Reviewerly/exHarmony huggingface dataset:
Osmo Kaleidoscope: Co-designing and Problem-solving with Children in S.T.E.A.M. App Development

Open-ended childhood investigations of old-fashioned handheld kaleidoscopes have offered generations of children the unique sense of wonder and beauty that comes with explorations of kaleidoscopic symmetry. Similarly, contemporary digital App Stores include numerous screen-based kaleidoscope apps that empower children to play with symmetrical patterns on their family smartphones or classroom tablets. However, when making Osmo Kaleidoscope, we sought to combine opportunities from both of these types of kaleidoscopes, as we aimed to create an innovative Kaleidoscope that leverages a tangible user interface so children could employ enriching tangible/sensory investigations within their digital experiences. The two children shown below demonstrate both of these elements in their investigation of the design tool called Osmo Kaleidoscope. One child holds a nubby pinecone after carefully arranging fall leaves into view, while the other player moves a digital slider bar to select the specific level of complexity she desires for this particular design. A leaf-shaped stencil was selected from a menu of thematic shapes relevant to elementary students. The collaboration between these children is a product of a design process that included inventive students, teachers, and professionals. Here we aim to offer an IDC’23 demo and a description of our Osmo Kaleidoscope project in support of childhood co-design opportunities in project-based learning at school.
Self-Talk with Superhero Zip: Supporting Children’s Socioemotional Learning with Conversational Agents

Socioemotional competencies are fundamental for children’s growth and success, and prior work shows that in some instances, technology can support children in acquiring these skills. Here, we examine whether children can learn to use a socioemotional strategy known as “self-talk” from a conversational agent (CA). To investigate this question, we designed and built “Self-Talk with Superhero Zip,” an interactive CA experience, and deployed it for one week in ten family homes to pairs of siblings between the ages of five and ten (N = 20). We found that children could recall and accurately describe the lessons taught by the intervention, and we saw indications of children applying self-talk in daily life. Targeting sibling pairs rather than individual users proved to be a design challenge in its own right, and families suggested design ideas for supporting this context, such as UI to manage conversational flow and reduce competition, and visuals and embodied activities to encourage focus. The dual-user context coupled with the audio modality prompted “preinput huddles” in which children conversed in whispers before responding to the system. We contribute evidence that CAs can support children in learning to use self-talk as well as design guidance for creating multi-user conversational interfaces.
Co-Designing a Virtual Reality Intervention for Supporting Cognitive Reappraisal Skills Development with Youth

Cognitive reappraisal is a critical emotion regulation skill required for youth mental health. While it has been well theorized psychologically and many therapeutic approaches exist, youth still struggle to develop these skills. We explore the design space of using virtual reality to create technologically-mediated cognitive reappraisal supports for youth. We developed four VR prototypes that we used as prompts for feedback in a ten-session co-design workshop with nine youth (aged 15-17). Our work contributes youth-derived directions for further research in the design space of VR and mental health; and highlights opportunities to use VR for cognitive reappraisal skills training by generating meaningful emotionally laden situations, supporting body-based experiential learning, and providing technology-mediated social connection.
The IDC Research and Design Challenge throughout the years: achievements, reflections and next steps

In this paper we describe and reflect upon the setup and outcomes of the Research and Design Competition at the Interaction Design and Children (IDC) conference over the years. We frame it against similar initiatives and discuss its achievements so far while suggesting the steps that could increase its visibility and popularity both inside and outside of our academic community. In fact, we believe such an initiative is a great opportunity to state and declare the community’s core principles and values. This is done by having children as protagonists, and listening to and acknowledging their contributions. Reaching out to, inspiring, and rewarding the other adults in the loop, such as parents and teachers, is essential to ensure children’s participation and empowerment. Equally important is actively promoting diversity and inclusion to maintain a healthy and balanced community’s growth. Here, we review progress to date, outline plans for moving forward and establishing this initiative as a fun place for children to interact with new technology, as researchers and practitioners better understand their needs and expectations. Therefore, our primary contribution is a set of guidelines and best practices for it to develop, which is based on a thorough examination of the challenge so far.
Digital Drum Circles

This demo presents a “digital drum circle”, a collection of bucket drums augmented with sensors that form a collective digital musical instrument. Through the drum circle prototype, we are exploring an alternative approach to computer science (CS) education that draws from cultural practices of collaborative music-making to create more engaging and meaningful learning experiences for young people. Within this context, we are interested in understanding how CS education might be remade through more equitable and culturally sustaining pedagogy approaches grounded in music and the performing arts.
DYSIGN: Towards Computational Screening of Dyslexia and Dysgraphia Based on Handwriting Quality

