Reviewerly/exHarmony huggingface dataset:
Identifying the Importance of UX Dimensions for Different Software Product Categories

Authors:
raw_author_name: Ehsan Mortazavi
raw_author_name: Philippe Doyon-Poulin
raw_affiliation_string: Polytechnique Montreal Department of Mathematics and Industrial Engineering, , 2500 Chem, de Polytechnique, QC H3T 1J4, Montréal
raw_author_name: Daniel Imbeau
raw_affiliation_string: Polytechnique Montreal Department of Mathematics and Industrial Engineering, , 2500 Chem, de Polytechnique, QC H3T 1J4, Montréal
raw_author_name: Jean-Marc Robert
raw_affiliation_string: Polytechnique Montreal Department of Mathematics and Industrial Engineering, , 2500 Chem, de Polytechnique, QC H3T 1J4, Montréal

Abstract: Abstract Billions of users around the world use mobile applications and computer software to achieve their professional and personal goals. This situation drives User Experience (UX) researchers and practitioners to assess the importance of UX dimensions across different products, to facilitate the design, development and evaluation of new products. To that end, this study surveyed a group of 200 end users and 8 UX experts from Canada to document the importance of 21 UX dimensions for 15 software product categories. The results confirmed that the importance of UX dimensions varies between product categories. Comparing the findings to those of similar studies conducted in Germany and Indonesia revealed that, while culture influences the rating of UX dimensions, the importance of UX dimensions is still determined by the product category. Comparisons between the importance ratings of UX dimensions between end users and experts and within end users were not significant in 77% and 97% of cases, respectively. Results showed that task-based product categories rely more on pragmatic dimensions (i.e. functionality and usability) while leisure-based products value hedonic dimensions (i.e. pleasure) as well. This study benefits researchers and practitioners by enabling them to select the most important UX dimensions for evaluating their products. CCS CONCEPTS: • Human-centered computing • Human-Computer Interaction (HCI) • HCI design and evaluation methods. Additional Keywords and Phrases: User experience, UX dimension, UX evaluation, culture.
Joint Alignment Networks For Few-Shot Website Fingerprinting Attack

Authors:
raw_author_name: Qiang Zhou
raw_affiliation_string: School of Computer Science and Communication Engineering, University of Jiangsu, Xuefu Street, 212013, Zhenjiang, Jiangsu, China
raw_author_name: Liangmin Wang
raw_affiliation_string: School of Cyber Science and Engineering, Southeast University, Moling Street, 211102, NanJing, Jiangsu, China
raw_author_name: Huijuan Zhu
raw_affiliation_string: School of Computer Science and Communication Engineering, University of Jiangsu, Xuefu Street, 212013, Zhenjiang, Jiangsu, China
raw_author_name: Tong Lu
raw_affiliation_string: School of Computer Science and Communication Engineering, University of Jiangsu, Xuefu Street, 212013, Zhenjiang, Jiangsu, China
raw_author_name: Heping Song
raw_affiliation_string: School of Computer Science and Communication Engineering, University of Jiangsu, Xuefu Street, 212013, Zhenjiang, Jiangsu, China

Abstract: Abstract Website fingerprinting (WF) attacks based on deep neural networks pose a significant threat to the privacy of anonymous network users. However, training a deep WF model requires many labeled traces, which can be labor-intensive and time-consuming, and models trained on the originally collected traces cannot be directly used for the classification of newly collected traces due to the concept drift caused by the time gap in the data collection. Few-shot WF attacks are proposed for using the originally and few-shot newly collected labeled traces to facilitate anonymous trace classification. However, existing few-shot WF attacks ignore the fine-grained feature alignment to eliminate the concept drift in the model training, which fails to fully use the knowledge of labeled traces. We propose a novel few-shot WF attack called Joint Alignment Networks (JAN), which conducts fine-grained feature alignment at both semantic-level and feature-level. Specifically, JAN minimizes a distribution distance between originally and newly collected traces in the feature space for feature-level alignment, and utilizes two task-specific classifiers to detect unaligned traces and force these traces mapped within decision boundaries for semantic-level alignment. Extensive experiments on public datasets show that JAN outperforms the state-of-the-art few-shot WF methods, especially in the difficult 1-shot tasks.
Analyzing third-party data leaks on online pharmacy websites

