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Detecting Anomalous Programmable Logic Controller Events Using Process Mining 15th

Detecting Anomalous Programmable Logic Controller Events Usi...

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15th IFIP WG 11.10 international conference on Critical Infrastructure Protection, ICCIP 2021

作者： Yau, Ken Chow, Kam-Pui Yiu, Siu-Ming Computer Science University of Hong Kong Hong Kong

ISBN: (纸本)9783030935108

Programmable logic controllers that monitor and control industrial processes are attractive targets for cyber attackers. Although techniques and tools have been developed for detecting anomalous programmable logic controller behavior, they rely heavily on knowledge of the complex programmable logic controller control programs that perform process monitoring and control. To address this limitation, this chapter describes an automated process mining methodology that relies on event logs comprising programmable logic controller inputs and outputs. the methodology discovers a process model of normal programmable logic controller behavior, which is used to detect anomalous behavior and support forensic investigations. Experiments involving a popular Siemens SIMATIC S7-1212C programmable logic controller and a simulated traffic light system demonstrate the utility and effectiveness of the methodology. © 2022, IFIP international Federation for Information Processing.

关键词： Anomaly detection

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SSANet: Side-by-Side Additive Network for Knee Osteoarthritis Severity Detection from X-Ray Images 4th

SSANet: Side-by-Side Additive Network for Knee Osteoarthrit...

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4th international conference on Frontiers in Computing and Systems, COMSYS 2023

作者： Tewari, Rakhi Bhattacharjee, Debotosh Roy, Hiranmoy Krejcar, Ondrej Department of Computer Science and Engineering Jadavpur University Kolkata700032 India Department of Information Technology RCC Institute of Information Technology Canal South Road Beliaghata Kolkata700015 India Faculty of Informatics and Management Center for Basic and Applied Science University of Hradec Kralove Rokitanskeho 62 Hradec Kralove50003 Czech Republic

ISBN: (纸本)9789819726103

Knee Osteoarthritis (KOA) is becoming one of the most frequent degenerative and irreversible diseases in elderly people. Its early detection and diagnosis is also a difficult and time-intensive task. the recent advances in Machine Learning (ML) and computer Vision (CV) created new paths to automatically detect KOA in the early stages. Detecting and grading the severity of KOA requires a dedicated ML model. this paper proposes a Side-by-Side Additive Network (SSANet)-based model for detecting and grading the severity of KOA. Since KOA affects the knee joint space by narrowing the gap between the joints, we preprocess the X-ray images to detect the Region of Interest (ROI) before the training of the model. From experimental results, it is evidenced that the ROI selection is really improving the detection accuracy of the model. Unlike sequentially connected convolution layer-based deep learning models, the proposed SSANet is based on a parallel convolution layer to reduce the degradation of feature quality. Again, to enhance the edge feature maps, we apply an additive layer that couples the previous layers’ convolutional output. SSANet captures both high-level and low-level features to predict the KOA severity effectively. the presence of a smaller number of layers and fewer numbers of learnable parameters than the existing popular deep learning models, make SSANet a time and space-efficient network. Moreover, the performance of the proposed SSANet in detecting KOA severity is significantly superior to that of other popular state-of-the-art networks. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolution

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Multi-Person Speech Curator for Minutes of Meetings along with Meeting Summarization and Language Translation 23

Multi-Person Speech Curator for Minutes of Meetings along wi...

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Proceedings of the 2023 7th international conference on advances in Artificial Intelligence

作者： Divija Sanapala Kabita Choudhary Sujala Shetty Computer science Birla Institute of Technology & Science Pilani Dubai Campus U.A.E

ISBN: (纸本)9798400708985

Multi-Person Speech Curator for Minutes of Meetings refers to a system where we can generate the summary of a meeting from the audio recording of the meeting using automatic speech recognition (ASR) and NLP algorithms. the system can identify and differentiate speakers and generate transcript with time stamp. ASR is used to transcribe the audio of the meeting and then NLP algorithm used to for speaker diarization and then extract the summary from the transcript. In this paper we use Whisper API from OpenAI for speech to text conversion. the model facebook/bart-large-cnn is used to generate the summary from the transcript. the model is fine-tuned with Samsum dataset that contains about 16k messenger-like conversations. mBART model is used for summary translation. the objective of the paper is to create minutes of meeting which has concise information about the complete meeting discussions, decisions, and actions. the system has the potential to significantly save the time and resources as it eliminates manual transcript and captures all important points of the meeting.

关键词： BART(Bidirectional Auto-Regressive Transformers) BERT (Bidirectional Encoder Representations from Transformers) NLP(Natural Language Processing) OpenAI Speech-text-summarization Whisper API automatic speech recognition mBART(multilingual Bidirectional Encoder Representations from Transformers) machine learning speaker diarization text summarization

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Optimizing Multilingual Summarization: Advanced Corpus Creation and Filtering Approach

Optimizing Multilingual Summarization: Advanced Corpus Creat...

