Reviewerly/exHarmony huggingface dataset:
UX-MAPPER: A User eXperience Method to Analyze App Store Reviews

The mobile app market has grown over the last decades. With the rise of app stores, users can easily choose an app from thousands, making them less tolerant of low-quality apps. More than ever, users are looking for apps that provide not only valuable functionalities but pleasurable experiences. Hence, User eXperience (UX) became the differential to stand out from competitors. By understanding what factors affect UX, practitioners could focus on factors that lead to positive UX while mitigating those that affect UX negatively. In this context, reviews from app stores emerged as a valuable source of information to investigate such factors. However, analyzing millions of reviews is costly and time-consuming. This paper presents UX-MAPPER, an approach to analyzing app store reviews and supporting practitioners in identifying factors affecting UX. We applied the Design Science Research method to design UX-MAPPER iteratively and grounded on a solid theoretical background. We performed exploratory studies to investigate the problem, a systematic mapping study to identify factors that affect UX, and an empirical study with 14 participants with experience in requirements engineering to determine the relevance and acceptance of our proposal from practitioners’ perspectives. The participants considered it useful to improve the quality of existing apps and explore the reviews of competing apps to identify functionalities and features that users are requesting, liking, or hating. They were also willing to use it when it became available, highlighting our proposal’s usefulness and relevance in software development.
Usability Considerations in Social Mobile Maps to Encourage Social Change

Maps play an essential social role in serving as a basis for understanding phenomena and proposing social changes. The use of mobile devices is a possible solution to enhance the spread of these maps, given their practicality and familiarity with most of the users. However, using mobile maps has several usability challenges, many of which have already been investigated in others studies. When dealing with social maps, different challenges could be found, making it difficult to understand the mapped reality and, consequently, to make decisions. This research discusses usability in mobile social maps and aspects that can improve usability to visualize social data. For this, a study was carried out on usability in mobile maps. Usability tests were applied to a mobile application resulting from a project with social mapping. We also discuss how hypotheses raised by other authors may or may not be applied to social maps. As a result, we propose considerations that discuss how design decisions can support the stimulation of critical thinking about the social reality, aiming to encourage social change.
Interaction Design in Distributed Software Development: practices, challenges, recommendations and research gaps

Human-Computer Interaction (HCI) is an area focused on human interaction with/through computational systems, among which tangent studies on interaction design stand out. In HCI, interaction design has been consolidated as a practice with the potential to support interactive systems projects. On the other hand, more and more organizations are developing software with geographically distributed teams. However, interaction design in distributed software development (DSD) has yet to be explored, mapped, or structured in the scientific literature. Although there are reports in the scientific literature about interaction design in DSD and some proposed solutions, it is still being determined how interaction design occurs in DSD. This research investigated how interaction design has been practiced in DSD, seeking to understand its characteristics, challenges, and limitations. The research methodology was based on Charles S. Peirce’s semiotic methodeutics, bibliographic research method, and mixed methods research to investigate the current state of knowledge and practice on interaction design in DSD. We hope that the results pointed out by this research contribute to the body of knowledge about interaction design at the research frontier between HCI and DSD by (i) providing an overview of research efforts on interaction design in DSD, (ii) providing an overview of the practice of interaction design in DSD, (iii) identify research gaps and discuss future research directions, and (iv) conceiving a set of recommendations for interaction design in DSD.
Feasibility Study of a Model that evaluates the Learner Experience: A Quantitative and Qualitative Analysis

Learner eXperience (LX) is a concept derived from User eXperience (UX) and it can be defined as the perceptions, answers, and performances of learners interacting with learning environments, educational products, and resources. Evaluating the LX to obtain experiences that support and facilitate learning and knowledge mastery is important. Thus, we developed the LEEM to assess and improve the learner’s experience using Digital Communication and Information Technologies during learning. The LEEM is a generic evaluation model and can be used for any level of education; it can be worked independently of the discipline and used with any educational technology. Therefore, this paper presents a feasibility study to evaluate the LEEM steps and sentences from the perspective of potential users. Nineteen teachers from different levels of education participated in this study. The study results were analyzed and generated in a new version of LEEM. The results showed positive points of LEEM, such as a practical, objective, easy-to-use, and useful model. In addition, opportunities for improving some items and sentence of LEEM was obtained. The teachers also suggested adding a description at the ends of the scales to facilitate the response to the items. This study contributes to creating a body of knowledge about LEEM, analyzing its use feasibility and evolution.
Revolutionizing Enterprise Architecture: Unleashing TOGAF's Potential with the Architecture Content Framework

