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Using No-code Platform to Accelerate Digital Transformation - The Study for Feasibility Analysis and Solution Development of Performance Visualization Cloud-computing Management System (PVCMS) 23

Using No-code Platform to Accelerate Digital Transformation ...

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5th Asia Pacific Information Technology Conference, APIT 2023

作者： Hsu, Shun-Fa Lee, Yi-In Hsu, Ching-Tzu Hsu, Ching-Yin Intelligent Cloud-computing and Big-Data Center Chung Chou University of Science and Technology Taiwan Department of International Business Tunghai University Taiwan Civil Aeronautics Administration Ministry of Transportation and Communications Taiwan Department of East Asian Studies National Taiwan Normal University Taiwan

ISBN: (纸本)9781450399500

This study is based on government's digital transformation policy and the industry's digital development trend. Micro-Small Enterprises(MSEs) are the target customers to meet the organizational productivity management needs of key performance indicators (KPI) and project management (PjM) as the research subject, combined with management by objectives (MBO) and performance appraisal organizational management practice as a methodology, using Google's No-code smart cloud-computing service as a practical platform, integrating data input/processing/analysis/ visualization (D/IPAV®) workflows to customized develop the "Performance Visualization Cloud-computing Management System"(PVCMS) of the organization's operational data monitoring and decision-making digital dashboard. This project sets PVCMS as the core product and develops a set of indicators construction, analysis and visual monitoring that meet the performance management needs of the industry. Diagnosis and Consultant model as a solution, to follow-up actual commercialization standard operating procedure to be followed, and quick import templates for various performance management visualization modules. Its operation process is mainly divided into five stages: (1). Define performance management requirements, (2). Establish KPIs, (3). Link Indicator with performance evaluation, (4). Build performance management system, (5). Track Indicators and decision-making application. © 2023 ACM.

关键词： Cloud computing

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Skeleton-OOD: An End-to-End Skeleton-Based Model for Robust Out-of-Distribution Human Action Detection

arXiv

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arXiv 2024年

作者： Xu, Jing Zhu, Anqi Lin, Jingyu Ke, Qiuhong Chen, Cunjian Department of Data Science and AI Monash University MelbourneVIC3800 Australia Department of Computing and Information Systems University of Melbourne MelbourneVIC3052 Australia

Human action recognition is crucial in computer vision systems. However, in real-world scenarios, human actions often fall outside the distribution of training data, requiring a model to both recognize in-distribution (ID) actions and reject out-of-distribution (OOD) ones. Despite its importance, there has been limited research on OOD detection in human actions. Existing works on OOD detection mainly focus on image data with RGB structure, and many methods are post-hoc in nature. While these methods are convenient and computationally efficient, they often lack sufficient accuracy, fail to consider the exposure of OOD samples, and ignore the application in skeleton structure data. To address these challenges, we propose a novel end-to-end skeleton-based model called Skeleton-OOD, which is committed to improving the effectiveness of OOD tasks while ensuring the accuracy of ID recognition. Through extensive experiments conducted on NTU-RGB+D 60, NTU-RGB+D 120, and Kinetics-400 datasets, Skeleton-OOD demonstrates the superior performance of our proposed approach compared to state-of-the-art methods. Our findings underscore the effectiveness of classic OOD detection techniques in the context of skeleton-based action recognition tasks, offering promising avenues for future research in this field. Code is available at https://***/YilliaJing/***. Copyright © 2024, The Authors. All rights reserved.

关键词： Musculoskeletal system

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Contamination-Resilient Anomaly Detection via Adversarial Learning on Partially-Observed Normal and Anomalous data 41

Contamination-Resilient Anomaly Detection via Adversarial Le...

