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Software Engineering and Computer Science (CSECS), International Conference on

作者： Mayra Rivera Pricila Lucero José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Group: Health Computing and Intelligent System Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Water leakage in distribution networks is a significant challenge, especially in regions with limited infrastructure like Huancayo, Peru, where losses account for 32.82% of the distributed volume. This study introduces a machine learning-based approach to detect leaks using four algorithms: Autoencoder LSTM, Isolation Forest, One-Class SVM, and K-Nearest Neighbors (KNN). The methodology involved preprocessing historical consumption data (2018–2024) into 12-month temporal sequences per client and evaluating the models based on F1 Score, Precision, and Mean Absolute Error (MAE). Among the algorithms, the Autoencoder LSTM demonstrated superior performance with the highest precision (0.89) and the lowest MAE (0.00402). Its robustness in high-variability contexts enables early and reliable leak detection, providing a cost-effective solution for optimizing water management in resource-constrained environments.

关键词： Support vector machines Machine learning algorithms Autoencoders Distribution networks Forestry Nearest neighbor methods Robustness Leak detection Long short term memory Water resources

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Experimental Evaluation of a Checklist-Based Inspection Technique to Verify the Compliance of Software systems with the Brazilian General Data Protection Law

arXiv

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arXiv 2023年

作者： Cerqueira, Diego André de Mello, Rafael Maiani Travassos, Guilherme Horta Systems Engineering and Computer Science Graduate Program Federal University of Rio de Janeiro Brazil Computing Institute Federal University of Rio de Janeiro Brazil

Background/Context: Recent laws to ensure the security and protection of personal data establish new software requirements. Consequently, new technologies are needed to guarantee software quality under the perception of privacy and protection of personal data. Therefore, we created a checklist-based inspection technique (LGPDCheck) to support the identification of defects in software artifacts based on the principles established by the Brazilian General Data Protection Law (LGPD). Objective/Aim: To evaluate the effectiveness and efficiency of LGPDCheck for verifying privacy and data protection (PDP) in software artifacts compared to ad-hoc techniques. Method: To assess LGPDCheck and ad-hoc techniques experimentally through a quasi-experiment (two factors, five treatments). The data will be collected from IoT-based health software systems built by software engineering students from the Federal University of Rio de Janeiro. The data analyses will compare results from ad-hoc and LGPDCheck inspections, the participant's effectiveness and efficiency in each trial, defects' variance and standard deviation, and time spent with the reviews. The data will be screened for outliers, and normality and homoscedasticity will be verified using the Shapiro-Wilk and Levene tests. Nonparametric or parametric tests, such as the Wilcoxon or Student's t-tests, will be applied as appropriate. © 2023, CC BY-NC-ND.

关键词： Data privacy

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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Computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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Word reuse and combination support efficient communication of emerging concepts

arXiv

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arXiv 2024年

作者： Xu, Aotao Kemp, Charles Frermann, Lea Xu, Yang Department of Computer Science University of Toronto Canada School of Computing and Information Systems University of Melbourne Australia School of Psychological Sciences University of Melbourne Australia Cognitive Science Program University of Toronto Canada

A key function of the lexicon is to express novel concepts as they emerge over time through a process known as lexicalization. The most common lexicalization strategies are the reuse and combination of existing words, but they have typically been studied separately in the areas of word meaning extension and word formation. Here we offer an information-theoretic account of how both strategies are constrained by a fundamental tradeoff between competing communicative pressures: word reuse tends to preserve the average length of word forms at the cost of less precision, while word combination tends to produce more informative words at the expense of greater word length. We test our proposal against a large dataset of reuse items and compounds that appeared in English, French and Finnish over the past century. We find that these historically emerging items achieve higher levels of communicative efficiency than hypothetical ways of constructing the lexicon, and both literal reuse items and compounds tend to be more efficient than their non-literal counterparts. These results suggest that reuse and combination are both consistent with a unified account of lexicalization grounded in the theory of efficient communication. © 2024, CC BY-NC-ND.

