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2nd Workshop on Sentiment Analysis and Linguistic Linked Data, SALLD 2022

作者： McNamee, Brad Varde, Aparna S. Razniewski, Simon Computational Linguistics Program Montclair State University MontclairNJ United States Department of Computer Science Montclair State University MontclairNJ United States Max Planck Institute for Informatics Saarbrucken Germany Max Planck Institute for Informatics Germany

ISBN: (纸本)9791095546764

As climate change alters the physical world we inhabit, opinions surrounding this hot-button issue continue to fluctuate. This is apparent on social media, particularly Twitter. In this paper, we explore concrete climate change data concerning the Air Quality Index (AQI), and its relationship to tweets. We incorporate commonsense connotations for appeal to the masses. Earlier work focuses primarily on accuracy and performance of sentiment analysis tools/models, much geared towards experts. We present commonsense interpretations of results, such that they are not impervious to the masses. Moreover, our study uses real data on multiple environmental quantities comprising AQI. We address human sentiments gathered from linked data on hashtagged tweets with geolocations. Tweets are analyzed using VADER, subtly entailing commonsense reasoning. Interestingly, correlations between climate change tweets and air quality data vary not only based upon the year, but also the specific environmental quantity. We anticipate that this study will shed light on possible areas to increase awareness of climate change, and methods to address it, by the scientists as well as the common public. In line with Linked Data initiatives, we aim to make this work openly accessible on a network, published with the Creative Commons license. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.

关键词： Linked data

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 2824-2835页

作者： Chiu, Ching-Yu Muller, Meinard Davies, Matthew E. P. Su, Alvin Wen-Yu Yang, Yi-Hsuan National Cheng Kung University Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701401 Taiwan International Audio Laboratories Erlangen Erlangen91058 Germany University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems Coimbra3004-531 Portugal National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Taiwan AI Labs Yating Music Team Taipei103622 Taiwan Academia Sinica Research Center for IT Innovation Taipei11529 Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called 'predominant local pulses' (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2014 IEEE.

关键词： Hidden Markov models

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Feature Weighting Optimization: Genetic Algorithms and Random Forest for Classification of Pregnant Potential Risk

Feature Weighting Optimization: Genetic Algorithms and Rando...

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2022 International Conference on Information Technology Research and Innovation, ICITRI 2022

作者： Ramdhani, Yudi Maulidia, Dea Setiadi, Ahmad Alamsyah, Doni Purnama Universitas Adhirajasa Reswara Sanjaya Informatics Engineering Program Bandung Indonesia Universitas Adhirajasa Reswara Sanjaya Computer Science Program Bandung Indonesia Universitas Bina Sarana Informatika Information System Program Jakarta Indonesia Bina Nusantara University Entrepreneurship Department Jakarta Indonesia

ISBN: (数字)9781665461849

ISBN: (纸本)9781665461849

Many pregnant women still do not want to check their pregnancy to recognize the signs of high-risk pregnant women. High-risk pregnancies refer to abundant women with several risky conditions requiring special care during and after delivery. This study aims to predict pregnancy risk levels with early detection efforts. The method used in this research is the Random Forest method for classification and the Genetic Algorithm for optimization. The results obtained from the Maternal Health Risk dataset analysis using the Random Forest method get an accuracy value of 73.37%. After optimizing the features using a genetic algorithm, the accuracy value in the classification of the health risk level of pregnant women has increased significantly to 90.20%. The results of the t-Test in this study showed a value of less than 0.05, and the t-Test value obtained is 0.000218269. That is, the genetic algorithm has excellent performance to improve the performance of the classification algorithm. Based on the results of the study to get a good accuracy value, it can be used by medical personnel to detect the level of health risk in pregnant women. It can be a reference material for further research to implement this research into a program. © 2022 IEEE.

关键词： Genetic algorithms

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A Study on Text Feature Selection Using Ant Colony and Grey Wolf Optimization 7

A Study on Text Feature Selection Using Ant Colony and Grey ...

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7th International Conference on informatics and Computing, ICIC 2022

作者： Widians, Joan Angelina Wardoyo, Retantyo Hartati, Sri Universitas Gadjah Mada Doctoral Program Dept. of Computer Science and Electronics Indonesia Universitas Mulawarman Faculty of Engineering Dept. of Informatics Samarinda Indonesia Universitas Gadjah Mada Faculty of Mathematics and Natural Sciences Dept. of Computer Science and Electronics Yogyakarta Indonesia

ISBN: (纸本)9798350345711

Text classification (TC) is widely used for organizing digital documents. The issues in TC are numerous characteristics and high-element dimensions. Many pattern classification issues require feature selection (FS), which is pertinent. FS removes unneeded and redundant data from the dataset. The Ant Colony Optimization (ACO) and Grey Wolf Optimizer (GWO) for FS are the main topics of our thorough assessment of the literature on the Swarm Intelligence (SI) algorithm. Furthermore, it illustrates how the hybrid SI technique is used in FS across various sectors. The hybrid SI technique uses applicable data from various FS methods to find feature subsets with smaller sizes and better classification performance than those found by regular FS algorithms. © 2022 IEEE.

