User Interaction Research Articles

Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework

This study presents an approach that uses session and page view data collected through the CAWAL framework, enriched through specialized processes, for advanced predictive modeling and anomaly detection in web usage mining (WUM) applications. Traditional WUM methods often rely on web server logs, which limit data diversity and quality. Integrating application logs with web analytics, the CAWAL framework creates comprehensive session and page view datasets, providing a more detailed view of user interactions and effectively addressing these limitations. This integration enhances data diversity and quality while eliminating the preprocessing stage required in conventional WUM, leading to greater process efficiency. The enriched datasets, created by cross-integrating session and page view data, were applied to advanced machine learning models, such as Gradient Boosting and Random Forest, which are known for their effectiveness in capturing complex patterns and modeling non-linear relationships. These models achieved over 92% accuracy in predicting user behavior and significantly improved anomaly detection capabilities. The results show that this approach offers detailed insights into user behavior and system performance metrics, making it a reliable solution for improving large-scale web portals’ efficiency, reliability, and scalability.

Multichannel EMG-based gesture recognition utilizing advanced machine learning techniques: A random forest classifier for high-precision signal classification

This research examines how advanced machine learning algorithms can be used to classify multichannel electromyographic (EMG) signals with a high level of accuracy to assist in recognizing hand gestures. The goal is to create a robust and scalable system for gesture-based virtual control using EMG signals with potential applications in assistive technologies, rehabilitation, and human-computer interaction. Data were gathered using a MYO Thalmic bracelet containing eight EMG sensors on thirty-six subjects, and a Random Forest classifier was trained to identify seven distinct types of hand gestures (rest, fist clench, wrist flexion/extension, and radial/ulnar deviations). The machine learning pipeline included extensive preprocessing (i.e., EMG signal normalization and signal feature extraction; root mean square, waveform length, and zero crossing rate) and several hyperparameter tuning procedures to improve model performance. The Random Forest model (100 decision trees) achieved an overall classification accuracy of 98.68%, with a range of accuracies for each class (e.g., 95.2% wrist flexion and 91.8% ulnar deviation) when evaluated using cross-validation (i.e., average F1-score = 0.92, precision = 0.94, recall = .91). Overall, the study provides strong evidence for the effectiveness of ensemble learning methods at analyzing complex, multidimensional EMG signals. The high classification accuracy reported, in particular, demonstrates that the system could function for real-time recognition of hand gestures in a virtual environment. Ultimately, the initial work sets the stage for future exploration of a model that may be integrated with actuation models to control prosthetic limbs, virtual actors/avatars, and robotic devices. By demonstrating a scalable and efficient method of gesture recognition using EMG signals, these early findings enable future pathways and possibilities to design innovative, assistive solutions for digital systems that increase accessibility and interaction for users who are motor impaired or have a limited range of motion.

StegaCrypt Integrating Hybrid Cryptography and Image Steganography

This paper discusses the combination of cryptography and steganography for digital data security. Cryptography and steganography are two of the most significant techniques in information security, to be used differently: data is secured against unauthorized use and ensured integrity by way of transforming it into some unreadable format, whilst steganography hides a file inside another file. The project aim is to synthesize the techniques for a robust system to protect secure communication and data. We use RSA cryptography for the encryption, which is the asymmetric algorithm of high-level security, and image-based steganography to place the encrypted data within a digital image. The processes of cryptography will ensure decrypting the data only in the hands of authorized recipient parties, and steganography provides additional protection on the data invisibility front. This project shows that this integration of two technologies will safeguard sensitive information from all types of threats. In view of various methods to combine cryptography and steganography, this project puts forward the significance of incorporating multi-layer security to combat different types of cyber-attacks. The implementation also includes an elaborated coding using algorithms and development of a GUI to facilitate user interaction with the system. This further ensures that it is not only technically sound but also user-friendly. The last system is to send confidential data toward the broader field of secure communications and information protection, efficiently, securely, and in a user-friendly way

