Reviewerly/exHarmony huggingface dataset:
S<sup>2</sup>LD: Semi-Supervised Landmark Detection in Low Resolution Images and Impact on Face Verification

Landmark detection algorithms trained on high resolution images perform poorly on datasets containing low resolution images. This degrades the performance of facial verification, recognition and modeling that rely on accurate detection of landmarks. To the best of our knowledge, there is no dataset consisting of low resolution face images along with their annotated landmarks, making supervised training infeasible. In this paper, we present a semi-supervised approach to predict landmarks on low resolution images by learning them from labeled high resolution images. The objective of this work is to show that predicting landmarks directly on low resolution images is more effective than the current practice of aligning images after rescaling or super-resolution. In a two-step process, the proposed approach first learns to generate low resolution images by modeling the distribution of target low resolution images. In the second stage, the model learns to predict landmarks for target low resolution images from generated low resolution images. With extensive experimentation, we study the impact of the various design choices and also show that prediction of landmarks directly in low resolution, improves performance on the critical task of face verification in low resolution images. As a byproduct, the proposed method also achieves competitive land mark detection results for high resolution images, with a single U-Net.
Affordances of Shape-Changing Interfaces

Affordances is an important term in the field of Shape-Changing Interfaces (SCI). But the field is challenged by a multitude of vaguely defined concepts around affordances such as dynamic, spatial, or tactile affordances. In line with this, Alexander et al [1] recently pointed to lack of theory as one of the grand challenges for SCI. This paper proposes a re-analysis of Gibson for resolving the conceptual cacophony in the field of SCI. Essential to the ecological approach to affordances, is that perception is dynamic and people pick up information through exploring objects and environments over time. From this perspective, affordances of Shape-Changing Interfaces become a matter of providing Information - through the design - regarding a) the interfaces' ability to transform its shape and b) how movement suggests level of animacy. Through analyzing scholarly SCI prototypes, from this perspective, we provide an initial set of design strategies for affordances of shape-changing interfaces.
ScagCNN

Scagnostics is a set of visual features that characterizes the data distribution of a 2D scatterplot and has been used in a wide range of applications. However, calculating the scagnostics scores involves computationally expensive algorithms. Moreover, the algorithms are sensitive to the slight changes in the underlying data distribution within the scatterplot. Therefore, this work provides a machine learning model, called ScagCNN, to estimate the scagnostics scores. This model aims to improve the scagnostics computation time and to reduce the sensitivity to the small shifts in the data distribution. This work also provides a web prototype to explore the predictive performance of the model and to give a visual explanation about whether a prediction is accurate. Furthermore, we test the performance of our solution on datasets of various sizes.
Voiceye

People with physical impairments who are unable to use traditional input devices (i.e. mouse and keyboard) are often excluded from technical professions (e.g. web development). Alternative input methods such as eye gaze tracking and speech recognition have become more readily available in recent years with both being explored independently to support people with physical impairments in coding activities. This paper describes a novel multimodal application ("Voiceye") that combines voice input, gaze interaction, and mechanical switches as an alternative approach for writing code. The system was evaluated with non-disabled participants who have coding experience (N=29) to assess the feasibility of the application in writing HTML and CSS code. Results found that Voiceye was perceived positively and enabled successful completion of coding tasks. A follow-up study with disabled participants (N=5) demonstrated that this method of multimodal interaction can support people with physical impairments in writing and editing code.
A Survey of the Model Transfer Approaches to Cross-Lingual Dependency Parsing

Cross-lingual dependency parsing approaches have been employed to develop dependency parsers for the languages for which little or no treebanks are available using the treebanks of other languages. A language for which the cross-lingual parser is developed is usually referred to as the target language and the language whose treebank is used to train the cross-lingual parser model is referred to as the source language. The cross-lingual parsing approaches for dependency parsing may be broadly classified into three categories: model transfer, annotation projection, and treebank translation. This survey provides an overview of the various aspects of the model transfer approach of cross-lingual dependency parsing. In this survey, we present a classification of the model transfer approaches based on the different aspects of the method. We discuss some of the challenges associated with cross-lingual parsing and the techniques used to address these challenges. In order to address the difference in vocabulary between two languages, some approaches use only non-lexical features of the words to train the models while others use shared representations of the words. Some approaches address the morphological differences by chunk-level transfer rather than word-level transfer. The syntactic differences between the source and target languages are sometimes addressed by transforming the source language treebanks or by combining the resources of multiple source languages. Besides cross-lingual transfer parser models may be developed for a specific target language or it may be trained to parse sentences of multiple languages. With respect to the above-mentioned aspects, we look at the different ways in which the methods can be classified. We further classify and discuss the different approaches from the perspective of the corresponding aspects. We also demonstrate the performance of the transferred models under different settings corresponding to the classification aspects on a common dataset.
Automated System for Indian Banknote Recognition using Image Processing and Deep Learning

