Reviewerly/exHarmony huggingface dataset:
Processing a large collection of historical tabular images

Processing automatically historical document images to allow the search of textual information requires the preparation of ground-truth data for training and evaluation. This process is an expensive and arduous task, especially when the historical document images contain specialized vocabulary and/or tabular information. In the latter case, relevant decisions have to be taken to annotate the tabular parts. This paper presents a complex collection of historical document images and the resulting database, which is called HisClima. In this database, half of the images are in tabular format and half as running text. Both types of images contain pre-printed and handwritten text. The textual information is plenty of abbreviations and specific vocabulary related to weather conditions and old ships. This database can be used to research technologies related to historical document image processing and analysis, both for tabular and running text recognition. Baseline results are presented for Document Layout Analysis, Text Recognition, and Probabilistic Indexing. Although these results are good, there is still room for improvement and some indications are provided in this direction.
Centralized Accessibility of VM for Distributed Trusted Cloud Computing

Cloud computing platforms constructed on Trusted Computing Technology are commonly referred to as trusted cloud computing. The core component of this technology is known as Trusted Platform Module (TPM). Its specifications are given by the consortium known as Trusted Computing Group (TCG). Though TPM is an immovable module and is fixed inside any computer, yet its credentials can be shared with other devices. This paper presents the accessibility model for Virtual Machine (VM) of a distributed trust protocol which is based on TPM, through which consumer's credentials are shared to other trusted devices. These credentials essentially describe the integrity of VM.
Designing an Algal Relay Computer: A Critical Orientation in Exploring More-than-Human Temporality

The re-evaluation of our industrial, anthropocentric relationships with time has led to the emergence of new conceptualizations of temporality beyond clock time in design-oriented HCI research. In order to provoke discussions on how design may (or may not) account for temporality of nonhumans, we conceptualized and prototyped Algal Relay Computer (ARC), a "slow" computer that incorporates the growth of algae in its calculation process. The paper presents theoretical groundings and technical details of ARC and offers preliminary insights into multiplicity of more-than-human temporality uncovered through the design process and exhibition of this artefact.
HybridMingler: Towards Mixed-Reality Support for Mingling at Hybrid Conferences

Mingling, the activity of ad-hoc, private, opportunistic conversations ahead of, during, or after breaks, is an important socializing activity for attendees at scheduled events, such as in-person conferences. The Covid-19 pandemic had a dramatic impact on the way conferences are organized, so that most of them now take place in a hybrid mode where people can either attend on-site or remotely. While on-site attendees can resume in-person mingling, hybrid modes make it challenging for remote attendees to mingle with on-site peers. In addressing this problem, we propose a collaborative mixed-reality (MR) concept, including a prototype, called HybridMingler. This is a distributed MR system supporting ambient awareness and allowing both on-site and remote conference attendees to virtually mingle. HybridMingler aims to provide both on-site and remote attendees with a spatial sense of co-location in the very same venue location, thus ultimately improving perceived presence.
Enabling Voice-Accompanying Hand-to-Face Gesture Recognition with Cross-Device Sensing

Gestures performed accompanying the voice are essential for voice interaction to convey complementary semantics for interaction purposes such as wake-up state and input modality. In this paper, we investigated voice-accompanying hand-to-face (VAHF) gestures for voice interaction. We targeted on hand-to-face gestures because such gestures relate closely to speech and yield significant acoustic features (e.g., impeding voice propagation). We conducted a user study to explore the design space of VAHF gestures, where we first gathered candidate gestures and then applied a structural analysis to them in different dimensions (e.g., contact position and type), outputting a total of 8 VAHF gestures with good usability and least confusion. To facilitate VAHF gesture recognition, we proposed a novel cross-device sensing method that leverages heterogeneous channels (vocal, ultrasound, and IMU) of data from commodity devices (earbuds, watches, and rings). Our recognition model achieved an accuracy of 97.3% for recognizing 3 gestures and 91.5% for recognizing 8 gestures (excluding the "empty" gesture), proving the high applicability. Quantitative analysis also shed light on the recognition capability of each sensor channel and their different combinations. In the end, we illustrated the feasible use cases and their design principles to demonstrate the applicability of our system in various scenarios.
Investigating the Synonyms of Conversational Agents to Aid Cross-Disciplinary CA Research