关键词： Information theory

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Mobile Application for Elderly Care 2

Mobile Application for Elderly Care

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Jose, Fernando Livaldo Mahardhika, Mozan Hafiz Sukiman, Nathanael Adolf Warnars, Harco Leslie Hendric Spits Ramadhan, Arief Utomo, Wiranto Herry Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Bina Nusantara Univerisity Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia President University Faculty of Computing Department of Magister Science of Information Technology West Java17530 Indonesia

ISBN: (纸本)9781665400527

This paper discusses the design and implementation process of mobile applications used by nurses to communicate with the elderly or with people appointed to represent the elderly in using this mobile application. This mobile application is expected to help caregivers or nurses monitor the health condition of the elderly, and this mobile application has several functions that make it complete. These functions include submitting weekly reports, communicating with others via forums, and providing relevant information about the elderly and how to deal with them, including news and government regulations on elderly care. This mobile application uses case diagrams to describe business processes and class diagrams to illustrate database design. As for the implementation, android studio and MySQL database are used for database storage. This paper is limited to 9 references for literature review previous similar research by searching for publication titles with the sentences 'mobile application'+elderly+care using quotation marks and without quotation marks for the words elderly and care for all years of publication. © 2022 IEEE.

关键词： Mobile computing

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Deviations in Representations Induced by Adversarial Attacks

arXiv

引用

arXiv 2022年

作者： Steinberg, Daniel Munro, Paul Intelligent Systems Program University of Pittsburgh United States School of Computing and Information University of Pittsburgh United States

Deep learning has been a popular topic and has achieved success in many areas. It has drawn the attention of researchers and machine learning practitioners alike, with developed models deployed to a variety of settings. Along with its achievements, research has shown that deep learning models are vulnerable to adversarial attacks. This finding brought about a new direction in research, whereby algorithms were developed to attack and defend vulnerable networks. Our interest is in understanding how these attacks effect change on the intermediate representations of deep learning models. We present a method for measuring and analyzing the deviations in representations induced by adversarial attacks, progressively across a selected set of layers. Experiments are conducted using an assortment of attack algorithms—on the CIFAR-10 dataset—with plots created to visualize the impact of adversarial attacks across different layers in a network. Copyright © 2022, The Authors. All rights reserved.

关键词： Network layers

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Data Selection Based on Phoneme Affinity Matrix for Electrolarynx Speech Recognition

Data Selection Based on Phoneme Affinity Matrix for Electrol...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： I-Ting Hsieh Chung-Hsien Wu Shu-Wei Tsa Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University Tainan Taiwan Dept. of Otolaryngology National Cheng Kung University Hospital Taiwan

Electrolarynx (EL) is a communicative aid for the patient after laryngectomy to generate communicable speech. Since EL speech exhibits low speech intelligibility and produces loud noise, understanding the content of the speech remains challenging for listeners, even if the patient is proficient in using the EL device. Accordingly, it is important to develop the tools that offer additional communication methods. Automatic speech recognition (ASR) of EL speech emerges as a method worth considering in this regard. However, the problem of under-resourced data dramatically degrades the recognition performance of EL speech. Data augmentation is one of the viable solutions for addressing the issue of under-resourced speech data. However, even with an increased health training corpus, the improvement in EL speech recognition may not be satisfactory. Because the characteristics of the EL speech still differ significantly from those of health speech. This paper proposes a data selection method using the phoneme affinity matrix to prioritize the selection of health speech that closely resembles EL speech for data augmentation. The affinity between two phonemes is defined as the similarity of the Phone Posteriorgrams(PPGs) of the two phonemes, considering the phoneme models. The experimental results demonstrate that the approach utilizing data selection based on the phoneme affinity matrix yields superior results compared to both the baseline and the method employing random sampling to select the augmented health speech corpus.

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U-Net and SegNet performances on lesion segmentation of breast ultrasonography images

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Research on Biomedical Engineering 2021年第2期37卷 171-179页

作者： Vianna, Pedro Farias, Ricardo de Albuquerque Pereira, Wagner Coelho Biomedical Engineering Program COPPE/Federal University of Rio de Janeiro Av. Horácio Macedo 2030 Room H327 Rio de JaneiroRJ Brazil Systems and Computing Engineering Program COPPE/Federal University of Rio de Janeiro Rio de JaneiroRJ Brazil

