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arXiv 2023年

作者： Vu, Thuy-Trang He, Xuanli Haffari, Gholamreza Shareghi, Ehsan Department of Data Science & AI Monash University Department of Computer Science University College London United Kingdom

In very recent years more attention has been placed on probing the role of pre-training data in Large Language Models (LLMs) downstream behaviour. Despite the importance, there is no public tool that supports such analysis of pre-training corpora at large scale. To help research in this space, we launch Koala, a searchable index over large pretraining corpora using compressed suffix arrays with highly efficient compression rate and search support. In its first release we index the public proportion of OPT 175B pretraining data. Koala provides a framework to do forensic analysis on the current and future benchmarks as well as to assess the degree of memorization in the output from the LLMs. Koala is available for public use at https://***/. © 2023, CC BY.

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Exact MAX tests in genetic association analysis

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中国科学技术大学学报 2024年第12期54卷 33-38,I0005,I0009页

作者： Yi Li Jianan Tian Chenliang Xu Yaning Yang Min Yuan Department of Statistics and Finance School of ManagementUniversity of Science and Technology of ChinaHefei 230026China School of Mathematical Sciences University of Science and Technology of ChinaHefei 230026China Department of Health Data Science School of Health ManagementAnhui Medical UniversityHefei 230032China

The three common genetic models(or modes of inheritance)in association analysis are the dominant,additive,and recessive *** is known that the Cochran-Armitage trend test(CATT)which correctly incorporates information from genetic models,is more powerful than the commonly used Pearson’s chi-square ***,the true genetic model is usually unknown in practice,and the power of the CAT test could be substantially reduced with a wrongly specified genetic *** achieve a power that is close to that of a correctly specified CAT test,it is natural to apply trend tests under different possible genetic models and to report the most significant test *** results in a MAX-type testing procedure,and it was found that this test is usually more powerful than the Pearson’s chi-square *** the signi-ficance(i.e.,p value)of the MAX-type test can be accessed by either large sample approximation or permutation methods,requirements for sample size or simulation replicates are demanding with respect to accuracy and *** paper proposes an approach to calculate the exact p values of MAX-type tests based on the combinatorial counting *** simulation results show that the exact method is more accurate than the large sample approximation methods and more computationally efficient than the permutation method,and our method can be readily applied to genome-wide association studies(GWASs).The proposed methodis built in an R package,MaXact,which is available at the https://***/Myuan 2019/MaXact/.

关键词： exact test MAX test normal approximation permutation

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Bounds on the covariance matrix of the Sherrington–Kirkpatrick model

arXiv

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arXiv 2022年

作者： Alaoui, Ahmed El Gaitonde, Jason Department of Statistics and Data Science Cornell University United States Department of Computer Science Cornell University United States

We consider the Sherrington-Kirkpatrick model with no external field and inverse temperature β 8. Our result follows by establishing an approximate formula for the covariance matrix which we obtain by differentiating the TAP equations and then optimally controlling the associated error terms. We complement this result by showing diverging lower bounds on the operator norm, both at the critical and low temperatures. © 2022, CC BY.

关键词： Covariance matrix

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RoyaltyChain: A Blockchain-Powered Big data Framework for Accurate Music Royalties Prediction 21

RoyaltyChain: A Blockchain-Powered Big Data Framework for Ac...

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21st IEEE India Council International Conference, INDICON 2024

作者： Shah, Megh H. Sheth, Parth V. Kumari, Aparna Tanwar, Sudeep Chawra, Vrajesh Kumar, Sunil Institute of Technology Nirma University Department of Computer Science and Engineering Ahmedabad India Guru Jambheshwar University of Science & Technology Department of Ai and Data Science Haryana Hisar India

ISBN: (纸本)9798350391282

The growth of the commodification of music in the present age has made royalties allocation in an efficient, straight-forward manner to the stakeholders, in general, a complex issue. To address these challenges, this paper introduces RoyaltyChain, a new framework powered by predictive analytics and blockchain. Using factors like streaming data, social media statistics, and geographical factors, Random Forest Regression is employed to predict royalties in a big data environment, with a blockchain-backed system ensuring secure and transparent distribution. Here Ethereum blockchain-based smart contracts are designed in Remix IDE and the Interplanetary File System (IPFS) is incorporated to handle data storage cost issues in blockchain. The performance evaluation shows an increase in the overall efficiency in predicting outcomes and the high scalability of our system in comparison with the related techniques with respect to accuracy, scalability, etc. RoyaltyChain provides a scalable framework to establish efficient and fair distribution of money from the consumption of music in this increasingly complex world. © 2024 IEEE.

关键词： Blockchain

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Enhancing Urdu Intrinsic Plagiarism Detection Through Stylometry Features and Machine Learning

Enhancing Urdu Intrinsic Plagiarism Detection Through Stylom...