Authors:
raw_author_name: Sampsa Rauti
raw_affiliation_string: Department of Computing, University of Turku, Turku, Finland
raw_author_name: Sini Mickelsson
raw_affiliation_string: Faculty of Law, University of Turku, Turku, Finland
raw_author_name: Tuomas Mäkilä
raw_affiliation_string: Department of Computing, University of Turku, Turku, Finland
raw_author_name: Elina Pirjatanniemi
raw_affiliation_string: Institute for Human Rights, Åbo Akademi University, Turku, Finland
raw_author_name: Ville Leppänen
raw_affiliation_string: Department of Computing, University of Turku, Turku, Finland

Abstract: Abstract Purpose With digitalization, using essential digital services such as online services has become increasingly common. These services process sensitive health related data, such as customers’ prescription medicine orders, which makes ensuring stringent data privacy crucial. The current study examines third parties such as analytics services on Finnish pharmacy websites and investigates the nature and contents of data leaks on these websites. Methods We perform an extensive network traffic analysis to reveal data leaks among 163 Finnish online pharmacies. We also study a set of privacy policies of these online pharmacies, and provide a legal analysis regarding the interpretation of the concept of data concerning health in the context of online pharmacies. Results Our findings reveal serious data leaks among Finnish online pharmacies. We found 145 pharmacies had third-party services on their websites and only 18 did not. Out of all 163 online pharmacies, 57 (35.0 %) leaked a specific prescription medicine name connected with identifying personal data on the customer. We argue that the information concerning purchases on the prescription medicines should be interpreted as data concerning health to ensure efficient protection of customers’ right to data protection and privacy. Conclusions We hope that these concerning results will serve as a wake-up call for the developers and maintainers of online pharmacies and other web services processing sensitive data. Any third-party services incorporated into websites processing sensitive personal data should be closely inspected in terms of data leaks, or preferably not used at all.
Software development metrics: to VR or not to VR

Authors:
raw_author_name: David Moreno-Lumbreras
raw_affiliation_string: Universidad Rey Juan Carlos, Fuenlabrada, Spain
raw_author_name: Gregorio Robles
raw_affiliation_string: Universidad Rey Juan Carlos, Fuenlabrada, Spain
raw_author_name: Daniel Izquierdo-Cortázar
raw_affiliation_string: Bitergia, Leganés, Spain
raw_author_name: Jesus M. Gonzalez-Barahona
raw_affiliation_string: Universidad Rey Juan Carlos, Fuenlabrada, Spain

Abstract: Abstract Context Current data visualization interfaces predominantly rely on 2-D screens. However, the emergence of virtual reality (VR) devices capable of immersive data visualization has sparked interest in exploring their suitability for visualizing software development data. Despite this, there is a lack of detailed investigation into the effectiveness of VR devices specifically for interacting with software development data visualizations. Objective Our objective is to investigate the following question: “How do VR devices compare to traditional screens in visualizing data about software development?” Specifically, we aim to assess the accuracy of conclusions derived from exploring visualizations for understanding the software development process, as well as the time required to reach these conclusions. Method In our controlled experiment, we recruited N=32 volunteers with diverse backgrounds. Participants interacted with similar data visualizations in both VR and traditional screen environments. For the traditional screen setup, we utilized a commercially available set of interactive dashboards based on Kibana, commonly used by Bitergia customers for data insights. In the VR environment, we designed a set of visualizations, tailored to provide an equivalent dataset within a virtual room. Participants answered questions related to software evolution processes, specifically code review and issue tracking, in both VR and traditional screen environments, for two projects. We conducted statistical analyses to compare the correctness of their answers and the time taken for each question. Results Our findings indicate that the correctness of answers in both environments is comparable. Regarding time spent, we observed similar durations, except for complex questions that required examining multiple interconnected visualizations. In such cases, participants in the VR environment were able to answer questions more quickly. Conclusion Based on our results, we conclude that VR immersion can be equally effective as traditional screen setups for understanding software development processes through visualization of relevant metrics in most scenarios. Moreover, VR may offer advantages in comprehending complex tasks that require navigating through multiple interconnected visualizations. However, further experimentation is necessary to validate and reinforce these conclusions.
<scp>O‐SCDR</scp>: Optimal cluster with attention based shared‐account cross‐domain sequential recommendation using deep reinforcement learning technique

