Reviewerly/exHarmony huggingface dataset:
A Meta-Analysis on Emotion and Mathematics Education Research

The purpose of this study is to identify the trends of emotion-related mathematics education research conducted in Korea so far, and compare them with those of international studies, and explore the desirable directions for the future emotion-related mathematics education research. Emotion-related domestic research were collected on research published in domestic academic journals and were analyzed and used as a research data for international comparative study. The findings of the Eligio(2017) were used as counterpart research data. Comparing the number of publications, research subjects and research topics of domestic and international studies on emotion, it was found to be difficult to determine the trends due to the lack of the number of domestic studies, but they generally have shown similar patterns. The future directions of domestic mathematics education research on emotion were proposed as follows. Since it is difficult to collect reasonable and reliable data on pure emotion due to the nature of emotion in mathematics education research, it is suggested to introduce the concept of emotional intelligence that is the combination of emotion and cognition. Also it was recommended to utilize the concept of meta-emotion for future study on emotion related research.
Flexible Integration of Voice Recognition Components for an Automotive Android Platform: A Design Science Research

There are several voice recognition interfaces available on the market capable ofbeing integrated into a variety of platforms. Mobile phones, speakers, and evenrefrigerators can be equipped with a voice assistant. Having a fully integrated voiceassistant in a car has been shown to increase the performance of the driver and reducedistractions. The subcontractor Aptiv aims to provide their Android platformwith a voice assistant meant to be sold to car manufacturers, but they aspire toavoid locking their platform to one voice component. This thesis investigates thepossibilities of mitigating the difficulties of integrating multiple voice recognitioncomponents into one unified framework using a design science research approachover the course of two cycles.Data has been collected through both formal and informal interviews that addressthe problem at hand. An Ishikawa diagram was constructed to illustrate an overviewof the problem space at Aptiv and through evaluation, a new narrowed scope couldbe determined. The new scope resulted in an investigation of the four voice recognitioncomponents Aptiv showed interest in, which were Amazon Alexa, GoogleAssistant, Mycroft, and Houndify. This investigation lead to the creation of a toolcontaining 15 parameters developers can use to compare the functionality of differentvoice recognition components. The tool was then evaluated by developers atAptiv through a workshop.The tool together with its illustrations is a helpful start for developers tasked withintegrating, either one or several, voice recognition components for an Android system.The presented details of each component are prone to change in the future dueto uncertainties regarding their future development. Further investigation of eachvoice recognition system is necessary in order to fully create the general solutionAptiv aims for. Other companies may benefit from the process that has been usedin this thesis when trying to integrate components. The artifact presented in thisthesis could be applied to other cases, where multiple components are present andthe causes and effects of choosing one or several have implications that need to beconsidered.
Self-training for Few-shot Transfer Across Extreme Task Differences

Most few-shot learning techniques are pre-trained on a large, labeled base dataset. In problem domains where such large labeled datasets are not available for pre-training (e.g., X-ray, satellite images), one must resort to pre-training in a different problem domain (e.g., ImageNet), which can be very different from the desired target task. Traditional few-shot and transfer learning techniques fail in the presence of such extreme differences between the source and target tasks. In this paper, we present a simple and effective solution to tackle this extreme domain gap: self-training a source domain representation on unlabeled data from the target domain. We show that this improves one-shot performance on the target domain by 2.9 points on average on the challenging BSCD-FSL benchmark consisting of datasets from multiple domains. Our code is available at this https URL.
A Light-Weight Monocular Depth Estimation with Edge-Guided Occlusion Fading Reduction

Self-supervised monocular depth estimation methods suffer occlusion fading, which is a result of a lack of supervision by the ground truth pixels. A recent work introduced a post-processing method to reduce occlusion fading; however, the results have a severe halo effect. This work proposes a novel edge-guided post-processing method that reduces occlusion fading for self-supervised monocular depth estimation. We also introduce Atrous Spatial Pyramid Pooling with Forward-Path (ASPPF) into the network to reduce computational costs and improve inference performance. The proposed ASPPF-based network is lighter, faster, and better than current depth estimation networks. Our light-weight network only needs 7.6 million parameters and can achieve up to 67 frames per second for $$256\,\times \,512$$ inputs using a single nVIDIA GTX1080 GPU. The proposed network also outperforms the current state-of-the-art methods on the KITTI benchmark. The ASPPF-based network and edge-guided post-processing produces better results, both quantitatively and qualitatively than the competitors.
On the assessment of face image quality based on handcrafted features