Purpose: Screening is the predominant strategy for the early detection of breast cancer. However, image analysis depends on the experience of the radiologist, inserting subjective factors in the evaluation of findings. Most biopsies performed on women with suspicious lesions on breast imaging tests show benignity. This medical technique is invasive and consumes time and resources;therefore, there is a demand to reduce these unnecessary procedures. Computer-aided diagnostics has been increasingly used as a second-reader tool lately, decreasing the diagnostic uncertainty of specialists. Based on this fact, the development of such systems is crucial and can be accomplished by the refinement of its steps, i.e., more accurate segmentation and classification. Considering this scenario, the present work describes the implementation of two well-known convolutional neural networks, U-Net and SegNet, for the segmentation of lesions found in breast ultrasonography. Defining which architecture is most appropriate for this task can ultimately help in reducing the number of biopsies performed. Methods: Convolutional neural networks are used in segmentation by classifying each pixel in an image, based on self-trained weights. Using a dataset of 2054 images, obtained in partnership with the National Cancer Institute, we compared the automatic segmentation performed by the networks with a manual segmentation made by a specialist. Results: Among the two proposed architectures, U-Net obtained better results in this task, obtaining a dice coefficient of 86.3%, and took 68.3% less training time than the SegNet, which achieved a dice score of 81.1%. Conclusion: The two networks can segment ultrasound images with useful accuracy depending on their configuration, with U-Net being faster to train and more accurate. © 2021, Sociedade Brasileira de Engenharia Biomedica.

关键词： Semantic Segmentation

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Evaluation of Healthprompt for Zero-shot Clinical Text Classification

Evaluation of Healthprompt for Zero-shot Clinical Text Class...

引用

IEEE International Conference on Healthcare Informatics (ICHI)

作者： Sonish Sivarajkumar Yanshan Wang School of Computing and Information Intelligent Systems Program University of Pittsburgh Pittsburgh USA Department of Health Information Management School of Health and Rehabilitation Sciences University of Pittsburgh Pittsburgh USA

This paper presents an evaluation of the Healthprompt, a prompt-based zero-shot clinical text classification framework. The lack of publicly available datasets and the expensive data annotation in the clinical domain make traditional NLP models difficult to train. To overcome this issue, Healthprompt utilizes Pre-trained Language Models (PLMs) and prompt-based Zero-Shot Learning (ZSL) to perform clinical NLP tasks without additional training data. However, the original Healthprompt paper missed the error analysis and ablation study of the framework. This paper conducts an error analysis and an ablation study on Electronic Health Records(EHR) notes to understand the capabilities and limitations of the Healthprompt framework for clinical text classification. The results provide insight into the potential and limitations of prompt-based zeroshot learning for clinical NLP tasks and offer suggestions for improvements to the Healthprompt framework, and for the future development of prompt-based ZSL.

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Learning Adaptation and Generalization from Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Analysis 6

Learning Adaptation and Generalization from Human-Inspired M...

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6th IEEE International Conference on Artificial Intelligence and Knowledge Engineering, AIKE 2023

作者： Ho, Joshua Wang, Chien-Min King, Chung-Ta You, Yi-Hsin Feng, Chi-Wei Chen, Yen-Min Kuo, Bo-Yi Institute of Information Science Academia Sinica Taipei11529 Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu30013 Taiwan Department of Computer Science National Tsing Hua University Hsinchu30013 Taiwan Department of Computer Science National Taiwan University Taipei10631 Taiwan Center of Intelligent Healthcare National Taiwan University Hospital Taipei10022 Taiwan

ISBN: (纸本)9798350331288

Over the last decades, there has been growing interest in research in multiple and interdisciplinary fields of human-AI computing. In particular, approaches integrating the intersecting design with reinforcement learning (RL) have received more attention. However, the current research on RL may need to consider its enhancement from a humaninspired approach further. In the present work, we focus on enabling a meta-reinforcement learning (meta-RL) agent to achieve adaptation and generalization according to modeling Markov decision processes using Bayesian knowledge and analysis. By introducing a novel framework called human-inspired meta-RL (HMRL), we incorporate the agent performing resilient actions to leverage the dynamic dense reward based on the knowledge and prediction of a Bayesian analysis. The proposed framework can make the agent learn generalization and prevent the agent from failing catastrophically. The experimental results show that our approach helps the agent reduce computational costs with learning adaptation. Finally, we conclude and anticipate that integrating human-inspired meta-RL can enable learning more formulations relating to robustness and scalability, leading to promising directions and more complex AI goals in the future. © 2023 IEEE.

关键词： Reinforcement learning

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