Reviewerly/exHarmony huggingface dataset:
Quantum state interferography with heralded single photons

Quantum state tomography is the standard method used for quantum state reconstruction. Recently, a new technique called, quantum state interferography, has been introduced for the direct single-shot measurement of the quantum state of a qubit [Phys. Rev. Lett. 125 (2020) 123601]. In the present work, we report a first-of-its-kind experimental demonstration of quantum state interferography of legitimate single photon polarization states derived from spontaneous parametric down conversion source. Further, we provide a more general and a simpler mathematical treatment of the QSI technique. While the theoretical treatment with d-dimensional state vectors in the earlier work is limited only to pure qudits, our approach readily lends itself to the case of mixed qudit systems. The basic arguments and the results of this extension are presented with a qutrit state as an example.
MED-TEX: Transfer and Explain Knowledge with Less Data from Pretrained Medical Imaging Models

Deep learning methods usually require a large amount of training data and lack interpretability. In this paper, we pro-pose a novel knowledge distillation and model interpretation framework for medical image classification that jointly solves the above two issues. Specifically, to address the data-hungry issue, a small student model is learned with less data by distilling knowledge from a cumbersome pretrained teacher model. To interpret the teacher model and assist the learning of the student, an explainer module is introduced to highlight the regions of an input that are important for the predictions of the teacher model. Furthermore, the joint framework is trained by a principled way derived from the information-theoretic perspective. Our framework outperforms on the knowledge distillation and model interpretation tasks com-pared to state-of-the-art methods on a fundus dataset.
BIDMIR: Bi-Directional Medical Image Registration with Symmetric Attention and Cyclic Consistency Regularization

The past years witness the remarkable progress in developing deep learning-based image registration methods, which leverage convolutional neural networks (CNNs) for efficient and end-to-end regression of deformation fields from an image pair. Identified limitations of existing methods include (a) ignorance of intra- and inter-image long-range spatial relevance, leading to failure in finding semantically meaningful correspondences of anatomical structures; and (b) single direction of image registration with no enforcement of topology preservation, resulting in loss of structural information. To address these issues, we propose a novel bi-directional medical image registration method, referred as BIDMIR, integrating symmetric attention with cyclic consistency regularization. The proposed method consists of a learnable volumetric embedding module, a symmetric attention module for feature enhancement, and a bi-directional registration field inference module. The symmetric attention module explicitly models the intra- and inter-image long-range relevance in the embedding, facilitating bi-directional correspondence of semantically meaningful structures. Cyclic consistency regularization is additionally proposed to encourage topology preservation. Results demonstrate the efficacy of our approach.
Design of Superior Parameterized Quantum Circuits for Quantum Image Classification

In this paper, we present a novel quantum neural network (QNN) algorithm enhanced with transfer learning to perform multi-class image classification. The proposed QNN extracts quantum image encoding measurements through the quantum state tomography framework and passes the sampled features through the classical neural network architecture to the proposed learnable parameterized quantum circuit (PQC) followed by gradient update via quantum backpropagation. We benchmark three different PQCs to demonstrate that our proposed algorithm outperforms similar classical CNN architecture in test accuracy on CIFAR10 and MNIST datasets. Present results more prominently establish the success of PQC designs which will be further used in the design of 2D quantum convolutional neural network (QCNN).
Habilyzer: A User-Driven Open-Ended Sensor Kit for Office Workers

Office work presents health and wellbeing challenges, triggered by working habits or environmental factors. While technological interventions gain popularity in the workplace, they often fall short of acknowledging personal needs. Building on approaches from personal informatics, we present our vision on the use of user-driven, situated sensor probes in an office context and how the community might deal with complex yet timely questions around the use of data to empower people in becoming explorers of their own habits and experiences. We demonstrate Habilyzer, an open-ended sensor toolkit for office workers, which enables user-driven explorations in self-tracking their work routines. This research contributes an alternative approach to improving working habits and vitality in the workplace, moving from solution-oriented technologies to inquiry-enabling tools. Through this demonstration, we also aim to trigger discussions on the use of sensors and data in the office context, in the light of privacy, consent and data ownership.
Identifying an Aurally Distinct Phrase Set for Text Entry Techniques

In the last decade, interest in accessible and eyes-free text entry has continued to grow. However, little research has been done to explore the feasibility of using audibly distinct phrases for text entry tasks. To better understand whether preexisting phrases used in text entry research are sufficiently distinct for eyes-free text entry tasks, we used Microsoft’s and Apple’s desktop text-to-speech systems to generate all 500 phrases from MacKenzie and Soukoreff’s set [32] using the default male and female voices. We then asked 392 participants recruited through Amazon’s Mechanical Turk to transcribe the generated audio clips. We report participant transcription errors and present the 96 phrases that were observed with no comprehension errors. These phrases were further tested with 80 participants who identified as low-vision and/or blind recruited through Twitter. We contribute the 92 phrases that were observed to maintain no comprehension errors across both experiments.
Virtual Lab by Quantum Flytrap: Interactive simulation of quantum mechanics

