User Usability Research Articles

E-Connect

In an era of digital proliferation, the need for centralized, intuitive, and user-driven platforms for video content management is growing. This project introduces a Video Directory Application designed using React.js and integrated with Firebase Firestore. The platform provides a seamless experience with core components such as user registration, login, and signup, alongside dynamic features like video exploration, personal profile management, and content uploading for videos and blogs. The application addresses critical challenges in content discovery and personalization by integrating advanced search capabilities and user-specific data management. With a responsive design optimized for various devices, the platform ensures accessibility and usability for diverse users. By focusing on personalization, security, and scalability, the Video Directory Application is poised to empower users to explore, manage, and contribute high-quality video content efficiently. This application exemplifies a modern approach to content management, blending robust front-end interfaces with a scalable backend to foster engagement and creativity among its users. Key Words: Video Directory , Firebase Firestore , Content Management , User Authentication , Web Application Development.

TimbreSense: Timbre Abnormality Detection for Bel Canto with Smart Devices

With the rise of mobile devices, bel canto practitioners increasingly utilize smart devices as auxiliary tools for improving their singing skills. However, they frequently encounter timbre abnormalities during practice, which, if left unaddressed, can potentially harm their vocal organs. Existing singing assessment systems primarily focus on pitch and melody and lack real-time detection of bel canto timbre abnormalities. Moreover, the diverse vocal habits and timbre compositions among individuals present significant challenges in cross-user recognition of such abnormalities. To address these limitations, we propose TimbreSense, a novel bel canto timbre abnormality detection system. TimbreSense enables real-time detection of the five major timbre abnormalities commonly observed in bel canto singing. We introduce an effective feature extraction pipeline that captures the acoustic characteristics of bel canto singing. By applying temporal average pooling to the Short-Time Fourier Transform (STFT) spectrogram, we reduce redundancy while preserving essential frequency-domain information. Our system leverages a transformer model with self-attention mechanisms to extract correlation and semantic features of overtones in the frequency domain. Additionally, we employ a few-shot learning approach involving pre-training, meta-learning, and fine-tuning to enhance the system’s cross-domain recognition performance while minimizing user usage costs. Experimental results demonstrate the system’s strong cross-user domain recognition performance and real-time capabilities.

Advancements in Nepali Speech Recognition: A Comparative Study of BiLSTM, Transformer, and Hybrid Models

In today's world, leveraging Automatic Speech Recognition (ASR) technology to process and understand spoken language is highly desirable. Our proposed Nepali Speech Recognition employs an advanced generation to recognize and interpret spoken Nepali language. It approaches Nepali speech, allowing it to reply to user queries effectively. To attain this, we rent a mixture of superior neural network fashions. We extract Mel-frequency cepstral coefficients (MFCCs) from the preprocessed audio information; these MFCCs capture crucial spectral characteristics of Nepali speech and serve as essential input features for our neural network model. To design a top-rated version for textual content-based query processing, we make use of convolutional neural networks (CNN), residual networks (ResNet), and bidirectional long short-term memory (BiLSTM) layers. The CNN layers excel at extracting neighborhood patterns and spatial features from the MFCC input; the ResNet layers capture deeper representations to enhance performance. The BiLSTM layers are also employed to model temporal dependencies in the textual content-based query processing, we make use of convolutional neural networks (CNN), residual networks (ResNet), and bidirectional long short-term memory (BiLSTM) layers. The CNN layers excel at extracting neighborhood patterns and spatial features from the MFCC input; the ResNet layers capture deeper representations to enhance performance. The BiLSTM layers are also employed to model temporal dependencies in the textual content records. We hired the Connectionist Temporal classification (CTC) loss feature to enable sequence-to-series mapping, aligning the input speech with corresponding text outputs. This approach permits our gadget to successfully process textual content queries and provide correct responses, enhancing the user's usefulness. The model, after being trained with 1.55 million parameters in about 1 lakh 57 thousand audio datasets for 47 epochs, achieved a CTC of 17.98% (82.02%-character accuracy rate) with this model.

