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2023 IEEE International Conference on Automatic Control and Intelligent systems, I2CACIS 2023

作者： De Jesus, Noelyn Castro, Eltimar T. Buyan, Cory Antonio Batangas State University College of Informatics & Computing Sciences Batangas Philippines Technological Institute of the Philippines Graduate Program Department Manila Philippines Ballard Power Systems Business Technology Burnaby Canada

ISBN: (纸本)9798350321302

Interactive Whiteboard (IWB) systems are increasingly being used to replace traditional whiteboards in educational activities, making them more efficient and interesting for both teachers and students. However, no studies have been conducted to investigate how age, educational level, and employment status can impact the adoption and usage of IWB systems in the educational setting. The study aims to analyze the acceptance and the influencing factors on faculty members' intention and usage behavior towards IWB systems in teaching, utilizing the Modified-Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. 135 faculty members from various colleges and departments across Batangas State University ARASOF-Nasugbu Campus were surveyed and data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS software for hypotheses testing. The results of the study indicate that faculty members' behavioral intention to use interactive whiteboard systems in teaching is positively influenced by performance expectancy and social influence. Moreover, facilitating conditions and habit positively impact usage behavior. This study, therefore, concludes that there is significant roles of performance expectancy and social influence as well as the moderating effect of age on the behavioral intention to use interactive whiteboard (IWB) systems in education. Furthermore, the study confirms that effort expectancy, hedonic motivation, and status as moderating variables significantly influence faculty members' usage behavior when using IWB systems in teaching. © 2023 IEEE.

关键词： Economic and social effects

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Evaluation of Healthprompt for Zero-shot Clinical Text Classification 11

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

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11th IEEE International Conference on Healthcare Informatics, ICHI 2023

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

ISBN: (纸本)9798350302639

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. © 2023 IEEE.

关键词： Learning systems

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Early Detection of Retinitis Pigmentosa 11

Early Detection of Retinitis Pigmentosa

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11th IEEE International Conference on Intelligent computing and Information systems, ICICIS 2023

作者： El Gazoly, Adham Mohammed Mahmoud, Karma Yasser Ismail El-Tohamy, Ahmed Hassan, Ali Ahmed Mohammed, Omar Mohammed Hamed Al-Berry, Maryam Nabil Mohammed, Hasnaa Sayed Abdo Al Amir, Yousra Samir Ain Shams University Faculty of Computer and Information Sciences Bioinformatics Program Information Systems Department Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Scientific Computing Department Cairo Egypt

ISBN: (纸本)9798350322101

Retinitis pigmentosa (RP) is a debilitating inherited retinal disorder that affects a significant number of individuals worldwide. Existing diagnostic methods have limitations in detecting early-stage RP and they highlight the need for using additional imaging modalities. In this paper, a groundbreaking approach for RP diagnosis is proposed using three imaging modalities and a CNN-based classification model achieving 97.7% accuracy. The proposed model demonstrates superior performance compared to previous work as it achieves higher accuracy. A segmentation model was also proposed to segment pigment spots and provide doctors with effective monitoring of the disease. The proposed segmentation model outperforms previously used methods by achieving 99.61% accuracy and higher values in most of the performance metrics, including specificity, precision, and Area Under Curve (AUC). The Segmentation and classification models were integrated into a web application providing a user-friendly and intuitive tool for RP diagnosis and paving the way for more accessible and effective diagnostic tools. This innovative approach democratizes healthcare by making RP diagnosis affordable and accessible to more people and improving the speed of clinical decision-making. © 2023 IEEE.

关键词： Semantics

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Confidentialchain: Enhancing the Security of a Notarization System Based on Blockchain 6

Confidentialchain: Enhancing the Security of a Notarization ...

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6th International Conference on Blockchain computing and Applications, BCCA 2024

作者： Cavalcante, Magno Alves Xexéo, José Antônio Moreira dos Santos, Anderson Fernandes Pereira Petróleo Brasileiro S.A. - PETROBRAS Rio de Janeiro Brazil Graduate Program in Systems and Computing Instituto Militar de Engenharia Rio de Janeiro Brazil Venturus Centro de Inovação Tecnológica Campinas Brazil

ISBN: (纸本)9798350351538

Blockchain platforms offer traceability, integrity, immutability and availability by native construction, providing resources for auditors to attest the security and temporal traceability of data stored in the blocks. However, permissioned blockchain platforms do not provide confidentiality and non-repudiation natively for sensitive user data. In this work, Confidentialchain is proposed as a hybrid cryptography schema to aggregate the characteristics of confidentiality and non-repudiation to a confidential data storage system, which is executed on a permissioned blockchain network, ensuring sufficient confidence for the subsequent secure use of sensitive user data that are stored on the blockchain network. © 2024 IEEE.