Specific Learning Difficulties such as Dyslexia and Dysgraphia are characterized by struggles in reading and writing. Their diagnosis and intervention are critical as if left unattended, they can cause hindrance in academic activity, self-esteem, and long-term quality of life. Owing to the complex traditional processes for diagnosis, social stigma, and the general lack of availability of remedial therapists and clinical psychologists in Pakistan, this study explores the potential of handwriting quality features to be used in computationally screening for SLDs to make screening more accessible. This project consists of exploratory data analysis of handwriting scans of 25 children thus far, in the age group of 5 to 15, generating various handwriting quality features and using classification models to assess their potential. Our preliminary results are promising, with approximately 80% accuracy, thus showing potential for increased accuracy when paired with larger data samples and further feature generation.
Imagining Alternative Visions of Computing: Photo-Visuals of Material, Social, and Emotional Contexts from Family Creative Learning

This pictorial presents visuals of families engaging with creative technologies as “knowledge-building artifacts” to provoke reflection on the social, material, and emotional context of designed interactions (“things that make you think”) as well as provocations to re-value these contexts and promote alternative visions in what and how engagement with computing can look like (“things that matter”). The selected images are from a large and ongoing collection of documentation from a family technology program. The images were captured using the Reggio Emilia documentation approach to documentation, which aims to “make learning visible.”
From Child-Centered to Family-Centered Interaction Design

The goal of this workshop is to have interdisciplinary discussions on family-centered interaction design of technology as an extension to child-centered design. The workshop will discuss the potential benefits of a family-centered approach to design, as well as the challenges and open questions that designers may face when adopting this approach. Through discussions and interactive activities, participants will have the opportunity to discuss and share ideas on how to effectively incorporate a family-centered perspective into their own design processes. A family-centered approach to design has the potential to create more meaningful and contextual experiences for children and their families.
Supporting Children’s Metacognition with a Facial Emotion Recognition based Intelligent Tutor System

The present study aims to investigate the relationship between emotions experienced during learning and metacognition in typically developing (TD) children and those with autism spectrum disorder (ASD). This will assist us in using machine learning (ML) to develop a facial emotion recognition (FER) based intelligent tutor system (ITS) to support children’s metacognitive monitoring process in order to enhance their learning outcomes. In this paper, we first report the results of our preliminary research, which utilized an ML-based FER algorithm to detect four spontaneous epistemic emotions (i.e., neutral, confused, frustrated, and boredom) and six spontaneous basic emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise). Subsequently, we adapted an application (‘BrainHood’) to create the ‘Meta-BrainHood’, that embedded our proposed ML-based FER algorithm to examine the relationship between facial emotion expressions and metacognitive monitoring performance in TD children and those with ASD. Finally, we outline the future steps in our research, which adopts the outcomes of the first two steps to construct an ITS to improve children’s metacognitive monitoring performance and learning outcomes.
MotiCards: Developing Designer Cards for Children’s Intrinsic Motivation of Daily Tasks

Intrinsic motivation plays an important role in learning or engagement, and the factors that identifies intrinsic motivation have been insufficiently taken into account at the early stages of technology design for children. In this work-in-progress paper, we describe how we develop cards for children’s intrinsic motivation in carrying out everyday tasks in home environment. The content of the cards were derived from 50 parents’ views extracted through an online survey as well as a desk research literature review. Then we evaluated the cards in a focus group with four experts from different backgrounds in developmental research. We draw on our findings to set forth design considerations and possible refinements that make age specific knowledge about 9-13-year-old children’s intrinsic motivations to inform technology design in child-computer interaction field.
Designing Textual Information in AR Headsets to Aid in Adults’ and Children's Task Performance

Augmented reality (AR) headsets are being utilized in different task-based domains (e.g., healthcare, education) for both adults and children. However, prior work has mainly examined the applicability of AR headsets instead of how to design the visual information being displayed. It is essential to study how visual information should be presented in AR headsets to maximize task performance for both adults and children. Therefore, we conducted two studies (adults vs. children) analyzing distinct design combinations of critical and secondary textual information during a procedural assembly task. We found that while the design of information did not affect adults' task performance, the location of information had a direct effect on children's task performance. Our work contributes new understanding on how to design textual information in AR headsets to aid in adults’ and children's task performance. In addition, we identify specific differences on how to design textual information between adults and children.
Exploring Embodied Approaches for Large Age Gap Sibling Communication through Technology Probes

