Reviewerly/exHarmony huggingface dataset:
Material-Guided Siamese Fusion Network for Hyperspectral Object Tracking

Hyperspectral videos (HSVs) have more potential in target tracking than color videos thanks to the material identification capability provided by abundant spectral bands. Due to limited HSVs for training, most current hyperspectral trackers are based on hand-crafted features rather than deeply learned ones, resulting in poor tracking performance. This paper introduces a material-guided Siamese fusion network (SiamF) for hyperspectral object tracking to make up this gap. Belonging to the Siamese tracker family and SiamF aims to model the appearance of hyperspectral objects using backbone networks trained on color images. Specifically, SiamF splits each hyperspectral frame into multiple groups of false-color images according to their band importance. Then SiamF employs a hyperspectral feature fusion (HFF) module with a dense connection architecture to integrate the extracted features from different layers and band groups, producing a multi-scale multilevel spatial-spectral representation of the targets. Instead of direct addition or concatenation, HFF employs global-local channel attention for feature fusion, so that yielded features capture the global and local structure of a specific object. Moreover, online spatial and material classifiers are developed to inject spatial and material appearance changes information into SiamF for adaptively online tracking. Experimental results demonstrate our tracker outperforms alternative methods.
Exploring Complementarity of Global and Local Spatiotemporal Information for Fake Face Video Detection

The spread of fake face videos leads to severe social concerns, which promotes the development of detection methods for these videos. Existing patch-based methods focus on local regions to find forgery common clues, while ignoring the important role of the global information. In this paper, a novel spatiotemporal network is proposed which can better utilize the implicit complementary advantages of global and local information. Specifically, the spatial module consists of the global information stream and local information stream extracted from patches selected by attention layers. Then, the fusion features of these two streams are fed into the temporal module to further capture temporal clues. Besides, a regularization loss is designed to guide the selection of local information and the extraction of fusion temporal information with the reference to the global information. Extensive experiments on different datasets demonstrate the superiority of our framework.
Robust Thermal Infrared Pedestrian Detection By Associating Visible Pedestrian Knowledge

Recently, pedestrian detection on thermal infrared images has shown the robust pedestrian detection performance. In this paper, we propose a novel thermal infrared pedestrian detection framework which can associate and utilize the complementary pedestrian knowledge from visible images. Motivated by that humans can associate useful information from other sensors to perform a more reliable decision, we devise a Visible-sensory Pedestrian Associating (VPA) Memory to conduct the robust pedestrian detection by utilizing complementary visible-sensory pedestrian knowledge explicitly. The VPA Memory is trained to store the pedestrian information of visible images and associate it with a given thermal infrared pedestrian knowledge via the memory associating learning. We verify the effectiveness of the proposed framework with extensive experiments, and it achieves state-of-the-art pedestrian detection performance on thermal infrared images.
How Immersion and Self-Avatars in VR Affect Learning Programming and Computational Thinking in Middle School Education

We present an empirical evaluation of immersion and self-avatars as compared to desktop viewing in Virtual Reality (VR) for learning computer programming and computational thinking in middle school education using an educational VR simulation. Students were asked to programmatically choreograph dance performances for virtual characters within an educational desktop application we built earlier called Virtual Environment Interactions (VEnvI). As part of a middle school science class, 90 students from the 6th and 7th grades participated in our study. All students first visually programmed dance choreography for a virtual character they created in VEnvI on a laptop. Then, they viewed and interacted with the resulting dance performance in a between-subjects design in one of the three conditions. We compared and contrasted the benefits of embodied immersive virtual reality (EVR) viewing utilizing a head-mounted display with a body-scaled and gender-matched self-avatar, immersive virtual reality only (IVR) viewing, and desktop VR (NVR) viewing with VEnvI on pedagogical outcomes, programming performance, presence, and attitudes towards STEM and computational thinking. Results from a cognition questionnaire showed that, in the learning dimensions of Knowledge and Understanding (Bloom's taxonomy) as well as Multistructural (SOLO taxonomy), participants in EVR and IVR scored significantly higher than NVR. Also, participants in EVR scored significantly higher than IVR. We also discovered similar results in objective programming performance and presence scores in VEnvI. Furthermore, students' attitudes towards computer science, programming confidence, and impressions significantly improved to be the highest in EVR and then IVR as compared to NVR condition. Our work suggests that educators and developers of educational VR simulations, who want to enhance knowledge and understanding as well as simultaneous acquisition of multiple abstract concepts, can do so by employing immersion and self-avatars in VR learning experiences.
Robust gamma correction based on chord distribution coding considering projector defocusing