In Pursuit of Architectural Happiness, on the Potential for Change in Legal Frameworks for Accessibility.

Modern architecture has generated measurable requirements about users' interactions with the built environment to create user-oriented architecture through meticulous full-scale testing with subsequent standardization. These requirements have entered the legal framework as minimum threshold values for basic demands that regulate building and physical planning. In the construction of the modern welfare society, building-related requirements have been associated with fundamental ethical values for the societal construction, e.g., equity, health, inclusion, and sustainable development. The programming of architecture through building requirements can be seen as giving the users a dosage of happiness through architecture. The modern welfare state defines an architectural happiness through is legal frameworks. This study analyzed thirteen questions from a 37-question questionnaire, distributed to 122 informants, in thirteen countries. The study aimed at establishing the contemporaneous understandings and uses of the concept of accessibility. The study concluded that, currently, the concept of accessibility has turned into a technical instrument that limits the quest for an increased fit between user demands and needs with the architectural design. Furthermore, there is a loose link between accessibility and sustainability. In conclusion, architectural happiness for the upcoming sustainable society of the 2030s needs to update the concept of accessibility so that the outcome - usability for future users - becomes apparent.

RPerf: Mining user reviews using topic modeling to assist performance testing: An industrial experience report

Software performance affects the user-perceived quality of software. Therefore, it is important to analyze the performance issues that users are concerned with. In this paper, we document our experience working with our industry partner on analyzing user reviews to identify and analyze performance issues users are concerned with. In particular, we designed an approach, RPerf, which automatically analyzes unstructured user reviews and generates a performance analysis report that can assist performance engineers with performance testing. In particular, RPerf uses BERTopic to uncover performance-related topics in user reviews. RPerf then maps the derived topics to performance KPIs (key performance indicators) such as response time. Such performance KPIs better help performance test design and allocate performance testing resources. Finally, RPerf extracts user usage scenarios from user reviews to help identify the causes. Through a manual evaluation, we find RPerf achieves a high accuracy (over 93%) in identifying the performance-related topics and performance KPIs from user reviews. RPerf can also accurately extract usage scenarios in over 80% of user reviews. We discuss the performance analysis report that is generated based on RPerf. We believe that our findings can assist practitioners with analyzing performance-related user reviews and inspire future research on user review analysis.

A Self-Administered Gamified Mobile Application for Secondary Prevention of Heart Disease in Patients Following a Cardiac Event (MyHeartMate): Process Evaluation from a Randomized Controlled Trial.

Objective: This study reports the process evaluation of a randomized controlled trial of the MyHeartMate app for patients with coronary heart disease (CHD). Materials and Methods: Data were collected on engagement in the intervention group from the app platform logs and self-reported usage and via interviews for perspectives of usefulness and acceptability. Participants' (n = 194) data logs showed 80.4% entered baseline data. Tracking (≥30 days) occurred for body mass index (23.2%), blood pressure (BP) (21.1%), and exercise (23.7%). Missions completed on ≥4 days were 47.5% for healthy eating and 30.9% for exercise; 50.5% used the word game. One quarter (26.8%) was classified as "engaged" (tracked exercise and/or BP ≥5 times and completed ≥18 missions in the first 30 days). There were no differences between participants who were "engaged" or not in baseline characteristics or 6-month outcomes, although engaged users were more likely to meet 4/5 recommended risk factor guideline levels (30.8% vs. 17.6%, P = 0.03). Results: Participants perceived tracking, prompts, and rewards as useful for recovery and risk factor-related lifestyle change, and games were enjoyed. Engagement decreased with time. Acceptability was limited when app processes were difficult to understand, not personalized enough and/or the overall game concept was disliked. Conclusions: Insight into user's perceptions and use of gamified app components is essential to address barriers to uptake and optimize potential health benefits. The target audience for a serious game app for CHD remains uncertain.

