Reviewerly/exHarmony huggingface dataset:
A Pipelined Approach to Deal with Image Distortion in Computer Vision

Image classification is a well-established problem in computer vision. Most state-of-the-art models rely on Convolutional Neural Networks to achieve near-human performance in that task. However, CNNs have shown to be susceptible to image manipulation, which undermines the trustability of perception systems. This property is critical, especially in unmanned systems, autonomous vehicles, and scenarios where light cannot be controlled. We investigate the robustness of several Deep-Learning based image recognition models and how the accuracy is affected by several distinct image distortions. The distortions include ill-exposure, low-range image sensors, and common noise types. Furthermore, we also propose and evaluate an image pipeline designed to minimize image distortion before the image classification is performed. Results show that most CNN models are marginally affected by mild miss-exposure and Shot noise. On the one hand, the proposed pipeline can provide significant gain on miss-exposed images. On the other hand, harsh miss-exposure, signal-dependent noise, and impulse noise, incur in a high impact on all evaluated models.
Psychological and pedagogical components of training future teachers to interact with the family of students

The article analyzes the main difficulties in the interaction of teachers with the parents of students. On this basis, measures are proposed to improve professional training in pedagogical universities. It is shown that the development of communication, design and predictive components in future professional activities can bring the training of students to a new qualitative level.
CaseGuide: Making Cheap Smartphones Accessible to Individuals with Visual Impairments in Informal Settlements

Individuals with visual impairments in informal settlements (IVIIS) depend highly on others for access to basic services. Smartphones can help provide assistive technology and access to basic services but are too expensive for IVIIS or lack accessibility features. This study explores and promotes a low-cost concept that uses a static interface overlay app in conjunction with a button enabled phone case, to enable the use of cheap smartphones and increase IVIIS autonomy and inclusion in society. Using existing research and an observational study of YouTube videos, design requirements were determined. A low-fidelity prototype was developed and usertested on one visually impaired and two blindfolded participants. Although usertests showed promising results, research and usertesting were limited. Future research and usertests with IVIIS are needed to validate if CaseGuide is a desirable solution for IVIIS.
Ray-Casting-based 3D pointing and Dragging Interface for Naked-Eye Stereoscopic Displays

In this work, a ray-casting-based three-dimensional (3D) pointing and dragging interface for naked-eye stereoscopic displays is proposed. When a user holds a stylus and points it to a display, the proposed system displays a ray, which extends from the stylus to the virtual space in the display. This ray can be used to interact with objects in the virtual space. By conducting a user study, we found that the proposed method allows users to perform 3D pointing with smaller hand movements compared to a hand-capture-based interface. A model, which extends Fitts’s law, is also proposed. This model is capable of well predicting the time required for a 3D pointing task.
Analog Circuit Faulty Parameter Identification Based on Two-Objective Evolutionary Algorithm

Faulty parameter identification is vital to prediction of remaining useful life of circuit under test (CUT). Based on the transfer function, we use measured faulty response to deduce the possible faulty parameters. It is known that the measured response of the CUT is the function of the analog parameters. Usually, the number of independent response is much less than that of the analog component. The underdetermined equations have infinite solutions. In other words, there are lots of combinations of analog parameters that can generate the same faulty response. Hence, it is more valuable to deduce the possible faulty parameter range than to estimate a single faulty parameter. The deduction is accomplished by using a modified two-objective genetic algorithm. The individual that minimizes the deviation, viz., difference between the simulated and measured faulty responses, gives the possible faulty parameter. Finding the possible faulty parameter range equals to finding the minimal and maximal faulty parameters that can zero the deviation. Hence, we have to simultaneously minimize (and maximize) the faulty parameters and minimize the deviation. It is transformed into a two-objective optimization problem. We use Pareto dominated method to minimize and maximize the faulty parameter. The effectiveness of the proposed method is examined by using filter circuit examples.
Prototype-Based Online Learning on Homogeneously Labeled Streaming Data

Algorithms in machine learning commonly require training data to be independent and identically distributed. This assumption is not always valid, e. g. in online learning, when data becomes available in homogeneously labeled blocks, which can severely impede especially instance-based learning algorithms. In this work, we analyze and visualize this issue, and we propose and evaluate strategies for Learning Vector Quantization to compensate for homogeneously labeled blocks. We achieve considerably improved results in this difficult setting.
A Novel Approach to Categorize News Articles From Headlines and Short Text