关键词： Ant colony optimization

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Blockchain-based Smart Contract in Service-Oriented Business Processes 7

Blockchain-based Smart Contract in Service-Oriented Business...

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7th International Conference on Information Technology Systems and Innovation, ICITSI 2022

作者： Parjuangan, Sabam Suhardi Nughara, I Gusti Bagus Baskara Khoirunnisa, Elvin Doctoral Program of School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung40132 Indonesia School of Electrical Engineering and Informatics Institut Teknologi Bandung Bandung40132 Indonesia University of Twente Faculty of Electrical Engineering Mathematics Computer Science Enschede Netherlands

ISBN: (数字)9781665465557

ISBN: (纸本)9781665465557

Service systems' business processes show the existence of mutual interaction between users. Running a particular business subdomain requires the role of each user. This role is in line with the concept contained in blockchain-based smart contracts. However, determining the subdomains that require smart contract coding is challenging in developing business processes that will run on the blockchain. We aim to determine the service-oriented business process (in the case of marketplace service) and the subdomain of the business process that requires smart contract coding. This article is essential because it shows how developing IT services without a smart contract differs from developing IT services with a smart contract. The main differences between developing services that need smart contracts and those that do not are that certain process can be run automatically and the record of activity in blockchain cannot be changed by anyone (tamper-proof). We outlined methods for designing marketplace business processes and defined subdomains to be encoded into smart contract code. After reviewing the three marketplace platforms, we determined eight subdomains of the marketplace business process. Two subdomains require smart contract coding from eight, namely the payment and complaint subdomains. © 2022 IEEE.

关键词： Smart contract

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Prediction Tool on Fine Particle Pollutants and Air Quality for Environmental Engineering

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SN computer science 2022年第3期3卷 184页

作者： Varde, Aparna S. Pandey, Abidha Du, Xu Dept. of Computer Science Environmental Science and Management PhD Program Montclair State University Montclair NJ United States Visiting Researcher at Max Planck Institute for Informatics Saarbrücken Germany Department of Computer Science Montclair State University Montclair NJ United States Department of Earth and Environmental Science Environmental Science and Management PhD Montclair State University Montclair NJ United States

This article focuses on the research, design and implementation of a prediction tool for air quality to estimate pollutant concentrations, contributing to environmental engineering. It addresses prediction of fine particle air pollutants of diameter less than 2.5 µm (particulate matter 2.5), their concentration being substantially influenced by urban traffic. We collect worldwide multicity data from health-related public sources on which mining is performed using classical data mining/machine learning paradigms: association rules, clustering and classification. Challenges include adapting appropriate techniques based on data, and capturing subtle domain-specific aspects. The prediction tool is built using knowledge discovered by mining, leveraging health standards, catering to novice, intermediate and expert users. The prediction output is accurate, efficient, interpretable and useful as evident from our experiments. The tool is helpful for urban decision support. This work is beneficial in developing software systems such as intelligent tutors, mobile device apps and smart city tools. It contributes to smart environment, mobility and living, making a positive impact on smart cities and sustainability. In this work, we claim that classical computational paradigms in their fundamental form can be adapted to solve environmental engineering problems, with easy comprehension, as per the Occam’s razor principle that advocates simplicity. This article constitutes applied research: using computational techniques to solve domain-specific problems. Future work includes exploring models in deep learning such as CNN and Bi-LSTM, and considering different types of pollutants as well as other sources besides multicity traffic data, to conduct further studies. This would address additional challenges with enhancements. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Applied research Decision support Environmental engineering Health informatics Machine learning Urban studies

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Leveraging EEG Signals and Machine Learning for Schizophrenia Classification

Leveraging EEG Signals and Machine Learning for Schizophreni...

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Computing and informatics (ICCI), International Conference on

作者： Abdelrahman Elfarsy Sara El-Metwally Medical Informatics Program Faculty of Computers and Information Mansoura University Mansoura Egypt Computer Science Department Faculty of Computers and Information Mansoura University Mansoura Egypt

Schizophrenia is a neurological disorder known for its potential to disrupt brain function and cause erratic behavior. Timely diagnosis and intervention are crucial for improving patient outcomes. This paper conducts a comprehensive comparative study of three machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) to classify EEG signals associated with schizophrenia. The dataset utilized in this study comprises EEG data obtained from 28 individuals, data preprocessing techniques, including artifact removal, filtering, and normalization, were applied to enhance data quality. A set of informative statistical features was extracted from the EEG signals to capture relevant information. The three machine learning models are trained and evaluated using various performance metrics, including accuracy, precision, recall, F1-score, and Area Under the Curve (AUC). Random Forest achieved the highest accuracy (96 % ), while SVM demonstrated strong precision and recall (95 % ). These findings highlight the potential of machine learning in aiding early schizophrenia diagnosis through EEG signal analysis.

