Reviewerly/exHarmony huggingface dataset:
Study on the Identification of Disruptive Technology, Evidence from Nano Science

Disruptive technology is an innovation that initially performs poorly in mainstream markets, but eventually disrupts and replaces existing product. The concept of disruptive technology has been studied at both micro and macro levels. The identification of disruptive technology includes the approaches based on subjective data and objective data. Understanding the nature and dynamics of disruptive technology is important for businesses and policymakers as it can have significant impacts on industries and society as a whole. In this paper, 259,706 nanotechnology papers from 1999 to 2015, are collected to identify disruptive technology in nano field. 53 highly cited paper with more than 1000 citations are analysed, using Disruption index. Study results show that Phosphorene and Polymer solar cells are the top high disruptive technology. In terms of team sizes, disruptive technology in highly cited papers are small teams.
Opening up Smart Learning Cities - Building Knowledge, Interactions and Communities for Lifelong Learning and Urban Belonging

This paper revisits issues arising from prior research carried out by the author examining citizen informal learning through interactions with the real world via augmented reality interfaces triggering place based knowledge. Topics discussed in this paper were not part of the research yet deserve further discussion in that context. Two areas are of particular interest: place based digital knowledge content delivery and user generated content related to place. In other words, how users might freely and easily access knowledge content that relates to features and places they pass through or live in, and how they might digitally interact with their local environment to contribute to a community of memory associated with place [29, 30, 39]. This compiling of the knowledge archive of place, both expert and citizen generated, might be described as the reading and writing of the city, somewhat like [26] or [19], reflecting ideas going back to the Berkeley Community Memory bulletin-boards of the 1970s [7]. Discussion includes the concept of community mapping, briefly examining examples from literature and a prototype, the ‘Learner Feedback Map’, developed by the author but not used in the final research. The challenges of finding and delivering knowledge content, and of uploading and hosting user-generated content are briefly considered in the context of decentralised networks and the Fediverse.
Industrial Design Education and Immersive Virtual Reality: Perceptions on Utility and Integration

The use of Immersive Virtual Reality (IVR) in collaborative and co-design contexts has proven valuable to product or industrial design specializations, yet integration has been notoriously slow within Higher Education (HE). This work sheds some light on the “why?” based on primary data sourced from two groups of design educators, through a mixed-methods design. The first group confirms the importance of digital and connected technologies in today’s design-centered learning environment and depict current stances regarding the use IVR as a tool for teaching and learning. The second, composed solely of educators operating in an industrial design HE program, add specificity by offering qualitative insight into aspects concerning IVR utility and integration into this particular specialization. Results foresee the technology furthering learning both on-campus and from afar, with richer virtual interactions, digital re-enactment of experiential learning, or using simulation to create new or replicate existing contexts virtually. However, the envisioned benefits appear to hinge on a series of factors, including critics over an already exceeding amount of technology in the learning environment, personal competence, skepticism over the technology merits, and other more concrete such as time, resources, familiarization or lack of technical support. Overall, participants envisage a more virtually interactive educational experience with IVR, though its reputed complexity or lack of a solid and pressing need seems to be keeping integration at bay.
Designing Tangible Interactive Artifacts for Religious and Spiritual Purposes

From candles to prayer beads to icons and more, tangible artifacts have long played a central role in religious and spiritual life. However, despite growing interest in HCI on design within religious and spiritual contexts, the intentional design of tangible interactive artifacts for religious or spiritual purposes is still relatively uncommon. This workshop aims at advancing our understanding of tangible interactive artifact design in religious and spiritual contexts and how religious and spiritual purposes may offer alternative approaches to design. We invite those interested from all professional and religious/spiritual backgrounds to imagine and design tangible interactive artifacts for religious or spiritual purposes such as beliefs, practices, or rituals. The workshop will include the demonstration of (imagined) artifacts, reflection on designing for religious/spiritual purposes, and hands-on design sessions.
Does Robotic Telepresence Make the Classroom Accessible?

