Publications

3D modeling using traditional computer-aided design (CAD) tools often poses challenges, particularly to novice users. Several prior works have explored virtual reality-based CAD systems. However, most face major problems such as loss of accuracy. To address these challenges, we present BoxCraft a tangible mixed-reality (MR) system for intuitive 3D modeling with two modalities. BoxCraft allows users to create and edit 3D models in an MR space by using physical tokens enhanced with visual overlays for immediate spatial feedback. We conducted a participatory design workshop (n=6) and an exploratory user study (n=7) to gather initial qualitative feedback on interaction design and user experience. Preliminary results show that, although tangible tokenbased interaction requires a longer initial learning phase, participants, especially beginners, preferred it over controller-based interaction and traditional CAD …

Artificial Intelligence-enhanced learning pathways contribute to the development of cognitive and metacognitive abilities in students within a skills-based educational framework. Artificial Intelligence tools such as adaptive learning systems, intelligent tutoring platforms, chatbots, and real-time analytics create personalised learning environments that support diverse thinking processes. AI strengthens cognitive abilities by improving attention, memory, information processing, critical thinking, and problem-solving through interactive, responsive, and data-driven instructional methods. Simultaneously, AI fosters metacognitive growth by guiding learners to set goals, monitor progress, evaluate performance, and reflect on their learning strategies. Grounded in psychological principles, it highlights how AI reduces cognitive load, enhances motivation, increases self-efficacy, and promotes autonomous learning. While AI presents powerful opportunities to transform learning, the chapter also acknowledges key challenges, such as data privacy concerns, overdependence on technology, and unequal access. Overall, the chapter emphasises that when integrated ethically and thoughtfully, AI serves as a transformative educational experience, cultivating analytical, reflective, and self-directed learners prepared for future academic and professional demands.

Data-driven social science research is inherently slow, relying on iterative cycles of observation, hypothesis generation, and experimental validation. While recent data-driven methods promise to accelerate parts of this process, they largely fail to support end-to-end scientific discovery. To address this gap, we introduce EXPERIGEN, an agentic framework that operationalizes end-to-end discovery through a Bayesian optimization inspired two-phase search, in which a Generator proposes candidate hypotheses and an Experimenter evaluates them empirically. Across multiple domains, EXPERIGEN consistently discovers 2-4x more statistically significant hypotheses that are 7-17 percent more predictive than prior approaches, and naturally extends to complex data regimes including multimodal and relational datasets. Beyond statistical performance, hypotheses must be novel, empirically grounded, and actionable to drive real scientific progress. To evaluate these qualities, we conduct an expert review of machine-generated hypotheses, collecting feedback from senior faculty. Among 25 reviewed hypotheses, 88 percent were rated moderately or strongly novel, 70 percent were deemed impactful and worth pursuing, and most demonstrated rigor comparable to senior graduate-level research. Finally, recognizing that ultimate validation requires real-world evidence, we conduct the first A/B test of LLM-generated hypotheses, observing statistically significant results with p less than 1e-6 and a large effect size of 344 percent.

BoxCraft is a tangible mixed reality system to support beginners in 3D CAD modelling through phygital interaction. The system has two interfaces: VR controller and tangible token-based interface to build and manipulate 3D models. We conducted a participatory design(n=6) workshop for gathering early design insights and then ran a comparative user study (n=7) to measure task performance using SUS and Raw NASA-TLX. The controller-based interface allowed for faster completion of tasks with lower workload, although the tangible token-based interface reduced CAD complexity and offered higher conceptual clarity at an early stage, making it potentially useful for early-stage CAD modelling.

Curtains are functional textiles that play a crucial yet invisible role in our daily lives. Despite their widespread use for decoration, blocking heat, privacy, etc., they are still passive in our surroundings. This paper presents a design exploration and development of an interactive curtain interface using capacitive sensing. We augment the capabilities of everyday curtains into touch-sensitive surfaces to facilitate embodied interactions through physical manipulation and gestures. By interfacing these curtains with a smart home environment to control lights, fans, and other appliances, we show that interactive curtains are technically feasible, thus paving the way for novel Curtain UIs as a medium of tangible, embodied, and embedded interaction in a ubiquitous computing scenario.