Survey Paper on Virtual Reality in Gaming

Abstract: This paper examines the integration of virtual reality (VR) technology within gaming, focusing on the development of immersive virtual environments that enhance user interaction, presence, and satisfaction. With advancements in VR hardware and software, gaming experiences are becoming more lifelike and socially engaging. The VR-based metaverse concept explored in this research aims to provide interconnected virtual worlds with dynamic, persistent interactions. This paper discusses the benefits and challenges of VR in gaming, including technical limitations, high costs, and potential for immersive storytelling. The findings offer insights into the potential impact of VR in gaming and entertainment

Worst case analysis, a video analysis approach to injury potential in traffic

This paper presents a method for evaluating the potential for injury in any traffic scenario involving road user interactions based on trajectory data from a few hours of data of typical traffic conditions. The basic idea was to evaluate to what extent interactions at a location are limited in speed, direction, and whether the interactions happen at a safe speed. Specifically, the method works by applying an existing injury model to the highest observed values at a specific point in space. The injury model is used to estimate a worst-case scenario at that point in space. By generating a heat map, it is possible to analyse a location and evaluate the injury potential at a location. The method does not provide a complete safety analysis with exposure or risk but instead focuses specifically on the potential consequences. The method is suitable for preliminary safety analyses that focus on determining areas of particular interest for further study.

Unveiling the potential of digital human avatars in modern marketing strategies

PurposeThis study aims to develop a theoretical framework that marketing practitioners and scholars can adopt to enhance their understanding of how firms can effectively deploy and use digital human avatars as part of their global digital marketing strategy. By doing so, we inform investors of ongoing digital transformations of marketing practices that will equip marketeers to provide scalable, tailored, reliable and relevant digital self-service interactions to users, consequently improving the user/customer experience.Design/methodology/approachThematic analysis was used to discover factors to enable the successful implementation of digital human avatars, drawing on in-depth interviews with fourteen executives of digital human avatars developer companies worldwide and analysis of ten podcasts and webinars with artificial intelligence (AI) experts.FindingsDigital human avatars revitalise the international dynamic marketing capabilities (IDMCs) of firms by integrating advanced technologies that transform user interactions, improve engagement and facilitate knowledge acquisition, dissemination and usage across various sectors and business units globally. This integration promotes a dynamic approach to international brands, customer relationships and marketing knowledge management capabilities, offering profound value to users and firms.Research limitations/implicationsOur first limitation is a lack of diversity in data sources. As digital human avatars are an emerging field, we had to limit our study to 14 experts in AI and 10 podcasts. While this method provides deep insights into the perspectives of those directly involved in the development and implementation of digital human avatars, it may not capture the views of end-users or consumers who interact with these avatars, which can be an avenue for further research. Our second limitation is the potential bias in the interpretation of our interview data and podcasts. This study’s approach to data analysis, where themes are derived from the data itself, carries a risk of subjective interpretation by the researchers. Future studies are encouraged to investigate the impact of digital human avatars across different organisational contexts and ecosystems, especially focusing on how these technologies are integrated and perceived in various international markets.Practical implicationsThe novel framework has direct implications for innovators and marketing practitioners who aim to adopt digital human avatars in their marketing practices to enhance the effectiveness of international marketing strategies.Social implicationsThe adoption of digital human avatars can alleviate loneliest elderly and vulnerable people by being a companion. The human-like characteristics can impact sense of presence and attachment.Originality/valueThe novelty of our study lies in exploring the characteristics of technologies and practical factors that maximise the successful adoption of digital human avatars. We advance and contribute to the emerging theory of avatar marketing, IDMCs and absorptive capacity by demonstrating how digital human avatars could be adopted as part of a firm’s global digital marketing strategy. We focus specifically on six dimensions: outcomes and benefits, enhancements and capabilities, applications and domains, future implications, foundational elements and challenges and considerations. This framework has direct implications for innovators and marketing practitioners who aim to adopt digital human avatars in their marketing practices to enhance the effectiveness of international marketing strategies.

