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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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Cross-center Early Sepsis Recognition by Medical Knowledge Guided Collaborative Learning for Data-scarce Hospitals 23

Cross-center Early Sepsis Recognition by Medical Knowledge G...

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32nd ACM World Wide Web Conference, WWW 2023

作者： Ding, Ruiqing Rong, Fangjie Han, Xiao Wang, Leye Key Lab of High Confdence Software Technologies Peking University Ministry of Education China School of Computer Science Peking University Beijing China School of Mathematical Science Peking University Beijing China School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai China

ISBN: (纸本)9781450394161

There are significant regional inequities in health resources around the world. It has become one of the most focused topics to improve health services for data-scarce hospitals and promote health equity through knowledge sharing among medical institutions. Because electronic medical records (EMRs) contain sensitive personal information, privacy protection is unavoidable and essential for multi-hospital collaboration. In this paper, for a common disease in ICU patients, sepsis, we propose a novel cross-center collaborative learning framework guided by medical knowledge, SofaNet, to achieve early recognition of this disease. The Sepsis-3 guideline, published in 2016, defines that sepsis can be diagnosed by satisfying both suspicion of infection and Sequential Organ Failure Assessment (SOFA) greater than or equal to 2. Based on this knowledge, SofaNet adopts a multi-channel GRU structure to predict SOFA values of different systems, which can be seen as an auxiliary task to generate better health status representations for sepsis recognition. Moreover, we only achieve feature distribution alignment in the hidden space during cross-center collaborative learning, which ensures secure and compliant knowledge transfer without raw data exchange. Extensive experiments on two open clinical datasets, MIMIC-III and Challenge, demonstrate that SofaNet can benefit early sepsis recognition when hospitals only have limited EMRs. © 2023 ACM.

关键词： Electronic data interchange

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CodeSwap: Symmetrically Face Swapping Based on Prior Codebook 24

CodeSwap: Symmetrically Face Swapping Based on Prior Codeboo...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Luo, Xiangyang Zhang, Xin Xie, Yifan Tong, Xinyi Yu, Weijiang Chang, Heng Ma, Fei Yu, Fei Richard Guangdong Shenzhen China Tsinghua Shenzhen International Graduate School Tsinghua University Guangdong Shenzhen China School of Computer Science Sun Yat-sen University Guangdong Guangzhou China Tsinghua-Berkeley Shenzhen Institute Tsinghua University Guangdong Shenzhen China College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen China Carleton University Ottawa Canada

ISBN: (纸本)9798400706868

Face swapping, the technique of transferring the identity from one face to another, merges as a field with significant practical applications. However, previous swapping methods often result in visible artifacts. To address this issue, in our paper, we propose CodeSwap, a symmetrical framework to achieve face swapping with high-fidelity and realism. Specifically, our method firstly utilizes a codebook that captures the knowledge of high quality facial features. Building on this foundation, the face swapping is then converted into the code manipulation task in a code space. To achieve this, we design a Transformer-based architecture to update each code independently, which enable more precise manipulations. Furthermore, we incorporate a mask generator to achieve seamless blending of the generated face with the background of target image. A distinctive characteristic of our method is its symmetrical approach to processing both target and source images, simultaneously extracting information from each to improve the quality of face swapping. This symmetry also simplifies the bidirectional exchange of faces in a singular operation. Through extensive experiments on ClelebA-HQ and FF++, our method is proven to not only achieve efficient identity transfer but also substantially reduce the visible artifacts. © 2024 ACM.

关键词： Codes (symbols)

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Heterogeneous Experts and Hierarchical Perception for Underwater Salient Object Detection

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IEEE Transactions on Image Processing 2025年 34卷 3703-3717页

作者： Zha, Mingfeng Wang, Guoqing Pei, Yunqiang Li, Tianyu Tang, Xiongxin Li, Chongyi Yang, Yang Shen, Heng Tao University of Electronic Science and Technology of China Center for Future Media School of Computer Science and Engineering Chengdu 611731 China Chinese Academy of Science Institute of Software Beijing 100190 China Nankai University School of Computer Science Tianjin 300350 China Tongji University School of Computer Science and Technology Shanghai 201804 China Peng Cheng Laboratory Shenzhen 518066 China

Existing underwater salient object detection (USOD) methods design fusion strategies to integrate multimodal information, but lack exploration of modal characteristics. To address this, we separately leverage the RGB and depth branches to learn disentangled representations, formulating the heterogeneous experts and hierarchical perception network (HEHP). Specifically, to reduce modal discrepancies, we propose the hierarchical prototype guided interaction (HPI), which achieves fine-grained alignment guided by the semantic prototypes, and then refines with complementary modalities. We further design the mixture of frequency experts (MoFE), where experts focus on modeling high and low-frequency respectively, collaborating to explicitly obtain hierarchical representations. To efficiently integrate diverse spatial and frequency information, we formulate the four-way fusion experts (FFE), which dynamically selects optimal experts for fusion while being sensitive to scale and orientation. Since depth maps with poor quality inevitably introduce noises, we design the uncertainty injection (UI) to explore high uncertainty regions by establishing pixel-level probability distributions. We further formulate the holistic prototype contrastive (HPC) loss based on semantics and patches to learn compact and general representations across modalities and images. Finally, we employ varying supervision based on branch distinctions to implicitly construct difference modeling. Extensive experiments on two USOD datasets and four relevant underwater scene benchmarks validate the effect of the proposed method, surpassing state-of-the-art binary detection models. Impressive results on seven natural scene benchmarks further demonstrate the scalability. © 1992-2012 IEEE.

