Miracle-dz/UniFAR-FTUs huggingface dataset:
Personalizing Ambience and Illusionary Presence: How People Use “Study with me” Videos to Create Effective Studying Environments

“Study with me” videos contain footage of people studying for hours, in which social components like conversations or informational content like instructions are absent. Recently, they became increasingly popular on video-sharing platforms. This paper provides the first broad look into what “study with me” videos are and how people use them. We analyzed 30 “study with me” videos and conducted 12 interviews with their viewers to understand their motivation and viewing practices. We identified a three-factor model that explains the mechanism for shaping a satisfactory studying experience in general. One of the factors, a well-suited ambience, was difficult to achieve because of two common challenges: external conditions that prevent studying in study-friendly places and extra cost needed to create a personally desired ambience. We found that the viewers used “study with me” videos to create a personalized ambience at a lower cost, to find controllable peer pressure, and to get emotional support. These findings suggest that the viewers self-regulate their learning through watching “study with me” videos to improve efficiency even when studying alone at home.
Resisting the Medicalisation of Menopause: Reclaiming the Body through Design

The menopause transition involves bodily-rooted, socially-shaped changes, often in a context of medicalisation that marginalises people based on their age and gender. With the goal of addressing this social justice matter with a participatory design approach, we started to cultivate partnerships with people going through menopause. This paper reports on interviews with 12 women and a design workshop with three. Our data analysis highlights their experiences from a holistic perspective that reclaims the primacy of the body and acknowledges the entanglement of the physical and the psychosocial. Participants’ design concepts show how design can come close the body to make space for menopause experiences, recognising and transforming them. We discuss how HCI can actively engage with the body to promote appreciation for it during menopause, and call for design that accompanies people in resisting the medicalisation of menopause as an enactment of social justice in everyday life.
Predicting Performance Anomalies in Software Systems at Run-time

High performance is a critical factor to achieve and maintain the success of a software system. Performance anomalies represent the performance degradation issues (e.g., slowing down in system response times) of software systems at run-time. Performance anomalies can cause a dramatically negative impact on users’ satisfaction. Prior studies propose different approaches to detect anomalies by analyzing execution logs and resource utilization metrics after the anomalies have happened. However, the prior detection approaches cannot predict the anomalies ahead of time; such limitation causes an inevitable delay in taking corrective actions to prevent performance anomalies from happening. We propose an approach that can predict performance anomalies in software systems and raise anomaly warnings in advance. Our approach uses a Long-Short Term Memory neural network to capture the normal behaviors of a software system. Then, our approach predicts performance anomalies by identifying the early deviations from the captured normal system behaviors. We conduct extensive experiments to evaluate our approach using two real-world software systems (i.e., Elasticsearch and Hadoop). We compare the performance of our approach with two baselines. The first baseline is one state-to-the-art baseline called Unsupervised Behavior Learning. The second baseline predicts performance anomalies by checking if the resource utilization exceeds pre-defined thresholds. Our results show that our approach can predict various performance anomalies with high precision (i.e., 97–100%) and recall (i.e., 80–100%), while the baselines achieve 25–97% precision and 93–100% recall. For a range of performance anomalies, our approach can achieve sufficient lead times that vary from 20 to 1,403 s (i.e., 23.4 min). We also demonstrate the ability of our approach to predict the performance anomalies that are caused by real-world performance bugs. For predicting performance anomalies that are caused by real-world performance bugs, our approach achieves 95–100% precision and 87–100% recall, while the baselines achieve 49–83% precision and 100% recall. The obtained results show that our approach outperforms the existing anomaly prediction approaches and is able to predict performance anomalies in real-world systems.
Ninja Hands: Using Many Hands to Improve Target Selection in VR

Selection and manipulation in virtual reality often happen using an avatar’s hands. However, objects outside the immediate reach require effort to select. We develop a target selection technique called Ninja Hands. It maps the movement of a single real hand to many virtual hands, decreasing the distance to targets. We evaluate Ninja Hands in two studies. The first study shows that compared to a single hand, 4 and 8 hands are significantly faster for selecting targets. The second study complements this finding by using a larger target layout with many distractors. We find no decrease in selection time across 8, 27, and 64 hands, but an increase in the time spent deciding which hand to use. Thereby, net movement time still decreases significantly. In both studies, the physical motion exerted also decreases significantly with more hands. We discuss how these findings can inform future implementations of the Ninja Hands technique.
Exploring Awareness of Breathing through Deep Touch Pressure

