Reviewerly/exHarmony huggingface dataset:
The Reflective Make-AR In-Action: Using Augmented Reality for Reflection-based Learning of Makerskills

Recent work on reflective learning supports self-paced learning of skills like breadboarding and using power tools in makerspaces through a reflection exercise toolkit. This toolkit monitors the learners' performances in real-time and prompts them to reflect both in-action and on-action i.e., during and after their maker activities. In this paper, we build on this prior work and use an augmented reality system to monitor, prompt, and record in-action reflections, i.e., while the maker activity is in progress. In particular, we propose a framework to design multi-modal reflective prompts for self-learning exercises using augmented reality with three specific goals - (1) adding real-world contextualization, (2) overlaying personalized multimodal contextual information for supporting in-action reflections, and (3) maintaining an immersive experience during the reflection exercises. We conclude with a discussion of three application case studies for reflective AR maker exercises.
Understanding occupants’ experiences in quantified buildings: results from a series of exploratory studies.

Quantified smart buildings increasingly utilise data-rich technologies (such as embedded sensors and personal wearables). Research and development however, rarely addresses occupants’ experiences and expectations in such environments, which is critical for designing ethical and occupant-centred workspaces. To support the design of human-centred smart buildings, a series of 4 workshops was conducted with a total of 27 participants, over 2 months, with occupants of a smart office building. Workshops used discursive (focus group) and projective (design fiction) techniques to qualitatively explore occupants’ perceptions of and concerns around the collection, processing and use of data within the building. Workshop data was thematically analysed, resulting in design implications for improving occupant experience in current smart workplaces, while also contributing implications for increasing the perceivability, accessibility and usability of data in such buildings. Contributing to discourses around Human-Building Interaction the paper concludes with discussion of future research challenges for occupant-centred development of quantified buildings.
Kaleidoscope: A Reflective Documentation Tool for a User Interface Design Course

Documentation can support design work and create opportunities for learning and reflection. We explore how a novel documentation tool for a remote interaction design course provides insight into design process and integrates strategies from expert practice to support studio-style collaboration and reflection. Using Research through Design, we develop and deploy Kaleidoscope, an online tool for documenting design process, in an upper-level HCI class during the COVID-19 pandemic, iteratively developing it in response to student feedback and needs. We discuss key themes from the real-world deployment of Kaleidoscope, including: tensions between documentation and creation; effects of centralizing discussion; privacy and visibility in shared spaces; balancing evidence of achievement with feelings of overwhelm; and the effects of initial perceptions and incentives on tool usage. These successes and challenges provide insights to guide future tools for design documentation and HCI education that scaffold learning process as an equal partner to execution.
An improved metaheuristic-based MPPT for centralized thermoelectric generation systems under dynamic temperature conditions

This paper proposes a multi-peak maximum power point tracking (MPPT) method based on the Global Flying Squirrel Search-Particle Swarm Optimization (GFSS-PSO) for centralized thermoelectric generator (TEG) systems operating under uneven temperature distribution conditions. Conventionally, metaheuristic-based MPPT methods mainly focused on indicators such as tracking speed, oscillation amplitude, and system efficiency. However, the real-time global search ability of conventional metaheuristic-based MPPT methods designed for photovoltaic systems may not be suitable for the gradual temperature change in the thermoelectric scene. A strong global search capability also can add to the computational burden and increase the power loss in the search process. To solve these problems, the GFSS-PSO algorithm introduces improved position updating method and multi-threshold restart mechanisms to reduce energy loss and improve the dynamic performance under temperature change. The proposed method has been compared with the perturb and observe method and several state-of-the-art metaheuristic-based MPPT algorithms. Simulation results confirm that GFSS-PSO demonstrates exceptional performance and generates higher energy levels compared to perturb and observe, grey wolf optimizer, and flying squirrel search optimization methods during the search phase under dynamic temperature conditions. The improvements achieved by GFSS-PSO are remarkable, with energy levels increasing by 118.3%, 105%, and 102.2% respectively. Finally, experiments are conducted to verify the effectiveness of the proposed algorithm in a real-time digital system.
Going, Going, Gone: Exploring Intention Communication for Multi-User Locomotion in Virtual Reality

