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A Model-Driven Approach for Interoperability Among SaaS and DaaS/DBaaS: The MIDAS Case

SN Computer Science 2022年第4期3卷 301页

作者： Mane, Babacar Mascarenhas, Ana Patrícia F. Magalhães Quinteiro, Gustavo Maciel, Rita Suzana Pitangueira Claro, Daniela Barreiro Formalisms and Semantic Applications Research Group (FORMAS) Institute of Computing Federal University of Bahia Av. Milton Santos Ondina Bahia Salvador 40170-110 Brazil Models for Software Engineering (MAtisse) Department of Exact Sciences and Earth State University of Bahia 2555 Silveira Martins St. Cabula Bahia Salvador Brazil Post Graduate Program in Computing and Systems Salvador University 251 Dr. Jose Peroba St. Bahia Salvador Brazil Models for Software Engineering (MAtisse) Institute of Computing Federal University of Bahia Av. Milton Santos Ondina Bahia Salvador 40170-110 Brazil

Cloud Platforms are heterogeneous, and users may face interoperability issues migrating applications or exchanging data among distinct clouds due, for instance, to the lack of standards solutions. Several solutions have been proposed to overcome lock-in situations, and middleware platforms are one of them. A semantic interoperability solution named middleware for Data as a Service (DaaS)/Database as a Service (DBaaS) and Software as a Service (SaaS)—MIDAS has been developed to overcome this lock-in issue. It is an intermediate communication layer to retrieve data from DaaS or DBaaS through a Structured Query Language (SQL) or a Not Only SQL (NoSQL) created at the SaaS level. MIDAS is a platform for software execution, but software development needs support for its entire life cycle. Therefore, we propose the Model drIven Approach for MIDAS (MIAMI), which enables the specification of platform-independent middleware models and their use to generate code on different cloud platforms. MIAMI comprises a Domain-Specific Modeling Language (DSML) that enables middleware models and a transformation specification, which defines how these models can be converted to code. MIAMI offers a strategy for MIDAS specification and code generation phases to help middleware developers’ activities. MIAMI was applied to code generation specifications in Cloud Foundry, Amazon Web Services, OpenShift, and Heroku providers to evaluate our approach. This specification shows MIAMI’s feasibility and points out that MDD is a promising approach to improving cloud interoperability solutions. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Cloud computing Interoperability Metamodel Model-driven development

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Cyberoception: Finding A Painlessly-Measurable New Sense In The Cyberworld Towards Emotion-awareness In computing 25

Cyberoception: Finding A Painlessly-Measurable New Sense In ...

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Proceedings of the 2025 CHI Conference on Human Factors in computing systems

作者： Tadashi Okoshi Zexiong Gao Yi Zhen Tan Takumi Karasawa Takeshi Miki Wataru Sasaki Rajesh Krishna Balan Faculty of Environment and Information Studies Keio University Fujisawa Kanagawa Japan Graduate School of Interdisciplinary Information Studies The University of Tokyo Tokyo Tokyo Japan School of Computing and Information Systems Singapore Management University Singapore Singapore Singapore Graduate School of Media and Governance Keio University Fujisawa city Kanagawa Pref Japan Department of Engineering University of Cambridge Cambridge Cambridge United Kingdom Graduate School of Science and Technology Nara Institute of Science and Technology Ikoma Nara Japan

ISBN: (纸本)9798400713941

In Affective computing, recognizing users' emotions accurately is the basis of affective human-computer interaction. Understanding users' interoception contributes to a better understanding of individually different emotional abilities, which is essential for achieving inter-individually accurate emotion estimation. However, existing interoception measurement methods, such as the heart rate discrimination task, have several limitations, including their dependence on a well-controlled laboratory environment and precision apparatus, making monitoring users' interoception challenging. This study aims to determine other forms of data that can explain users' interoceptive or similar states in their real-world lives and propose a novel hypothetical concept "cyberoception,"a new sense (1) which has properties similar to interoception in terms of the correlation with other emotion-related abilities, and (2) which can be measured only by the sensors embedded inside commodity smartphone devices in users' daily lives. Results from a 10-day-long in-lab/in-the-wild hybrid experiment reveal a specific cyberoception type "Turn On"(users' subjective sensory perception about the frequency of turning-on behavior on their smartphones) significantly related to participants' emotional valence. We anticipate that cyberoception to serve as a fundamental building block for developing more "emotion-aware", user-friendly applications and services. © 2025 Copyright held by the owner/author(s).

