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Predicting Workload in Virtual Flight Simulations Using EEG Spectral and Connectivity Features

Predicting Workload in Virtual Flight Simulations Using EEG ...

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IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Bas Verkennis Evy van Weelden Francesca L. Marogna Maryam Alimardani Travis J. Wiltshire Max M. Louwerse Department of Cognitive Science and Artificial Intelligence Tilburg University Tilburg Netherlands Faculty of Science Computer Science Vrije Universiteit Amsterdam Amsterdam Netherlands

ISBN: (数字)9798331521578

ISBN: (纸本)9798331521585

Effective cognitive workload management has a major impact on the safety and performance of pilots. Integrating brain-computer interfaces (BCIs) presents an opportunity for real-time workload assessment. Leveraging cognitive workload data from high-fidelity virtual reality (VR) flight simulations allows for dynamic adjustments to training scenarios. While prior studies have predominantly concentrated on EEG spectral power for workload prediction, delving into intra-brain connectivity may yield deeper insights. This study assessed the predictive value of EEG spectral and connectivity features in distinguishing high vs. low workload periods during simulated flight in VR and Desktop conditions. Using an ensemble approach, a stacked classifier was trained to predict workload from the EEG signals of 52 participants. Results showed that the mean accuracy of the model incorporating both spectral and connectivity features improved by 28% compared to the model that solely relied on spectral features. Further research on other connectivity metrics and deep learning models in a large sample of pilots is essential to validate the potential of a real-time workload-prediction BCI. This could contribute to the development of an adaptive training system for safety-critical operational environments.

关键词： Training Solid modeling Adaptation models Accuracy Aerospace simulation Virtual reality Brain modeling Electroencephalography Real-time systems Brain-computer interfaces

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Assessing IT Job Candidates Through Interpretable Machine Learning Model

Assessing IT Job Candidates Through Interpretable Machine Le...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Andrew Sukardi Jayagatha Saputra Sebastian Hariman Alexander Agung Santoso Gunawan Karli Eka Setiawan Computer Science Department Data Science Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

The escalating demand for skilled IT professionals underscores the increasing significance of the recruitment process. Traditional methods often fall short in identifying individuals poised for success in the dynamic technology industry. This paper aims to address the challenges in recruiting proficient applicants by proposing an interpretable machine learning model for assessing IT job candidates. The study conducts experiments comparing various machine learning algorithms, including Logistic Regression, Random Forest, and Decision Tree, using data from the ‘IT job applicant’ public dataset. Evaluation methods such as the confusion matrix, accuracy, recall, and precision are employed to assess the model's performance. The findings reveal that the decision tree exhibits an accuracy exceeding 99.8%, establishing it as the top-performing model. The key contributing factor to this accuracy is identified as the 'computerSkills' feature. Additionally, within the decision tree, *** and Microsoft SQL Server are highlighted as influential factors positively impacting individual decision interpretation.

关键词： Industries Measurement Accuracy Systematics Computational modeling Decision making Servers Random forests Regression tree analysis Recruitment

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BERT and ULMFiT Ensemble for Personality Prediction from Indonesian Social Media Text

BERT and ULMFiT Ensemble for Personality Prediction from Ind...

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2022 International Symposium on Information Technology and Digital Innovation, ISITDI 2022

作者： Halimawan, Noptovius Suhartono, Derwin Gema, Aryo Pradipta Yunanda, Rezki Binus Graduate Program-Master Of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia School Of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Epigene Labs Paris France

ISBN: (数字)9781665461160

ISBN: (纸本)9781665461160

Predicting personality is a growing topic in the field of natural language processing. The study of personality prediction has been proven to benefit the development of recommender systems and automated personality assessments by previous studies. Additionally, the widespread usage of social media in Indonesia such as Twitter has served as a potential source of data for developing such models. Existing personality prediction models has explored the implementation of both traditional machine learning models and deep learning models, with the latter proven to perform better with more data. Despite so, there is not much development of deep learning-based personality prediction models in the domain of Indonesian text. We propose a deep learning-based ensemble model based on Universal Language Model Fine Tuning (ULMFiT) and Bidirectional Encoder Representations from Transformers (BERT), the state-of-the-art pre-trained models for text classification. The model is trained with Indonesian tweets labelled with Five Factor Model (FFM) personality traits along with the users' metadata. The model is tested with five predetermined scenarios to prove the robustness of its performance. The final evaluation of the model proves that it has successfully achieved optimal performance of 76.41 % macro-average accuracy and 75.55% macro-average F-1 score, exceeding the state-of-the-art BERT and ULMFiT © 2022 IEEE.

关键词： Classification (of information)

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Enhancing Tourism Industry Insights: LeveragingLDA and KMeans for Topic Extraction in Indonesian OTAs' Reviews

Enhancing Tourism Industry Insights: LeveragingLDA and KMean...