Virtual Lab by Quantum Flytrap explores novel ways to represent quantum phenomena in an interactive and intuitive way. It is an online laboratory with a real-time simulation of an optical table, supporting up to 3 entangled particles. We have created a highly visual no-code interface and an introductory Quantum Game to make the Virtual Lab approachable for users without prior exposure to quantum mechanics. At the same time, a configurable sandbox mode with customizable elements makes it useful for students, lecturers, and scientists. Virtual Lab provides a way to explore exact quantum states, entanglement, and the probabilistic nature of quantum physics. We provide ways to simulate quantum computing (e.g. Deutsch-Jozsa algorithm), use quantum cryptography (e.g. BB84 protocol), explore counterintuitive quantum phenomena (e.g. quantum teleportation), and recreate historical experiments (e.g. Michaelson-Morley interferometer). Virtual Lab is available at: https://lab.quantumflytrap.com/.
InfinityWall – Vertical Locomotion in Virtual Reality using a Rock Climbing Treadmill

Current commodity Virtual Reality (VR) hardware allows for free, even wireless roaming, however it is still confined by a finite tracking space. To overcome this issue, past research has introduced different methods for vertical locomotion, ranging from walking on a treadmill to interaction paradigms such as teleportation. Recently, researchers have integrated rock climbing on a physical wall into VR experiences. The space available is naturally confined by the dimensions of the wall. Building upon this, we present implementation details and future research directions of a VR system for vertical locomotion on a rock climbing treadmill.
Two-Gimbal PAT: An artistic installation combining mechanical and acoustic rotation of levitated content

Interactive applications using ultrasonic phased arrays of transducers (PATs) provide novel opportunities to create 3D displays. They show visual content using levitated particles such as polystyrene beads, thread or fabric, that dynamically change to render volumetric shapes in mid-air. Typically, this technology uses fixed arrays of transducers (e.g., two boards vertically or horizontally aligned) to display interactive content. That is, a fixed system of reference (SoR), defined by the levitation device, and dynamic mid-air content. This demo proposes a novel setup, featuring a dynamic-SoR for the device with the dynamic mid-air content. We do this by combining a PAT-based levitation device mounted onto a two-gimbal mechanism, resulting into a device with a dynamic coordinate system for volumetric animations. We describe our setup and demonstration, which aims to create an intriguing experience, where both the content and the device itself can freely move in 3D space, with the aim of challenging the user's expectations about the content displayed or even the principles enabling it.
A Blockchain Approach to Academic Assessment

In this paper, we propose a novel method for academic assessment inspired by the decentralized applications made possible by blockchain technology. The proposed method applies to a wide range of academic material, including assignments, exams, academic papers, etc and tackles issues regarding potential personal bias and makes assessment possible without the need to rely on a few assessors. We examine the challenges and possibilities that arise with this method and further explore more general applications in areas such as education. In the experiments conducted for this research, poll results show generally positive views toward the fairness of this system compared to the traditional methods.
Metaphorical Visualization: Mapping Data to Familiar Concepts

We present a new approach to visualizing data that is well-suited for personal and casual applications. The idea is to map the data to another dataset that is already familiar to the user, and then rely on their existing knowledge to illustrate relationships in the data. We construct the map by preserving pairwise distances or by maintaining relative values of specific data attributes. This metaphorical mapping is very flexible and allows us to adapt the visualization to its application and target audience. We present several examples where we map data to different domains and representations. This includes mapping data to cat images, encoding research interests with neural style transfer and representing movies as stars in the night sky. Overall, we find that although metaphors are not as accurate as the traditional techniques, they can help design engaging and personalized visualizations.
Categorizing Open Government Data Users by Exploring their Challenges and Proficiency

Developing user personas has been a crucial aspect of human-centered system design for decades as it helps in understanding and segregating users based on their prominent characteristics. However, such a technique has not been applied in developing and improving systems for supporting open government data (OGD) users. Therefore, this paper explores OGD users’ characteristics and creates relevant personas for them. Open coding-based content analysis and k-means clustering were performed on posts of an online community managed by a U.S. local-level OGD portal, where users’ characteristics such as purposes, challenges, and proficiency in civic data domain knowledge and computational skills were used as features for clustering. Through manually analyzing the output clusters, we identified three personas with their distinct behavior patterns based on their proficiency. The proposed personas can facilitate in personalizing informational and technological support for OGD forum users.
Human-Centered Prior-Guided and Task-Dependent Multi-Task Representation Learning for Action Recognition Pre-Training