Deep Pressure Therapy relies on exerting firm touch to help individuals with sensory sensitivity. We performed first-person explorations of deep pressure enabled by shape-changing actuation driven by breathing sensing. This revealed a novel design space with rich, evocative, aesthetically interesting interactions that can help increase breathing awareness and appreciation through: (1) applying symmetrical as well as asymmetrical pressure on the torso; (2) using pressure to direct attention to muscles or bone structure involved in different breathing patterns; (3) apply synchronous as well as asynchronous feedback following or opposing the user’s breathing rhythm through applying rhythmic pressure. Taken together these explorations led us to design (4) breathing correspondence interactions – a balance point right between leading and following users’ breathing patterns by first applying deep pressure – almost to the point of being unpleasant – and then releasing in rhythmic flow.
Bubble Coloring to Visualize the Speech Emotion

To explore the emotional effect of the chat bubble’s background color on voice messages, we carried out a user survey with Facebook Messenger, WeChat, and KakaoTalk, which use blue, green, and yellow, respectively, as the default color for chat bubbles. We provided the colors in orange, dark red, dark grey, and pale blue when the voice message seemed to be in an excited, angry, sad, or serene mood, respectively. With the exception of the serene, the emotion was intensified through the background color across the three messengers. Concerning willingness to use, the likelihood was reduced, particularly in negative emotions. Based on the empirical evidence, we discussed the potentials and concerns when the method is implemented in voice messaging.
Mediating Intimacy with DearBoard: a Co-Customizable Keyboard for Everyday Messaging

Co-customizations are collaborative customizations in messaging apps that all conversation members can view and change, e.g. the color of chat bubbles on Facebook Messenger. Co-customizations grant new opportunities for expressing intimacy; however, most apps offer private customizations only. To investigate how people in close relationships integrate co-customizations into their established communication app ecosystems, we built DearBoard: an Android keyboard that allows two people to co-customize its color theme and a toolbar of expression shortcuts (emojis and GIFs). In a 5-week field study with 18 pairs of couples, friends, and relatives, participants expressed their shared interests, history, and knowledge of each other through co-customizations that served as meaningful decorations, interface optimizations, conversation themes, and non-verbal channels for playful, affectionate interactions. The co-ownership of the co-customizations invited participants to negotiate who customizes what and for whom they customize. We discuss how co-customizations mediate intimacy through place-making efforts and suggest design opportunities.
When the Tab Comes Due:Challenges in the Cost Structure of Browser Tab Usage

Tabs have become integral to browsing the Web yet have changed little since their introduction nearly 20 years ago. In contrast, the internet has gone through dramatic changes, with users increasingly moving from navigating to websites to exploring information across many sources to support online sensemaking. This paper investigates how tabs today are overloaded with a diverse set of functionalities and issues users face when managing them. We interviewed ten information workers asking about their tab management strategies and walk through each open tab on their work computers four times over two weeks. We uncovered competing pressures pushing for keeping tabs open (ranging from interaction to emotional costs) versus pushing for closing them (such as limited attention and resources). We then surveyed 103 participants to estimate the frequencies of these pressures at scale. Finally, we developed design implications for future browser interfaces that can better support managing these pressures.
Disagree? You Must be a Bot! How Beliefs Shape Twitter Profile Perceptions

In this paper, we investigate the human ability to distinguish political social bots from humans on Twitter. Following motivated reasoning theory from social and cognitive psychology, our central hypothesis is that especially those accounts which are opinion-incongruent are perceived as social bot accounts when the account is ambiguous about its nature. We also hypothesize that credibility ratings mediate this relationship. We asked N = 151 participants to evaluate 24 Twitter accounts and decide whether the accounts were humans or social bots. Findings support our motivated reasoning hypothesis for a sub-group of Twitter users (those who are more familiar with Twitter): Accounts that are opinion-incongruent are evaluated as relatively more bot-like than accounts that are opinion-congruent. Moreover, it does not matter whether the account is clearly social bot or human or ambiguous about its nature. This was mediated by perceived credibility in the sense that congruent profiles were evaluated to be more credible resulting in lower perceptions as bots.
Human-AI Interactive and Continuous Sensemaking: A Case Study of Image Classification using Scribble Attention Maps