Over the last few years the world has experienced a surge in the number of online news portals. This has caused the volume of news articles to reach an all time high; which will only get higher with time. Thus, an efficient system of categorization and organization of the articles has become a necessity for various information systems like- news aggregation and association in search engines. It is impractical to employ humans to label this expansive volume of text data, prompting the growth of automated text categorization systems. And so, we devised a deep learning model that effectively categorizes news articles from the headlines and short text descriptions. The prime foci of our work were to design, develop, and measure the performance metrics of our proposed model.
Homogeneous Pools to Heterogeneous Ensembles for Unsupervised Outlier Detection

A member selection method is presented here for ensembles of unsupervised outlier detection. The key challenge of ensemble construction in unsupervised scenario is the absence of labeled training set. Thus, an alleged outlier set needs to be formed first. Existing methods construct this set, called as target set, comprising all random detectors at input. Our argument is that such target formation may itself be erroneous as an outcome of such no filtered random results. Hence, complete reliance on it may mislead entire selection process. Herein, we propose an ensemble construction approach HEnS (Heterogeneous Ensemble Selector), which selects members from different sets of homogeneous detectors to build a heterogeneous one. Heterogeneity is ensured to induce diversity to form a good target outlier set, first, by considering those detectors at input which are characteristically distinct, and second, by selecting minimal subset of detectors of each type. Accuracy is maintained, first by pruning the relatively highly inaccurate detectors out of the process, and second, by forming a better target which includes only two extreme parameter instantiations from each type. This work primarily aims at building a heterogeneous ensemble which comprises of best or relatively better detectors from each of the homogeneous groups. Experimental results on benchmark datasets show notably improved prediction accuracy of ensembles constructed using proposed method, which in turn supports our claim of better target construction and enhanced diversity.
Camera calibration based on phase calibration targets

For traditional camera calibration methods, the calibration accuracy of camera parameters is highly dependent on the feature extraction of the control points of the calibration target. However, due to problems such as perspective and lens distortion, the commonly used checkerboards, circular dots, and other calibration patterns will inevitably undergo large deformation, resulting in a serious impact on the accuracy of the extraction of control points. To solve this problem, a camera calibration method based on active phase calibration targets is proposed. The method uses the MI Pad that can automatically display the structured light patterns as the calibration target. Compared with the method of using a checkerboard calibration target, this method does not require pattern detection and avoids the process of manual marking. Also, based on the phase information of the structured light gratings, a large number of dense, high-precision control point coordinates can be obtained even in areas where the image edge distortion is severe. Not only is it highly automated, but it is also suitable for defocused cameras. Experimental results show that the reprojection error of our method is only onetenth of the comparison method, and it has better robustness and accuracy.
Application of Open GL Function in Computer Graphics

Abstract In order to make computer graphics from abstract to concreteness, OpenGL visual programming technology is widely used in our life. The illumination, material and atomization in OpenGL function are used to set up the triangular mesh model. OpenGL function is introduced to achieve the goal of rebuilding the object.
CLASS-O, A Cooperative Language Assessment System with Ontology

This paper describes the design, development and test of a novel Computerized Adaptive Testing (CAT) system for English language based on a testing process using features of Item Response Theory and an Ontology of English Language (ELO). The testing process employs a variable branching procedure to deliver the best next test item to the examinee based on the ability level shown so far. The formative assessment part is based on ELO, which acts as knowledge base for giving efficient and detailed feedback to the examinee. The summative part of the feedback consists of the percentage of correct answers regarding the three sections covered in the test: vocabulary, reading comprehension, and structure. It includes the proficiency level and places examinees into one of three groups: high, medium and low proficiency. After the test has terminated, each examinee contributes to a new version of the item bank by feeding back the individual response parameters, which refines item data for subsequent tests.
Health status prediction of airborne systems based on transfer learning

Abstract In this paper, based on the prediction of the decay mode of the system health state, a health pattern recognition and prediction method based on transfer learning is proposed. In the context of big data, the system's healthy decline mode is summarized from the massive historical flight data, and then the research on the health status of the airborne system based on the recognition results is carried out. Firstly, this paper demonstrates the feasibility of transfer learning applied to the prediction of the health status of airborne systems. Then, a HMM-based parameter migration health state prediction method is proposed. Finally, the model is verified by the hydraulic system of a certain type of aircraft. The results show that the model can predict the time when the health state changes.
Silhouette Analysis of Hand Gesture Dataset Using Histogram Profile Feature Extraction