Recently, much progress has been made for self-supervised action recognition. Most existing approaches emphasize the contrastive relations among videos, including appearance and motion consistency. However, two main issues remain for existing pre-training methods: 1) the learned representation is neutral and not informative for a specific task; 2) multi-task learning-based pre-training sometimes leads to sub-optimal solutions due to inconsistent domains of different tasks. To address the above issues, we propose a novel action recognition pre-training framework, which exploits human-centered prior knowledge that generates more informative representation’ and avoids the conflict between multiple tasks by using task-dependent representations. Specifically, we distill knowledge from a human parsing model to enrich the semantic capability of representation. In addition, we combine knowledge distillation with contrastive learning to constitute a task-dependent multi-task framework. We achieve state-of-the-art performance on two popular benchmarks for action recognition task, i.e., UCF101 and HMDB51, verifying the effectiveness of our method.
Art is Not Research. Research is not Art.

Art is not Research. Research is not Art. is a multimedia, multi-site participatory installation by a collective of artists and researchers from Calgary, Toronto, and Lancaster; it is informed by these contexts. It reflects the tensions between how "participants" are treated in participatory art and interaction research. It offers a framework through which we can explore how epistemologies might evolve in a blending between Art and Research. Visitors download the paper to read, critically reflect on the relationship between art and research, and experientially engage with the material through a series of creative prompts. A performance variation of the piece will be performed in-person and online through the ACM SIGCHI Conference on Human Factors in Computing Systems alt.chi track.
“Where did you first meet the owner?” – Exploring Usable Authentication for Smart Home Visitors

Visitors in smart homes might want to use certain device features, as far as permitted by the device owner (e.g., streaming music on a smart speaker). At the same time, protecting access to features from attackers is crucial, motivating a need for authentication. However, it is unclear if and how smart home visitors should authenticate as they usually do not have access to respective interfaces. We explore considerations for the design of authentication for visitors evolving around, e.g., the visitors themselves as well as the environment and concrete mechanisms. Moreover, we suggest a concrete idea: security questions to authenticate visitors in smart homes. In an interview study (N = 24), we found that owners and visitors appreciated the low effort and would adapt our approach. We conclude with future research directions that we hope will spark further discussions around the design of authentication for smart homes, considering visitors and owners alike.
“Careful with that Roam, Edu”: experimental analysis of Eduroam credential stealing attacks

Eduroam, which stands for education roaming, is a world-wide Wi-Fi access and roaming service largely exploited by the international research and education community. Eduroam’s authentication relies on the same long-term credentials used by students and professors to access critical education/research services. To assess the real-world security of Eduroam, we i) implemented a credential stealing attack based on a rogue Eduroam setup, ii) ran a controlled experiment with 37 relatively skilled real world users (mainly electrical or computer engineering students), and iii) for four heterogeneous selected devices, we investigated their more detailed dependence on different WPA-enterprise configurations and certificate settings. The aftermath is that, even with a completely passive attack (users were keeping devices in their pocket), we stole credentials from more than one third of the participants. While most of the Eduroam vulnerabilities employed in this work should be considered somewhat known (being disclosed in former technical papers), our work appears to raise a threefold concern: i) most pragmatic Eduroam configurations appear to be grossly insecure; ii) no Apple’s iPhone felt in our attack, owing to its reduced possibility for an user to misconfigure the terminal; and iii) there is a limited awareness of Wi-Fi authentication threats even in relatively skilled end users.
Biomaterial Playground: Engaging with Bio-based Materiality

Recently, HCI researchers have shown interest in sustainability in the context of both new making methodologies and materials. In this work, we introduce a range of sustainable biomaterials (materials that are bio-based) that we use to create unique interactive interfaces. These biomaterials include ReClaym, a clay-like material made from compost; Alganyl, an algae-based bioplastic; Dinoflagellates, bioluminescent algae; SCOBY, symbiotic cultures of bacteria and yeast; and Spirulina, nutrient-dense blue-green algae. We describe how the implementation of these materials in our designs highlight the importance of utilizing materials that can biodegrade. We also call attention to the importance of care, patience, and understanding during the design processes to facilitate the creation of playful designs that respect the agency of each biomaterial. Lastly, we discuss the gained deeper sense of intimacy and understanding with our biomaterials which not only lead to more personally meaningful interfaces but more sustainable ones.
Multi-robot Collision Avoidance Based on Buffered Voronoi Diagram

