Reviewerly/exHarmony huggingface dataset:
An Efficient Method to Accurately Cluster Large Number of High Dimensional Facial Images

Accurately clustering large, high dimensional datasets is a challenging problem in unsupervised learning. K-means is considered to be a fast, widely used and accurate centroid based data partitioning algorithm for spherical datasets. However, its non-determinism and heavy dependence on the selection of initial cluster centers along with vulnerability to noise make it a poor candidate for clustering large datasets with high dimensionality. To overcome these, we develop a novel, nature inspired, centroid based clustering algorithm, inspired from the principles of particle physics. Our method ensures that the convergence to local optima and non-deterministic outputs are avoided. We experiment the method on large datasets of human face images. Besides, our method addresses the problem of outliers and presence of not well-separated data in these datasets. We use a deep learning model for extracting facial features into a vector of 128 dimensions. We validate the quality and accuracy of our methods using different statistical parameters like <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f-measure, accuracy, error rate, average in group proportion and normalized cluster size rand index</i> . These evaluations show that our method exhibits better accuracy and quality in clustering large face image datasets, in comparison with other existing mechanisms. The strength of our algorithms is more visible as the size of the dataset grows.
User Preference and Performance using Tagging and Browsing for Image Labeling

Visual content must be labeled to facilitate navigation and retrieval, or provide ground truth data for supervised machine learning approaches. The efficiency of labeling techniques is crucial to produce numerous qualitative labels, but existing techniques remain sparsely evaluated. We systematically evaluate the efficiency of tagging and browsing tasks in relation to the number of images displayed, interaction modes, and the image visual complexity. Tagging consists in focusing on a single image to assign multiple labels (image-oriented strategy), and browsing in focusing on a single label to assign to multiple images (label-oriented strategy). In a first experiment, we focus on the nudges inducing participants to adopt one of the strategies (n=18). In a second experiment, we evaluate the efficiency of the strategies (n=24). Results suggest an image-oriented strategy (tagging task) leads to shorter annotation times, especially for complex images, and participants tend to adopt it regardless of the conditions they face.
Detecting Email Components with Constraints: Expressive and Extensible Models in Answer Set Programming

Detecting structures and components in business emails is vital for editor software to convert third-party emails so that designers can edit them without needing to know how HTML works. In a production environment, the challenge is to make the model easy to be understood and maintained by different stakeholders. We propose detecting email components with a collection of constraints written in Answer Set Programming (ASP). Hard constraints can detect well-defined components like email layouts, and soft constraints can incorporate ML to detect custom components like buttons and titles in emails. Using constraints, developers can apply their domain knowledge to the model and express them in a concrete, extensible, and deterministic form. We demonstrate the effectiveness with a prototype and evaluations from real datasets.
From Grasshoppers to Secondhand Cars: Understanding the Smartphone-Enabled Marketplace in Peri-urban Tanzania

The evolving socio-technical landscape in peri-urban Tanzania has paved the way for a dramatic increase in smartphone-supported micro and small enterprises. We conduct surveys and focus groups with 46 such entrepreneurs, shedding light on the internal mechanisms and external networks of their businesses. We uncover the new trust dynamics encountered in online interactions, the gendered aspects of this emerging business model, and the means through which people with low capital are reclaiming economic empowerment through entrepreneurship.
MFS Design in Appstore-enabled Smart Featurephones for Low-literate, Marginalized Communities

Mobile Financial Services (MFS) has gained significant popularity during the COVID-19 pandemic, especially among marginalized and low-income, low-literate communities around the world. Such communities have not been traditionally considered while designing MFS services via smartphone apps or USSD services in featurephones. Financial constraints limit such end-users towards basic featurephones, where recent appstore support has made it possible to deploy app-based MFS solutions beyond USSD. This new featurephone platform is a relatively underexplored area in terms of addressing design issues related to aforementioned end-users while developing MFS solutions. Our work addresses this gap by presenting qualitative findings on barriers to technology access focused on MFS solutions in marginal communities. We present a prototype non-USSD, app-based solution on an appstore-supported featurephone platform designed via a human-centered approach. This work has the potential to increase the financial inclusivity of marginalized communities in cashless MFS transactions via low-cost, appstore-enabled featurephones.
Imagine That! Imaginative Suggestibility Affects Presence in Virtual Reality