Despite the government of India's admirable efforts to assist tribal people and help them develop, many tribal problems persist in India. This paper seeks to explore attitudes and barriers toward child health, maternal health, and educational services among the Ladakhi and the Balli tribal people. In March 2016, qualitative research was conducted through a self-developed questionnaire. This interview schedule was used to assess the concept and usage of child and maternal healthcare services and educational services with samples aged 25 to 50 years from the Ladakhi (50) and the Balti (50) tribes of I adakh. The study found that the primary barriers to utilizing healthcare facilities are direct and indirect financial barriers; travel distances to medical facilities; subpar public transportation; hospital staff members perceived to act negatively toward patients; and inadequate infrastructure. The location of schools, weak

Welding training has been an important job training process in the industry and usually demands a large amount of resources. In real practice, the strong magnetic force and intense heat during the welding processes often frighten novice welders. In order to provide safe and effective welding training, this study developed a visuo-haptic extended reality (VHXR)–based hands-on welding training system for training novice welders to perform a real welding task. Novice welders could use the VHXR-based system to perform a hands-on manual arc welding task, without exposure to high temperature and intense ultraviolet radiation. Real-time and realistic force and visual feedback are provided to help trainees to maintain a constant arc length, travel speed, and electrode angle. Compared to the traditional video training, users trained using the VHXR-based welding training system significantly demonstrated better performance in real welding tasks. Trainees were able to produce better-quality joints by performing smoother welding with less mistakes, inquiry times, and hints.

Lithium-ion batteries (LIBs) have received enormous attention as the core components of Electric vehicles (EVs). An unavoidable issue is that battery performance will continue to degrade as materials age and cycle time increases. Accurately predicting the Remaining useful life (RUL) of LIBs is an important prerequisite to ensure the safe driving of EVs. However, the actual battery management system may encounter sensor or communication system failures, resulting in missing or incomplete data, which will result in inaccurate battery RUL predictions. This article presents a novel method based on hybridization of Generative adversarial imputation nets (GAIN) and Stacked denoised autoencoder with Kriging (SDAE-Kriging) for the prediction of RUL of LIBs in scenarios of missing and incomplete data. In the proposed method, the GAIN is leveraged to realize the filling of missing and incomplete data. The SDAE

Extended reality (XR) technology has been proven an effective human–computer interaction tool to increase the perception of presence. The purpose of this study is to develop an interactive XR-based welding tutorial system to enhance the learning and hands-on skills of novice welders. This study is comprised of two parts: (1) fundamental manual metal arc welding (MMAW) science and technology tutoring in a virtual reality (VR)-based environment, and (2) hands-on welding training in a mixed reality (MR)-based environment. Using the developed tutorial system, complicated welding process and the effects of welding process parameters on weld bead geometry can be clearly observed and comprehended by using a 3D interactive user interface. Visual aids and quantitative guidance are displayed in real time to guide novice welders through the correct welding procedure and help them to maintain a proper welding position. A user study was conducted to evaluate the learnability, workload, and usability of the system. Results show that users obtained significantly better performance by using the XR-based welding tutorial system, compared to those who were trained using the conventional classroom training method.

Urine sediment analysis plays a critical role in diagnosing the kidney related diseases, urinary tract infections and many related disorders. Historically, the urine sediment microscopic image samples were manually examined under a microscope by the clinical experts and trained professionals. This traditional manual analysis is slow, time consuming, labor intensive and prone to variability and errors. In recent times, computer vision models have been extensively used in automated urine sediment analysis but they often struggle in accurately locating and identifying smaller and irregular shaped urine sediments. In order to overcome these challenges we have worked with two distinct approaches to improve the performance of computer vision models in urine sediment analysis. In this work, we have used two state-of-the-art computer vision models YOLOv12 and RT-DETR in our experimental results on the publicly

Children in institutional care often experience abandonment and emotional neglect, which shape their emerging self-concept. These early adversities may hinder identity development, leading to internalized feelings of worthlessness and invisibility. Traditional assessment tools may fail to capture their emotional realities, making projective techniques valuable. Aim: This study aimed to explore the self-image of institutionalized orphan children using the House-Tree-Person (HTP) test and examine gender-based emotional differences in their drawings. Methodology: A qualitative, ex post facto design was used with a purposive sample of 160 children (80 boys and 80 girls) aged 10–14 years residing in government-run orphanages in Kanpur and Lucknow. Participants were assessed using the HTP test, and drawings were analyzed through thematic and content analysis to uncover emotional and symbolic …

