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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Kulyk, Oksana Kickbusch, Jari Mayer, Peter IT University of Copenhagen Copenhagen Denmark Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798400713958

Studies on civilian costs of cyberwarfare operations are crucial in understanding how to protect the population in large-scale cyberattacks conducted by state actors. In this preliminary study, we conduct interviews (N=5) focusing on the harms experienced by Ukrainian civilians due to Russian cyberwarfare operations since the start of the full-scale invasion in 2022 as well as mitigation measures for protection of the civil society. The findings include harms such as lack of information, lack of access to essential services, physical harms, anxiety and lack of trust in the government. The study shows that cyberwarfare operations have the potential of causing severe harm to civilians and that there is a need for future studies to be conducted and adopted by institutions in order to develop measures to mitigate, not only in Ukraine but in any countries in risk of such attacks. © 2025 Copyright held by the owner/author(s).

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Metasystems in the Strategic Management of the Region

Metasystems in the Strategic Management of the Region

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Alexander Solomatin Department of Mathematical Methods of Regional Programming Federal Research Center “Computer Science and Control” of RAS Moscow Russia

ISBN: (数字)9798350375718

ISBN: (纸本)9798350375725

Instead of the traditional “system-environment” pair for objects of the regional economy, it is proposed to consider metasystems - these objects together with all interacting objects of the microenvironment. Each object is considered as a point in the multidimensional space of characteristics. Objects are visualized to reflect their development and interaction with other objects.

关键词： Visualization Reflection Large-scale systems

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Optimal Control in the Problem With Two Gas Fields

Optimal Control in the Problem With Two Gas Fields

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International Conference on Management of Large-Scale System Development (MLSD)

作者： Alexander Skiba Department of Mathematical Methods of Regional Programming Federal Research Center “Computer Science and Control” of RAS Moscow Russia

ISBN: (数字)9798350375718

ISBN: (纸本)9798350375725

We explore a continuous aggregated dynamic model for developing two gas fields. The new borehole commissioning rates are the control parameters. Changes in the average flow rate of producing boreholes and current natural gas production are proportional. It is necessary to solve the problem of maximizing discounted accumulated income for two gas fields. We analyze the optimal control problem with an unrestricted right end and a fixed time. The Pontryagin maximum principle is a main tool for solving the problem. The special optimal control mode is of particular interest. We highlight the critical aspects of all possible optimal controls.

关键词： Drilling Road transportation Limiting Optimal control Switches Production Trajectory Planning Large-scale systems Natural gas

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Visualising Bibliographic Metadata Using CAQDAS in the Research on the Gender Gap in STEM Studies in Higher Education 7th

Visualising Bibliographic Metadata Using CAQDAS in the Resea...

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7th World Conference on Qualitative Research, WCQR 2023

作者： Verdugo-Castro, Sonia Sánchez-Gómez, Mª Cruz García-Holgado, Alicia García-Peñalvo, Francisco José Costa, António Pedro GRIAL Research Group Department of Didactics Organization and Research Methods Universidad de Salamanca Salamanca Spain GRIAL Research Group Computer Science Department Universidad de Salamanca Salamanca Spain University of Aveiro Aveiro Portugal

ISBN: (纸本)9783031313455

Besides providing the consulted publication’s findings, the literature review can offer information through metadata. computer-Assisted Qualitative Data Analysis Software (CAQDAS) can support visualising bibliometric metadata through RIS files. The usefulness is to provide an interactive image of the reality and actuality of scientific production. This article presents a case study to exemplify how metadata can be analysed and visualised using CAQDAS. The topic for the case study is the gender gap in STEM studies in higher education. The study aims to identify the value and usefulness of data visualisation in representing bibliometric data to support literature review processes. The phenomenon of the gender gap in the STEM education sector is used as a case study. The research questions addressed by the study are: (1) What does CAQDAS contribute to the results obtained?;(2) What are the possible causes of the gender gap? The analysis concludes that the cultural, social, educational, family, and peer group environment generates positive and negative force fields when deciding which studies to pursue;some people follow the patterns expected of them according to their gender. Finally, data visualisation helps understand the scientific evolution of a phenomenon and supports the research on a particular topic. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Metadata

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DEEP GENERATIVE MODELING ON LIMITED DATA WITH REGULARIZATION BY NONTRANSFERABLE PRE-TRAINED MODELS 11

DEEP GENERATIVE MODELING ON LIMITED DATA WITH REGULARIZATION...