关键词： blockchain confidentiality hybrid cryptography non-repudiation notarization secure data schema

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An Approach to Detect Abnormal Submissions for CodeWorkout Dataset 8

An Approach to Detect Abnormal Submissions for CodeWorkout D...

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8th Educational Data Mining in Computer Science Education Workshop, CSEDM 2024

作者： Hicks, Alex Shi, Yang Lekshmi-Narayanan, Arun-Balajiee Yan, Wei Marwan, Samiha Dept of Computer Science Virginia Tech Blacksburg VA Dept of Computer Science Utah State University Logan UT Intelligent Systems Program University of Pittsburgh Pittsburgh PA School of Informatics Computing and Cyber Systems North Arizona University Flagstaff AZ Dept. of Computer Science University of Virginia wCharlottesville VA

Students’ interactions while solving problems in learning environments (i.e. log data) are often used to support students’ learning. For example, researchers use log data to develop systems that can provide students with personalized problem recommendations based on their knowledge level. However, anomalies in the students’ log data, such as cheating to solve programming problems, could introduce a hidden bias in the log data. As a result, these systems may provide inaccurate problem recommendations, and therefore, defeat their purpose. Classical cheating detection methods, such as MOSS, can be used to detect code plagiarism. However, these methods cannot detect other abnormal events such as a student gaming a system with multiple attempts of similar solutions to a particular programming problem. This paper presents a preliminary study to analyze log data with anomalies. The goal of our work is to overcome the abnormal instances when modeling personalizable recommendations in programming learning environments. © 2024 Copyright for this paper by its authors.

关键词： CS1 Dataset Cleaning Dataset Standards Educational Data Mining Introductory programming

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Analysis of User Experience on Short Video Services: Instagram Reels and Tiktok Comparison

Analysis of User Experience on Short Video Services: Instagr...

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International Conference on Information Management and Technology (ICIMTech)

作者： Michael Siek Fakhran Hartmanda Fariz Information Systems Department Business Information Systems Program School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

Rapid development of internet and technologies has contributed much to the quality of human's daily life. One of the breakthrough innovations is the implementation of the video streaming platform distributed over internet. With the resurgence of online video streaming platform, sharing video has never been easier. Therefore, users can easily share, comment, and like other video that different users uploaded. Brand awareness and customer trust could be significantly enhanced by means of short form videos as elements of online user experiences. Nowadays, there are many short form video streaming platforms on the internet like Instagram Reels and TikTok. This paper discusses the importance of user experience on Instagram Reels and TikTok. Every application needs to have some uniqueness so they can stand out and keep retention of their users. The User Experience Honeycomb was utilized in the form of quantitative research as a factor the user experience influencing on the user satisfaction. The major factors effecting user satisfaction were identified to help Instagram Reels and TikTok improve their user experience.

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Extraction of c- F os Protein Regions from Mouse Brain Slice Images using Multi-channelization, 1 x 1 Convolution and U -Net 13

Extraction of c- F os Protein Regions from Mouse Brain Slice...

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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： Wang, Ziyue Okamura, Hibiki Luo, Shijie Hayashi, Yu Takizawa, Hotaka Master's Program in Computer Science University of Tsukuba Tsukuba Japan International Institute for Integrative Sleep Medicine University of Tsukuba Ph.D. Program in Humanics Tsukuba Japan International Institute for Integrative Sleep Medicine University of Tsukuba Tsukuba Japan Institute of Systems and Information Engineering Academic Computing and Communications Center University of Tsukuba Tsukuba Japan

ISBN: (纸本)9798350373332

This study proposed a novel extraction method of c-Fos protein regions in DAB(3,3'-diaminobenzidine)-stained mouse brain slice images using the U-Net model combined with the multi-channelization and 1×1 convolution techniques. Our U-Net m odel, called 1× 1 conv U-Net, was applied to three DAB- strainedmouse slice images whose resolutions were 1000x1000 pixels. The experimental results demonstrated that the 1×1 conv U-Net outperformed the conventional U-Net. © 2024 IEEE.