Telepresence robots are meant to provide physical access to digital remote spaces. However, they have a range of limitation that make spaces less accessible for remote participants. In this study we reveal, catalogue, and examine telepresence limitations in the context of the classroom. Based on ongoing field study results from participatory observations, surveys and interviews with 22 participants we discuss the extent to which telepresence robots make the classroom accessible and whether the telepresence robots are "disabled."
Bringing Emotions into Practice: The Role of AI in Supporting Emotional Work in Informal Caregiving

The demanding nature of informal caregiving has negative consequences on caregivers’ emotional and physical health. HCI community has increasingly examined ways to support caregivers, but the solutions mostly focus on the negative emotions of caregivers. Informal caregivers go through a range of emotional experiences, both positive and negative. In this dissertation work, I aim to understand the emotional work of informal caregivers and how AI systems can be better designed to support this work. I start with conducting user studies and interviews to understand the valence and intensity of emotions in caregiving work and how this affects the caregiving burden. Then, I plan to use the Emotion Practice Theory to understand the complex emotional work from studies 1 and 2 through parts that AI systems can help caregivers with. This dissertation aims to provide a framework for designing AI systems to support the emotional work of informal caregivers.
Unlogical instrument: Material-driven gesture-controlled sound installation

This proposal describes the design and demonstration of the Unlogical Instrument. This textile-based gesture-controlled sound interface invites the audience to generate sound by interacting with the textile using different gestures, such as poking, flicking, and patting. Unlogical is a statement about breaking the dogmatic approach of commonly used musical instruments. It brings resilience to the form of musical expression and interaction for the audience. It introduces an interactive mechanism mediated by intuitive human gestures and the curiosity to explore tangible material. Unlogical Instrument locates the focus on the textile. This artwork investigates the relationship between the gestures and the sound perception that originates from the textile through a material-driven approach. Inkjet printing is applied to transfer the original textile into a sensorial interface. The audience will listen to the sound while interacting with the textile surface, exploring and understanding this instrument through free-form playing.
ZoombaTogether: A Video Conference Add-on for Generating Interactive Visual Feedback for Online Group Exercise Through On-The-Fly Pose Tracking

Exercise benefits people not only in physical health but also in mental health. Compared to individual workouts, many people prefer group exercise because of the additional peer support like guidance from experienced peers, and socializing benefits like the opportunities to meet new friends when exercising with others as a group. However, during the pandemic, many group exercise sessions were shifted from in-person to online through video conferencing platforms. Such change significantly decreased the exercise feedback and social signals that participants could receive during the exercise, such as the immediate self-checking with a studio mirror, the group movement calibration with co-exercisers, and the sense of social presence (i.e., the existence of other co-exercisers) in the small community. To address these challenges, we present ZoombaTogether, a video platform add-on that enhances the remote Zumba dancing experience with on-the-fly pose detection and visualization techniques. It tracks and evaluates how well remote dancers follow the instructor with minor latency and provide them with visual feedback to simulate a joint exercise experience with group members. ZoombaTogether visualizes the participants’ performance at both individual and group levels to help them understand their movement similarity to the instructor and other dancers with a set of extensible visualization modules. Meanwhile, ZoombaTogether addresses the participants’ privacy concerns by supporting on-device computer vision-based body posture estimation so that they could keep their personal video private from other group members. In this work, we provide a solution to use visual feedback as a communication channel for exercise participants and demonstrate the possibility of enhancing the online group exercise experience. We encourage the community to carry on our work and explore solutions to the challenges of missing social signals in online group exercises.
Sand-in-the-loop: Investigating embodied co-creation for shared understandings of generative AI

Generative Artificial Intelligence (AI) applications in creative practices have grown tremendously over the past few years. However, these generative AI applications lack clarity on how they work and operate for their users. Revealing how generative AI tools work enables designers to understand these tools’ limitations and capabilities. We developed a tangible interface with sand as a medium for human-AI co-creation, Sand Playground. It extends the work in human-AI drawing practices beyond two-dimensional digital surfaces. Sand playground has three co-drawing modes, artistic mimicry, zen garden and doodling. We conducted a user study with ten designers. One of our findings is that the interface enabled users to predict the AI agent’s actions. Our research introduces novel insights into the role of tangible interactions and physical interfaces in generative AI literacy and explainability in design tools.
The General Staff Library in the second half of the 19-th century

