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AI Integration in the IT Professional Workplace: A Scoping Review and Interview Study with Implications for Education and Professional Competencies 2024

AI Integration in the IT Professional Workplace: A Scoping R...

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29th Working Group Reports on Innovation and Technology in computer science Education, ITiCSE WGR 2024

作者： Clear, Tony Cajander, Åsa Clear, Alison Mcdermott, Roger Daniels, Mats Divitini, Monica Forshaw, Matthew Humble, Niklas Kasinidou, Maria Kleanthous, Styliani Kultur, Can Parvini, Ghazaleh Polash, Mohammad Zhu, Tingting Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand Department of Information Technology Uppsala University Uppsala Sweden School of Business Eastern Institute of Technology Auckland New Zealand School of Computing Robert Gordon University Aberdeen United Kingdom Dpt. of Computer Science Norwegian University of Science and Technology Trondheim Norway The Alan Turing Institute Newcastle Upon Tyne United Kingdom Open University of Cyprus Nicosia Cyprus The Open University of Cyprus Nicosia Cyprus School of Computer Science Carnegie Mellon University PittsburghPA United States Manning College of Information and Computer Sciences University of Massachusetts Amherst Amherst United States School of Computer Science University of Sydney Sydney Australia Department of Geography Geomatics and Environment University of Toronto Mississauga Toronto Canada School of Computer Science Newcastle University Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798400712081

As Artificial Intelligence (AI) continues transforming workplaces globally, particularly within the Information Technology (IT) industry, understanding its impact on IT professionals and computing curricula is crucial. This research builds on joint work from two countries, addressing concerns about AI's increasing influence in IT sector workplaces and its implications for tertiary education. The study focuses on AI technologies such as generative AI (GenAI) and large language models (LLMs). It examines how they are perceived and adopted and their effects on workplace dynamics, task allocation, and human-system interaction. IT professionals, noted as early adopters of AI, offer valuable insights into the interplay between AI and work engagement, highlighting the significant competencies required for digital workplaces. This study employs a dual-method approach, combining a systematic and multi-vocal literature review and qualitative research methods. These included a thematic analysis of a set of 47 interviews conducted between March and May of 2024 with IT professionals in two countries (New Zealand and Sweden). The research aimed to understand the implications for computing students, education curricula, and the assessment of emerging professional competencies. The literature review found insufficient evidence addressing comprehensive AI practice methodologies, highlighting the need to both develop and regulate professional competencies for effective AI integration. Key interview findings revealed diverse levels of GenAI adoption, ranging from individual experimentation to institutional integration. Participants generally expressed positive attitudes toward the technology and were actively pursuing self-learning despite some concerns. The themes emerging from the interviews included AI's role in augmenting human tasks, privacy and security concerns, productivity enhancements, legal and ethical challenges, and the evolving need for new competencies in the workplace.

关键词： artificial intelligence

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An Intelligent Resource Scheduling Method with Edge Channel Deployment for BPM 19

An Intelligent Resource Scheduling Method with Edge Channel ...

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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Liu, Bowen Dou, Wanchun Zhou, Xiaokang Zhang, Xuyun Qi, Lianyong Dai, Fei Chen, Chaochao Nanjing University State Key Laboratory for Novel Software Technology Nanjing China Shiga University Faculty of Data Science Hikone Japan Macquarie University Department of Computing Sydney Australia College of Computer and Software Qingdao China Southwest Forestry University College of Big Data and Intelligent Engineering Kunming China Zhejiang University College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350346558

Edge computing is a novel computing paradigm that offers kinds of resources at the network edge. In edge computing, terminal users are connected to edge servers via the wireless network and there are various channels in each wireless link. These wireless channels are limited resource while different channel has different cost and service ability. The dynamic changes of users' status make it harder to find an appropriate method to satisfy the BPM requirements of channel deployment. With this observation, it is a tricky challenge to make a trade-off between the system cost(rental price) and the service ability(number of users). In view of this challenge, an intelligent resource scheduling method, named EdgeIRS, is proposed in this paper. In the technical sense, the EdgeIRS method can accommodate most users at the edge with a minimum cost of deploying channel resources in an online way. Its performance is analyzed theoretically and the experiments verify the superiority of the method. © 2022 IEEE.

