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1. The Skills Imperative 2035: What Does the Literature Tell Us about Essential Skills Most Needed for Work? Working Paper 1

Author

National Foundation for Educational Research (NFER) (United Kingdom), Taylor, Amanda, Nelson, Julie, O'Donnell, Sharon, Davies, Elizabeth, and Hillary, Jude

Abstract

Calls are intensifying for workforce reskilling and a re-engineering of education and training to meet the demands of the future. Current policy in England focuses on technical, digital and green economy skills, underpinned by strong literacy and numeracy and a knowledge-rich school curriculum. National Foundation for Educational Research's Nuffield-funded research study, "The Skills Imperative 2035: Essential skills for tomorrow's workforce" investigates: (1) which essential employment skills will be most needed in 2035; (2) what will their likely supply be and where will the gaps be; (3) which occupations and workers are most at risk of not having these skills; (4) which skills will affected workers need to develop to transition into new employment opportunities; and (5) the role of educators and employers in helping to prepare young people and workers for the future labour market. This first report, a review drawing on a wide-ranging and growing evidence base, sets the scene for the wider research study by bringing together what the literature suggests about: (1) what the world of work will look like in 2035; and (2) which essential employment skills will be in demand and how what should be done to prepare.

Published
2022

2. Artificial Intelligence on Campus: Parameters for Thoughtful Action

Author

Alec Thomson

Abstract

Artificial intelligence tools have presented many challenges and opportunities to transform teaching and learning on college campuses. These changes are significant enough to require colleges to take action to create a framework by which faculty and students can navigate the proper usage of these tools. Rather than working to create entirely new policies strictly for addressing these new technologies, they should instead edit their existing academic integrity and intellectual property statements to incorporate explicit mentions of artificial intelligence.

Published
2024

3. LLM potentiality and awareness: a position paper from the perspective of trustworthy and responsible AI modeling

Author

Iqbal H. Sarker

Subjects
LLM, Automation, Risk factors, Trustworthy AI, Responsible AI, Text analytics, Computational linguistics. Natural language processing, P98-98.5, Electronic computers. Computer science, QA75.5-76.95
Abstract

Abstract Large language models (LLMs) are an exciting breakthrough in the rapidly growing field of artificial intelligence (AI), offering unparalleled potential in a variety of application domains such as finance, business, healthcare, cybersecurity, and so on. However, concerns regarding their trustworthiness and ethical implications have become increasingly prominent as these models are considered black-box and continue to progress. This position paper explores the potentiality of LLM from diverse perspectives as well as the associated risk factors with awareness. Towards this, we highlight not only the technical challenges but also the ethical implications and societal impacts associated with LLM deployment emphasizing fairness, transparency, explainability, trust and accountability. We conclude this paper by summarizing potential research scopes with direction. Overall, the purpose of this position paper is to contribute to the ongoing discussion of LLM potentiality and awareness from the perspective of trustworthiness and responsibility in AI.

Published
2024
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5. Declarative Representation of UML State Machines for Querying and Simulation

Author

Mehrafrooz, Zohreh, Jannatpour, Ali, Constantinides, Constantinos, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Tekinerdoğan, Bedir, editor, Spalazzese, Romina, editor, Sözer, Hasan, editor, Bonfanti, Silvia, editor, and Weyns, Danny, editor

Published
2024
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7. IoT Enabled Framework for Smart Home Automation Using Artificial Intelligence and Blockchain Technology

Author

Mazid, Abdul, Kirmani, Sheeraz, Manaullah, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Challa, Rama Krishna, editor, Aujla, Gagangeet Singh, editor, Mathew, Lini, editor, Kumar, Amod, editor, Kalra, Mala, editor, Shimi, S. L., editor, Saini, Garima, editor, and Sharma, Kanika, editor

Published
2024
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9. Automated Reporting of Code Quality Issues in Student Submissions

Author

Karnalim, Oscar, Simon, Chivers, William, Panca, Billy Susanto, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Keane, Therese, editor, Lewin, Cathy, editor, Brinda, Torsten, editor, and Bottino, Rosa, editor