Authors:
raw_author_name: M. Nanthini
raw_affiliation_string: Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India
raw_author_name: K. Pradeep Mohan Kumar
raw_affiliation_string: Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur India

Abstract: Abstract Sequential recommendation involves suggesting subsequent items in a series of user activities. When recommending relevant items to users within the same account, the challenge lies in discerning diverse user behaviours to provide tailored recommendations based on individual preferences and timing. Cross‐domain sequential recommendation (CDSR) focuses on accurately extracting cross‐domain user preferences from both within‐sequence and between‐sequence interactions among items. Current approaches typically concentrate on learning preferences within a single domain using intra‐sequence item interactions, followed by a transfer module for cross‐domain preferences. However, this sequential process and implicit method are constrained by the effectiveness of the transfer module and may overlook inter‐sequence item associations. In this study, we propose an optimal cluster with attention‐based shared‐account cross‐domain sequential recommendation (O‐SCSR) system using deep reinforcement learning techniques. Our approach commences by formulating a modified hummingbird optimization (MHO) algorithm for clustering, effectively identifying latent users who share the same account to enhance the understanding of user interactions within shared‐account scenarios. Additionally, we design a domain filter based on quantum classic deep reinforcement learning (QCDRL), intelligently selecting interactions contributing to O‐SCSR. By quantifying rewards from transferred domain knowledge, the QCDRL‐based filter retains only valuable interactions for the task of SCDR. Finally, we validate the efficacy of our proposed O‐SCDR method using real‐world datasets, namely HVIDEO and HAMAZON. Through simulation results comparing the O‐SCDR system with existing state‐of‐the‐art systems, we demonstrate its effectiveness and legitimacy.
Evaluation of the number of clusters in a data set using <i>p</i> -values from multiple tests of hypotheses

Authors:
raw_author_name: Dr. Soumita Modak
raw_affiliation_string: Faculty, Department of Statistics, University of Calcutta, Basanti Devi College, Kolkata, India

Abstract: This article proposes a novel, nonparametric, interpoint distance-based measure to investigate whether there exist any groups in a set of given data, and if so then, how many groups are prevailing in total. It is a cluster accuracy index useful for arbitrary-dimensional data set, in association with any clustering algorithm having the number of groups specified a priori. We perform univariate, nonparametric, multiple statistical tests of hypotheses, where as many dependent tests as the sample size are carried out using the interpoint distances. They possess p-values to be combined to reach a decision, which is taken in a step-wise process for a possible number of clusters. It reduces unnecessary computations compared with the other accuracy measures from the literature. Data study establishes the proposed index's efficiency and superiority.
Advances in automated support for requirements engineering: a systematic literature review

Authors:
raw_author_name: Muhammad Aminu Umar
raw_affiliation_string: Department of Informatics, King’s College London, Strand, London, WC2R 2LS, UK
raw_author_name: Kevin Lano
raw_affiliation_string: Department of Informatics, King’s College London, Strand, London, WC2R 2LS, UK

