Reviewerly/exHarmony huggingface dataset:
MyDJ: Sensing Food Intakes with an Attachable on Your Eyeglass Frame

Various automated eating detection wearables have been proposed to monitor food intakes. While these systems overcome the forgetfulness of manual user journaling, they typically show low accuracy at outside-the-lab environments or have intrusive form-factors (e.g., headgear). Eyeglasses are emerging as a socially-acceptable eating detection wearable, but existing approaches require custom-built frames and consume large power. We propose MyDJ, an eating detection system that could be attached to any eyeglass frame. MyDJ achieves accurate and energy-efficient eating detection by capturing complementary chewing signals on a piezoelectric sensor and an accelerometer. We evaluated the accuracy and wearability of MyDJ with 30 subjects in uncontrolled environments, where six subjects attached MyDJ on their own eyeglasses for a week. Our study shows that MyDJ achieves 0.919 F1-score in eating episode coverage, with 4.03 × battery time over the state-of-the-art systems. In addition, participants reported wearing MyDJ was almost as comfortable (94.95%) as wearing regular eyeglasses.
”I don’t want to feel like I’m working in a 1960s factory”: The Practitioner Perspective on Creativity Support Tool Adoption

With the rapid development of creativity support tools, creative practitioners (e.g., designers, artists, architects) have to constantly explore and adopt new tools into their practice. While HCI research has focused on developing novel creativity support tools, little is known about creative practitioner's values when exploring and adopting these tools. We collect and analyze 23 videos, 13 interviews, and 105 survey responses of creative practitioners reflecting on their values to derive a value framework. We find that practitioners value the tools' functionality, integration into their current workflow, performance, user interface and experience, learning support, costs and emotional connection, in that order. They largely discover tools through personal recommendations. To help unify and encourage reflection from the wider community of CST stakeholders (e.g., systems creators, researchers, marketers, educators), we situate the framework within existing research on systems, creativity support tools and technology adoption.
Annotating Line Charts for Addressing Deception

Deceptive visualizations are visualizations that, whether intentionally or not, lead the reader to an understanding of the data which varies from the actual data. Examples of deceptive visualizations can be found in every digital platform, and, despite their widespread use in the wild, there have been limited efforts to alert laypersons to common deceptive visualization practices. In this paper, we present a tool for annotating line charts in the wild that reads line chart images and outputs text and visual annotations to assess the line charts for distortions and help guide the reader towards an honest understanding of the chart data. We demonstrate the usefulness of our tool through a series of case studies on real-world charts. Finally, we perform a crowdsourced experiment to evaluate the ability of the proposed tool to educate readers about potentially deceptive visualization practices.
ControllerPose: Inside-Out Body Capture with VR Controller Cameras

We present a new and practical method for capturing user body pose in virtual reality experiences: integrating cameras into handheld controllers, where batteries, computation and wireless communication already exist. By virtue of the hands operating in front of the user during many VR interactions, our controller-borne cameras can capture a superior view of the body for digitization. Our pipeline composites multiple camera views together, performs 3D body pose estimation, uses this data to control a rigged human model with inverse kinematics, and exposes the resulting user avatar to end user applications. We developed a series of demo applications illustrating the potential of our approach and more leg-centric interactions, such as balancing games and kicking soccer balls. We describe our proof-of-concept hardware and software, as well as results from our user study, which point to imminent feasibility.
Stylette: Styling the Web with Natural Language

End-users can potentially style and customize websites by editing them through in-browser developer tools. Unfortunately, end-users lack the knowledge needed to translate high-level styling goals into low-level code edits. We present Stylette, a browser extension that enables users to change the style of websites by expressing goals in natural language. By interpreting the user's goal with a large language model and extracting suggestions from our dataset of 1.7 million web components, Stylette generates a palette of CSS properties and values that the user can apply to reach their goal. A comparative study (N=40) showed that Stylette lowered the learning curve, helping participants perform styling changes 35% faster than those using developer tools. By presenting various alternatives for a single goal, the tool helped participants familiarize themselves with CSS through experimentation. Beyond CSS, our work can be expanded to help novices quickly grasp complex software or programming languages.
Impact and User Perception of Sandwich Attacks in the DeFi Ecosystem

Decentralized finance (DeFi) enables crypto-asset holders to conduct complex financial transactions, while maintaining control over their assets in the blockchain ecosystem. However, the transparency of blockchain networks and the open mechanism of DeFi applications also cause new security issues. In this paper, we focus on sandwich attacks, where attackers take advantage of the transaction confirmation delay and cause financial losses for victims. We evaluate the impact and investigate users’ perceptions of sandwich attacks through a mix-method study. We find that due to users’ lack of technical background and insufficient notifications from the markets, many users were not aware of the existence and the impact of sandwich attacks. They also had a limited understanding of how to resolve the security issue. Interestingly, users showed high tolerance for the impact of sandwich attacks on individuals and the ecosystem, despite potential financial losses. We discuss general implications for users, DeFi applications, and the community.
ChildGAN: Face aging and rejuvenation to find missing children