关键词： Emotion / Affective computing Cyberoception Interoception Sensing Mobile Devices Wearable Devices Personalization

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Transcriptomic analysis of Andrias davidianus meat and experimental validation for exploring its bioactive components as functional foods

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Food Science and Human Wellness 2024年第1期13卷 166-172页

作者： Changge Guan Zhenglin Tan Shucheng Li Yi Wang Naoyuki Yamamoto Chong Zhang Songjun Wang Junjie Chen Xinhui Xing Key Laboratory for Industrial Biocatalysis Ministry of Education of ChinaDepartment of Chemical EngineeringTsinghua UniversityBeijing 100084China School of Life Science and Technology Tokyo Institute of TechnologyYokohama 226-0026Japan Sensing System Research Center National Institute of Advanced Industrial Science and TechnologyTsukuba 305-8565Japan Center for Synthetic and Systems Biology Tsinghua UniversityBeijing 100084China Henan Giant Salamander Protection and Development Association Luoyang 471000China Intelligent Computing Research Center Harbin Institute of TechnologyShenzhen 518071China Institute of Biopharmaceutical and Health Engineering Tsinghua Shenzhen International Graduate SchoolShenzhen 518055China Institute of Biomedical Health Technology and Engineering Shenzhen Bay LaboratoryShenzhen 518067China

Andrias davidianus(Chinese giant salamander,CGS)is the largest and oldest extant amphibian species in the world and is a source of prospective functional food in ***,the progress of functional peptides mining was slow due to lack of reference genome and protein sequence *** this study,we illustrated full-length transcriptome sequencing to interpret the proteome of CGS meat and obtain 10703 coding DNA *** functional annotation and amino acid composition analysis,we have discovered various genes related to signal transduction,and 16 genes related to *** have also found vast variety of functional peptides through protein coding sequence(CDS)analysis by comparing the data obtained with the functional peptide ***-Pro-Ile predicted by the CDS analysis was released from the CGS meat through enzymatic hydrolysis,suggesting that our approach is *** study suggested that transcriptomic analysis can be used as a reference to guide polypeptide mining in CGS meat,thereby providing a powerful mining strategy for the bioresources with unknown genomic and proteomic sequences.

关键词： Chinese giant salamander Transcriptomic analysis Bioactive components Functional peptides mining

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Analyzing the Impact of Deep Learning Approaches on Real-Time Data Analysis in Machine Learning

Analyzing the Impact of Deep Learning Approaches on Real-Tim...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Deepti Nalluri, Mounika Mupparaju, Chinna Babu Rongali, Aruna Sri Dhar, Sohong Ajitha, P. Sloan School of Management Mit United States Computer Information Systems Murray State University United States University of Central Missouri Computer Science warrensburg United States University of the Cumberlands Graduate Doctoral Information Technology Information Technology Emphasis/Information Technology United States Azure Microsoft United States Library & Information Science Jadavpur University India School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798350357769

Deep learning is a powerful subset of synthetic intelligence, and it has the potential to significantly enhance the skills of machine getting to know. it's miles primarily based on the idea of artificial neural networks, which might be laptop fashions that are capable of gaining knowledge of from records and making decisions, just like the manner a human mind works. Deep mastering isn't the same as traditional system mastering algorithms in that it can technique complex inputs with multiple layers of abstraction, main to greater accurate evaluation. Deep studying algorithms are capable of unexpectedly examine raw facts and generate styles, making them perfect for actual-time statistics analysis. Deep studying is capable of as it should be predicting behaviors and trends, which may be used to optimize procedures and enhance operational efficiencies. Deep studying models can also be used for automated fault detection and predictive renovation, permitting businesses to count on and prevent capacity troubles. Additionally, deep mastering models frequently offer quicker consequences than other device studying or conventional techniques, as well as permitting an extra diploma of control over the resolution of the data being analyzed. This has widespread implications for real-time programs, such as medical diagnostics, independent automobiles, and manufacturing monitoring. © 2023 IEEE.

