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Dynamic Optimization of Provider-Based Scheduling for HPC Workloads 31

Dynamic Optimization of Provider-Based Scheduling for HPC Wo...

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31st international conference on software, Telecommunications and Computer Networks, SoftCOM 2023

作者： Marino, Jacopo Risso, Fulvio Bighi, Mauro Politecnico di Torino Dept. of Control and Computer Engineering Torino Italy PUNCH Torino S.p.A Dept. of Information Technology Torino Italy

ISBN: (纸本)9798350301076

The vast array of cloud providers present in today's market proffer a suite of High-Performance Computing (HPC) services. However, these offerings are characterized by significant variations in execution times and cost structures. Consequently, selecting the optimal cloud provider and configuring the features of the chosen computing instance (e.g. virtual machines) proves to be a challenging task for users intending to execute HPC workloads. This paper introduces a novel component designed for effortless integration with existing HPC scheduling systems. This module's primary function is to facilitate the selection of the most appropriate cloud provider for each distinct job, thereby empowering dynamic and adaptive cost-minimization strategies. Through the application of data augmentation techniques and the employment of Continuous machine learning, the system is endowed with the capability to operate efficiently with cloud providers that have not been previously utilized. Furthermore, it is capable of tracking the evolution of jobs over time. Our results show that this component can achieve consistent economic savings, based on the quality of the data used in the training phase. © 2023 University of Split, FESB.

关键词： machine learning

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Distance protection schemes in distributed generation connected Transmission system combined with machine learning 3

Distance protection schemes in distributed generation connec...

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3rd IEEE international conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Prabhakar, Saurabh Iit Delhi Department of Electrical Engineering New Delhi India

ISBN: (纸本)9798350304732

Day by day, the demand for electricity is increasing worldwide. Due to the generation of electricity by fossil fuels, our environment is getting worse. To overcome this problem, many countries are increasing the use of renewable energy sources (RES) in electricity generation. So, future challenges to the power system will increase as RES penetrates more into the system. Distributed Generations (DGs) connected to the power system network interfaced with a power electronics converter create challenges in protecting the power system. Protective relays need to change the required setting according to the influence of RES on the power network. The variable connection states of distributed generation also impact the selectivity of a protective relay. The above-mentioned problem requires a different approach to recognize the state of the network and make decisions for the relay about whether it is required to modify the setting. Thus, In this paper, the impacts of distributed generation on distance protection are simulated using DIgSILENT PowerFactory software. Then, fault classification and fault location are done using machine learning that enhances the adaptive protection of microgrids. © 2023 IEEE.

关键词： Distance Protection Distributed Generation machine learning Transmission Network Windfarm

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An Architecture for Adaptive machine learning Models using Adversarial and Transfer learning 56

An Architecture for Adaptive Machine Learning Models using A...

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56th CIRP international conference on Manufacturing Systems, CIRP CMS 2023

作者： Kamm, Simon Kumar, Praveen Jazdi, Nasser Weyrich, Michael University of Stuttgart Institute of Industrial Automation and Software Engineering Pfaffenwaldring 47 Stuttgart70550 Germany

machine learning and especially deep neural networks are more and more used in industrial applications, because they offer new possibilities to gain knowledge from the available data. The algorithms therefore should work during the lifetime of the industrial system, to which the algorithms are applied for improving the overall performance of the system. However, over the lifetime of an industrial system or application, data sources are likely to change. A sensor may be damaged and delivers faulty values or a new sensor is added to the system. A classical deep neural network will deliver incorrect outputs in case of a faulty sensor or cannot use the additional information coming from a new sensor. In both cases, a retraining with the new input space would be necessary, which requires effort for data collection and training of the network. To address these challenges, this paper proposes an architecture that combines separate approaches, namely the Fast Gradient Sign Method and Transfer learning. To evaluate the combined architecture, it is applied to a real-world task of failure detection in the Air Pressure System (APS) of an automobile. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the 56th CIRP international conference on Manufacturing Systems 2023.

关键词： Adaptive machine learning Adversarial learning Failure Detection Regularization Transfer learning

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Enhancing Deep Reinforcement learning with Executable Specifications 23

Enhancing Deep Reinforcement Learning with Executable Specif...