With the advent of artificial intelligence, the CHI community has regained a large interest in Conversational Agents (CAs). Designing CAs involves cross-disciplinary efforts, such as that of computer science (e.g., language models), psychology (e.g., cognition and emotions), linguistics (e.g., conversation design), or communication (e.g., trust, communication theory). However, CAs are named differently depending on the discipline and purpose (e.g., chatbot, embodied avatar, virtual butler). This divergence of vocabulary on CA brings challenges to researchers and designers in researching the full landscape of CA literature and sharing cross-disciplinary knowledge. We performed bibliometric and qualitative analyses to systematically assess divergent terms used for CA nomenclatures. We present 54 CA-terms and how these terms are used differently depending on the discipline and design characteristics (e.g., voice vs. non-verbal vs. text-based). Our work contributes to helping CA researchers effectively review the CA literature and build on each other's work for novel cross-disciplinary CA research.
Comparative study between delivery modalities in higher education during emergency remote teaching due to COVID-19

Despite the difficulties faced during Emergency Remote Teaching (ERT) because of the COVID-19 pandemic, it is also true that such a situation has left a series of learnings that educational institutions around the world should capitalize on. Under this scenario, interest arose in studying three delivery modalities (face-to-face, hybrid, and remote) at the university level, aiming to compare the students’ learning level and their perceptions of each delivery modality. The present study was developed in a private university in Mexico, following a quantitative methodological approach involving 360 students and 14 professors from various schools and geographical locations. Data were collected through pre-and post-tests and a perception questionnaire for students. Findings suggest that the students’ learning level in every modality varies by school and that students positively perceive the three delivery modalities, albeit identifying factors that foster and hinder their learning process in each one. The results of this study contribute to strengthening the research field on teaching during ERT, allowing educational institutions to make better decisions regarding the quality of the educational offer.
Personalized Quest and Dialogue Generation in Role-Playing Games: A Knowledge Graph- and Language Model-based Approach

Procedural content generation (PCG) in video games offers unprecedented opportunities for customization and user engagement. Working within the specialized context of role-playing games (RPGs), we introduce a novel framework for quest and dialogue generation that places the player at the core of the generative process. Drawing on a hand-crafted knowledge base, our method grounds generated content with in-game context while simultaneously employing a large-scale language model to create fluent, unique, accompanying dialogue. Through human evaluation, we confirm that quests generated using this method can approach the performance of hand-crafted quests in terms of fluency, coherence, novelty, and creativity; demonstrate the enhancement to the player experience provided by greater dynamism; and provide a novel, automated metric for the relevance between quest and dialogue. We view our contribution as a critical step toward dynamic, co-creative narrative frameworks in which humans and AI systems jointly collaborate to create unique and user-specific playable experiences.
Modeling reciprocal adaptation in HCI: a Multi-Agent Reinforcement Learning Approach

Adaptation between users and computers is difficult because of the reciprocal long-term adaptation between the user and an adaptive tool. In this work in progress, we present a novel method for designing adaptive systems, by simulating reciprocal adaptation between a user and an intelligent tool via Multi-Agent Reinforcement Learning (MARL). We report on a simple target-selection simulation in which a simulated, rational user tries to reach a target and is aided by an intelligent tool that manipulates the user's environment. The user and the tool's behavior are jointly determined by an adaptive algorithm that maximizes their utility. Our approach shows that both agents are capable of learning realistic policies, with the user and tool combination being 30% more effective than the user alone.
Developing an Urdu Lemmatizer Using a Dictionary-Based Lookup Approach

Lemmatization aims at returning the root form of a word. The lemmatizer is envisioned as a vital instrument that can assist in many Natural Language Processing (NLP) tasks. These tasks include Information Retrieval, Word Sense Disambiguation, Machine Translation, Text Reuse, and Plagiarism Detection. Previous studies in the literature have focused on developing lemmatizers using rule-based approaches for English and other highly-resourced languages. However, there have been no thorough efforts for the development of a lemmatizer for most South Asian languages, specifically Urdu. Urdu is a morphologically rich language with many inflectional and derivational forms. This makes the development of an efficient Urdu lemmatizer a challenging task. A standardized lemmatizer would contribute towards establishing much-needed methodological resources for this low-resourced language, which are required to boost the performance of many Urdu NLP applications. This paper presents a lemmatization system for the Urdu language, based on a novel dictionary lookup approach. The contributions made through this research are the following: (1) the development of a large benchmark corpus for the Urdu language, (2) the exploration of the relationship between parts of speech tags and the lemmatizer, and (3) the development of standard approaches for an Urdu lemmatizer. Furthermore, we experimented with the impact of Part of Speech (PoS) on our proposed dictionary lookup approach. The empirical results showed that we achieved the best accuracy score of 76.44% through the proposed dictionary lookup approach.
Inhabiting Interconnected Spaces: How Users Shape and Appropriate Their Smart Home Ecosystems

