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EloMetrics: Advanced Outcome Prediction for Chess Matches with Elo Ratings and Logistic Regression

EloMetrics: Advanced Outcome Prediction for Chess Matches wi...

引用

international conference on Electronics, Information and Communications (ICEIC)

作者： Ma. Julianna Re-an DG. Reyes Eirnan Dicreto Emmanuel Gabriel D. Santos Daniella Franxene P. Limbag Gabriel Avelino Sampedro College of Computer Studies De La Salle University Manila Philippines Networks and Distributed Systems Laboratory University of the Philippines Diliman Quezon City Philippines

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

Chessis a complex game characterized by diverse strategies and time constraints, making quick decision-making essential for success. While Elo ratings are widely recognized as indicators of player skill, the predictability of match outcomes based solely on these ratings remains a challenge. The study aims to develop a model for accurately predicting the outcome of chess games using logistic regression, focusing on Elo rating differences and the number of moves in each game. The dataset includes over 20,000 games from ***, and only games with decisive outcomes (excluding draws) were used. The research categorizes Elo ratings into various classes and evaluates model performance across these ranges. The model achieves a predictive accuracy of 68.18%, demonstrating the significance of Elo ratings and move counts in determining game results. Performance metrics, including precision, recall, and Fl-score, further validate the model's effectiveness. The study concludes that while Elo ratings and move count are strong predictors of chess outcomes, further refinement is needed to improve performance at high-ranking skill levels. The model performs well for experts but loses accuracy in master-level games, reflecting higher skill-level complexities. The insights gained from this research contribute to a deeper understanding of predictive modeling in chess, sug-gesting potential avenues for further investigation into additional Influencing factors.

关键词： Training measurement Analytical models Logistic regression Accuracy Focusing Psychology Games Predictive models Time factors

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29th international conference on Technologies and Applications of Artificial Intelligence, TAAI 2024

29th International Conference on Technologies and Applicatio...

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29th international conference on Technologies and Applications of Artificial Intelligence, TAAI 2024

ISBN: (纸本)9789819645886

The proceedings contain 24 papers. The special focus in this conference is on Technologies and Applications of Artificial Intelligence. The topics include: Spectator Support systems for Mahjong Novices by Displaying Candidate Tiles for Winning;effects of Online News Comments on Attitude Formation of Readers;QCI Robot with Generative AI Knowledge Graph for Taiwanese/Japanese Co-Learning Model Application;optimal Assignment of Immediate Tasks in Multi-agent Pickup and Delivery;enhancement of the Speech Ability of an Intelligent Dialogue Agent by Distributed Text Representation and Sentiment Analysis;comicFaves: Organizing Shelves for Favorite Comics Based on User’s Affection;procedurally Generating Natural-Looking Villages in Minecraft with Ant Colony Optimization Algorithms;proposal of modeling Personal Values Using Large Language Models for Extracting Mentions of Item Attributes and Evaluation Polarity from Review Texts;an Eye Pupil Detection-Based Review Support System for Online Video-Based Learning;A Study of LLM Generated Pseudo-Data for Improving Small-Scale Models in Human Values Estimation;Exploring Optimal Color Filter Array Patterns for Demosaicing with SwinIR;proposal for a New Evaluation Index for Human Fallibility in Shogi;systematic Selection of N-Tuples for Game 2048 Using Neural-Network Function Approximator;a User Interactive Shift Scheduling and Timetabling System Based on Evolutionary Computation for Private Tutoring Schools;fish Robot Locomotion Control Using Central Pattern Generator Based on Kuramoto Oscillator Model;a Crow Search Algorithm for Solving Virus Disease Transmission;physics-Informed Neural Network for Shock Absorber Design;exploring Compromised Accounts in Social Media by Change Point Detection;a Comparison of Deep Learning Methods for Multi-Class Classification of Bloodstain Patterns;genetic Algorithms for Variational Quantum Eigensolvers with Non-orthogonal Quantum State Encoding;AI-Based Microsection measurement Framework Using C

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29th international conference on Technologies and Applications of Artificial Intelligence, TAAI 2024

29th International Conference on Technologies and Applicatio...

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29th international conference on Technologies and Applications of Artificial Intelligence, TAAI 2024

ISBN: (纸本)9789819645954

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Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual Metrics

Adversarially Robust CLIP Models Can Induce Better (Robust) ...

