Reviewerly/exHarmony huggingface dataset:
Multi-View Stereo by Temporal Nonparametric Fusion

We propose a novel idea for depth estimation from multi-view image-pose pairs, where the model has capability to leverage information from previous latent-space encodings of the scene. This model uses pairs of images and poses, which are passed through an encoder--decoder model for disparity estimation. The novelty lies in soft-constraining the bottleneck layer by a nonparametric Gaussian process prior. We propose a pose-kernel structure that encourages similar poses to have resembling latent spaces. The flexibility of the Gaussian process (GP) prior provides adapting memory for fusing information from previous views. We train the encoder--decoder and the GP hyperparameters jointly end-to-end. In addition to a batch method, we derive a lightweight estimation scheme that circumvents standard pitfalls in scaling Gaussian process inference, and demonstrate how our scheme can run in real-time on smart devices.
ADVANTAGES AND DISADVANTAGES OF USING MACHINE TRANSLATION IN TRANSLATION PEDAGOGY FROM THE PERSPECTIVE OF INSTRUCTORS AND LEARNERS

Purpose of the study: This paper embodies research on the introduction of machine translation (MT) into translation teaching and learning from the perspectives of learners and instructors/teachers. Four suppositions of employment of MT in translation classes are observed and examined here: MT as a weak (or peripheral) tool, MT as a useful (or essential) tool; MT as a professional treatment; and MT as a CATI tool.&#x0D; Methodology: The objective is achieved using an experimental-survey method with a theory of ‘action about reasons’ (technology acceptance model) adapted from Davis, Bagozzi, and Warshaw’s (1989) work as its framework. The survey tool is done through a closed and open-ended questionnaire while the ‘experiment’ takes the form of MT introduction practice exercises in the classroom. One hundred Iranian undergraduate students from a translation course with MT in its syllabus and thirty translation instructors make up the population for this study.&#x0D; Main Findings: In general, students found MT to be useful for producing their translation and seemed, with good exposure through practice, encouraged to use it. The translation educators too saw its benefits but would only be persuaded seriously to utilize it in their translation classrooms when MT is found to produce a much higher quality of output. Otherwise, the disadvantages might outweigh the benefits and thus make the integration of MT into translation teaching not worthwhile.&#x0D; Applications of this study: Understanding reservations and motivations of translation students and translation instructors from their responses enable translation educators and programmers to redesign their teaching to lessen the challenges and at the same grow their confidence in handling MT and guide them towards efficient and effective use.&#x0D; Novelty/Originality of this study: To date, the testing of MT in teaching has been done in language education per se. In this study, MT is examined as a tool for better translation teaching, and not as a mode of translation as opposed to human translation. This lends originality to the study.
An Integrated Fuzzy DEMATEL and Fuzzy Cognitive Mapping Methodology for Prioritizing Smart Campus Investments

Based on the development of the technologies, traditional education systems are in a transformation to catch the era. This transformation is mainly based on 4 dimensions: Strategic plan of the university, functionality for the users, financial aspects, and physical infrastructure. Based on these dimensions, there are 4 most attracted investment areas in the literature review: smart buildings, parking systems, energy savings, e-learning systems. In this study, a preliminary work for a smart campus project of a public university is constructed to prioritize the investment alternatives based on the dimensions’ sub-criteria. For the evaluation process, fuzzy DEMATEL and cognitive mapping methods are applied. Fuzzy DEMATEL method is applied to obtain influences of the criteria and investments based on the expert judgments. Then, cognitive mapping is conducted to prioritize the investments together with the sub-criteria.
Target Tracking Algorithm Based on Density Clustering

The traditional Siamese network-based target tracking algorithm needs to use the convolution feature of the target to scan around the target location when predicting the location of the target in the next frame image, and perform similarity calculation to obtain the similarity score matrix with the highest score. It is the next frame target position. The highest similarity score often does not represent the precise target position of the target, which is often affected by the sliding step size during scanning. Aiming at this problem, this paper proposes a target tracking method based on density clustering. By combining the Siamese network to predict the next frame target position, and adding the target’s motion trajectory information, the direction of the target motion is given more weight, the other directions are given a smaller weight, and finally the target position is predicted by the density clustering method. The results show that the proposed algorithm effectively improves the accuracy of the target location prediction of the Siamese network when tracking targets.
Review on Methods in Touch Tracking for Tabletop Projections