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International Conference of Information and Communication Technology (ICoICT)

作者： Laurentia Alyssa Castilani Natasha Hartanti Winata Nathania Christy Nugraha Alexander Agung Santoso Gunawan Karli Eka Setiawan Lili Ayu Wulandari Computer Science Department Data Science Program School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350363432

ISBN: (纸本)9798350363449

Indonesia's tourism sector, boosted by its captivating landscapes, has seen a rise in the popularity of Online Travel Agencies (OTAs) like Traveloka, ***, and Agoda. To effectively assist tourists, OTAs are anticipated to comprehend tourists' needs and interests, with insights gathered through scraping data from the Google Play Store. Through topic extraction from customers' reviews, this research aims to optimize the tourist experience and offer actionable insights for the tourism industry's marketing strategies. Furthermore, it endeavors to eliminate the reliance on brute force approaches in determining the candidate number of topics. To achieve the research goal, this research employs topic modeling, specifically Latent Dirichlet Allocation (LDA) enhanced through the K- means elbow method. The evaluation of the optimal topic number utilizes a coherence score, while human judgement serves as a quantitative metric for overall performance, with a remarkable validity rate across three platforms: Agoda (91%), *** (88%), and Traveloka (94%). This highlights the approach's effectiveness in accurately identifying and categorizing topics. In conclusion, the model accurately discerned essential topics, revealing that the majority of the reviews on these OTAs focus on the transaction and refund systems on each platform. Furthermore, this model offers promising recommendations to enhance the understanding and response to customers' reviews, facilitating faster and more effective responses. These insights are invaluable for continuous development and improvement strategies within the dynamic changes in the tourism industry across these influential platforms.

关键词： Measurement Adaptation models Analytical models Technological innovation Sentiment analysis Reviews Tourism industry Market research Real-time systems Resource management

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Optimizing Big Data Implementation to Create Business Value and Architecture Proposed in the Banking Industry: A Systematic Review

Optimizing Big Data Implementation to Create Business Value ...

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2023 International Conference on computer science, Information Technology and Engineering, ICCoSITE 2023

作者： Kristiana, Idha Meyliana, Meyliana Hidayanto, Achmad Nizar Prabowo, Harjanto Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Information Systems Information Systems Department Jakarta11480 Indonesia Universitas Indonesia Faculty of Computer Science Depok Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Management Departement Jakarta11480 Indonesia

ISBN: (纸本)9798350320954

The exponential growth of data is compelling organizations to employ data in decision-making. As one of the businesses with an ecosystem that contributes to data growth, banks have challenges in generating insight. A high level of data security is frequently linked to a high level of data access difficulties. This poses a challenge to the implementation of data-driven business, where taking control of our data is one of the best ways to ensure that we not only own data but also have the ability to process and use data to extract business value. Through literature review, a number of challenges to optimizing the implementation of big data analytics in the banking industry were discovered. Data governance refers to the methods and procedures that assist banks in managing and securing data. A big data architecture is presented to address the highlighted issues, particularly with a multi-tiered approach to big data structures. With the adoption of this architecture, it will be simpler to generate business-value-generating insights for the banking industry using big data with accessibility and protection of data. © 2023 IEEE.

关键词： Big data

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Development of social media opinion leaders during international periodic events 33

Development of social media opinion leaders during internati...

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33rd Australasian Conference on Information Systems: The Changing Face of IS, ACIS 2022

作者： Basyurt, Ali Sercan Brünker, Felix Stieglitz, Stefan Buhl, Florian Neuberger, Christoph Department of Computer Science and Applied Cognitive Science University of Duisburg-Essen Duisburg Germany Department of Political and Social Sciences Free University of Berlin Berlin Germany

Social networks like Twitter are a central communication channel, where people share their opinions, emotions, and views with the world which is also true during live TV events. These opinions are able to start a discussion and influence the opinion of others. This influence is able to impact the results of televised events such as the Eurovision Song Contest. In the process of sharing information, key groups could be able to be more effective and have a bigger reach at different points of an event. Therefore, their identification is relevant for research, event operators, participants, and fans. We aim to create an understanding of how groups and opinion leaders change over the event period of the contest by conducting a social network analysis based on Tweets. Word clouds will be used to confirm findings from the social network analysis as well as to identify topics within tweets for further research. Copyright © 2022 Ali Sercan Basyurt, Felix Brünker, Stefan Stieglitz, Florian Buhl, Christoph Neuberger.

关键词： Social networking (online)

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Evaluating Public Opinion on the 2024 Indonesian Presidential Election Candidate: An IndoBERT Approach to Twitter Sentiment Analysis

Evaluating Public Opinion on the 2024 Indonesian Presidentia...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Rafael Nicholas Tanaja Andrew Widjaya Johnny Alexander Agung Santoso Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

As social media significantly shapes societal norms and ethical paradigms, understanding real-time public sentiment provides valuable insights for political parties to evaluate their candidates, key issues, and campaign strategies. This study aims to gauge each presidential candidate's public sentiment from August 1, 2023, until October 31, 2023, using Twitter data. In this study, the authors would like to conduct an IndoBERT with a Fully Connected Layer to predict the sentiment analysis of Indonesian public sentiment toward the 2024 upcoming presidential election. The paper concluded that the IndoBERT model achieved an accuracy of 84.7%. In conclusion, the findings of this study lay the groundwork for more extensive analyses, enriching our comprehension of the intricate dynamics of public opinion in the political sphere. Furthermore, Sentiment Analysis is dynamic and will change as significant events occur. As we look to the future, integrating cutting-edge language models like IndoBERT with real-time social media data emerges as a transformative force in reshaping political analysis.