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Secure and Trustworthy Machine Learning (SaTML), IEEE conference on

作者： Francesco Croce Christian Schlarmann Naman Deep Singh Matthias Hein EPFL Switzerland Tübingen AI Center University of Tübingen Germany

ISBN: (数字)9798331517113

ISBN: (纸本)9798331517120

Measuring perceptual similarity is a key tool in computer vision. In recent years perceptual metrics based on features extracted from neural networks with large and diverse training sets, e.g. CLIP, have become popular. At the same time, the metrics extracted from features of neural networks are not adversarially robust. In this paper we show that adversarially robust CLIP models, called R-CLIP F , obtained by unsupervised adversarial fine-tuning induce a better and adversarially robust perceptual metric that outperforms existing metrics in a zero-shot setting, and further matches the performance of state-of-the-art metrics while being robust after fine-tuning. Moreover, our perceptual metric achieves strong performance on related tasks such as robust image-to-image retrieval, which becomes especially relevant when applied to “Not Safe for Work” (NSFW) content detection and dataset filtering. While standard perceptual metrics can be easily attacked by a small perturbation completely degrading NSFW detection, our robust perceptual metric maintains high accuracy under an attack while having similar performance for unperturbed images. Finally, perceptual metrics induced by robust CLIP models have higher interpretability: feature inversion can show which images are considered similar, while text inversion can find what images are associated to a given prompt. This also allows us to visualize the very rich visual concepts learned by a CLIP model, including memorized persons, paintings and complex queries.

关键词： measurement Training Visualization Filtering Computational modeling Perturbation methods Neural networks Feature extraction Robustness Standards

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A Deep CNN Self-Attention Model for Multidimensional Speech Quality Prediction Using Crowdsourced Datasets

A Deep CNN Self-Attention Model for Multidimensional Speech ...

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Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), international conference on

作者： Annapurna Gummadi Chamandeep Kaur Deepak Gupta Vuda Sreenivasa Rao Santosh Tripurana P. Nagaraj Department of CSE (Data Science) CVR College of Engineering Hyderabad India Computer Science & Information Technology Department Jazan University Jazan Kingdom of Saudi Arabia Department of Mechanical Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Mechanical Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram A.P. India Dept of ECE Vignan's Institute of Engineering for Women Visakhapatnam Andhra Pradesh India Department of AI & ML K. Ramakrishnan College of Engineering Trichy Tamil nadu India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Predicting voice quality is critical for improving communication systems and the user experience. In this article, we offer a unique technique to multidimensional speech quality prediction using a Deep Convolutional Neural Network (CNN) enriched with self-attention techniques and crowdsourced datasets. The proposed model incorporates both spatial and temporal information from speech signals via numerous convolutional layers, enabling it to accurately represent the intricate patterns and features seen in speech data. Furthermore, self-attention methods are used to let the algorithm to focus on relevant parts of the input data, increasing its discriminative capacity and tolerance to interference. Transfer learning is utilized to leverage pre-trained representations from large-scale speech datasets, enabling the model to generalize well to diverse speech characteristics and environments. By fine-tuning the pre-trained model on the specific crowdsourced dataset, we adapt it to the task of speech quality prediction while mitigating the need for large annotated datasets, which are often costly and time-consuming to obtain. Experimental results demonstrate the effectiveness of the proposed approach, achieving state-of-the-art performance on various metrics for multidimensional speech quality prediction tasks. Furthermore, extensive ablation studies are conducted to analyze the contributions of different components of the model, highlighting the importance of both the CNN architecture and self-attention mechanisms. Overall, our findings suggest that the proposed deep CNN self-attention model, combined with transfer learning from crowdsourced datasets, presents a promising approach for accurate and efficient speech quality prediction in diverse real-world scenarios

关键词： measurement Adaptation models Convolution Computational modeling Transfer learning Interference Predictive models User experience Data models Convolutional neural networks

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Harnessing AI and Machine Learning to Transform English Language Learning Experiences

Harnessing AI and Machine Learning to Transform English Lang...

