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Identifying the Barriers to Human-Centered Design in the Workplace: Perspectives from UX Professionals

Proceedings of the ACM on Human-Computer Interaction 2025年第2期9卷 1-25页

作者： Gorichanaz, Tim College of Computing & Informatics Drexel University PhiladelphiaPA United States

Human-centered design, a theoretical ideal, is sometimes compromised in industry practice. Technology firms juggle competing priorities, such as adopting new technologies and generating shareholder returns, which may conflict with human-centered design values. This study sought to identify the types of workplace situations that present barriers for human-centered design, going beyond the views and behaviors of individual professionals. Q methodology was used to analyze the experiences of 14 UX professionals based in the United States. Five factors were identified, representing workplace situations in which human-centered design is inhibited, despite the involvement of UX professionals: Single-Minded Arrogance, Competing Visions, Moving Fast and Breaking Things, Pragmatically Getting By, and Sidestepping Responsibility. Underpinning these five factors are the dimensions of speed and clarity of vision. This paper demonstrates connections between the literature on UX ethics and human-centered design practice, and its findings point toward opportunities for education and intervention to better enable human-centered and ethical design in practice. © 2025 Owner/Author.

关键词： Professional aspects

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Leveraging Machine Learning for Threat Detection: A Study on Malicious URL Classification

Leveraging Machine Learning for Threat Detection: A Study on...

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2024 IEEE International Conference on computing, ICOCO 2024

作者： Sheng, Justin Wong Junn Mathivanan, Norsyela Muhammad Noor Ghani, Nor Azura Md School of Computing and Creative Media University of Wollongong Malaysia Shah Alam Malaysia School of Computing and Creative Media University of Wollongong Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia

ISBN: (纸本)9798331530303

In today's digital age, the internet serves as a vital resource. However, it poses significant risks from malicious websites that threaten cybersecurity. This study focuses on developing effective threat detection methods to identify harmful URLs using machine learning models, specifically Random Forest and XGBoost, to classify URL types such as benign, phishing, malware, and defacement. A comprehensive feature extraction process trains the classification models for real-time analysis of URL threats. The findings indicate that the Random Forest model outperforms XGBoost, particularly in classifying phishing URLs, while demonstrating robust accuracy despite challenges with overfitting. This research culminates in the creation of an Android-based Malicious URL Detector mobile application that integrates the trained model, aiming to enhance cybersecurity defenses. By leveraging machine learning techniques, this study contributes to more efficient and effective detection of malicious URLs, fostering a safer online environment for users. ©2024 IEEE.

关键词： Phishing

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M3GNAS: Multi-modal Multi-view Graph Neural Architecture Search for Medical Outcome Predictions

M3GNAS: Multi-modal Multi-view Graph Neural Architecture Sea...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Al-Sabri, Raeed Gao, Jianliang Chen, Jiamin Oloulade, Babatounde Moctard Wu, Zhenpeng Abdullah, Monir Hu, Xiaohua Central South University School of Computer Science and Engineering Changsha China College of Science and Engineering Hamad Bin Khlifa University Division of Information and Computing Technology Doha Qatar College of Computing and Information Technology University of Bisha Saudi Arabia College of Computing & Informatics Drexel University United States

ISBN: (纸本)9798350386226

Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework's superior performance compared to state-of-the-art models. © 2024 IEEE.

关键词： Contrastive Learning

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Oral Ulcer Classification System Using CNN 5

Oral Ulcer Classification System Using CNN

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5th International Conference on Artificial Intelligence and Data Sciences, AiDAS 2024

作者： Azam, Ahmad Ikmal Naim Mod Ali, Azliza Mohd Latif, Wannoraini Abdul Kamil, Wan NurHazirah Wan Ahmad Zainal, Mukarramah Hamid, Nurzeatul Hamimah Abdul College of Computing Informatics and Mathematics Universiti Teknologi Mara Selangor Shah Alam Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Melaka Jasin Malaysia Universiti Teknologi Mara Faculty of Dentistry Kampus Sungai Buloh Selangor Sungai Buloh Malaysia Selangor Shah Alam Malaysia

ISBN: (纸本)9798331528553

This study presents a Convolutional Neural Network (CNN) model developed to address the limitations of current diagnostic methods for oral ulcers. Specifically, it targets the classification of three types of oral ulcers: Oral Malignant Ulcer, Recurrent Aphthous Ulcer, and Traumatic Ulcer. Traditional diagnostic approaches often suffer from limitations such as subjective interpretation, variability in diagnostic accuracy, and inadequate automation. To overcome these challenges, our approach involves constructing a CNN from scratch and rigorously optimizing key hyperparameters, including batch size and learning rate. Under optimal conditions, with a batch size of 12 and a learning rate of 0.001, our CNN achieved an accuracy of 98.61% and a loss rate of 4.78%. In contrast, alternative configurations resulted in an accuracy of 90.62% and a loss of 17.12%, highlighting the model's sensitivity to parameter adjustments. These results demonstrate that careful tuning of CNN hyperparameters can significantly improve diagnostic performance and accuracy. The findings suggest that the proposed CNN model offers a promising enhancement over traditional methods, with the potential to improve diagnostic precision and clinical outcomes for oral ulcers. Future work will focus on validating the model in real-world clinical settings and exploring its applicability to other medical imaging domains, with the aim of broadening its impact and utility in healthcare diagnostics. © 2024 IEEE.