Over the last decade, smart home technology (SHT) has become an integral part of modern households. As a result, smart home ecosystems blend with daily social life, appropriated and integrated into personalised domestic environments. The lived experience of inhabiting smart home ecosystems, however, is not yet understood, resulting in a mismatch between ecosystem design and inhabitants' needs. Drawing on contextual inquiry methods, we conducted an explorative interview study (N=20) with SHT users in their homes. Our thematic analysis reveals how users shape their smart home ecosystems (SHEs), considering social relationships at home, perceived ownership of SHTs, and expected key benefits. Notably, our analysis shows that household members consciously choose 'their' level of SHT interconnectedness, reflecting social, spatial and functional affinities between systems. Following our findings, we formulate five implications for designing future SHTs. Our work contributes insights on the dynamics and appropriation of smart home ecosystems by their inhabitants.
Automated Report Generation: A GRU Based Method for Chest X-Rays

Radiology reports are the primary medium through which physicians communicate with patients and share diagnoses from medical scans. Examples include radiology reports for chest X-Rays and CT scans. Chest X-Ray images are frequently employed in clinical screening and diagnosis. However, writing medical reports for the X-Ray is tedious, error-prone, and time-consuming, even for experienced radiologists. The modern world of clinical practice demands that a radiologist with specialized training manually evaluate chest X-Ray and report the findings. Therefore, this paper explores the ability of artificial intelligence (AI) to automate diagnosing diseases through chest X-Rays and accurately generate radiology reports to alleviate the burdens of medical doctors. Automating this manual process could streamline a clinical workflow, and healthcare quality could be improved. The conventional AI-based abstract methods provide fluent but clinically incorrect radiology reports. The proposed Gated Recurrent Unit (GRU) based model provides both stan-dard language generation and clinical coherence. The model is evaluated on the Indiana University dataset with commonly-used metrics BLEU and ROUGE-L. Empirical evaluations illustrate that the proposed approach can make more precise diagnoses and generate more fluent and precise reports than existing baselines.
Programming Quantum Neural Networks on NISQ Systems: An Overview of Technologies and Methodologies

Noisy Intermediate-Scale Quantum (NISQ) systems and associated programming interfaces make it possible to explore and investigate the design and development of quantum computing techniques for Machine Learning (ML) applications. Among the most recent quantum ML approaches, Quantum Neural Networks (QNN) emerged as an important tool for data analysis. With the QNN advent, higher-level programming interfaces for QNN have been developed. In this paper, we survey the current state-of-the-art high-level programming approaches for QNN development. We discuss target architectures, critical QNN algorithmic components, such as the hybrid workflow of Quantum Annealers and Parametrized Quantum Circuits, QNN architectures, optimizers, gradient calculations, and applications. Finally, we overview the existing programming QNN frameworks, their software architecture, and associated quantum simulators.
Demonstrating CleAR Sight: Transparent Interaction Panels for Augmented Reality

In this work, we demonstrate our concepts for transparent interaction panels in augmented-reality environments. Mobile devices can support interaction with head-mounted displays by providing additional input channels, such as touch & pen input and spatial device input, and also an additional, personal display. However, occlusion of the physical context, other people, or the virtual content can be problematic. To address this, we previously introduced CleAR Sight, a concept and research platform for transparent interaction panels to support interaction in HMD-based mixed reality. Here, we will demonstrate the different interaction and visualization techniques supported in CleAR Sight that facilitate basic manipulation, data exploration, and sketching & annotation for various use cases such as 3D volume visualization, collaborative data analysis, and smart home control.
A Survey on Machine Learning Approaches in Cryptocurrency: Challenges and Opportunities

Blockchain research is now heavily centred on cryptocurrency, which has drawn the attention of financial academics. The availability of numerous types of data and a wealth of resources in bitcoin research facilitates the application of machine learning algorithms. However, a through analysis and a complete study of machine learning-based cryptocurrencies need further research. Cryptocurrency price prediction is the most pertinent research topic and the algorithms being used in cryptocurrency research are not unique. Various researcher are using combination of multiple machine learning algorithms. Therefore, in this study, the research related to cryptocurrency price prediction using machine learning is summarized along with prominent research challenges related to application of machine learning algorithms in cryptocurrency.
Online Proctoring: Privacy Invasion or Study Alleviation?

