Reviewerly/exHarmony huggingface dataset:
Extracting Data from General Practitioners' XML Reports in Bulgarian Healthcare to Comply with ISO/EN 13606

Software applications that are used in the Bulgarian healthcare area generate a lot of data, describing the medical state of the patients. At the moment this data is mostly used for financial reports for the Bulgarian National Health Insurance Fund, based on which the medical entities (general practitioners, hospitals, healthcare centers, etc.) are paid. In case the data coming from different systems is constantly analyzed it can be used for other purposes -- statistics, analysis, and analytics. In this article, we analyze the information coming from XML reports generated from general practitioners' applications and propose an approach to transform it to comply with the standard ISO/EN 13606, which is approved in EU.
Copula Grow-Shrink Algorithm for Structural Learning

The PC algorithm is the most known constraint-based algorithm for learning a directed acyclic graph using conditional independence tests. For Gaussian distributions the tests are based on Pearson correlation coefficients. PC algorithm for data drawn from a Gaussian copula model, Rank PC, has been recently introduced and is based on the Spearman correlation. Here, we present a modified version of the Grow-Shrink algorithm, named Copula Grow-Shrink; it is based on the recovery of the Markov blanket and on the Spearman correlation. By simulations it is shown that the Copula Grow-Shrink algorithm performs better than the PC and the Rank PC algorithms, according to the structural Hamming distance. Finally, the new algorithm is applied to Italian energy market data.
Accurate image dehazing with three simultaneously captured hazed images

In this work, we capture hazed images with three similar cameras possessing a sufficiently large depth of field (wide angle lenses). So, the foreground, middle-ground and background of the scene appear sharp and clear. The cameras are located at the vertices of an isosceles right triangle and take the same part of the scene. We propose a restoration algorithm based on three observed degraded images. It is assumed that degraded images contain information about an original image, hazing function, and noise. A dehazing algorithm explicitly solves a linear system of equations derived from a quadratic objective function. Experimental results obtained with the proposed method are presented and discussed.
Deep Compact Person Re-Identification with Distractor Synthesis via Guided DC-GANs

We present a dual-stream CNN that learns both appearance and facial features in tandem from still images and, after feature fusion, infers person identities. We then describe an alternative architecture of a single, lightweight ID-CondenseNet where a face detector-guided DC-GAN is used to generate distractor person images for enhanced training. For evaluation, we test both architectures on FLIMA, a new extension of an existing person re-identification dataset with added frame-by-frame annotations of face presence. Although the dual-stream CNN can outperform the CondenseNet approach on FLIMA, we show that the latter surpasses all state-of-the-art architectures in top-1 ranking performance when applied to the largest existing person re-identification dataset, MSMT17. We conclude that whilst re-identification performance is highly sensitive to the structure of datasets, distractor augmentation and network compression have a role to play for enhancing performance characteristics for larger scale applications.
Distributed Single-Source Shortest Path Algorithms with Two-Dimensional Graph Layout

Single-source shortest path (SSSP) is a well-known graph computation that has been studied for more than half a century. It is one of the most common graph analytical analyses in many research areas such as networks, communication, transportation, electronics, and so on. In this chapter, we propose scalable SSSP algorithms for distributed memory systems. Our algorithms are based on a ∆-stepping algorithm with the use of a two-dimensional (2D) graph layout as an underlying graph data structure to reduce communication overhead and improve load balancing. The detailed evaluation of the algorithms on various large-scale, real-world graphs is also included. Our experiments show that the algorithm with the 2D graph layout delivers up to three times the performance (in TEPS), and uses only one-fifth of the communication time of the algorithm with a one-dimensional layout.
Transparent Heterogeneous Cloud Acceleration

This work proposes a cloud computing platform (PaaS) with a novel micro-service architecture designed to support transparent acceleration on large-scaled heterogeneous cloud infrastructures with hardware accelerators such as FPGAs.
A Human-Oriented Term Rewriting System

We introduce a fully automatic system, implemented in the Lean theorem prover, that solves equality problems of everyday mathematics. Our overriding priority in devising the system is that it should construct proofs of equality in a way that is similar to that of humans. A second goal is that the methods it uses should be domain independent. The basic strategy of the system is to operate with a subtask stack: whenever there is no clear way of making progress towards the task at the top of the stack, the program finds a promising subtask, such as rewriting a subterm, and places that at the top of the stack instead. Heuristics guide the choice of promising subtasks and the rewriting process. This makes proofs more human-like by breaking the problem into tasks in the way that a human would. We show that our system can prove equality theorems simply, without having to preselect or orient rewrite rules as in standard theorem provers, and without having to invoke heavy duty tools for performing simple reasoning.
Message Security Implementation by Using a Combination of Hill Cipher Method and Pixel Value Differencing Method in Mozilla Thunderbird Email Client

