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Are Preservice Teachers "Totally PACKaged"? A Quantitative Study of Pre-service Teachers' Knowledge and Skills to Ethically Integrate Artificial Intelligence (AI)-Based Tools into Education 4th

Are Preservice Teachers "Totally PACKaged"? A Quantitative S...

引用

4th International Conference on Digital Technologies and Applications (ICDTA)

作者： Gregorio, Thrixia Angela D. Alieto, Ericson O. Natividad, Eric-Roland R. Tanpoco, Manuel R. Western Mindanao State Univ Normal Rd Zamboanga City Philippines De La Salle Univ 2401 Taft Ave Manila Philippines

ISBN: (纸本)9783031686597;9783031686603

Advancements in modern technologies have resulted in the integration of artificial intelligence (AI) in the field of education. It is imperative that educators are knowledgeable and skilled with various AI-based tools. However, little is known about the extent of the knowledge and skills of preservice teachers in utilizing AI-based tools in education or about the extent of their ethical considerations when using AI-based tools. Preservice teachers are the vanguard of 21st century teaching and it is important that they have the capacity to incorporate technology into pedagogy. This study employed a descriptive-quantitative-correlational approach to determine the extent of knowledge and skills of 212 preservice teachers in using AI-based tools, as well as the extent of the respondents' ethical considerations in utilizing AI-based tools in education, with the use of the Intelligent-TPACK survey form adapted from Celik (2023). The data gathered were analyzed through descriptive analysis, namely mean, standard deviation and percentage, as well as inferential statistics of Pearson bivariate correlation to determine if there was a significant interrelationship among technological knowledge (TK), technological pedagogical knowledge (TPK), technological content knowledge (TCK) and technological pedagogical and content knowledge (TPACK). The results revealed that preservice teachers possess high levels of TK, TPK, TCK, and TPACK. Furthermore, the participants are capable of skillfully utilizing technology into teaching, which indicates that preservice teachers are up to date with the trends in artificial intelligence. In addition, the participants were revealed to have high levels of ethical consideration in utilizing AI-based tools, where they can discern which tools will be best in improving the students' works and give fair feedback in assessing the works of the students. Finally, significant interrelationships among TK, TPK, TCK, and TPACK were also found, in which compet

关键词： Artificial Intelligence Preservice Teachers TPACK

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Decision Aid on Blockchain Project: A Cost and Time Management Tool 7th

Decision Aid on Blockchain Project: A Cost and Time Manageme...

引用

7th International Conference on Logistics Operations Management (GOL)

作者： Diop, Mor Tazi, Said Ducq, Yves Univ Bordeaux IMS CNRS UMR 5218 F-33405 Talence France Univ Pau & Pays Adour LIUPPA UPPA E2S F-64600 Anglet France

ISBN: (纸本)9783031686337;9783031686344

Initially developed for financial transactions, blockchain has become a subject of discussion in other sectors due to its capacity for security and transparency. Like any newtechnology, blockchain requires readjustment and improvement to reach maturity. In the supply chain, efforts are increasingly being made to adapt and apply this technology within operations. This paper proposes a blockchain decision aid tomanage costs and lead times, which have long been real bottlenecks in these kinds of projects. The result demonstrates the effectiveness of our approach in anticipating, managing and documenting these risks.

关键词： Blockchain Decision aid management

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Valohai-CSC Integration: A Machine Learning Management Platform for Finnish Academic Institutions 21st

Valohai-CSC Integration: A Machine Learning Management Platf...

引用

21st International Conference on Smart Technologies and Education (STE) on Smart Technologies for a Sustainable Future

作者： Espinosa-Leal, Leonardo Shcherbakov, Andrey Westerlund, Magnus Arcada Univ Appl Sci Grad Sch & Res Jan Magnus Janssons Pl 1 Helsinki 00560 Finland Arcada Univ Appl Sci Sch Engn Culture & Wellbeing Jan Magnus Janssons Pl 1 Helsinki 00560 Finland

ISBN: (纸本)9783031619045;9783031619052

We present and review the results of the integration of Valohai (https://***), a machine learning management platform that supports training, pipeline and version control machine learning, and deep learning network capabilities in combination with the computational resources from the CSC-IT Center for Science (https://www. ***), a Finnish center of expertise in information technology provider of supercomputer capabilities owned by the Finnish state and higher education institutions. We tested this integration against different commercial cloud services by training a YOLOv3 model with the COCO2014 dataset as benchmarking. Our findings reflect the technical advantages of using this infrastructure for research and teaching in higher education institutions in Finland.