关键词： Convolutional neural networks

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Towards Enhancing Linked Data Retrieval in Conversational UIs Using Large Language Models 25th

Towards Enhancing Linked Data Retrieval in Conversational U...

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25th International Conference on Web Information Systems Engineering, WISE 2024

作者： Mussa, Omar Rana, Omer Goossens, Benoît Orozco-terWengel, Pablo Perera, Charith School of Computer Science and Informatics Cardiff University Cardiff United Kingdom School of Biosciences Cardiff University Cardiff United Kingdom College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

ISBN: (纸本)9789819605729

Despite the recent broad adoption of Large Language Models (LLMs) across various domains, their potential for enriching information systems in extracting and exploring Linked Data (LD) and Resource Description Framework (RDF) triplestores has not been extensively explored. This paper examines the integration of LLMs within existing systems, emphasising the enhancement of conversational user interfaces (UIs) and their capabilities for data extraction by producing more accurate SPARQL queries without the requirement for model retraining. Typically, conversational UI models necessitate retraining with the introduction of new datasets or updates, limiting their functionality as general-purpose extraction tools. Our approach addresses this limitation by incorporating LLMs into the conversational UI workflow, significantly enhancing their ability to comprehend and process user queries effectively. By leveraging the advanced natural language understanding capabilities of LLMs, our method improves RDF entity extraction within web systems employing conventional chatbots. This integration facilitates a more nuanced and context-aware interaction model, critical for handling the complex query patterns often encountered in RDF datasets and Linked Open Data (LOD) endpoints. The evaluation of this methodology shows a marked enhancement in system expressivity and the accuracy of responses to user queries, indicating a promising direction for future research in this area. This investigation not only underscores the versatility of LLMs in enhancing existing information systems but also sets the stage for further explorations into their potential applications within more specialised domains of web information systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Structured Query Language

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An Adaptive Method For Selecting Cluster Head Using Analytical Hierarchy Process(Ahp)

An Adaptive Method For Selecting Cluster Head Using Analytic...

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2022 IEEE International Conference on computing, ICOCO 2022

作者： Khalid, Nor Azimah Anuar, Nadhirah Mohammad Noor, Noorhayati Mohamed Universiti Teknologi Mara School of Computing Sciences College of Computing Informatics and Media Malaysia

ISBN: (纸本)9781665489966

Nowadays, Wireless Sensor Network had made a major contribution to surveillance, target tracking and healthcare. Due to the nature of its complex functions in sensing and monitoring the environment at a diverse area, energy efficiency is one of the primary objectives that need to be considered to prolong the network lifetime. Cluster based is one the most common use and suitable protocol in enhancing energy efficiency in WSN. However, an efficient cluster head (CH) selection mechanism is still needed to ensure that the most appropriate CH is selected. Selecting CH based on single criteria could lead to inappropriate decision. Thus, a holistic view of the CH considering multiple criteria is more promising. Four criteria were considered for the CH selection in our approach;number of neighbour nodes (NNN), residual energy (RE), initial energy (IE) and distance of nodes to base station (DTBS). In this paper, we demonstrate that the use of Analytic Hierarchy Process (AHP) helps in CH selection better and we managed to identify the distance as the important criteria in the selection of CH. © 2022 IEEE.

关键词： Energy efficiency

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Research on Stock Prediction Model using Mode Decomposition Algorithm and Support Vector Regression 24

Research on Stock Prediction Model using Mode Decomposition ...

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2024 Guangdong-Hong Kong-Macao Greater Bay Area International Conference on Digital Economy and Artificial Intelligence, DEAI 2024

作者： Sun, Yu Mutalib, Sofianita Omar Peng Guangdong University of Science and Technology Dongguan Guangdong523083 China School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia

ISBN: (纸本)9798400717147

Stock market forecasting has always been one of the most difficult challenges faced by the stock market. In the issue of stock price prediction, its noisy nature, complexity, as well as the country's governance system, economic situation, epidemic will be mentioned. In recent years, scholars have conducted many stock market prediction studies using traditional and artificial intelligence technologies since the lower the error in stock prediction, the more investment risk can be reduced. In this research, a novel prediction method based on mode decomposition algorithm and optimized Support Vector Regression (SVR) is proposed. Mode decomposition algorithm is used to smooth the original signal and reduce the impact of noise. SVR extracts knowledge from data and ultimately utilizes the knowledge to predict new stock data. Bayesian optimization (BO) algorithm is adopted to optimize the core parameters of SVR and prevent overfitting so as to improve predictive performance. Finally, The CEEMDAN-BO-SVR model proposed in this study is compared with several other methods, including CEEMDAN-SVR, BO-SVR, SVR, RNN, LSTM, and ensemble learning algorithms, then analyzed through evaluation indicators. The experimental results comparing different models indicate that the CEEMDAN-BO-SVR model has the best fitting ability. © 2024 ACM.