关键词： Diagnosis

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Diversity and stylization of the contemporary user-generated visual arts in the complexity-entropy plane

arXiv

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arXiv 2024年

作者： Kim, Seunghwan Lee, Byunghwee Lee, Wonjae Graduate School of Culture Technology KAIST Daejeon34141 Korea Republic of Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Institute of Basic Science Sungkyunkwan University Suwon16419 Korea Republic of

The advent of computational and numerical methods in recent times has provided new avenues for analyzing art historiographical narratives and tracing the evolution of art styles therein. Here, we investigate an evolutionary process underpinning the emergence and stylization of contemporary user-generated visual art styles using the complexity-entropy (C-H) plane, which quantifies local structures in paintings. Informatizing 149,780 images curated in DeviantArt and Bēhance platforms from 2010 to 2020, we analyze the relationship between local information of the C-H space and multi-level image features generated by a deep neural network and a feature extraction algorithm. The results reveal significant statistical relationships between the C-H information of visual artistic styles and the dissimilarities of the multi-level image features over time within groups of artworks. By disclosing a particular C-H region where the diversity of image representations is noticeably manifested, our analyses reveal an empirical condition of emerging styles that are both novel in the C-H plane and characterized by greater stylistic diversity. Our research shows that visual art analyses combined with physics-inspired methodologies and machine learning, can provide macroscopic insights into quantitatively mapping relevant characteristics of an evolutionary process underpinning the creative stylization of uncharted visual arts of given groups and time. © 2024, CC BY.

关键词： Deep neural networks

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How Enterprise Architecture Enhances the National Health Information System in Supporting the COVID-19 Response in Indonesia 32

How Enterprise Architecture Enhances the National Health Inf...

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32nd Australasian Conference on Information systems: Information systems for a Sustainable Future, Connectedness, and Social Good, ACIS 2021

作者： Dilnutt, Rod Kurnia, Sherah Khoir, Safirotu Fuad, Anis Sanjay, Guardian Y. Adnan, Hafizh Rafizal Hidayanto, Achmad Nizar School of Computing and Information Systems The University of Melbourne VIC Australia Graduate School of Information and Library Management Universitas Gadjah Mada Yogyakarta Indonesia Graduate School of Information Universitas Gadjah Mada Yogyakarta Indonesia Universitas Gadjah Mada Yogyakarta Indonesia Universitas Indonesia Jakarta Indonesia

The COVID-19 pandemic has negative impacts on human well-being with significant consequences on social, economic, political, legal, and technological aspects of society. A key challenge in managing the pandemic particularly in developing countries is the lack of integrated health data. Previous research indicates that an Enterprise Architecture (EA)-driven national initiative has the potential to mitigate data integration issues. However, in-depth study of how EA initiatives provide a systematic solution to national health data integration in the context of COVID-19 data management is lacking. Using Indonesia as a study context, we conducted an in-depth case study to systematically explore factors affecting the current COVID-19 data integration issues and investigate how EA helps overcome the problems. In this research in progress paper, we present our initial research findings and outline our future research plans. When completed, our study contributes to IS research specifically in the EA domain and has important practical implications. Copyright © 2021 authors.

关键词： COVID-19

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ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicular CAN Bus Networks

ZeroCAN: Anomaly-Based Zero-Day Attack Detection in Vehicula...

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Euromicro Conference on Parallel, Distributed and Network-Based Processing

作者： Jonathan Rendel William Balte Harrison Kurunathan Hazem Ismail Ali Alexandre Dos Santos Roque Wagner Ourique De Morais Mahdi Fazeli Department for Computing and Electronics for Real-time and Embedded Systems (CERES) School of Information Technology (ITE) Halmstad University Halmstad Sweden CISTER/ISEP Polytechnic Institute of Porto Portugal Graduate Program of Electrical Engineering Federal University of Rio Grande do Sul - UFRGS Porto Alegre Brazil

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Zero-day attacks present a significant security threat to vehicular networks, exploiting vulnerabilities at both software and hardware levels within such systems that remain undiscovered. Mitigating these threats is essential to ensuring the safety and security of vehicular systems. Support Vector Machine (SVM) is a good candidate for anomaly detection of zero-day attacks within vehicular networks because it can handle highdimensional data and effectively distinguish between normal and abnormal patterns in complex and dynamic environments. A trained SVM on the normal operation data of in-vehicular network can identify flag deviations, thus making it effective in the detection of any previously unknown attack patterns, which is a common behaviour of zero-day attacks. In this paper, we introduce an anomaly detection method called “ZeroCAN” which models the behaviour of every single electronic control unit on the network with a separate SVM and a set of high-level features that capture the timing and data payload aspects of CANbus traffic. This approach achieves an anomaly detection rate of over $\mathbf{9 9 \%}$ and a false positive rate below $\mathbf{0. 0 1 \%}$ during normal operation in most cases.