SmartStudy-Learning Path Recommender system using DS and ML

Learning Path Recommender Systems (LPRS) utilize machine learning and data-driven approaches to personalize educational experiences, adapting content sequences based on individual learners’ needs and progress. With the rising demand for tailored learning, LPRS have emerged as vital tools to guide students through educational content, enhancing engagement and achievement. This review covers state-of-the-art algorithms, including collaborative filtering and reinforcement learning, used in LPRS to optimize learning paths. We discuss model accuracy, user interaction data, and adaptive feedback mechanisms, providing insights into the potential of LPRS to improve learning outcomes

Harmonizing human-computer interaction: Exploring evolution and integration in media and computing

Abstract. This paper delves into the intricate relationship between Human-Computer Interaction (HCI), media, and computing, examining its historical evolution and contemporary challenges. From the rudimentary interfaces of the 1970s to the immersive digital experiences of the present day, HCI has undergone a profound transformation, driven by advancements in technology and changing user expectations. The convergence of media and computing technologies has blurred traditional boundaries, reshaping user interactions and opening new frontiers for research and innovation. Through quantitative analysis and mathematical modeling, researchers gain insights into user behavior, preferences, and interactions, informing the design of more intuitive and engaging interactive systems. However, this integration also poses challenges related to cross-platform compatibility, ethical considerations, and accessibility. By embracing user-centered design principles, ethical stewardship, and technological innovation, HCI researchers and practitioners can navigate these challenges and shape the future of HCI-Media-Computing integration.

From attraction to monetization: How do knowledge influencers trigger users’ willingness to subscribe to knowledge products?

PurposeThis study examines the phenomenon of “knowledge influencers,” individuals who convey expert information to non-expert audiences and attract users to subscribe to their self-created knowledge products. It seeks to address two research questions: (1) What are the antecedents that promote perceived attractiveness of knowledge influencers? and (2) How does perceived attractiveness of knowledge influencers affect users’ willingness to subscribe to knowledge products?Design/methodology/approachGuided by self-branding theory, which suggests that individuals strategically shape user perceptions and interactions to create an appealing image, the study employed a sequential mixed-methods approach. Qualitative interviews were conducted with knowledge influencers and their subscribers, followed by a quantitative survey of users with knowledge subscription experience to validate the findings.FindingsResults suggested that knowledge influencers could enhance their attractiveness to users by promoting perceived professionalism, perceived familiarity, and perceived connectedness. Perceived attractiveness of knowledge influencers could directly affect users’ willingness to subscribe or indirectly through the role of users’ attachment to knowledge influencers.Practical implicationsBy understanding the factors driving users’ subscription intentions, platform operators and influencers can refine their strategies to enhance user attachment and optimize monetization opportunities through personalized interactions and tailored content offerings.Originality/valueThis study contributes to the literature by elucidating the relationship between perceived attractiveness and users’ subscription intentions, offering new insights into the dynamics of online knowledge consumption.

Point-of-interest recommendation based on the spatial-temporal graph

Purpose Point-of-interest (POI) recommendation techniques play a crucial role in mitigating information overload and delivering tailored services. To address limitations in conventional POI recommendation systems, constrained by sparse user-POI interactions and incomplete consideration of temporal dynamics, POI recommendation based on the spatial-temporal graph (STG-POI) is proposed. Design/methodology/approach Spatial-temporal sequence graphs from geographical locations and user interaction history data are constructed, which are used to mine spatial-temporal sequence information. Using the data filtered by the band-pass filter, graph neural networks with distance-awareness and sequence-awareness are applied to capture high-order spatial-temporal connections within diverse graph topologies. The model leverages contrastive learning for self-supervised disentanglement of graph representations, providing self-supervised signals for sequential and geographical intent perception, thereby achieving more precise POI personalization. Findings Compared to the baseline model GSTN, experiments on the Foursquare and Gowalla data sets reveal that STG-POI improves testing AUC by 2.0%, 2.1%, 2.0% and decreases logloss by 1.9%, 3.3%, 0.3%, respectively. These results indicate the model’s effectiveness in capturing spatial-temporal information, surpassing mainstream POI recommendation baseline models. Originality/value This approach constructs a dual graph from user interaction data, harnessing sequential and geographical information as self-supervised signals. It yields decoupled representations of these influences, offering a comprehensive insight into user behaviors and preferences within location-based social networks, thus enhancing recommendation accuracy and interpretability. This approach addresses the challenge in graph convolutional network where only rough and smooth features are conducive to recommendation by using band-pass filters to significantly reduce computational complexity, thereby enhancing recommendation speed by filtering out noise data that does not contribute to recommendation performance. Experimental results indicate that this model surpasses current mainstream approaches in POI recommendation tasks, effectively integrating both geographical and temporal features.