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45th IEEE/ACM international conference on software engineering (ICSE)

作者： Yerushalmi, Raz Weizmann Inst Sci Dept Comp Sci & Appl Math Rehovot Israel

ISBN: (纸本)9798350322637

Deep reinforcement learning (DRL) has become a dominant paradigm for using deep learning to carry out tasks where complex policies are learned for reactive systems. However, these policies are "black-boxes", e.g., opaque to humans and known to be susceptible to bugs. For example, it is hard - if not impossible - to guarantee that the trained DRL agent adheres to specific safety and fairness properties that may be required. This doctoral dissertation's first and primary contribution is a novel approach to developing DRL agents, which will improve the DRL training process by pushing the learned policy toward high performance on its main task and compliance with such safety and fairness properties, guaranteeing a high probability of compliance while not compromising the performance of the resulting agent. The approach is realized by incorporating domain-specific knowledge captured as key properties defined by domain experts directly into the DRL optimization process while leveraging behavioral languages that are natural to the domain experts. We have validated the proposed approach by extending the AI-Gym Python framework [1] for training DRL agents and integrating it with the BP-Py framework [2] for specifying scenario-based models [3] in a way that allows scenario objects to affect the training process through reward and cost functions, demonstrating dramatic improvement in the safety and performance of the agent. In addition, we have validated the resulting DRL agents using the Marabou verifier [4], confirming that the resulting agents indeed comply (in full) with the required safety and fairness properties. We have applied the approach, training DRL agents for use cases from network communication and robotic navigation domains, exhibiting strong results. A second contribution of this doctoral dissertation is to develop and leverage probabilistic verification methods for deep neural networks to overcome the current scalability limitations of neural network verificat

关键词： machine learning deep reinforcement learning scenario-based modeling rule-based specifications domain expertise

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Heart Disease Prediction Using machine learning: A Systematic Literature Review 10

Heart Disease Prediction Using Machine Learning: A Systemati...

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10th international conference on Information Technology, Computer, and Electrical engineering, ICITACEE 2023

作者： Prakoso, Sulistyo Damas Permanasari, Adhistya Erna Pratama, Azkario Rizky Universitas Gadjah Mada Department of Electrical and Information Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350322729

Heart disease, categorized as a cardiovascular condition, stands as a prominent factor in worldwide mortality, accounting for approximately 32% of all deaths globally. It manifests when the buildup of arterial plaque obstructs the circulation of blood to the heart or brain, potentially resulting in a stroke or heart attack. Early identification of heart disease is needed to reduce mortality rates and improve decision-making in the prevention and treatment of high-risk individuals. This can be done by using a prediction model. To offer a comprehensive examination of machine learning research about the prediction of cardiac disease, a systematic literature review (SLR) was undertaken. Based on these papers, researchers can focus on four main research questions. The UCI dataset was commonly used in 25 out of 32 papers. Random forest was the most popular machine learning algorithm, used in six papers. The limitations identified by the authors mainly revolve around the dataset, including the need for a larger one. Other limitations involve the inability to detect subtle changes and the use of oversimplified algorithms for prediction. To address these limitations, future research can explore new and larger datasets, experiment with different algorithms, and consider advancements in software and hardware. © 2023 IEEE.

关键词： Forecasting

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Analysis of Water Quality for Taal Lake Using machine learning Classification Algorithm 20

Analysis of Water Quality for Taal Lake Using Machine Learni...

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20th international Joint conference on Computer Science and software engineering, JCSSE 2023

作者： Tanega, Michelle C. Fajardo, Arnel Limbago, Jomel S. Technological Institute of the Philippines Manila Philippines Isabela State University-Cauayan Philippines Cavite State University Naic Philippines

ISBN: (纸本)9798350300505

The rapid population growth in the Philippines has increased the demand for food and aquatic commodities, making fishing a crucial source of income for coastal households [1]. Ensuring the water quality of Philippine lakes is essential to maintaining the environment's integrity and protecting communities that rely on aquatic resources. In this work, we applied machine learning classification algorithms such as Random Forest, Decision Tree, and Support Vector to calculate Taal Lake, Philippines's Water Quality Index (WQI) and Water Quality Classification (WQC). The Weighted Arithmetic Water Quality Index (WAWQI) approach was employed to classify the water quality in Taal Lake. Our results showed the lake's water quality was unsuitable between 2018 and 2022 at five selected stations. Moreover, we evaluated the classification model against two other algorithms and demonstrated that it outperformed Precision, Recall, and the F-1 score. Random Forest achieved the highest overall accuracy rate of 95.0% compared to the other models tested. This study emphasizes the importance of utilizing machine learning algorithms to monitor and classify water quality in the Philippines. © 2023 IEEE.

关键词： Water quality

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A Step to Achieve Personalized Human Centric Privacy Policy Summary 18

A Step to Achieve Personalized Human Centric Privacy Policy ...

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18th international conference on Evaluation of Novel Approaches to software engineering (ENASE)

作者： Simon, Ivan Haggag, Sherif Haggag, Hussein Univ Adelaide Fac Sci Engn & Technol Adelaide SA Australia Umea Univ Dept Comp Sci Umea Sweden

ISBN: (纸本)9789897586477

Online users continuously come across privacy policies for the service they use. Due to the complexity and verbosity of policies, majority of the users skip the tedious task of reading the policy and accept it. Without reading and evaluating the document users risk giving up all kinds of rights to their personal data and for the most part, are unaware of the data sharing and handling process. Efforts have been made to address the complex and lengthy structure of privacy policies by creating a standardized machine-readable format of privacy policies for the web browsers to process it automatically, a repository of crowdsourced summarized versions of some privacy policies, or by using natural language processing to summarize the policies. PirvacyInterpreter is one unique tool that acknowledges human-centric factors while summarising the policy. Thus, it generates a personalised summary of the privacy policy for the user providing relevant information to appease their privacy concerns. This paper presents the conceptualization of PrivacyInterpreter and implements a proof-of-concept model using configured RoBERTa(base) model to classify a privacy policy and produce a summary based on privacy aspects that reflect users' privacy concerns.