This work proposes to develop an automated system for Indian banknote recognition using image processing and deep learning techniques which will be invariant of orientation and independent of the sides or faces of the notes. The images of currency notes are preprocessed to compensate for rotation and then passed through a technique to detect whether they are Indian banknotes or not. If found to be Indian banknote, then the denomination is classified in two different ways: (i) extracting colour and texture features to form feature vectors and the test samples are classified using k-NN based on those features, (ii) images of Indian banknotes are directly fed to a Convolutional Neural Network (CNN) for classification.
Engaging with Children Using Augmented Reality on Clothing to Prevent Them from Smoking

Smoking is a harmful habit, causing a range of severe consequences which could lead to premature death. This habit is still prevalent amongst young people. In order to protect children, effective early interventions supported by public instances need to be set in place. Raising awareness and educating the youth is crucial to change their mindset about the severity of smoking. Emerging technologies, such as augmented reality (AR) on mobile devices, have been shown to be useful in providing engaging experiences and educating children about a range of issues, including health and anatomy. This chapter presents a research which explores the use of AR as an exciting and engaging medium to effectively help educating children from 5 to 13 years about the effects of smoking. A mobile application, called SmokAR, was developed. This app includes AR visualization amongst other functionalities, whereby children are presented a realistic model of the human lungs of a healthy person and of a smoker. The aim of this research is to propose a transformative experience in order to put children off this dangerous habit whilst they gain knowledge about the effect of smoking on their organs. The anatomical accuracy of the 3D models and animations proposed by the app has been verified by an expert anatomist. A group of children (n = 17) also took part in usability and knowledge acquisition testing at the Glasgow Science Centre. Findings showed a significant high usability suggesting a user-friendly app design. Moreover, results also suggested that participants gained knowledge to a certain extent and felt discouraged from smoking after seeing the model of the smoker's lungs. Although there were several limitations to the study, the potential of the app to support learning and raising awareness is encouragingly positive. In addition, user testing in a more controlled environment, such as a classroom, can help gain further insights into the effectiveness and usability of the app. In the future, this simple but engaging approach to raise public awareness and support education could be used to further communicate with children about negative health effects of other harmful habits such as alcohol or drug consumption.
"A Postcard from Your Food Journey in the Past"

Food-posting, a pervasive practice on social media platforms, opens a window for introspection on personal food intake, physical health, and mental well-being. Existing self-reflection tools on food intake usually require manual logging of dietary information and inadequately support retrospective reviews beyond the data. To facilitate in-depth, non-judgmental self-reflection on information hidden in food-posting, we propose a design to transform general food posts into "a postcard from a past food journey". The postcards are procedurally created from food posts, and encode nutritional values together with the user's emotional status extracted from photos and texts. After validating the visual design, we evaluate the auto-generated postcards with 20 participants to explore how they reflect on the data, context, action, and value subjects. Qualitative feedback indicates that our designs encourage users to review their physical and mental well-being differently from conventional visualization. We conclude by discussing issues identified with the non-judgmental postcard design.
Realistic simulation of virtual multi-scale, multi-modal patient trajectories using Bayesian networks and sparse auto-encoders

Abstract Translational research of many disease areas requires a longitudinal understanding of disease development and progression across all biologically relevant scales. Several corresponding studies are now available. However, to compile a comprehensive picture of a specific disease, multiple studies need to be analyzed and compared. A large number of clinical studies is nowadays conducted in the context of drug development in pharmaceutical research. However, legal and ethical constraints typically do not allow for sharing sensitive patient data. In consequence there exist data “silos”, which slow down the overall scientific progress in translational research. In this paper, we suggest the idea of a virtual cohort (VC) to address this limitation. Our key idea is to describe a longitudinal patient cohort with the help of a generative statistical model, namely a modular Bayesian Network, in which individual modules are represented as sparse autoencoder networks. We show that with the help of such a model we can simulate subjects that are highly similar to real ones. Our approach allows for incorporating arbitrary multi-scale, multi-modal data without making specific distribution assumptions. Moreover, we demonstrate the possibility to simulate interventions (e.g. via a treatment) in the VC. Overall, our proposed approach opens the possibility to build sufficiently realistic VCs for multiple disease areas in the future.
Solving visual object ambiguities when pointing: an unsupervised learning approach