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Emerging Smart Computing and Informatics (ESCI), conference on

作者： Sreela B P.Bindhu M. Mythili P Vijaya Kumar E. Pearlin Noor Kaylan Hamid Department of English Prathyusha Engineering College India Department of Humanities and Science Rajalakshmi Institute of Technology Chennai India Department of English Nandha Engineering College(Autonomous) Erode India Dept of English Sagi Rama Krishnam Raju Engineering College (A) Bhimavaram Andhra Pradesh India Department of English Panimalar Engineering College Chennai India Department of Computer Education Al Kitab University Kirkuk Iraq

ISBN: (数字)9798331515683

ISBN: (纸本)9798331515690

This study presents a methodology for evaluating argumentative essays from English language learners (ELLs) in grades 8 through 12, utilizing the ELLIPSE corpus. The essays are assessed across six critical metrics: coherence, syntax, vocabulary, phraseology, grammar, and conventions, with ratings from 1.0 to 5.0. The process begins with data collection, followed by preprocessing steps to prepare the text for machine learning. This includes removing punctuation, stopwords, stemming, lemmatization, and expanding contractions to enhance text quality for analysis. Advanced feature extraction techniques are then applied, utilizing word embeddings to convert textual data into dense vector representations. These embeddings capture the syntactic and semantic nuances of the text, making it suitable for machine learning models. The vectorized data is input into Long Short-Term Memory (LSTM) networks, which are adept at handling sequential data. LSTMs manage information flow through cell states and gates, allowing them to capture long-term dependencies in the text effectively. The LSTM network processes the text sequentially, leveraging previous words to make informed classification decisions. The resulting model provides high-accuracy classifications of essays based on the six metrics, offering insights into learners' proficiency levels. This methodology demonstrates a robust application of AI and machine learning in enhancing English language learning, with potential uses in personalized education and automated assessment systems. The study highlights the effectiveness of integrating advanced feature extraction and sequential modeling techniques to assess and improve language proficiency among students. The proposed Word Embedding-based LSTM Model, however, gave out a great result where it produced an accuracy of 98% and it is implemented in python.

关键词： measurement Vocabulary Text categorization Semantics Machine learning Transforms Syntactics Feature extraction Vectors Long short term memory

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Characterizing Periodic tasks are common in and interpreting periodic behavior in computer systems

Characterizing Periodic tasks are common in and interpreting...

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1992 ACM SIGMETRICS joint international conference on measurement and modeling of computer systems, SIGMETRICS/PERFORMANCE 1992

作者： Berry, Robert F. Hellerstein, Joseph L IBM Research Division Yorktown HeightsNY10598 United States

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Micro and macro views of discrete-state markov models and their application to efficient simulation with phase-type distributions 12

Micro and macro views of discrete-state markov models and th...

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12th joint international conference on measurement and modeling of computer systems, ACM SIGMETRICS/Performance 2012

作者： Reinecke, Philipp Telek, Miklós Wolter, Katinka Freie Universität Berlin Institute for Computer Science Takustraße 9 14195 Berlin Germany Budapest University of Technology and Economics Dept. of Telecommunications Magyar tudósok krt. 2 1117 Budapest Hungary Freie Universität Berlin Germany Newcastle University United Kingdom

No abstract available.

ISBN: (纸本)9781450310970

No abstract available.

关键词： phase-type distributions micro/macro views efficient evaluation

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Pedcad: A framework for performance evaluation of object database applications 95

Pedcad: A framework for performance evaluation of object dat...

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1995 ACM SIGMETRICS joint international conference on measurement and modeling of computer systems, SIGMETRICS 1995/PERFORMANCE 1995

作者： Hughes, Eric Winslett, Marianne Department of Computer Science University of Illinois at Urbana-Champaign 1304 West Springfield Avenue UrbanaIL61801 United States

No abstract available.

ISBN: (纸本)0897916956

No abstract available.

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Multi-hop network tomography: Path reconstruction and per-hop arrival time estimation from partial information 12

Multi-hop network tomography: Path reconstruction and per-ho...

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12th joint international conference on measurement and modeling of computer systems, ACM SIGMETRICS/Performance 2012

作者： Keller, Matthias Beutel, Jan Thiele, Lothar Computer Engineering and Networks Laboratory ETH Zurich Switzerland

ISBN: (纸本)9781450310970

In the context of low-power wireless sensor networks, this paper presents multi-hop network tomography (MNT), a novel, non-intrusive algorithm for reconstructing the path, the per-hop arrival order, and the per-hop arrival time of individual packets at runtime. While explicitly transmitting this information over the radio would negatively impact the performance of the system under investigation, information is instead reconstructed after packets have been received at the sink. © 2012 Authors.

关键词： Wireless sensor networks

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