关键词： Cost effectiveness

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Optimizing Weighted Ensemble ML IDS Using Parallelization and Batch Learning in Healthcare Networks

Optimizing Weighted Ensemble ML IDS Using Parallelization an...

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International Conference on e-health Networking, Applications and Services (HealthCom)

作者： Blake Ward Daniel Gonzalez Jake Alt Dongfeng Fang Shengjie Xu Department of Computer Science and Software Engineering College of Engineering California Polytechnic State University San Luis Obispo CA USA Department of Cyber Intelligence and Information Operations College of Applied Science and Technology University of Arizona Tucson AZ USA

ISBN: (数字)9798350350548

ISBN: (纸本)9798350350555

The increasing adoption of loT devices in healthcare necessitates the development of lightweight and fast intrusion de-tection systems (IDS). This paper proposes a weighted ensemble ML IDS with parallelized batch learning to optimize runtime while maintaining ML performance. The proposed method is compared to other solutions, evaluating ML performance and runtime across three different datasets. Experimental results demonstrate that although batch learning introduces feedback delay that slightly degrades ML performance, parallel computing significantly reduces runtime.

关键词： Performance evaluation Degradation Runtime Accuracy Wireless personal area networks Medical services Machine learning Parallel processing Delays

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EEG-EMG FAConformer: Frequency Aware Conv-Transformer for the fusion of EEG and EMG

EEG-EMG FAConformer: Frequency Aware Conv-Transformer for th...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： ZhengXiao He Minghong Cai Letian Li Siyuan Tian Ren-Jie Dai College of Electronic and Information Engineering Tongji University Shanghai China School of Software Engineering Tongji University Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Motor pattern recognition paradigms are the main forms of Brain-computer Interfaces(BCI) aimed at motor function rehabilitation and are the most easily promoted applications. In recent years, many researchers have suggested encouraging patients to perform real motor control execution simultaneously in MI-based BCI rehabilitation training systems. Electromyography (EMG) signals are the most direct physiological signals that can assess the execution of movements. Multimodal signal fusion is practically significant for decoding motor patterns. Therefore, we introduce a multimodal motion pattern recognition algorithm for EEG and EMG signals: EEG-EMG FAConformer, a method with several attention modules correlated with temporal and frequency information for motor pattern recognition. We especially devise a frequency band attention module to encode EEG information accurately and efficiently. What’s more, modules like Multi-Scale Fusion Module, Independent ChannelSpecific Convolution Module(ICSCM), and Fuse Module which can effectively eliminate irrelevant information in EEG and EMG signals and fully exploit hidden dynamics are developed and show great effects. Extensive experiments show that EEGEMG FAConformer surpasses existing methods on Jeong2020 dataset, showcasing outstanding performance, high robustness and impressive stability.

关键词： Training Motor drives Fuses Convolution Motors Electromyography Electroencephalography Robustness Stability analysis Physiology

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Artificial Neural Network using Image Processing for Digital Forensics Crime Scene Object Detection 2

Artificial Neural Network using Image Processing for Digital...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Devasenapathy, Deepa Raja, M. Dwibedi, Rajat Kumar Vinoth, N. Jayasudha, T. Ganesh, V. Dilli Computing & Software Engineering U.A. Whitaker College of Engineering Florida Gulf Coast University United States Cmr Institute of Technology Department of Computer Science and Engineeting Bengaluru India Aarupadai Veedu Institute of Technology Vmrf Chennai India Chennai Institute of Technology Department of Mechatronics Engineering Chennai India Kpr Institute of Engineering and Technology Department of English Coimbatore India Chennai India

ISBN: (纸本)9798350347579

Digital forensics science places a significant emphasis on the detection of objects as one of the most vital areas of study. Several industries and institutions may benefit from the object detection method, including those concerned with medical diagnostic scanning, traffic monitoring, airport security, law enforcement, and data rescue on a local and global scale. This study aims to detect weapons in video surveillance images by using various enhancement, segmentation, feature extraction, and classification methods by Artificial Neural Network to improve the detection accuracy. Yet, several mathematical and algorithmic models are computed to provide the appropriate approaches. © 2023 IEEE.