This paper studies the assessment of the quality of face images, predicting the utility of face images for automated recognition. The utility of frontal face images from a publicly available dataset was assessed by comparing them with each other using commercial off-the-shelf face recognition systems. Multiple face image features delineating face symmetry and characteristics of the capture process were analysed to find features predictive of utility. The selected features were used to build system-specific and generic random forest classifiers.
Governance, Blockchain, Cyberspace: How Technology Implies Normative Power and Regulation

Technologies and their inherent design choices create normative structures that affect governance. This chapter aims to illustrate how block-chain technology in particular introduces new norms into a legal framework. We first analyze the different forms of governance by distinguishing between old and new governance. With a view to code that functions as legal norms, Block-chain technology is particularly suited to create governance structures and mechanisms. However, one needs to be aware of the norms that are implicitly introduced into the legal system by a specific block-chain technology. We look at the block-chain technology that underlies cryptocurrencies such as Bitcoin. This block-chain introduces a decentralized, transparent, cryptographic-ally locked and thus immutable shared ledger. In summary, these design choices have normative powers over the user and over user interaction. If this is indeed the case, then regulators have to actively assess newly introduced digital ledger technology and other technologies for their effect on the normative and legal system.
Entanglement swapping of d-dimension bipartite and multi-particle entangled states

We consider entanglement swapping schemes for several d-level entangled states, including d-dimension maximally entangled states, GHZ-class states, and cat-like states, where each scheme is realized by generalized Bell measurements. We also consider the entanglement swapping chains proposed by Hardy et al. [Phys. Rev. A. 62(5) 052315] for maximally entangled states. Applications of the proposed entanglement swapping schemes in quantum cryptography are described.
Speeding it Up: Perception of High-Frame Rate Videos

Modern streaming video providers continuously seek to improve consumer experiences by delivering higher-quality, denser content. An important direction that bears study is high-frame rate (HFR) videos, which present unique problems involving balances between frame rate, video quality, and compression. I will describe new large-scale perceptual studies that we have conducted that are focused on these issues. I will also describe new computational video quality models that address highly practical questions, such as frame rate selection versus compression, and how to combine space-time sampling with compression. My hopes are that these contributions will help further advance the global delivery of HFR video content
The Effects of L2 Motivational Self on English Achievement with a Mediating Role of English Learning Strategies: Comparison of General and Special-purpose High Schools

The purpose of the study is to compare the impact of L2 Motivational Self, ideal L2 self and ought-to L2 self, on English achievement with a mediating role of English learning strategies with a focus on the types of high schools(both general high school and special purpose high school). A total of 518 high school students from five major cities/provinces participated in this study and completed questionnaires. The results are as follows. First, students from special purpose high schools scored significantly higher in ideal L2 self, English learning strategies and English achievement than students from general high schools. Second, both high schools demonstrated the same in which ideal L2 self affected English achievement through the mediating role of English learning strategies but the degree of impact was different. Instead, ought-to L2 self only affected students from general high school. Ought-to L2 self increased English achievement and English learning strategies played a full mediating effect. The study identified the relationships between L2 Motivational Self and English learning and reaffirmed the importance of ideal L2 self. The study also suggests that ought-to L2 self should not always be underestimated since it supports some students.
Deep Neural Networks for Predicting Affective Responses from Movies

In this work, we develop deep neural networks for predicting affective responses from movies taking both audio and video streams into account. This study also tackles the issue of how to build a representation of video and audio in order to predict emotions that movies elicit in viewers. Besides, we analyse and identify helpful features extracted from video and audio streams that are important for the design of a good emotion prediction model. Fusion techniques are also taken into account with the aim to obtain the highest prediction accuracy.
Binocular Multi-CNN System for Real-Time 3D Pose Estimation

The current practical approaches for depth-aware pose estimation convert a human pose from a monocular 2D image into 3D space with a single computationally intensive convolutional neural network (CNN). This paper introduces the first open-source algorithm for binocular 3D pose estimation. It uses two separate lightweight CNNs to estimate disparity/depth information from a stereoscopic camera input. This multi-CNN fusion scheme makes it possible to perform full-depth sensing in real time on a consumer-grade laptop even if parts of the human body are invisible or occluded. Our real-time system is validated with a proof-of-concept demonstrator that is composed of two Logitech C930e webcams and a laptop equipped with Nvidia GTX1650 MaxQ GPU and Intel i7-9750H CPU. The demonstrator is able to process the input camera feeds at 30 fps and the output can be visually analyzed with a dedicated 3D pose visualizer.
“Emergency Remote Teaching”: A Case Study of the Online English Literature Course