Optimization of Network Intrusion Detection Model Based on Big Data Analysis

As user usage grows, so do security threats to networks, the Internet, websites, and organizations. Detecting intrusions in such a big data situation is complex. A feature-optimized network intrusion detection model based on extensive data analysis is designed to overcome the limitations of current network intrusion detection models and obtain more ideal results. Firstly, the current modeling status of network intrusion detection is studied, and the influence of features on the results of network intrusion detection is analyzed. Then, the feature optimization mathematical model of network intrusion detection is established. The solution of the feature optimization mathematical model is searched by an adaptive genetic algorithm simulating natural biological evolution. The optimal feature subset of intrusion detection is obtained by back coding the optimal solution. Finally, according to the optimal feature subset, the learning sample of network intrusion detection is modeled, and the optimal network intrusion detection model is designed. Using the standard data set of network intrusion detection for simulation and comparison tests, the average accuracy of this paper’s network intrusion detection model is about 95%, while other current network intrusion detection models are below 95%. Meanwhile, the time of training and the detection of intrusion detection modeling in this model is significantly reduced, and better efficiency of network intrusion detection can be obtained.

Enhancing Learning Through Universally Designed Augmented Reality: A Comparative Study of Augmented and Traditional Learning Materials.

This paper examines the impact and effectiveness of educational Universally Designed (UD) Augmented Reality (AR) applications compared to traditional paper-based counterparts. The study evaluates accessibility, usability, user experience, and short-term learning outcomes in marine biology, human anatomy, and cultural history. Thirty-six participants with diverse skills and abilities, including visual impairments and dyslexia, participated in two experiments. The findings reveal that UD-designed AR applications significantly improve accessibility and usability for users with special needs and enhance learning performance for sighted users, particularly in spatial tasks. Users with dyslexia demonstrated effective information recall, while blind users could interact with and learn from the AR content without needing third-party assistive technologies, which is not possible with paper-based solutions. This study underscores the importance of incorporating UD principles in AR development to enhance accessibility and usability, ensuring that immersive technologies facilitate digital inclusion.

Connecting with fetus: The use of app-based fetal movement counting and experiences during pregnancy and birth

BackgroundPregnant people are often introduced to fetal movement counting to monitor the health of their fetus. This study aims to examine the impacts of app-based fetal movement counting on experiences during pregnancy and birth. MethodThis study used two types of secondary data including individual user's app use and their end of pregnancy survey data collected on the Count the Kicks (CTK) app. CTK is a free mobile app providing a virtual platform for pregnant people to conduct their daily kick counting. The study sample includes 1,147 pregnant people. Descriptive analyses were used to examine the number of kick counts using CTK and pregnant women's experiences with the app, their pregnancy, and childbirth. Bivariate analyses were used to examine the relationships between the frequency of kick counts and pregnant people's experience with their pregnancy and birth. Logistic regressions were used to model pregnant people's experiences: anxiety level related to their pregnancy and bonding with their baby. FindingsThe study found that there is inadequate compliance with daily fetal movement counting recommendations in third trimester among pregnant people. However, results showed that frequent use of fetal movement counting is associated with lower anxiety level related to their pregnancy, and more bonding with their baby. These positive pregnancy experiences are associated with healthy birth. ConclusionTo benefit from the impacts of fetal movement counting on positive pregnancy and birth experience, the app developers and public health agencies need to develop strategies to increase daily use of fetal movement counting.

Harnessing AI for Enhanced Searching in Digital Libraries: Transforming Research Practices

The application of Artificial Intelligence (AI) technologies in digital libraries is changing the paradigm in which users search and interact with digital content. This article is focused on how the use of AI technologies improves the search in digital libraries, demonstrating the benefits of such technologies in overcoming the challenges associated with formulation of query, information retrieval, and end user usage. Most search engine systems face the problems of user queries, which are inherent in online searches. Many user queries are vague, imprecise or ambiguous with regards to the context and as a result lead to less than satisfactory search results. AI technologies such as Natural Language Processing (NLP) and Machine Learning (ML) solve this challenge effectively as they enhance comprehension of queries, contextual understanding, and relevance of the results returned. With the capabilities of NLP incorporated in search engines, search queries which are in the form of natural language can be understood and executed which leads to efficiency in targeting the information needed. Comprehensive restructuring of standard information systems is used by computer learning algorithms for searches by inference and instills usage in users. This study also examines the place of AI in query formulation and the different ways it can be employed; fully automated, semi-automated and/or manually so that the users are less faced with the challenges that are usually present during the query formulation process. Such increases in the level of difficulty and scope of information retrieval suggest that search AI improved the scenario.