Published
2023
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10. Markov Decision Process for Automatic Cyber Defense

Author

Zhou, Xiaofan, Enoch, Simon Yusuf, Kim, Dong Seong, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, You, Ilsun, editor, and Youn, Taek-Young, editor

Published
2023
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11. Automatic Control System Based on Industry 4.0, PLC, and SCADA

Author

Kibulungu, Jonathan Wilondja, Laseinde, Opeyeolu Timothy, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nagar, Atulya K., editor, Singh Jat, Dharm, editor, Mishra, Durgesh Kumar, editor, and Joshi, Amit, editor

Published
2023
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12. Deep Learning Based Automated Smart Cart With Inventory Management For Sustainable Development Using IoT

Author

Jain, Aman, Nagrath, Ishu, Bankawat, Ayush, Arora, Simran, Sharma, Pavika, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Whig, Pawan, editor, Silva, Nuno, editor, Elngar, Ahmed A., editor, Aneja, Nagender, editor, and Sharma, Pavika, editor

Published
2023
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13. Integrating Industry 4.0 Technologies for Enhanced Safety Engineering: A Comprehensive Review and Analysis

Author

Hutchins, Savannah, Jhaveri, Niral, Duffy, Vincent G., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Duffy, Vincent G., editor, Krömker, Heidi, editor, A. Streitz, Norbert, editor, and Konomi, Shin'ichi, editor

Published
2023
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14. Performance Losses with Virtualization: Comparing Bare Metal to VMs and Containers

Author

Baumgartner, Jonatan, Lillo, Christophe, Rumley, Sébastien, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Bienz, Amanda, editor, Weiland, Michèle, editor, Baboulin, Marc, editor, and Kruse, Carola, editor

Published
2023
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15. Swarm Maneuver of Combat UGVs on the Future Digital Battlefield

Author

Nohel, Jan, Stodola, Petr, Flasar, Zdeněk, Křišťálová, Dana, Zahradníček, Pavel, Rak, Luděk, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mazal, Jan, editor, Fagiolini, Adriano, editor, Vašík, Petr, editor, Bruzzone, Agostino, editor, Pickl, Stefan, editor, Neumann, Vlastimil, editor, Stodola, Petr, editor, and Lo Storto, Stefano, editor

Published
2023
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16. Quality Control Automation System for Furniture Connecting Fittings to Achieve Zero Defect

Author

Mertöz, Cihan, Şen, Çağla, Utuş, Ömür, Karaarslan, Nurettin, Başçı, Hüseyin, Fırat, Sedat, Alev, Uğur, Vanlı, Ali Serdar, Akdoğan, Anıl, Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Durakbasa, Numan M., editor, and Gençyılmaz, M. Güneş, editor

Published
2023
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17. Possibilities and Barriers in Implementing of AI-Based Automation Techniques in Management

Author

Dolle, Nicolas, Kuzmina-Merlino, Irina, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kabashkin, Igor, editor, Yatskiv, Irina, editor, and Prentkovskis, Olegas, editor

Published
2023
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18. Value Entity Recognition Task in the Air Transportation on the Base of E-Texts Analysis

Author

Zervina, Olga, Stukalina, Yulia, Pavlyuk, Dmitry, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kabashkin, Igor, editor, Yatskiv, Irina, editor, and Prentkovskis, Olegas, editor

Published
2023
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20. Industry 4.0: Implementation of Technologies in Medical Manufacturing Companies

Author

Guadalupe Mori, Victor Hugo, Ogosi Auqui, José Antonio, Rosales Huamani, Jimmy Aurelio, Arenas Ñiquin, José Luis, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nagar, Atulya K., editor, Singh Jat, Dharm, editor, Mishra, Durgesh Kumar, editor, and Joshi, Amit, editor

Published
2023
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21. Optimization of the control paper production system of using the extremum seeking control method

Author

Olga Ermilina and Vladimir Chemodanov

Subjects
Automation, Extremum seeking controller, Papermaking, Synchronous accumulation, Synchronous detection, Science, Technology
Abstract

Article highlights Automated control system for paper production has been developed, which has increased the accuracy and stability of regulating the weight of paper web. Fiber consumption rates decreased by 2%. The standard deviation of the weight of the paper web decreased by 1.5-2%.

