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IEEE International Workshop on Sport, Technology and Research (IEEE STAR)

作者： Benzakour, Yassine Cabado, Bruno Giancola, Silvio Cioppa, Anthony Ghanem, Bernard Van Droogenbroeck, Marc Univ Liege Inst Montefiore Liege Belgium Univ A Coruna Cinfo La Coruna Spain Univ A Coruna CITIC Res Ctr La Coruna Spain KAUST IVUL Thuwal Saudi Arabia

ISBN: (纸本)9798350351460;9798350351453

Action spotting is crucial in sports analytics as it enables the precise identification and categorization of pivotal moments in sports matches, providing insights that are essential for performance analysis and tactical decision-making. The fragmentation of existing methodologies, however, impedes the progression of sports analytics, necessitating a unified codebase to support the development and deployment of action spotting for video analysis. In this work, we introduce OSL-ActionSpotting, a python library that unifies different action spotting algorithms to streamline research and applications in sports video analytics. OSL-ActionSpotting encapsulates various state-of-the-art techniques into a singular, user-friendly framework, offering standardized processes for action spotting and analysis across multiple datasets. We successfully integrated three cornerstone action spotting methods into OSL-ActionSpotting, achieving performance metrics that match those of the original, disparate codebases. This unification within a single library preserves the effectiveness of each method and enhances usability and accessibility for researchers and practitioners in sports analytics. By bridging the gaps between various action spotting techniques, OSL-ActionSpotting significantly contributes to the field of sports video analysis, fostering enhanced analytical capabilities and collaborative research opportunities. The scalable and modularized design of the library ensures its long-term relevance and adaptability to future technological advancements in the domain.

关键词： Video understanding Action spotting Sports Analytics python library Benchmark Algorithms

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TAT-HUM: Trajectory analysis toolkit for human movements in python

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BEHAVIOR RESEARCH METHODS 2024年第4期56卷 4103-4129页

作者： Wang, Xiaoye Michael Welsh, Timothy N. Univ Toronto Fac Kinesiol & Phys Educ Ctr Motor Control Toronto ON Canada

Human movement trajectories can reveal useful insights regarding the underlying mechanisms of human behaviors. Extracting information from movement trajectories, however, can be challenging because of their complex and dynamic nature. The current paper presents a python toolkit developed to help users analyze and extract meaningful information from the trajectories of discrete rapid aiming movements executed by humans. This toolkit uses various open-source python libraries, such as NumPy and SciPy, and offers a collection of common functionalities to analyze movement trajectory data. To ensure flexibility and ease of use, the toolkit offers two approaches: an automated approach that processes raw data and generates relevant measures automatically, and a manual approach that allows users to selectively use different functions based on their specific needs. A behavioral experiment based on the spatial cueing paradigm was conducted to illustrate how one can use this toolkit in practice. Readers are encouraged to access the publicly available data and relevant analysis scripts as an opportunity to learn about kinematic analysis for human movements.

关键词： Human movement analysis Kinematic analysis Trajectory analysis python library Spatial cueing paradigm

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CBRkit: An Intuitive Case-Based Reasoning Toolkit for python 32nd

CBRkit: An Intuitive Case-Based Reasoning Toolkit for Python

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32nd International Conference on Case-Based Reasoning Research and Development (ICCBR)

作者： Lenz, Mirko Malburg, Lukas Bergmann, Ralph Trier Univ Univ Ring 15 D-54296 Trier Germany Branch Trier Univ German Res Ctr Artificial Intelligence DFKI Behringstr 21 D-54296 Trier Germany

ISBN: (纸本)9783031636455;9783031636462

Developing Case-Based Reasoning (CBR) applications is a complex and demanding task that requires a lot of experience and a deep understanding of users. Additionally, current CBR frameworks are not as usable as Machine Learning (ML) frameworks that can be deployed with only a few lines of code. To address these problems and allow users to easily build hybrid Artificial Intelligence (AI) systems by combining CBR with techniques such as ML, we present the CBRkit library in this paper. CBRkit is a python-based framework that provides generic and easily extensible functions to simplify the creation of CBR applications with advanced similarity measures and case representations. The framework is available from GitHub and PyPI under the permissive MIT license. An initial user study indicates that it is easily possible even for non-CBR experts and users who only have limited python programming skills to develop their own customized CBR application.

关键词： Case-Based Reasoning Machine Learning Hybrid AI CBR Frameworks python library

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A Hand Gesture Exergame for Dementia Development Suppression and Its Applications to Elders

A Hand Gesture Exergame for Dementia Development Suppression...