User Experience and Usability of Voice User Interfaces: A Systematic Literature Review

As voice user interfaces (VUIs) rapidly transform the landscape of human–computer interaction, their potential to revolutionize user engagement is becoming increasingly evident. This paper aims to advance the field of human–computer interaction by conducting a bibliometric analysis of the user experience associated with VUIs. It proposes a classification framework comprising six research categories to systematically organize the existing literature, analyzes the primary research streams, and identifies future research directions within each category. This systematic literature review provides a comprehensive analysis of the development and effectiveness of VUIs in facilitating natural human–machine interaction. It offers critical insights into the user experience of VUIs, contributing to the refinement of VUI design to optimize overall user interaction and satisfaction.

The Relationship of EMR (Electronic Medical Record) Documentation Performance with Patient Satisfaction

Technological developments have an impact on developments in the world of health, especially information technology in hospitals. The use of EMR makes medical records more orderly and improves health services that are faster, more precise, valuable and accountable. User usage and satisfaction will influence individual impact. Methods: An analysis of the relationship between EMR documentation and nurse satisfaction is needed. This study used a cross-sectional, The sampling technique in this research used purposive sampling correlational design with a sample of 80 patient at RSUD Dr. Soetomo. Research data was collected through questionnaires from the EMR documentation performance variables and nurse satisfaction. The results of the validity test of the EMR Documentation Performance instrument obtained a Parson correlation value in the range 0.590-0.926 > r table 0.514 and the results of the reliability test in this research showed a Cronbach's Alpha value of 0.966 so it is reliable. results of the validity and reliability of the patient satisfaction instrument Cronbach α reliability coefficient was high, ranging from 0.900 to 0.940. Research data was processed using the Spearmen rho test with a p value <0.05. Results: The results of the study showed that EMR documentation performance was related to patient satisfaction (0.009). Discussion: Good electronic medical record documentation performance can increase patient satisfaction.

Liver Disease Detection using Machine Learning

Abstract: Liver disease is a serious worldwide health issue, and prompt diagnosis and treatment are essential for successful outcomes. Traditional diagnostic techniques, however, may be expensive and time-consuming and can require intrusive procedures. In this work, we suggest a machine learning-based method for liver disease identification that makes use of the Support Vector Machine (SVM) and Random Forest Decision Tree algorithms. Our approach uses a large dataset with pertinent clinical characteristics including biochemical signs and patient demographics to categorize people into liver disease-positive or - negative groups. Furthermore, we incorporate an intuitive UI with Streamlit, improving accessibility and usability for end users and healthcare providers alike. By means of meticulous testing and assessment, we exhibit the efficiency and functionality of our suggested framework. In terms of precision, specificity, and sensitivity, our model performs admirably, demonstrating its promise as a trustworthy instrument for the identification of liver illness. Moreover, the incorporation of Streamlit improves the system's practicality and usability, making it easier to install and use in actual healthcare environments.

A VIKOR-Based Decision Support System for Prioritizing Public Facility Improvements in Malang City with Geotagging Integration

Public facilities play a crucial role in driving economic growth and development. Nevertheless, the dearth of public information concerning facility enhancements fosters a sense of public distrust towards the government. Additionally, numerous facilities, which should be prioritized for improvement, have not received adequate attention. In contrast to several prior studies, the present study encompasses a broader scope and incorporates geotagging techniques to precisely identify the location of complaints and determine the optimal route to reach them. Moreover, an analysis process utilizing the VIKOR method has been devised to assess the priority of public facility improvements. This method yielded an accuracy rate of 89,7%, signifying a commendable level of precision and a 16% increase in accuracy based on confusion matrix method compared to previous studies. Through user usability testing, it was determined that the majority of users agreed that this system can facilitate public reporting, enable progress monitoring of public facility improvements, and aid in prioritizing such improvements.