Smart hospital transformation in hospitals is currently needed to increase hospital services' effectiveness and efficiency. One of the smart hospital implementations is the use of SIM RS (Hospital Management Information System). The implementation of SIM RS is required for every hospital following the Regulation of the Minister of Health of the Republic of Indonesia Number 82 of 2013. The method used in this research uses the adoption of the TOGAF framework with the Architecture Content Framework component consisting of five phases: Architecture Vision, Architecture Requirements, Business Architecture, Information Systems Architecture, and Architecture Realization. The findings of this research involve designing an Enterprise Architecture design using the TOGAF framework approach, specifically focusing on the Architecture Content Framework component, it can be concluded that the research results are in the form of an enterprise architecture blueprint which can be used as a solution in developing information systems and data integration in organizations to improve service quality and achieve the goals and strategic plans of Sidoarjo Regional Hospital.
Transformer-Based Model for Malicious URL Classification

In recent years, cyber threats including malicious software, virus, spam, and phishing have grown aggressively via compromised Uniform Resource Locators (URLs). However, the current phishing URL detection solutions based on supervised learning use labeled data for training and classification, leading to the dependency on known attacking patterns. These approaches have limitations in fighting against evolving phishing tactics, resulting in a lack of robustness and sustainability. In this study, an unsupervised transformer model is proposed to address the drawbacks of the existing methods which use supervised learning to combat zero-day phishing attacks. Specifically, Bidirectional Encoder Representations from Transformers (BERT) is adopted in this paper to classify malicious URLs. The proposed model was trained on a public dataset and benchmarked with various baseline models using several performance metrics. Results obtained from the experiments showed that BERT-Medium achieved the highest detection accuracy of 98.55% among numerous transformer based models and outperformed other text embedding and deep learning techniques, indicating that the proposed solution is effective and robust in detecting phishing URLs.
Gender Nuances in Human-Computer Interaction Research

Many studies are being done in the Human-Computer Interaction (HCI) area to identify gender nuances in how hardware and software are thought and built. According to bias prevention, identification, and resolution techniques, the objective is to establish an inclusive scenario. This paper presents a systematic literature review on gender and HCI, synthesizing studies where these concepts are analyzed on how HCI research discusses gender nuances, aspects, and issues. Also, to comprehend how interventions have been suggested and the frequency of those studies proposing interventions, as well as how they have been applied to diminish gender disparities in technological processes. The results indicated that the way of dealing with gender in technology has changed over the years, after including experiences of intersectional and indeterminate genders, especially considering methods submitted by academic research, where “Feminist HCI” was the most considered technique for working with gender issues, but it was mainly present from 2012 to 2021. Similarly, it was shown that most of the proposed interventions are focused on detecting gender bias in technologies, not specifically on solving them.
Detection of XSS Attacks with One Class SVM Using TF-IDF and Devising a Vectorized Vocabulary

Cross Site Scripting (XSS) attacks are one of the most serious security risks on web applications, in which attackers force victims to execute malicious scripts on a browser. By applying machine learning to detection methods, several researches have been conducted on methods to detect Reflected XSS attacks and Stored XSS attacks. This paper proposes a method to detect XSS attacks using TF-IDF vectorization and One Class SVM, a machine learning model for unsupervised anomaly detection. One Class SVM eliminates the need to include XSS attack data in training data, allowing effective detection even when data for new XSS attack techniques is not prepared enough. We conducted two experiments on the Kaggle XSS dataset by vectorizing the TF-IDF and comparing each result. The first type uses all the vocabulary on the dataset, and another uses only the vocabulary contained in XSS attacks from the dataset. Since One Class SVM can identify the characteristics of XSS attacks from vectorized data, the latter type achieves higher performance, with an accuracy of 0.993, precision of 0.987, recall of 1.000, and F-measure of 0.993. The contribution of this paper is that our proposed method is based on unsupervised learning, which means a large number of new XSS attack data for retraining the model is not required. XSS attack detection is realized with high accuracy compared to previous research based on supervised learning methods.
Corporate ICT Governance of Indonesian State-Owned Companies: Governance Structure and Decision Making Archetype