Advances in Artificial Intelligence (AI), especially the stunning achievements of Deep Learning (DL) in recent years, have shown AI/DL models possess remarkable understanding towards the logic reasoning behind the solved tasks. However, human understanding towards what knowledge is captured by deep neural networks is still elementary and this has a detrimental effect on human’s trust in the decisions made by AI systems. Explainable AI (XAI) is a hot topic in both AI and HCI communities in order to open up the blackbox to elucidate the reasoning processes of AI algorithms in such a way that makes sense to humans. However, XAI is only half of human-AI interaction and research on the other half - human’s feedback on AI explanations together with AI making sense of the feedback - is generally lacking. Human cognition is also a blackbox to AI and effective human-AI interaction requires unveiling both blackboxes to each other for mutual sensemaking. The main contribution of this paper is a conceptual framework for supporting effective human-AI interaction, referred to as interactive and continuous sensemaking (HAICS). We further implement this framework in an image classification application using deep Convolutional Neural Network (CNN) classifiers as a browser-based tool that displays network attention maps to the human for explainability and collects human’s feedback in the form of scribble annotations overlaid onto the maps. Experimental results using a real-world dataset has shown significant improvement of classification accuracy (the AI performance) with the HAICS framework.
Making Sense of Risk Information amidst Uncertainty: Individuals’ Perceived Risks Associated with the COVID-19 Pandemic

During a global pandemic such as COVID-19, laypeople bear a large burden of responsibility for assessing risks associated with COVID-19 and taking action to manage risks in their everyday lives, yet epidemic-related information is characterized by uncertainty and ambiguity. People perceive risks based on partial, changing information. We draw on crisis informatics research to examine the multiple types of risk people perceive in relation to the COVID-19 pandemic, the information sources that inform perceptions of COVID-19 risks, and the challenges that people have in getting the information they need to understand risks, using qualitative interviews with individuals across the United States. Participants describe multiple pandemic-related threats, including illness, secondary health conditions, economic, socio-behavioral, and institutional risks. We further uncover how people draw on multiple information sources from technological infrastructures, people, and spaces to inform the types of their risk perceptions, uncovering deep challenges to acquiring needed risk information.
Expressive Skin: An extended prosthesis that reflects content from the virtual world onto the user's original body

In this paper, we introduce Expressive Skin, a 3D-printed, haptic display interface that links the virtual world with the real world through the user's body. This research's vision is to imagine an advanced version of the human body with an extended prosthesis that can leverage human cognitive abilities. We believe that by immersing our bodies in virtual content, we foster creative self-expression, imagination, and thus design new lifestyles. This is the concept of Extensive Immersion. The Express Skin is equipped with a projector on its back, four pyramid-shaped projection modules on its front, and a LED lights system strengthening the visualization of gaming content by reacting simultaneously with the projections and with the tactile interaction (Figure 1: Full sketch of high-fidelity prototype). Through the creation of Expressive Skin, we want to ignite the fuse of fashion and gaming, as a viable way to achieve Extensive Immersion.
Studying the Formation of an Older Adult-Led Makerspace

Makerspaces are being introduced in a wide variety of settings, including community settings such as schools and libraries. Older adults are one group for whom making agendas are being pursued, with envisioned outcomes such as supporting agency and well-being. However, research on making and DIY with older adults typically study individuals who are already engaged in making practices or bring individuals in to a technology environment that has already been created. In this paper, we study the older adult-driven formation of a makerspace in an independent living community. Through an ethnographically-informed approach, we studied the ways that individuals considered appropriate allocation of resources towards a makerspace, scoped activities, evaluated goals, and made trade-offs. Our analysis is centered around describing the way that this makerspace formed as well as three ways that individuals made sense of the makerspace as the planning unfolded: the openness of a space that promises to cater to interests of the population; the promise of a makerspace to involve more residents in technology, but the need to obscure the technology to make it appealing; and a valuation of the return on investment for limited financial and space resources. Our discussion contributes to supporting and studying early adoption of technology by older adults, complicates visions of “making for all,” and presents considerations regarding the often under-specified community of a makerspace.
ProxiMic: Convenient Voice Activation via Close-to-Mic Speech Detected by a Single Microphone