关键词： Machine Learning Valohai CSC MLOps

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Deriving the Design Requirements to Make Mobile Apps Accessible for Older Adults

Deriving the Design Requirements to Make Mobile Apps Accessi...

引用

Intelligent systems Conference

作者： Ling, Ruiqi NYU Comp Sci New York NY 10012 USA

ISBN: (纸本)9783031663352;9783031663369

A lot of mobile apps are not accessible to older adults, who are not the target audience when these apps are designed. However, mobile apps are critical to people's lives, including older adults. In this research, we take one mobile app as an example and investigate the challenges older adults have when interacting with similar mobile apps. Specifically, we collect survey responses from 137 people, from which we summarize the challenges and design requirements for the mobile app to be accessible to older adults. We then created a high-fidelity prototype of a redesign of the mobile app implementing the design requirements we identified. We then evaluated the redesign with 3 users and collected their feedback on the usability and accessibility of the redesigned version of the app.

关键词： Accessibility Older adults Mobile app design Figma

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LSTM Network and Box and Jenkins Methodology for Time Series Forecasting: Solar Energy Production 7th

LSTM Network and Box and Jenkins Methodology for Time Series...

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7th International Conference on Logistics Operations Management (GOL)

作者： Riahi, Mohamed Hedi Maalaoui, Hiba Hedhli, Amel Ncib, Lotfi ESPRIT Sch Engn Ariana Tunisia RIDCHADATA Montigny Le Bretonneux France

ISBN: (纸本)9783031686276;9783031686283

Solar power generation experiences a significant surge, the forecasting of incoming solar energy is becoming increasingly crucial. The aim of this study is twofold. First, it aims to show how to overcome some of the shortcomings of standard methods of forecasting solar energy production using time series models and to propose an alternative empirical approach based on Recurrent neural network (LSTM algorithm). Secondly, it aims to fill the research gaps in forecasting in the Sfax region. In this regard, for the production of solar energy from June 2019 to March 2020, we use the ARIMA model for solar energy forecasting. Based on the metrics, we evaluate the effectiveness of time series models. By implementing the LSTM method, we note that, empirically, the LSTM models give better results in forecasting solar energy production for the Sfax region.

关键词： Time series Predictions Box and Jenkins method solar energy deep learning LSTM network

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Customisable Training Programme on Best Industrial Practices for Sustainability, Circular Economy and Energy Efficiency 14th

Customisable Training Programme on Best Industrial Practices...

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14th International Conference on Learning Factories (CLF)

作者： Panagiotopoulou, Vasiliki C. Paraskevopoulou, Aikaterini Margaityte, Milda Petkova, Elitsa Ivova Stavropoulos, Panagiotis Univ Patras Dept Mech Engn & Aeronaut Lab Mfg Syst & Automat Patras 26504 Greece Smart Mfg Technol Ctr InTechCentras Vilnius Lithuania Cleantech Bulgaria Krakra St 18 Sofia 1504 Bulgaria

ISBN: (纸本)9783031654107;9783031654114

Industry is considered one of the top contributors of carbon emissions and it is vital to be fully aligned with the European GreenDeal, which aims to make Europe the first carbon neutral continent by 2050. However, only a few industrial companies have implemented technologies and strategic approaches towards green transition. This delay is not only due to poor implementation of measures towards carbon footprint reduction, but most importantly due to a lack of knowledge around basic concepts and latest developments in key green strategies (Sustainability, Circular Economy, Energy Efficiency). The focus of the current paper is identifying the level of sustainability readiness of small and medium enterprises (SMEs) and the main drives and obstacles towards implementing green strategies. For that, a questionnaire was created for companies, with the results used to develop a training programme (TP) to fit the companies' needs. The TP included asynchronous and synchronous learning, with the latter available in physical and virtual settings. The asynchronous learning was used as preparatory material, while all events included a questionnaire to assess the learners' comprehension. For the synchronous learning, participants were asked to develop their own roadmap towards improving the sustainability level of a use case, showing their ability to implement these concepts and best practices in real-life. Despite the plethora of available material, only few TPs consider the industrial needs, to provide a tailor-made curriculum, providing background knowledge and current practices. This allows companies to create their own roadmap towards improving the sustainability level in practice.