Personality characteristics can affect how much presence an individual experiences in virtual reality, and researchers have explored how it may be possible to prime users to increase their sense of presence. A personality characteristic that has yet to be explored in the VR literature is imaginative suggestibility, the ability of an individual to successfully experience an imaginary scenario as if it were real. In this paper, we explore how suggestibility and priming affect presence when consulting an ancient oracle in VR as part of an educational experience – a common VR application. We show for the first time how imaginative suggestibility is a major factor which affects presence and emotions experienced in VR, while priming cues have no effect on participants' (n=128) user experience, contrasting results from prior work. We consider the impacts of these findings for VR design and provide guidelines based on our results.
Towards Personalized User Interface Design For News Chatbots: A Pilot Study

Numerous mainstream news organizations have adopted chatbots as an emerging channel for delivering personalized news services. However, the news presentation style of chatbots may not satisfy users' personalization needs due to the "one-size-fits-all" design. In this context, we conducted two within-subject studies to investigate the impacts of news reader types, namely "dipper", "tracker", and "reviewer" on users' preference for different interfaces for news selection and reading. Our preliminary findings reveal that all these types of news readers perceived a higher level of ease of selection when using the carousel design. For "trackers" and "reviewers", the question-driven design resulted in a higher level of perceived ease of reading than the in-conversation design. Finally, I discuss my findings and provide insights into personalized user interface design for news chatbots.
Supporting Collaborative Discussions In Surgical Teleconsulting Through Augmented Reality Head Mounted Displays

Although Augmented Reality (AR) has been touted as the future of surgery, its contribution to distributed collaboration such as in surgical teleconsulting has not been articulated. We propose AR-Head Mounted Displays (AR-HMD) to tackle two previously-identified challenges: operating surgeons needing to view and interact with imaging systems that reside away from the operative field, and, their lack of gesturing tools to point and annotate on the shared images and physical environment. We report on a controlled lab experiment where 12 expert gynecology surgeons perform a tumor localisation task guided by a remote radiologist (confederate) via an AR-HMD. We find that bringing the shared images to the place of work reduces the need for clarifications and provides opportunistic access to information when required, and, that pointing and annotating provides opportunities to further support verbal instruction in deictic communication. Our results inform the design of intraoperative AR-HMD systems for surgical telecollaboration.
Analyzing the Use of Public and In-house Secure Development Guidelines in U.S. and Japanese Industries

Secure development guidelines contribute to improving software security from the development stage by making developers aware of the risks to be assumed, the necessary security countermeasures, and how to implement them. In this study, we investigated the actual utilization of guidelines and their usability in the industry through a survey of software development professionals in the U.S. and Japan (N=396 in the U.S. and N=474 in Japan). Our quantitative analysis revealed that "in-house" guidelines not examined in most existing studies are in fact widely utilized in the industry and also clarified how they are related to the use of public guidelines. In addition, we found that the practices for implementing guidelines recommended by existing studies are difficult for software development professionals with certain attributes, e.g., those who are working on small projects. The findings demonstrate the need for lightweight recommended practices taking into account organizational issues at industrial development sites that are easy for developers to implement.
BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractor

Random forests are machine learning methods characterised by high performance and robustness to overfitting. However, since multiple learners are combined, they are not as interpretable as a single decision tree. In this work we propose a novel method that is Building Explanations through a LocalLy AccuraTe Rule EXtractor (Bellatrex), which is able to explain the forest prediction for a given test instance with only a few diverse rules. Starting from the decision trees generated by a random forest, our method 1) pre-selects a subset of the rules used to make the prediction, 2) creates a vector representation of such rules, 3) projects them to a low-dimensional space, 4) clusters such representations to pick a rule from each cluster to explain the instance prediction. We test the effectiveness of Bellatrex on 89 real-world datasets and we demonstrate the validity of our method for binary classification, regression, multi-label classification and time-to-event tasks. To the best of our knowledge, it is the first time that an interpretability toolbox can handle all these tasks within the same framework. We also show that Bellatrex is able to approximate the performance of the corresponding ensemble model in all considered tasks, and it does so while selecting at most three rules from the whole forest. Finally, a comparison with similar methods in literature also shows that our proposed approach substantially outperforms other explainable toolboxes in terms of predictive performance.
Performative Vocal Synthesis for Foreign Language Intonation Practice

Typical foreign language (L2) pronunciation training focuses mainly on individual sounds. Intonation, the patterns of pitch change across words or phrases is often neglected, despite its key role in word-level intelligibility and in the expression of attitudes and affect. This paper examines hand-controlled real-time vocal synthesis, known as Performative Vocal Synthesis (PVS), as an interaction technique for practicing L2 intonation in computer aided pronunciation training (CAPT).
The Civil and Political Rights in the Press Discourse of Britain’s Most Prominent News Websites