Wake-up-free techniques (e.g., Raise-to-Speak) are important for improving the voice input experience. We present ProxiMic, a close-to-mic (within 5 cm) speech sensing technique using only one microphone. With ProxiMic, a user keeps a microphone-embedded device close to the mouth and speaks directly to the device without wake-up phrases or button presses. To detect close-to-mic speech, we use the feature from pop noise observed when a user speaks and blows air onto the microphone. Sound input is first passed through a low-pass adaptive threshold filter, then analyzed by a CNN which detects subtle close-to-mic features (mainly pop noise). Our two-stage algorithm can achieve 94.1% activation recall, 12.3 False Accepts per Week per User (FAWU) with 68 KB memory size, which can run at 352 fps on the smartphone. The user study shows that ProxiMic is efficient, user-friendly, and practical.
SEMOUR: A Scripted Emotional Speech Repository for Urdu

Designing reliable Speech Emotion Recognition systems is a complex task that inevitably requires sufficient data for training purposes. Such extensive datasets are currently available in only a few languages, including English, German, and Italian. In this paper, we present SEMOUR, the first scripted database of emotion-tagged speech in the Urdu language, to design an Urdu Speech Recognition System. Our gender-balanced dataset contains 15,040 unique instances recorded by eight professional actors eliciting a syntactically complex script. The dataset is phonetically balanced, and reliably exhibits a varied set of emotions as marked by the high agreement scores among human raters in experiments. We also provide various baseline speech emotion prediction scores on the database, which could be used for various applications like personalized robot assistants, diagnosis of psychological disorders, and getting feedback from a low-tech-enabled population, etc. On a random test sample, our model correctly predicts an emotion with a state-of-the-art 92% accuracy.
Conceptualizing Smart Government: Interrelations and Reciprocities with Smart City

Smart government (SG) is an emerging topic, which increasingly attracts attention from scholars who work in public administration, political, and information sciences. Smart city (SC) on the other hand, is an emerging and multidisciplinary domain of study. It is not clear whether the two terms SG and SC co-exist or concern different domains that interrelate and interact. The aim of this paper is to investigate the term SG; to conceptualize it with components; to define the importance of these components to the SG with their relative strengths; and to clarify its relationship with the SC term. In this respect, this paper follows a multi-method approach: a comprehensive literature review to define and conceptualize the SG, and determine its components, and a Delphi study for validating the literature findings and for calculating the relative components’ strengths. The SG definitions that are in literature have several weaknesses and the authors proposed a definition to the SG that overcomes them, while a model with three rings, three dimensions and 13 components conceptualizes it. The Delphi study showed that all the SG conceptual entities are useful, and highlighted that Citizens Engagement, Economic Growth, and Accountability are more important compared to the others, but it is hard to decide about the less important component. Third, the ICT Innovation entity appears to be the most important compared to emerging technologies and data. Finally, SC and SG are indeed related but, SC is proved to be a complimentary part of the broader SG term.
FlexTruss: A Computational Threading Method for Multi-material, Multi-form and Multi-use Prototyping

3D printing, as a rapid prototyping technique, usually fabricates objects that are difficult to modify physically. This paper presents FlexTruss, a design and construction pipeline based on the assembly of modularized truss-shaped objects fabricated with conventional 3D printers and assembled by threading. To create an end-to-end system, a parametric design tool with an optimal Euler path calculation method is developed, which can support both inverse and forward design workflow and multi-material construction of modular parts. In addition, the assembly of truss modules by threading is evaluated with a series of application cases to demonstrate the affordance of FlexTruss. We believe that FlexTruss extends the design space of 3D printing beyond typically hard and fixed forms, and it will provide new capabilities for designers and researchers to explore the use of such flexible truss structures in human-object interaction.
Green synthesis and characterization of silver nanoparticles using aqueous petal extract of the medicinal plant Combretum indicum

For the first time, any type of plant extract from the medicinally important plant Combretum indicum has been used for the biosynthesis of silver nanoparticles (AgNPs). The present investigation reports the synthesis and characterization of AgNPs using the flower petal extract of Combretum indicum. For monitoring the formation and optical properties of the synthesized nanoparticles, they were analyzed using UV-visible spectroscopy. Apart from this, the luminescence properties were also studied by photoluminescence (PL) spectroscopy. Scanning electron microscopy (SEM) analysis revealed the formation of AgNPs and the surface morphology has been determined. The mean particle diameter using the dynamic light scattering (DLS) technique ranged from 50–120 nm depending upon the reaction time. The atomic percentage of Ag in synthesized NPs and the crystallinity were determined by energy dispersive x-ray (EDX) and x-ray diffraction (XRD), respectively. This green approach of synthesizing AgNPs, using a biologically important plant extract is found to be cost effective, economical, eco-friendly and convenient in synthesis.