Geo-spatial hypertext in virtual reality: mapping and navigating global news event spaces

ABSTRACT Every day, a myriad of events take place that are documented and shared online through news articles from a variety of sources. As a result, as users navigate the Web, the volume of data can lead to information overload, making it difficult to find specific details about an event. We present News in Time and Space (NiTS) to address this issue: NiTS is a fully immersive system integrated into Va.Si.Li-Lab that organises textual information in a geospatial hypertext system in virtual reality. With NiTS, users can visualise, filter and interact with information currently based on GDELT on a virtual globe providing document networks to analyse global events and trends. The article describes NiTS, its event semantics and architecture. It evaluates NiTS in comparison to a classic search engine website, extended by NiTS's information filtering capabilities to make it comparable. Our comparison with this website technology, which is directly linked to the user's usage habits, shows that NiTS enables comparable information exploration even if the users have little or no experience with VR. That is, we observe an equivalent search result behaviour, but with the advantage that VR allows users to get their results with a higher level of usability without distracting them from their tasks. Through its integration with Va.Si.Li-Lab, a simulation-based learning environment, NiTS can be used in simulations of learning processes aimed at studying critical online reasoning, where Va.Si.Li-Lab guarantees that this can be done in relation to individual or groups of learners.

How do software practitioners perceive human-centric defects?

Context:Human-centric software design and development prioritises the way users prefer to complete their jobs, rather than expecting users to adapt to the software. Software users can have different genders, ages, cultures, languages, disabilities, socioeconomic statuses, and educational backgrounds, among many other differences. Due to the inherently varied nature of these differences and their impact on software usage, preferences and issues of users can vary, resulting in user-specific defects that we term as ‘human-centric defects’ (HCDs). Objective:This research aims to understand the perception and current management practices of such HCDs by software practitioners, identify key challenges in reporting, understanding and fixing them, and provide recommendations to improve HCDs management in software engineering. Methods:We conducted a survey and interviews with software engineering practitioners to gauge their knowledge and experience on HCDs and the defect tracking process. Results:We analysed fifty (50) survey- and ten (10) interview-responses from SE practitioners and identified that there are multiple gaps in the current management of HCDs in software engineering practice. There is a lack of awareness regarding human-centric aspects, causing them to be lost or under-appreciated during software development. Our results revealed that handling HCDs could be improved by following a better feedback process with end-users, a more descriptive taxonomy, and suitable automation. Conclusion:HCDs, given their diverse end-user base, present a major challenge to software practitioners. In the software engineering domain, research on HCDs has been limited and requires effort from research and practice communities to create awareness and support for human-centric aspects.

Scenario planning for climate adaptation and management: a high-level synthesis and standardization of methodology

Scenario planning is a tool used to explore a set of plausible futures shaped by specific trajectories. When applied in participatory contexts, it is known as participatory scenario planning (PSP), which has grown in its usage for planning, policy, and decision-making within the context of climate change. There has been no high-level synthesis of systematic reviews covering the overall state and direction of PSP for climate adaptation and management. We draw from four systematic reviews on PSP published between 2015 and 2021 to substantiate the credibility of the process and identify a set of standard practices to make PSP a more accessible and usable tool for not only researchers, but policymakers, practitioners, and other end users who may benefit from PSP. We summarize and synthesize the range of PSP processes and characteristics, highlighting four common trends that provoke additional inquiry: PSP’s contribution to social learning and bias, the varying use of quantitative information in scenario development, issues related to carrying out monitoring and evaluation, and the varying completion of practices recommended by established PSP literature. We propose four processes as integral to maximizing PSP’s usability for end users and recommend these areas for further study: identifying social imbalances throughout the PSP process, recognizing bias as inherent to PSP, explicitly addressing, and incorporating uncertainty, and allocating resources for monitoring and evaluation.