Exploring virtual worlds together with others adds a social component to the Virtual Reality (VR) experience that increases connectedness. In the physical world, joint locomotion comes naturally through implicit intention communication and subsequent adjustments of the movement patterns. In VR, however, discrete locomotion techniques such as point&teleport come without prior intention communication, hampering the collective experience. Related work proposes fixed groups, with a single person controlling the group movement, resulting in the loss of individual movement capabilities. To close the gap and mediate between these two extremes, we introduce three intention communication methods and explore them with two baseline methods. We contribute the results of a controlled experiment (n=20) investigating these methods from the perspective of a leader and a follower in a dyadic locomotion task. Our results suggest shared visualizations support the understanding of movement intentions, increasing the group feeling while maintaining individual freedom of movement.
Visual Attention Quality Research for Social Media Applications: A Case Study on Photo Sharing Applications

With the rapid growth of smart terminal industry and User-Generated Content products, Social Media Applications (SMA) products, represented by TikTok, have been booming growth. How to capture and maintain users’ attention in order to prolong their stay on the product and increase customer stickiness has become the focus of the industry. Therefore, according to the duration and characteristics of users using SMA, this paper takes the layout of SMA as an example to study the visual attention quality. Firstly, through the literature review, the study of visual attention quality in SMA was summarized into four aspects, including the span, maintenance, allocation, and switch of attention. Secondly, eye movement and behavior experiments were designed based on common content consumption tasks of SMA users to verify the hypotheses proposed in this paper. According to the results: (1) In terms of attention maintenance, linear layout outperformed masonry layout, masonry layout outperformed matrix layout, and in terms of attention span, the reverse was true; (2) In attention allocation, linear layout showed the best anti-interference characteristics; The masonry layout is suitable to be used in the task search stage and also in the dual-task execution stage; (3) In terms of attention switch, the energy cost of attention switch increased in the order of matrix layout, masonry layout and linear layout. (4) In terms of interaction, double-clicking interaction had more advantages in attention allocation and cognitive load than swiping interaction. The research in this paper has provided objective and quantitative data support for the design and operation of SMA products.
LCA-GAN: Low-Complexity Attention-Generative Adversarial Network for Age Estimation with Mask-Occluded Facial Images

Facial-image-based age estimation is being increasingly used in various fields. Examples include statistical marketing analysis based on age-specific product preferences, medical applications such as beauty products and telemedicine, and age-based suspect tracking in intelligent surveillance camera systems. Masks are increasingly worn for hygiene, personal privacy concerns, and fashion. In particular, the acquisition of mask-occluded facial images has become more frequent due to the COVID-19 pandemic. These images cause a loss of important features and information for age estimation, which reduces the accuracy of age estimation. Existing de-occlusion studies have investigated masquerade masks that do not completely occlude the eyes, nose, and mouth; however, no studies have investigated the de-occlusion of masks that completely occlude the nose and mouth and its use for age estimation, which is the goal of this study. Accordingly, this study proposes a novel low-complexity attention-generative adversarial network (LCA-GAN) for facial age estimation that combines an attention architecture and conditional generative adversarial network (conditional GAN) to de-occlude mask-occluded human facial images. The open databases MORPH and PAL were used to conduct experiments. According to the results, the mean absolution error (MAE) of age estimation with the de-occluded facial images reconstructed using the proposed LCA-GAN is 6.64 and 6.12 years, respectively. Thus, the proposed method yielded higher age estimation accuracy than when using occluded images or images reconstructed using the state-of-the-art method.
Blockchain technologies in accounting: bibliometric analysis

The purpose of the paper is to conduct a bibliometric analysis of scientific literature on the use of blockchain technologies in accounting for the period 2013–2022 based on the use of a number of special methods and tools, including Scopus and WoS, VOSviewer, Publish or Perish, Google Trends, and Google Books Ngram Viewer. Based on the results of the bibliometric analysis of relevant scientific publications, a map of the relationships between the concepts of «blockchain» and «accounting» with other categories was formed, which allowed identifying seven clusters. Based on the results of the analysis of the evolutionary time block of blockchain technology research, it was possible to identify several main periods during which the main accents in this area changed. It has been proven that it was in 2020–2021 that the interest of researchers was directed to considering blockchains as tools for working with financial and accounting information. An analysis of the space-time dimension of bibliometric analysis in Ukraine showed that the publication activity of research on the use of blockchain technologies in accounting has intensified since the second half of 2021. Analysis using Google Books and Google Trends confirmed the absence of analogies in the dynamics of changes in search queries «blockchain» and «accounting». The use of bibliometric analysis tools allowed identifying the most relevant works by the searched terms. Relevance was mostly determined by the significance of the scientific work and the scientific cluster. On the basis of these works, key theses are presented, which will become the basis for further empirical research on the chosen topic.
ARctic Escape: Promoting Social Connection, Teamwork, and Collaboration Using a Co-Located Augmented Reality Escape Room