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41st International Conference on Machine Learning, ICML 2024

作者： Lv, Wenxi Su, Qinliang Wan, Hai Xu, Hongteng Xu, Wenchao School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Gaoling School of Artifical Intelligence Renmin University of China Beijing China Department of Computing The Hong Kong Polytechnic University Hong Kong

Many existing anomaly detection methods assume the availability of a large-scale normal dataset. But for many applications, limited by resources, removing all anomalous samples from a large unlabeled dataset is unrealistic, resulting in contaminated datasets. To detect anomalies accurately under such scenarios, from the probabilistic perspective, the key question becomes how to learn the normal-data distribution from a contaminated dataset. To this end, we propose to collect two additional small datasets that are comprised of partially-observed normal and anomaly samples, and then use them to help learn the distribution under an adversarial learning scheme. We prove that under some mild conditions, the proposed method is able to learn the correct normal-data distribution. Then, we consider the overfitting issue caused by the small size of the two additional datasets, and a correctness-guaranteed flipping mechanism is further developed to alleviate it. Theoretical results under incomplete observed anomaly types are also presented. Extensive experimental results demonstrate that our method outperforms representative baselines when detecting anomalies under contaminated datasets. Copyright 2024 by the author(s)

关键词： Adversarial machine learning

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A New Sentiment Analysis-based Approach for the Prediction of Users Beliefs

A New Sentiment Analysis-based Approach for the Prediction o...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Sadia Abdulhalim Rakif Pathan Belkacem Chikhaoui Department of Data Science and Artificial Intelligence College of Computing and Information Technology University of Doha for Science and Technology Doha Qatar Department of Science and Technology Institute of Applied Artificial Intelligence TELUQ University Montreal Canada

ISBN: (数字)9798350373974

ISBN: (纸本)9798350373981

This paper proposes a new approach based on sentiment analysis for predicting the relevance of religious beliefs in contemporary relationships within the context of online dating. The motivation for this research lies in understanding how shared values, common missions, and rituals in various religions might influence decision-making in relationships, the study aims to predict users’ religious beliefs based on OkCupid profiles. Our approach starts first by 1) extracting relevant features using sentiment analysis from text data, and 2) developing machine learning models based on state-of-the-art algorithms such as random forests, logistic regression, and decision trees. The results obtained demonstrated the effectiveness of our proposed approach in accurately predicting religious beliefs and its superiority compared to the state-of-the-art methods. Through this investigation, the paper seeks insights into the interplay between religious beliefs and romantic choices.

关键词： Sentiment analysis Logistic regression Machine learning algorithms Shape Predictive models Feature extraction Prediction algorithms Decision trees Information technology Random forests

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Few-shot Prompting for Pairwise Ranking: An Effective Non-Parametric Retrieval Model

arXiv

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arXiv 2024年

作者： Sinhababu, Nilanjan Parry, Andrew Ganguly, Debasis Samanta, Debasis Mitra, Pabitra Centre for Computational and Data Sciences IIT Kharagpur India School of Computing Science University of Glasgow United Kingdom Department of Computer Science and Engineering IIT Kharagpur India

A supervised ranking model, despite its advantage of being effective, usually involves complex processing - typically multiple stages of task-specific pre-training and fine-tuning. This has motivated researchers to explore simpler pipelines leveraging large language models (LLMs) that are capable of working in a zero-shot manner. However, since zero-shot inference does not make use of a training set of pairs of queries and their relevant documents, its performance is mostly worse than that of supervised models, which are trained on such example pairs. Motivated by the existing findings that training examples generally improve zero-shot performance, in our work, we explore if this also applies to ranking models. More specifically, given a query and a pair of documents, the preference prediction task is improved by augmenting examples of preferences for similar queries from a training set. Our proposed pairwise few-shot ranker demonstrates consistent improvements over the zero-shot baseline on both in-domain (TREC DL) and out-domain (BEIR subset) retrieval benchmarks. Our method also achieves a close performance to that of a supervised model without requiring any complex training pipeline. © 2024, CC BY.