Interactive tabletop projections overlay an interface into the spatial environment and enable new and frameless fields of interaction. The continually evolving technology has potential in several application areas. In contrast to traditional invasive technology setups such as displays, capacitive foils, or infrared grids, projected systems do not require any construction of the interaction surface. This overview intends to present the current state of the art in touch tracking methods for tabletop projections and identify the potential of such environments as a new generation of interfaces for current and future research in this emerging field. It reviews selected novel methods for touch input, using sensors, on a flat surface projection, including a timeline of selected recent work, and a summary showing the characteristics, primary strengths, and drawbacks. Methods using machine learning are promising and linked to research in mid-air fingertip recognition, as implementations using depth and infrared sensors are more sophisticated in terms of fingertip recognition and accuracy.
Black-Box Emotion Detection: On the Variability and Predictive Accuracy of Automated Emotion Detection Algorithms

The current research demonstrates considerable variability in predictive accuracy across major emotion detection systems (such as Google ML or Microsoft Cognitive Services) with lower (higher) classification accuracy for negative (positive) discrete emotions. We provide two modeling strategies to improve prediction accuracy by either combining feature sets or using ensemble methods.
Comparison of Clustering Algorithms in Text Clustering Tasks

The purpose of this paper is to compare the performance and accuracy of several clustering algorithms in text clustering tasks. The text preprocessing were realized by using the Term Frequency - Inverse Document Frequency in order to obtain weights for each word in each text and then obtain weights for each text. The Cosine Similarity was used as the similarity measure between the texts. The clustering tasks were realized over the PAN dataset and three different algorithms were used: Affinity Propagation, K-Meansand Spectral Clustering. This paper presents the results in comparative tables: ID of the task, ground truth clusters and the clusters generated by the algorithms. A table with precision, recall and f-measure scores is presented.
Closure hyperdoctrines, with paths.

Spatial logics are modal logics whose modalities are interpreted using topological concepts of neighbourhood and connectivity. Recently, these logics have been extended to (pre)closure spaces, a generalization of topological spaces covering also the notion of neighbourhood in discrete structures. In this paper we introduce an abstract theoretical framework for the systematic investigation of the logical aspects of closure spaces. To this end we define the categorical notion of closure (hyper)doctrine, which are doctrines endowed with inflationary operators (and subject to suitable conditions). The generality and effectiveness of this notion is demonstrated by many examples arising naturally from topological spaces, fuzzy sets, algebraic structures, coalgebras, and covering at once also known cases such as Kripke frames and probabilistic frames (i.e., Markov chains). In order to model also surroundedness, closure hyperdoctrines are then endowed with paths; this construction allows us to cover all the logical constructs of the Spatial Logic for Closure Spaces. By leveraging general categorical constructions, we provide a first axiomatisation and sound and complete semantics for propositional/regular/first order logics for closure operators. Therefore, closure hyperdoctrines are useful both for refining and improving the theory of existing spatial logics, but especially for the definition of new spatial logics for various applications.
ABOUT MUSICAL THINKING

This article discusses the problem of the development of musical thinking of children. The purpose of the study is to reveal the features of the thinking process and its many properties such as: abstraction, synthesis analysis. The article reveals pedagogical recommendations for future vocalists, musicians in the selection of musical works. In conclusion, the materials and manuals on which the author worked are disclosed. We think the article will be interesting to teachers and students of creative universities.
Proactive Smart City Interactions

The following paper describes how an application for a Smart City Interaction is being developed at the small city of Lohr a.Main in Bavaria Germany. The project is ongoing, but a first prototype has been developed and was introduced to the local community. Results of those first interaction tests are being presented and implications for the project are being discussed.
The Way of Pooling p-values

Pooling p-values arises both in practical (in any science and engineering applications) and theoretical (statistical) issues.The p-value (sometimes p value) is a probability used as a statistical decision quantity: in practical applications, it is used to decide if an experiment er has to believe that his/her collected data confirm or disconfirm his/her hypothesis about the "reality" of a phenomenon.It is a real number, determination of a Random Variable, uniformly distributed, related to the data provided by the measurement of a phenomenon.Almost all statistical software provides p-values when statistical hypotheses are considered, e.g. in Analysis of Variance and regression methods.Combining the p-values from various samples is crucial, because the number of degrees of freedom (df) of the samples we want to combine is influencing our decision: forgetting this can have dangerous consequences.One way of pooling p-values is provided by a formula of Fisher; unfortunately, this method does not consider the number of degrees of freedom.We will show other ways of doing that and we will prove that theory is more important than any formula which does not consider the phenomenon on which we have to decide: the distribution of the Random Variables is fundamental in order to pool data from various samples.Manager, professors and scholars should remember Deming's profound knowledge and Juran's ideas; profound knowledge means "understanding variation (type of variation)" in any process, production or managerial; not understanding variation causes cost of poor quality (more than 80% of sales value) and do not permits a real improvement.
An improved algorithm for target trajectory prediction in the active protection system based on converted measurement Kalman filter