关键词： Investments

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Work Design Impact on the Performance of Pakistani Academic Librarians

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International Information and Library Review 2024年第4期56卷 352-365页

作者： Altaf, Asif Masrek, Mohamad Noorman Husaini, Haslinda Library and Learning Resource Centre Royal College of Surgeons in Ireland–Medical University of Bahrain Busaiteen Bahrain College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam Malaysia

The performance of academic librarians is essential to the success of universities, yet limited research has explored the impact of work design on their performance, particularly in developing countries like Pakistan. This study addresses this gap by examining the relationship between work design elements—task significance, task autonomy, task variety, and task identity—and work performance among academic librarians in Pakistan. The research objective was to assess librarians’ perceptions of their work design and performance and to explore how these perceptions influence both task and contextual performance. A quantitative approach was used, with data collected through a structured survey of academic librarians from various Pakistani universities. Statistical analysis was conducted to test the hypotheses and evaluate the relationships between work design and performance outcomes. The findings reveal a strong positive relationship between all four work design elements and both task and contextual performance. This suggests that when librarians perceive their work as meaningful, varied, and autonomous, and when they feel connected to their tasks, their performance improves. These results align with previous studies in different contexts, underscoring the importance of effective job design in enhancing employee performance. The study provides valuable insights for university administrators, library managers, and policymakers, emphasizing the need for well-structured jobs to boost librarian performance and contribute to institutional success. It also adds to the theoretical understanding of job design by applying these concepts to the academic library context in a developing country setting. © 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： academic library librarians Work design work performance

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Cloud Seeding Potential Areas From Remote Sensing of Low-Level Cloud 20

Cloud Seeding Potential Areas From Remote Sensing of Low-Lev...

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20th IEEE International Colloquium on Signal Processing and Its Applications, CSPA 2024

作者： Anuar, Siti Nabila Shamsul Narashid, Rohayu Haron Razak, Tajul Rosli Hashim, Suhaila Rahim, Ashnita Bohari, Sharifah Norashikin Universiti Teknologi Mara Perlis Branch College Of Built Environment Arau Campus Perlis Arau02600 Malaysia Universiti Teknologi Mara School Of Computing Science College Of Computing Informatics And Mathematics Shah Alam40450 Malaysia

ISBN: (纸本)9798350382310

Cloud seeding is carried out to increase the amount of precipitation, which is useful during drought, especially in agricultural areas. However, the success rate of cloud seeding operations in Malaysia was not fully achieved due to location and temporal factors. The cumulus clouds at low elevation levels are practical in cloud seeding operations. Nowadays, low-level clouds' location can be detected using satellite remote sensing. Therefore, this study aims to determine the potential areas for cloud seeding operations with remotely sensed low-level cloud distribution. The potential areas were retrieved from the low-level cloud distribution of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) with cloud masking method and single reflective band and mapped with Geographic Information System (GIS) overlay analysis. It was found that the locations were coincidently used in previous cloud seeding operations conducted by the Malaysian Meteorological Department (MET) in 2019 which were above the 0.14 threshold. Thus, the potential areas based on low-level clouds for cloud seeding operations can be detected and mapped using Remote Sensing satellite images. The approach can potentially be utilised by related agencies to identify the appropriate areas for cloud seeding operations practically. © 2024 IEEE.

关键词： Remote sensing

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Paddy Diseases Multi-Class Classification using CNN Variants 24

Paddy Diseases Multi-Class Classification using CNN Variants

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24th International Arab Conference on Information Technology, ACIT 2023

作者： Aziz, Danial Izzat Anwar Bin Abdul Yusoff, Marina Ibrahim, Nurain Alazaidah, Raed College of Computing Informatics and Mathematics Universiti Teknologi Mara Shah Alam Malaysia College of Computing Informatics and Mathematics Universiti Teknologi Mara Shah Alam Malaysia Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798350384307

Agriculture is critical to ensuring and securing food supply for human consumption. Paddy crops are an agricultural practice that produces one of the highest food yields in agriculture. Paddy diseases must now be detected and diagnosed as soon as possible rather than later. Farmers currently use the naked eye to observe suspected infected paddies. The suspected infected paddy will then be sampled and sent to the laboratory for further examination. These methods are highly inefficient, time-consuming, and error prone. Most paddy farmers lack the knowledge to make an informed and accurate diagnosis of which paddies are infected by which diseases. This paper explores using deep learning algorithms to detect and classify various paddy diseases automatically. This method will take an image of paddy leaves and classify it based on the disease category indicated by the idea. The PaddyDoctor dataset from the Kaggle website was used to train and test the deep-learning models. Convolutional Neural Network (CNN) and Transfer Learning models, including MobileNet, MobileNetv2, and InceptionV3, have been constructed. The results of an experiment involving different dataset split ratios, learning rates, and batch sizes. CNN with MobileNetV2 outperformed CNN with MobileNetV1 and CNN with InceptionV3 by 98.05 percent, 80.08 percent, and 83.98 percent, respectively. © 2023 IEEE.

关键词： Agriculture Convolutional Neural Network Deep Learning Paddy Disease

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