This paper presents an innovative approach aimed at seamlessly integrating physical and digital environments via World Building techniques employing a sand table, specifically designed for wargaming applications. By leveraging simulation and gamification, our approach broadens the scope of traditional wargaming, offering users a comprehensive and immersive gaming experience. We redefine the capabilities of physical sand tables by transforming them into platforms for creating digital virtual environments conducive to adversarial combat scenarios, facilitated through tangible user interface (TUI)-based interactions. Our system incorporates a diverse array of military-inspired tokens and instruments for manipulating the sand table, further extending into a metaverse reflective of the physical sand model. To enable these functionalities,the system harnesses cutting-edge technologies including machine learning …

In this study, we use Augmented Reality (AR) to project and let users interact with a 3D-replicated model of a fenced space to understand how AR can improve navigation and enhance space retention by improving wayfinding via recall. We recreated a fenced space in 3D using Blender. Users can see their current position and, using on-screen buttons, see routes to their destinations. We found that users could associate the 3D models of the buildings with their real-world counterparts. We observed that users were better able to navigate the campus after using the application.

In the contemporary academic landscape, university students frequently deliver presentations in front of their peers and faculty, often leading to heightened levels of Public Speaking Anxiety (PSA). This study explores the potential of Virtual Reality Exposure Therapy (VRET) to alleviate PSA among students. Our study introduces "Manch," a realistic VR environment that simulates classroom public speaking scenarios with lifelike audience interactions and a slide-deck presentation feature. The study was conducted with N=28 participants, showing a significant reduction in PSA levels post-VR exposure, thereby establishing VR's efficacy in mitigating PSA. Additionally, we also incorporated a unique qualitative analysis through participant interviews, offering deeper insights into individual experiences with VRET. Manch shows great promise as a tool for future studies and interventions aimed at reducing PSA, particularly

This paper presents a demonstration of the developed prototype showcasing a way to preserve the Intangible Cultural Heritage of Uttarakhand, India. Aipan is a traditional art form practiced in the Kumaon region in the state of Uttarakhand. It is typically used to decorate floors and walls at places of worship or entrances of homes and is considered auspicious to begin any work or event. This art is associated with a great degree of social, cultural as well as religious significance and is passed from generation to generation. However, in the present era of modernization and technological advancements, this art form now stands on the verge of depletion. This study presents a humble attempt to preserve this vanishing art form through the use of Virtual Reality (VR). Ethnographic studies were conducted in Almora, Nainital, and Haldwani regions of Uttarakhand to trace the origins as well as to gain a deeper understanding of this art form. A total of ten (N =10) Aipan designers were interviewed. Several interesting insights are revealed through these studies that show the potential to be incorporated as a VR experience.

Amid the evolving landscape of education, evaluating the impact of educational video content on students remains a challenge. Existing methods for assessment often rely on heuristics and self-reporting, leaving room for subjectivity and limited insight. This study addresses this issue by leveraging physiological sensor data to predict student-perceived content effectiveness. Within the realm of educational content evaluation, prior studies focused on conventional approaches, leaving a gap in understanding the nuanced responses of students to educational materials. To bridge this gap, our research introduces a novel perspective, building upon previous work in multimodal physiological data analysis. Our primary contributions encompass two key elements. First, we present the ’Effecti-Net’ architecture, a sophisticated deep learning model that integrates data from multiple sensor modalities, including Electroencephalogram (EEG), Eye Tracker, Galvanic Skin Response (GSR), and Photoplethysmography (PPG). Second, we introduce the ’DECEP’ dataset, a repository comprising 597 minutes of multimodal sensor data. To assess the effectiveness of our approach, we benchmark it against conventional methods. Remarkably, our model achieves a lowest MSE of 0.1651 and MAE of 0.3544 on the DECEP dataset. It offers educators and content creators a comprehensive framework that promotes the development of more engaging educational content.