关键词： Industrial needs Roadmap to Sustainability Sustainability level Circular economy Energy efficiency

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Digital Twins: Revolutionizing Automotive Supply Chains 7th

Digital Twins: Revolutionizing Automotive Supply Chains

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7th International Conference on Logistics Operations Management (GOL)

作者： Triantafyllou, Maria Al-Bazi, Ammar Ahmad, Mahmood Abdulsattar Univ Warwick Warwick Mfg Grp Coventry CV4 7AL W Midlands England Aston Univ Aston Business Sch Birmingham B4 7ET W Midlands England

ISBN: (纸本)9783031686276;9783031686283

This paper explores the transformative potential ofDigital Twins (DTs) in the automotive sector, particularly in the context of the 4th industrial revolution. It delves into the evolution of DTs from their origins to their current applications to highlight the rapid technological advancement and the increasing influence DTs have on various industries and contexts. It discusses how DTs, powered by advanced technologies like IoT, Big Data analytics, and simulation techniques, are being adopted by automotive manufacturers to enhance the lifecycle management of vehicles and optimize supply chain processes. It examines the role of DTs in various stages of a vehicle's lifecycle, from its conceptualization and design to predictive maintenance and disposal to highlight the importance for interoperability and improved integration with other advanced technologies. Moreover, it identifies key research gaps that need to be addressed for further advancement in this field. It emphasizes the importance of fostering DT integration with other disruptive technologies and developing robust data management strategies. It calls for collaborative efforts in research and industry to bridge the existing gaps and fully unlock the potential of DTs in the automotive sector.

关键词： Digital twins automotive supply chains Internet of Things AI

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Variational Inference Driven Drug Protein Binding Prediction 11th

Variational Inference Driven Drug Protein Binding Prediction

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11st World Conference on Information systems and Technologies (WorldCIST)

作者： Kumar, Neeraj Narang, Anish IIT Delhi New Delhi India AAAI Washington DC 20001 USA

ISBN: (纸本)9783031456473;9783031456480

The identification of drug-protein interactions (DPIs) is a key task in drug discovery, where drugs are chemical compounds and targets are proteins. Traditional DPI prediction methods are either time consuming (simulation-based methods) or heavily dependent on domain expertise (similarity-based and feature-based methods). Recent explorations involving deep learning either exploit 3D structure of the proteins and/or use GNNs (graph convolutions) to capture neighbour relationships. In this paper, we present a novel end-end deep learning architecture, VED-BI, that leverages variational inference based encoder and decoder along with GraphSage based approach to predict drug-protein interaction. Due to better generalization, our architecture is able to deliver better results on the drug protein binding prediction. Detailed experimental comparative analysis using Precision, Recall and AUC metrics, has been performed across all the approaches on metador and bindingDB datasets. Our proposed novel architecture performs better on bindingDB dataset as compared to key state-of-the-art results.

关键词： variational architecture drug-protein interactions transductive inference

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Predicting Hanoi House Prices Using Machine Learning 9th

Predicting Hanoi House Prices Using Machine Learning

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9th International Congress on Information and Communication Technology (ICICT)

作者： Nguyen Hong Van Vu Thu Diep Nguyen Quang Thang Phan Thanh Ngoc Phan Duy Hung FPT Univ Hanoi Vietnam HaNoi Univ Sci & Technol Hanoi Vietnam VNU Univ Engn & Technol Hanoi Vietnam

ISBN: (纸本)9789819732982;9789819732999

Nowadays, predicting house prices has long been a fundamental challenge in the real estate industry and finance. Machine learning methods are utilized to uncover valuable models beneficial to both home buyers and sellers. This study is to predict house prices in Hanoi using data from the website *** and applying different algorithms such as linear regression, Support Vector Machine, decision tree, and random forest. Many factors affect house prices, including physical factors such as area and location, as well as economic factors. In this paper, we use root mean squared error, mean absolute error, and R-squared as measures of model effectiveness. From there, we utilize and contrast these metrics to identify the model that exhibits the highest level of accuracy, contributing to a deeper understanding of the Hanoi real estate market.

关键词： Housing prices Linear regression SVM Decision tree Random forest regression

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Stock Market Prediction: Integrating Explainable AI with Conv2D Models for Candlestick Image Analysis 12th

Stock Market Prediction: Integrating Explainable AI with Con...

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12th World Conference on Information systems and Technologies (WorldCIST)

作者： Euko, Joao Paulo Santos, Flavio Novais, Paulo Univ Fed Sergipe Comp Sci Dept Sao Cristovao Brazil Univ Fed Pernambuco IT Ctr Recife Brazil Univ Minho ISLAB Braga Portugal

ISBN: (纸本)9783031602177;9783031602184

The term "candlestick" refers to a graphical representation of price movements in financial markets. Predicting candlesticks is crucial for anticipating market trends and making informed investment decisions. Interpretability is essential to understand the rationale behind model predictions, particularly in complex financial environments. In conclusion, this research leverages explainability techniques like SHAP and GRAD-CAM to improve the interpretability of Conv2D models for candlestick prediction in stock market images.

关键词： xAI deep learning stock market

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