Abstract: Abstract Requirements Engineering (RE) has undergone several transitions over the years, from traditional methods to agile approaches emphasising increased automation. In many software development projects, requirements are expressed in natural language and embedded within large volumes of text documents. At the same time, RE activities aim to define software systems' functionalities and constraints. However, manually executing these tasks is time-consuming and prone to errors. Numerous research efforts have proposed tools and technologies for automating RE activities to address this challenge, which are documented in published works. This review aims to examine empirical evidence on automated RE and analyse its impact on the RE sub-domain and software development. To achieve our goal, we conducted a Systematic Literature Review (SLR) following established guidelines for conducting SLRs. We aimed to identify, aggregate, and analyse papers on automated RE published between 1996 and 2022. We outlined the output of the support tool, the RE phase covered, levels of automation, development approach, and evaluation approaches. We identified 85 papers that discussed automated RE from various perspectives and methodologies. The results of this review demonstrate the significance of automated RE for the software development community, which has the potential to shorten development cycles and reduce associated costs. The support tools primarily assist in generating UML models (44.7%) and other activities such as omission of steps, consistency checking, and requirement validation. The analysis phase of RE is the most widely automated phase, with 49.53% of automated tools developed for this purpose. Natural language processing technologies, particularly POS tagging and Parser, are widely employed in developing these support tools. Controlled experimental methods are the most frequently used (48.2%) for evaluating automated RE tools, while user studies are the least employed evaluation method (8.2%). This paper contributes to the existing body of knowledge by providing an updated overview of the research literature, enabling a better understanding of trends and state-of-the-art practices in automated RE for researchers and practitioners. It also paves the way for future research directions in automated requirements engineering.
Trajectory-based alignment for optical see-through HMD calibration

Authors:
raw_author_name: Lingling Chen
raw_affiliation_string: School of Artificial Intelligence and Data Science, Hebei University of Technology, 300130, Tianjin, People’s Republic of China
raw_author_name: Wei Chen
raw_affiliation_string: Defense Innovation Institute, Academy of Military Sciences (AMS), 100071, Beijing, People’s Republic of China
raw_author_name: Zhongchen Shi
raw_affiliation_string: Defense Innovation Institute, Academy of Military Sciences (AMS), 100071, Beijing, People’s Republic of China
raw_author_name: Ye Yan
raw_affiliation_string: Defense Innovation Institute, Academy of Military Sciences (AMS), 100071, Beijing, People’s Republic of China
raw_author_name: Erwei Yin
raw_affiliation_string: Defense Innovation Institute, Academy of Military Sciences (AMS), 100071, Beijing, People’s Republic of China

Abstract: Abstract In order to align the virtual and real content precisely through augmented reality devices, especially in optical see-through head-mounted displays (OST-HMD), it is necessary to calibrate the device before using it. However, most existing methods estimated the parameters via 3D-2D correspondences based on the 2D alignment, which is cumbersome, time-consuming, theoretically complex, and results in insufficient robustness. To alleviate this issue, in this paper, we propose an efficient and simple calibration method based on the principle of directly calculating the projection transformation between virtual space and the real world via 3D-3D alignment. The proposed method merely needs to record the motion trajectory of the cube-marker in the real and virtual world, and then calculate the transformation matrix between the virtual space and the real world by aligning the two trajectories in the observed view. There are two advantages associated with the proposed method. First, the operation is simple. Theoretically, the user only needs to perform four alignment operations for calibration without changing the rotation variation. Second, the trajectory can be easily distributed throughout the entire observation view, resulting in more robust calibration results. To validate the effectiveness of the proposed method, we conducted extensive experiments on our self-built optical see-through head-mounted display (OST-HMD) device. The experimental results show that the proposed method can achieve better calibration results than other calibration methods.
Personnel Training for Common Facility Management Issues in Thermal-Energy-Storage Chiller Plant using a Serious 3D Game

Authors:
raw_author_name: Mirza Rayana Sanzana
raw_affiliation_string: University of Nottingham Malaysia, Malaysia
raw_author_name: Mostafa Osama Mostafa Abdulrazic
raw_affiliation_string: University of Nottingham Malaysia, Malaysia
raw_author_name: Jing Ying Wong
raw_affiliation_string: University of Nottingham Malaysia, Malaysia
raw_author_name: Chun-Chieh Yip
raw_affiliation_string: University Tunku Abdul Rahman, Malaysia

