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GROUP EQUIVARIANT NEURAL POSTERIOR ESTIMATION 10

GROUP EQUIVARIANT NEURAL POSTERIOR ESTIMATION

引用

10th international conference on Learning Representations, ICLR 2022

作者： Dax, Maximilian Green, Stephen R. Gair, Jonathan Deistler, Michael Schölkopf, Bernhard Macke, Jakob H. Max Planck Institute for Intelligent Systems Tübingen Germany Max Planck Institute for Gravitational Physics Potsdam Germany Machine Learning in Science University of Tübingen Tübingen Germany

Simulation-based inference with conditional neural density estimators is a powerful approach to solving inverse problems in science. However, these methods typically treat the underlying forward model as a black box, with no way to exploit geometric properties such as equivariances. Equivariances are common in scientific models, however integrating them directly into expressive inference networks (such as normalizing flows) is not straightforward. We here describe an alternative method to incorporate equivariances under joint transformations of parameters and data. Our method-called group equivariant neural posterior estimation (GNPE)-is based on self-consistently standardizing the "pose" of the data while estimating the posterior over parameters. It is architecture-independent, and applies both to exact and approximate equivariances. As a real-world application, we use GNPE for amortized inference of astrophysical binary black hole systems from gravitational-wave observations. We show that GNPE achieves state-of-the-art accuracy while reducing inference times by three orders of magnitude. © 2022 ICLR 2022 - 10th international conference on Learning Representationss. All rights reserved.

关键词： Gravity waves

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High-order network analysis for financial crash identification 10

High-order network analysis for financial crash identificati...

引用

10th international conference on Monitoring, Modeling and Management of Emergent Economy, M3E2-MLPEED 2022

作者： Bielinskyi, Andrii O. Soloviev, Vladimir N. Hushko, Serhii V. Kiv, Arnold E. Matviychuk, Andriy V. Kryvyi Rih State Pedagogical University 54 Gagarin Ave. Kryvyi Rih50086 Ukraine State University of Economics and Technology 16 Medychna Str. Kryvyi Rih50005 Ukraine Kyiv National Economic University named after Vadym Hetman 54/1 Peremogy Ave. Kyiv03680 Ukraine Ben-Gurion University of the Negev P.O.B. 653 Beer Sheva8410501 Israel South Ukrainian National Pedagogical University named after K. D. Ushynsky 26 Staroportofrankivska Str. Odesa65020 Ukraine

Network analysis is a powerful method to characterize the complexity and dynamics of socio-economic systems. However, traditional network analysis often ignores the higher-order dependencies that arise from the interactions of more than two nodes. In this paper, we propose to use high-order networks, which are generalized network structures that capture the higher-order dependencies, to study the temporal evolution of the Dow Jones Industrial Average (DJIA) index. We construct high-order networks from the DJIA time series using the visibility graph method, and we measure the topological complexity of the high-order networks using various metrics. We find that the complexity of the system changes drastically during crisis events, indicating that high-order network analysis can be used as an indicator (indicator-precursor) of financial crashes. We also show that high-order network analysis and topology can provide more insights into the nonlinear and nonstationary behavior of the DJIA index than traditional tools of financial time series analysis. © 2023 Copyright for this paper by its authors.

关键词： Complex networks

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Outlier Detection in Network Traffic Monitoring 10th

Outlier Detection in Network Traffic Monitoring

引用

10th international conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Michalak, Marcin Wawrowski, Lukasz Sikora, Marek Kurianowicz, Rafal Kozlowski, Artur Bialas, Andrzej Inst Innovat Technol EMAG Res Network Lukasiewicz Ul Leopolda 31 PL-40189 Katowice Poland

ISBN: (纸本)9789897584862

Network traffic monitoring becomes, year by year, an increasingly more important branch of network infrastructure maintenance. there exist many dedicated tools for on-line network traffic monitoring that can defend the typical (and known) types of attacks by blocking some parts of the traffic immediately. However, there may occur some yet unknown risks in network traffic whose statistical description should be reflected as slow-in-time changing characteristics. Such non-rapidly changing variable values probably should not be detectable by on-line tools. Still, it is possible to detect these changes with the data mining method. In the paper the popular anomaly detection methods with the application of the moving window procedure are presented as one of the approaches for anomaly (outlier) detection in network traffic monitoring. the paper presents results obtained on the real outer traffic data, collected in the Institute.

关键词： Anomaly Detection Network Traffic Monitoring Outlier analysis Data Mining

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Innovative analysis methods of energy performance of buildings

Innovative analysis methods of energy performance of buildin...