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IEEE International Conference on Multi Topic

作者： Muhammad Faraz Manzoor Muhammad Shoaib Farooq Muhammad Haseeb Uzma Farooq Adnan Abid Ahmer Saeed Department of Computer Science University of Management and Technology Lahore Pakistan Department of Data Science University of the Punjab Lahore Pakistan Department of Computer Science Aircod Technologies Lahore Pakistan

The creation of digital content and the easy accessibility of information have led to a surge in academic and textual plagiarism. Plagiarism detection in multiple languages is essential to maintain the integrity of academic and literary works. In the context of the Urdu language, there is a growing need for effective plagiarism detection methods that are tailored to its unique linguistic characteristics. Existing Urdu plagiarism detection tools often rely on external sources or lack robustness in handling intrinsic forms of plagiarism, where the copied content is slightly modified or paraphrased. This research aims to bridge this gap by developing an intrinsic plagiarism detection system for the Urdu language, using a combination of machine learning, ensemble learning and Multi-Layer Perceptron (MLP). Furthermore, to train and evaluate our plagiarism detection models, we manually curate a corpus comprising a substantial collection of 1807 documents in Urdu. This corpus forms the foundation of our research, enabling us to develop and fine-tune our detection algorithms to effectively identify instances of intrinsic plagiarism in Urdu text. To comprehensively assess the unique stylistic fingerprints of documents, we employ a diverse set of word based stylometry features. This multifaceted approach enhances our ability to pinpoint instances of plagiarism in a robust manner. This research contributes to the ongoing efforts to combat plagiarism and uphold the integrity of written content, particularly in the context of the Urdu language, while also showcasing the effectiveness of different word based stylometry features in addressing this critical issue.

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Smartphone-based serious games for mental health: a scoping review

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Multimedia Tools and Applications 2024年第36期83卷 84047-84094页

作者： Gómez-Cambronero, Águeda Mann, Anna-Lisa Mira, Adriana Doherty, Gavin Casteleyn, Sven GEOTEC Research Group Institute of New Imaging Technologies Universidad Jaime I Castellon Spain School of Computer Science and Statistics Trinity College Dublin Dublin 2 Dublin Ireland Personality Evaluation and Psychological Treatments Department University of Valencia Av. de Blasco Ibáñez 13 Valencia46010 Spain

The use of smartphone-based Serious Games in mental health care is an emerging and promising research field. Combining the intrinsic characteristics of games (e.g., interactiveness, immersiveness, playfulness, user-tailoring and engaging nature) with the capabilities of smartphones (e.g., versatility, ubiquitous connectivity, built-in sensors and anywhere–anytime nature) yields great potential to deliver innovative psychological treatments, which are engaging, effective, fun and always available. This article presents a scoping review, based on the PRISMA (scoping review extension) guidelines, of the field of smartphone-based serious games for mental health care. The review combines an analysis of the technical characteristics, including game design, smartphone and game-specific features, with psychological dimensions, including type and purpose of use, underlying psychological frameworks and strategies. It also explores the integration of psychological features into Serious Games and summarizes the findings of evaluations performed. A systematic search identified 40 smartphone-based Serious Games for mental health care. The majority consist of standalone and self-administrable interventions, applying a myriad of psychological strategies to address a wide range of psychological symptoms and disorders. The findings explore the potential of Serious Games as treatments and for enhancing patient engagement;we conclude by proposing several avenues for future research in order to identify best practices and success factors. © The Author(s) 2024.

关键词： Smartphones

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data Quality Assessment of Substation data in Bangladesh: Insights from Handwritten data Digitization 10

Data Quality Assessment of Substation Data in Bangladesh: In...

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10th IEEE International Conference on Power Systems, ICPS 2023

作者： Al Amin, Murshed Jawad-Al-Mursalin Hoque, M.D. Nazzum, Zakia Sayed, Md Abu Tanveer Ahmed Rumee, Sarker Zaber, Moinul Islam Military Institute of Science and Technology Bangladesh Independent University Bangladesh Data and Design Lab Dhaka1000 Bangladesh University of Dhaka Department of Computer Science & Engineering Dhaka1000 Bangladesh United Nations University Guimarães Portugal

ISBN: (纸本)9798350318739

The assessment of power quality is vital for evaluating the existing state of the electricity supply, pinpointing its deficiencies, and guiding enhancements to guarantee a stable and consistent power supply, particularly in the face of rising demands from both industrial and residential sectors. In the context of Bangladesh, however, most substation data are still recorded manually, despite ongoing digitization efforts. This paper focuses on the quality of digitized transcription of handwritten substation data to determine its suitability for further analytical use. Our analysis revealed that the paper-based data from the substations exhibited significant gaps, undermining its reliability and accuracy. Through comprehensive evaluation, we concluded that the data, in its current state, is inadequate for in-depth analysis without the application of robust imputation methods. By tackling these challenges, our research aims to contribute to more informed decision-making and the efficient management of Bangladesh's electrical infrastructure. This study not only diagnoses the current shortcomings in data quality but also lays the groundwork for future improvements in the country's approach to energy data management. © 2023 IEEE.