The study delves into the growing trend of implementing Information Technology (IT) governance principles across companies and organizations, particularly in the public sector. This approach has gained momentum due to its significance in enhancing performance, risk management, and aligning IT strategies with corporate objectives. The paper highlights the role of COBIT 2019, a standard that assesses IT governance effectiveness and its alignment with organizational goals. The integration of IT governance practices with strategic and operational policies in State-Owned Enterprises (SOEs) is explored, focusing on aspects like IT master planning, framework organization, IT investment management, and risk mitigation. Additionally, the study emphasizes the significance of the RACI method, a tool for delineating responsibilities within organizations, in enhancing governance clarity and effectiveness. By mapping ministerial regulations to COBIT 2019, the research establishes a robust foundation for aligning IT practices with organizational goals. Furthermore, it underscores the importance of internal control evaluation, as internal audit plays a vital role in ensuring effective control mechanisms. The aim, the paper advocates for a comprehensive integration of IT governance, COBIT 2019, and RACI methodologies to enhance IT management practices in SOEs, ultimately promoting efficient and effective organizational operations. Further research could explore practical implementation challenges and strategies to optimize the proposed frameworks
Methods for Estimating Seniors' Source Code Helpful for Next Challenge Depending on a Series of Programs Written by Each Student in Recent Exercises

In programming exercises, analysis of the source code created by learners is often used to support instructors. However, most existing studies used this analysis for estimating the learner's understanding or identifying learners who need support. We developed methods for automatically estimating seniors' source code helpful for next challenge depending on a series of codes written by each learner in recent exercises. Specifically, we built a machine learning model that outputs the labeled source code (helpful code) by receiving a series of programs each learner wrote. To build this model, we generate abstract syntax trees by compiling source codes and then convert them to token columns and weights. These are then classified into different solutions (i.e., program logic) using clustering and labeled accordingly. Results of evaluation have shown that our methods can estimate helpful source code with high accuracy.
Towards the Selection of Distance Metrics for k-NN Classifier in Students' Performance Prediction Modeling

This paper investigates the impact of changing distance metrics on the performance of the k-NN classifier. The study investigates the variation in models performance with changing distance metric and value of k in the context of students' performance prediction models. The research utilizes datasets specifically designed for students' performance prediction modeling. Starting with a I-NN model, the experiments increment the value of k by 2 until the size of the dataset is reached. The experiments are repeated with different distance metrics derived from Minkowski derivation, including Euclidean, Manhattan, and Chebyshev. The findings indicate that there is no unanimously dominant distance metric for every dataset. However, the Euclidean and Manhattan distance metrics emerge effective, while Chebyshev exhibits lower performance. The research concludes Euclidean and Manhattan distance metrics as appropriate metrics for students' performance prediction modeling using the k-NN classifier.
BoZFEx – A New Feature Extraction Based on Bounded-Zone Method for Handwriting Character Recognition

The selection of a suitable set of features to represent input samples is one of the most influential elements in a successful optical character recognition, OCR pipeline. The feature extraction phase plays a vital role in extracting the appropriate features that yield minimum classification error, especially in cursive or fibrous handwriting. This study proposed a new feature extraction; BoZFEx - Bounded-Zone Feature Extraction, which focuses on the character recognition of children's handwriting. Minnesota handwriting assessment (MHA) - an established handwriting assessment was used in data collection that uses English alphabets. A total of 4841 characters were collected from 90 children with two font sizes. The proposed feature extraction method is integrated into the optical character recognition (OCR) pipeline. Among font size 36, there were 4 vowel letters, a, e, o, and u showing the consistency of the number of pixels in zone 11 and zone l2. This method was compared with the established character recognition method using a machine learning algorithm, i.e., a multi-input convolutional neural network and achieved promising results with 0.78 accuracy.
Leveraging Federated Learning and XAI for Privacy-Aware and Lightweight Edge Training in Network Traffic Classification

The exponential growth of internet traffic causes significant challenges for network traffic classification, such as maintaining data privacy and requiring more computing resources. To address such challenges, moving the processing to data instead of the opposite shows some promise. Related technologies, such as edge computing, present a potential solution as they reduce latency and bandwidth and consider data privacy. However, edge computing often runs on resource-constraint devices, thus making complex model training like deep learning challenging. As such, this paper proposes a distributed and lightweight edge training method for network traffic classification using federated learning and XAI, which are currently underexplored in the domain. In doing so, we managed to run deep learning on edge devices and preserve data privacy. We evaluate the proposed method using the ISCXVPN2016 dataset on three NVIDIA Jetson devices to emulate edge devices. The results show a 36.71% improvement in F1 score, training time, memory, and GPU utilization when benchmarked against the more conventional centralized training.
Sentiment Analysis on Mixed-Language Social Media Post