关键词： Privacy Policy Text Summarization Human-Centric machine learning

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A Systematic Survey of Human Ear Biometrics Recognition by using 2D images 3

A Systematic Survey of Human Ear Biometrics Recognition by u...

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3rd IEEE international Maghreb Meeting of the conference on Sciences and Techniques of Automatic Control and Computer engineering, MI-STA 2023

作者： Elmarzaki, Howayda Abedallah Gargoum, Bothaina F. Abdo, Amina A University of Benghazi Department of Software Engineering Benghazi Libya University of Benghazi Department of Computer Science Benghazi Libya Collage of Computer Technology Department of Artificial Intelligence Benghazi Libya

ISBN: (纸本)9798350319897

Nowadays, one of the most serious issues is secure verification, especially with the advent of artificial intelligence and machine learning and deep learning algorithms. As a result, the research field of recognizing human biometrics has grown significantly, and various types of algorithms have been proposed to help with security. From the available literature, we noticed that researchers were focusing on various systematic surveys for the recognition of human biometrics, including faces, eyes, finger prints, palms, and etc. Despite the popularity of machine and deep learning methods in the field of human hearing prediction, surveys of such methods are rare. In this work, we present an up-to-date survey of the ear recognition field by using 2D images and display the most relevant papers from the most popular databases by applying different techniques based on several criteria. This review presents an overview of the similarities and differences between different works and provides a statistical analysis view of common techniques commonly used in ear recognition literature. The results presented in this report provide insight on the patterns of this field's research. © 2023 IEEE.

关键词： Biometrics

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Research on Food Image Recognition of Deep learning Algorithms 4

Research on Food Image Recognition of Deep Learning Algorith...

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4th international conference on Computers, Information Processing and Advanced Education, CIPAE 2023

作者： Luo, Lihua Guangdong University of Science and Technology College of Software Engineering Dongguan523000 China

ISBN: (纸本)9798350342710

In recent years, food image recognition has been a research field that has received much attention. Diet is an essential part of people's daily life. Through food image recognition and classification, dietary evaluation is conducted to achieve the goal of healthy eating. Manual features and classification methods in traditional target detection algorithms can no longer meet the requirements of detection accuracy and speed, so it is necessary to introduce depth learning-based techniques into food image recognition. Deep learning is a complex machine learning algorithm that allows computers to simulate the human brain's thinking, judge new problems through data sets (human experience), and improve themselves to obtain relatively accurate models, just like humans. Among them, convolutional neural networks have developed most rapidly. They are applied to image recognition with the expectation of being able to automate recognition through machine learning, thereby reducing the workload of image recognition and providing more objective analysis results. Deep learning has achieved great success in intelligent image processing, image classification, and recognition and has excellent development potential and social value. This article implements automatic recognition of food images based on the recognition algorithm of the YOLOv5 convolutional neural network structure, which can detect food images with high detection efficiency. It explores the combination of deep learning to identify food images, providing a reference for people to eat healthily, especially for obese people to control calorie intake, and providing theoretical support for food image recognition. © 2023 IEEE.

关键词： Image recognition

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Multi Model Approach to Evaluate and Enhance Student - Teacher Interactions 5

Multi Model Approach to Evaluate and Enhance Student - Teach...

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5th international conference on Advancements in Computing, ICAC 2023

作者： Daksith, Nisal Wanaguru, Sathsara Kolonne, Uresha Dolawatta, Amaya Abeywardhana, Lakmini Kasthurirathna, Dharshana Sri Lanka Institute of Inoformation Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Inoformation Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

Advancements in artificial intelligence (AI) and machine learning (ML) have enabled the development of tools to address issues in Virtual Classrooms. This research focuses on utilizing AI and ML to monitor student behavior in virtual classrooms by analyzing facial expressions, background noise, and keystroke patterns. An AI based approach is proposed to analyze real-time webcam data, detecting patterns of inattention, and notifying teachers accordingly. The application also monitors background noise levels, identifying non-conducive learning environments, and analyzes keystroke patterns to detect unrelated activities. The research draws from academic literature to evaluate the effectiveness of the application while considering ethical implications such as potential bias and student privacy protection. Overall, this study contributes to the existing literature on AI and ML-based applications in education, demonstrating how these technologies can revolutionize student behavior monitoring, leading to enhanced outcomes for virtual classroom learning. © 2023 IEEE.

关键词： machine learning

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