关键词： Zero-shot learning

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Probabilistic Reduced-Dimensional Vector Autoregressive Modeling for Dynamics Prediction and Reconstruction with Oblique Projections

Probabilistic Reduced-Dimensional Vector Autoregressive Mode...

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IEEE Conference on Decision and Control

作者： Yanfang Mo Jiaxin Yu S. Joe Qin Hong Kong Institute for Data Science City University of Hong Kong Hong Kong School of Data Science and Hong Kong Institute for Data Science City University of Hong Kong Hong Kong Department of Computing and Decision Science Institute of Data Science Lingnan University Hong Kong

In this paper, we propose a probabilistic reduced-dimensional vector autoregressive (PredVAR) model with oblique projections. This model partitions the measurement space into a dynamic subspace and a static subspace that do not need to be orthogonal. The partition allows us to apply an oblique projection to extract dynamic latent variables (DLVs) from high-dimensional data with maximized predictability. We develop an alternating iterative PredVAR algorithm that exploits the interaction between updating the latent VAR dynamics and estimating the oblique projection, using expectation maximization (EM) and a statistical constraint. In addition, the noise covariance matrices are estimated as a natural outcome of the EM method. A simulation case study of the nonlinear Lorenz oscillation system illustrates the advantages of the proposed approach over two alternatives.

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Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review

arXiv

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arXiv 2024年

作者： Chan, Yi Hao Girish, Deepank Gupta, Sukrit Xia, Jing Kasi, Chockalingam He, Yinan Wang, Conghao Rajapakse, Jagath C. College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Department of Computer Science and Engineering Indian Institute of Technology Punjab Ropar140001 India

Graph neural networks (GNN) have emerged as a popular tool for modelling functional magnetic resonance imaging (fMRI) datasets. Many recent studies have reported significant improvements in disorder classification performance via more sophisticated GNN designs and highlighted salient features that could be potential biomarkers of the disorder. However, existing methods of evaluating their robustness are often limited to cross-referencing with existing literature, which is a subjective and inconsistent process. In this review, we provide an overview of how GNN and model explainability techniques (specifically, feature attributors) have been applied to fMRI datasets for disorder prediction tasks, with an emphasis on evaluating the robustness of potential biomarkers produced for psychiatric disorders. Then, 65 studies using GNNs that reported potential fMRI biomarkers for psychiatric disorders (attention-deficit hyperactivity disorder, autism spectrum disorder, major depressive disorder, schizophrenia) published before 9 October 2024 were identified from 2 online databases (Scopus, PubMed). We found that while most studies have performant models, salient features highlighted in these studies (as determined by feature attribution scores) vary greatly across studies on the same disorder. Reproducibility of biomarkers is only limited to a small subset at the level of regions and few transdiagnostic biomarkers were identified. To address these issues, we suggest establishing new standards that are based on objective evaluation metrics to determine the robustness of these potential biomarkers. We further highlight gaps in the existing literature and put together a prediction-attribution-evaluation framework that could set the foundations for future research on discovering robust biomarkers of psychiatric disorders via GNNs. © 2024, CC BY-SA.

关键词： Graph neural networks

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Towards a Sustainable Future: Analyzing Water Quality Changes for Environmental Sustainability 1

Towards a Sustainable Future: Analyzing Water Quality Change...

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1st IEEE International Conference on Smart Technology, ICE-SMARTec 2023

作者： Mansor, Nur Suhaili Awang, Hapini Harun, Nor Hazlyna Hassan, Mohamad Ghozali Shehu Malami, Sarkin Tudu Shamsuddin, Noraishah Institute for Advanced and Smart Digital Opportunities School of Computing Universiti Utara Malaysia Sintok Malaysia School of Computing Universiti Utara Malaysia Data Science Research Lab Sintok Malaysia School of Technology Management and Logistics Universiti Utara Malaysia Sintok Malaysia Sokoto State University Faculty of Science Department of Computer Science and Information Technology Sokoto Nigeria Ici in Smart Manufacturing Sirim Bhd Shah Alam Malaysia

