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Joint Ontology Workshops 2023, Episode IX: The Quebec Summer of Ontology, JOWO 2023

作者： Ravenschlag, Anna Löhnert, Bianca Guizzardi, Giancarlo da Silva Teixeira, Maria das Graças Denissen, Monique Hutzler, Florian University of Salzburg Department of Psychology Centre for Cognitive Neuroscience Austria University of Salzburg Department of Computer Science Database Research Group Austria University of Twente Department of Mathematics and Computer Science Netherlands Universidade Federal do Espírito Santo Departamento de Computação e Eletrônica Brazil

cognitive neuroscience is a data-intensive and theory-driven discipline that seeks to explain how human experience and behavior is related to physiological, behavioral, and neural measurements. The investigated cognitive concepts (e.g., memory or attention) are, however, latent, unobservable constructs that are assessed via observable and objectifiable measurements obtained in carefully designed experimental settings. Because their definitions and assumed interrelations may vary depending on the underlying cognitive theories, cognitive concepts must be defined and interpreted in the context of those theories. For communication, however, the cognitive neuroscience community is accustomed to use the same linguistic terms for denoting cognitive concepts that have varying definitions in different theories, effectively introducing terminological ambiguity. An ontology for the domain of cognitive neuroscience thus needs to be capable of representing these varying definitions of cognitive concepts that depend on different theories while preserving the relation to their linguistic terms to meet the communication needs of the community. To address this problem, we propose a cognitive Theory Ontology (CoTOn) that provides the means to represent and relate 1. the objectifiable knowledge about observable entities of the experimental setting, 2. the theory-dependent conceptualizations of latent cognitive concepts, and 3. the community-specific use of the same linguistic terms for differently defined cognitive concepts. In this paper, we ontologically analyse the relevant entities in the cognitive neuroscience domain and derive a reference model and an operational version of CoTOn on the level of general types. We implement this initial version of CoTOn in Protégé and show its applicability for retrieving objectifiable as well as theory-dependent knowledge. © 2023 Copyright for this paper by its authors.

关键词： Ontology

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Cybersecurity Detection Using Neural Networks for Chemical Industry 1

Cybersecurity Detection Using Neural Networks for Chemical I...

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1st International Conference on Technology, Engineering, and Computing Applications, ICTECA 2023

作者： Pratama, Ilham Fitrah Eka Mantoro, Teddy Wijayanti, Rika Persadha, Pratama Dahlian Postgraduate Program of Medical Intelligence National Intelligence College Bogor Indonesia Sampoerna University Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350360554

Cybersecurity threats affect all sectors of the economy, especially the chemical industry. The chemical industry is a critical infrastructure vulnerable to cyberattacks. In addition to financial and reputational losses, they can also cause serious accidents (such as loss of control of hazardous materials) with serious consequences for workers, society, and the environment. Industrial control panel systems are typically embedded in a variety of physical devices. The safety of control systems is crucial for ensuring the secure functioning of industrial chemical processes. Nevertheless, the susceptibility of control systems to cyber-attacks has intensified due to the extensive deployment of actuators, sensor measurements, and wireless communications. These attacks have the potential to propagate swiftly and result in severe industrial accidents. This study was conducted as part of a systematic literature review. The purpose of this study is to analyze cybersecurity threats to the chemical industry. Research shows that although cyber attacks are highly vulnerable to the chemical industry, they can be detected through neural network-based integration (NN) without having to know the process model when large quantities of historical data are available and easy to apply. It has been shown that it can be done. It is hoped that the policy recommendations obtained from this research will provide more effective guidelines for protecting the chemical industry from the risks of cyber threats. © 2023 IEEE.

关键词： Chemical industry

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Comparison of LSTM and Transformer for Time Series Data Forecasting

Comparison of LSTM and Transformer for Time Series Data Fore...

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International Conference on Informatics and Computational sciences (ICICoS)

作者： Ilvico Sonata Yaya Heryadi Computer Science Department Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350375886

ISBN: (纸本)9798350375893

Time series data is data that is collected periodically and has certain time intervals. Time series data is widely available in the fields of finance, meteorology, signal processing, health, and economics. Weather data, stock prices, and sales data are examples of time series data. Time series data analysis can be used to predict future conditions based on patterns and values from previous data through a forecasting process. These forecasting results are useful for identifying trends and data patterns for decision making. The rapid development of deep learning models can now be used as a method for forecasting time series data. Deep learning models that are suitable for forecasting include sequence-to-sequence models such as Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Transformer. LSTM and Transformer are sequence-to-sequence models that are widely used for forecasting time series data. Research to compare the accuracy of the two models is still very limited. This article will discuss the use of the LSTM and Transformer models for the time series data forecasting process using Hewlett Packard stock price data from 1962 to 2022. The accuracy results show that the LSTM model outperforms the Transformer model in forecasting Hewlett Packard stock prices.

关键词： Accuracy Recurrent neural networks Biological system modeling Time series analysis Predictive models Transformers Data models

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The Role of Biosafety and Biosecurity in Biotechnology to Prevent Bioterrorism Threats 1

The Role of Biosafety and Biosecurity in Biotechnology to Pr...