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9th international conference on New Energy and Future Energy systems (NEFES 2024)

作者： Dániel László Hegedűs Olivér Tóth Árpád Hajdu Kitti Ajtayné Károlyfi Tamás Horváth Dóra Szalai Department of Architecture and Building Construction Széchenyi István University H-9026 Győr Egyetem tér 1 Hungary Department of Structure and Geotechnical Engineering Széchenyi István University H-9026 Győr Egyetem tér 1 Hungary

ISBN: (数字)9781837241880

In the current era of architecture, sustainability and energy efficiency are becoming increasingly important, while at the same time, advanced technological tools and analytical methods are reshaping the design and construction of buildings. Architects must think responsibly and globally, as buildings account for a significant proportion of the world's energy use. As architects, we have a responsibility to create ecologically optimal facilities for the long term. With this in mind, we would like to present applications that trace the chronological milestones in the development of energy analysis. this paper provides a detailed overview of the different methods of energy analysis. the methods include software developed specifically for energy analysis, an analysis add-on built into modeling software, and among the more innovative technologies, we have also examined parametric design and methodologies based on artificial intelligence algorithms. We have tried to select these methodologies and software in a diversified way to get a more comprehensive picture of how they work. the main aim of this paper is to compare the conclusions drawn from case studies of our previous energy research and from the studies of these energy software, partly subjectively and partly with an objective perspective that tightens subjectivity. As such, a set of criteria we have defined will guide the structure of this analysis. In this article, we will try to highlight the advantages and disadvantages of each method, and we will also try to consider the importance of 3D model-based analysis.

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Design and construction of a Popularizing Internet Resources System Using External Ranking Factors 6

Design and Construction of a Popularizing Internet Resources...

引用

6th international conference on Computational Linguistics and Intelligent systems, COLINS 2022 - Volume I: Main

作者： Basyuk, Taras Vasyliuk, Andrii Lviv Polytechnic National University Bandera str.12 Lviv79013 Ukraine

the article analyzes the existing approaches and software tools for popularizing Internet resources using external ranking factors. As the analysis showed, today there is a lot of software that provides tools for automating the promotion of Internet resources, but all of them are characterized by such main disadvantages as: commercial application, lack of recommendations for promotion and, in most cases, limited functionality. the work carried out was aimed at developing an algorithm for popularizing Internet resources using external ranking factors, which was reflected using the algebra of algorithms and optimized by the number of uniterms. the system was designed using an object-oriented approach in the CASE tool IBM Rational Rose. the analysis of the available methods for solving the task was conducted, the class of tasks that will be solved by each of them was determined, and the tools that would ensure maximum efficiency were selected for the implementation of each of the functions of the system being developed. the system was designed using modern software tools, namely, Angular was used to implement the client part, and the server part was designed using the *** Core Web API. the performed verification of the work confirmed the correctness of the obtained results and the possibility of its application. Further research will be aimed at creating related software modules in order to expand functionality and test their work. © 2022 Copyright for this paper by its authors.

关键词： Software testing

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Movie Recommendation System Based On Emotions

Movie Recommendation System Based On Emotions

引用

international conference on Communications and Signal Processing

作者： J. Ashok Kumar N. Parimala R. Pitchai M Sravya Reddy G.Gita Rishika Vadakattu Manvith Department of ECE B V Raju Institute of Technology Narsapur India Department of ECE Marri Laxman Reddy Institute of Technology and Management Dundigal Hyderabad Telangana Department of CSE B V Raju Institute of Technology Narsapur India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In this paper, we offer an engaging emotion-based movie recommendation system. With our approach, people may effortlessly convey their feelings through natural language in a search box. Using cutting-edge natural language processing tools, we examine and understand the user’s emotional condition. Its recommendation structures make use of strategies. the first technique is content-based filtering, which generates suggestions based on a variety of criteria, including actors, directors, and movie-related content. the algorithm makes recommendations for films based on an analysis of these features and films that the user has expressed interest in. Using the TF-IDF Vectorizer from the sci-kit-learn module, we apply TF-IDF vectorization to the anticipated genre to improve the precision of genre-based movie recommendations. With the help of this vectorization technique, we depict the anticipated genre. We used cosine similarity to recommend 10 movies that match the user’s preferences.

关键词： Filtering Films Signal processing algorithms Signal processing Motion pictures Natural language processing Recommender systems

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Applying Moral and Civic Education to the Operating System Course

Applying Moral and Civic Education to the Operating System C...

引用

international conference on systems and Informatics (ICSAI)

作者： Fengshan Shen School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

Curriculum moral and civic education is the concrete implementation of the construction of socialist spiritual civilization in the school classroom. It is not only a means of moral and civic moral education, but also a way of thinking to learn new knowledge, and it is the unity of moral education and thinking methods. Taking the course of operating system as an example, this paper introduces popular and vivid moral and civic examples from the need of understanding the course knowledge, and explains the course knowledge from a new perspective. these examples include the methodology of understanding things, and at the same time convey healthy and progressive moral and civic themes such as outlook on life, values, social outlook, ethics and scientific thinking methods. the analysis shows that curriculum moral and civic education is not only an external requirement, but also an internal demand of the curriculum itself.