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Bloom-epistemic and sentiment analysis hierarchical classification in course discussion forums

arXiv

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arXiv 2024年

作者： Toba, Hapnes Hernita, Yolanda Trixie Ayub, Mewati Wijanto, Maresha Caroline Master of Computer Science Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia Bachelor of Informatics Study Program Faculty of Information Technology Maranatha Christian University Bandung Indonesia

Online discussion forums are widely used for active textual interaction between lecturers and students, and to see how the students have progressed in a learning process. The objective of this study is to compare appropriate machine-learning models to assess sentiments and Bloom's epistemic taxonomy based on textual comments in educational discussion forums. The proposed method is called the hierarchical approach of Bloom-Epistemic and Sentiment Analysis (BE-Sent). The research methodology consists of three main steps. The first step is the data collection from the internal discussion forum and YouTube comments of a Web programming channel. The next step is text preprocessing to annotate the text and clear unimportant words. Furthermore, with the text dataset that has been successfully cleaned, sentiment analysis and epistemic categorization will be done in each sentence of the text. Sentiment analysis is divided into three categories: positive, negative, and neutral. Bloom's epistemic is divided into six categories: remembering, understanding, applying, analyzing, evaluating, and creating. This research has succeeded in producing a course learning subsystem that assesses opinions based on text reviews of discussion forums according to the category of sentiment and epistemic analysis. © 2024, CC BY-SA.

关键词： Sentiment analysis

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Utilization of Linguistic Data for Learner Assessment on e-Learning: Instrument and Processing 7

Utilization of Linguistic Data for Learner Assessment on e-L...

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7th International Conference on informatics and Computing, ICIC 2022

作者： Yuniarti, Wenty Dwi Hartati, Sri Priyanta, Sigit Surjono, Herman Dwi Doctoral Program of Computer Science Universitas Gadjah Mada Yogyakarta Indonesia Universitas Islam Negeri Walisongo Semarang Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia Universitas Negeri Yogyakarta Faculty of Engineering Department of Informatics Engineering Education Yogyakarta Indonesia

ISBN: (纸本)9798350345711

The increasingly massive use of e-Learning illustrates the speed and need for innovation in learning. According to the National Higher Education Standards (SN-Dikti), constructive alignment is required between learning outcomes, processes, and assessments to properly implement learning in e-Learning. Assessment is an essential component of e-Learning. Unfortunately, the problem of assessment in e-Learning is still found. One of them is the limitations of e-Learning in accommodating and processing various assessment data, primarily linguistic. Even though the variety of assessment data, both numerical, linguistic, and a combination of the two, supports a comprehensive assessment. On the other hand, the accommodation of linguistic data raises problems regarding how to process of unifying linguistic data is carried out. Research related to linguistic data using computing with words has been carried out, but it still needs more precise results from the unification of the linguistic data. Therefore, this study proposes providing an assessment instrument to accommodate linguistic data in e-Learning, as well as showing how to process of unifying linguistic data is carried out using 2-Tuple Fuzzy Linguistic. This approach can avoid the loss of assessment information by presenting more informative and precise results in a 2-tuple (s, α) where s indicates the ability level, and α shows a comparison of abilities with other learners and the potential of the learner to achieve higher abilities. This proposal has the potential to be applied in a learner assessment system for higher education e-Learning. © 2022 IEEE.

关键词： Linguistics

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Analyzing the Effectiveness of Smartphone Applications on Disease Management and Healthcare Systems

Analyzing the Effectiveness of Smartphone Applications on Di...

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International Conference on Application of Information and Communication Technologies (AICT)

作者： Rabei Raad Ali Wisam Subhi Al-Dayyeni Christy Atika Sari Eko Hari Rachmawanto Candra Irawan Omar M. Jihad Department of Computer Engineering Technology Northern Technical University Mosul Iraq School of Information Technologies and Engineering ADA University Baku Azerbaijan Study Program in Informatics Engineering Faculty of Computer Science University of Dian Nuswantoro Semarang Indonesia Study Program in Information Systems Faculty of Computer Science University of Dian Nuswantoro Semarang Indonesia

ISBN: (数字)9798350387537

ISBN: (纸本)9798350387544

In today’s complex global environments, it is highly challenging to achieve effective healthcare management. The critical challenges faced by healthcare management are being met by the revolution of m-health where mobile cloud computing plays a major role. The taxonomy of mobile computing comprises operational aspects, end-user issues, and service quality and mobility management. The use of smartphone technologies and applications has become a highly significant approach to improving healthcare management. Mobile health services such as mobile pathology, mobile neurosurgery, cancer treatment, and behavioral/psychological disorders are gaining significance where smartphone applications are being used. Portability, flexibility, and convenience are major characteristics of mobile computing that have helped patients and doctors to develop better relationships through coordination and communication. The relationship between smartphone technologies and applications and healthcare management can be understood in several broad aspects. This article aims to analyze the current and future implications of these technologies on healthcare and disease management systems.

关键词： Pathology Cloud computing Taxonomy Cancer treatment Medical services Benchmark testing Neurosurgery Information and communication technology Mobile computing Diseases

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