We present ARctic Escape, a co-located augmented reality (AR) escape room designed to promote collaboration between dyads through play. While physical escape rooms provide groups with fun, social experiences, they require a gameplay venue, props, and a game master, all of which detract from their ease of access. Existing AR escape rooms demonstrate that AR can make escape room experiences easier to access. Still, many AR escape rooms are single-player and therefore fail to maintain the social and collaborative elements of their physical counterparts. This paper presents ARctic Escape, a two-person AR escape room with clues emphasizing player interaction and teamwork. We evaluated ARctic Escape by conducting semi-structured interviews with four dyads to learn about participants’ interpersonal dynamics and experiences during gameplay. We found that participants thought the experience was fun, collaborative, promoted discussion, and inspired new social dynamics, but sometimes the escape room’s reliance on virtual content was disorienting.
Data as a Material for Design: Alternative Narratives, Divergent Pathways, and Future Directions

This one-day workshop will bring together a diverse group of practitioners and researchers within the CHI community to discuss and explore data's increasing use as a material for design. This workshop encourages the submission of design exemplars, i.e., physical or digital works (in progress), design processes, or provocative or controversial pieces on the topic of data as a design material. If we are to continue to explore what data means as a design material and how we will continue to co-exist with them in our everyday lives through new and exciting ways and means, we must develop new strategies, tactics, tools, and outcomes. By bringing together products, processes, and provocations, this workshop will nurture and extend the continuation of research inquiring into data as a design material in its many forms. Our workshop will be conducted through physical and digital activities before, during, and after the onsite event at CHI 2023.
Digital Sovereignty: What it is and why it matters for HCI

In recent years, digital sovereignty has become a central term in digital policy discourses. Both authoritarian and democratic states use digital sovereignty as a base for their digital policy, although, the individual interpretation and resulting policy and power balance might be quite different. Given the importance of this term, the HCI community and the users as the core of our research would benefit from taking up the discussion by finding own definitions of human-centred digital sovereignty, contributing to policy discourses, and to strengthening the position of users under non-sovereign conditions. This paper aims to initiate and provoke such discourse within the community by (1) introducing the policy term to HCI and providing an overview of how it is used, (2) arguing for the relevancy of the term, and (3) proposing possible ways forward.
InExChange: Fostering Genuine Social Connection through Embodied Breath Sharing in Mixed Reality

InExChange is an interactive mixed reality experience centering around an inflatable vest which conveys a physical sense of shared breathing on the diaphragm between two or more participants. The experience is composed of three acts in which the participants’ breaths are transformed into metaphorical projected representations: expansive waves, flowing light trails, and growing tree branches. The inflatable wearable devices physically enact in near real-time the inhale/exhale pattern of the other person’s breath, varying in intensity level to create an attention interplay between the embodied sensation and the projection. Through this embodied sense of playful shared breathing, we aim to cultivate a genuine feeling of connection and contribute to the integration of somaesthetic design principles in mixed reality HCI.
Virtual self-conversation using motivational interviewing techniques to promote healthy eating and physical activity: A usability study

We aim to examine the usability of a Virtual Reality (VR) platform, called ConVRSelf, which has been designed to address the needs of People Living With Obesity (PLWO).Fourteen participants with a desire to eat healthier and exercise more (6 normal weight and 8 PLWO; Mean age = 41.86, SD = 13.89) were assigned to the experimental group (EG) or the control group (CG). EG participants, after being trained on motivational interviewing skills, engaged in a virtual self-conversation using embodiment and body swapping techniques, which aimed to normalize and resolve their ambivalence to change lifestyle habits. CG participants, embodied in their virtual bodies, participated in a pre-established discourse with a virtual counselor giving them psychoeducational advice about how to change lifestyle habits. A mixed-methods design was used, involving a semi-structured interview and self-report questionnaires, including readiness to change habits (Readiness Rulers), body ownership (Body Ownership Questionnaire, BOQ), and system usability (System Evaluation Questionnaire, SEQ). Thematic content analysis was carried out for qualitative data while statistical data analysis was carried out using SPSS 20.0.Participants from both groups showed high readiness to change lifestyle (Readiness Rulers) before engaging with the virtual experiences, which was maintained at the same level after the interventions and their scores on the SEQ and BOQ were satisfactory. Regarding qualitative information obtained from the interviews, almost all participants found the VR experience to be novel, interesting, and enjoyable. A higher acceptability was observed among PLWO from the EG than normal weight participants from the same group, a promising finding for the ConVRSelf platform, which had been specifically designed to address the needs of PLWO.The ConVRSelf system is well-accepted by participants and is ready to be tested with PLWO in a clinical setting.
A novel verifiable weighted threshold quantum secret sharing scheme