关键词： Ethics Operating systems Education Informatics

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analysis of Markov Jump Processes under Terminal Constraints 1

引用

27th international conference on tools and algorithms for construction and analysis of systems (TACAS)

作者： Backenkoehler, Michael Bortolussi, Luca Grossmann, Gerrit Wolf, Verena Saarland Univ Saarbrucken Grad Sch Comp Sci Saarland Informat Campus E1 3 Saarbrucken Germany Univeristy Trieste Trieste Italy Saarland Univ Saarland Informat Campus E1 3 Saarbrucken Germany

ISBN: (数字)9783030720162

ISBN: (纸本)9783030720155;9783030720162

Many probabilistic inference problems such as stochastic filtering or the computation of rare event probabilities require model analysis under initial and terminal constraints. We propose a solution to this bridging problem for the widely used class of population-structured Markov jump processes. the method is based on a state-space lumping scheme that aggregates states in a grid structure. the resulting approximate bridging distribution is used to iteratively refine relevant and truncate irrelevant parts of the state-space. this way, the algorithm learns a well-justified finite-state projection yielding guaranteed lower bounds for the system behavior under endpoint constraints. We demonstrate the method's applicability to a wide range of problems such as Bayesian inference and the analysis of rare events.

关键词： Bayesian Inference Bridging problem Smoothing Lumping Rare Events

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A Hybrid Approach for Sentiment analysis of Arabic Tweets

A Hybrid Approach for Sentiment Analysis of Arabic Tweets

引用

international conference of Signal Processing and Intelligent systems (ICSPIS)

作者： Maryam Saidi Kian Shahi Najme Zare Hossein Hassanpoor Bioelectric Department Electrical Engineering Faculty Shahrood University of Technology Shahrood Iran Faculty of Biomedical Engineering Amirkabir University of Technology Tehran Iran Department of Management Faculty of Economics and Administrative Sciences Ferdowsi University of Mashhad Mashhad Iran Faculty of Engineering Modern Technologies Amol University of Special Modern Technologies Amol Iran

ISBN: (数字)9798331532543

ISBN: (纸本)9798331532550

Sentiment analysis is an emerging field that focuses on determining positive and negative sentiments, emotions, and measuring their intensity. One application of sentiment analysis is to provide a clearer picture of the general public's opinion on specific topics, such as events, campaigns, services, or specific news, which may be in the form of articles, posts, brief comments, tweets, or even images. While various sentiment analysis tools for English texts have been developed, tools for Arabic text sentiment analysis remain limited and often basic. Not only is a vast amount of content generated online in Arabic, but its strategic importance cannot be overlooked. the aim of this paper is to conduct a hybrid approach to sentiment analysis on Arabic text using the Semeval-2018 Task-1 dataset. the proposed method uses a combination of lexicon-based features, word embedding as well as character embedding as features classified by an ensemble of CNN, DNN, GBT, and bi-LSTM models. Results show a 4% improvement over the Semeval-2018 competition's winner. the knowledge gained from this method has been localized, making it implementable for future applications.

关键词： Sentiment analysis Accuracy Signal processing Vectors Intelligent systems Long short term memory

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Revolutionizing Consumer Power Management: Unveiling Power Grid Feasibility analysis Using Machine Learning

Revolutionizing Consumer Power Management: Unveiling Power G...

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international conference on Power systems (ICPS)

作者： Minhazur Rahman Sheik Erfan Ahmed Himu Abdullah Al Shahid Chowdhury Arafat Ibne Ikram Samiul Hoque Kashfi Md Imtiaz Uddin MD Faisal Department of Electrical and Electronic Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Electrical and Mechanical Engineering Nagoya Institute of Technology Nagoya Japan

As distributed energy sources become more prevalent, maintaining power grid stability is increasingly challenging. By integrating machine intelligence and communication technologies, traditional power networks could transform into smart grids. through machine learning and artificial intelligence, these smart grids can adeptly respond to unexpected changes in consumer demand, power interruptions, surges in renewable energy production, and other crucial situations. Variations in power generation and loads, along with changes in the power system’s structure, lead to varying shifts in the entire network’s active power. Detecting these fluctuations through manual analysis is laborious, but machine learning can be highly impactful in this regard. this study aims to collect comprehensive data for analyzing grid stability, utilizing machine learning tools to thoroughly examine the data, and adopting a multimodal approach to compare model outcomes for an improved solution strategy. Our analysis reveals an accuracy exceeding 0.97, indicating strong potential for practical application and implications.

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