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Adding Flexibly in Distributed Simulation of Space Missions by Enhancing the SpaceFOM Standard 28

Adding Flexibly in Distributed Simulation of Space Missions ...

引用

28th IEEE/ACM International Symposium on Distributed Simulation and Real Time Applications, DS-RT 2024

作者： De Grande, Robson E. Falcone, Alberto Garro, Alfredo Brock University Depart. of Computer Science St. Catharines Canada University of Calabria Depart. of Informatics Modeling Electronics and Systems Engineering Italy

ISBN: (纸本)9798331527211

SpaceFOM is the reference standard adopted by space agencies for simulating space missions. Although specifically designed for handling space systems, it currently faces a significant limitation when simulating interplanetary missions: a fixed Federation Time Step. This constraint hinders accurate and flexible modeling of space missions, which limits the dynamic changes of simulation pace, especially during critical phases that require particular temporal granularities. This work proposes extending the SpaceFOM standard to address this issue by enabling dynamic adjustment of Federation Time Step granularity. The proposed solution allows fine-grained time steps for mission-critical phases and coarse-grained steps for extended phases while smoothly combining continuous temporal progression. Furthermore, the proposed solution is general-purpose and can be applied to other domains requiring dynamic temporal granularity. © 2024 IEEE.

关键词： Interplanetary flight

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Leveraging Artificial Intelligence for Innovative Technopreneurial Business Models 3

Leveraging Artificial Intelligence for Innovative Technopren...

引用

3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Sudaryono Yusuf, Inayatul Izzati Diana Maulana, Sabda Pratiangga, Aptanta Apriliasari, Dwi University of Raharja Department of Informatics Engineering Tangerang Indonesia Bina Bangsa University Department of Informatics Engineering Serang Indonesia Bosindo Department of Digital Business Tangerang Indonesia Rey Department of Computer Science Tangerang Indonesia Pandawan Sejahtera Indonesia Department of Information System Tangerang Indonesia

ISBN: (纸本)9798350367492

This study examines the rapid growth of startups integrating artificial intelligence (AI) into their business models, aiming to analyze and compare these startups with traditional IT organizations to understand their differences. Analyzing 162 global startups, this research identifies four unique archetypal business models, introducing novel methodologies in AI integration and data utilization that contrast with conventional IT paradigms. The focus is on technologies providing data analytics solutions, AI products and services, and the expansion of AI use. Key components of AI startup business models are highlighted: (1) advantages of AI in new value propositions, (2) data use to create value, and (3) the impact of AI on business decision-making. This research enhances the understanding of AI startup business models and suggests future research directions in entrepreneurship. The developed taxonomy highlights four distinct AI business models: the Deep Tech Researcher, Data Analytics Provider, AI Product/Service Provider, and AI Development Facilitator, each offering unique value propositions and leveraging AI technologies in different ways to address specific market needs. The research framework fosters entrepreneurial efforts with taxonomies and models as useful instruments. © 2024 IEEE.

关键词： Artificial Intelligence Data Analytics Startup Business Models Technopreneurship

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Gamification of cyber ranges in cybersecurity education 20

Gamification of cyber ranges in cybersecurity education

引用

20th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2022

作者： Jelo, Martin Helebrandt, Pavol Slovak University of Technology Institute of Computer Engineering and Applied Informatics Bratislava Slovakia

ISBN: (数字)9798350320336

ISBN: (纸本)9798350320336

There is a lack of cybersecurity experts, and this problem is getting worse with the increasing number and importance of IT systems together with rising number and sophistication of attacks. Rapid development in cybersecurity necessitates constant training and practice to keep up with the latest threats. Studying IT, and especially cybersecurity is considered difficult and there is an urgent need to encourage interest of students in this field. One promising avenue to improving the situation is gamification. Gamification has been used in education to improve enthusiasm of students, increasing their numbers, information uptake and retention. Leveraging social connections and human competitiveness can further motivate students to take part and engage in the learning activities they would otherwise not consider. Virtualized environments offer means to scale up the practical training as well as share latest teaching methods. Cyber ranges used by educational institutions often are such open platforms that provide such practical cybersecurity training. Moreover, with some thoughtful planning they can prove to be popular serious games. In this paper, we explore opportunities for hands-on cybersecurity education by using gamified scenarios in cyber range virtualized environment. We focus on cyber testing environments, what their types are, and what it includes. We place emphasis on creating basic game scenario close to real-world situations. © 2022 IEEE.