关键词： Mammals

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TERMS: textual emotion recognition in multidimensional space

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Applied Intelligence 2023年第3期53卷 2673-2693页

作者： Ghafoor, Yusra Jinping, Shi Calderon, Fernando H. Huang, Yen-Hao Chen, Kuan-Ta Chen, Yi-Shin Social Networks and Human-Centered Computing Taiwan International Graduate Program Institute of Information Science Academia Sinica. Institute of Information Systems and Applications National Tsing Hua University Hsinchu City Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu City Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu City Taiwan

Microblogs generate a vast amount of data in which users express their emotions regarding almost all aspects of everyday life. Capturing affective content from these context-dependent and subjective texts is a challenging task. We propose an intelligent probabilistic model for textual emotion recognition in multidimensional space (TERMS) that captures the subjective emotional boundaries and contextual information embedded in a text for robust emotion recognition. It is implausible with discrete label assignment;therefore, the model employs a soft assignment by mapping varying emotional perceptions in a multidimensional space and generates them as distributions via the Gaussian mixture model (GMM). To strengthen emotion distributions, TERMS integrates a probabilistic emotion classifier that captures the contextual and linguistic information from texts. The integration of these aspects, the context-aware emotion classifier and the learned GMM parameters provide a complete coverage for accurate emotion recognition. The large-scale experimentation shows that compared to baseline and state-of-the-art models, TERMS achieved better performance in terms of distinguishability, prediction, and classification performance. In addition, TERMS provide insights on emotion classes, the annotation patterns, and the models application in different scenarios. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Recognition

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Aspect Based Sentiment Analysis: Restaurant Online Review Platform in Indonesia with Unsupervised Scraped Corpus in Indonesian Language 1

Aspect Based Sentiment Analysis: Restaurant Online Review Pl...

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1st International Conference on Computer Science and Artificial Intelligence, ICCSAI 2021

作者： Tedjojuwono, Samuel Mahatmaputra Neonardi, Clement Bina Nusantara University Business Information Systems Program Faculty of Computing and Media Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

The paper has designed a dynamic dashboard that will show a summarized information of restaurants in Indonesia on four distinct metrics which are Food, Service, Ambience and Covid Safety. Each metrics shown will have their own ratings which shows the detailed score for each aspect of the restaurant. The data inside the dashboard have been developed by using semi supervised learning of aspect-based sentiment analysis approach. The idea is to analyze past reviews/comments of each restaurant in the current restaurant's online review platform and extract the sentiment as well as the aspect of each of the reviews. The restaurant lists and the reviews have been collected through web scraping method on one of the most used online review platforms in Indonesia which is Tripadvisor. Scraped data has been cleaned through several process of data pre-processing by utilizing Sastrawi and NLTK library for Indonesian languages. The machine learning tools that will extract the aspect and sentiments in every of the reviews will be built by applying Monkeylearn machine learning platform through APIs. Cleaned datasets have been imported into the platform for data annotations of model training to identify the set of words belongs in each aspect categories as well as their sentiment values. Although after reaching the end of the analysis, this paper has concluded that accuracy of the analysis may not be ideal due to lack of negative sentiment dataset being gathered which affects the model during the training process. In conclusion, the feature has successfully been built and implemented as well as deployed into a web server which supported by Ngrok services however, there are still more room for improvement regarding the analysis of the model. © 2021 IEEE.

关键词： Sentiment analysis

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Converging on mutual harmony of knowledge-based expert system and technology consultant 2

Converging on mutual harmony of knowledge-based expert syste...

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2nd International Conference on Biospheric Harmony Advanced Research, ICOBAR 2020

作者： Siek, M. Urian, T.W. Business Information Systems Program Information Systems Department Faculty of Computing Media Bina Nusantara University Jakarta11480 Indonesia

In the Society 5.0, the adoption of advanced technologies in many fields has large positive impacts on the way people think and behave as well as how the companies provide services for any other businesses. In adopting new technology, however, the businesses require significant efforts and deep insights on how to implement such technology that offers large benefits for their businesses. This attempt is typically in line with the one conducted by many consulting firms which provide solution services on how the new innovation and technologies can be implemented and aligned with business goals. With regards to that, this paper focuses on the implementation of a knowledge-based expert system as a technology consultant to recommend suitable technological solutions in supporting the existing teaching and learning activities at educational institutions. A number of tasks have been executed in the research in order to build a reliable expert system, such as properly using design knowledge ontology, formulating if-then rules and reasoning, expert system structure and user interface, and so forth. The resulting knowledge-based expert system demonstrates promising performance and permits to its integration with machine learning techniques for automated knowledge acquisition. This knowledge-based expert system can be a mutual supports in harmony to the human consultants on providing their technology consultancy services. © Published under licence by IOP Publishing Ltd.

关键词： Expert systems

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