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international conference on computer and Information technology (ICCIT)

作者： Md Mehrab Hossain Nafiz Nahid Gourab Roy Md. Abu Naser Mojumder M. Shahidur Rahman Department of Computer Science and Engineering Sylhet Engineering College Sylhet Bangladesh Department of Computer Science and Engineering Shahjalal University of Science & Technology Sylhet Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Summarization is essential for effectively understanding vast information, and machine learning is fundamental. to progressing this field. However, a major obstacle is the lack of high-quality datasets, especially for languages that are less commonly studied. To address this, we developed a large dataset on five low-resource languages in terms of input text and corresponding summary quality such as Arabic, Afrikaans, Azerbaijani, Albanian, and Bengali. Here, we commenced by translating texts and summaries from the prevalent Daily Mail CNN dataset into these languages. To ensure the quality and relevance, we introduced a filtering approach by using robust metrics such as LaBSE, ROUGE, BLEU, and BERTScore to filter the data, eliminating outliers and ensuring quality. this procedure yielded a substantial dataset comprising 567,462 varied and high-quality instances, with each language containing more than 100,000 text-summary ***, this study uses extensive comparative analysis, focusing on all five languages to demonstrate that the abstractive summarization model performs better compared to the existing work. the trained MT5-base model on our developed dataset outperforms the state-of-the-art by 7% in BERTScores, 8% in BLEU, and 16% in *** highlighting the importance of high-quality datasets and models, this work advances abstractive multilingual summarization.

关键词： Measurement Analytical models Translation Filtering Computational modeling Focusing Machine learning Multilingual Information technology Postal services

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Sector-wise expert input selection for electricity load forecasting

Sector-wise expert input selection for electricity load fore...

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Recent advances and Innovations in Engineering (ICRAIE)

作者： Megha Sharma Namita Mittal Anukram Mishra Arun Gupta Department of Computer Science and Engineering Malaviya National Institute of Technology Jaipur India Genus Power Infrastructures Limited Jaipur India

In Smart Grid, forecasting power demand plays a vital role in demand side management. Forecasting medium-term load is important for power system planning. People depend on electricity for livelihood in the residential sector and for good productivity in the commercial, industrial and agricultural sectors. the sector-wise importance of characteristics for accurate load forecasting has been identified in this paper. First, we analyze the power consumption data in various sectors with different parameters, then various parameters (weather data and seasonal data) are identified that affect the power consumption pattern of the region, finally, a machine learning model is used to identify the sector-wise feature importance for load prediction. the Random Forest Regressor outperforms Multiple Linear Regression for load prediction.

关键词： Productivity Technological innovation Load forecasting Linear regression Predictive models Smart grids Forecasting

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Contrasting Parallelized with Sequential Sorting

Contrasting Parallelized with Sequential Sorting

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Recent advances and Innovations in Engineering (ICRAIE)

作者： Anurag Dutta Manjari Saha Department of Computer Science Government College of Engineering and Textile Technology Serampore Calcutta India

Sorting is one of the important Data Management Techniques that is quite commonly operated on Data Structures. It has been proved that best, any sorting algorithm can do, in asymptotic versions is φlog 2 φ, when the iterations are made to run in sequential manner. But what if we parallelize them? Architectural advancements, like Amdahl’s Law have made use of the concept of Parallelism, and have successfully revolutionized the computing power of the microprocessors. In this paper, we will try to push the limit of computing, by making use of Graphical Processing unit and will briefly compare the efficiency of the algorithms acquainted with both the techniques, sequential as well and parallel in terms of running time. In the first section, we have included some of the preexisting works on this topic, and have mentioned about scopes of further improvement. From the next section onwards, we have slowly put together all the pillars required to support our work.

关键词： Technological innovation Microprocessors Graphics processing units Parallel processing Data structures Sorting

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Recommendation System Using Deep Learning

Recommendation System Using Deep Learning

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Recent advances and Innovations in Engineering (ICRAIE)

作者： Raghavendra C K Gagana Dhananjaya Lavanya Ds Priyanka Lakur Krishnamurthy Anika Shreya Pawar Computer Science & Engineering BNM Institute of Technology Bangalore

Recommendation systems are a commom feature of many services nowadays. Using a significant quantity of user history that has been stored, this system forecasts what a user will use next. Recommendation systems are frequently used in a variety of industries, including e-commerce, social services and movies. We are attempting to compare three distinct deep learning models—RBM, Autoencoder, and DNN and comparing their efficiency in order to determine which model will perform best given the supplied dataset. We conducted experiments on real world data like MovieLens, which has four csv files to compare the efficacy of different Deep Learning models. these experimental results show that the query features and item features which Deep Neural Network can readily incorporate may help identify a user’ s interests and increase the relevancy of recommendations.