关键词： Power quality

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Development of a Virtual Patient Model for Kampo Medical Interview: New Approach for Enhancing Empathy and Understanding of Kampo Medicine Pathological Concepts 13

Development of a Virtual Patient Model for Kampo Medical Int...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Takata, Tomoki Yamada, Rie Oliveira Nzinga Rene, Antonio Xu, Kuangzhe Fujimoto, Makoto Toyama Prefectural University Department of Electrical and Computer Engineering Toyama Japan University of Toyama Department of Adult Nursing Toyama Japan Toyama Prefectural University Department of Data Science Toyama Japan Institute for Promotion of Higher Education Hirosaki University Hirosaki Japan University of Toyama Department of Japanese Oriental Medicine Toyama Japan

ISBN: (纸本)9798350373332

Global interest in complementary and alternative medicine has increased in recent years, with Kampo medicine in Japan gaining greater trust and use. Detailed patient interviews are essential in Kampo medicine, as the physician's empathy is critical to diagnostic precision. Typically, medical students develop empathy and deepen their understanding of Kampo's pathological concepts through clinical practice. However, the COVID-19 pandemic has imposed significant restrictions on clinical training. To address this challenge, we propose a novel educational approach to enhance empathy and understanding of Kampo medicine by developing a virtual patient application. This application leverages generative artificial intelligence to simulate realistic patient interactions, enabling students to practice Kampo medical interviews in a safe, controlled environment. The AI-generated conversations are designed to reflect the emotional nuances of real-life dialogue, with the virtual patients' facial expressions synchronized to these emotions, thus enhancing the realism of the training. The suggested method allows repeated practice at any time and fosters the development of essential diag-nostic and empathetic skills. While promising challenges remain in improving these simulations' accuracy, further refinements are still under consideration. © 2024 IEEE.

关键词： Virtual environments

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Machine Learning Approaches for the Classification of Knee Osteoarthritis

Machine Learning Approaches for the Classification of Knee O...

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International Conference on

作者： Tayyaba Tariq Zobia Suhail Zubair Nawaz Department of Computer Science University of the Punjab Lahore Pakistan Department of Data Science University of the Punjab Lahore Pakistan

Machine learning systems have gained tremendous attention in solving complex tasks. For instance, image classification using machine learning has multiple applications in the medical field, where different imaging modalities are used for diagnosing, detecting, and classifying diseases. This study aims to analyze different machine-learning approaches for detecting knee osteoarthritis using X-rays. We used different machine learning algorithms to classify X-ray images of the knee to various levels of osteoarthritis severity. We have solved the problem as a binary classification task, multi-class classification, and ordinal regression task. The Histogram of Oriented Gradients and Haralick Features are used for feature extraction in the binary classification task. For Binary Classification, six experiments are performed using different machine learning classifiers. Logistic regression has a maximum accuracy of 84.50%, F measure of 81%, AUC of 0.84, Mathew’s Correlation Coefficient of 0.69, and Cohen’s Kappa 0.66. 10-Fold cross-validation also reveals that the models’ performance is consistent across all folds. Furthermore, texture and shape features are extracted for ordinal classification using the histogram of oriented gradients and dense DAISY descriptors. Finally, separate experiments are performed, and results for both descriptors are compared. Our implemented approaches have shown state-of-the-art results.

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Transforming Heart Disease Detection with BERT: Novel Architectures and Fine-Tuning Techniques

Transforming Heart Disease Detection with BERT: Novel Archit...

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Frontiers of Information Technology (FIT)

作者： Muhammad Usman Javeed Muhammad Shafqat Ali Asma Iqbal Muhammad Azhar Shafqat Maria Aslam Ifra Shabbir Department of Computer Science Comsats University Islamabad Sahiwal Pakistan Department of Applied Data Science Hong Kong Shue Yan University Hong Kong

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

The prediction of heart disease is crucial for effective prevention and treatment. However, extracting clinical infor-mation such as CAD, smoking, hypertension, hyperlipidemia, obesity, and family history of CAD from unstructured electronic health records (EHRs) poses significant challenges to clinicians. This research introduces a novel approach that leverages an ensemble of transfer learning algorithms combined with a multi-head attention mechanism to automatically extract heart disease risk factors from EHRs. Various deep learning models, including BERT, BioBERT, BioClinical BERT, RoBERTa, and XLNet, were initially trained on medical data sets and subsequently fine-tuned on the i2b2 clinical data set. Individual models delivered strong results, with RoBERTa achieving the highest accuracy of 95. 27% and an F1 score of 94. 94%. BioBERT, BioClinical BERT, XLNet, and BERT also performed well, with precision ranging from 94. 73% to 95. 03%. However, the proposed ensemble model with multi-head attention outperformed all, achieving an accuracy of 96.35% and the F1-score of 95.76%. These findings highlight the superior ability of the ensemble model to capture complex inter-dependencies between heart disease risk factors, making it a robust tool for clinical prediction.

关键词： Heart Solid modeling Obesity Accuracy Biological system modeling Prevention and mitigation Transfer learning Predictive models Reliability Diseases

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