Abstract Threshold quantum secret sharing is a cryptography technique for dividing and reconstructing secret information. The dealer shares a secret based on quantum mechanics principles, and a subset of participants cooperate to recover that secret. For example, in <?CDATA $\left(k,n\right)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mfenced close=")" open="("> <mml:mrow> <mml:mi>k</mml:mi> <mml:mo>,</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:mfenced> </mml:math> quantum threshold scheme and <?CDATA $\left(n,n\right)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mfenced close=")" open="("> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>,</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:mfenced> </mml:math> quantum threshold scheme, the former can only reconstruct the secret when at least k shares are collected. The latter requires all n shares to be collected to reconstruct the secret. However, in real scenarios, the priority of participant identities may vary. In order to meet the actual needs of participants with different rights, this paper proposes a <?CDATA $\left(w,\omega ,n\right)$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mfenced close=")" open="("> <mml:mrow> <mml:mi>w</mml:mi> <mml:mo>,</mml:mo> <mml:mi>ω</mml:mi> <mml:mo>,</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:mfenced> </mml:math> quantum secret sharing (QSS) scheme. When the sum of participants’ weights w is greater than or equal to a threshold ω , they can cooperate to reconstruct the secret. In order to achieve this, this paper uses the entangled state of single particles and constructs a verifiable QSS scheme based on the Chinese remainder theorem. Significantly, in case of a dishonest participant, the dealer can delete the participant before recovering the secret to ensure the security of the communication. This scheme is more flexible than other threshold protocols. At the same time, it reduces the communication cost of the participants. In addition, security analysis shows that the scheme resists typical external and internal attacks.
Kernel-attentive weight modulation memory network for optical blur kernel-aware image super-resolution

Recently, imaging systems have exhibited remarkable image restoration performance through optimized optical systems and deep-learning-based models. Despite advancements in optical systems and models, severe performance degradation occurs when the predefined optical blur kernel differs from the actual kernel while restoring and upscaling the images. This is because super-resolution (SR) models assume that a blur kernel is predefined and known. To address this problem, various lenses could be stacked, and the SR model could be trained with all available optical blur kernels. However, infinite optical blur kernels exist in reality; thus, this task requires the complexity of the lens, substantial model training time, and hardware overhead. To resolve this issue by focusing on the SR models, we propose a kernel-attentive weight modulation memory network by adaptively modulating SR weights according to the shape of the optical blur kernel. The modulation layers are incorporated into the SR architecture and dynamically modulate the weights according to the blur level. Extensive experiments reveal that the proposed method improves peak signal-to-noise ratio performance, with an average gain of 0.83 dB for blurred and downsampled images. An experiment with a real-world blur dataset demonstrates that the proposed method can handle real-world scenarios.
Spectrum-irrelevant fine-grained representation for visible–infrared person re-identification

Visible–infrared person re-identification (VI-ReID) is an important and practical task for full-time intelligent surveillance systems. Compared to visible person re-identification, it is more challenging due to the large cross-modal discrepancy. Existing VI-ReID methods suffer from heterogeneous structures and the different spectra of visible and infrared images. In this work, we propose the Spectrum-Insensitive Data Augmentation (SIDA) strategy, which effectively alleviates the disturbance in the visible and infrared spectra and forces the network to learn spectrum-irrelevant features. The network also compares samples with both global and local features. We devise a Feature Relation Reasoning (FRR) module to learn discriminative fine-grained representations according to the graph reasoning principle. Compared to the most commonly used uniform partition, our FRR better adopts to the case of VI-ReID, in which human bodies are difficult to align. Furthermore, we design the dual center loss for learning the global feature in order to maintain the intra-modality relations, while learning the cross-modal similarities. Our method achieves better convergence in training. Extensive experiments demonstrate that our method achieves state-of-the-art performance on two visible–infrared cross-modal Re-ID datasets.