Large age differences and geographic distance between siblings can often hinder the development of close relationships, as they may have different social circles and schedules. Younger siblings may also have limited access to digital technology, which can further complicate communication and interaction between them. In this paper, we developed two technology probes, Haptic Bubble and Emoji Board. Both of these systems utilize embodied interaction, which has been shown to be an effective way to engage children in remote communication. Our work focused on three main goals through the development and preliminary study: the system design goal involved testing the feasibility of the embodied design approach, the empirical goal was to collect information on how siblings use embodied communication technology and the design goal was to inspire new kinds of technology to support large gap siblings’ needs.
Shape of Music: AR-based Tangible Programming Tool for Music Visualization

Integrating music into Computer Science (CS) education can stimulate children's creativity, change the stereotypical perspective of CS, and encourage women, ethnic or cultural minorities involved in the Computer Science area. In this paper, we use Augmented Reality (AR) technology to design a tangible programming system - AR-MPro for children, acting as a bridge between programming and music. It allows children to create customized AR effects to visualize music with low-cost materials by constructing tangible program sequences. AR-MPro is expected to broaden participation in computing, and be more intuitive, intriguing and instructional to enrich children's creating and programming experiences.
Caring for Children’s Health and Wellbeing Through Understanding and Designing for Health Data Literacy

Data-driven health technologies, such as wearables and medical devices, are increasingly part of children’s daily lives. These technologies help children collect, reflect on, and use their health data. The hope is that they develop healthy habits and skills and prepare themselves to manage their health and wellbeing. However, past research has shown that children, especially those of young age, have limited interactions with these technologies and often struggle to understand their health data. My dissertation research contributes to a comprehensive understanding of how children and their caregivers (i.e., parents and healthcare providers) perceive data-driven health technologies, and use the resulting data to improve children’s health. Drawing on findings from a systematic literature review, content analysis, qualitative interviews, and participatory design workshops, my dissertation research fosters discussion of children’s health data literacy and identifies design opportunities to build children’s health capacity through supporting their health data literacy.
EdibleToy: Empowering Children to Create Their Own Meals with a DIY Wafer Paper Kit

Existing methods in human-computer interaction to enhance children’s eating habits predominantly rely on digital interactive technologies, which pose the risk of increasing sensory stimulation and diverting children’s attention away from the food itself. Drawing inspiration from shape-changing food research, we propose an approach that combines deformable wafer paper for food preparation. We summarize the principles of wafer paper controllable deformation and develop a toolkit to facilitate its use. We support children in creating personalized, transformable food items using this method, aiming to provide a playful, convenient, and safe food-making experience tailored for children, thereby enhancing children’s mealtime engagement and habits.
A Variational Ansatz for Taylorized Imaginary Time Evolution

Being able to predict molecular properties and interactions is of utmost interest for academia as well as industry. But the vast complexity of strongly correlated molecular systems limits the performance of classical algorithms. In contrast, quantum computation has the potential to be a game changer in the field of molecular simulations. Despite the hope in quantum computation, the capabilities of current quantum computers are still insufficient for handling molecular systems of interest. In this paper, we propose a variational ansatz for today's noisy quantum computers to calculate the ground state with the help of imaginary time evolution. Although the imaginary time evolution operator is not unitary, it can be implemented on a quantum computer by a linear decomposition and subsequent Taylor series expansion. This has the advantage that only a set of shallow circuits needs to be computed on a quantum computer. The parallel nature of this algorithm can be exploited to speed-up simulations even further, if a privileged access to quantum computers is granted.
Detection and parameter estimation of intra-pulse modulated radar signals in complex interference environments+