Streaming Large-Scale Microscopy Data to a Supercomputing Facility.

Data management is a critical component of modern experimental workflows. As data generation rates increase, transferring data from acquisition servers to processing servers via conventional file-based methods is becoming increasingly impractical. The 4D Camera at the National Center for Electron Microscopy generates data at a nominal rate of 480 Gbit s-1 (87,000 frames s-1), producing a 700 GB dataset in 15 s. To address the challenges associated with storing and processing such quantities of data, we developed a streaming workflow that utilizes a high-speed network to connect the 4D Camera's data acquisition system to supercomputing nodes at the National Energy Research Scientific Computing Center, bypassing intermediate file storage entirely. In this work, we demonstrate the effectiveness of our streaming pipeline in a production setting through an hour-long experiment that generated over 10 TB of raw data, yielding high-quality datasets suitable for advanced analyses. Additionally, we compare the efficacy of this streaming workflow against the conventional file-transfer workflow by conducting a postmortem analysis on historical data from experiments performed by real users. Our findings show that the streaming workflow significantly improves data turnaround time, enables real-time decision-making, and minimizes the potential for human error by eliminating manual user interactions.

The Feasibility of AgileNudge+ Software to Facilitate Positive Behavioral Change: Mixed Methods Design

Background In today’s digital age, web-based apps have become integral to daily life, driving transformative shifts in human behavior. “AgileNudge+” (Indiana University Center for Health Innovation and Implementation Science) is a web-based solution to simplify the process of positive behavior change using nudging as an intervention. By integrating knowledge from behavioral economics with technology, AgileNudge+ organizes multiple steps, simplifies complex tasks, minimizes errors by enhancing user engagement, and provides resources for creating and testing nudge interventions. Objective This paper aimed to outline the design process, methodologies, and usefulness of “AgileNudge+” for the development of evidence-based nudges. It used a mixed methods approach to evaluate the software’s interface usability and usefulness for creating and testing nudge interventions. Methods AgileNudge+ was developed through iterative processes integrating principles from behavioral economics and user-centered design. The content of AgileNudge+ operationalizes an Agile science–based process to efficiently design, embed, and disseminate evidence-based nudges that encourage positive behavior change without limiting choice. Using a mixed methods approach, we tested AgileNudge+ software’s ability to organize and simplify the nudge intervention process, allowing a diverse range of scholars with limited knowledge of Agile science to use nudges. Usability testing assessed the tool’s usefulness and interface with a sample of 18 health care professionals, each asked to interact with the software and create a nudge intervention to solve a problem within their professional project’s sphere. Results The study was funded in August 2022, with data collection occurring from June 2023 to July 2024. As of July 2024, we have enrolled 18 participants. Quantitative results found a mean usefulness rating of AgileNudge+ of 3.83 (95% CI 3.00-4.66). Qualitative results highlighted ways to modify the language used in AgileNudge+ to be more comprehensible to a diverse user base and promoted modifications to the software that facilitate real-time assistance and prioritize time efficiency in user interactions. Feedback further supported the positive impact of gamification on participant motivation when using the software. Conclusions AgileNudge+ is an effective assistive tool for simplifying the positive behavior change process using nudge interventions, with tailored content and interactions to meet users’ needs and demands. Building onto the current design, future iterations of AgileNudge+ will use artificial intelligence to process large volumes of data while reducing the time and mental energy required to scan for existing cognitive biases and nudge prototypes. The software is also being upgraded to build on current gamification efforts, encouraging more sustained motivation by increasing the temporal resolution of the digital interface. These modifications stay true to the agility and user-centered aspects of AgileNudge+, emphasizing the novelty of the constantly evolving software design process.