Multimedia content, such as advertisements and story videos, exhibit a rich blend of creativity and multiple modalities. They incorporate elements like text, visuals, audio, and storytelling techniques, employing devices like emotions, symbolism, and slogans to convey meaning. There is a dearth of large annotated training datasets in the multimedia domain hindering the development of supervised learning models with satisfactory performance for real-world applications. On the other hand, the rise of large language models (LLMs) has witnessed remarkable zero-shot performance in various natural language processing (NLP) tasks, such as emotion classification, question-answering, and topic classification. To leverage such advanced techniques to bridge this performance gap in multimedia understanding, we propose verbalizing long videos to generate their descriptions in natural language, followed by performing video-understanding tasks on the generated story as opposed to the original video. Through extensive experiments on fifteen video-understanding tasks, we demonstrate that our method, despite being zero-shot, achieves significantly better results than supervised baselines for video understanding. Furthermore, to alleviate a lack of story understanding benchmarks, we publicly release the first dataset on a crucial task in computational social science on persuasion strategy identification.

Recent research has focused on the effectiveness of Virtual Reality (VR) in games as a more immersive method of interaction. However, there is a lack of robust analysis of the physiological effects between VR and flatscreen (FS) gaming. This paper introduces the first systematic comparison and analysis of emotional and physiological responses to commercially available games in VR and FS environments. To elicit these responses, we first selected four games through a pilot study of 6 participants to cover all four quadrants of the valence-arousal space. Using these games, we recorded the physiological activity, including Blood Volume Pulse and Electrodermal Activity, and self-reported emotions of 33 participants in a user study. Our data analysis revealed that VR gaming elicited more pronounced emotions, higher arousal, increased cognitive load and stress, and lower dominance than FS gaming. The Virtual Reality and Flat Screen (VRFS) dataset, containing over 15 hours of multimodal data comparing FS and VR gaming across different games, is also made publicly available for research purposes. Our analysis provides valuable insights for further investigations into the physiological and emotional effects of VR and FS gaming.

Emotional intelligence (EI), a critical aspect of regulating emotions and behavior in daily life, holds paramount significance in both psychology research and real-world applications. Understanding and assessing EI are essential for informed decision-making, nurturing relationships, and facilitating efficient communication. As human–computer interaction (HCI) continues to evolve, there is a growing need to develop systems capable of comprehending human emotions, personality traits, and moods through recognition models. This research endeavors to explore the potential of recognizing EI in the context of effective HCI. To address this challenge, we have developed a novel computational model based on electroencephalogram (EEG) data. Our work encompasses a carefully curated EEG dataset, featuring recordings from 40 participants who were exposed to a set of 16 emotional video clips selected from distinct quadrants of the valence-arousal (VA) space. Participants’ emotional responses were meticulously annotated through self-assessment of emotional dimensions for each video stimulus. In addition, participants’ feedback on the big-five personality traits and their responses to the trait emotional intelligence questionnaire (TEIQue) served as our ground truth for further analysis. Our study includes a comprehensive correlation analysis, using Pearson correlations to establish the relationships between personality traits and EI. Furthermore, we conducted EEG-based analysis to uncover connections between EEG signals and emotional attributes. Remarkably, our analysis reveals that EEG signals excel at capturing differences in EI levels. Leveraging machine learning algorithms, we have constructed binary classification models that yield average $F1$ scores of 0.72, 0.71, and 0.62 for emotions, personality traits, and EI, respectively. These experimental outcomes underscore the potential of EEG signals in the recognition of EI, personality traits, and emotions. We envision our proposed model as a foundational element in the development of effective HCI systems, enabling a deeper and better understanding of human behavior.

Student attention is an indispensable input for uncovering their goals, intentions, and interests, which prove to be invaluable for a multitude of research areas, ranging from psychology to interactive systems. However, most existing methods to classify attention fail to model its complex nature. To bridge this gap, we propose AttentioNet, a novel Convolutional Neural Network-based approach that utilizes Electroencephalography (EEG) data to classify attention into five states: Selective, Sustained, Divided, Alternating, and relaxed state. We collected a dataset of 20 subjects through standard neuropsychological tasks to elicit different attentional states. The average across-student accuracy of our proposed model at this configuration is 92.3% (SD=3.04), which is well-suited for end-user applications. Our transfer learning-based approach for personalizing the model to individual subjects effectively addresses the issue of individual variability in EEG signals, resulting in improved performance and adaptability of the model for real-world applications. This represents a significant advancement in the field of EEG-based classification. Experimental results demonstrate that AttentioNet outperforms a popular EEGnet baseline (p-value < 0.05) in both subject-independent and subject-dependent settings, confirming the effectiveness of our proposed approach despite the limitations of our dataset. These results highlight the promising potential of AttentioNet for attention classification using EEG data.

