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SSRN 2022年

作者： Lee, Juhyun Park, Sangsung Lee, Junseok Institute of Engineering Research Korea University Seoul02841 Korea Republic of Department of Big Data and Statistics Cheongju University Cheongju28503 Korea Republic of Machine Learning Big Data Institute Korea University Seoul02841 Korea Republic of

A network is a structure that can represent an organic relationship of observations. Network representation learning has the advantage of extracting latent features in a network. In recent years, various algorithms have been developed to embed network representation in a low-dimensional feature space. Conventional algorithms learn a sequence of vertices that are randomly searched from a network and convert a vertex into a vector. This study focused on the representation learning of document networks. Thus, this study proposes a scalable network representation learning algorithm that preserves the structure and content of the document network and reduces data sparsity. To do this, we used the shortest walks to extract a sequence of vertices that reflected semantic similarity from a network. The shortest walk is a method for finding several shortest paths that connect similar vertices. After this, the sequence of vertices was inputted into the language model, assuming that the sequence was a sentence. Experiments were conducted by collecting 29,353 patents, which were scientific documents. The results showed that the proposed algorithm exhibited the lowest error of the tested methods in multi-class classification tasks and link prediction tasks. They also verified that our algorithm employed a smaller sequence of vertices and shorter learning time than other methods. © 2022, The Authors. All rights reserved.

关键词： Semantics

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An Improved Uniform Convergence Bound with Fat-Shattering Dimension

arXiv

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arXiv 2023年

作者： Colomboni, Roberto Esposito, Emmanuel Paudice, Andrea Department of Computer Science Università degli Studi di Milano Via Giovanni Celoria 18 Milan20131 Italy Computational Statistics and Machine Learning Istituto Italiano di Tecnologia Via Enrico Melen 83 Genoa16152 Italy

The fat-shattering dimension characterizes the uniform convergence property of real-valued functions. The state-of-the-art upper bounds feature a multiplicative squared logarithmic factor on the sample complexity, leaving an open gap with the existing lower bound. We provide an improved uniform convergence bound that closes this gap. Copyright © 2023, The Authors. All rights reserved.

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Building a data Lake for Power BI in the Cloud: A Review on Utilizing Cloud Storage Services for Large datasets

Building a Data Lake for Power BI in the Cloud: A Review on ...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Viraj Vijay Gurbade Prateek Verma Swapnil Gundewar Vijendra Singh Bramhe Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education & Research (DU) Wardha Maharashtra India School of Computing Science and Engineering VIT Bhopal University Sehore Madhya Pradesh India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

A data lake is a centralized repository where you may store both structured and unstructured data at any scale. Unlike typical data warehouses, which need data structure for storage, data lakes allow you to store raw data in its natural state. This adaptability makes data lakes suitable for storing large volumes of various data kinds, including photographs, videos, log files, sensor data, and others. When integrating a data lake with Power Business Intelligence (BI), organizations can harness the power of both technologies to gain valuable insights from their data. In the realm of data-driven decision-making, organizations face the challenge of managing and extracting insights from vast and diverse datasets, a task conventional on-premises storage solutions often struggle with due to the exponential growth of data. Thus, the adoption of more scalable and flexible alternatives becomes imperative. Cloud-based data lakes emerge as transformative solutions, offering unparalleled scalability, accessibility, cost-effectiveness, and advanced security protocols. This paper discuss the concept of constructing a cloud-based data lake optimized for Power BI, leveraging leading cloud storage services like Azure data Lake Storage (ADLS) and Amazon S3. By seamlessly integrating Power BI with the cloud data lake, organizations can overcome traditional storage limitations and reap numerous benefits. The practical applications in advanced analytics, customer insights, IoT data management, and data discovery underscore the real-world utility of cloud data lakes. The integration of Power BI with a cloud data lake augments efficient data analysis and self-service analytics potential, offering organizations a comprehensive solution for deriving actionable insights from their data assets.

关键词： Cloud computing Scalability Decision making Buildings Organizations Big data applications Security data mining Business intelligence Self-service

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An Adaptive Modelling Approach to Employee Burnout in the Context of the Big Five Personality Traits

SSRN

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SSRN 2022年

作者： Bashkirova, Anna Compagner, Annelies Henningsen, Diana M. Treur, Jan King's College London Institute of Psychiatry Psychology and Neuroscience United Kingdom Vrije Universiteit Amsterdam Department of Computer Science Social AI Group Netherlands University of Copenhagen Department of Computer Science Machine Learning and Data Science Denmark

