South Wales Australia School of Computer Science University of Sydney Australia Faculty of Data Science City University of Macau China College of Computing & Data Science Nanyang Technological University Singapore

Large Language Models (LLMs) are foundational to AI advancements, facilitating applications like predictive text generation. Nonetheless, they pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information from their vast datasets. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns, offering techniques for LLMs to selectively discard certain data. This paper reviews the latest in machine unlearning for LLMs, introducing methods for the targeted forgetting of information to address privacy, ethical, and legal challenges without necessitating full model retraining. It divides existing research into unlearning from unstructured/textual data and structured/classification data, showcasing the effectiveness of these approaches in removing specific data while maintaining model efficacy. Highlighting the practicality of machine unlearning, this analysis also points out the hurdles in preserving model integrity, avoiding excessive or insufficient data removal, and ensuring consistent outputs, underlining the role of machine unlearning in advancing responsible, ethical AI. © 2024, CC BY.

关键词： Computational linguistics

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The Necessary Requirements for Utilizing AI in data Access Setting 15

The Necessary Requirements for Utilizing AI in Data Access S...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Swapnil, K. Sharma, Bhuvnesh Singh, Manpreet Yadav, Rakesh Kumar Devaraj Verma, C. Umarani, K. Vishwakarma Institute of Technology Department of Artificial Intelligence &data Science Pune India Vivekananda Global University Department of Mechanical Engineering Jaipur India Maharishi University of Technology Maharishi School of Engineering and Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Computer Science and Engineering Karnataka Ramnagar District562112 India Chitkara University Institute of Engineering and technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350370249

AI has been an increasing number of adopted in records get admission to settings to improve the overall performance of statistics get admission to and extract significant insights, using extended productiveness and value-effectiveness. However, the success implementation of AI calls for positive essential requirements to be met, together with facts accuracy, connectivity, security, and scalability. First, facts accuracy is critical for AI applications in facts get entry to settings. Information gathered need to be of excessive quality and accuracy, as AI working on erroneous facts can yield unreliable and probably harmful effects. Records should be of suitable granularity, taking pictures the most relevant elements, to make certain a complete and steady training performance.2nd, linked statistics assets, networks, and services are had to leverage the capability of AI in information get entry to settings. This gives an interconnected and up to date dataset at scale, while requiring included answers for uninterrupted get entry to. Moreover, single sign-on and alertness orchestration abilities are vital to decorate user revel in. © 2024 IEEE.

关键词： Connectivity Gathered Increasing Productiveness Security

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Autonomous Multi-Robot Collaboration in Virtual Environments to Perform Tasks in Industry 4.0 2

Autonomous Multi-Robot Collaboration in Virtual Environments...

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2nd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2022

作者： Alsamhi, Saeed Hamood Saif, A.A.F. Curry, Edward Kumar, Santosh Hawbani, Ammar University of Galway Insight Centre for Data Analytics Galway Ireland IBB Univeristy Faculty of Engineering Ibb Yemen Netstars Co Ltd Tokyo Japan IIIT-Naya Raipur Department of Computer Science and Engineering Chhattisgarh India University of Science and Technology of China School of Computer Science and Technology Hefei230027 China

ISBN: (数字)9781665461818

ISBN: (纸本)9781665461818

In Industry 4.0, multi-robot efficient coordination is a critical enabler for many commercial and industrial automation activities. This paper describes how a group of robots may dynamically produce actions from a common. We focus on examining the performance of autonomous multi-robot collaboration effectively and efficiently to achieve a common task and plan for the action of future task allocation dynamically, autonomously, and in a decentralized fashion. The common goal for multi-robots here is to bring the ball from the current position to predefine the desk target. Unreal Engine (UE4) is used for designing and implementing the platform of robots. The results show significant achievement in accurate trajectories for the ball taken via multi-robot collaboration in an autonomous and decentralized fashion. As a result, multi-robot collaboration can be available for numerous applications in Industry 4.0, warehouse automation, transport, risky environments, search and rescue (SAR) teams, and disaster recovery. © 2022 IEEE.

关键词： Industry 4.0

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"Two Means to an End Goal": Connecting Explainability and Contestability in the Regulation of Public Sector AI

arXiv

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arXiv 2025年

作者： Schmude, Timothée Yurrita, Mireia Alfrink, Kars Goff, Thomas L.E. Viard, Tiphaine University of Vienna Faculty of Computer Science Research Network Data Science Doctoral School Computer Science Austria Delft University of Technology Department of Sustainable Design Engineering Netherlands i3 Télécom Paris Institut Polytechnique de Paris France

Explainability and its emerging counterpart contestability have become important normative and design principles for the trustworthy use of AI as they enable users and subjects to understand and challenge AI decisions. However, the regulation of AI systems spans technical, legal, and organizational dimensions, producing a multiplicity in meaning that complicates the implementation of explainability and contestability due to the difficulty of defining them. Resolving this conceptual ambiguity requires specifying and comparing the meaning of both principles across regulation dimensions, disciplines, and actors. This process, here defined as translation, is essential to provide guidance on the principles’ realization. To this end, we present the findings of a semi-structured interview study with 14 interdisciplinary AI regulation experts. We report on the experts’ understanding of the intersection between explainability and contestability in public AI regulation, their advice for a decision subject and a public agency in a welfare allocation AI use case, and their perspectives on the connections and gaps within the research landscape. We provide differentiations between descriptive and normative explainability, judicial and non-judicial channels of contestation, and individual and collective contestation action. We further outline three main translation processes pertaining to the alignment of top-down and bottom-up regulation, the assignment of responsibility for interpreting regulations, and the establishment of interdisciplinary collaboration. Our contributions include an empirically grounded conceptualization of the intersection between explainability and contestability and recommendations on implementing these principles in public institutions. We believe our contributions can inform policy-making and regulation of these core principles and enable more effective and equitable design, development, and deployment of trustworthy public AI systems. Copyright © 2025, T

关键词： Energy policy

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HYCOT: A TRANSFORMER-BASED AUTOENCODER FOR HYPERSPECTRAL IMAGE COMPRESSION

arXiv

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arXiv 2024年

作者： Fuchs, Martin Hermann Paul Rasti, Behnood Demir, Begüm Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

The development of learning-based hyperspectral image (HSI) compression models has recently attracted significant interest. Existing models predominantly utilize convolutional filters, which capture only local dependencies. Furthermore, they often incur high training costs and exhibit substantial computational complexity. To address these limitations, in this paper we propose Hyperspectral Compression Transformer (HyCoT) that is a transformer-based autoencoder for pixelwise HSI compression. Additionally, we apply a simple yet effective training set reduction approach to accelerate the training process. Experimental results on the HySpecNet-11k dataset demonstrate that HyCoT surpasses the state of the art across various compression ratios by over 1 dB of PSNR with significantly reduced computational requirements. Our code and pre-trained weights are publicly available at https://***/rsim/hycot. © 2024, CC BY.

关键词： Image compression

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