Creating a Quasi-Resonant Induction Cooktop Integrating Zero-Voltage Switching (ZVS) and Load Management

This study aims to elucidate the development and construction of a durable induction cooktop, with key considerations including efficiency, power customization, and safety features. The intricate processes involved in crafting a 3.5 kW induction burner are thoroughly examined, encompassing simulations for quasi-resonant inverters, the meticulous selection of induction coils and capacitors, the implementation of practical Analog-to-Digital Converter (ADC) filtration, pulse width modulation (PWM) driving techniques, and the integration of protection mechanisms. Leveraging an ARM-based microcontroller enabled the attainment of diverse objectives such as Zero-Voltage Switching (ZVS), safeguarding IGBTs, facilitating user interaction through a user-friendly interface, and enabling load detection capabilities. Furthermore, the capability to gauge and adjust output power based on user preferences was incorporated. Subsequently, rigorous testing was conducted to evaluate the functionality and applicability of the device in real-world scenarios.

Recommender systems use in weight management mHealth interventions: A scoping review.

The use of recommender systems in mobile health apps for weight control has grown, but user app uptake and engagement remain limited. The objective of our scoping review was to explore the influence of recommender systems on mHealth app user engagement, identify the theoretical frameworks that have been applied on digital health interventions designed for weight management, and examine the key aspects that support tailoring user engagement through recommender systems. Based on existing literature, we identified 13 articles on recommender systems for weight management. Themes emerged, including theoretical underpinnings, authors' domain knowledge, user motivation, and design. Most studies used constructs from the social cognitive theory. We found inconsistencies between authors' domain knowledge and the intervention's content, with few professionals from the health and psychology fields. Only 46% of articles measured user engagement, whereas gamification and tailored messages were common app features. Despite some positive weight change results, more attention is needed toward implementing behavior theory and other strategies to promote app user engagement. Future studies should more accurately measure user motivation and identify the best features and behavioral constructs to increase app user interaction. Larger studies with a more diverse population are needed to generalize findings and evaluate weight loss maintenance and physical activity habits among users of recommender system.

The Role of Memory in LLMs: Persistent Context for Smarter Conversations

Memory in LLMs has given way to more logical and sensible interactions between the system and the user. This is different from other models that are session bound, such that the responses to any one query are not related to past and future interactions with the same user, but memory-enabled LLMs retain information across sessions and continually update interactions with the person they are communicating with. The role of permanent memory in LLMs is considered in this work, provided through the analysis of the role of memory mechanisms in maintaining conversation flows, improving user interaction, and supporting practical applications in various industries, including customer service, healthcare, and education. Discussing how the idea and architectures of memory correspond to storage and retrieval procedures and the management of memory in LLMs this paper outlines the opportunities and challenges for AI systems that want to include contextual intelligence but also remain ethical. The synthesis of important concepts underlines the promising prospects of memory-augmented models in improving the communication with users and points to the imperatively important aspect of controlling the memory process at the design stage of LLMs. We also offer recommendations for privacy and ethical concerns that should be avoided in the case of future AI memory advancements in an effort to pursue sustainable technological progress while also incorporating user-oriented values into the process.

Eye Tracking and Human Influence Factors’ Impact on Quality of Experience of Mobile Gaming

Mobile gaming accounts for more than 50% of global online gaming revenue, surpassing console and browser-based gaming. The success of mobile gaming titles depends on optimizing applications for the specific hardware constraints of mobile devices, such as smaller displays and lower computational power, to maximize battery life. Additionally, these applications must dynamically adapt to the variations in network speed inherent in mobile environments. Ultimately, user engagement and satisfaction are critical, necessitating a favorable comparison to browser and console-based gaming experiences. While Quality of Experience (QoE) subjective evaluations through user surveys are the most reliable method for assessing user perception, various factors, termed influence factors (IFs), can affect user ratings of stimulus quality. This study examines human influence factors in mobile gaming, specifically analyzing the impact of user delight towards displayed content and the effect of gaze tracking. Using Pupil Core eye-tracking hardware, we captured user interactions with mobile devices and measured visual attention. Video stimuli from eight popular games were selected, with resolutions of 720p and 1080p and frame rates of 30 and 60 fps. Our results indicate a statistically significant impact of user delight on the MOS for most video stimuli across all games. Additionally, a trend favoring higher frame rates over screen resolution emerged in user ratings. These findings underscore the significance of optimizing mobile gaming experiences by incorporating models that estimate human influence factors to enhance user satisfaction and engagement.