Child-face aging and rejuvenation have amassed considerable active research interest, owing to their immense impact on a broad range of social and security applications, e.g., digital entertainment, fashion and wellness, and searching for long-lost children using childhood photos. All current face aging approaches based on generative adversarial networks (GANs) focus on adult images or long-term aging. We present a new large-scale longitudinal Indian child (ICD) benchmark dataset to facilitate face age progression and regression, cross-age face recognition, age estimation, gender prediction, and kinship face recognition to alleviate these issues. Furthermore, we propose an automatic child-face age progression and regression model, namely, ChildGAN, that generates visually realistic images for enhanced face-identification accuracy while preserving the identity. Consequently, we have trained state-of-the-art (SOTA) face aging models on ICD for comprehensive qualitative and quantitative evaluations. We also present a multi-racial experiments dataset named Multi-Racial Child Dataset (MRCD) containing 64,965 child face images. The images are selected from publicly available datasets and web crawling. Finally, we investigate the generalization of ChildGAN by experimenting with White, Black, Asian, and Indian races. The experimental results suggest that the proposed ChildGAN and SOTA models can aid in reconnecting young children, who were lost at a young age as victims of child trafficking or abduction, with their families. The model and the MRCD web crawled images are available at https://github.com/praveenkumarchandaliya/ChildGAN_Tamp1/.
Vulnerability as an ethical stance in soma design processes

We articulate vulnerability as an ethical stance in soma design processes and discuss the conditions of its emergence. We argue that purposeful vulnerability – an act of taking risk, exposing oneself, and resigning part of one's autonomy – is a necessary although often neglected part of design, and specifically soma design, which builds on felt experience and stimulates designers to engage with the non-habitual by challenging norms, habitual movements, and social interactions. With the help of ethnography, video analysis, and micro-phenomenological interviews, we document an early design exploration around drones, describing how vulnerability is accomplished in collaboration between members of the design team and the design materials. We (1) define vulnerability as an active ethical stance; (2) make vulnerability visible as a necessary but often neglected part of an exploratory design process; and (3) discuss the conditions of its emergence, demonstrating the importance of deliberating ethics within the design process.
Dare to Dream, Dare to Livestream: How E-Commerce Livestreaming Empowers Chinese Rural Women

China has witnessed rapid growth in its e-commerce markets and livestreaming communities in recent years. The commercialization of livestreaming has led to the rise of e-commerce livestreamers, among which rural women constitute a substantial portion. To understand the motivations underlying these women's choices to engage in livestreaming activities and probe the extent to which they are empowered by this new form of entrepreneurship, we conducted an interview-based study with rural female livestreamers. We found that these women chose to be livestreamers for practical and self-presentation purposes and they gained a sense of self-empowerment through economic, social, intellectual, psychological, and spiritual dimensions. At the same time, however, they experienced and confronted social stigmas rooted in rural societies and the strategies they used to deal with these biases were vastly different. Our work contributes to the HCI community by providing a nuanced understanding of the motives and lived experiences of rural female livestreamers and offers design implications that could improve the everyday experiences of these livestreamers.
Blockchain for decentralised rural development and governance

Rural areas are steadily being marginalised in a global economy where ‘core/periphery’ models of development are dominant. To overcome this, rural areas have experimented with decentralised governance. However, this process is fraught with political, fiscal, and institutional difficulties. These often revolve around transparency and accountability issues and low participation rates. Blockchain technology could act as a social innovation to overcome issues in decentralised governance, and rural areas could even prove to be a fertile environment for future innovation. In this conceptual paper, the potential of blockchain technology is theoretically positioned in regional development discourses. After exploring how blockchain could be applied to rural governance and the barriers it needs to overcome to reach mass adoption, a new distributed model of governance is suggested.
A novel approach for text categorization by applying hybrid genetic bat algorithm through feature extraction and feature selection methods

