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IEEE International Workshop on Measurements and Networking Proceedings (M&N)

作者： Annamaria Ficara Maria Fazio Orazio Pellegrino Alessia Ruggeri Armando Ruggeri Giuseppe Sollazzo Gaetano Bosurgi Department of Cognitive Science Education and Cultural Studies University of Messina Italy Department of Mathematics Computer Science Physics and Earth Science University of Messina Italy Department of Engineering University of Messina Italy

ISBN: (数字)9798350370539

ISBN: (纸本)9798350370546

Digital transition is characterizing all the components of our world. Considering transport infrastructures, for maximizing safety and comfort of users while driving, the exploitation of multiple powerful sensors and data analysis capabilities is a primary research and development goal. In this context, the digital road transport infrastructures (“Smart Roads”) will represent complex and integrated systems combining physical and digital assets for ensuring roads with information, sensing, and acting capabilities. In view of these goals and this transition, the management of the asset condition plays a strategic role, especially considering road physical components and, mainly, the pavement, on which vehicles move. In this paper, accordingly, the authors describe a methodological approach for implementing an innovative architecture of advanced digital services in Smart Roads, based on the distributed computing paradigms of Edge and Cloud. This approach combines continuous and multiple data acquired by reliable, simple, smart, and connected sensors in vehicles on a cloud-based management platform to estimate the pavement “health-state”. This may significantly improve control of condition of the asset critical segments, for optimizing planning of more accurate high-performance survey and, thus, implement maintenance and rehabilitation programs.

关键词： Surveys Data analysis Roads Sensor phenomena and characterization Real-time systems Maintenance Safety

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Scheduled Sampling for Recursive Multi-Step GPU Temperature Forecasting

Scheduled Sampling for Recursive Multi-Step GPU Temperature ...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Harold Wang Xunfei Jiang Mahdi Ebrahimi Department of Cognitive Science University of California San Diego San Diego CA Department of Computer Science California State University Northridge Northridge CA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Energy-efficient workload management is increasingly important in data centers as demand for cloud computing continues to grow. Recent findings in workload management point to thermal-aware load balancers as an effective approach to reduce energy consumption via cooling costs. Due to the high energy cost of Data Centers (DCs) and possible risk of disrupting critical services, simulations can be used to evaluate temperature-aware load-balancing algorithms. In this paper, we leverage an existing DC simulator's environmental parameters for integrating a temperature prediction model to construct a temperature-aware workload distribution simulation. Using scheduled sampling, we implemented a custom recursive multi-step forecasting model that reduced the discrepancy between training error and simulation predictions. Compared with a baseline GPU temperature prediction model, our hybrid CNN-LSTM model achieved a reduction in RMSE from 2.54 to 0.73.

关键词： Training Data centers Temperature distribution Costs Graphics processing units Predictive models Thermal management Thermal loading Forecasting Load modeling

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Bibliometric Analysis of Smart Technology for Virtual Museum Research 9

Bibliometric Analysis of Smart Technology for Virtual Museum...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Carollina, Donna Purbasari, Mita Sidharta Bina Nusantara University School of Design Visual Communication Design Department Jakarta Indonesia Bina Nusantara University Graphic Design & New Media Program Visual Communication Design Department Jakarta Indonesia Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350370126

In recent years, the development of intelligent technology has had a significant impact on the development of virtual museums. Therefore, knowledge mapping is needed to describe and analyze trends, implementation, and benefits of using smart technology in virtual museums. This study used bibliometric methods. The results is smart technology for virtual museum research tends to a downward trend in research productivity. The productive year for this study was 2019. While the type of documents related to this research on Scopus indexation is dominated by conference papers with 51 documents or 63.7%. The dominant publication source for this research is Lecture Notes in computer science including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics with 11 documents. The affiliation with the most documents is MARA University of Technology with 2 documents, and the most productive country in this study is China with 15 documents. From the results of research mapping, it was found that research in the field of smart technology for virtual museums still has great opportunities to be applied to applicable themes such as the application of local wisdom content that can enrich research topics. © 2023 IEEE.

关键词： Mapping

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Learning Channels to Support Interaction and Collaboration in CourseMapper 22

Learning Channels to Support Interaction and Collaboration i...

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Proceedings of the 14th International Conference on Education Technology and computers

作者： Qurat Ul Ain Mohamed Amine Chatti Shoeb Joarder Ilia Nassif Benjamine Stella Wobiwo Teda Mouadh Guesmi Rawaa Alatrash Social Computing Group Department of Computer Science and Applied Cognitive Science University of Duisburg-Essen Germany

ISBN: (纸本)9781450397766

Massive Open Online Courses (MOOCs) have proved to be revolutionary in the development and widespread of educational technology over the past few years. It has been identified that these systems, despite all the benefits, lack interaction and collaboration around learning materials. This paper explores how to leverage learning channels to support interaction and collaboration in MOOC environments. We present the design, implementation, and evaluation details of learning channels in CourseMapper, a MOOC platform which supports collaboration and interaction around learning materials using collaborative Pdf and video annotations. More specifically, we report on the application and results of a human-centered design (HCD) approach used to systematically design learning channels. Three design iterations with five new users in each iteration (N=15) were conducted from an initial user interview to the final design. After the development, user experience with the system was evaluated with end users (N=10) based on the Technology Acceptance Model (TAM). The evaluation results show that the introduction of learning channels provides a valuable mechanism to support effective interaction and collaboration in a MOOC environment.