Published
2023
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22. Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning.

Author

Salimi Y, Mansouri Z, Hajianfar G, Sanaat A, Shiri I, and Zaidi H

Subjects
Humans, Radiography, Abdominal, Abdomen diagnostic imaging, Machine Learning, Tomography, X-Ray Computed, Contrast Media, Image Processing, Computer-Assisted methods, Automation
Abstract

Background: Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is usually missing on public datasets and not standardized in the clinic even in the same region and language. This is a barrier to effective use of available CECT images in clinical research., Purpose: The aim of this study is to detect contrast media injection phase from CT images by means of organ segmentation and machine learning algorithms., Methods: A total number of 2509 CT images split into four subsets of non-contrast (class #0), arterial (class #1), venous (class #2), and delayed (class #3) after contrast media injection were collected from two CT scanners. Seven organs including the liver, spleen, heart, kidneys, lungs, urinary bladder, and aorta along with body contour masks were generated by pre-trained deep learning algorithms. Subsequently, five first-order statistical features including average, standard deviation, 10, 50, and 90 percentiles extracted from the above-mentioned masks were fed to machine learning models after feature selection and reduction to classify the CT images in one of four above mentioned classes. A 10-fold data split strategy was followed. The performance of our methodology was evaluated in terms of classification accuracy metrics., Results: The best performance was achieved by Boruta feature selection and RF model with average area under the curve of more than 0.999 and accuracy of 0.9936 averaged over four classes and 10 folds. Boruta feature selection selected all predictor features. The lowest classification was observed for class #2 (0.9888), which is already an excellent result. In the 10-fold strategy, only 33 cases from 2509 cases (∼1.4%) were misclassified. The performance over all folds was consistent., Conclusions: We developed a fast, accurate, reliable, and explainable methodology to classify contrast media phases which may be useful in data curation and annotation in big online datasets or local datasets with non-standard or no series description. Our model containing two steps of deep learning and machine learning may help to exploit available datasets more effectively., (© 2024 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Published
2024
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23. GPTZero vs. Text Tampering: The Battle That GPTZero Wins

Author

David W. Brown and Dean Jensen

Abstract

The growth of Artificial Intelligence (AI) chatbots has created a great deal of discussion in the education community. While many have gravitated towards the ability of these bots to make learning more interactive, others have grave concerns that student created essays, long used as a means of assessing the subject comprehension of students, may be at risk. The bot's ability to quickly create high quality papers, sometimes complete with reference material, has led to concern that these programs will make students too reliant on their ability and not develop the critical thinking skills necessary to succeed. The rise in these applications has led to the need for the development of detection programs that are able to read the students submitted work and return an accurate estimation of if the paper is human or computer created. These detection programs use natural language processing's (NLP) ideas of perplexity, or randomness of the text, and burstiness, or the tendency for certain words and phrases to appear together, plus sophisticated algorithms to compare the essays to preexisting literature to generate an accurate estimation on the likely author of the paper. The use of these systems has been found to be highly effective in reducing plagiarism among students, however concerns have been raised about the limitations of these systems. False positives, false negatives, and cross language identification are three areas of concern amongst faculty and have led to reduced usage of the detection engines. Despite the limitations however, these systems are a valuable tool for educational institutions to maintain academic integrity and ensure that students are submitting original work. [For the full proceedings, see ED656038.]

Published
2023

24. The Image-Based Automatic Detection Method for Cutter Ring Edge Wear of Shield Machine

Author

Bo, Wen, Li, Fengyuan, Cai, Xiaofeng, Zhang, Changyou, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Sun, Yuqing, editor, Lu, Tun, editor, Cao, Buqing, editor, Fan, Hongfei, editor, Liu, Dongning, editor, Du, Bowen, editor, and Gao, Liping, editor

Published
2022
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26. Virtual Police Station System with Chat Bot Using FSM