DSL‐Driven Approaches and Metamodels for Chatbot Development: A Systematic Literature Review

ABSTRACTChatbots have emerged as ubiquitous tools for enhancing user interaction across various platforms, from customer service to personal assistance. They are computer programs that simulate and process human conversation, either written, spoken or both. However, developing efficient chatbots remains a challenge, primarily due to the intricate nature of critical components of chatbots like natural language understanding (NLU) requiring a subscription from intent recognition providers like Dialogflow and Amazon Lex. This makes chatbots closely linked to NLP services and can be locked in. Recently, various research studies have provided solutions to reduce the workload of developers and designers. These approaches have proposed model‐driven development via domain‐specific languages (DSLs), which make the chatbot development process more accessible and more automated. This advancement aims to enhance effectiveness in chatbot development by leveraging DSLs. This study aims to provide a comprehensive overview of DSLs for developing chatbots, with the first contribution comprising various research topics, tools, approaches, and technologies employed to implement DSLs. Second, this work aims to assess and contrast the primary DSLs currently available for chatbot development, focusing on presenting the key elements used in constructing these DSLs. Third, this study identifies the challenges and limitations of using DSLs in chatbot development.

Parking Assistant with the Simulation of Application

Smart Parking can be used for any city. It aims to improve the efficiency and sustainability of urban transportation systems by using technology to manage parking systems. This article provides an overview of the different components of smart parking systems. The increasing vehicular population in urban areas has led to significant challenges related to parking management, exacerbating traffic congestion, environmental impact, and user dissatisfaction. The app facilitates real- time information on available parking spaces, reservation capabilities, and payment integration, thereby enhancing user experience and operational efficiency. We summarize the methodologies applied in developing the app, which include sensor integration for dynamic parking space monitoring and mobile application development for user interaction. The article concludes with a consideration of the future of smart parking and its role in the overall development of smart cities. In conclusion, the findings serve as a basis for future advancements in intelligent parking systems, ensuring improved service delivery for urban commuters.

An interactive dashboard for analyzing user interaction patterns in the i2b2 clinical data warehouse.

Clinical data warehouses provide harmonized access to healthcare data for medical researchers. Informatics for Integrating Biology and the Bedside (i2b2) is a well-established open-source solution with the major benefit that data representations can be tailored to support specific use cases. These data representations can be defined and improved via an iterative approach together with domain experts and the medical researchers using the platform. To facilitate these discussions, it is important to understand how users interact with the system. The objective of this work was to develop metrics for describing user interactions with clinical data warehouses in general and i2b2 in particular. Moreover, we aimed to develop a dashboard featuring interactive visualizations that inform data engineers and data stewards about potential improvements. We first identified metrics for different data usage dimensions and extracted the relevant metadata about previous user queries from the i2b2 database schema for further analysis. We then implemented associated visualizations in Python and integrated the results into an interactive dashboard using Dash. The identified categories of metrics include frequency of use, session duration, and use of functionality and features. We created a dashboard that extends our local i2b2 data warehouse platform, focusing on the latter category, further broken down into the number of queries, frequently queried concepts, and query complexity. The implementation is available as open-source software. A range of metrics can be derived from metadata logged in the i2b2 database schema to provide data engineers and data stewards with a comprehensive understanding of how users interact with the platform. This can help to identify the strengths and limitations of specific instances of the platform for specific use cases and aid their iterative improvement.

Self-Sovereign Identity-Based E-Portfolio Ecosystem

In Taiwan, traditional student assessments, covering academic and extracurricular achievements, have shifted from paper to electronic portfolios (e-portfolios). However, limited trust among institutions restricts students from freely sharing and using their educational data. This paper introduces a self-sovereign identity-based infrastructure aimed at enhancing personal data security within the e-portfolio ecosystem. The proposed system includes two core components: (1) a decentralized identity chain, aligning user identities across platforms and granting users full self-sovereign control; and (2) an e-portfolio application chain to manage user interactions and access permissions within the ecosystem. A trusted educational authority also audits data sources, ensuring data integrity and reliability. This infrastructure empowers users to control who can access their data, safeguarding their security, with the identity chain preventing unauthorized access and the application chain recording authorization statuses to restrict data visibility to approved parties only.