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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Petousakis, Giannis Cangelosi, Angelo Stolkin, Rustam Chiou, Manolis University of Manchester Cognitive Robotics Lab Department of Computer Science United Kingdom University of Birmingham School of Metallurgy and Materials Extreme Robotics Lab United Kingdom Queen Mary University of London United Kingdom

ISBN: (纸本)9781665410205

This paper presents a novel method to quantify Trust in HRI. It proposes an HRI framework for estimating the Robot Trust towards the Human in the context of a narrow and specified task. The framework produces a real-time estimation of an AI agent's Artificial Trust towards a Human partner interacting with a mobile teleoperation robot. The approach for the framework is based on principles drawn from Theory of Mind, including information about the human state, action, and intent. The framework creates the ATTUNE model for Artificial Trust Towards Human Operators. The model uses metrics on the operator's state of attention, navigational intent, actions, and performance to quantify the Trust towards them. The model is tested on a pre-existing dataset that includes recordings (ROSbags) of a human trial in a simulated disaster response scenario. The performance of ATTUNE is evaluated through a qualitative and quantitative analysis. The results of the analyses provide insight into the next stages of the research and help refine the proposed approach. © 2024 IEEE.

关键词： Chatbots

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Time-series Analysis of Correlation between Climatic Parameters and Dengue Fever in Indonesia 4

Time-series Analysis of Correlation between Climatic Paramet...

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4th International Conference on Informatics, Multimedia, Cyber and Information System, ICIMCIS 2022

作者： Nirwantono, Rudi Trinugroho, Joko Pebrianto Sudigyo, Digdo Hidayat, Alam Ahmad Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Master of Computer Science Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665473279

Dengue fever is mainly caused by female mosquitoes from the genus Aedes. It predominantly occurs in equatorial to sub-tropical regions. The weather factors play a significant role in the mosquito life cycle, so a study on the impact of climate parameters on dengue incidence is required. This study aimed to understand the statistical association between the annual fluctuation of dengue cases with climate factors in Indonesia. Climatic data, annual rainfall, and annual mean ambient temperature of Indonesia were obtained from *** from 2010 to 2020, while the data on the annual incidence of dengue fever in Indonesia were collected from the Indonesian Health Profile by the Ministry of Health of Indonesia from 2010 to 2020. The association between climatic factors with dengue cases was calculated with Spearman statistical test after the data was indicated as not normally distributed. In addition, we performed Poisson regression analysis to examine how the changes in climatic parameters affect the dengue cases. The rain precipitation was insignificantly correlated with the dengue incidence in Indonesia during the study time. In contrast, the ambient temperature statistically showed a strong association with the dengue cases. The association between climatic factors and the number of dengue cases suggests that the model would likely be prospectively applied to the dengue early warning system as a nationwide intervention to reduce the epidemic impact in Indonesia. © 2022 IEEE.

关键词： Data mining

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Improving learning material repositories using student profiles

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Soft Computing 2025年第5期29卷 2417-2431页

作者： Silva Bravo, Natalie Ferraz Martins, André Ferreira de Souza Fonseca Rodrigues, Thales Brito Machado, Marcelo Bernardino, Heder Soares Vieira, Alex Borges Barbosa, Hélio José Corrêa de Souza, Jairo Francisco LApIC Research Group Federal University of Juiz de Fora Minas Gerais Juiz de Fora36036-900 Brazil Postgraduate Program in Computer Science Federal University of Juiz de Fora Minas Gerais Juiz de Fora36036-900 Brazil Postgraduate Program in Informatics Federal University of the State of Rio de Janeiro Rio de Janeiro22290-255 Brazil Department of Computer Science Federal University of Juiz de Fora Minas Gerais Juiz de Fora36036-900 Brazil

aaaa The adaptive learning community seeks to provide solutions to customize and enhance students’ learning experiences when accessing web-based learning systems. The adaptation usually occurs from the use of learning materials and user information data, which turns the adaptation process highly dependent on the quality of the repositories. Then, the best adaptation a system may offer might still not satisfy the users’ needs. In this work, we propose an approach to assist teachers and stakeholders in understanding repositories’ characteristics and their gaps according to students’ needs. Our approach, first, selects the best sequence of learning materials for each student, which is a well-known problem called Adaptive Curriculum Sequencing. Then, based on the selected sequences, we use optimization approaches, such as GRASP and Simulated Annealing, to generate new learning materials possibilities that can improve ACS recommendations. This way, our new approach assists teachers in assembling their learning materials. We have evaluated our approach by comparing it to a traditional approach using a real dataset, and the results are promising. In fact, it is possible to design customized materials using a combination of GRASP and brute force algorithms on the characteristics of the learning materials. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Federated learning

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Systematic Literature Review on Machine Learning Predictive Models for Indoor Climate in Smart Solar Dryer Dome 4

Systematic Literature Review on Machine Learning Predictive ...