关键词： Human-Centered Design (HCD) Technology-Enhanced Learning (TEL) Learning channels Collaborative annotation Collaborative learning Massive Open Online Course (MOOC)

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XLS-R Deep Learning Model for Multilingual ASR on Low- Resource Languages: Indonesian, Javanese, and Sundanese

arXiv

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arXiv 2024年

作者： Arisaputra, Panji Handoyo, Alif Tri Zahra, Amalia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

This research paper focuses on the development and evaluation of Automatic Speech Recognition (ASR) technology using the XLS-R 300m model. The study aims to improve ASR performance in converting spoken language into written text, specifically for Indonesian, Javanese, and Sundanese languages. The paper discusses the testing procedures, datasets used, and methodology employed in training and evaluating the ASR systems. The results show that the XLS-R 300m model achieves competitive Word Error Rate (WER) measurements, with a slight compromise in performance for Javanese and Sundanese languages. The integration of a 5-gram KenLM language model significantly reduces WER and enhances ASR accuracy. The research contributes to the advancement of ASR technology by addressing linguistic diversity and improving performance across various languages. The findings provide insights into optimizing ASR accuracy and applicability for diverse linguistic contexts. © 2024, CC BY-SA.

关键词： Linguistics

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Smart Campus and Education: The Study of Individual Performance in E-Learning Environments 11

Smart Campus and Education: The Study of Individual Performa...

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11th International Conference on ICT for Smart Society, ICISS 2024

作者： Purnama Alamsyah, Doni Morika, Doni Siswanto, Boby Othman, Norfaridatul Akmaliah Binus Business School Bina Nusantara University Entrepreneurship Department Jakarta11480 Indonesia School of Design Bina Nusantara University Design Interior Study Program Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Study Program Jakarta11480 Indonesia Universiti Teknikal Malaysia Melaka Faculty of Technology Management and Technopreneurship Department of Technology Management Melaka76100 Malaysia

ISBN: (纸本)9798350389654

The Learning Management System (LMS) is a component of the Smart Campus and is implemented with e-learning. E-learning can be effective if it gives satisfaction to the user and improves academic performance. Therefore, it is important to evaluate online learning users to support learning outcomes. The purpose of this study is to examine the relationship model of individual performance with support for user satisfaction and flexibility in an e-learning environment. This study was carried out using a survey to 615 e-learning users. Users are among the universities students in the city of Bandung (Indonesia). Data were collected quantitatively through questionnaires distributed online. Data processing is done with SmartPLS, where there are two model tests, namely the PLS Algorithm and Bootstrapping. Results of this study model test found that user satisfaction can be increased by information quality from e-learning. Online learning has perceived flexibility that is assessed by users and can increase user satisfaction and individual performance. It was found that user satisfaction was able to support the individual performance achievements of students as e-learning users. The results from this study provide valuable insights into the role of user satisfaction as a mediator in controlling individual student performance, highlighting the significance of integrating Smart Campus solutions. © 2024 IEEE.

关键词： Individual Performance Information Quality Perceived Flexibility User Satisfaction

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The CAT Effect: Exploring the Impact of Casual Affective Triggers on Online Surveys' Response Rates 22

The CAT Effect: Exploring the Impact of Casual Affective Tri...

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2022 CHI Conference on Human Factors in Computing Systems, CHI 2022

作者： Chounta, Irene-Angelica Nolte, Alexander Department of Computer Science and Applied Cognitive Science University of Duisburg-Essen Duisburg Germany University of Tartu Tartu Estonia Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781450391573

We explore the impact of Casual Affective Triggers (CAT) on response rates of online surveys. As CAT, we refer to objects that can be included in survey participation invitations and trigger participants' affect. The hypothesis is that participants who receive CAT-enriched invitations are more likely to respond to a survey. We conducted a study where the control condition received invitations without affective triggers, and the experimental condition received CAT-enriched invitations. We differentiated the triggers within the experimental condition: one-third of the population received a personalized invitation, one-third received a picture of the surveyor's cat, and one-third received both. We followed up with a survey to validate our findings. Our results suggest that CATs have a positive impact on response rates. We did not find CATs to induce response bias. © 2022 ACM.

关键词： Surveys

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Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Classification 4

Fine-Tuned MobileNetV2 and VGG16 Algorithm for Fish Image Cl...