关键词： Students

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LASS: A Novel and Economical Data Augmentation Framework Based on Language Models for Debiasing Opinion Summarization 31

LASS: A Novel and Economical Data Augmentation Framework Bas...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Yanyue Li, Pengfei Lai, Yilong He, Yulan Zhou, Deyu School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King's College London United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9798891761964

As more than 70% of reviews in the existing opinion summary data set are positive, current opinion summarization approaches are hesitant to generate negative summaries given the input of negative texts. To address such sentiment bias, a direct approach without the reliance on a specific structure is to generate additional data based on large language models to balance the emotional distribution of the dataset. However, large-scale data augmentation based on large language models faces an apparent disadvantage, the expensive costs. Therefore, in this paper, we propose LASS, a novel data augmentation framework based on both LArge and Small language models for debiaSing opinion summarization. Specifically, a small number of synthesized negative reviews is obtained by rewriting the positive text via a large language model. Then, a disentangle reconstruction model is trained based on the generated data. After training, a large amount of synthetic data can be obtained by decoding the new representation obtained from the combination of different sample representations and filtering based on perplexity degree and sentiment classification. Experiments have proved that LASS can effectively alleviate emotional bias, similar to using only large models, but in a more economical way. © 2025 Association for Computational Linguistics.

关键词： Data assimilation

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Early Prediction of Chronic Heart Disease Based on Electronic Triage Dataset by using Machine Learning

Early Prediction of Chronic Heart Disease Based on Electroni...

引用

2023 Al-Sadiq International Conference on Communication and Information Technology, AICCIT 2023

作者： Muhammed, Saleh Mahdi Abdul-Majeed, Ghassan Mahmoud, Mahmoud Shuker Informatics Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Department of Computer Science Baghdad Iraq University of Baghdad College of Engineering Department of Computer Science Baghdad Iraq Al/Mansour University College Department of Communications Baghdad Iraq

ISBN: (纸本)9798350341881

Chronic Heart Disease (CHD) is the main reason for mortality around the world, constituting about 15% of all natural deaths, which confirms the importance of early detection to mitigate these rates. CHD can be detected by measuring multiple health parameters such as blood pressure, SpO2, ECG, and other parameters. Therefore, there is a persistent need for effective and precise prediction methods. Traditional techniques for prediction and analysis are limited due to the complexity of the data and correlations. First approach for developing these techniques is by using machine learning (ML) algorithms. ML can examine vast amounts of patient information, including medical records, test outcomes, and imaging scans to identify patterns and predict the possibility of developing heart disease or having a cardiac event. This study aims to improve the accuracy of prediction by utilizing ML techniques to predict CHD using medical data, reduce the incidence of misdiagnosis, and ultimately save lives. Supervised learning techniques, such as Support Vector Machine (SVM), Artificial Neural Networks (ANN), AdaBoost, Logistic Regression (LR), K-Nearest Neighbor (KNN) and others are employed to identify correlations in CHD data and enhance prediction rates. The results indicate that SVM-derived probabilistic models outperform other algorithms in predicting CHD and the accuracy of the model was 96.2%, as revealed through various performance evaluation metrics. © 2023 IEEE.

关键词： Support vector machines

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Hepatitis Cluster Model with K-Means Algorithm

Hepatitis Cluster Model with K-Means Algorithm

引用

2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Chusyairi, Ahmad Nurdiawan, Odi Sambath, Khoem Hayat, Rachmad Nur Arie Wijaya, Yudhistira Bina Insani University Informatics Engineering Bekasi Indonesia STMIK IKMI Cirebon Informatics Management Bekasi Indonesia NPIC of Cambodia Computer Science and Networking Phnom Penh Cambodia STMIK IKMI Cirebon Informatics Management Cirebon Indonesia

ISBN: (纸本)9798350320954

Indonesia is currently being hit by cases of mysterious hepatitis, the Indonesian Ministry of Health announced on May 18, 2022, that there have been 14 cases of hepatitis, consisting of 1 probable case and 13 pending classification cases, with information on 1 probable case from West Sumatra, 7 cases in Jakarta, 1 case in Jambi, and 3 cases in East Java. At the time when this paper was written, Jaundice that age under 10, and the absence of any other etiology are common symptoms of strange hepatitis. Fever, diarrhea, stronger urine, and pale stools are symptoms that surface. The use of intelligent techniques appears to be necessary to interpret the complicated data set's structure and effectively support doctors during the diagnosis process. As a result, this research effort provides an intelligent hybrid technique for the diagnosis of hepatitis disease by merging enhanced k-means clustering and ensemble learning algorithms. The benefit of employing ensemble learning is that it creates a set of hypotheses by utilizing numerous students to address a particular issue. The data in this study uses a primary data model, which means that the data is taken directly from the source. In this study, the data is obtained from ***, which is then performed by feature selection leaving several features, namely age, bilirubin, alk-phosphate, sgot, albumin, and protime which are the important feature to be used on K-Means clustering. © 2023 IEEE.

关键词： K-means clustering

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Knowledge-Enriched Domain Specific Chatbot on Low-resource Language 11

Knowledge-Enriched Domain Specific Chatbot on Low-resource L...