Author

Gupta, Bhoomi, Rustagi, Parth, Sinha, Priyanshu, Sroa, Rohit, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Nagar, Atulya K., editor, Jat, Dharm Singh, editor, Marín-Raventós, Gabriela, editor, and Mishra, Durgesh Kumar, editor

Published
2022
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27. IRIS Advanced Threat Intelligence Orchestrator- A Way to Manage Cybersecurity Challenges of IoT Ecosystems in Smart Cities

Author

Bilali, Vasiliki-Georgia, Kosyvas, Dimitrios, Theodoropoulos, Thodoris, Ouzounoglou, Eleftherios, Karagiannidis, Lazaros, Amditis, Angelos, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, González-Vidal, Aurora, editor, Mohamed Abdelgawad, Ahmed, editor, Sabir, Essaid, editor, Ziegler, Sébastien, editor, and Ladid, Latif, editor

Published
2022
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28. Functionality Testing in the Automation of Scientific Application Workflows in an HPC Environment

Author

de Jesús Orozco Luna, Felipe, González, Jesús Manuel Alemán, Dueñas, Veronica Lizette Robles, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Navaux, Philippe, editor, Barrios H., Carlos J., editor, Osthoff, Carla, editor, and Guerrero, Ginés, editor

Published
2022
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30. Co-designing Prototypes for User Experience and Engagement in Automation : Case Study of London-based Airport Future Workplace

Author

Saadati, Parisa, Abdelnour-Nocera, José, Clemmensen, Torkil, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Bhutkar, Ganesh, editor, Barricelli, Barbara R., editor, Xiangang, Qin, editor, Clemmensen, Torkil, editor, Gonçalves, Frederica, editor, Abdelnour-Nocera, José, editor, Lopes, Arminda, editor, Lyu, Fei, editor, Zhou, Ronggang, editor, and Hou, Wenjun, editor

Published
2022
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31. Co-design Workshops as a Step Towards Pilot Implementation for Complex Workplaces : A Case Study of London-Based Airport Future Workplace

Author

Saadati, Parisa, Abdelnour-Nocera, José, Clemmensen, Torkil, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Ardito, Carmelo, editor, Lanzilotti, Rosa, editor, Malizia, Alessio, editor, Larusdottir, Marta, editor, Spano, Lucio Davide, editor, Campos, José, editor, Hertzum, Morten, editor, Mentler, Tilo, editor, Abdelnour Nocera, José, editor, Piccolo, Lara, editor, Sauer, Stefan, editor, and van der Veer, Gerrit, editor

Published
2022
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32. One-Word Approach in Text-Mining for Value Identification

Author

Zervina, Olga, Stukalina, Yulia, Pavlyuk, Dmitry, Rubens, Neil, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kabashkin, Igor, editor, Yatskiv, Irina, editor, and Prentkovskis, Olegas, editor

Published
2022
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33. Human-Centered Artificial Intelligence: Beyond a Two-Dimensional Framework

Author

Pacailler, Matthew, Yahoodik, Sarah, Sato, Tetsuya, Ammons, Jeremiah G., Still, Jeremiah, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Chen, Jessie Y. C., editor, Fragomeni, Gino, editor, Degen, Helmut, editor, and Ntoa, Stavroula, editor

Published
2022
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35. Self Driving Car in a Constrained Environment

Author

Harish, B., Thenmozhi, Durairaj, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Fernando, Xavier, editor, Lu, Joan, editor, Piramuthu, Selwyn, editor, and Chandrabose, Aravindan, editor

Published
2022
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36. Smart License Approval Using AIOT

Author

Jagati, Anika, Raja, Maheswari, Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Goedicke, Michael, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Tröltzsch, Fredi, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, Kreps, David, Editorial Board Member, Reis, Ricardo, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Fernando, Xavier, editor, Lu, Joan, editor, Piramuthu, Selwyn, editor, and Chandrabose, Aravindan, editor

Published
2022
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37. Artificial Intelligence Techniques for Automating Management and Leadership Tasks: Literature Review

Author

Kuzmina-Merlino, Irina, Dolle, Nicolas, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Kabashkin, Igor, editor, Yatskiv, Irina, editor, and Prentkovskis, Olegas, editor

Published
2022
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38. Assembling Automation for Furniture Fittings to Gain Durability and to Increase Productivity

Author

Kocaman, Musaddin, Mertöz, Cihan, Gökçe, Rıza, Fırat, Sedat, Akdoğan, Anıl, Vanlı, Ali Serdar, Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, di Mare, Francesca, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Durakbasa, Numan M., editor, and Gençyılmaz, M. Güneş, editor

Published
2022
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40. Automated Search for Logistic Knowledge Tracing Models

Author

Pavlik, Philip I. and Eglington, Luke G.