Abstract Fast and accurate prediction of the trajectory is the prerequisite for the successful interception of the incoming target by the active protection system (APS) of armoured vehicles. The traditional least-squares algorithm is susceptible to large errors in the initial stage of the tracking process, and the prediction of interception points converges slowly. This paper proposes an interception point prediction algorithm that combines the Kalman filter and least-squares estimation. By dynamically determining the convergence of the least-squares prediction, a converted measurement Kalman filter is used in the initial tracking stage to obtain higher prediction accuracy of the interception point. Simulations are performed in the scenario of the interception process of the APS. The results prove that the algorithm can effectively accelerate the convergence of the interception point prediction process.
IDC events related to volcanic activity at Kamchatka Peninsula

&amp;lt;p&amp;gt;International Monitoring System (IMS) is designed to detect and locate nuclear test explosions as part of Comprehensive Nuclear Test-Ban Treaty (CTBT) verification regime. This network can be also used for civil applications, such as the remote monitoring of volcanic activity.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Events related to volcanic eruptions, which are listed in the International Data Centre (IDC) bulletins, are typically detected by infrasound stations of the IMS network. Infrasound station IS44 and primary seismic station PS36 are situated in Kamchatka, Russian Federation, in the vicinity of several active volcanoes. These two stations recorded seismo-acoustic events generated by volcanic eruptions. In addition to atmospheric events, the IMS network has the potential of detecting underwater volcanic activity. Under favourable conditions, the hydroacoustic stations located in the Pacific Ocean and PS36 may detect underwater events close to the shore of Kamchatka Peninsula.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The aim of this presentation is to show examples of volcanic eruptions at Kamchatka Peninsula recorded by the IMS network. Supplementary information obtained by other observing networks can be found in reports issued by Kamchatkan Volcanic Eruption Response Team (KVERT) or Tokyo Volcanic Ash Advisory Center (VAAC). Such information can be compared with events listed in IDC bulletins.&amp;lt;/p&amp;gt;
Do We Need a Criminalistic Characterization of a Crime in the Criminalistic Methodology?

The paper deals with the essence of criminalistic characterization of the crime, its necessity and role in the criminalistic methodology. The attitude of the scientific community to this scientific category for half a century of its existence has undergone dramatic changes: from enthusiastic exaltation as a universal means of solving crimes to a useless “phantom” of criminalistics. However, despite criticism from a number of highly eminent scholars, criminalistic characterization of the crime has not disappeared from academic intercourse and has firmly enshrined its role in criminalistic methodology. The paradox is explained by its systemic need in the arsenal of this section of criminalistics. As the only acceptable indication of the empirical knowledge of the crime and the ways of its disclosure developed by criminalistics, the criminalistic characterization of the crime not only amounts to core of individual forensic techniques, but also defines the structure of the entire forensic methodology as a branch of the science. Attempting to abandon it leads to the emergence of various substitutes that are similar in content, but less successful with regard to terms. For these reasons, the criminalistic characterization of a crime cannot be rejected by forensics despite any criticism. It seems that the “phantom” is not the very idea of criminalistic characterization of the crime, but the method by which it was “filled.” A systematically necessary and successfully named design was unsuccessfully methodologically connected by its creators with statistical measurements of correlation dependencies between the main criminalistically significant elements of a crime. However, such measurements are unsuitable for a specific investigative methodology. The “filling” of criminalistic characterization with relevant information concerning criminal activity should be provided by the totality of methods available for empirical sciences without being limited to statistical measurements of the links between its elements.
Dynamic Game Based CAPTCHA-A New Approach to Tackle Web Robots

Abstract: stands for Complete Automated Public Turning Test [1], is a security mechanism designed to differentiate between online bots and human. It is a challenged- response test for computing to determine whether the user is human or not. It is used to defend against the malicious bots programs. As use of Internet has been become vital issue and many web application facing a threat of web bots (Robots). Web Bots or, Robots is an automated script which executes over an Internet and occupy web space and increases the network traffic [6] [7]. The complication with currently used CAPTCHA i.e., text-based CAPTCHA or, graphic-based CAPTCHA is that, it is it is troublesome to read even for the human and Image based or, voice-based CAPTCHA has been broken many times. In this CAPTCHA, We can store 3-4 GUI game on our server. If the user successfully completes the game then only he/she will login. This CAPTCHA game will change as many times user login. As the game will change randomly, so for the web bots it would be difficult to solve this CAPTCHA but for the human it would be easy. This paper discusses the existing CAPTCHA and dynamic game-based CAPTCHA. Keywords: CAPTCHA, Text-Based/Graphic-Based CAPTCHA, Image-Based/Audio-Based CAPTCHA, Face-Recognition CAPTCHA, Dynamic Game Based CAPTCHA Cite this Article: Megha Saloni. Dynamic Game Based CAPTCHA - A New Approach to Tackle Web Robots. International Journal of Distributed Computing and Technology. 2019; 5(2): 1–7p.
Cost and Benefit of Computer-Based National Examinations in Indonesia

