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Generative AI as a Catalyst for Transforming Transnational engineering Education: Opportunities, Challenges, and Future Directions

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Generative AI as a Catalyst for Transforming Transnational E...

IEEE Education engineering (EDUCON)

作者： Sami Ahmed Haider Khwaja Mutahir Ahmad Jehan Akbar Mukesh Soni Ismail Keshta Azzah AlGhamdi Hafiza Mahrukh Shahzadi School of Engineering and Physical Sciences Heriot-Watt University EH144AS Edinburgh United Kingdom School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu P.R. China Glasgow College University of Glasgow Glasgow United Kingdom Centre for Research & Development Chandigarh University Mohali Punjab India College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia

ISBN: (数字)9798331539498

ISBN: (纸本)9798331539504

Generative artificial intelligence (GAI) is emerging as a transformative force that empowers transnational education (TNE) in engineering. Recent trends indicate a significant shift in the application of generative AI in engineering policies, academic research, business practices, and educational settings throughout TNE. Governments and organizations are transitioning from restrictive stances to developing guiding frameworks for its application, enabling cross-border collaboration in TNE. Numerous universities have permitted and even promoted the utilization of GAI. Furthermore, academic research around the world is looking into the pros and cons of GAI in engineering education, focusing on how it can help teachers and keep students interested. Industrial applications are diversifying, extending across disciplines, and TNE is occurring in engineering contexts, including cross-border programs. GAI possesses the capacity to transform TNE by revolutionizing talent development, reformulating engineering models, and facilitating scientific assessment across multinational frameworks. However, problems like the generative illusion, ethical and ideological risks, lack of trust between teachers and students, and new threats to TNE in engineering equity in global settings require substantial focus. This study examines these concerns and outlines potential strategies to leverage GAI for transnational education in engineering, offering stakeholders the opportunity to prioritize AI literacy among educators and learners. This work emphasizes that cross-disciplinary and collaborative R&D, following national and international standards, should tackle application hurdles while guaranteeing safety and inclusion. This study also addresses several future directions that can contribute to creating a unified framework and cost-effective solutions. These solutions, integrated with platforms like the National Smart Education Platform, can bridge digital divides, ensuring equitable access a

关键词： Ethics Generative AI Catalysts Collaboration Transforms Market research Safety Stakeholders engineering education Research and development

Building an Italian-Chinese Parallel Corpus for Machine Translation from the Web 20

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Building an Italian-Chinese Parallel Corpus for Machine Tran...

6th EAI International Conference on Smart Objects and Technologies for Social Good, GOODTECHS 2020

作者： Tse, Rita Mirri, Silvia Tang, Su-Kit Pau, Giovanni Salomoni, Paola School of Applied Sciences - Macao Polytechnic Institute Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence Ministry of Education - Macao China Department of Computer Science and Engineering University of Bologna Bologna Italy Computer Science Department - Ucla Los AngelesCA United States

ISBN: (纸本)9781450375597

In an increasingly globalized world, being able to understand texts in different languages (even more so in different alphabets and charsets) has become a necessity. This can be strategic even while moving and travelling across different countries, characterized by different languages. With this in mind, bilingual corpora become critical resources since they are the basis of every state-of-the-art automatic translation system;moreover, building a parallel corpus is usually a complex and very expensive operation. This paper describes an innovative approach we have defined and adopted to automatically build an Italian-Chinese parallel corpus, with the aim of using it for training an Italian-Chinese Neural Machine Translation. Our main idea is to scrape parallel texts from the Web: we defined a general pipeline, describing each specific step from the selection of the appropriate data sources to the sentence alignment method. A final evaluation was conducted to evaluate the goodness of our approach and its results show that 90% of the sentences were correctly aligned. The corpus we have obtained consists of more than 6,000 sentence pairs (Italian and Chinese), which are the basis for building a Machine Translation system. © 2020 ACM.

关键词： Neural machine translation

Asynchronous control for Markov jump systems subject to actuator saturation

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Asynchronous control for Markov jump systems subject to actu...

第33届中国控制与决策会议

作者： Chao Wang Lei Liu Wei Wang Xiaobing Luo Huijin Fan the National Key Laboratory of Science and Technology of Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology the School of Automation Science and Electrical Engineering and the Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Computer Technology and Software Engineering Wuhan Polytechnic

In this paper,the asynchronous controller problem is considered for Markov jump systems(MJSs) subject to actuator *** asynchronous phenomenon between the controller and the plant is addressed by introducing a hidden Markov model and the closed-loop hidden MJSs are *** on the closed-loop hidden MJSs and using convex hull technique and linear matrix inequality technique,a controller design algorithm is derived to ensure that the closed-loop hidden MJSs satisfy the stochastic ***,a numerical example is provided to demonstrate the effectiveness of the proposed asynchronous controller design algorithm.