Abstract

This paper presents a tool for creating student models in logistic regression. Creating student models has typically been done by expert selection of the appropriate terms, beginning with models as simple as IRT or AFM but more recently with highly complex models like BestLR. While alternative methods exist to select the appropriate predictors for the regression-based models (e.g., stepwise selection or LASSO), we are unaware of their application to student modeling. Such automatic methods of model creation offer the possibility of better student models with either reduced complexity or better fit, in addition to relieving experts from the burden of searching for better models by hand with possible human error. Our new functions are now part of the preexisting R package LKT. We explain our search methods with two datasets demonstrating the advantages of using the tool with stepwise regression and regularization (LASSO) methods to aid in feature selection. For the stepwise method using BIC, the models are simpler (due to the BIC penalty for parameters) than alternatives like BestLR with little lack of fit. For the LASSO method, the models can be made simpler due to the fitting procedure involving a regularization parameter that penalizes large absolute coefficient values. However, LASSO also offers the possibility of highly complex models with exceptional fit. [For the complete proceedings, see ED630829.]

Published
2023

41. Using Graph-Based Heuristics to Analyze a Systematic Literature Review and Validate an Expert-Based Taxonomy

Author

Nadine Schlomske-Bodenstein, Bernhard Standl, and Pablo Pirnay-Dummer

Abstract

The study presented in this paper uses heuristics from computer linguistics and graph theory to analyze a systematic literature review on educational technology. A literature review was conducted to validate an expert-based taxonomy which was developed to ontologize delivered teaching and learning for easy reuse. The sample includes N = 121 publications. The findings indicate that the yielded key words that were generated through T-MITOCAR Artemis contained key features that were rated as being relevant to the experts and caused the expert-based taxonomy to be changed and restructured. The results of this study provide directions on how time-consuming elements of systematic literature reviews could partially be automated. [For the full proceedings, see ED636095.]

Published
2023

42. Beyond the Obvious Multi-Choice Options: Introducing a Toolkit for Distractor Generation Enhanced with NLI Filtering

Author

Andreea Dutulescu, Stefan Ruseti, Denis Iorga, Mihai Dascalu, and Danielle S. McNamara

Abstract

The process of generating challenging and appropriate distractors for multiple-choice questions is a complex and time-consuming task. Existing methods for an automated generation have limitations in proposing challenging distractors, or they fail to effectively filter out incorrect choices that closely resemble the correct answer, share synonymous meanings, or imply the same information. To overcome these challenges, we propose a comprehensive toolkit that integrates various approaches for generating distractors, including leveraging a general knowledge base and employing a T5 LLM. Additionally, we introduce a novel strategy that utilizes natural language inference to increase the accuracy of the generated distractors by removing confusing options. Our models have zero-shot capabilities and achieve good results on the DGen dataset; moreover, the models were fine-tuned and outperformed state-of-the-art methods on the considered dataset. To further extend the analysis, we introduce human annotations with scores for 100 test questions with 1085 distractors in total. The evaluations indicated that our generated options are of high quality, surpass all previous automated methods, and are on par with the ground truth of human-defined alternatives. [This paper was published in: "AIED 2024, LNAI 14830," edited by A. M. Olney et al., Springer Nature Switzerland, 2024, pp. 242-50.]

Published
2024
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43. Automated Search for Logistic Knowledge Tracing Models

Author

Pavlik, Philip I. and Eglington, Luke G.