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Setiawan, Karli Eka Elwirehardja, Gregorius Natanael Pardamean, Bens Bina Nusantara University BINUS Graduate Program Master of Computer Science Program Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

Precise predictions of indoor climate conditions are required in the implementation of Smart Solar Dryer Dome (SDD). Trend development of prediction models is discussed in this review from 15 selected research papers (2018-2022) on indoor climate prediction which was obtained from research paper databases The output shows that the most used model for predicting indoor climate is Artificial Neural Network (ANN), especially Recurrent Neural Network (RNN) such as LSTM and GRU. However, there are some potential methods such as Transformer, Combined Support Vector Machines (SVM)-Deep Learning, and sequence-to-sequence which could outperform other commonly used models. Based on findings various opportunities exist to improve the precision of indoor climate prediction, which can bring power consumption efficiency and others benefit to Smart SDD users. Such studies may further be explored to produce more accurate machine learning models. © 2022 IEEE.

关键词： Domes

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Trend Intelligent Tutoring System 2018-2022 : Systematic Literature Review 3

Trend Intelligent Tutoring System 2018-2022 : Systematic Lit...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Suwita, Jaka Prabowo, Harjanto Hendric Spits Warnars, Harco Leslie Meyliana, Meyliana Binus Graduate Program Computer Science Bina Nusantara University Departement of Computer Science Jakarta11480 Indonesia Stmik Insan Pembangunan Department of Information System Tangerang15810 Indonesia Binus Business School Undergraduate Program Bina Nusantara University Management Department Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350381931

ITS is a method of teaching that uses technology to give lessons without the need for an instructor to be there. A systematic literature review (SLR) was conducted in this research to identify the components of an intelligent tutoring system, and the results are presented. The method used in this literature review is the prism method. The steps in the prism method are slr protocol, search for literature, select literature, summarize evidence and disseminate results. The slr protocol stage, it consists of determining a research question, searching for scientific reference sources with the keyword intelligent tutoring system, then sorting out the appropriate articles and making final conclusions. This article gives a detailed look at how the intelligent tutoring system changed over time during the pandemic epoch. Our contribution is complete information on the components of ITS in times of epidemic at universities, with the goal of improving their learning and practical use of the technology. © 2024 IEEE.

关键词： Prisms

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The Impact of Recommendation System Provided Based on Online Learning Readiness in the MOOC Environment 24

The Impact of Recommendation System Provided Based on Online...

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Murad, Dina Fitria Toha, Mohamad Mayatopani, Hendra Dewi, Meta Amalya Leandros, Riyan Murad, Silvia Ayunda Heryadi, Yaya Wijanarko, Bambang Dwi Bina Nusantara University Information Systems Department Binus Online Learning Jakarta Indonesia Universitas Terbuka Jakarta Indonesia Pradita University Department of Information System Tangerang Indonesia Bina Nusantara University Computer Science Department Binus Graduate Program Doctor of Computer Science Jakarta Indonesia Bina Nusantara University School of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350384307

MOOCs are one of the developments or impacts of e-learning or online learning. Massive Open Online Course (MOOC) is one of the choices for students to increase their knowledge through non-formal channels. Our research aims to determine the level of readiness of students regarding MOOC learning and opportunities for utilizing recommendation systems in students' decision-making regarding selecting MOOCs that suit their interests and needs. We conducted a survey regarding the constraints and needs of learner readiness through 5 aspects, namely 1) technical aspect, (2) social aspect, (3) communication aspect, (4) lecture readiness aspect, and (5) online discussion. The results of this research show that the main challenges that MOOC participants may face in a MOOC environment are Technical Aspects (28.25%), Social Aspects (21.47%), and Communication Aspects (18.93%). Meanwhile, the greatest strength of online learning in the MOOC environment is the social aspect (21%), the most influential and continued with technical and other aspects. Meanwhile, the lecture readiness aspect is quite good, and the online discussion aspect is the biggest aspect that influences the success of MOOC learning. Other results of this research indicate the importance of a recommendation system for MOOC students to make it easier for students to choose the right MOOC according to their interests and needs. © 2023 IEEE.

关键词： MOOC personalization Recommendation system student competency Technology Enhanced Learning

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Intersectionality in Developing a Virtual Community of Practice Platform on Abortion 16

Intersectionality in Developing a Virtual Community of Pract...