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2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Anggraini, Irin Tri Xiao, Yanqi Funabiki, Nobuo Shih, Cheng-Liang Huang, Wan-Chia Fan, Chih-Peng Okayama University Dept. of Information and Communication Systems Okayama Japan National Chung Hsing University Dept. of Electrical Engineering Taichung Taiwan

ISBN: (纸本)9798350324136

Currently, Dementia is a leading cause of disability and dependency among the elderly worldwide. The World Health Organization (WHO) lists it as the seventh largest cause of mortality. Exercise using hands and fingers can be crucial in suppressing the development of dementia. Thus, we are studying an hand gesture exergame system to play video games while doing hand exercises. We have implemented the prototype system. In this paper, we collect four video games and define five hand gestures to control a game. Then, we applied the system to 10 elders in Indonesia to evaluate the applicability where their questionnaire results confirmed it. © 2024 IEEE.

关键词： dementia exergame hand gesture python library senior

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Development of AI Educational Datasets library Using Synthetic Dataset Generation Method 5

Development of AI Educational Datasets Library Using Synthet...

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5th International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Kim, Seul Ki Kim, Kwihoon Kim, Taeyoung Korea Natl Univ Educ Dept Comp Educ Cheongju South Korea Korea Natl Univ Educ Dept Artificial Intelligence Convergence Educ Cheongju South Korea

ISBN: (纸本)9781665456456

Recently, as the impact of AI technology on society has increased, education to develop students' AI capabilities has been emphasized. Along with the importance of AI technology, the importance of datasets, which are an axis of technological development, is being emphasized, and many studies on datasets for AI are being conducted. In order to provide meaningful AI education to students from an educational perspective, this paper reconstructs libraries to utilize synthetic dataset generation libraries in different classroom instruction environments and confirms the applicability of educational purposes.

关键词： Artificial Intelligence Education Synthetic data Teaching and learning materials python library

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The EsnTorch library: Efficient Implementation of Transformer-Based Echo State Networks 29th

The EsnTorch Library: Efficient Implementation of Transforme...

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29th International Conference on Neural Information Processing

作者： Cabessa, Jeremie Hernault, Hugo Lamonato, Yves Rochat, Mathieu Levy, Yariv Z. Playtika Ltd CH-1003 Lausanne Switzerland Univ Paris Saclay UVSQ Lab DAVID F-78000 Versailles France Czech Acad Sci Inst Comp Sci Prague 8207 8 Czech Republic Ecole Polytech Fed Lausanne Math Sect SMA CH-1015 Lausanne Switzerland

ISBN: (纸本)9789819916474;9789819916481

Transformer-based models have revolutionized NLP. But in general, these models are highly resource consuming. Based on this consideration, several reservoir computing approaches to NLP have shown promising results. In this context, we propose EsnTorch, a library that implements echo state networks (ESNs) with transformer-based embeddings for text classification. EsnTorch is developed in PyTorch, optimized to work on GPU, and compatible with the transformers and datasets libraries from Hugging Face: the major data science platform for NLP. Accordingly, our library can make use of all the models and datasets available from Hugging Face. A transformer-based ESN implemented in EsnTorch consists of four building blocks: (1) An embedding layer, which uses a transformer-based model to embed the input texts;(2) A reservoir layer, which can implements three kinds of reservoirs: recurrent, linear or null;(3) A pooling layer, which offers three kinds of pooling strategies: mean, last, or None;(4) And a learning algorithm block, which provides six different supervised learning algorithms. Overall, this work falls within the context of sustainable models for NLP.

关键词： reservoir computing echo state networks natural language processing (NLP) text classification transformers BERT python library Hugging Face

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SRAI: Towards Standardization of Geospatial AI 6

SRAI: Towards Standardization of Geospatial AI

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6th ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery (GeoAI)

作者： Gramacki, Piotr Lesniara, Kacper Raczycki, Kamil Wozniak, Szymon Przymus, Marcin Szymanski, Piotr Wroclaw Univ Sci & Technol Dept Artificial Intelligence Wroclaw Poland Kraina AI Wroclaw Poland

ISBN: (纸本)9798400703485

Spatial Representations for Artificial Intelligence (srai) is a python library for working with geospatial data. The library can download geospatial data, split a given area into micro-regions using multiple algorithms and train an embedding model using various architectures. It includes baseline models as well as more complex methods from published works. Those capabilities make it possible to use srai in a complete pipeline for geospatial task solving. The proposed library is the first step to standardize the geospatial AI domain toolset. It is fully open-source and published under Apache 2.0 licence.