Abstract

This paper presents a tool for creating student models in logistic regression. Creating student models has typically been done by expert selection of the appropriate terms, beginning with models as simple as IRT or AFM but more recently with highly complex models like BestLR. While alternative methods exist to select the appropriate predictors for the regression-based models (e.g., step-wise selection or LASSO), we are unaware of their application to student modeling. Such automatic methods of model creation offer the possibility of better student models with either reduced com-plexity or better fit, in addition to relieving experts from the bur-den of searching for better models by hand with possible human error. Our new functions are now part of the preexisting R pack-age LKT. We explain our search methods with two datasets demonstrating the advantages of using the tool with stepwise re-gression and regularization (LASSO) methods to aid in feature selection. For the stepwise method using BIC, the models are simpler (due to the BIC penalty for parameters) than alternatives like BestLR with little lack of fit. For the LASSO method, the models can be made simpler due to the fitting procedure involving a regularization parameter that penalizes large absolute coefficient values. However, LASSO also offers the possibility of highly complex models with exceptional fit. [This paper was published in: "Proceedings of the 16th International Conference on Educational Data Mining," edited by M. Feng et. al., International Educational Data Mining Society, 2023, pp.17-27.]

Published
2023
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45. REVIEW PAPER BIOSENSORS FOR EARLY DIAGNOSIS AND AUTOMATED DRUG DELIVERY IN PANCREATIC CANCER.

Author

S., ANAND

Subjects
CANCER diagnosis, DRUG delivery systems, PANCREATIC cancer, MACHINE learning, CANCER prognosis
Abstract

Pancreatic cancer remains one of the most challenging malignancies to diagnose and treat effectively, resulting in poor patient outcomes due to late-stage detection and limited therapeutic options. The emergence of biosensors has revolutionized cancer diagnosis and therapy, providing new avenues for early detection and personalized treatment. This paper explores the development and integration of biosensors within a unique expert system for pancreatic cancer diagnosis and drug delivery automation. It discusses the principles, types, and applications of biosensors in pancreatic cancer diagnosis, their role in automating drug delivery, and the design of an expert system that leverages these technologies to enhance patient outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
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47. Desirable and Realistic Futures of the University: A Mixed-Methods Study with Teachers in Denmark

Author

Magda Pischetola, Maria Hvid Stenalt, Liv Nøhr, Danielle Elizabeth Hagood, and Morten Misfeldt

Abstract

In this paper, we put in dialogue the local dimension of a nation-state with the global challenges faced by universities worldwide. We focus on the case of Denmark, a nation that was exceptionally active in implementing international university reforms and where digitalisation is a high priority of the public sector governance. The article seeks to contribute to speculative research and critical studies by presenting a mixed-methods study that explores higher education teachers' views about desirable and realistic future scenarios in Denmark. The study draws on data from a survey applied at a large Danish university, analysed both quantitatively (N = 755) and qualitatively (N = 53). The findings show that teachers share clusters of concern about uncontrolled digitalisation and teaching automation, commodification of education, and modularisation of university courses, the latter being a contested but realistic perspective in the latest political reforms. They reaffirm the mission of the university as preparing students for solving real problems and contributing to the challenges of the present time. The paper concludes with a call for university management to recalibrate future imaginaries to the values expressed by the teachers, and the university they wish to create.

Published
2024
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48. A Proposal for Combining Project Based Learning and Lean Six Sigma to Teach Robotic Process Automation Development and Enhance Systems Integration

Author

Money, William H. and Mew, Lionel Q.