Sentiment analysis is a powerful tool that can be used by businesses and organizations to gather valuable data about public opinion towards a brand, product, topic, event, and much more. However, most Malaysians post on social media using a mix of English and Malay words, also known as Manglish, which are not catered to by existing sentiment analysis models. Malaysian-centric companies that are interested to analyze the Malaysian posts would have to do so manually, which is costly and time-consuming. Motivated by this issue, this paper aims to propose a method of performing sentiment analysis on posts using the power of machine learning. Several machine learning algorithms were identified and trained to classify a Manglish post as either positive or negative. Steps are also taken to ensure the reliability of the model and to improve it after the first training experiment. We found that this method is successful in producing a model that can predict social media post sentiment with reliable and acceptable accuracy.
Performance Assessment of U-Net for Semantic Segmentation of Liquid Spray Images with Gaussian Blurring

Semantic segmentation plays a vital role in various computer vision applications. One of the convolutional networks which have been scholarly accepted as the prominent solution for semantic segmentation is U-Net, known for its effectiveness in capturing spatial context with fewer training images. In real-world applications of analysing liquid spray images, motion blur and defocus blur distortions likely appear due to camera positioning. The robustness of U-Net segmenting the object in the presence of image distortions still needs to be explored in the context of liquid spray images. This study aims to analyse the visual impact of U-Net for semantic segmentation in liquid spray images. Gaussian blurring is applied with different levels of distortion to the test image to simulate the blurring effect caused by the camera defocusing and unstable motion-positioning. Surprisingly, the experimental results showed that U-Net could recognise many initially unrecognisable objects in the test image after applying Gaussian blurring. Following this finding, a hypothesis is proposed that inducing an appropriate blurring artefact into the test image will significantly maximise the contour boundaries visibility, allowing the U-N et to segmentise the recognisable contour efficiently. A closer inspection of the pixel histogram revealed a narrow range of high-frequency values. The reduction of pixel variation makes the object appear connected without sharp separation, improving the continuity of the contour boundary. This quantitative observation is harmonised with the above hypothesis. Code is available at https://github.com/lynerlwl/unet-gaussian-blurring
A Systematic Literature Review: Metaverse in Educational Learning for TVET Student

Metaverse has essential for today technology due to enhance the assessment study in distance learning. A good application allows the competency of student with quality academic. However, metaverse in educational setting for Technical Vocational Education Training (TVET) student may require a characteristic to produce effective in teaching and learning specifically for TVET student that requires hands on or practical assessment. Therefore, this paper provides a gap of study for existing work on Metaverse educational learning for supporting TVET student in teaching and learning regarding their market commercial and research purposes. The methodology of this study was conducted using Systematic Literature Review (SLR). Result: two findings investigate 1) identify the relevant Metaverse in education learning for TVET Student and 2) method for education learning for TVET Student. The strengths and weaknesses of the features and utilities behind each study are also presented to provide further understanding of the gaps and weaknesses of each body of research. We conclude that these studies are still lacking and require further evaluation and improvement.
Methods for Organization of the Contexts in Complicated Elaboration of Research Documents

In research activities, multiple tasks such as survey, discussion, presentation of work progress and writing a paper are repeatedly carried out while refining several documents. To conduct such work successfully, it is important to satisfactorily understand the deliverables and contexts of document-elaboration work and use them for later work. We developed methods for supporting the understanding of work circumstances by organizing research documents together with work histories and visualizing them. We describe a series of methods for generating an elaboration-context graph. We also describe the dynamic summarization of the graph depending on key documents the user focuses on and work scene. Finally, we describe an experiment on generating an elaboration-context graph using at prototype support system that is based on our methods and discuss the characteristics of our methods on the basis of the results.
A Futuristic Proposed Solution to a Global Challenge: The Promise of AI for Social Distancing Compliance

Infectious disease is a terrible pandemic around the world, the economy and even people's lifestyles are affected while implementing a lot of standard operating procedures (SOPs) to control the spread of the virus. One of the most effective ways is to control the physical distance between people. Therefore, a Social Distancing Monitoring (SDM) system can be used to track the safe distance between people. In this study, a comparison between algorithms will be made to identify the most efficient algorithm based on human detection results. As a result, the OpenPose algorithm has outstanding human detection results compared with YOLOv4 and YOLOv4-pedestrian. Furthermore, a distinctive bounding box method for human detection has been proposed, which is defined from the human skeleton using the OpenPose algorithm. Hence, SDM will be efficient after a perspective transformation to calculate the distance between people, since most of the people in the frames have been detected. Finally, this study will discuss how the power of AI enhances the SDM system in detecting humans and calculating the distance between people detected.