关键词： Sentiment analysis Analytical models Accuracy Social networking (online) Shape Voting Dynamics Blogs Data models Real-time systems

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Image-Based Natural Disaster Classification with Deep Learning: A Comparative Analysis

Image-Based Natural Disaster Classification with Deep Learni...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Samuel Eka Alamsyah Simen Ferdinand Djamhari Salomo Hendrian Sudjono Alexander A S Gunawan Karli Eka Setiawan Data Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350353464

ISBN: (纸本)9798350353471

Natural disasters, including earthquakes, cyclones, floods, and wildfires, cause significant environmental damage and have emerged as a major global issue. These events can result in loss of life and disrupt communities and economy. Effective disaster management relies on timely and accurate information. With social media becoming a primary source of real-time information during such events, many images of these disasters are shared, though some may not be accurately labeled. Therefore, the purpose of this study is classifying natural disaster images, mainly cyclone, wildfire, flood, earthquake from social media images. This paper is to conduct a comparative analysis over 4 models namely VGG16, VGG19, EfficientNetB0, and ResNet-50. This paper uses a dataset that contains 4428 human-annotated images including cyclones, earthquakes, floods, and wildfires. Results concluded that ResNet-50 and EfficientNetB0 give the best result with both got over 96% average accuracy. ResNet-50 delivers more stable and more convergent results compared to EfficientNetB0, but it delivers slightly less classification accuracy. VGG16 and VGG19 are both outclassed in terms of accuracy, but better in training time, with VGG19 having a more convergent result.

关键词： Training Accuracy Wildfires Disasters Computational modeling Biological system modeling Earthquakes

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Emotion Recognition from Eye Movements Using Multi-way Autoregressive Model

Emotion Recognition from Eye Movements Using Multi-way Autor...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Tian-Fang Liu, Xuan-Hao Zheng, Wei-Long Lu, Bao-Liang Shanghai Jiao Tong University Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai China

ISBN: (纸本)9798350386226

The application of physiological signals in emotion recognition is a popular research topic in human-computer interactions. Eye movement, as an important physiological signal, plays an essential role in medicine, psychology, cognitive science, and other scientific research fields. Previous studies have successfully identified human emotions by combining various eye-related measurements, selecting features, and utilizing machine learning techniques. However, the exploration of eye movement signals in emotion recognition remains insufficient. In this study, we utilize eye tracking heatmap and eye movement trajectory data for emotion recognition for the first time. Based on the two types of eye movement data, we develop a multi-way autoregressive model capable of processing multi-view eye movement data. Compared to traditional deep learning baseline models, our model better adapts to the structure of eye movement data and significantly improves the classification performance. Furthermore, we integrate heatmap and trajectory data with commonly used eye-related measurement features, which further enhance the performance of emotion recognition beyond previous methods. © 2024 IEEE.

关键词： Emotion Recognition

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A Design science Artefact for Cyber Threat Detection and Actor Specific Communication 33

A Design Science Artefact for Cyber Threat Detection and Act...

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33rd Australasian Conference on Information Systems: The Changing Face of IS, ACIS 2022

作者： Eyilmez, Kaan Basyurt, Ali Sercan Stieglitz, Stefan Fuchss, Christoph Kaufhold, Marc-André Reuter, Christian Mirbabaie, Milad Virtimo AG Berlin Germany Department of Computer Science and Applied Cognitive Science University of Duisburg-Essen Duisburg Germany Department of Computer Science Technical University of Darmstadt Darmstadt Germany Department of Information Systems Paderborn University Paderborn Germany

Over the past few decades, the number and variety of cyberattacks and malware patterns have increased immensely. As a countermeasure, computer emergency response teams were established with the responsibility of securing the cyber environment. However, recent studies revealed that currently performed manual processes and the unavailability of adequate tools impede the achievement of cybersecurity. To address these challenges, we followed the Design science paradigm to develop an artefact that improves the evaluation of open-source intelligence obtained from Twitter as well as the actor-specific communication of cyber threat information. Subsequently, the implemented artefact will be evaluated through semi-structured interviews with subject matter experts. This research in progress article presents the identified research gap and describes the development process and the endeavor to contribute to the cybersecurity domain theoretically with design principles for the development of an instrument and practically by implementing an artefact that supports domain experts in their work. Copyright © 2022 Kaan Eyilmez, Ali Sercan Basyurt, Stefan Stieglitz, Christoph Fuchss, Marc-André Kaufhold, Christian Reuter, Milad Mirbabaie.

关键词： Design

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