Hand gesture dataset is a collection of hand gesture images. Several hand gesture datasets are freely available and can be used for various purposes, such as comparison or method testing. Processing the distribution of hand gesture image quality in the dataset has the opportunity to find potential models of hand gesture image quality for further research. This study tries to provide answers by exploring the quality of hand gesture images based on various datasets of public hand gestures. Then perform feature extraction based on the image histogram profile to get an overview of the range of color intensity values from the hand gesture image. The Herarchical Clustering method is used to build clusters based on histogram characteristics. The feasibility of the relationship between clusters was tested based on the silhouette index clustering method. The total number of hand gesture test images is 16 thousand data taken from 6 dataset sources that have been used in hand gesture recognition research. Based on the results of the processing, it is shown that the three clusters have no relationship feasibility or in other words the image clusters are independent.
Clarity of Shoe Impressions in Different Soil Types

Forensic science is a vastly expanding field as many different types of evidence and methods of analysis are being developed.  One key type of evidence is impression evidence, and this experiment focused on shoe impressions made in soil.  The research was based on how different types of soil at different moisture levels affects the clarity of shoe impressions.  Shoe impressions were made in four various “types” of soil and three different impressions were made in each type.  Molds were taken in plaster and then analyzed to see the clarity at which the shoe impressions could be made out.  It was noted that none of the molds displayed individual characteristics and it was very different to observe even the basic tread of the shoe.  The results show that the soil with the higher moisture levels made it overall easier to see and analyze the tread of the shoe and that soil that is more malleable, but not muddy, allows for shoe impressions to be more easily read.
Accurate and Robust System Calibration Method for Fringe Projection Profilometry

Fringe projection profilometry technique has attracted more and more attention in the computer vision and found wide applications in the areas of high-precision measurement. This paper presents an accurate and robust system calibration method to formulate the phase-to-height conversion. This method uses the Levenberg-Marquardt nonlinear optimization algorithm to fit the phase value of a virtual reference plane, which is parallel to the line between the optical centers of the camera and the projector. The final expression just has three coefficients. With no need to manually adjust the position of components, several experiments were conducted to evaluate the performance of proposed method and demonstrated that the novel system calibration outperforms state-of-the-art methods in terms of stability and accuracy.
Methods of Machine Learning in Oncology

Malignant diseases are among the leading causes of mortality. The registration of malignant diseases in Bulgaria is mandatory. The information from the National Cancer Registry about the registered patients, their psychosocial distress and the medical establishments provides an opportunity to conduct research with machine learning methods on a large volume of data. The study aims to apply three machine learning algorithms - CHART, Boosting and Bagging to create classification models for determining the rating of a medical institution and the level of distress of a patient. We present results obtained in a comparative study of different classification methods.
When complementation gets specific: A study of collocational preferences in verb–object combinations in Norwegian

Abstract This study investigates to what extent there are collocational preferences in the verb–object combinations of a large corpus of Norwegian and how important recurrent combinations are in usage. The material has been extracted from a large web-corpus of 700 million tokens and consists of dependency-based verb–object combinations. The overall importance of collocational preferences is demonstrated by the fact that the most frequent 5% of the verb–object combinations account for as much as 64% of the verb–object tokens in the material. The database of verb–object combinations contains measures of collocational strength and thereby allows us to model the mutual strength between the exemplars in the clusters found with individual verbs. Based on some studies of individual verbs and verb pairs, it seems safe to assume that speakers do distinguish between and prefer certain conventional verb–object combinations to other equally grammatical, equally transparent and equally understandable alternatives, and that speakers have access to complementation information at the level of exemplars.
Detection of Image Significance in Film Editing

In film editing graphics size, image content diversity, complexity, and specific practical application scene, how to quickly and efficiently using prior knowledge and accurately detect significant area, accurately evaluate significant spectral quality in order to enhance the robustness of the algorithm, the use of significance in solving practical application is still the direction to solve the problem. In this background, this paper is based on significant researches on the detection method and its application, explores the pretreatment stage and objective prior rapid generation method, the spectrum generated significant stage significant detection method based on the background, the post-processing stage no reference quality evaluation based on network of significant spectral selection methods, significant test in the film the application of the video clip.
Non-Markovianity leading to coherence revivals in an open quantum system