Abstract The main aim of this review is to describe in detail an advanced technique to detect and estimate the prior unknown parameters of intra-pulse modulated signals and the verification with typical radar signals. The method is approved for detecting parameters successfully, including chirp rate, carrier frequency, and pulse width. The review presents already-done research on detecting and estimating single and multi-component linear frequency modulation (LFM) and binary phase shift keying (BPSK) signals in a strong noise environment and studies the technique on one more case, a mixture of LFM and BPSK signals. Firstly, the accuracy of the revised technique is shown in detecting and estimating parameters of a single and multi-component LFM signal in white noise and a mixture of continuous wave signals and noise, or a single BPSK signal in strong white noise. All of them have been done in the existing studies. This method is continuously tested in the second part by detecting a mixture of LFM and BPSK signals and estimating their parameters in intense noise. The tested experimental results demonstrate that the technique can detect single and multi-component real-time LFM signals, single BPSK as well as verification with a mixture of real-time LFM and BPSK signals with $$SNR \ge - 12{\text{ dB}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>S</mml:mi> <mml:mi>N</mml:mi> <mml:mi>R</mml:mi> <mml:mo>≥</mml:mo> <mml:mo>-</mml:mo> <mml:mn>12</mml:mn> <mml:mrow> <mml:mspace /> <mml:mtext>dB</mml:mtext> </mml:mrow> </mml:mrow> </mml:math> is performed. As a result, the technique outperforms the existing detection methods based on machine learning and artificial intelligence.
Thread-Level Attack-Surface Reduction

Existing debloating techniques designed to prevent buffer-overflow exploits through return-oriented programming do not differentiate roles within a process or binary, allowing all threads access to the full program functionality. For example, a worker thread that handles client connections (highest attack exposure) still has access to all the code that the management thread needs (highest potential fallout).
Knowledge Distillation Across Vision and Language

Recent years have witnessed the fast development of Vision and Language (VL) learning with deep neural architectures, benefiting from large amounts of unlabeled or weakly labeled, and heterogeneous forms of data. A notable challenge arises when deploying these cross-modal models on an edge device that usually has the limited computational power to be undertaken. It is impractical for real-world applications to exploit the power of prevailing models under a constrained training/inference budget, especially when dealing with abundant multi-modal data that requires significant resources. As an important technique for deep model compressing, Knowledge DistillationKnowledge distillation (KD) has been widely applied to various tasks that aim to build a small yet powerful student model from a large teacher model. In the furtherance of gaining a more real-world practical compact Vision-language model, it is essential to exploit knowledge distillation techniques to improve VL learning on compact models. KD + VL, is of great practical value but is less explored in the previous literature. This inspires the studies of KDKnowledge distillation on different modalities (Vision, Language); learning tasks (Self-supervised learning, Contrastive Learning); and also will serve as a vital role for many sub-disciplines of cross-modal tasks, such as image/video captioning, visual-question answering, image/video retrieval, etc. This chapter summarizes and discusses in-depth this emerging area of research. We first retrospect the existing KD algorithmsDistillation algorithms comprehensively, and mainly focuses on their applications for Vision-Language tasks as well as the recent coming-up works leverage the self-distillationSelf-distillation technique for training joint VL representation. The chapter also features recent works at the forefront of research on this topic leveraging KD for Vision-Language representation learningRepresentation Learning, as well as its application to downstream VL tasks, captioning, visual question answering, and so forth. The chapter further discusses and studies variations of KDKnowledge distillation applied to Vision and Language-related topics.
A Geometric Perspective on Feature-Based Distillation

Feature-based Knowledge Distillation (FKD) is a method for guiding the activations at the intermediate layers of a Convolutional Neural NetworkConvolutional Neural Network (CNN) (CNN) during training. It has recently gained significant popularity as either a standalone or complementary method of Knowledge Distillation (KD)Knowledge distillation. Most techniques however, handle the teacher-to-student knowledge transfer in a statistical or probabilistic manner. In this chapter, we propose a family of efficient loss functions that aim to transfer the geometry of activations from intermediate layers of a teacher CNNConvolutional Neural Network (CNN) to the activations of a student CNN model at matching spatial resolutions through a novel Feature-based Knowledge Distillation (FKD) method. We discuss the challenges of geometric methods, provide connections with manifold-to-manifold comparison and regularization techniques, and draw some interesting connections to the field of random graphs. Experiments on benchmark tasks show evidence that by focusing on replicating the feature relationships only across local neighborhoods, results in better performance. Furthermore, the definition of neighborhoods important for sufficient performance, with neighborhoods defined over parsimonious graphs such as the Minimal Spanning Tree achieving better results that standard kNN rule. Finally, we present a case study on data-free Knowledge Distillation in the field of offline handwritten signature verification. The case study demonstrates a way to harness knowledge from an expert CNNConvolutional Neural Network (CNN) model to enhance the training of a new model with different architecture, using only external (task-irrelevant) data. Results indicate that both geometric FKD and its combination with standard KDKnowledge distillation techniques can effectively create new models with better performance than the expert teacher model.