ISBN: (纸本)9798350306156

The development of industrial areas is frequently thought to have a good impact on society because it helps to meet the population's requirements. However, it also negatively impacts the environment, such as river pollution. If a country experiences rapid economic growth without considering the effects of industrial development on the environment, then human well-being will be affected. In this context, advanced development is attained by a developed nation while ensuring the well-being of society. Uncontrolled and unsustainable development increases environmental pollution, adversely affecting human health. As water is essential for human life, water resources, such as rivers, must be properly managed and protected to ensure human well-being. Thus, the study conducted on the changes in the water quality of the Kim Kim River provides insights into how unintended consequences of urban and industrial development can be identified and addressed using three sets of satellite imagery data from 2015, 2019 and 2020. The study was led to identify research problems and achieve the study objectives to obtain research results. The results of image classification analyses indicate that the development area near Sungai Kim Kim increased to 50%, with 38% from industrial areas. The RMSE for the image classification is 1.51, significantly better than the minimum allowed value of 2.00. It provides an understanding of the change in river water quality because of urban and industrial development. By highlighting the importance of sustainable development and the need to balance economic growth with environmental protection, this study contributes to the development of effective policies and strategies for the sustainable development of industrial areas. © 2023 IEEE.

关键词： Water quality

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Proving Linearizability of Concurrent Queues

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Journal of Computers (Taiwan) 2024年第5期35卷 91-103页

作者： Peng, Jie Wen, Tangliu Jiang, Dongming Department of Information Engineering Gannan University of Science and Technology Ganzhou China Ganzhou Cloud Computing and Big Data Research Center Ganzhou China School of Big Data Jiangxi Science & Technology Normal University Nanchang China

Linearizability is a commonly accepted correctness criterion for concurrent data structures. Concurrent queues are among the most fundamental concurrent data structures. In this paper, we present necessary and sufficient conditions for proving linearizability of concurrent queues, which make use of linearization of dequeue operations. The verification conditions intuitively express the "FIFO" semantics of concurrent queues and can be verified just by reasoning about the happened-before order of operations. We have successfully applied the proof technique to prove several challenging concurrent queues. We believe that our proof technique can be extended to the concurrent data structures which have the ordering requirements when their elements are removed. © 2024 Codon Publications. All rights reserved.

关键词： data structures

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Blood Test Report-Based Prediction and Classification of Diseases Using Artificial Neural Networks

Blood Test Report-Based Prediction and Classification of Dis...

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Women in computing (InCoWoCo), International Conference for

作者： Ashwini Kodipalli N Prajwal Vamshi Vignesh B Trupthi Rao Gargi N Shridhar B. Devamane Dept. Artificial Intelligence & Data Science Global Academy of Technology Bangalore Karnataka India Department of Computer Science School of Computing Amrita Vishwavidyapeetam Mysuru India

ISBN: (数字)9798331518943

ISBN: (纸本)9798331518950

Through early intervention and individualized treatment plans, timely disease detection and personalized healthcare can advance patient results and reduce healthcare costs. With the aim to categorize medical conditions, we present a dataset in this study that includes blood sample parameters like haemoglobin, cholesterol, and glucose. To analyse these parameters, we created a three-layered artificial neural network (ANN). Achieving an accuracy of 93.42% on the test set, the ANN was trained and validated on the dataset. With the aim of reducing overfitting and enhance model generalization, dropout layers were added. Furthermore, a comparative analysis with various optimizers was carried out. The results highlight the ANN's potential as a means for timly diagnosis, personalized healthcare, improving patient outcomes and lowering long-term healthcare costs. They also demonstrate the ANN's ability to accurately classify diseases.

关键词： Costs Accuracy Medical services Artificial neural networks Glucose Diabetes Medical diagnosis Blood Medical diagnostic imaging Diseases

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