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1st International Conference on Technology, Engineering, and Computing Applications, ICTECA 2023

作者： Subariyah, Riski Mantoro, Teddy Ratmono, Barito Mulyo Postgraduate Program of Medical Intelligence National Intelligence College Bogor Indonesia Sampoerna University Department of Computer Science Jakarta Indonesia

ISBN: (纸本)9798350360554

'Society 5.0' is characterized as a high degree of integration of cyberspace and physical space capable of promoting economic growth and addressing social problems in a modern information society based on cutting-edge technology advancements for data processing and knowledge creation. Recent developments in the field of biotechnology have been demonstrated by gene editing techniques, especially CRISPR-Cas9, such as basic research, applied biotechnology, and biomedical research Most biotechnology is essentially 'dual use' because its development can result in both profitable and nefarious goals, all depending on the perspective and motivation of the perpetrator. The purpose of this research is a better understanding of biosecurity, biosecurity, and bioterrorism threats, so as to provide a foundation for policymakers, researchers, and health professionals to improve global health security so as to realize effective biodefense systems. The research was conducted by library research, namely data collection with literature studies. By implementing biosafety and biosecurity laboratory measures in accordance with established guidelines and guidelines can help protect people, animals, the environment, and national security. Medical intelligence is vital to monitoring and assessing the threat of bioterrorism through the process of data collection, which is then analyzed, and data dissemination. The medical intelligence community can help protect public safety and prevent bioterrorism by understanding and implementing strong biosafety and biosecurity practices in laboratories. Bioterrorism attacks that can occur at any time must be anticipated by medical intelligence by conducting research and assessment of bioterrorism threats, analyzing information so that it can be passed on to the government to take appropriate policies to be able to detect and monitor the spread of infectious diseases quickly, and developing strategies to prevent the spread of disease. Combining health s

关键词： Bioterrorism

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Sentiment Analysis of Hashtag Activism on Social Media Twitter

Sentiment Analysis of Hashtag Activism on Social Media Twitt...

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

作者： Sidharta, Sidharta Heryadi, Yaya Lukas, Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University BINUS Graduated Program Computer Science Computer Science Department Jakarta11480 Indonesia Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Currently, the performance of the police in Indonesia is often in the spotlight of the public with cases that occur, both on a national and regional scale, including personal experiences who also feel disappointed with the performance of the police. The cases, such as the rape of three children by a biological father in East Luwu, and the barrage of other cases that have occurred, in their handling have left the public disappointed, giving rise to the hashtag percumlaporpolisi on social media, especially on twitter. This hashtag became trending and was widely responded to by the public. Using free hashtag keywords, tweet data is collected using Twitter's API for sentiment analysis. The datasets obtained from the crawling results is the latest data with a total of 100 datasets. This study applies the naïve bayes algorithm using textblob which aims to determine the value of polarity and subjectivity. From the results of the analysis carried out, the words that appear most often are useless police, police chief, and policeman. While the results of the sentiment analysis that has been carried out are 40 percent positive sentiment, 40 percent are neutral sentiment and 10 percent are negative sentiment from the hashtags used. From these results, according to the keywords used, the percentage of negative sentiment should be greater than positive and neutral sentiment. It is necessary to conduct a more in-depth study related to the model used in the next research. © 2023 IEEE.

关键词： Sentiment analysis

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Stress detection system for social media users

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Procedia computer science 2023年 216卷 672-681页

作者： Mochamad Rizky Febriansyah Nicholas Rezki Yunanda Derwin Suhartono Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Software Engineering Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Stress detection is a growing topic in the field of natural language processing. The study of stress detection for mental health prediction has been proven to benefit the development of recommender systems and automated mental health assessments in previous studies. Additionally, the widespread usage of social media has served as a potential data source for developing such models. Our research tried to detect whether the users of social media were under stress or not. We used a dataset from Dreaddit consisting of posts from one of the popular social media platforms, Reddit. We propose a machine learning model consisting of Support Vector Machine (SVM), Naïve Bayes, Decision Tree, Random Forest, Bag of Words, and Term Frequency – inverse document frequency (TF-IDF) for stress detection. The final evaluation of the model achieved an 80.00% F-1 Score and 75.00% accuracy, and both were scored by SVM.

关键词： Machine Learning Stress Detection Social Media Natural Language Processing

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Suitability of Latest Version of YOLOv11 in Drone Development Studies

Suitability of Latest Version of YOLOv11 in Drone Developmen...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Mohammad Faisal Riftiarrasyid Ford Lumban Gaol Haryono Soeparno Yulyani Arifin Computer Science Department Binus Graduate Program – Computer Science Bina Nunsantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

Although drones have become increasingly advanced and sophisticated, they still face challenges such as limited computing power and high-power consumption. Optimizing their features is a key strategy to address these limitations. One critical feature that can be further improved is the algorithm for specialized tasks like obstacle detection. Recent studies have primarily relied on earlier versions of YOLO for this purpose. However, with the release of the latest YOLO versions, YOLOv10 and YOLOv11, there is an opportunity to assess their suitability for drone applications. In this study, we trained YOLOv10-nano and YOLOv11-nano using a drone-specific dataset and compared their performance with similar studies. The results indicate that YOLOv11-nano outperforms YOLOv10-nano in bounding-box prediction, achieving a higher mAP of 0.957 with a 0.22 difference, as well as faster inference times with a 74.1 ms improvement, resulting in a frame rate gain of 0.0421 FPS. However, when compared to earlier studies, YOLOv11-nano demonstrates superior precision but lags in real-time processing speed. These findings highlight the trade-off between accuracy and inference time, highlighting the suitability of YOLOv11-nano for applications in drone development where accuracy takes precedence over processing speed.