Multi-robot path planning is an essential research topic for coordinated robots. In this paper, we aim to enable teams of robots to navigate in a common environment samultanuously without collision. Our method is based on Voronoi diagram, an optimal spatial area assignment strategy. We take the localization inaccuracy in to consideration, which always exist in real-world senarioss and set a buffer to the ordinary Voronoi diagram for collision-free navigation amoung the robot team. We also combine the left-handed principle to handle the situation of deadlock, which may happen when robots reach the connors or edges of their Voronoi cells. We use MATLAB simulation to test the efficacy of our proposed algorithms in an open environment with twelve mobile robots. The results show that all robots reach the target efficiently without collision. We also compare our method with several related multi-robot path planning algorithms to demonstrate the efficiency of our proposed method.
NeurTEx: A Neural Framework for Template Extraction from Flat Images

With the increasing demands of digital creation, a creative user often starts by looking for inspirational resources online. Once they find such inspirations, they recreate certain design elements using tracing-like applications to internalize the design for their purpose. We aim to accelerate this process by extracting key semantics from an inspirational image, and converting it into a ready-to-use template using a multimodal information extraction setup. We propose NeurTEx, a holistic algorithm that takes an inspirational banner image as input and extracts multimodal design semantics: layout, text elements (actual text along with the font style), image elements (including semantics like logos and background/foreground objects) and shapes. Our technique uses a segmentation framework followed by a region based depth-first search to extract and identify different elements and their semantics. We process these regions to extract finer details such as font styles, texts and logos. We believe that such fine–grained semantics would accelerate the design process for a creator helping them rapidly adapt designs from inspirations for their requirements. With the help of metric-based and human survey based evaluations, we not only demonstrate the effectiveness of the proposed approach to extract the style & design components from an inspirational image but also illustrate how these extractions can accelerate the creation process thus aiding novices and professionals alike.
Enhanced Videogame Livestreaming by Reconstructing an Interactive 3D Game View for Spectators

Many videogame players livestream their gameplay so remote spectators can watch for enjoyment, fandom, and to learn strategies and techniques. Current approaches capture the player’s rendered RGB view of the game, and then encode and stream it as a 2D live video feed. We extend this basic concept by also capturing the depth buffer, camera pose, and projection matrix from the rendering pipeline of the videogame and package them all within a MPEG-4 media container. Combining these additional data streams with the RGB view, our system builds a real-time, cumulative 3D representation of the live game environment for spectators. This enables each spectator to individually control a personal game view in 3D. This means they can watch the game from multiple perspectives, enabling a new kind of videogame spectatorship experience.
Extracting Handwritten Annotations from Printed Documents Via Infrared Scanning

Despite ever improving digital ink and paper solutions, many people still prefer printing out documents for close reading, proofreading, or filling out forms. However, in order to incorporate paper-based annotations into digital workflows, handwritten text and markings need to be extracted. Common computer-vision and machine-learning approaches require extensive sets of training data or a clean digital version of the document. We propose a simple method for extracting handwritten annotations from laser-printed documents using multispectral imaging. While black toner absorbs infrared light, most inks are invisible in the infrared spectrum. We modified an off-the-shelf flatbed scanner by adding a switchable infrared LED to its light guide. By subtracting an infrared scan from a color scan, handwritten text and highlighting can be extracted and added to a PDF version. Initial experiments show accurate results with high quality on a test data set of 93 annotated pages. Thus, infrared scanning seems like a promising building block for integrating paper-based and digital annotation practices.
CamFi: An AI-driven and Camera-based System for Assisting Users in Finding Lost Objects in Multi-Person Scenarios

It is important to study how to help people quickly find misplaced objects. However, previous studies have focused on single-person scenarios without considering the influence of other people in public places. Based on the technology of object detection and face recognition, our system can help reduce the burden upon people's memory. It can provide useful information, whether the user forgets where the object is or because someone else has moved the object. The system includes a camera, processing server and smartphone application. To evaluate our approach, we conducted a quantitative and qualitative user study with participants (n=12). We demonstrated the usability of this system in helping users find misplaced items in public settings with multiple people.
cARdLearner: Using Expressive Virtual Agents when Learning Vocabulary in Augmented Reality

Augmented reality (AR) has a diverse range of applications, including language teaching. When studying a foreign language, one of the biggest challenges learners face is memorizing new vocabulary. While augmented holograms are a promising means of supporting this memorization process, few studies have explored their potential in the language learning context. We demonstrate the possibility of using flashcard along with an expressive holographic agent on vocabulary learning. Users scan a flashcard and play an animation that is connected with an emotion related to the word they are seeing. Our goal is to propose an alternative to the traditional use of flashcards, and also introduce another way of using AR in the association process.