In phase-measurement profilometry (PMP), the gamma effect can cause severe nonlinear distortion of the phase pattern (i.e., water ripples on the surface profile). Gamma correction is an effective method to eliminate the gamma effects of commercial projectors. However, projector defocusing on the suppression of higher harmonics inevitably results in an estimated gamma deviation from the true value. In this study, gamma mapping is constructed using the duty ratio (DR) to code the chord distribution of the simulated distorted phase while considering projector defocusing. With the known gamma mapping, the accurate gamma is calculated by DR coding of the actual distorted phase under projector defocusing. Simulated experiments verified that the relative errors of the gamma calculated by the proposed method under different degrees of defocus were less than 3.5%. Furthermore, the experimental results demonstrate that the proposed gamma calculation method is robust to the defocus effect of the projector and that a smoother surface can be reconstructed after gamma correction.
A decade for the books: Bibliometric analysis of Economics Letters

This paper conducts a ten-year bibliometric analysis of Economics Letters journal to uncover the emerging trends in economic research, as well as their specifics. The results of the co-citation and co-word analysis provide information on the most popular references, scholarly approved journals and trendiest economics fields. For example, monetary policy is a highly investigated field, while panel data analysis and experiments are the most used statistical methods. These results could serve scholars as a guide by putting their scientific work in the current perspective and aiding them to discover future research paths.
Simplification of genetic programs: a literature survey

Abstract Genetic programming (GP), a widely used evolutionary computing technique, suffers from bloat—the problem of excessive growth in individuals’ sizes. As a result, its ability to efficiently explore complex search spaces reduces. The resulting solutions are less robust and generalisable. Moreover, it is difficult to understand and explain models which contain bloat. This phenomenon is well researched, primarily from the angle of controlling bloat: instead, our focus in this paper is to review the literature from an explainability point of view, by looking at how simplification can make GP models more explainable by reducing their sizes. Simplification is a code editing technique whose primary purpose is to make GP models more explainable. However, it can offer bloat control as an additional benefit when implemented and applied with caution. Researchers have proposed several simplification techniques and adopted various strategies to implement them. We organise the literature along multiple axes to identify the relative strengths and weaknesses of simplification techniques and to identify emerging trends and areas for future exploration. We highlight design and integration challenges and propose several avenues for research. One of them is to consider simplification as a standalone operator, rather than an extension of the standard crossover or mutation operators. Its role is then more clearly complementary to other GP operators, and it can be integrated as an optional feature into an existing GP setup. Another proposed avenue is to explore the lack of utilisation of complexity measures in simplification. So far, size is the most discussed measure, with only two pieces of prior work pointing out the benefits of using time as a measure when controlling bloat.
Letters from the Future: Exploring Ethical Dilemmas in the Design of Social Agents

We present a case-study of using Ethnographic Experiential Futures (EXF) to surface underlying divergences, tensions and dilemmas implicit in views of the futures of ”social agents” among professional researchers familiar with the state of the art. Based on expert interviews, we designed three ”letters from the future,” research probes that were mailed to 15 participants working in the field, to encounter and respond to. We lay out the elements and design choices that shaped these probes, present our remote and asynchronous study design, and discuss lessons learned about the use of EXF. We find that this form of hybrid design/futures intervention has the potential to provide professional communities with opportunities to grapple with potential ethical dilemmas early on. However, the knowledge and tools for doing so are still in the making. Our contribution is a step towards advancing the potential benefits of experiential futures for technology designers and researchers.
Paper Trail: An Immersive Authoring System for Augmented Reality Instructional Experiences

Prior work has demonstrated augmented reality’s benefits to education, but current tools are difficult to integrate with traditional instructional methods. We present Paper Trail, an immersive authoring system designed to explore how to enable instructors to create AR educational experiences, leaving paper at the core of the interaction and enhancing it with various forms of digital media, animations for dynamic illustrations, and clipping masks to guide learning. To inform the system design, we developed five scenarios exploring the benefits that hand-held and head-worn AR can bring to STEM instruction and developed a design space of AR interactions enhancing paper based on these scenarios and prior work. Using the example of an AR physics handout, we assessed the system’s potential with PhD-level instructors and its usability with XR design experts. In an elicitation study with high-school teachers, we study how Paper Trail could be used and extended to enable flexible use cases across various domains. We discuss benefits of immersive paper for supporting diverse student needs and challenges for making effective use of AR for learning.
“Rewind to the Jiggling Meat Part”: Understanding Voice Control of Instructional Videos in Everyday Tasks