Coherence and quantum correlations have been identified as fundamental resources for quantum information tasks. As recently shown, these resources can be interconverted. In multipartite systems, entanglement represents a prominent case among quantum correlations, one which can be activated from coherence. All this makes coherence a key resource for securing the operational advantage of quantum technologies. When dealing with open systems, decoherence hinders full exploitation of quantum resources. Here, we present a protocol that allows reaching the maximal achievable amount of coherence in an open quantum system. By implementing our protocol, or suitable variants of it, coherence losses might be fully compensated, thereby leading to coherence revivals. We provide an experimental proof of principle of our protocol through its implementation with an all-optical setup.
White Noise of the Ecosphere: Ontology in Digital Sound

This essay offers a critique of the ontological turn in digital media theory, focusing on the presence the voice is presumed to confer on sound, even in digital media. The voice is not understood b...
Choosing the Best Interpolation Data in Images with Noise

We introduce and discuss shape-based models for finding the best interpolation data in the compression of images with noise. The aim is to reconstruct missing regions by means of minimizing a data fitting term in the L2-norm between the images and their reconstructed counterparts using time-dependent PDE inpainting. We analyse the proposed models in the framework of the Γ-convergence from two different points of view. First, we consider a continuous stationary PDE model, obtained by focusing on the first iteration of the discretized time-dependent PDE, and get pointwise information on the 'relevance' of each pixel by a topological asymptotic method. Second, we introduce a finite dimensional setting of the continuous model based on 'fat pixels' (balls with positive radius), and we study by Γ-convergence the asymptotics when the radius vanishes. Numerical computations are presented that confirm the usefulness of our theoretical findings for non-stationary PDE-based image compression.
Approximate Nearest-Neighbour Fields via Massively-Parallel Propagation-Assisted K-D Trees

Nearest neighbour fields accurately and intuitively describe the transformation between two images and have been heavily used in computer vision. Generating such fields, however, is not an easy task due to the induced computational complexity, which quickly grows with the sizes of the images. Modern parallel devices such as graphics processing units depict a viable way of reducing the practical run time of such compute-intensive tasks. In this work, we propose a novel parallel implementation for one of the state-of-the-art methods for the computation of nearest neighbour fields, called p ropagation-assisted k -d trees. The resulting implementation yields valuable computational savings over a corresponding multi-core implementation. Additionally, it is tuned to consume only little additional memory and is, hence, capable of dealing with high-resolution image data, which is vital as image quality standards keep rising.
Designing for Orchestration in Mixed and Virtual Reality: Challenges and Best Practices

Mixed and Virtual Reality (XR) have seen an important development since the advent of low-cost technologies. The application space is still diversifying, opening up new opportunities and application areas for such technologies. An important common feature of Mixed and Virtual Reality applications is that designers and engineers need to orchestrate virtual content and user experience. We regard orchestration of Mixed and Virtual Reality as invisible work: in many cases, orchestration is hidden within the task of story scripting of applications. It is often occluded by designing and programming Mixed and Virtual Reality. As such, orchestration is regarded as a means to a goal, which occludes its importance for the design of XR systems. We would like to invite the research community to address this issue, by focusing this workshop on highlighting and exemplifying orchestration challenges and best practices, and to systematise the orchestration topic in the context of the recent technology advances. We will consider a wide range of orchestration cases, from completely pre-orchestrated to live-orchestrated Mixed and Virtual Reality. Based on that knowledge, we aim to consider novel tools to support designers and users in their orchestration work. We invite scholars and practitioners to contribute to our workshop, with cases from their work.
Specification of Software Requirements for Condition Monitoring of Automation Systems

Condition monitoring is a fundamental technology that enables predictive maintenance of automation systems. However, as automation systems increase in complexity, the development of condition monitoring software becomes a challenging task that requires extensive knowledge from multiple engineering disciplines. In this context, the identification and specification of condition monitoring software requirements play a key role. Neglecting these tasks often results in costly problems during later stages of systems development. Currently, means to support interdisciplinary requirements engineering within condition monitoring software development are missing. In particular, there is a need for a systematic process that supports modeling condition monitoring requirements. In this paper, we present our solution - a profile based on the extension of the SysML, which is commonly used to engineer requirements in automation systems. The profile allows specification of condition monitoring software requirements and thus enables a more domain-specific requirements engineering approach. We illustrate this approach on a heat exchanger condition monitoring system, explain the particular modeling steps, and present lessons learned.