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11th International Conference on Learning Representations, ICLR 2023

作者： Zhong, Yong Liu, Hongtao Liu, Xiaodong Bao, Fan Shen, Weiran Li, Chongxuan Gaoling School of AI Renmin University of China Beijing China Beijing Key Lab of Big Data Management and Analysis Methods Beijing China Department of Computer Science Technology Tsinghua University Beijing China

Deep generative models (DGMs) are data-eager because learning a complex model on limited data suffers from a large variance and easily overfits. Inspired by the classical perspective of the bias-variance tradeoff, we propose regularized deep generative model (Reg-DGM), which leverages a nontransferable pre-trained model to reduce the variance of generative modeling with limited data. Formally, Reg-DGM optimizes a weighted sum of a certain divergence and the expectation of an energy function, where the divergence is between the data and the model distributions, and the energy function is defined by the pre-trained model w.r.t. the model distribution. We analyze a simple yet representative Gaussian-fitting case to demonstrate how the weighting hyperparameter trades off the bias and the variance. Theoretically, we characterize the existence and the uniqueness of the global minimum of Reg-DGM in a non-parametric setting and prove its convergence with neural networks trained by gradient-based methods. Empirically, with various pre-trained feature extractors and a data-dependent energy function, Reg-DGM consistently improves the generation performance of strong DGMs with limited data and achieves competitive results to the state-of-the-art methods. Our implementation is available at https://***/ML-GSAI/Reg-ADA-APA. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

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The Reliability of Information Sources: Student Perceptions and Educational Opportunities 7

The Reliability of Information Sources: Student Perceptions ...

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7th IEEE International Conference and Workshop Obuda on Electrical and Power Engineering, CANDO-EPE 2024

作者： Kollar, Anita Mihalovicsne Katona, Jozsef Obuda University Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Budapest Hungary John Von Neumann University GAMF Faculty of Engineering and Computer Science Kecskemet Hungary Budapest Business University Faculty of Finance and Accountancy Department of Applied Quantitative Methods Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary

ISBN: (纸本)9798331531119

The modern digital age demands the skills to be able to assess information. In the current world, access to information and knowledge is abundant from different sources. Indeed, high-quality data are much needed in education and professional development, as the quality and accuracy of information directly influence the process of learning and decision-making. The study aims to investigate the perceptions of computer science students at the University of Dunaújváros related to the credibility of diverse information sources. Within a questionnaire, on a voluntary answer basis, 68 students took part, 59 males and nine females. This work is aimed at assessing the degree to which the surveyed students evaluate information sources based on a five-point Likert scale. The data shows that students find print books and textbooks as the most reliable sources;social media are the least reliable. The study also supports the importance of source credibility evaluation, for which lateral reading techniques and AI-based news source credibility identification systems can be helpful. These findings suggest the necessity of bettering the educational approaches to foster better source criticism skills in the students, especially for online content and social media. The study generally contributes to understanding the information society and helps prepare students to obtain and use reliable and accurate information in their future careers. Using AI-based tools and systems can then further support these educational efforts with robust mechanisms to evaluate information credibility in an ever more complex digital landscape. © 2024 IEEE.

关键词： Data reliability

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Automatic Enrichment of Abstract Meaning Representations 16

Automatic Enrichment of Abstract Meaning Representations

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16th Linguistic Annotation Workshop, LAW 2022

作者： Ji, Yuxin Williamson, Gregor Choi, Jinho D. Department of Quantitative Theory and Methods Emory University AtlantaGA30322 United States Department of Computer Science Emory University AtlantaGA30322 United States

ISBN: (纸本)9782493814081

Meaning Representation (AMR) is a semantic graph framework which inadequately represent a number of important semantic features including number, (in)definiteness, quantifiers, and intensional contexts. Several proposals have been made to improve the representational adequacy of AMR by enriching its graph structure. However, these modifications are rarely added to existing AMR corpora due to the labor costs associated with manual annotation. In this paper, we develop an automated annotation tool which algorithmically enriches AMR graphs to better represent number, (in)definite articles, quantificational determiners, and intensional arguments. We compare our automatically produced annotations to gold-standard manual annotations and show that our automatic annotator achieves impressive results. © 2022 European Language Resources Association (ELRA).

关键词： Wages

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Two-stage shadow inclusion estimation: an IV approach for causal inference under latent confounding and collider bias 24

Two-stage shadow inclusion estimation: an IV approach for ca...