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

作者： Fitrianah, Devi Suryaningrum, Kristien Margi Sagala, Noviyanti Tri Maretta Ayumi, Vina Lim, Siew Mooi School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia School of Computer Science Bina Nusantara University Mathematics Statistics Department Jakarta Indonesia Universitas Mercu Buana Informatics Engineering Program Faculty of Computer Science Jakarta Indonesia Tunku Abdul Rahman University College Faculty of Computing and Information Technology Computer Science Department Kuala Lumpur Malaysia

ISBN: (纸本)9781665473279

Study on the identification and classification of fish is challenging and valuable because of its role in advancing the marine and agricultural fields. This research has benefits interms of monitoring fish populations and ecosystems in a particular area. Furthermore, this research helps monitor fish that are considered threatened or endangered so that it makes iteasier to map prohibited areas for fishing. This research aims to know performance of MobileNetV2 and VGG16 with parameter tuning process by identifying the value of batch size, epoch, learning rate, and optimizer for fish image dataset. The proposed research phase consists of five main stages, including experimental setup, dataset construction, dataset preprocessing, dataset training and modelling and evaluation. As the result, VGG16 obtained the highest accuracy value. For VGG16 without fine-tuning, the testing accuracy is 98.07%. For VGG16 with fine-tuning, the testing accuracy is 96.56%. © 2022 IEEE.

关键词： Fish

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AI-Driven Market Demand Prediction: An Artificial Neural Network Model for Horticultural Commodities

AI-Driven Market Demand Prediction: An Artificial Neural Net...

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International Conference on Information Technology (INCIT)

作者： Petrus Wolo Aina Musdholifah Sri Mulyana Doctoral Program in Computer Science Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Gadjah Mada University Yogyakarta Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Gadjah Mada University Yogyakarta Indonesia

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

The annual increase in population drives a corresponding rise in demand for goods and services, which in turn directly impacts agricultural needs at both regional and national levels. In Sikka Regency, the demand for horticultural commodities has grown alongside population expansion and increasing prosperity. However, the lack of proper planting distance management has disrupted efficient distribution, leading to logistical inefficiencies. As a result, it is crucial to develop an optimal market demand plan for horticultural commodities to prevent surplus accumulation. To address this challenge, this study proposes a predictive model for monthly market demand in 2024, focusing on 12 types of horticultural commodities in Sikka Regency. Food consumption time series data from 2019 to 2023 are used in the model. A back-propagation algorithm is utilized by an Artificial Neural Network (ANN) to make this prediction. With a Mean Absolute Error (MAE) of 0.166 and a Root Mean Square Error (RMSE) of 0.201, the model's performance evaluation showed an average training accuracy. The average testing accuracy, on the other hand, had an MAE of 0.303 and an RMSE of 0.338. These experimental results show that the suggested strategy might be a useful tool for horticulture commodities market participants and provide insightful information about market dynamics.

关键词： Training Performance evaluation Accuracy Time series analysis Weather forecasting Artificial neural networks Predictive models Prediction algorithms Root mean square Testing

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Towards a Healthy AI Tradition: Lessons from Biology and Biomedical science

arXiv

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arXiv 2024年

作者： Kasif, Simon Department of Biomedical Engineering Bioinformatics Graduate Program Department of Computer Science Boston University United States Department of Computer Science Tel Aviv University

AI is a magnificent field that directly and profoundly touches on numerous disciplines ranging from philosophy, computer science, engineering, mathematics, decision and data science and economics, to cognitive science, neuroscience and more. The number of applications and impact of AI is second to none and the potential of AI to broadly impact future science developments is particularly thrilling. While attempts to understand knowledge, reasoning, cognition and learning go back centuries, AI remains a relatively new field. In part due to the fact it has so many wide-ranging overlaps with other disparate fields it appears to have trouble developing a robust identity and culture. Here we suggest that contrasting the fast-moving AI culture to biological and biomedical sciences is both insightful and useful way to inaugurate a healthy tradition needed to envision and manage our ascent to AGI and beyond (independent of the AI Platforms used). After all, the human brain is a biological organ produced by evolution and human intelligence is a remarkable bi-product of nature and nurture and their complex interaction. In this perspective, we focus on traditions and culture, namely the commonly observed practices of evaluating, recognizing applauding, critiquing, debating and managing all progress including useful advances and discovery of challenging limitations. We are not discussing specific scientific exchanges between AI and Biology that include interdisciplinary cross fertilization of scientific methods, technology, ideas and applications that have been amply demonstrated and will continue to be transformative in the future. In a previous perspective, we suggested that biomedical laboratories or centers can usefully embrace logistic traditions in AI labs that will allow them to be highly collaborative, improve the reproducibility of research, reduce risk aversion and produce faster mentorship pathways for PhDs and fellows. This perspective focuses on the benefits of AI a

关键词： Biological organs

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