The author investigates into the history of the main military library of the Russian Empire. In the mid 19-th century, the General Staff Library became one of the largest book depositories in Russia. The author sets the goal to review the main events related to the library during the period when F. F. Kruse and A. N. Petrov were heading the institution. This period was characterized by consistent collection development and collaborations with military historians, and by the new trends of expanding openness of the institution. Owing to the reform by Dmitry Milyutin, Minister of War, the library became accessible for all military personnel a nd, through s pecial o rder, f or c ivilians. T he s ources f or the s tudy a re unpublished materials from the Russian State Military Historical Archive.Based on these sources and literature, the author describes the efforts of the librarians F. F. Kruse and A. N. Petrov, analyzes the internal structure of the institution, the sources for collection development and structure. Despite the fact that the institution was run by professional soldiers – the above-mentioned librarians had a military education, and Petrov even took part in the Crimean War – the library was developed properly and performed efficiently.
Synthetic-real image domain adaptation for indoor camera pose regression using a 3D model

Deep learning-based camera pose regression approaches have achieved outstanding performance for visual indoor localisation. However, these approaches are limited by the availability of images with known camera poses, and they often require a comprehensive mapping of the indoor scenes, which is labour-intensive and often impractical. Recent studies have shown that synthetic images derived from simple 3D building models can be used to train deep learning models to perform cross-domain synthetic-to-real visual localisation. But the performance of such cross-domain localisation models is degraded due to the domain gap between the real and synthetic images. In this study, we propose a domain adaptation approach based on a Generative Adversarial Network (GAN) framework, which is trained on a set of unpaired synthetic and real images to generate real-looking synthetic images and synthetic-looking real images. These images are then used to train the current deep learning-based camera pose regression networks. We develop two domain adaptation strategies for the localisation task, namely, synthetic-to-real and real-to-synthetic image style transfer. The results show that the best model is obtained for the real-to-synthetic style transfer with a localisation error of 0.31 m and orientation error of 3.36°, which is approximately three times lower compared to the previously reported results.
Novel Deep Learning Techniques to Design the Model and Predict Facial Expression, Gender, and Age Recognition

For computer and human interaction, human facial recognition is crucial. Our goal is to anticipate the expression of a human face, gender, and age as quickly and accurately as possible in real-time. Understanding human behavior, detecting mental diseases, and creating synthetic human expressions are only a few of the applications of automatic human facial recognition . Salespeople can employ age, gender, and emotional state prediction to help them better understand their consumers. Convolutional Neural Network one of the Deep Learning techniques is utilized to design the model and predict emotion, age, and gender, using the Haar-Cascade frontal face algorithm to detect the face. This model can predict from video in real-time. The goal is to create a web application that uses a camera to capture a live human face and classify it into one of seven expressions, two ages, and eight age groups. The process of detecting face, pre-processing, feature extraction, and the prediction of expression, gender, and age is carried out in steps.
Bias Analysis in Stable Diffusion and MidJourney Models

In recent months, all kinds of image-generating models got the spot-light, opening many possibilities for further research direction, and from the commercial side, many teams will be able to start experimenting and building products on top of them. A sub-area of image generation that picked the most interest in the eye of the public is text-to-image models, most notably Stable Diffusion and MidJourney. Open sourcing Stable Diffusion and free tier of MidJourney allowed many product teams to start building on top of them with little to no resources. However, applying any pre-trained model without proper testing and experimentation creates unknown risks for companies and teams using them. In this paper, we are demonstrating what might happen if such models are used without additional filtering and testing through bias detection.
Detection of Cyber-attacks in Food Industry using Multi-Layer Perceptron and Convolution Neural Network

Food insecurity refers to the lack of access to sufficient food, and this is a critical problem that many countries are currently tackling. While there are various causes contributing to this issue, cyberattacks are identified as the primary factor impacting the food industry after the concept of Industry 4.0 was introduced. The impact of cyberattacks on the food industry has been documented by several authors based on the technologies and the devices used in real-time, revealing the need for better detection methods. This study employs the use of three deep learning algorithms, multi-layer perceptron, Artificial neural network and convolution neural networks, to detect cyber-attacks based on the equipment used in food industry. The study achieves its objective by using multiple hidden layers trained for accurate detection. Although MLP has 96.04% accuracy CNN has the highest accuracy rate of 96.77%.
Predictive Analytics in Financial Services Using Explainable AI