The COVID-19 pandemic has brought about unprecedented changes in the landscape of education across the world. Following the first few-weeks’ mass closures of educational institutions in March 2020, all the universities and colleges in Korea began the spring semester online. Nationwide switching to online during such a short period of time may have proved both our strength of IT infrastructure and operational effectiveness of all those involved in educational system, but the salient features of this instant transition in all levels of educational practices ask for shared on-going discussions about ‘pandemic’ pedagogy, especially as the threat of COVID-19 is now being ever greater. This study thus aims to be a part of such discussions, first, briefly examining the notions and approaches that the current emergency education compels for a deliberation, and then sharing the experience of the shift-to-online teaching of the Nineteenth-century English literature course. With a critical caution against rushed ideas of post-corona education, and also to draw attention to contextual appreciation for pandemic education, the paper takes on “emergency remote teaching” suggested as an alternative term by online education scholars, and goes on to scrutinize the “Film Narrative and English Literature” course implemented as such. Its focus is more on the major aspects of the modification in instructional design, operational strategies, and student supporting required for an asynchronous model of “emergency remote teaching,” rather than the course content, so that its context and process elements can be underlined.
Activities and challenges of brazilian university libraries during the covid 19 pandemic

In a context marked by the worsening of the global social, economic and political crisis, the emergency caused by COVID-19 and the adoption of extraordinary measures of social distance and quarantine, new challenges are imposed on libraries In this scenario, the purpose of this paper was to characterize the activities of the Brazilian university libraries, as well as the main challenges to keep them functioning through remote products and services, from the perspective of librarians A survey of data is carried out through online questionnaires and a quantitative and qualitative analysis of 188 responses from participants linked to 64 higher education institutions, from all Brazilian regions Almost all respondent libraries have implemented remote working, using information and communication technologies (with emphasis on social networks), to carry out reference services, disseminate information, as well as to offer access to databases Technical processes and development of collections are carried out as well, albeit with some limitations Some libraries have created specific support services for research on COVID-19 Main challenges are related to the sudden shift from personal attendance and face-to-face work, to remote working and virtual attendance, without prior preparation (performance protocols, infrastructure, professional skills) Reopening of libraries is another major challenge, since it includes changes in facilities, protective equipment, and hygiene protocols It is concluded that university libraries have activities and challenges going beyond pure technical aspects, including ethical and political dimensions looking forward to building better societies © 2020, Centro Nacional de Informacion de Ciencias Medicas All rights reserved
Grassmannian codes from paired difference sets

An equiangular tight frame (ETF) is a sequence of vectors in a Hilbert space that achieves equality in the Welch bound and so has minimal coherence. More generally, an equichordal tight fusion frame (ECTFF) is a sequence of equi-dimensional subspaces of a Hilbert space that achieves equality in Conway, Hardin and Sloane's simplex bound. Every ECTFF is a type of optimal Grassmannian code, that is, an optimal packing of equi-dimensional subspaces of a Hilbert space. We construct ECTFFs by exploiting new relationships between known ETFs. Harmonic ETFs equate to difference sets for finite abelian groups. We say that a difference set for such a group is with a difference set for its Pontryagin dual when the corresponding subsequence of its harmonic ETF happens to be an ETF for its span. We show that every such pair yields an ECTFF. We moreover construct an infinite family of paired difference sets using quadratic forms over the field of two elements. Together this yields two infinite families of real ECTFFs.
Augmented Museum Experience with Interactive Visualization: Korean Mummy

We have developed interactive content about Korean mummy to augment the exhibition that is already going on at the Korean Natural History Museum in Korea. This paper introduces the concept of employment of visualization techniques to bring out the intriguing story of a Korean mummy. Haptic rendering and volume rendering are the main algorithms implemented for excavation of a mummy and exploration of the mummy’s internal body. Touch screen interface and a haptic device were utilized for intuitive interaction for general public use, especially for children. The virtual contents were formulated based on the information displayed on the wall panels and exhibited at the museum on site. The interactive display fetched more attention to the exhibit and provided a better understanding of the contents. From our survey, it is encouraging to learn that the children who experienced the interactive exhibition showed their interest to revisit the museum.
And, Action! Towards Leveraging Multimodal Patterns for Storytelling and Content Analysis