关键词： Neural networks

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Birth Mode Prediction Using Bagging Ensemble Classifier: A Case Study of Bangladesh

Birth Mode Prediction Using Bagging Ensemble Classifier: A C...

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2021 International Conference on Information and Communication Technology for Sustainable Development, ICICT4SD 2021

作者： Bin Alam, Md. Sakib Patwary, Muhammed J. A. Hassan, Maruf Asian University for Women Department of Computer Science Bangladesh Big Data Institute College of Computer Science and Software Engineering Shenzhen University China International Islamic University Chittagong Department of Computer Science and Engineering Bangladesh

ISBN: (纸本)9781665414609

Maternal mortality and childbirth complications are major delivery issues in most developing countries, especially in rural areas. The proper identification of risks associated with the delivery of an expecting woman at an earlier stage can substantially reduce the mortality rate. A few studies have been conducted on using Machine Learning (ML) techniques for predicting birth mode i.e. caesarean section or normal delivery. The most commonly used methods are Decision Tree (DT), K-Nearest Neighbour (KNN), Naive Bayes (NB) and Support Vector Machine (SVM). In this study we have implemented Bagging Ensemble Classifiers based on these traditional machine learning algorithms, which is a novel approach in the area of birth mode prediction. This paper examines the performance of four ML algorithms, with bagging ensemble classifiers (DT-Bagging, KNN-Bagging, NB-Bagging, SVM-Bagging). The result shows that bagging ensemble models outperformed the traditional models in this domain. Besides, we have identified the association between important factors and caesarean section. This study may later be used to create a decision support system by extracting knowledge from the hidden patterns in data to reduce the rate of caesarean delivery in Bangladesh. © 2021 IEEE.

关键词： Decision trees

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Using Formal Knowledge to Support Episodic Evidence-Based Nursing Care

Using Formal Knowledge to Support Episodic Evidence-Based Nu...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Jaffe, Shai Ben Shahar, Bruria Shahar, Yuval Goldstein, Ayelet Shalom, Erez Selivanova, Maya Rimon, Ephraim Cohen, Odeya Department of Nursing Faculty of Health Sciences Ben-Gurion University of the Negev Israel Nursing Informatics lab Faculty of Health Sciences Ben-Gurion University of the Negev Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Israel Computer Science Department Hadassah Academic College Israel Herzfeld Geriatric Rehabilitation Medical Center Israel

ISBN: (纸本)9781643685335

Applying evidence-based medicine prevents medical errors highlighting the need for applying Clinical Guidelines (CGs) to improve patient care by nurses. However, nurses often face challenges in utilizing CGs due to patient-specific needs. Developing a Clinical Decision Support System (CDSS) can provide real-time context-sensitive CG-based recommendations. Therefore, there is a need to acquire and represent CGs in a machine-applicable manner. Also, there is a need to be able to provide recommendations episodically, only when requested, and not continuously, and to assess previous partial performance of evidence-based actions on a continuous scale. This study evaluated the feasibility of acquiring and representing major nursing CGs, in a machine-applicable manner for episodic use. Using data from an Israeli geriatric center, the results suggest that an episodic CDSS effectively supports the application of formalized nursing knowledge. © 2024 The Authors.

关键词： Nursing

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An Intelligent and Interpretable Intrusion Detection System for Unmanned Aerial Vehicles

An Intelligent and Interpretable Intrusion Detection System ...

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IEEE International Conference on Communications (ICC)

作者： Danish Javeed Tianhan Gao Prabhat Kumar Shifa Shoukat Ijaz Ahmad Randhir Kumar Software College Northeastern University Shenyang China Department of Software Engineering School of Engineering Science LUT University Lappeenranta Finland Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China Department of Computer Science and Engineering SRM University AP AP India