Abstract: Background This study introduces an innovative personnel training method for facility management and maintenance of Thermal-Energy-Storage (TES) chiller plants using a serious 3D game. Training games can improve the decision-making of personnel where they can learn to deal with management of TES chiller plants in the context of this study in an active learning approach. Intervention The research aims to investigate the effectiveness of an immersive learning experience with computerized simulation and synthetic task environments as a training game for facility managers. The serious 3D game adopts a first-person perspective, allowing players to assume the role of a facility manager and actively learn how to address maintenance issues commonly encountered in chiller plants. Methods The study implemented a first-person-based serious 3D game centered around a TES chiller plant. Participants, in the role of facility managers, engaged with the game and followed instructions to gain practical knowledge in managing maintenance issues within a controlled and simulated setting. Results The findings demonstrate increased engagement and interest among personnel when learning how to manage chiller plant issues within a serious 3D game environment. Notably, personnel experienced reduced pressure compared to real-life scenarios, as they navigated the challenges without the presence of a supervisor. Limitations The study’s limitations include a higher proportion of male participants in the qualitative content analysis, which may affect the generalizability of the findings. Additionally, the absence of a control group limits the ability to make direct comparisons with traditional training methods. Conclusion The results suggest that serious 3D games hold potential as an effective training tool for facility management and maintenance personnel. By engaging in active training within simulated and synthetic task environments, personnel can enhance their skills and decision-making capabilities. However, further research with a more diverse participant sample and a control group is warranted to fully evaluate the effectiveness of this innovative personnel training approach.
Self‐supervised multicontrast super‐resolution for diffusion‐weighted prostate MRI

Authors:
raw_author_name: Batuhan Gundogdu
raw_affiliation_string: Department of Radiology University of Chicago Chicago Illinois USA
raw_author_name: Milica Medved
raw_affiliation_string: Department of Radiology University of Chicago Chicago Illinois USA
raw_author_name: Aritrick Chatterjee
raw_affiliation_string: Department of Radiology University of Chicago Chicago Illinois USA
raw_author_name: Grace Lee
raw_affiliation_string: Department of Radiology University of Chicago Chicago Illinois USA
raw_author_name: Aytekin Oto
raw_affiliation_string: Department of Radiology University of Chicago Chicago Illinois USA
raw_author_name: Gregory S. Karczmar
raw_affiliation_string: Department of Radiology University of Chicago Chicago Illinois USA

Abstract: This study addresses the challenge of low resolution and signal-to-noise ratio (SNR) in diffusion-weighted images (DWI), which are pivotal for cancer detection. Traditional methods increase SNR at high b-values through multiple acquisitions, but this results in diminished image resolution due to motion-induced variations. Our research aims to enhance spatial resolution by exploiting the global structure within multicontrast DWI scans and millimetric motion between acquisitions.We introduce a novel approach employing a "Perturbation Network" to learn subvoxel-size motions between scans, trained jointly with an implicit neural representation (INR) network. INR encodes the DWI as a continuous volumetric function, treating voxel intensities of low-resolution acquisitions as discrete samples. By evaluating this function with a finer grid, our model predicts higher-resolution signal intensities for intermediate voxel locations. The Perturbation Network's motion-correction efficacy was validated through experiments on biological phantoms and in vivo prostate scans.Quantitative analyses revealed significantly higher structural similarity measures of super-resolution images to ground truth high-resolution images compared to high-order interpolation (p <$$ < $$ 0.005). In blind qualitative experiments, 96.1%$$ 96.1\% $$ of super-resolution images were assessed to have superior diagnostic quality compared to interpolated images.High-resolution details in DWI can be obtained without the need for high-resolution training data. One notable advantage of the proposed method is that it does not require a super-resolution training set. This is important in clinical practice because the proposed method can easily be adapted to images with different scanner settings or body parts, whereas the supervised methods do not offer such an option.