Burnout has been on the rise in the past decade, especially amongst the younger working generation. While work environmental aspects play an important role in predicting burnout, variations in personality traits are integral for understanding the syndrome's risk factors, processes, and outcomes. This paper studies the complex interaction of personality factors on the one hand and work environment aspects on the other through the relatively novel adaptive causal network modelling paradigm. Due to the adaptive nature of the model, it can investigate the effects of changes in particular job demands and resources on the symptoms of burnout and their dependence on different personality traits. The model can also demonstrate how an individual’s personality traits, environmental perception, and burnout symptoms can adaptively be altered by individual therapy, in this case, mindfulness-based cognitive therapy. Using the dedicated software environment to simulate the designed adaptive causal network model, two main scenarios were explored, focusing on the neuroticism personality *** demonstrate that neuroticism increases due to interpersonal conflict, indicating that neuroticism can be treated as an adaptive trait. Furthermore, when mindfulness-based cognitive therapy was introduced into the simulation, the likelihood of developing burnout decreased because the perception of the work environment was positively changed due to the therapy. This model contributes to the field of burnout modelling by representing personality traits as adaptive factors that can be changed through individual interventions. More detailed research is needed to understand how organisational-level interventions can also impact burnout development through changes in environmental perception and personality. © 2022, The Authors. All rights reserved.

关键词： Employment

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A unified view of label shift estimation 20

A unified view of label shift estimation

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Proceedings of the 34th International Conference on Neural Information Processing Systems

作者： Saurabh Garg Yifan Wu Sivaraman Balakrishnan Zachary C. Lipton Machine Learning Department Department of Statistics and Data Science Carnegie Mellon University

ISBN: (纸本)9781713829546

Under label shift, the label distribution p(y) might change but the class-conditional distributions p(x|y) do not. There are two dominant approaches for estimating the label marginal. BBSE, a moment-matching approach based on confusion matrices, is provably consistent and provides interpretable error bounds. However, a maximum likelihood estimation approach, which we call MLLS, dominates empirically. In this paper, we present a unified view of the two methods and the first theoretical characterization of MLLS. Our contributions include (i) consistency conditions for MLLS, which include calibration of the classifier and a confusion matrix invertibil-ity condition that BBSE also requires; (ii) a unified framework, casting BBSE as roughly equivalent to MLLS for a particular choice of calibration method; and (iii) a decomposition of MLLS's finite-sample error into terms reflecting miscalibration and estimation error. Our analysis attributes BBSE's statistical inefficiency to a loss of information due to coarse calibration. Experiments on synthetic data, MNIST, and CIFAR10 support our findings.

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data Governance in the Big data Era: A Scalable Framework for Effective Management and Regulatory Compliance

Data Governance in the Big Data Era: A Scalable Framework fo...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Vasanth S G Saravanan Keerthana S Madhu Karthick A Data Science and Applications Indian Institute of Technology Chennai Madras India Department of ECE Sri Sai Ram Institute of Technology Chennai West Tambaram India Department of Artificial Intelligence and Machine Learning St. Joseph's College of Engineering OMR Chennai India Department of CSE (Cyber Security) R.M.K. College of Engineering and Technology Thiruvallur India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

While big data enhances the capacity of organizations to make choices, the traditional data governance approaches are not productive enough to manage the explosion of volume, variety, and velocity of data. This inadequateness leads to loss of data integrity, inabilities to adhere to legal frameworks and poor use of available data. This study provides a solution by providing an easier way to apply data governance concepts using a framework that is elastic and scalable enough for today's data environment. In addition to this, study helps a firm maintain quality of data, compliance, and interactivity through the use of metadata strategies. This relatively new tool helps to manage data governance more effective and innovatively so that the organization can harness the business aspects within big data through the proper deployment of the data governance structure.

关键词： Scalability data integrity Medical services Big data Metadata Regulation data governance Safety Security Usability

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Towards Fixing Clever-Hans Predictors with Counterfactual Knowledge Distillation

arXiv

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arXiv 2023年

作者： Bender, Sidney Anders, Christopher J. Chormai, Pattarawat Marxfeld, Heike Herrmann, Jan Montavon, Grégoire Machine Learning Group Technische Universität Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Max Planck School of Cognition Leipzig Germany BASF SE Ludwigshafen am Rhein Germany Department of Mathematics and Computer Science Freie Universität Berlin Germany

This paper introduces a novel technique called counterfactual knowledge distillation (CFKD) to detect and remove reliance on confounders in deep learning models with the help of human expert feedback. Confounders are spurious features that models tend to rely on, which can result in unexpected errors in regulated or safety-critical domains. The paper highlights the benefit of CFKD in such domains and shows some advantages of counterfactual explanations over other types of explanations. We propose an experiment scheme to quantitatively evaluate the success of CFKD and different teachers that can give feedback to the model. We also introduce a new metric that is better correlated with true test performance than validation accuracy. The paper demonstrates the effectiveness of CFKD on synthetically augmented datasets and on real-world histopathological datasets. © 2023, CC0.