Detecting fraud during online exams using proctoring software comes with substantial privacy challenges. Previous work argues students experience heightened anxiety and have privacy concerns. However, little is known about which specific aspects of online proctoring cause these concerns. This study contributes such insights by using the Contextual Integrity (CI) framework to discover how students (N = 456) rate the acceptability of 1064 proctoring information flows with varying information types, recipients, and transmission principles. We find that the acceptability varies considerably depending on the context. Besides exposing obvious privacy violations, we find that, under certain conditions, students consider it acceptable to share data with teachers - despite their lack of involvement in proctoring. Also, the acceptability of sharing highly sensitive information - which should under no circumstances be shared - sometimes increases. We discuss the implications of these and other findings and provide concrete recommendations for educational institutions using online proctoring.
Making Usability Test Data Actionable! A Quantitative Test-Driven Prototyping Approach

In recent years, the availability and use of prototyping and usability testing tools has increased massively in practice. In parallel, research introduced multiple novel approaches and tools to assist novice and expert designers in prototyping. However, existing research has not looked into how to assist designers in conducting usability tests and making the collected quantitative data directly available in prototyping tools. In this paper, we distill three design goals from literature and propose a novel quantitative test-driven prototyping approach. The approach in its current form consists of a process and two user interface artifacts supporting selected actions of our process. We conducted a formative study to gather feedback from potential users on our approach. We use the collected feedback to refine our approach, identify trade-offs, and propose future research directions. We contribute by providing a novel quantitative test-driving prototyping approach that tightly connects prototyping with asynchronous usability testing.
FoodFighters - Improving Memory Retention of Food Items through a Mobile Serious Game

Food waste in private households of industrialised countries is becoming an increasing concern, as its impact on greenhouse gas emissions is comparable to that of the aviation industry. One particular unnecessary contribution to food waste is a person forgetting which food items they have in their household, which often leads to food items expiring before they are used. This paper introduces FoodFighters a mobile serious game aimed at encouraging young adult players to interact with their food items thereby making them more memorable. FoodFighters works by taking pictures of food items, that then can be customized into so called Food Fighters, which are then used to battle other players/friends fighters. In two preliminary lab studies, we validated that Food Fighters actually improved retention of food items and found that players found the game enjoyable based on a GEQ-questionnaire.
Understanding Personal Data Tracking and Sensemaking Practices for Self-Directed Learning in Non-classroom and Non-computer-based Contexts

Self-directed learning is becoming a significant skill for learners. However, learners may suffer from difficulties such as distractions, a lack of motivation, and so on. While self-tracking technologies have the potential to address these challenges, existing tools and systems mainly focused on tracking computer-based learning data in classroom contexts. Little is known about how students track and make sense of their learning data from non-classroom learning activities and which types of learning data are personally meaningful for learners. In this paper, we conducted a qualitative study with 24 users of Timing, a mobile learning tracking application in China. Our findings indicated that users tracked a variety of qualitative learning data (e.g., videos, photos of learning materials, and emotions) and made sense of this data using different strategies such as observing behavioral and contextual details in videos. We then provided implications for designing non-classroom and non-computer-based personal learning tracking tools.
Creepy Assistant: Development and Validation of a Scale to Measure the Perceived Creepiness of Voice Assistants

Voice assistants have afforded users rich interaction opportunities to access information and issue commands in a variety of contexts. However, some users feel uneasy or creeped out by voice assistants, leading to a decreased desire to use them. As there has yet to be a comprehensive understanding of the factors that cause users to perceive voice assistants as being creepy, this research developed an empirical scale to measure the creepiness inherent in various voice assistants. Utilizing prior scale creation methodologies, a 7-item Perceived Creepiness of Voice Assistants Scale (PCAS) was created and validated. The scale measures how creepy a new voice assistant would be for users of voice assistants. The scale was developed to ensure that researchers and designers can evaluate the next generation of voice assistants before such voice assistants are released to the wider public.
Designing Virtual Environments for Social Engagement in Older Adults: A Qualitative Multi-site Study

Virtual reality (VR) is increasingly used as a platform for social interaction, including as a means for elders to maintain engagement. However, not many empirical studies have been conducted to examine features of social VR that are most relevant to elders' experiences. The current study qualitatively analyzed the behavior of older adults in a collaborative VR environment and evaluated aspects of design that affected their engagement outcomes. We paired 36 participants over the age of 60 from three diverse geographic locations to interact in collaborative VR modules. Video-based observation methods and thematic analyses were used to study their interactions. The results indicated a strong link between conversations about personal lives in VR and social engagement, highlighting the need for social VR to encourage users to create their own stories and share their life experiences. The study provides new insights into design guidelines that could improve social VR for older adults.