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

The increasing adoption of Unmanned Aerial Ve-hicles (UAV s) in various critical applications necessitates robust security measures to protect these systems from cyber threats. In response, this research introduces an innovative Intrusion Detection System (IDS) specifically tailored for UAV s. The proposed IDS leverages Hierarchical Attention-based Long Short-Term Memory (H-LSTM) networks to effectively model the intricate temporal dependencies in UAV data. This architecture allows for comprehensive surveillance of UAV behavior, capturing both short-term anomalies and long-term deviations from expected patterns. The hierarchical attention mechanism enables the system to focus on salient features within the data, enhancing detection accuracy and robustness. To address the critical need for interpretable AI in cybersecurity, we incorporate Shapley Ad-ditive Explanations (SHAP) into our IDS. SHAP values provide a coherent and intuitive explanation of the IDS's decisions by emphasizing the specific features and their contributions to the intrusion detection process. The performance of the proposed system is rigorously evaluated using the N-BaIoT dataset. Our experiments demonstrate that the H-LSTM-based IDS outper-forms traditional methods, achieving a higher detection rate while minimizing false positives. Moreover, the incorporation of SHAP explanations facilitates rapid incident analysis, allowing security professionals to discern between genuine threats and benign anomalies effectively.

关键词： Industries Federated learning Surveillance Intrusion detection Autonomous aerial vehicles Feature extraction Robustness

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Implementation of an Effective Framework in Merging Cybersecurity and software engineering

Implementation of an Effective Framework in Merging Cybersec...

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Women in Data science at Prince Sultan University (WiDS PSU), International Conference of

作者： Sara Khalid Alhuqail Nor Shahida Mohd Jamail Department of Software Engineering College of Computer Science and Information Sciences Prince Sultan University Riyadh Kingdom of Saudi Arabia

Cyberattacks have increased. Security throughout software development reduces the program's attack surface. Despite several fixes. new research indicates software security weaknesses and hackers are rising. SSD security methods must be studied. 28 academic and government SSD approaches are examined in this study. Security is implemented throughout SDLC. We studied organizational. behavioral. legal. policy. and governance issues as well as SSD approaches. Method effectiveness strategies were investigated. We conclude with scientific problems. Our survey should help academics an institutions understand software security development (SSD). Nontechnical software security experts may benefit from our bridge. Our literature gaps may improve software security. Today's fast-paced. modern world requires software security for product success. CIA-classified data. software. and services (Confidentiality. Integrity. and Availability).

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Research on Image Recognition Technology and Application Based on Neural Network 2nd

Research on Image Recognition Technology and Application Bas...

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2nd International Conference on Artificial Intelligence and Communication Technologies, ICAICT 2024

作者： Huang, Changquan Chen, Yikun Liu, Huanyu Wang, Sheng Li, Chongru Liu, Henan School of College of Artificial Intelligence Yango University Fujian China Department of Economic and Management Zhan Jiang Preschool Education College Guangdong China School of Computer and Software Engineering Huaiyin Institute of Technology Jiangsu China University of Aircraft ordos Institute of Technology Inner Mongolia Ordos China Hongde college buct Beijing University of Chemical Technology Beijing China Harbin University of Science and Technology Heilongjiang China

ISBN: (纸本)9789819600915

The aim of this study is to explore neural network-based image recognition techniques and their applicability and performance in different application scenarios. With the rapid development of artificial intelligence technology, image recognition technology is more and more widely used in various fields, especially the introduction of convolutional neural network (CNN), which makes image recognition technology show excellent performance in processing complex data. Traditional machine learning algorithms often show low recognition accuracy and generalisation ability when dealing with complex image data due to their shallow structure. Deep learning, on the other hand, has made significant progress in image recognition tasks by constructing a multi-layered neural network structure that can better extract and characterise complex features in images. In the study of this paper, an improved convolutional neural network model is proposed, which consists of a convolutional layer, a filter bank, a residual learning module, and a classifier. In order to validate the effectiveness of the model, three datasets (D1, D2, and D3) with different sizes and features are used for experiments in this paper. The experimental results show that the proposed CNN model exhibits high recognition accuracy when dealing with different datasets, especially in the classification task of specific targets and realistic scenes, the model is able to extract and recognise key features effectively, showing good convergence and stability. The experimental results not only demonstrate the excellent performance of the deep learning-based target recognition algorithm on different datasets, but also reflect the stability and gradual optimisation ability of the model during the training process. Although a slight overfitting phenomenon occurs on the validation set, these problems can be effectively mitigated and improved by means of appropriate regularisation and hyperparameter tuning. The research in this pa

关键词： Convolutional neural networks

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