关键词： Distillation

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Big data in Social Media: Analyzing Trends, Patterns and Challenges 2

Big Data in Social Media: Analyzing Trends, Patterns and Cha...

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2nd International Conference on machine learning and Autonomous Systems, ICMLAS 2025

作者： Jikar, Nayan Tale, Yash Tale, Abhay Barhate, Aditya Verma, Prateek Jikar, Aman Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Sawangi Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331505745

The huge volumes of data produced in social media provide both new possibilities and challenges to analytics. The present paper emphasizes Big data analytics and machine learning (ML) methods to uncover trends, patterns, and user behavior from social media-related data. Such vital concepts, like the 'Four Vs' of Big data: Volume, Velocity, Variety, and Veracity, and innovative tools for datacollection, storage, and preprocessing are covered here as well. Sentiment analysis, trend forecasting, and user behaviormodeling are some of the examples of what is possible using ML and Artificial Intelligence (AI) for deriving actionable insights. Marketing campaigns, crisis communications, and political campaigns are some of the scenarios that are covered to bring out real world applications of such technologies, along with their transformative effects. Lastly, data privacy, quality, and scalability issues are noted, along with ethical concerns and biases. Directions for the future point to developments in real-time data analysis, anonymization, and the changing role of AI in social media platforms. This research emphasizes the significance of Big data and machine learning in driving innovation and decision-making in the digital era. © 2025 IEEE.

关键词： Tweets

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Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission Capacity with Optimized Fault Current Limiters and HVDC Circuit Breaker Parameters

Giant Trevally Optimizer (GTO): Enhancing HVDC Transmission ...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： M. Siva Ramkumar Saranya. N Gokul Gopan Rahmath Ulla Baig Josha Daniel S Babu M Department of ECE SNS College of Technology India Department of Artificial Intelligence and Data Science Karpagam College of Engineering India Department of Mechanical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India Department of Artificial Intelligence and Machine Learning Cambridge Institute of Technology K R Puram Bangalore Department of EEE Hindustan College of Engineering and Technology India Master of Engineering in Embedded Systems Hindusthan College of Engineering and Technology India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The multi-terminal HVDC system relies heavily on circuit breakers (CBs) and fault current limiters (FCLs) for protection and performance reliability. This research describes a new Giant Trevally optimizer-based strategy for improving the performance of 4-terminal HVDC systems by optimizing crucial circuit breakers and fault current limiters. The proposed method uses time domain estimation to correctly capture fault dynamics, such as traveling waves and reflection coefficients and finds the worst-case fault locations. The multi-objective optimization seeks to optimize the overcurrent, overvoltage handling, fault clearance time, and energy absorption in arresters. This technology provides an effective, dependable protection solution that reduces fault recovery time and increases system stability. The output pulse of the HVDC system shows very low THD results 1.98% for the minimum load condition, 0.87% for median load, and 0.04% for peak load conditions. The results established the simulation of the model in a MATLAB environment.

关键词： HVDC transmission Circuit breakers Stability analysis Circuit faults Fault current limiters Time-domain analysis Protection Voltage control Integrated circuit reliability Optimization

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A unified view of label shift estimation

arXiv

引用

arXiv 2020年

作者： Garg, Saurabh Wu, Yifan Balakrishnan, Sivaraman Lipton, Zachary C. Machine Learning Department Department of Statistics and Data Science Carnegie Mellon University

Label shift describes the setting where although the label distribution might change between the source and target domains, the class-conditional probabilities (of data given a label) do not. There are two dominant approaches for estimating the label marginal. BBSE, a moment-matching approach based on confusion matrices, is provably consistent and provides interpretable error bounds. However, a maximum likelihood estimation approach, which we call MLLS, dominates empirically. In this paper, we present a unified view of the two methods and the first theoretical characterization of the likelihoodbased estimator. Our contributions include (i) conditions for consistency of MLLS, which include calibration of the classifier and a confusion matrix invertibility condition that BBSE also requires;(ii) a unified view of the methods, casting the confusion matrix as roughly equivalent to MLLS for a particular choice of calibration method;and (iii) a decomposition of MLLS's finite-sample error into terms reflecting the impacts of miscalibration and estimation error. Our analysis attributes BBSEâAZs statistical inefficiency to a loss of information due to coarse calibration. We support our findings with experiments on both synthetic data and the MNIST and CIFAR10 image recognition datasets. Copyright © 2020, The Authors. All rights reserved.

关键词： Image resolution

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