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16th International Congress on Nursing Informatics, NI 2024

作者： Abdulai, Abdul-Fatawu Czerniak, Efrat Duong, Cam Mehta, Aashay Chiu, Rachel Stroulia, Eleni Norman, Wendy V. School of Nursing University of British Columbia Vancouver Canada Cognitive Sciences University of British Columbia Vancouver Canada Department of Computer Science University of Alberta Edmonton Canada Department of Family Practice University of British Columbia Vancouver Canada

ISBN: (纸本)9781643685274

Abortion is an essential healthcare service in many countries including Canada. The number of people who seek abortion is disproportionately higher among equity-deserving populations. Yet the knowledge needed to provide evidence-based, culturally safe, and gender-affirming abortion services remain limited among healthcare professionals. Using an intersectional lens, we conducted focus group discussions with 14 healthcare professionals to understand how an abortion web-based platform, which is currently under development, can be adapted to meet the needs of equity deserving populations. The findings revealed the need for multi-lingual resources on abortion, information on funding coverage for undocumented migrants, educational resources on Indigenous cultural safety and gender-affirming practices, and a mapping tool to locate providers or pharmacists. Beyond presenting clinical guidelines on web platforms, this study revealed important considerations for the design of web platforms that can help advance access to abortion for equity-deserving populations. © 2024 The Authors.

关键词： Websites

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Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation 23

Unsupervised Skill Discovery for Robotic Manipulation throug...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Jansonnie, Paul Wu, Bingbing Perez, Julien Peters, Jan Naver Labs Europe 6 chemin de Maupertuis Meylan38240 France Department of Computer Science Tu Darmstadt Germany Le Kremlin-BicêtreFR-94276 France Systems Ai for Robot Learning Germany Center for Cognitive Science Tu Darmstadt Germany Germany

ISBN: (纸本)9798350373578

Learning skills that interact with objects is of major importance for robotic manipulation. These skills can indeed serve as an efficient prior for solving various manipulation tasks. We propose a novel Skill Learning approach that discovers composable behaviors by solving a large and diverse number of autonomously generated tasks. Our method learns skills allowing the robot to consistently and robustly interact with objects in its environment. The discovered behaviors are embedded in primitives which can be composed with Hierarchical Reinforcement Learning to solve unseen manipulation tasks. In particular, we leverage Asymmetric Self-Play to discover behaviors and Multiplicative Compositional Policies to embed them. We compare our method to Skill Learning baselines and find that our skills are more interactive. Furthermore, the learned skills can be used to solve a set of unseen manipulation tasks, in simulation as well as on a real robotic platform. © 2024 IEEE.

关键词： Adversarial machine learning

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BERT base model for toxic comment analysis on Indonesian social media

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Procedia computer science 2023年 216卷 714-721页

作者： Ghinaa Zain Nabiilah Simeon Yuda Prasetyo Zahra Nabila Izdihar Abba Suganda Girsang Computer Science Department Binus Graduate Program Master of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

Social media is an online media that functions as a platform for users to participate, share, create, and exchange information through various forums and social networks. The rapid increase in social media activity causes an increase in the number of comments on social media. This is prone to triggering debate due to the easy formation of open discussions between social media users. However, the debate often triggers the emergence of negative things, causing great fights on social media. Social media users often use comments containing toxic words to argue and corner a party or group. This study conducted an experiment to detect comments containing toxic sentences on social media in Indonesia using a Pre-Trained Model that was trained for Indonesian. This study performed a multilabel classification and evaluated the classification results generated by the Multilingual BERT (MBERT), IndoBERT, and Indo Roberta Small models. The optimal result of this study is to use the IndoBERT model with an F1 Score of 0.8897.

关键词： Toxic Comment MBERT IndoBERT IndoRoBERTa Small Indonesian social media

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exploreCOSMOS Interactive Exploration of Conditional Statistical Shape Models in the Web-Browser

arXiv

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arXiv 2024年

作者： Hahn, Maximilian Egger, Bernhard Cognitive Computer Vision Lab Department of Computer Science Friedrich-Alexander-Universtitat Erlangen-Nürnberg Germany

Statistical Shape Models of faces and various body parts are heavily used in medical image analysis, computer vision and visualization. Whilst the field is well explored with many existing tools, all of them aim at experts, which limits their applicability. We demonstrate the first tool that enables the convenient exploration of statistical shape models in the browser, with the capability to manipulate the faces in a targeted manner. This manipulation is performed via a posterior model given partial observations. We release our code and application on GitHub https://***/maximilian-hahn/exploreCOSMOS. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

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