This study comprehensively examines the Paper and Pencil-based National Examination (PPNE) model, which has recently morphed into the Computer-based National Examination (CNE) model, whether the new program is  more efficient and more effective.The benefit-cost ratio (BCR) of the CNE model was also measured in this study. This research uses secondary data in the form of PPNE and CNE budget realization reports. The data period spans from 2015 to2019. The study uses ratio analysis, differential analysis, and regression analysis. The results show that the CNE is more cost efficient than the PPNE. The research also discusses the result of cost effectiveness, in which the cost effectiveness of the CNE program is nearly 100%. Lastly, the benefit–cost ratio of the CNE, which exceeds 1 since it was first implemented in 2015has shown that the CNE is more benefits than costs.
Deep Claim: Payer Response Prediction from Claims Data with Deep Learning

Each year, almost 10% of claims are denied by payers (i.e., health insurance plans). With the cost to recover these denials and underpayments, predicting payer response (likelihood of payment) from claims data with a high degree of accuracy and precision is anticipated to improve healthcare staffs' performance productivity and drive better patient financial experience and satisfaction in the revenue cycle (Barkholz, 2017). However, constructing advanced predictive analytics models has been considered challenging in the last twenty years. That said, we propose a (low-level) context-dependent compact representation of patients' historical claim records by effectively learning complicated dependencies in the (high-level) claim inputs. Built on this new latent representation, we demonstrate that a deep learning-based framework, Deep Claim, can accurately predict various responses from multiple payers using 2,905,026 de-identified claims data from two US health systems. Deep Claim's improvements over carefully chosen baselines in predicting claim denials are most pronounced as 22.21% relative recall gain (at 95% precision) on Health System A, which implies Deep Claim can find 22.21% more denials than the best baseline system.
Hijab, Sport and Schooling in Finland – from Principles to Practice

This paper concerns the intersections between veiling, school and sport, focussing on both legislative elements and formal regulations, as well as the more micro-level practices of physical education teachers in school environments in Finland. Veiling is an extraordinarily politicised topic today, while also being an everyday dress practice engaged in by millions of women worldwide. Sport can be likewise politicised, and certainly is so in the case of veiling. Sometimes seen as resistance to patriarchal structures and cultural traditions, sometimes defended and justified using religious arguments, Muslim women’s physical activities may be understood as a conflictual social field, especially when the women either choose to veil or prefer gender segregated venues for sport. Bringing together realms such as politics, legislation, education, garment design and religion, the debates surrounding female Muslim bodies are at the centre of ideas to do with citizenship and integration in Muslim-minority contexts. In Finland, both the national law and local regulations allow for a great deal of independence for teachers working with veiling students, at the same time as guaranteeing high protection of an individual’s right to freedom of religion. Consequently, negotiation strategies between teachers and veiling students are central for the accommodation of religious dress practices. This is particularly so when teaching physical education, which has specific requirements for students’ outfits from the point of view of safety and practicality. I discuss the complexities created by the fields of law, education, religion, politics and design when they come together in the case of hijabs, sport and physical education.
A convolutional neural network reaches optimal sensitivity for detecting some, but not all, patterns

We investigate the performance of modern convolutional neural networks (CNN) and a linear support vector machine (SVM) with respect to spatial contrast sensitivity. Specifically, we compare CNN sensitivity to that of a Bayesian ideal observer (IO) with the signal-known-exactly and noise known statistically. A ResNet-18 reaches optimal performance for harmonic patterns, as well as several classes of real world signals including faces. For these stimuli the CNN substantially outperforms the SVM. We further analyzed the case in which the signal might appear in one of multiple locations and found that CNN spatial sensitivity continues to match the IO. However, the CNN sensitivity was far below optimal at detecting certain complex texture patterns. These measurements show that CNNs can have very large performance differences when detecting the presence of spatial patterns. These differences may have a significant impact on the performance of an imaging system designed to detect low contrast spatial patterns.