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Proceedings of the 41st International Conference on Machine Learning

作者： Baohong Li Anpeng Wu Ruoxuan Xiong Kun Kuang College of Computer Science and Technology Zhejiang University Hangzhou China Department of Quantitative Theory & Methods Emory University Atlanta

Latent confounding bias and collider bias are two key challenges of causal inference in observational studies. Latent confounding bias occurs when failing to control the unmeasured covariates that are common causes of treatments and outcomes, which can be addressed by using the Instrumental Variable (IV) approach. Collider bias comes from non-random sample selection caused by both treatments and outcomes, which can be addressed by using a different type of instruments, i.e., shadow variables. However, in most scenarios, these two biases simultaneously exist in observational data, and the previous methods focusing on either one are inadequate. To the best of our knowledge, no approach has been developed for causal inference when both biases exist. In this paper, we propose a novel IV approach, Two-Stage Shadow Inclusion (2SSI), which can simultaneously address latent confounding bias and collider bias by utilizing the residual of the treatment as a shadow variable. Extensive experimental results on benchmark synthetic datasets and a real-world dataset show that 2SSI achieves noticeable performance improvement when both biases exist compared to existing methods.

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Covid severity prediction: Who cares about the data quality?

Covid severity prediction: Who cares about the data quality?

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2022 International Conference on Frontiers of Information Technology, FIT 2022

作者： Nae, Teodora Krabbe, Johannes Bukhsh, Faiza Allah Jayasinghe Arachchige, Jeewanie Ahmed, Faizan University of Twente Faculty of Electrical Engineering Mathematics and Computer Science Enschede Netherlands Medische Spectrum Twente Enschede Netherlands University of Ruhuna Department of Computer Science Matara Sri Lanka University of Twente Formal Methods and Tools Group Enschede Netherlands

ISBN: (纸本)9798350345933

COVID-19 is an ongoing pandemic disrupting daily life and overwhelming the healthcare infrastructure. Since the outburst of the pandemic, researchers have used various techniques to predict many aspects of the disease, including mortality rate and severity. The reproducibility of this research is challenging due to varying methodologies used to collect data, data quality, vague description of methodological approach to training prediction models, over-relying on data imputation, and over-fitting. This paper focuses on these challenges and provides a short yet comprehensive review of research on COVID mortality and severity prediction. The emphasis is on the reproducibility of the results and data quality issues. To further elaborate on the issue, we report the development of severity prediction models using two data sets. CRISP-DM is used as a methodological approach. We analyze and criticize the quality of the used data sets and how they affect the performance and limitations of the trained models. We conclude this paper with comments on data quality issues, the importance of reproducibility, and suggestions to improve reproducibility. © 2022 IEEE.

关键词： COVID-19

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PREDICTING ACCIDENT SEVERITY: AN ANALYSIS OF FACTORS AFFECTING ACCIDENT SEVERITY USING RANDOM FOREST MODEL

arXiv

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arXiv 2023年

作者： Adefabi, Adekunle Olisah, Somtobe Obunadike, Callistus Oyetubo, Oluwatosin Taiwo, Esther Tella, Edward Department of Computer Science and Quantitative Methods Austin Peay State University TN United States

Road accidents have significant economic and societal costs, with a small number of severe accidents accounting for a large portion of these costs. Predicting accident severity can help in the proactive approach to road safety by identifying potential unsafe road conditions and taking well-informed actions to reduce the number of severe accidents. This study investigates the effectiveness of the Random Forest machine learning algorithm for predicting the severity of an accident. The model is trained on a dataset of accident records from a large metropolitan area and evaluated using various metrics. Hyperparameters and feature selection are optimized to improve the model's performance. The results show that the Random Forest model is an effective tool for predicting accident severity with an accuracy of over 80%. The study also identifies the top six most important variables in the model, which include wind speed, pressure, humidity, visibility, clear conditions, and cloud cover. The fitted model has an Area Under the Curve of 80%, a recall of 79.2%, a precision of 97.1%, and an F1 score of 87.3%. These results suggest that the proposed model has higher performance in explaining the target variable, which is the accident severity class. Overall, the study provides evidence that the Random Forest model is a viable and reliable tool for predicting accident severity and can be used to help reduce the number of fatalities and injuries due to road accidents in the United States. © 2023, CC0.

关键词： Forecasting

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