Voice interaction has long been envisioned as enabling users to transform physical interaction into hands-free, such as allowing fine-grained control of instructional videos without physically disengaging from the task at hand. While significant engineering advances have brought us closer to this ideal, we do not fully understand the user requirements for voice interactions that should be supported in such contexts. This paper presents an ecologically-valid wizard-of-oz elicitation study exploring realistic user requirements for an ideal instructional video playback control while cooking. Through the analysis of the issued commands and performed actions during this non-linear and complex task, we identify (1) patterns of command formulation, (2) challenges for design, and (3) how task and voice-based commands are interwoven in real-life. We discuss implications for the design and research of voice interactions for navigating instructional videos while performing complex tasks.
Microcalcification Discrimination in Mammography Using Deep Convolutional Neural Network: Towards Rapid and Early Breast Cancer Diagnosis

Breast cancer is among the most common types of cancer in women and under the cases of misdiagnosed, or delayed in treatment, the mortality risk is high. The existence of breast microcalcifications is common in breast cancer patients and they are an effective indicator for early sign of breast cancer. However, microcalcifications are often missed and wrongly classified during screening due to their small sizes and indirect scattering in mammogram images. Motivated by this issue, this project proposes an adaptive transfer learning deep convolutional neural network in segmenting breast mammogram images with calcifications cases for early breast cancer diagnosis and intervention. Mammogram images of breast microcalcifications are utilized to train several deep neural network models and their performance is compared. Image filtering of the region of interest images was conducted to remove possible artifacts and noises to enhance the quality of the images before the training. Different hyperparameters such as epoch, batch size, etc were tuned to obtain the best possible result. In addition, the performance of the proposed fine-tuned hyperparameter of ResNet50 is compared with another state-of-the-art machine learning network such as ResNet34, VGG16, and AlexNet. Confusion matrices were utilized for comparison. The result from this study shows that the proposed ResNet50 achieves the highest accuracy with a value of 97.58%, followed by ResNet34 of 97.35%, VGG16 96.97%, and finally AlexNet of 83.06%.
EpNet: Power lines foreign object detection with Edge Proposal Network and data composition

Power lines foreign object detection task is to detect objects suspending on power lines, they might be kites, plastic bags or anything else, which could be a potential risk to power system. However, this task remains a challenge due to the lack of data, because these data can only be produced by a few major video surveillance companies, who treat their data as valuable property and will not share it with others. Without massive training data, we could not obtain an excellent neural network. In this paper, we introduce a new data composition method to generate artificial data and help alleviate the data shortage problem. What is more, we propose a new detection method called Edge Proposal Network (EpNet) to reduce wrong proposal locations and increase detection performance. At last, we conduct several experiments to verify the effectiveness of the two methods, and some discussion experiments to gain a deeper understanding of the composited data.
VocabEncounter: NMT-powered Vocabulary Learning by Presenting Computer-Generated Usages of Foreign Words into Users’ Daily Lives

We demonstrate that recent natural language processing (NLP) techniques introduce a new paradigm of vocabulary learning that benefits from both micro and usage-based learning by generating and presenting the usages of foreign words based on the learner's context. Then, without allocating dedicated time for studying, the user can become familiarized with how the words are used by seeing the example usages during daily activities, such as Web browsing. To achieve this, we introduce VocabEncounter, a vocabulary-learning system that suitably encapsulates the given words into materials the user is reading in near real time by leveraging recent NLP techniques. After confirming the system's human-comparable quality of generating translated phrases by involving crowdworkers, we conducted a series of user studies, which demonstrated its effectiveness on learning vocabulary and its favorable experiences. Our work shows how NLP-based generation techniques can transform our daily activities into a field for vocabulary learning.
DreamStream: Immersive and Interactive Spectating in VR

Today spectating and streaming virtual reality (VR) activities typically involves spectators viewing a 2D stream of the VR user's view. Streaming 2D videos of the game play is popular and well-supported by platforms such as Twitch. However, the generic streaming of full 3D representations is less explored. Thus, while the VR player's experience may be fully immersive, spectators are limited to 2D videos. This asymmetry lessens the overall experience for spectators, who themselves may be eager to spectate in VR. DreamStream puts viewers in the virtual environment of the VR application, allowing them to look "over the shoulder" of the VR player. Spectators can view streamed VR content immersively in 3D, independently explore the VR scene beyond what the VR player sees and ultimately cohabit the virtual environment alongside the VR player. For the VR player, DreamStream provides a spatial awareness of all their spectators. DreamStream retrofits and works with existing VR applications. We discuss the design and implementation of DreamStream, and carry out three qualitative informal evaluations. These evaluations shed light on the strengths and weakness of using DreamStream for the purpose of interactive spectating. Our participants found that DreamStream's VR viewer interface offered increased immersion, and made it easier to communicate and interact with the VR player.
Investigating Daily Practices of Self-care to Inform the Design of Supportive Health Technologies for Living and Ageing Well with HIV