Abstract

This paper proposes a Project-based team instruction methodology with open-ended projects to teach students critical analysis, design and implementation steps of developing Robotic Process Automation (RPA) for information systems. The use of project-based learning is appropriate for teaching RPA analysis and design with lean Six Sigma tools because of its experimental approach and documentation of logical steps needed to learn how to implement RPA successfully. The approach systematically documents work currently performed and defines future actions of the process while ensuring significant benefits are achieved with the RPA enhanced process. This methodology is important because the application of RPA is not commonly taught in Management Information System (MIS) programs. MIS students may not understand the significance of combined methodology, RPA tool, and usefulness of RPA until they enter the workforce where RPA is rapidly becoming available and easier to implement. The lecture sessions and exercises are valuable because it is easy to communicate the value of RPA in terms of time, quality, volume of transactions, etc. using Lean Six Sigma analytic approaches. The exercises involve hands on activities to make this learning experience interesting for students to readily associate the theoretical process improvement agreement and visualize the practical value of RPA enhanced projects. The paper discusses the need for process changes (and new development approaches) in organization to match the properties and functions within enterprise systems and ERPs that has led to criticism of the enterprise systems. This criticism is attributable to the ERPs' many subfunctions and operations that have limited adaptability and reduced functional and operational flexibility. The RPAs require limited prior knowledge of ERPs or their sub-processes for the improvements that are made in the performance of the organization. Thus, students do not have to "learn" how these enterprise or ERP systems operate to make changes or task improvements. This paper presents a project-based methodology and design approach focusing on development of RPAs that help students learning how to make the improvements using the RPS tools. The students learn that projects can deliver significant and tangible benefits to organizations while engaging students in key activities of the analysis, design and development process from a low code-no code perspective.

Published
2023

49. A Framework for Measuring Relevancy in Discovery Environments: Increasing Scalability and Reproducibility.

Author

Galbreath, Blake, Merrill, Alex, and Johnson, Corey M.

Subjects
WORLD Wide Web, SERIAL publications, ECOLOGY, COMPUTER software, SEASONS, RESEARCH evaluation, CITATION analysis, NEWSPAPERS, STUDENTS, BIBLIOGRAPHICAL citations, BOOKS, BIBLIOGRAPHY, MEDICAL research, AUTOMATION, DATA analysis software
Abstract

Institutional discovery environments now serve as central resource databases for researchers in the academic environment. Over the last several decades, there have been numerous discovery layer research inquiries centering primarily on user satisfaction measures of discovery system effectiveness. This study focuses on the creation of a largely automated method for evaluating discovery layer quality, utilizing the bibliographic sources from student research projects. Building on past research, the current study replaces a semiautomated Excel Fuzzy Lookup Add-In process with a fully scripted R-based approach, which employs the stringdist R package and applies the Jaro-Winkler distance metric as the matching evaluator. The researchers consider the error rate incurred by relying solely on an automated matching metric. They also use Open Refine for normalization processes and package the tools together on an OSF site for other institutions to use. Since the R-based approach does not require special processing or time and can be reproduced with minimal effort, it will allow future studies and users of our method to capture larger sample sizes, boosting validity. While the assessment process has been streamlined and shows promise, there remain issues in establishing solid connections between research paper bibliographies and discovery layer use. Subsequent research will focus on creating alternatives to paper titles as search proxies that better resemble genuine information-seeking behavior and comparing undergraduate and graduate student interactions within discovery environments. [ABSTRACT FROM AUTHOR]

Published
2024
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50. Multitask Summary Scoring with Longformers

Author

Botarleanu, Robert-Mihai, Dascalu, Mihai, Allen, Laura K., Crossley, Scott Andrew, and McNamara, Danielle S.

Abstract

Automated scoring of student language is a complex task that requires systems to emulate complex and multi-faceted human evaluation criteria. Summary scoring brings an additional layer of complexity to automated scoring because it involves two texts of differing lengths that must be compared. In this study, we present our approach to automate summary scoring by evaluating a corpus of approximately 5,000 summaries based on 103 source texts, each summary being scored on a 4-point Likert scale for seven different evaluation criteria. We train and evaluate a series of Machine Learning models that use a combination of independent textual complexity indices from the ReaderBench framework and Deep Learning models based on the Transformer architecture in a multitask setup to predict concurrently all criteria. Our models achieve significantly lower errors than previous work using a similar dataset, with MAE ranging from 0.10-0.16 and corresponding R[superscript 2] values of up to 0.64. Our findings indicate that Longformer-based models are adequate for contextualizing longer text sequences and effectively scoring summaries according to a variety of human-defined evaluation criteria using a single Neural Network. [This paper was published in: "AIED 2022, LNCS 13355," edited by M. M. Rodrigo et al., Springer Nature Switzerland, 2022, pp. 756-761.]

Published
2022
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