Due to the rapid incline in the number of documents along with social media usage, text categorization has become an important concept. There are tasks required to be fulfilled during the text categorization, such as extracting useful data from different perspectives, reducing the high feature space dimension, and improving effectiveness. In order to accomplish these tasks, feature selection, and feature extraction gain importance. This paper investigates how to solve feature selection and extraction problems. Also, this study aims to decide which topics are the focus of a document. Moreover, the Twitter data-set is utilized as a document and an Uncapacitated P-Median Problem (UPMP) is applied to make clustering. In this study, UPMP is used on Twitter data collection for the first time to collect clustered tweets. Therefore, a novel hybrid genetic bat algorithm (HGBA) is proposed to solve the UPMP for our case. The proposed novel approach is applied to analyze the Twitter data-set of the Nepal earthquake. The first part of the analysis includes the data pre-processing stage. The Latent Dirichlet Allocation (LDA) method is applied to the pre-processed text. After that, a similarity (distance) matrix is generated by utilizing the Jensen Shannon Divergence (JSD) model. The study's main goal is to use Twitter to assess the needs of victims during and after a disaster. To evaluate the applicability of the proposed approach, experiments are conducted on the OR-Library data-set. The results demonstrate that the proposed approach successfully extracts topics and categorizes text.
Designing Visuo-Haptic Illusions with Proxies in Virtual Reality: Exploration of Grasp, Movement Trajectory and Object Mass

Visuo-haptic illusions are a method to expand proxy-based interactions in VR by introducing unnoticeable discrepancies between the virtual and real world. Yet how different design variables affect the illusions with proxies is still unclear. To unpack a subset of variables, we conducted two user studies with 48 participants to explore the impact of (1) different grasping types and movement trajectories, and (2) different grasping types and object masses on the discrepancy which may be introduced. Our Bayes analysis suggests that grasping types and object masses (≤ 500 g) did not noticeably affect the discrepancy, but for movement trajectory, results were inconclusive. Further, we identified a significant difference between (un)restricted movement trajectories. Our data shows considerable differences in participants' proprioceptive accuracy, which seem to correlate with their prior VR experience. Finally, we illustrate the impact of our key findings on the visuo-haptic illusion design process by showcasing a new design workflow.
Techno-economic analysis and energy forecasting study of domestic and commercial photovoltaic system installations in Estonia

The Baltic countries have good potential for solar photovoltaic (PV) energy generation, as on average 15 hours of sunlight is available in summer. Another potential option is to encourage the construction of nearly zero-energy buildings (NZEBs) according to the EU framework. This study focuses on solar irradiance and energy generation potential in different regions of Estonia as a case study. Techno-economic analysis of possible solutions to use differently rated domestic and commercial PV systems’ feasibility and payback periods are presented. The results illustrate that all PV systems studied in the research are self-sufficient while selling excess energy to the grid with a nominal payback period. Furthermore, for short-term energy management, we developed an efficient deep learning-based forecasting algorithm. Apart from the inherent non-linear nature of solar energy data, what makes forecasting particularly challenging is to efficiently cope with the issue of data regression and random noise. The RNN-LSTM algorithm is chosen for the prediction of solar energy. This is the first comprehensive report that can encourage potential Estonian users to invest in solar PV systems and gain economic benefits. The results presented in this study cover a broader perspective and are more useful keeping in mind the real market situation of the Baltic countries.
Circuit-level protocol and analysis for twist-based lattice surgery

Lattice surgery is a measurement-based technique for performing fault-tolerant quantum computation in two dimensions. When using the surface code, the most general lattice surgery operations require lattice irregularities called twist defects. However, implementing twist-based lattice surgery may require additional resources, such as extra device connectivity, and could lower the threshold and overall performance for the surface code. Here we provide an explicit twist-based lattice surgery protocol and its requisite connectivity layout. We also provide new stabilizer measurement circuits for measuring twist defects which are compatible with our chosen gate scheduling. We undertake the first circuit-level error correction simulations during twist-based lattice surgery using a biased depolarizing noise model. Our results indicate a slight decrease in the threshold for timelike logical failures compared to lattice surgery protocols with no twist defects in the bulk. However, comfortably below threshold (i.e., with CNOT infidelities below $5\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}3}$), the performance degradation is mild and in fact preferable over proposed alternative twist-free schemes. Lastly, we provide an efficient scheme for measuring $Y$ operators along boundaries of surface codes, which bypasses certain steps that were required in previous schemes.
Cohort-based federated learning services for industrial collaboration on the edge