关键词： expert learning Multimodal fusion uncertainty guidance underwater perception

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Secure Data Sharing for Cross-domain Industrial IoT Based on Consortium Blockchain 26

Secure Data Sharing for Cross-domain Industrial IoT Based on...

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26th International Conference on computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yu, Xinze Xie, Yunzhou Xu, Qiujie Xu, Zhuqing Xiong, Runqun Southeast University College of Software Engineering Suzhou China Imperial College London Department of Mathematics London United Kingdom Xi'an Jiaotong-Liverpool University School of Advanced Technology Suzhou China Southeast University School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350331684

Industrial Internet of Things (IIoT) is considered one of the most revolutionary technologies that can significantly improve manufacturing efficiency and realize intelligent production. With the increasing complexity of industrial manufacturing, the manufacturing process of a product often involves several different IoT domains (e.g., factories). To achieve a common production goal, devices from various domains share their data for cooperative work, which raises privacy and security concerns for cross-domain communication. Most existing data-sharing schemes rely on a trusted third party. Hence, the privacy and security issues in cross-domain data sharing are still challenging research directions. This paper proposes CBDS, a consortiumblockchain-based cross-domain IIoT data-sharing mechanism. Specifically, we introduce consortium blockchain to construct trust among different domains in IIoT. A group signature is presented to ensure each device's privacy to achieve anonymous authentication. In addition, the collected data should be stored in the ciphertext. The smart contract and proxy re-encryption are utilized in the CBDS to realize secure cross-domain data sharing. The experimental results demonstrate the effectiveness and efficiency of the proposed CBDS. © 2023 IEEE.

关键词： Smart contract

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Structural Balance Computation of Signed Hypergraphs via Memetic Algorithm 5

Structural Balance Computation of Signed Hypergraphs via Mem...

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5th International Conference on Data-Driven Optimization of Complex Systems, DOCS 2023

作者： Han, Xinchu Xu, Ruijie Ma, Lijia Shao, Zengyang Bai, Yuan Wang, Shanfeng College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Laboratory of Data Discovery for Health Limited Hong Kong999077 Hong Kong School of Electronic Engineering Xidian University Xi'an710071 China

ISBN: (纸本)9798350393521

The potential imbalances among entities and connections within complex systems can pose significant threats to their overall functionality. In order to enhance the functional security of these complex systems, it becomes imperative to address and alleviate these underlying imbalances. Structural balance computation has gained attention because of its ability to balance relationships among entities within systems. Traditional methods in structural balance computation aim to minimize imbalances within signed networkss. However, signed networkss have limitations in effectively representing complex data relationships in multi-modal or multi-type data. In order to address this constraint, we propose a novel framework called SHMA (Structural Balance of Signed Hypergraphs based on Memetic Algorithm) which aims to identify and minimize imbalances within signed hypergraphs. More specifically, we formulate the degree of imbalance in signed hypergraphs as an extended energy function, thereby transforming the computation of structural balance of the hypergraphs into an optimization problem. Then, we utilize a multi-level memetic algorithm to optimize the energy function and obtain a solution with the least imbalances. To assess the performance of SHMA, we performed experiments on various real-world datasets and conducted a comparative analysis. The experimental results illustrate that SHMA outperforms the alternative algorithms in terms of structural balance computation of signed hypergraphs. In addition, SHMA exhibits fast convergence, enabling it to reach a balanced state more efficiently. © 2023 IEEE.

关键词： Large scale systems

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Multi-MedVit: a deep learning approach for the diagnosis of COVID-19 with the CT images

Multi-MedVit: a deep learning approach for the diagnosis of ...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Cai, Yunjie Zheng, Zeqi Nie, Shanling Guo, Yue Zhang, Ruijie Yang, Hai East China University of Science and Technology Department of Computer Science and Engineering Shanghai200237 China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China University of Southampton School of Electronics and Computer Science United Kingdom

ISBN: (纸本)9781665468190

The grim situation of novel coronavirus pneumonia 2019 (COVID-19) and its terrible spreading speed have already constituted a severe risk to human life, so it is ultimately essential to rapidly and accurately diagnose for COVID-19 pneumonia. Based on this study's 746 lung CT images, we propose Multi-MedVit, a novel auxiliary COVID-19 diagnosis framework based on the multi-input Transformer. We compare Multi-MedVit with state-of-the-art deep learning methods, such as CNN, VGG16, and ResNet50. Multi-MedVit outperformed the other methods on the benchmark dataset and proved that multiscale data input for data augmentation helped enhance model stability. Based on an interpretable analysis of the input and output of Multi-MedVit, we found that with the support of the training set data, the model has been possible to accurately focus on the lesion area for diagnosis of COVID-19 without expert annotations, which can provide initial references containing more potential information to doctors more precisely and fleetly. © 2022 IEEE.