关键词： Asynchronous control actuator saturation Markov jump systems

Crystal Structure Prediction by Joint Equivariant Diffusion

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arXiv 2023年

作者： Jiao, Rui Huang, Wenbing Lin, Peijia Han, Jiaqi Chen, Pin Lu, Yutong Liu, Yang Dept. of Comp. Sci. & Tech. Institute for AI Tsinghua University China Institute for AIR Tsinghua University China Gaoling School of Artificial Intelligence Renmin University China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China National Supercomputer Center in Guangzhou School of Computer Science and Engineering Sun Yat-sen University China Stanford University United States

Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While CSP can be addressed by employing currently-prevailing generative models (e.g. diffusion models), this task encounters unique challenges owing to the symmetric geometry of crystal structures—the invariance of translation, rotation, and periodicity. To incorporate the above symmetries, this paper proposes DiffCSP, a novel diffusion model to learn the structure distribution from stable crystals. To be specific, DiffCSP jointly generates the lattice and atom coordinates for each crystal by employing a periodic-E(3)-equivariant denoising model, to better model the crystal geometry. Notably, different from related equivariant generative approaches, DiffCSP leverages fractional coordinates other than Cartesian coordinates to represent crystals, remarkably promoting the diffusion and the generation process of atom positions. Extensive experiments verify that our DiffCSP significantly outperforms existing CSP methods, with a much lower computation cost in contrast to DFT-based methods. Moreover, the superiority of DiffCSP is also observed when it is extended for ab initio crystal generation. Code is available at https://***/jiaor17/DiffCSP. © 2023, CC BY-SA.

关键词： Geometry

Fuzzychain: An equitable consensus mechanism for blockchain networks

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Journal of Network and computer Applications 2025年 241卷

作者： Ramos-Cruz, Bruno Andreu-Perez, Javier Quesada-Real, Francisco J. Martínez, Luis Computer Science Department University of Jaen Jaen Jaen23071 Spain Centre for Computational Intelligencer School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Andalusian Research Institute in Data Science and Computational Intelligence Jaen Jaen23071 Spain

Blockchain technology has become a trusted method for establishing secure and transparent transactions through a distributed, encrypted network. The operation of blockchain is governed by consensus algorithms, among which Proof of Stake (PoS) is popular yet has its drawbacks, notably the potential for centralising power in nodes with larger stakes or higher rewards. Our proposed novel solution, Fuzzychain, leverages fuzzy sets to define stake semantics, introducing a degree of softness in validator selection. This approach mitigates rigid threshold-based decision-making by allowing gradual transitions between stake levels, reducing sharp disparities among validators. By incorporating this enhanced stake evaluation, Fuzzychain promotes a more adaptive and distributed selection process, ensuring a fairer and more inclusive blockchain network. A thorough assessment of a real-time multi-agent blockchain system to examine validator selection and reduce inequality, promoting a more equitable distribution of stakes among validators compared to other consensus mechanisms. This fosters a more inclusive selection process and a more equitably distributed network. © 2025 The Authors

关键词： Consensus algorithm

Identification of Causal Relationship between Amyloid-β Accumulation and Alzheimer’s Disease Progression via Counterfactual Inference

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arXiv 2023年

作者： Dai, Haixing Hu, Mengxuan Li, Qing Zhang, Lu Zhao, Lin Zhu, Dajiang Diez, Ibai Sepulcre, Jorge Zhang, Fan Gao, Xingyu Liu, Manhua Li, Quanzheng Li, Sheng Liu, Tianming Li, Xiang School of Computing University of Georgia AthensGA United States State Key Lab of Cognitive Neuroscience and Learning Beijing Normal University Beijing100875 China School of Artificial Intelligence Beijing Normal University Beijing100875 China Department of Computer Science and Engineering The University of Texas at Arlington Arlington76019 United States Department of Radiology Massachusetts General Hospital Harvard Medical School Boston02114 United States Department of Radiology Brigham and Women’s Hospital Harvard Medical School Boston02115 United States School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai200240 China The MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China School of Data Science The University of Virginia Charlottesville22903 United States

Alzheimer’s disease (AD) is a neurodegenerative disorder that is beginning with amyloidosis, followed by neuronal loss and deterioration in structure, function, and cognition. The accumulation of amyloid-β in the brain, measured through 18F-florbetapir (AV45) positron emission tomography (PET) imaging, has been widely used for early diagnosis of AD. However, the relationship between amyloid-β accumulation and AD pathophysiology remains unclear, and causal inference approaches are needed to uncover how amyloid-β levels can impact AD development. In this paper, we propose a graph varying coefficient neural network (GVCNet) for estimating the individual treatment effect with continuous treatment levels using a graph convolutional neural network. We highlight the potential of causal inference approaches, including GVCNet, for measuring the regional causal connections between amyloid-β accumulation and AD pathophysiology, which may serve as a robust tool for early diagnosis and tailored care. © 2023, CC BY.