关键词： Support vector machines Telecommunication traffic Feature extraction Data models Timing Security Vehicle dynamics Anomaly detection Standards Payloads

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Minimizing the Number of Stunting Prevalence Using the Euclid Algorithm Clustering Approach

Minimizing the Number of Stunting Prevalence Using the Eucli...

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International Conference of Computer Science and Information Technology

作者： Muhammad Zarlis Tanty Oktavia Relita Buaton Ferda Ernawan Kevin Andrian Department BINUS Graduate Program - Master of Information System Management Information System Management Bina Nusantara University Jakarta Indonesia Informatics Engineering STMIK Kaputama Binjai Indonesia Faculty of Computing Universiti Malaysia Pahang Al-Sultan Abdullah Pekan Malaysia Department of Information Systems Management BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta Indonesia

Superior and quality human resources are based on healthy human resources with indicators of adequate nutritional intake according to age development. However, the world still faces the problem of hunger and malnutrition today. According to a UNICEF report, the number of people suffering from malnutrition in the world will reach 767.9 million people in 2021. The World Health Organization (WHO) said that malnutrition is a dangerous threat to the health of the world's population. Stunting also has an impact in Indonesia, the prevalence of toddlers experiencing stunting in Indonesia is 24.4% in 2021. The solution created is to classify and cluster the prevalence of stunting to produce a pattern that can be used as best practice to be transmitted to other affected areas. The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. The results of the classification and clustering of the best stunting prevalence in cluster one with a very small number, can be used as a source of accurate and updated information that can be used by the government in its efforts to optimize stunting handling in each district/city based on artificial intelligence which can provide handling and optimization patterns. stunting in every district/city.

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Multilabel Machine Learning System for Predicting Gender and Age Group from Human Gait

Multilabel Machine Learning System for Predicting Gender and...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Nunung Nurul Qomariyah Ahmad Nurul Fajar Vincent Yono Dimitar Kazakov Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia Information Systems Management Department Binus Graduate Program Master of Information System Management Bina Nusantara University Jakarta Indonesia Computer Science Department University of York United Kingdom

This paper explores the development of a multilabel machine learning system for predicting both gender and age from human gait patterns. Gait analysis, a non-intrusive method of identifying subtle nuances in human movement, has proven to be a rich source of information related to demographic characteristics. The research extends beyond traditional single-label classification approaches, adopting a multilabel framework to simultaneously predict gender and age *** study evaluates various multilabel machine learning algorithms, with the Random k-labeLsets (RAKEL) algorithm demonstrating superior performance in predicting gender and age labels from human gait datasets. The accuracy of the algorithm can reach up to 87%. We also compared the multilabel approach with several multiclass algorithms such as Decision Tree, Random Forest, Gradient Boosting, K-Neighbors and XGBoost. However, when we also considering the training time, Classifier Chain algorithm showed the best trade off with the accuracy of 86% and the training time is twice faster than the RAKEL algorithm.

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Mixed Reality System for Teaching and Learning: A Systematic Literature Review 5

Mixed Reality System for Teaching and Learning: A Systematic...

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5th IEEE International Conference on Information Technology, Information systems and Electrical Engineering, ICITISEE 2021

作者： Suryodiningrat, Satrio Pradono Prabowo, Harjanto Meyliana Hidayanto, Achmad Nizar Bina Nusantara University Faculty Of Computing And Media Computer Science Department BINUS Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Computer Science Graduate Program Jakarta Indonesia Bina Nusantara University School Of Information Systems Information Systems Department Jakarta Indonesia Universitas Indonesia Faculty Of Computer Science Depok Indonesia

ISBN: (纸本)9781665401968

Mixed reality is becoming more popular, and a lot of technology companies trying to develop and enhance mixed reality technology and using it not just in manufactures or industries but lately the technology is used for teaching and learning, either in school or for training purposes. While traditional teaching and learning processes are not effective anymore and most of the time, the learning objectives are not reached. This paper has an objective to point out the benefits of using mixed reality for teaching and learning. 99 articles have been collected and reviewed carefully as a quality assessment process, there are 41 articles has been selected for further review and top 5 benefits will be discussed. This review informs educators and scholars that there are common benefits, and the most popular benefit is better learning engagement. © 2021 IEEE.

关键词： Mixed reality

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