Abstract Whenever we are addressing a specific object or refer to a certain spatial location, we are using referential or deictic gestures usually accompanied by some verbal description. Particularly, pointing gestures are necessary to dissolve ambiguities in a scene and they are of crucial importance when verbal communication may fail due to environmental conditions or when two persons simply do not speak the same language. With the currently increasing advances of humanoid robots and their future integration in domestic domains, the development of gesture interfaces complementing human–robot interaction scenarios is of substantial interest. The implementation of an intuitive gesture scenario is still challenging because both the pointing intention and the corresponding object have to be correctly recognized in real time. The demand increases when considering pointing gestures in a cluttered environment, as is the case in households. Also, humans perform pointing in many different ways and those variations have to be captured. Research in this field often proposes a set of geometrical computations which do not scale well with the number of gestures and objects and use specific markers or a predefined set of pointing directions. In this paper, we propose an unsupervised learning approach to model the distribution of pointing gestures using a growing-when-required (GWR) network. We introduce an interaction scenario with a humanoid robot and define the so-called ambiguity classes. Our implementation for the hand and object detection is independent of any markers or skeleton models; thus, it can be easily reproduced. Our evaluation comparing a baseline computer vision approach with our GWR model shows that the pointing-object association is well learned even in cases of ambiguities resulting from close object proximity.
Sign language recognition based on concept learning

This paper presents a concept-based learning framework for sign language recognition. The recognition is based on the concept learning framework which transforms learning about high-level concepts into learning for low-level concepts, the structure of which is simpler and can be learned with fewer samples. The sign language samples were first segmented by a modified Time Varying Parameter (TVP) algorithm which segments the motion time series according to pause points, and the segments are used to build a primitive library with a selforganizing feature map (SOFM) network. Then the SL samples represented by the primitive library were recognized using template matching and model generation methods. Experimental results show that under the framework of concept learning, we achieve 3% to 4% accuracy improvement for the recognition of 95 classes Australian Sign Language (AUSL) using Dynamic Time Warping (DTW) and Hidden Markov Model (HMM) classifiers.
Research on Accuracy of Augmented Reality Surgical Navigation System Based on Multi-View Virtual and Real Registration Technology

The traditional navigation system used in spinal puncture is not able to monitor the surgical process in real time and the accuracy of navigation is unsatisfactory. In this study, an augmented reality surgical navigation system based on multi-view virtual and real registration is proposed to solve these problems. The theory of virtual and real registration in augmented reality technology is analyzed, and the methods of single-view and multi-view virtual and real registration are compared. The principle of coordinate transformation in the surgical navigation module is analyzed. The platform of augmented reality surgical navigation system based on multi-view virtual and real registration technology is designed. The experiments of the spinal model are used to verify the accuracy of virtual and real registration. The accuracy of the proposed navigation system is verified by the experiment of simulating puncture operation with robotic control of surgical tool. The experimental results show that the accuracy of single-view and multi-view virtual and real registration is 9.85±0.80mm and 1.62±0.22mm respectively. The accuracy of the augmented reality surgical navigation system added with multi-view virtual and real registration technology is 1.70±0.25mm, which is 35% higher than that of the augmented reality surgical navigation system not previously used. The augmented reality surgical navigation system for spinal puncture based on multi-view virtual and real registration proposed in this study can meet the requirements of physician on the accuracy of surgery. It can also help physician to monitor the surgical process in real time and improve the success rate of surgery.
Texture Recognization and Image Smoothing for Microcalcification and Mass Detection in Abnormal Region

The second most important cause of death is breast cancer in the country. In the early stages of the disease, primary treatment is difficult as its mechanisms are virtually unknown. Nonetheless, some common signatures of this disease can be used to improve early diagnostics approaches that are important for female Life quality. Mammograms of X-ray are the primary diagnostic and early diagnosis method and are the key to improving the prognosis of breast cancer examination and recovery. Good contrast and sometimes very fluidity of mass and healthy glandular tissue have been described to assist in their treatment, radiologists and internists. Many computerized diagnostics programs have been developed. The method presented in this paper is an important study of visual texture-based mammography for early-stage tumor detection. A few pictures from the digital data base were taken to screen and diagnose cancer mammograms. The suggested algorithm could be used to differentiate mass and micro calcifications by morphological operators from the context fabric and then to separate them using machine learning.
Bullet Screen Short Text Sentiment Analysis Algorithm