关键词： Deep learning Industries Technological innovation Neural networks Mean square error methods Motion pictures Data models

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A Novel Fabric Defect Detection Network Based on Attention Mechanism and Multi-Task Fusion 7

A Novel Fabric Defect Detection Network Based on Attention M...

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7th IEEE international conference on Network Intelligence and Digital Content, IC-NIDC 2021

作者： Peng, Zhengrui Gong, Xinyi Lu, Zhenfeng Xu, Xiangyi Wei, Bengang Prasad, Mukesh State Grid Shanghai Electric Power Research Institute Shanghai200437 China Institute of Automation Chinese Academy of Sciences Beijing100190 China China University of Mining and Technology School of Mechanical Electronic and Information Engineering Beijing100083 China Faculty of Engineering and Information Technology University of Technology Sydney Sydney2007 Australia

ISBN: (纸本)9781665405829

Fabric is an important material, which is applied in the entire process of textile manufacturing, such as spinning, weaving, dyeing, printing, and finishing, and garments manufacturing. As defects on the surface of the fabric are inevitable in the process of fabric production, the defect detection of fabric is significant for fabric manufacture. the current CNN-based defect detection methods face several challenges when tackling the fabric defect with a tiny shape, the low grayscale difference with background, and ambiguous defect type. To deal with the problem, we proposed a novel fabric defect detection network - AMTFNet based on the attention mechanism and multi-task fusion module in this paper. On one hand, the attention mechanism module forced networks to pay attention to defects. On the other hand, the multi-task fusion module helps AMTFNet to further improve the classification effect using feature concatenated. the experimental result indicates that the precision-score, recall-score, and F1-score of AMTFNet reach 0.980, 0.994, and 0.987, respectively. the proposed method can be successfully applied in the detection of industrial fabric material. © 2021 IEEE.

关键词： computer vision

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Analysis of Age Sage Classification for Students’ Social Engagement Using REPTree and Random Forest 1

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5th IFIP TC 12 international conference on Computational Intelligence in Data science, ICCIDS 2022

作者： Prajapati, Jigna B. Acharya Motibhai Patel Institute of Computer Studies Ganpat University Gujarat India

ISBN: (数字)9783031163647

ISBN: (纸本)9783031163630

Study and analysis of train dataset along with various ML algorithms is used widely in different sectors. the accuracy parameters can be clarified to have prediction of different score levels. this study covers the extension work of Students’ social engagement during covid-19 pandemic. the study was initiated with students’ social connection during the pandemic. We had compared various machine learning algorithms with its performance about the engagement of students in various social network. After studied, analyzed & compared, we derived that the most of students’ social engagement found in WhatsApp, YouTube & Instagram. the current study is foreseeing age wise social media connection. It correlates between student & their social engagement during the pandemic phase. In which age group, which social media is one of the most popular one. this study focuses on age wise classification using Machine Learning. In this paper, the decision-making classification is compared. the Reduced Error Pruning Tree (REPTree) and Random Forest algorithm is implemented on train dataset with diverse nodes. the attributes are focused as age & time spent on social media as per necessity of study. this paper includes the study and analysis of RAE & RMSE along with ML tree approach. the discoveries of this study can lead better classification in regards of students’ age and duration which they have spent on social media for derived social platform. © 2022, IFIP international Federation for Information Processing.

关键词： Decision trees

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An Exploration of Moving Robot Localization Assisted with a Static Monocular Camera 7

An Exploration of Moving Robot Localization Assisted with a ...

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7th IEEE international conference on Network Intelligence and Digital Content, IC-NIDC 2021

作者： Zhang, Yanting Shi, Jingru Wang, Qingxiang Wang, Zijian Yan, Cairong Donghua University School of Computer Science and Technology Shanghai201620 China

ISBN: (纸本)9781665405829

Simultaneous localization and mapping (SLAM) is critical for robots in exploring an unknown environment. the monocular camera mounted on the robot can capture images continuously. However, the localization and mapping process may fail when there are not enough structure features observed from the moving camera on the robot. In this paper, we explore to use an external static surveillance camera to calculate the realtime pose data for the moving robot. We perform an adaptive self-localization for the robot taking advantage the joint information both from the camera on the robot and the external static surveillance camera. the localization results from this coordination are fused to solve the problem that localization may be unreliable in the SLAM. Whenever the SLAM fails, the estimated poses from the other camera can effectively help with the localization for the moving robot. We set up an environment to perform the experiments and validate the feasibility of coordinated mining of multiple cameras. the results can be beneficial for autonomous driving and the deployment of intelligent infrastructures. © 2021 IEEE.

关键词： Cameras

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