关键词： geospatial data processing spatial embeddings urban data embeddings openstreetmap embeddings spatial representation learning standardization in geospatial domain python library

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Text Data Augmentation

Text Data Augmentation

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2023 International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2023

作者： Joshi, Deepali Shinde, Aryan Das, Shreya Deokar, Om Shetiya, Dipasha Jagtap, Simran Vishwakarma Institute of Technology Artificial Intelligence and Data Science Pune India

ISBN: (纸本)9798350344387

Natural language processing model performance and generalization are greatly enhanced by text data augmentation. This paper introduces nlpaug, a python library that provides a wide range of text data augmentation techniques. nlpaug offers functionalities for tasks such as synonym replacement, word insertion, word deletion, character-level modifications, and sentence shuffling. We present an overview of the nlpaug library, including its features and capabilities, and discuss the advantages of using nlpaug for text data augmentation. Through experimental evaluation, we demonstrate the effectiveness of nlpaug in enhancing model performance and its versatility in different NLP tasks. The paper concludes by highlighting the potential applications and future directions of text data augmentation using nlpaug. © 2023 IEEE.

关键词： applications character-level modifications experimental evaluation future directions generalization model performance natural language processing NLP tasks nlpaug python library sentence shuffling synonym replacement Text data augmentation versatility word deletion word insertion

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GrASP: A library for Extracting and Exploring Human-Interpretable Textual Patterns 13

GrASP: A Library for Extracting and Exploring Human-Interpre...

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13th International Conference on Language Resources and Evaluation (LREC)

作者： Lertvittayakumjorn, Piyawat Choshen, Leshem Shnarch, Eyal Toni, Francesca Imperial Coll London Dept Comp London England IBM Res Armonk NY USA

ISBN: (纸本)9791095546726

Data exploration is an important step of every data science and machine learning project, including those involving textual data. We provide a novel language tool, in the form of a publicly available python library for extracting patterns from textual data. The library integrates a first public implementation of the existing GrASP algorithm. It allows users to extract patterns using a number of general-purpose built-in linguistic attributes (such as hypernyms, part-of-speech tags, and syntactic dependency tags), as envisaged for the original algorithm, as well as domain-specific custom attributes which can be incorporated into the library by implementing two functions. The library is equipped with a web-based interface empowering human users to conveniently explore data via the extracted patterns, using complementary pattern-centric and example-centric views: the former includes a reading in natural language and statistics of each extracted pattern;the latter shows applications of each extracted pattern to training examples. We demonstrate the usefulness of the library in classification (spam detection and argument mining), model analysis (machine translation), and artifact discovery in datasets (SNLI and 20Newsgroups).

关键词： pattern extraction textual data exploration resource tool grasp python library model debugging model analysis data analysis

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Characterizing and detecting python version incompatibilities caused by inconsistent version specifications☆

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JOURNAL OF SYSTEMS AND SOFTWARE 2024年 222卷

作者： Li, Shuo Gao, Haocheng Chen, Wei Li, Yi Tian, Haoxiang Liu, Chengwei Ye, Dan Univ Chinese Acad Sci Inst Software Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Nanyang Technol Univ Nanyang Singapore Chinese Acad Sci Key Lab Syst Software Beijing Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Nanjing Inst Software Technol Nanjing Peoples R China China Singapore Int Joint Res Inst Guangzhou Peoples R China

python interpreter serves as the foundation for developing python projects. Configuring compatible python version is essential for packaging and reusing third-party libraries (TPLs) in the python ecosystem. When the python distribution version range constraint of a third-party library is incorrectly declared, it may lead to installation failures or runtime errors when the library is used by a client project. We call such errors python Distribution Version Specification Error (PDSpecErr). PDSpecErrs are not studied adequately and there still lacks a method to guarantee the correctness of python version specifications. To combat PDSpecErrs and mitigate their adverse impacts on downstream applications, we conducted the first exploratory study on 3000 real-world TPLs to investigate PDSpecErrs about their prevalence, symptom categories, and diagnostic patterns. Based on the comprehensive study, we designed and implemented PyChecker, a tool to automatically detect PDSpecErrs in TPLs, locate root causes, and generate fixing solutions. We evaluated PyChecker on 3000 TPL releases and PyChecker detected 842 PDSpecErrs, reporting their root causes and recommending fixing solutions. We have reported 52 PDSpecErrs to the concerned developers. So far, 32 issues have been confirmed and fixed according to our suggestions.

关键词： python library python version incompatibility Exploratory study

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