Bullet screen can express the feeling of the audience when watching video. If emotional analysis and research are carried out on it, it is helpful to improve the accuracy of user recommendation system. The existing emotional analysis method of bullet screen text separates emotional symbols from text information and ignores emotional expression of emotional symbols in bullet screen. To this end, an emotional symbol space multi attention convolutional neural network model (ES-MACNN) is proposed for video barrage sentiment analysis, the model selects emoji and kaomoji to construct emotional symbol space, uses attention mechanism to describe the important degree of emotional information, and obtains deeper emotional feature information to achieve the purpose of improving classification accuracy. Through experimental verification and analysis, the ES-MACNN model has significantly improved classification precision and recall rate compared with the traditional model.
Language Independent Multi-Class Sentiment Analysis

One of the common approaches for putting user review is social media. In the last year, several attempts have been dedicated to automatically mining view and feelings from natural language in social media posts, news and business product reviews. It is necessary to evaluate the user's emotion by studying the user's input text and feelings with the help of textual communication techniques. Till now the most of the work has been done on identifying the polarity of sentiment is classified into positive, negative , neutral only. Our system provides an emotional determination strategy from the text and emojis entered by the user with direct and indirect tweets. It classifies emotions more than three classes has been identified like happy, sad, angry, sarcasm, neutral using sentiword emoji's based dictionary The proposed method utilizes techniques of extraction of features and extraction of emotions, to determine emotion of the user. Some associated work is reviewed to design the system, which gives the concept of prior work done. It also deals with special words, emoji, direct and indirect emotions expressed in different languages. The classifier which is used is hybrid to avoid drawback of SVM, Naïve bayes and Maximum entropy.
Quantum speed limit for robust state characterization and engineering

In this paper we propose to use a quantum speed limit (QSL) as a measure of robustness of states, defining a state with a bigger QSL as more robust. From this perspective, it is important to have an explicitly computable QSL, because then we can formulate an engineering problem of the Hamiltonian that makes a target state robust against decoherence. Hence we derive an explicitly computable QSL that is applicable to general Markovian open quantum systems. This QSL is tighter than another explicitly computable QSL, in an important setup such that decoherence is small. Also, the Hamiltonian engineering problem with this QSL is a quadratic convex optimization problem, and thus it is efficiently solvable. The idea of robust state characterization and the Hamiltonian engineering, in terms of the QSL, is demonstrated with several examples.
Retinex Based Image Enhancement via General Dictionary Convolutional Sparse Coding

Retinex theory represents the human visual system by showing the relative reflectance of an object under various illumination conditions. A feature of this human visual system is color constancy, and the Retinex theory is designed in consideration of this feature. The Retinex algorithms have been popularly used to effectively decompose the illumination and reflectance of an object. The main aim of this paper is to study image enhancement using convolution sparse coding and sparse representations of the reflectance component in the Retinex model over a learned dictionary. To realize this, we use the convolutional sparse coding model to represent the reflectance component in detail. In addition, we propose that the reflectance component can be reconstructed using a trained general dictionary by using convolutional sparse coding from a large dataset. We use singular value decomposition in limited memory to construct a best reflectance dictionary. This allows the reflectance component to provide improved visual quality over conventional methods, as shown in the experimental results. Consequently, we can reduce the difference in perception between humans and machines through the proposed Retinex-based image enhancement.
The Potential of Using Dynamic Strains in Earthquake Early Warning Applications

Abstract We investigate the potential of using borehole strainmeter data from the Network of the Americas (NOTA) and the U.S. Geological Survey networks to estimate earthquake moment magnitudes for earthquake early warning (EEW) applications. We derive an empirical equation relating peak dynamic strain, earthquake moment magnitude, and hypocentral distance, and investigate the effects of different types of instrument calibration on model misfit. We find that raw (uncalibrated) strains fit the model as accurately as calibrated strains. We test the model by estimating moment magnitudes of the largest two earthquakes in the July 2019 Ridgecrest earthquake sequence—the M 6.4 foreshock and the M 7.1 mainshock—using two strainmeters located within ∼50 km of the rupture. In both the cases, the magnitude based on the dynamic strain component is within ∼0.1–0.4 magnitude units of the catalog moment magnitude. We then compare the temporal evolution of our strain-derived magnitudes for the largest two Ridgecrest events to the real-time performance of the ShakeAlert EEW System (SAS). The final magnitudes from NOTA borehole strainmeters are close to SAS real-time estimates for the M 6.4 foreshock, and significantly more accurate for the M 7.1 mainshock.