Semisupervised Learning to Boost hERG, Nav1.5, and Cav1.2 Cardiac Ion Channel Toxicity Prediction by Mining a Large Unlabeled Small Molecule Data Set.

Predicting drug toxicity is a critical aspect of ensuring patient safety during the drug design process. Although conventional machine learning techniques have shown some success in this field, the scarcity of annotated toxicity data poses a significant challenge in enhancing models' performance. In this study, we explore the potential of leveraging large unlabeled small molecule data sets using semisupervised learning to improve drug cardiotoxicity predictive performance across three cardiac ion channel targets: the voltage-gated potassium channel (hERG), the voltage-gated sodium channel (Nav1.5), and the voltage-gated calcium channel (Cav1.2). We extensively mined the ChEMBL database, comprising approximately 2 million small molecules, and then employed semisupervised learning to construct robust classification models for this purpose. We achieved a performance boost on highly diverse (i.e., structurally dissimilar) test data sets across all three targets. Using our built models, we screened the whole ChEMBL database and a large set of FDA-approved drugs, identifying several compounds with potential cardiac ion channel activity. To ensure broad accessibility and usability for both technical and nontechnical users, we developed a cross-platform graphical user interface that allows users to make predictions and gain insights into the cardiotoxicity of drugs and other small molecules. The software is made available as open source under the permissive MIT license at https://github.com/issararab/CToxPred2.

TeleCleft: Development and Evaluation of a Mobile Application for Monitoring Cleft Patients.

Development and evaluation of a mobile application for remote monitoring and guidance of pediatric patients diagnosed with cleft lip and/or palate (CL ± P) and their caregivers. This is a pilot cross-sectional, applied, and quantitative study. The study was conducted in two tertiary care treatment centers in Brazil. The participants included 20 caregivers and infants undergoing treatment with nasoalveolar molding (NAM) for CL ± P. The intervention involved using the TeleCleft mobile application for remote monitoring and guidance of caregivers and infants during NAM treatment. The main outcome measures included usability and satisfaction of users with the TeleCleft application. The results showed high usability and satisfaction ratings among users of the TeleCleft application. Most participants found remote monitoring to be effective and expressed positive opinions about its convenience and usefulness. TeleCleft could be a viable tool for remote monitoring and guidance, reducing the need for patients and caregivers to travel to treatment centers, which could potentially alleviate the burden of care faced by families during the journey of CL ± P treatment.

EXTERNALIZATION OF TACIT KNOWLEDGE IN THE MENTAL MODEL OF A USER OF AN ARTIFICIAL INTELLIGENCE SYSTEM

The subject of the study is the processes of forming the user's mental model in artificial intelligence systems. The construction of such a model is associated with solving the problem of opacity and incomprehensibility of the decision-making process in such systems for end users. To solve this problem, the system user needs to receive an explanation of the obtained decision. The explanation should take into account the user's perception of the decision and the decision-making process, which is formalized within the user's mental model. The mental model considers the user's use of explicit and implicit knowledge, the latter of which usually lacks formal representation. The externalization of such knowledge ensures its transformation into a formal form. The aim of the work is to develop an approach to the externalization of implicit knowledge based on identifying patterns and causal dependencies for the decision-making process in an intelligent system when constructing the user's mental model. To achieve this goal, the following tasks are solved: developing a user's mental model of an artificial intelligence system that takes into account both explicit and implicit knowledge and developing an approach to the externalization of implicit knowledge of the user of the artificial intelligence system. A user's mental model of an artificial intelligence system that accounts for both explicit and implicit knowledge of the user is proposed. The model considers the connections between the user's explicit and implicit knowledge regarding the artificial intelligence system, the decision-making process, the method of using the decisions, and the general concept of the intelligent system. This creates conditions for the externalization of the user's implicit knowledge and the subsequent use of this knowledge in forming explanations regarding the decision-making process in the artificial intelligence system. An approach to the externalization of knowledge from the statistical and semantic layers of the user's mental model is proposed. In practical terms, the approach makes it possible to translate into explicit form the conditions and constraints regarding the formation and use of decisions in the artificial intelligence system.