We report on a Diary Study investigating daily practices of Self-care by seven UK adults living with Human Immunodeficiency Virus (HIV), to understand their routines, experiences, needs and concerns, informing Self-care technology design to support living well. We advance a developing HCI literature evidencing how digital tools for self-managing health do not meet the complex needs of those living with long-term conditions, especially those from marginalised communities. Our evaluation of using a Self-care Diary as Design Probe responds to calls to study Self-care practices so that future digital health tools are better grounded in lived experiences of managing multi-morbidity. We contribute to HCI discourses including Personal Health Informatics, Lived Informatics and Reflection by illuminating psychosocial challenges for practicing and self-reporting on Self-care. We offer design implications from a Critical Digital Health perspective, addressing barriers to technology use related to trust, privacy, and representation, gaining new significance during the COVID-19 pandemic.
I Know It Is Not Real (And That Matters) Media Awareness vs. Presence in a Parallel Processing Account of the VR Experience

Inspired by the widely recognized idea that in VR/XR, not only presence but also encountered plausibility is relevant (Slater, Phil. Trans. R. Soc. B, 2009, 364 (1535), 3549–3557), we propose a general psychological parallel processing account to explain users’ VR and XR experience. The model adopts a broad psychological view by building on interdisciplinary literature on the dualistic nature of perceiving and experiencing (mediated) representations. It proposes that perceptual sensations like presence are paralleled by users’ belief that “this is not really happening,” which we refer to as media awareness . We review the developmental underpinnings of basic media awareness, and argue that it is triggered in users’ conscious exposure to VR/XR. During exposure, the salience of media awareness can vary dynamically due to factors like encountered sensory and semantic (in)consistencies. Our account sketches media awareness and presence as two parallel processes that together define a situation as a media exposure situation. We also review potential joint effects on subsequent psychological and behavioral responses that characterize the user experience in VR/XR. We conclude the article with a programmatic outlook on testable assumptions and open questions for future research.
SkyPort: Investigating 3D Teleportation Methods in Virtual Environments

Teleportation has become the de facto standard of locomotion in Virtual Reality (VR) environments. However, teleportation with parabolic and linear target aiming methods is restricted to horizontal 2D planes and it is unknown how they transfer to the 3D space. In this paper, we propose six 3D teleportation methods in virtual environments based on the combination of two existing aiming methods (linear and parabolic) and three types of transitioning to a target (instant, interpolated and continuous). To investigate the performance of the proposed teleportation methods, we conducted a controlled lab experiment (N = 24) with a mid-air coin collection task to assess accuracy, efficiency and VR sickness. We discovered that the linear aiming method leads to faster and more accurate target selection. Moreover, a combination of linear aiming and instant transitioning leads to the highest efficiency and accuracy without increasing VR sickness.
Understanding Interactions for Smart Wheelchair Navigation in Crowds

Shared control wheelchairs can help users to navigate through crowds by enabling the person to drive the wheelchair while receiving support in avoiding pedestrians. To date, research into shared control has largely overlooked the perspectives of wheelchair users. In this paper, we present two studies that aim to address this gap. The first study involved a series of semi-structured interviews with wheelchair users which highlighted the presence of two different interaction loops, one between the user and the wheelchair and a second one between the user and the crowd. In the second study we engaged with wheelchair users and designers to co-design appropriate feedback loops for future shared control interaction interfaces. Based on the results of the co-design session, we present design implications for shared control wheelchair around the need for empathy, embodiment and social awareness; situational awareness and adaptability; and selective information management.
ExposAR: Bringing Augmented Reality to the Computational Thinking Agenda through a Collaborative Authoring Tool

There is a growing focus on computational thinking (CT) in terms of supporting children's understanding of everyday technologies. But unlike other technologies, Augmented Reality (AR) has received limited attention. In this paper, we present ExposAR – a collaborative cross-device AR system enabling children to create, use, and reflect on AR technologies through the co-creation of a simple AR application. With ExposAR, we explore three core design principles: 1) reify computational concepts, 2) support collaborative cross-device authoring, and 3) incorporate the user's own world. These principles were developed through a co-design process with teachers and evaluated with 46 primary school students. We found that the collaborative authoring process with ExposAR supported students in understanding AR concepts and challenged their perspectives on AR. With these results, we bring AR to the CT agenda and contribute novel design principles for exposing the underlying mechanisms and implications of AR.