关键词： Neurodegenerative diseases

Kurdish Handwritten Character Recognition using Deep Learning Techniques

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arXiv 2022年

作者： Ahmed, Rebin M. Rashid, Tarik A. Fattah, Polla Alsadoon, Abeer Bacanin, Nebojsa Mirjalili, Seyedali Vimal, S. Chhabra, Amit IT Department Faculty of Applied Science Tishk International University Erbil Iraq Computer Science and Engineering Department University of Kurdistan Hewler Erbil Iraq Software and Informatics Engineering Salahaddin University-Erbil- Erbil Iraq School of Computing and Mathematics Charles Sturt University Sydney Australia Sydney Australia Singidunum University Belgrade Serbia Centre for Artificial Intelligence Research and Optimisation Torrens University Australia Yonsei Frontier Lab Yonsei University Seoul Korea Republic of Department of Artificial Intelligence and Data Science Ramco Institute of Technology North Venganallur Village Virudhunagar District Tamilnadu Rajapalayam626 117 India Department of Computer Engineering and Technology Guru Nanak Dev University Amritsar India

Handwriting recognition is one of the active and challenging areas of research in the field of image processing and pattern recognition. It has many applications that include: a reading aid for visual impairment, automated reading and processing for bank checks, making any handwritten document searchable, and converting them into structural text form, etc. Moreover, high accuracy rates have been recorded by handwriting recognition systems for English, Chinese Arabic, Persian, and many other languages. Yet there is no such system available for offline Kurdish handwriting recognition. In this paper, an attempt is made to design and develop a model that can recognize handwritten characters for Kurdish alphabets using deep learning techniques. Kurdish (Sorani) contains 34 characters and mainly employs an Arabic\Persian based script with modified alphabets. In this work, a Deep Convolutional Neural Network model is employed that has shown exemplary performance in handwriting recognition systems. Then, a comprehensive dataset was created for handwritten Kurdish characters, which contains more than 40 thousand images. The created dataset has been used for training the Deep Convolutional Neural Network model for classification and recognition tasks. In the proposed system, the experimental results show an acceptable recognition level. The testing results reported a 96% accuracy rate, and training accuracy reported a 97% accuracy rate. From the experimental results, it is clear that the proposed deep learning model is performing well and is comparable to the similar model of other languages' handwriting recognition systems. © 2022, CC BY.

关键词： Convolutional neural networks

A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects

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arXiv 2023年

作者： Wang, Jiapu Wang, Boyue Qiu, Meikang Pan, Shirui Xiong, Bo Liu, Heng Luo, Linhao Liu, Tengfei Hu, Yongli Yin, Baocai Gao, Wen Beijing Municipal Key Lab of Multimedia and Intelligent Software Technology Beijing Artificial Intelligence Institute Faculty of Information Technology Beijing University of Technology Beijing100124 China The Beacom College of Computer and Cyber Sciences Dakota State University MadisonSD United States Griffith University QLD Australia The Department of Computer Science University of Stuttgart Stuttgart70569 Germany The Department of Data Science and AI Monash University Melbourne Australia The Institute of Digital Media Peking University Beijing100871 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China

Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset. Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this paper, we provide a comprehensive review of TKGC methods and their details. Specifically, this paper mainly consists of three components, namely, 1) Background, which covers the preliminaries of TKGC methods, loss functions required for training, as well as the dataset and evaluation protocol;2) Interpolation, that estimates and predicts the missing elements or set of elements through the relevant available information. It further categorizes related TKGC methods based on how to process temporal information;3) Extrapolation, which typically focuses on continuous TKGs and predicts future events, and then classifies all extrapolation methods based on the algorithms they utilize. We further pinpoint the challenges and discuss future research directions of TKGC. Copyright © 2023, The Authors. All rights reserved.

关键词： Interpolation

Genetic engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems

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arXiv 2023年

作者： Sohrabi, Majid Fathollahi-Fard, Amir M. Gromov, Vasilii A. School of Data Analysis and Artificial Intelligence Faculty of Computer Science HSE University 11 Pokrovsky Boulevard Moscow109028 Russia Laboratory for Models and Methods of Computational Pragmatics HSE University Moscow Russia School of Management Sciences Université du Québec à Montréal B.P. 8888 Succ. Centre-ville MontréalQCH3C 3P8 Canada

Genetic Algorithms (GAs) are known for their efficiency in solving combinatorial optimization problems, thanks to their ability to explore diverse solution spaces, handle various representations, exploit parallelism, preserve good solutions, adapt to changing dynamics, handle combinatorial diversity, and provide heuristic search. However, limitations such as premature convergence, lack of problem-specific knowledge, and randomness of crossover and mutation operators make GAs generally inefficient in finding an optimal solution. To address these limitations, this paper proposes a new metaheuristic algorithm called the Genetic engineering Algorithm (GEA) that draws inspiration from genetic engineering concepts. GEA redesigns the traditional GA while incorporating new search methods to isolate, purify, insert, and express new genes based on existing ones, leading to the emergence of desired traits and the production of specific chromosomes based on the selected genes. Comparative evaluations against state-of-the-art algorithms on benchmark instances demonstrate the superior performance of GEA, showcasing its potential as an innovative and efficient solution for combinatorial optimization problems. © 2023, CC BY-NC-ND.

关键词： Genetic algorithms