This article deals with the civil and political rights in the press of the news websites, aims to identify the civil and
Modeling Temporal Target Selection: A Perspective from Its Spatial Correspondence

Temporal target selection requires users to wait and trigger the selection input within a bounded time window, with a selection cursor that is expected to be delayed. This task conceptualizes, for example, a variety of game scenarios such as determining the timing of shooting a projectile towards a moving object. In this work, we explore models that predict "when" users typically perform a selection (i.e., user selection distribution) and their selection error rates in such tasks. We hypothesize that users react to temporal factors including "distance", "width", and "delay" as how they treat the corresponding variables in spatial target selection. The derived models are evaluated in a controlled experiment and an MTurk-based online study. Our research contributes new knowledge on user behavior in temporal target selection tasks and its potential connection with its spatial correspondence. Our models and conclusions can benefit both users and designers of relevant interactive applications.
A Manifold Consistency Interpolation Method of Poisoning Attacks Against Semi-supervised Model

Semi-Supervised Learning (SSL) is an influential derivative that allows humans to uncover invisible knowledge, potentially substituting it for extensive labeling data. Despite the optimism generated by the availability of unlabeled data, its potential unreliability can result in numerous unknown security risks. Assailants may covertly contaminate data, leading to potentially catastrophic and unpredictable outcomes. We investigate poisoning attacks in triangular manifolds to understand how SSL models defend against attacks resulting from small perturbations. By inserting tiny amounts of artificially modified samples totaling 2% of the entire training set, we can deceive classification models into altering the prediction results of arbitrary categories. In addition, considering that the poisoned data in practical scenarios belong to a minority sample attack, which is typically only about 0.1%-2% of the total data, we employed outlier detection to examine all inserted instances and discovered that it could bypass discovery. Our poisoning strategy can work across multiple datasets, models, and application domains of images and network traffic. Experimental results prove that our proposed method is effective on at least seven semi-supervised models. The declining ratio of model detection accuracy of autoencoder with confidence (ConAE) is 52.55% at the lowest cost. Another persuasive result is that our model poisoning exceeds the state-of-the-art methods in the image domain. The extended conclusion corroborates that the more accurate classification models do not have a corresponding improvement in their ability to resist interference, which also provides a new standard for testing model robustness.
Emerging Transdisciplinary Perspectives to Confront Dark Patterns

Technology ethics is increasingly at the forefront of human-computer interaction scholarship, with increasing visibility not only to end users of technology, but also regulators, technology practitioners, and platforms. The notion of "dark patterns" has emerged as one common framing of technology manipulation, describing instances where psychological or perceptual tricks are used to decrease user agency and autonomy. In this panel, we have assembled a group of highly diverse early-career scholars that have built a transdisciplinary approach to scholarship on dark patterns, engaging with a range of socio-technical approaches and perspectives. Panelists will discuss their methodological approaches, key research questions to be considered in this emerging area of scholarship, and necessary connections between and among disciplinary perspectives to engage with the diverse constituencies that frame the creation, use, and impacts of dark patterns.
Audiovisual Learning Media Based on Local Wisdom Values of the Baduy Tribe Community to Grow Student Character

Character building can be done by integrating local wisdom values. The values of the local wisdom of the Baduy people can be integrated into learning to shape student character. The integration process can be done by developing learning media. The purpose of this study was to develop audio-visual learning media based on local wisdom values of the Baduy community on social studies theme 7 sub-theme 2 in class IV to grow student character. This type of research is R&amp;D research with the ADDIE model. The subjects of the research trial were 4th-grade elementary school students. Methods of data collection conducted through product validation and distribution of questionnaires. The data collection instrument was carried out, namely through the distribution of validation questionnaires and student character questionnaires. The steps in analysing the product developed are expert validation analysis consisting of material experts and media experts as well as student responses. Based on the results of the assessment, it can be concluded that audiovisual learning media based on the local wisdom values of the Baduy people have validity, feasibility, and effectiveness for use in developing student character. Good learning media can help students learn local wisdom in society.
“Information-Backward but Sex-Forward”: Navigating Masculinity towards Intimate Wellbeing and Heterosexual Relationships