Large financial institutions have a lot of products for earning money, but a major source of their income is from lending money. This income mainly depends on the ability of the loan taker to repay the loan. Every year these institutions incur huge losses due to loan defaulters. Through this research, we will try to predict the possibility of an individual defaulting on a loan using various features in our dataset. Then, will try to explain our results using the novel concept of explainable AI. This research uses a random forest classifier to predict if the individual will have difficulties in repayment of loans. The dataset is sourced from Kaggle. This research has followed a machine learning pipeline wherein we have pre-processed dataset, trained model, predicted outcomes using a model trained, and explained results using explainable ai. Random Forest and XGBoost algorithms are used. The models predicted results with accuracy as high as 97%. For the explainable part, we have used the Shapash library. In our study, we have explained the factor that contributes most to global as well as individual predictions. The results of these predictions can be used by financial institutions to enforce rules and restrictions surrounding this parameter to avoid losses. Furthermore, the explanations for the individual predictions can be used for personalized loan profile improvement recommendations.
The Role of Knowledge Management on Employees' Work Performance

The purpose of the study is to identify the effect of knowledge management on work performance, and identify the association between knowledge management, work performance, and socio-demographic characteristics of the sample study. The study is a descriptive cross-sectional design done in the health sector in Erbil city in Iraq. The study begins on 20.10.2022 to 25.12.2022. The estimated sample was 187 participants. Data collection was done by using a questionnaire, asking 30 questions as follows; 4 items for socio-demographics, 16 items for knowledge management, and 10 items for work performance. The findings of the study indicated that there was a positive effect of knowledge management on work performance, and there was a strong relationship between them. There was a significant association between age, education level, monthly income, and knowledge management. Also, there was a significant association between gender and work performance. While there was no significant association between gender and knowledge management. Also, age, education level, monthly income, and work performance. The result of the present study recommended that; to ensure that their knowledge management strategies are in line with their overarching aims and objectives, organizations should clearly articulate them. Also, organizations should foster an environment that promotes employee collaboration and knowledge sharing, and organizations should place a high priority on learning and development by providing employees with training and development opportunities that help them gain new knowledge and skills.
Temporal Tensions in Digital Story Mapping for Housing Justice: Rethinking Time and Technology in Community-Based Design

In this paper, we explore temporal and technological conjunctures of community-based design responsiveness based upon our experiences of making an interactive digital story map amid housing crises. Through reflexive ethnographic work with the Anti-Eviction Mapping Project, we trace three intertwined genres of temporality in our community-based design process: slow design with care; rapid design in response to the urgency of crises; and disruptive design that seeps in from Silicon Valley, proving to “move fast and break things.” We assess temporal tensions and contradictions in an evolving data landscape by examining struggles over our pace of practice, time-based media lifecycles, and tooling trade-offs. Theorizing community-based design dilemmas, we contemplate ways of mindfully designing with time, making the past present via digital storytelling, and reimagining technological relations over time.
The Cuteness Factor: An Interpretive Framework for Artists, Designers and Engineers

Cuteness in design can brighten our daily lives by bringing feelings of joy, comfort, and healing to our experiences. Previous HCI works have explored how cuteness can be leveraged to create more joyful user experiences across a variety of technologies, for example, in the design of cute interfaces for AI agents and the cute physical and behavioral aspects of robots. These works rely on the “Baby Schema” identified by ethologist Konrad Lorenz to apply cuteness as a decoration. This pictorial explores ways that artists and designers can contribute their cultural and aesthetic understandings of cuteness to create richer interactive experiences. Our interpretive framework serves as a provocation for discussing the value of style in the design of interactive systems, and provides guidance for defining cuteness building blocks to support interdisciplinary teams of artists, designers and engineers in the creation of cute user experiences.
Ethical Tensions in UX Design Practice: Exploring the Fine Line Between Persuasion and Manipulation in Online Interfaces

HCI researchers are increasingly concerned about the prevalence of manipulative design strategies in user interfaces, commonly referred to as “dark patterns”. The line between manipulation and persuasion strategies is often blurred, leading to legal and ethical concerns. This paper examines the tension between persuasive UX practices and manipulative designs. UX/UI design professionals (n=22), split into eight focus groups, conducted design activities on two fictitious scenarios. We qualitatively analysed their discussions regarding strategies for influencing user behaviours and their underlying reasoning. Our findings reveal a combination of classical UI design strategies like sticky interfaces and incentives as their most common practice to influence user behaviour. We also unveil that trust, transparency, and user autonomy act as guiding principles for the professionals in assessing their ideas. However, a thorough approach is missing; despite a general user-first attitude, they feel constrained by contextual factors. We explain how the tensions between principles and context contribute to manipulative designs online.