The care and upkeep of indigenous, migratory & endangered species of Aves is vital to the ecosystem as they are instrumental in various activities such as pollination and seed dispersal. We explore the possibility of the inclusion of an auditory machine learning model into a multi-platform application to help forest rangers based in remote locations of India identify such species and aid their study and care. The application will be connected to databases which would not only help in the recognition of various birds via sound but also fetch essential information respective to the conservation of identified birds. Forest rangers in remote locations will no longer need to depend on the primitive analogue techniques of bird recognition such as birding by ear or sight, they will be able to get accurate results just by scanning their surroundings using any common device such as a smartphone or a portable computer. This …

Several works have developed end-to-end pipelines for generating lip-synced talking faces with real-world applications, such as teaching and language translation in videos. However, these prior works fail to create realistic-looking videos since they focus little on people's expressions and emotions. Moreover, these methods' effectiveness largely depends on the faces in the training dataset, which means they may not perform well on unseen faces. To mitigate this, we build a talking face generation framework conditioned on a categorical emotion to generate videos with appropriate expressions, making them more realistic and convincing. With a broad range of six emotions, i.e., happiness, sadness, fear, anger, disgust, and neutral, we show that our model can adapt to arbitrary identities, emotions, and languages. Our proposed framework has a user-friendly web interface with a real-time experience for talking face generation with emotions. We also conduct a user study for subjective evaluation of our interface's usability, design, and functionality.

In the proposed research, we investigated whether the standardized neuropsychological tests commonly used to assess attention can be used to measure students’ engagement in online learning settings. Accordingly, we employed 73 students in three clinically relevant neuropsychological tests to assess three types of attention. Students’ engagement performance, as evidenced by their facial video, was also annotated by three independent annotators. The manual annotations observed a high level of inter-annotator reliability (Krippendorffs’ Alpha of 0.864). Further, by obtaining a correlation value of 0.673 (Spearmans’ Rank Correlation) between manual annotation and neuropsychological tests score, our results show construct validity to prove neuropsychological test scores’ significance as a latent variable for measuring students’ engagement. Finally, using non-intrusive behavioral cues, including facial action unit and eye gaze data collected via webcam, we propose a machine learning method for engagement analysis in online learning settings, achieving a low mean squared error value (0.022). The findings suggest a neuropsychological test-based machine learning technique could effectively assess students’ engagement in online education.

Thermal analysis is one of the cardinal studies essential for arc welding processes. Thermal field and temperature distribution in arc welds affect the quality of welds as they govern the microstructural and thermo-mechanical properties. Therefore, thorough understanding of the thermal behaviour in arc welds is an absolute necessity. Significant efforts have been made in the past to determine the temperature field associated with arc welding. However, for accurate determination of the temperature field/distribution, it is necessary to understand the heat source which influences the temperature distribution in welds. Rosenthal reported the first concept of modelling the heat source, which was then improvised and new models have been instituted through the years. This review article summarizes a collective study made on the heat source and the resulting temperature distribution in arc welds. Numerous methods have been developed to conduct transient temperature distribution studies on arc welds. Analytical approaches with constant material properties, numerical approaches with variable material properties, infrared imaging systems, machine vision systems with soft computing, etc. have been developed to facilitate understanding of transient temperature in arc welds. We first summarize heat source studies followed by literatures on various techniques and methods devoted to transient temperature investigations. Eventually, latest methods used for thermal studies, such as image processing, machine learning and intelligent systems are summarized and discussed.

People these days primarily focus on having a well-measured calorific diet which results in a healthy body but lacks mindfulness. The Ayurvedic diet system, which is more than thousands of years old, focuses on balancing different energies in the body which in turn promotes wellness of both body and mind. Due to lack of information available, most of the youth is unaware of its benefits and hence struggles to follow a proper dietary system. From the initial interviews with N = 16 participants, it was identified that many people are interested to know more about the Ayurvedic dietary system but lack the means to do so. The findings indicate a need to design an AI-based mobile application that presents information about the Ayurvedic dietary system in an actionable way, giving users the ability to track and compare diet with their peers and identify what to consume consciously according to their body type, life …