There has been a growing interest in reproductive health and intimate wellbeing in Human-Computer Interaction, increasingly from an ecological perspective. Much of this work is centered around women's experiences across diverse settings, emphasizing men's limited engagement and need for greater participation on these topics. Our research responds to this gap by investigating cisgender men's experiences of cultivating sexual health literacies in an urban Indian context. We leverage media probes to stimulate focus group discussions, using popular media references on men's fertility to elicit shared reflection. Our findings uncover the role that humor and masculinity play in shaping men's perceptions of their sexual health and how this influences their sense of agency and participation in heterosexual intimate relationships. We further discuss how technologies might be designed to support men's participation in these relationships as supportive partners and allies.
The HCI User Studies Toolkit: Supporting Study Designing and Planning for Undergraduates and Novice Researchers in Human-Computer Interaction

Planning and organizing user studies can be a challenge for novices and student researchers in human-computer interaction (HCI). We present a web-based study planning toolkit for researchers to support their planning and organization with human subjects. The free and open toolkit contains multiple interactive research applications for (1) the selection of quantitative and qualitative research methods in HCI, (2) automated study design planning for experimental user studies, (3) an automated informed consent generation for studies with human subjects, (4) calendar booking for experimental user studies in shared laboratories, and (5) automated participation confirmation certificates. The toolkit can be helpful for Ph.D. students in supervising novices and undergraduates who need support with their planning and study design choices. The toolkit was actively used over two semesters in multiple HCI lectures and formatively evaluated. We contribute with the whole toolkit and encourage other researchers to contribute and further improve the content.
Minimizing change aversion through mixed methods research: a case study of redesigning Spotify’s Your Library

Launching a radical change to a habitual feature, used by millions every day, presents a challenge both to the end user and the organization making the change. Change aversion to habitually used features is a known factor in users experience design, but at times major changes are needed to encompass feature growth ultimately benefiting users.
QWriter: Technology-Enhanced Alphabet Acquisition based on Reinforcement Learning

In Kazakhstan, the ongoing Cyrillic-to-Latin alphabet shift raises challenges for early literacy development and acquisition in the Kazakh language. This paper proposes the QWriter system to help young children learn the Latin-based Kazakh alphabet and its handwriting. The system consists of a humanoid robot NAO, a tablet with a stylus, and a Reinforcement Learning (RL) agent that learns a child's mistakes and progress to maximize alphabet learning in the shortest period of time by adapting the order of practice words according to the child's mistakes. To evaluate the effectiveness of the QWriter system, we conducted a between-subject design experiment with 59 Kazakh children aged 6-8 years old and compared their learning performance with a human tutor and the CoWriting Kazakh robot system. The results did not support our assumption, we found that the proposed system received significantly higher likability scores than the baseline human tutor.
Moral Agents for Sustainable Transitions: Ethics, Politics, Design

Artificial moral agents – systems that engage in explicit moral reasoning on their own and with users – present a potential new paradigm for behavior and system change for social and environmental sustainability. Moral agents could replace current individualist, prescriptive, inflexible, and opaque interventions with systems that transparently state their values and then openly deliberate and contest these with users, or agents that represent human and non-human stakeholders such as future generations, species, or ecosystems. Indeed, moral agents could mark a genuine new form of more-than-human interactions and human-technology relation, where we relate to artificial systems as a counterpart. To jointly articulate key questions and possible futures around moral agents, this workshop convenes HCI, AI, behaviour change, and critical and speculative design researchers and practitioners.
Interaction of Thoughts: Towards Mediating Task Assignment in Human-AI Cooperation with a Capability-Aware Shared Mental Model

The existing work on task assignment of human-AI cooperation did not consider the differences between individual team members regarding their capabilities, leading to sub-optimal task completion results. In this work, we propose a capability-aware shared mental model (CASMM) with the components of task grouping and negotiation, which utilize tuples to break down tasks into sets of scenarios relating to difficulties and then dynamically merge the task grouping ideas raised by human and AI through negotiation. We implement a prototype system and a 3-phase user study for the proof of concept via an image labeling task. The result shows building CASMM boosts the accuracy and time efficiency significantly through forming the task assignment close to real capabilities within few iterations. It helps users better understand the capability of AI and themselves. Our method has the potential to generalize to other scenarios such as medical diagnoses and automatic driving in facilitating better human-AI cooperation.
Escapement: A Tool for Interactive Prototyping with Video via Sensor-Mediated Abstraction of Time

We present Escapement, a video prototyping tool that introduces a powerful new concept for prototyping screen-based interfaces by flexibly mapping sensor values to dynamic playback control of videos. This recasts the time dimension of video mock-ups as sensor-mediated interaction.