关键词： COVID-19

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OnMKD: An Online Mutual Knowledge Distillation Framework for Passage Retrieval 12th

OnMKD: An Online Mutual Knowledge Distillation Framework for...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Deng, Jiali Li, Dongyang Zhang, Taolin He, Xiaofeng School of Computer Science and Technology East China Normal University Shanghai China School of Software Engineering East China Normal University Shanghai China NPPA Key Laboratory of Publishing Integration Development ECNUP Shanghai China

ISBN: (纸本)9783031446955

Dense passage retriever recalls a set of relevant passages from a large corpus according to a natural language question. The dual-encoder architecture is prevalent in dense passage retrievers, which is based on large-scale pre-trained language models (PLMs). However, existing PLMs usually have thick structures and bulky parameters, resulting in large memory and time consumption. To overcome the limitation of PLMs, in this paper we apply online distillation to passage retrieval and propose an Online Mutual Knowledge Distillation framework (OnMKD). Specifically, we obtain a lightweight retriever by simultaneously updating two peer networks with the same dual-encoder structure and different initial parameters, named Online Mutual Knowledge Refinement. To further interact with the latent knowledge of intermediate layers, we utilize a novel cross-wise contrastive loss to alternate the representation of questions and passages. Experimental results indicate that our framework outperforms other small baselines with the same number of layers on multiple QA benchmarks. Compared to the heavy PLMs, OnMKD significantly accelerates the inference process and reduces storage requirements with only a slight sacrifice in performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Distillation

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Adaptive and Guided Attention in Transformer for Visual Question Answering

Adaptive and Guided Attention in Transformer for Visual Ques...

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International Conference on Virtual Reality and Visualization (ICVRV)

作者： Shan Zhong Shiyang He Shengrong Gong Gengsheng Xie Yangtao Xue Xiaoyang Tang Silin Zhou School of Computer Science and Engineering Changshu Institute of Technology State Key Lab. for Novel Software Technology Nanjing University Changshu China School of Computer Science and Technology Soochow University Suzhou China School of Computer Science and Engineering Changshu Institute of Technology Changshu China School of Software Jiangxi Normal University Nanchang China

ISBN: (数字)9798331515447

ISBN: (纸本)9798331515454

Transformer-based techniques for Visual Question Answering (VQA) have shown commendable prowess in modeling global relationships. However, they often encounter a decrement in accuracy due to their limitations in capturing local dependencies. In response to this challenge, we propose a novel solution, the Adaptive and Guided Attention in Transformer (AGAT), tailored for the VQA task. AGAT constructs an adaptive selfattention method, where a neighborhood prediction module is adopted to dynamically fine-tune attention span and weight by sampling the query token from a default neighborhood. This innovative approach enables the comprehensive modeling of long-term dependency relationships and facilitates extracting contextual information from diverse image regions. The guided attention, which leverages information from the question encoder, is further integrated. It interplays between different modalities multiple times to align the text and image. Experimental results demonstrate that AGAT outperforms the representative methods significantly in terms of accuracy.

关键词： Visualization Adaptation models Solid modeling Accuracy Noise Virtual reality Transformers Question answering (information retrieval) Data mining Context modeling

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Multi-strategy learning-based particle swarm optimization algorithm for COVID-19 threshold segmentation

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computers in Biology and Medicine 2024年 176卷 108498-108498页

作者： Zhu, Donglin Shen, Jiaying Zheng, Yangyang Li, Rui Zhou, Changjun Cheng, Shi Yao, Yilin School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China School of Computer Science Shaanxi Normal University Xi'an710119 China College of Software Engineering Jiangxi University of Science and Technology Nanchang330013 China

With advancements in science and technology, the depth of human research on COVID-19 is increasing, making the investigation of medical images a focal point. Image segmentation, a crucial step preceding image processing, holds significance in the realm of medical image analysis. Traditional threshold image segmentation proves to be less efficient, posing challenges in selecting an appropriate threshold value. In response to these issues, this paper introduces Inner-based multi-strategy particle swarm optimization (IPSOsono) for conducting numerical experiments and enhancing threshold image segmentation in COVID-19 medical images. A novel dynamic oscillatory weight, derived from the PSO variant for single-objective numerical optimization (PSOsono) is incorporated. Simultaneously, the historical optimal positions of individuals in the particle swarm undergo random updates, diminishing the likelihood of algorithm stagnation and local optima. Moreover, an inner selection learning mechanism is proposed in the update of optimal positions, dynamically refining the global optimal solution. In the CEC 2013 benchmark test, PSOsono demonstrates a certain advantage in optimization capability compared to algorithms proposed in recent years, proving the effectiveness and feasibility of PSOsono. In the Minimum Cross Entropy threshold segmentation experiments for COVID-19, PSOsono exhibits a more prominent segmentation capability compared to other algorithms, showing good generalization across 6 CT images and further validating the practicality of the algorithm. © 2024 Elsevier Ltd

关键词： COVID-19

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