引用

11th Electrical Power, Electronics, Communications, Control, and informatics Seminar, EECCIS 2022

作者： Perdana, Rizal Setya Adikara, Putra Pandu Indriati Kurnianingtyas, Diva Brawijaya University Faculty of Computer Science Department of Informatics Engineering Malang Indonesia

ISBN: (数字)9781665406482

ISBN: (纸本)9781665406482

This study presents architecture in learning human-machine conversation using a human language known as chatbots. Given an utterance, a chatbot attempt to reply using human language by mimicking human intelligence in communication. Previous works in this domain were mostly implemented in English, thus it remains a problem when bringing to a low-resource language, e.g., Indonesian. The limited availability of human-to-human dialog history led to difficulty in the learning process of machine learning algorithms. Therefore, this study proposed a Knowledgeable Chatbot (KC), an enhanced pipeline that enables to receiving of transferred knowledge from another task. A data augmentation pipeline is proposed to handle the limited number of available. To deal with the low-resource language, this study proposed to incorporate a pre-trained language model to gain contextualized language understanding. As this research can be categorized as preliminary, extensive experiments are required to prove the effectiveness of each part. Standard automatic metrics for information retrieval and classification prove that KC excels in the ablation study. © 2022 IEEE.

关键词： Information retrieval

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Performance Evaluation of Intrusion Detection System with SVM 6

Performance Evaluation of Intrusion Detection System with SV...

引用

6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Diaz, Ricky Aurelius Nurtanto Putra, I. Ketut Gede Darma Sudarma, Made Sukarsa, I. Made Department of Computer Systems Faculty of Informatics and Computer Institut Teknologi dan Bisnis STIKOM Bali Bali Indonesia Department of Information Technology Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia Department of Electrical Engineering Faculty of Engineering Udayana University Jimbaran Hill Bali Indonesia

ISBN: (纸本)9798331539603

Various machine learning techniques have been proposed to improve the effectiveness of Intrusion Detection Systems (IDS), where IDS is one of the important parts of the network that functions to maintain network security. With its various capabilities and reputation, Support Vector Machine (SVM) is often applied in various classification cases. However, it has constraints with accuracy and computing time when it comes to data that has large dimensions. Utilizing metaheuristic techniques is one possibility that researchers can use to solve this problem. To overcome this, in this study, we will evaluate the performance of SVM for the IDS case by utilizing the metaheuristic Grey Wolf Optimization (GWO) algorithm. From the experiments with three different iteration models, the best results of the combination of SVM and GWO were found in a model with 10 GWO iterations and 10 populations with an accuracy of 97.47% using 15 features out of 48 original features of the UKM-IDS20 dataset. © 2024 IEEE.

关键词： Network intrusion

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Implementation of the SPECK Cipher Algorithm on Patient Blood Pressure Data Using the Internet of Things (IoT) 7

Implementation of the SPECK Cipher Algorithm on Patient Bloo...

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7th International Conference on Sustainable Information engineering and Technology, SIET 2022

作者： Santoso, Edy Nurwarsito, Heru Malik, Argandhi Adi Department of Informatics Engineering Faculty of Computer Science University of Brawijaya Malang Indonesia

ISBN: (纸本)9781450397117

Internet of Things technology allows humans to collect information about daily activities, for example to find out someone's health information, but at the time of data transmission it has an impact on one's privacy. Sniffing can record data sent from the user to the database server, this results in a leak of individual privacy. The SPECK algorithm is one of the cryptographic algorithms that guarantees confidentiality because the SPECK algorithm has varying key lengths and block sizes. Adding the SPECK cipher Algorithm to the node that holds sensor data can minimize privacy data leaks caused by data transmission on the Internet of Things. The SPECK algorithm has the same results as the test vectors in published journals and the resulting ciphertext can be returned to its original form by decryption. The encryption time is 0.089354 seconds and the decryption time on the server is 0.00610295 seconds. © 2022 ACM.

关键词： Internet of things

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Face Recognition Using Deep Learning as User Login on Healthcare Kiosk 14

Face Recognition Using Deep Learning as User Login on Health...

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14th International Conference on Information Technology and Electrical engineering, ICITEE 2022

作者： Aditya, Alvian Tedy Sigit, Riyanto Dewantara, Bima Sena Bayu Politeknik Elektronika Negeri Surabaya Dept of Informatics and Computer Engineering Surabaya Indonesia

ISBN: (数字)9781665460774

ISBN: (纸本)9781665460774

This paper proposes the development of a login system to a Healthcare Kiosk using facial images. The use of the face as an example of a unique biometric system other than fingerprint and iris is considered better than conventional systems using RFID cards that are prone to being lost or left behind, or passwords that are often forgotten. In this paper, we propose the use of faces as input for the login system to a healthcare kiosk by utilizing deep learning technology. We tested four types of Convolutional Neural Network (CNN) architectures such as VGG16, ResNet50, Xception and MobileNet. In the accuracy testing process, VGG16 got a total accuracy of 100% but still showed the wrong class during realtime detection testing, ResNet50 got a total accuracy of 99.531% and was able to show the correct class during realtime detection testing, Xception got a total accuracy of 80.018% but still shows the wrong class when testing realtime detection, and MobileNet gets a total accuracy of 92.934%. © 2022 IEEE.

关键词： Face recognition

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