Machine Learning (ML) is increasingly applied in industrial manufacturing, but often performance is limited due to insufficient training data. While ML models can benefit from collaboration, due to privacy concerns, individual manufacturers often cannot share data directly. Federated Learning (FL) enables collaborative training of ML models without revealing raw data. However, current FL approaches fail to take the characteristics and requirements of industrial clients into account. In this work, we propose an FL system consisting of a process description and a software architecture to provide FL as a Service (FLaaS) to industrial clients deployed to edge devices. Our approach deals with skewed data by organizing clients into cohorts with similar data distributions. We evaluated the system on two industrial datasets. We show how the FLaaS approach provides FL to client processes by considering their requests submitted to the Industrial Federated Learning (IFL) Services API. Experiments on both industrial datasets and different FL algorithms show that the proposed cohort building can increase the ML model performance notably. • Federated learning enables training a model without sharing clients' data. • Clients with skewed data can improve their model more by training with similar ones. • Federate learning services build cohorts of clients without retrieving their data. • Clients with skewed data improve their model using cohort-based federated services. • Edge device clients independently collaborate using federated learning services.
Polymorphic graph attention network for Chinese NER

Fusing lexicon information into Chinese characters, which has normally a number of meanings, has been proven to be effective for Chinese Named Entity Recognition (NER). However, the existing approaches to incorporating a matched Chinese word into its composition characters only take the word as a whole (no subdivision or part), which failed to capture fine-grained correlation in word-character space and failed to make full use of lexicon information. Moreover, existing approaches use the fixed (static) weights between words and characters. This limits the performance of NER. Considering the fact that the same word-character pairs have different interactions in different contexts, the weights of matched word-character pairs should be dynamic rather than fixed. In this paper, we propose a Polymorphic Graph Attention Network (PGAT), aiming at capturing dynamic correlation between characters and matched words from multiple dimensions, to enhance the character representation. By obtaining matched words of characters from lexicon, we carefully map the word-character in four positions, which are B (begin), M (middle), E (end) and S (single word). The proposed semantic fusion unit based on Graph Attention Network (GAT) can dynamically modulate attention of matched words and characters in the four dimensions B, M, E, and S. Thus, it can explicitly capture fine-grained correlation between characters and matched words across each dimension. Experiments on four Chinese NER datasets show that PGAT outperforms the baseline models. It demonstrates the significance of the attention capture and fusion capabilities of the proposed polymorphic graph. Furthermore, PGAT is used in character representation layer, which makes it easier to be combined with pre-trained models like BERT and other sequence encoding models like CNN and Transformer.
Improving End-to-End Contextual Speech Recognition with Fine-Grained Contextual Knowledge Selection

Nowadays, most methods for end-to-end contextual speech recognition bias the recognition process towards contextual knowledge. Since all-neural contextual biasing methods rely on phrase-level contextual modeling and attention-based relevance modeling, they may suffer from the confusion between similar context-specific phrases, which hurts predictions at the token level. In this work, we focus on mitigating confusion problems with fine-grained contextual knowledge selection (FineCoS). In FineCoS, we introduce fine-grained knowledge to reduce the uncertainty of token predictions. Specifically, we first apply phrase selection to narrow the range of phrase candidates, and then conduct token attention on the tokens in the selected phrase candidates. Moreover, we re-normalize the attention weights of most relevant phrases in inference to obtain more focused phrase-level contextual representations, and inject position information to help model better discriminate phrases or tokens. On LibriSpeech and an in-house 160,000-hour dataset, we explore the proposed methods based on an all-neural biasing method, collaborative decoding (ColDec). The proposed methods further bring at most 6.1% relative word error rate reduction on LibriSpeech and 16.4% relative character error rate reduction on the in-house dataset.
User-controllable Recommendation Against Filter Bubbles

Recommender systems usually face the issue of filter bubbles: overrecommending homogeneous items based on user features and historical interactions. Filter bubbles will grow along the feedback loop and inadvertently narrow user interests. Existing work usually mitigates filter bubbles by incorporating objectives apart from accuracy such as diversity and fairness. However, they typically sacrifice accuracy, hurting model fidelity and user experience. Worse still, users have to passively accept the recommendation strategy and influence the system in an inefficient manner with high latency, e.g., keeping providing feedback (e.g., like and dislike) until the system recognizes the user intention. This work proposes a new recommender prototype called UserControllable Recommender System (UCRS), which enables users to actively control the mitigation of filter bubbles. Functionally, 1) UCRS can alert users if they are deeply stuck in filter bubbles. 2) UCRS supports four kinds of control commands for users to mitigate the bubbles at different granularities. 3) UCRS can respond to the controls and adjust the recommendations on the fly. The key to adjusting lies in blocking the effect of out-of-date user representations on recommendations, which contains historical information inconsistent with the control commands. As such, we develop a causality-enhanced User-Controllable Inference (UCI) framework, which can quickly revise the recommendations based on user controls in the inference stage and utilize counterfactual inference to mitigate the effect of out-of-date user representations. Experiments on three datasets validate that the UCI framework can effectively recommend more desired items based on user controls, showing promising performance w.r.t. both accuracy and diversity.