关键词： YOLO Training Seminars Accuracy Cameras Prediction algorithms Real-time systems Intelligent systems Information technology Drones

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Evaluating Vision Transformers Efficiency in Image Captioning

Evaluating Vision Transformers Efficiency in Image Captionin...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Bintang Kevin Hizkia Samosir Sani Muhamad Isa Computer Science Department Binus Graduate Program – Master of Computer Science Binus University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

This study investigates the performance of Vision Transformer (ViT) variants—the Shifted Window Transformers (SWIN), Distillation with No Labels (DINO), and Data-efficient Image Transformers (DeIT)—in image captioning tasks using the Flickr8K dataset. While ViT architectures have shown promise in image classification, their effectiveness for image captioning, particularly with smaller datasets, remains unclear. The models' performance was evaluated using BLEU metrics, while training efficiency was analyzed through Pareto front analysis of computational time and accuracy. Among the tested variants, SWIN Transformers demonstrated superior performance (BLEU-1: 64.4, BLEU-2: 33.9, BLEU-3: 17.1, BLEU-4: 8.4), followed by DINO (BLEU-1: 63.1, BLEU-2: 32.7, BLEU-3: 16.4, BLEU-4: 7.5), while DeIT showed the weakest performance (BLEU-1: 61.6, BLEU-2: 31.1, BLEU-3: 14.7, BLEU-4: 6.5). SWIN Transformers achieved the shortest training time at 3 minutes 31 seconds per epoch, making it the most efficient model among ViT variants based on Pareto front analysis. While ViT variants achieved competitive BLEU-1 scores comparable to previous top models, they struggled with generating coherent, longer sentences, as evidenced by suboptimal BLEU-4 scores. These findings provide empirical evidence of how the lack of inductive bias in transformer architectures affects their ability to capture complex scene relationships, despite their strong feature detection capabilities, contributing to the understanding of transformer models' limitations in vision-language tasks, especially with limited data.

关键词： Training Measurement Analytical models Feature detection Computational modeling computer architecture Transformers Data models Computational efficiency Image classification

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Utilizing Sentiment Insights for Software Evalution with Aspect-Based Sentiment Analysis in App Reviews Using IndoBERT-Lite

Utilizing Sentiment Insights for Software Evalution with Asp...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Nikita Ananda Putri Masaling Haryono Soeparno Yulyani Arifin Ford Lumban Gaol Computer Science Department BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331519643

ISBN: (纸本)9798331519650

With mobile applications increasingly used daily, understanding user feedback is vital for software improvement. Manual analysis of user reviews on platforms is time-consuming and lacks scalability. This study utilizes aspect-based sentiment analysis with the IndoBERT-Lite model to automate the categorization and sentiment detection of feedback. Despite advancements, precise aspect classification and sentiment detection in Indonesian language reviews remain challenging due to limited resources. Using 5,000 Tokopedia reviews, labeled across five aspects, namely functionality, service, performance, usability, and design, the model achieved optimal results with batch size 32, showing 0.85 accuracy and 0.866 F1 score for aspect classification, and 0.998 across all metrics for sentiment classification.

关键词： Measurement Seminars Sentiment analysis Analytical models Accuracy Reviews Scalability Software Mobile applications Usability

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The Conceptual Model of the Student Learning Outcomes Monitoring System for Higher Education Institutions

The Conceptual Model of the Student Learning Outcomes Monito...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Bagus Mulyawan Harjanto Prabowo Spits Warnar Harco Leslie Hendric Iman H. Kartowisastro Department of Computer Science BINUS Postgraduate Program – Computer Science Bina Nusantara University Jakarta Indonesia

Monitoring learning outcomes is an activity that must be carried out by the study program and requires time. Implementation of a system that can monitor learning outcomes needs to be done to eliminate the gap between information needs and the monitoring process. We propose an architectural model to monitor and evaluate student learning outcomes. The architectural model proposed will be able to assist study programs in building the tools used for monitoring learning outcomes. The research method was carried out qualitatively by collecting primary data and secondary data. Primary data was obtained through interviews with heads of study programs of Tarumanagara University, while secondary data was obtained from literature studies, government policies, best practices, and other related documents. The results of the research are in the form of a conceptual model architecture which consists of the components needed to monitor student learning outcomes, which can help study programs, lecturers, and students monitor learning outcomes.

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