Humans perform intelligent tasks by productively leveraging relevant information from numerous sensory and experiential inputs, and recent scientific and hardware advances have made it increasingly possible for machines to attempt this as well. However, improved resource availability does not automatically give rise to humanlike performance in complex tasks [1]. In this talk, I discuss recent work towards three tasks that benefit from an elegant synthesis of linguistic and visual input: visual storytelling, visual question answering (VQA), and affective content analysis. I focus primarily on visual storytelling, a burgeoning task with the goal of generating coherent, sensible narratives for sequences of input images [2]. I analyze recent work in this area, and then introduce a novel visual storytelling approach that employs a hierarchical context-based network, with a co-attention mechanism that jointly attends to patterns in visual (image) and linguistic (description) input.
Learning Self-Supervised Multimodal Representations of Human Behaviour

Self-supervised learning of representations has important potential applications in human behaviour understanding. The ability to learn useful representations from large unlabeled datasets by modeling intrinsic properties of the data has been successfully employed in various fields of machine learning, often outperforming transfer learning or fully supervised training. My research interests lie in applying these ideas to multimodal human-centric data. In this extended abstract, I present the direction of research that I have followed during the first half of my PhD, along with ideas and work in progress for the second half. My completed research so far demonstrates the potential of cross-modal self-supervision for audio representation learning, especially on small downstream datasets. I want to explore similar ideas for visual and multimodal representation learning, and apply them to speech and emotion recognition and multimodal question answering.
SF-UDA$^{3D}$: Source-Free Unsupervised Domain Adaptation for LiDAR-Based 3D Object Detection

3D object detectors based only on LiDAR point clouds hold the state-of-the-art on modern street-view benchmarks. However, LiDAR-based detectors poorly generalize across domains due to domain shift. In the case of LiDAR, in fact, domain shift is not only due to changes in the environment and in the object appearances, as for visual data from RGB cameras, but is also related to the geometry of the point clouds (e.g., point density variations). This paper proposes SF-UDA$^{3D}$, the first Source-Free Unsupervised Domain Adaptation (SF-UDA) framework to domain-adapt the state-of-the-art PointRCNN 3D detector to target domains for which we have no annotations (unsupervised), neither we hold images nor annotations of the source domain (source-free). SF-UDA$^{3D}$ is novel on both aspects. Our approach is based on pseudo-annotations, reversible scale-transformations and motion coherency. SF-UDA$^{3D}$ outperforms both previous domain adaptation techniques based on features alignment and state-of-the-art 3D object detection methods which additionally use few-shot target annotations or target annotation statistics. This is demonstrated by extensive experiments on two large-scale datasets, i.e., KITTI and nuScenes.
STORIUM: A Dataset and Evaluation Platform for Machine-in-the-Loop Story Generation

Systems for story generation are asked to produce plausible and enjoyable stories given an input context. This task is underspecified, as a vast number of diverse stories can originate from a single input. The large output space makes it difficult to build and evaluate story generation models, as (1) existing datasets lack rich enough contexts to meaningfully guide models, and (2) existing evaluations (both crowdsourced and automatic) are unreliable for assessing long-form creative text. To address these issues, we introduce a dataset and evaluation platform built from STORIUM, an online collaborative storytelling community. Our author-generated dataset contains 6K lengthy stories (125M tokens) with fine-grained natural language annotations (e.g., character goals and attributes) interspersed throughout each narrative, forming a robust source for guiding models. We evaluate language models fine-tuned on our dataset by integrating them onto STORIUM, where real authors can query a model for suggested story continuations and then edit them. Automatic metrics computed over these edits correlate well with both user ratings of generated stories and qualitative feedback from semi-structured user interviews. We release both the STORIUM dataset and evaluation platform to spur more principled research into story generation.
You Have a Point There

Sophisticated user interaction in the automotive industry is a fast emerging topic. Mid-air gestures and speech already have numerous applications for driver-car interaction. Additionally, multimodal approaches are being developed to leverage the use of multiple sensors for added advantages. In this paper, we propose a fast and practical multimodal fusion method based on machine learning for the selection of various control modules in an automotive vehicle. The modalities taken into account are gaze, head pose and finger pointing gesture. Speech is used only as a trigger for fusion. Single modality has previously been used numerous times for recognition of the user's pointing direction. We, however, demonstrate how multiple inputs can be fused together to enhance the recognition performance. Furthermore, we compare different deep neural network architectures against conventional Machine Learning methods, namely Support Vector Regression and Random Forests, and show the enhancements in the pointing direction accuracy using deep learning. The results suggest a great potential for the use of multimodal inputs that can be applied to more use cases in the vehicle.