Abstract One of the problems in this field of communication is the insecurity of messages sent across computer networks. Even though sometimes the contents of a message can be something that is sensitive and prone to abuse. One method that can be used to overcome this problem is by using a combination of cryptography and steganography. In this research, an add-on menu for Mozilla application was developed to secure messages to be sent via e-mail using Hill Cipher’s cryptographic method on messages and insert them using steganographic pixel value differencing method on image files to be extracted and returned to the form originally. The results of this study indicate that with experiments using messages with length and image file extensions that are also different, namely. bmp, .jpg, .png, and .gif can insert the initial message into the image file and be returned to its original form with a maximum length limit message 1000 characters and no manipulation of the image file from the insertion message.
The reducibility of matrix sweeping operations: A computational issue in linear belief functions

Matrix sweeping operations are the basis for the knowledge representation and combination of linear belief functions. Yet, their basic properties are not well studied and often misunderstood. This paper attempts to fill the void and corrects a long lasting mistake in the literature on whether a block-level sweeping from a nonsingular submatrix can be reduced into sequential element-level sweepings from individual leading diagonal elements of the submatrix. It proposes a new alternative to workaround the failure of reducibility and introduces a prototype system, dubbed LMOS, in support of irreducible sweeping operations.
An Experimental Comparative Performance Study of Demosaicing Algorithms on General-purpose GPUs

The image registration by digital still cameras and video cameras requires color filters to be posed onto the photosensitive sensors (CCD or CMOS). The filters are arranged in patterns across the face of the image sensing array. The conventional color filter array (CFA) capture only one color component at each image pixel. The missing colors in the raw sensor data are interpolated by a process called CFA interpolation or demosaicing. Quality of the full-color reconstruction process is mostly relied on demosaicing method applied. Most of the current demosaicing methods are computationally expensive and often too slow for real-time scenarios. Many industrial applications require real-time and high quality demosaicing solutions, and quite often slow image reconstruction process is a real bottleneck. The purpose of this research is to present a comparative performance study of demosaicing algorithms on general-purpose GPUs. The experimental results of CUDA-based implementations of two state-of-the-art and widely applied in practice CFA algorithms are presented. The performance efficiency is assessed and analyzed by experimental studies on a set of real photographic test images on two general-purpose graphic cards. The obtained results demonstrated the benefit of exploiting the contemporary GPUs in speeding up the demosaicing process, especially for practical applications that need to meet real-time and high-speed video processing requirements combined with high quality of the full-color image reconstruction.
A MapReduce-Based Parallel Mining for Frequent Itemset

Abstract Frequent itemset mining is the most important step of association rule mining. Due to the large size of datasets, many parallel mining methods have been introduced to divide datasets or to distribute mining processes, which improve the efficiency of mining. In this paper, we propose a parallel algorithm of PrePost+ based on MapReduce (we call our parallel algorithm MR-PrePost+) to mine frequent itemsets. In our parallelization approach, the Reduce task performs a set of independent mining tasks, which eliminates data dependencies and computational dependencies between machines. The experimental results show that our MapReduce-based parallel algorithm MR-PrePost+ performs better than PFP in running time, which is one of the best frequent itemset mining parallel algorithms.
A creative dialog generator for fallout 4

This software demonstration describes a mod for Fallout 4 that will adapt in-game dialog to the context of the current state of the game. The dialog is generated by a computationally creative back-end software during the game play. The mod solves the problem of Fallout 4 not supporting dynamically generated dialog by showing dialog in an overlay application on top of the game window.
사회문화이론적 관점에서 본 한국어 학습자의 L1 사용 연구

The purpose of this study is to reveal the relationship between learner’s use of L1 and Korean language learning, and to derive educational implications of learner’s use of L1. To do this, this study examines Chinese learners’ learner-learner dialogues which were produced while they participated in role-play tasks, and analyzed the L1 use of learners included in the dialogues. As a result, in the case of this study, when learners construct the content of role-playing, they use their L1 to check if it is appropriate according to the instructions of the task, to discuss different opinions, and to communicate their opinions more accurately. In addition, learners used their L1 for the purpose of choosing the more appropriate Korean expression which is to be used in the role play and supplementing the lack of Korean knowledge of peer learners. Finally, learners used L1 to understand the task itself in order to carry out the task successfully. Thus, in this study, it was confirmed that L1 is used as a support tool for L2 learners to accomplish more successfully the task of role-playing, which is communicative task in Korean language learning. In the communicative language classroom, it is almost impossible not to control learner’s L1 use at all. However, since L1 use can support the accomplishment of the L2 task, it is necessary for the Korean language learner to use it partially considering the specific learning objectives set in the individual classroom and the characteristics of the learner. (Korea University)
Architecture trace diagrams for cyber-physical systems

Developing a suitable architecture for Cyber-Physical Systems requires architects to consider all necessary areas - software, hardware, networking - and have an understanding of how information is passed between the different components. Errors in some part might be caused by problems in another, thus a deeper understanding of the information flow is necessary to identify possible error sources. We present how Architecture Trace Diagrams (ATDs) can be used to model the architecture and information flow of Cyber-Physical Systems. In a study with several developers, we show that participants using ATDs achieved better results in terms of correctness, speed, and participant self-efficacy in comparison to UML diagrams.
Probabilistic Logic for Reasoning About Actions in Time

In this paper, we develop a probabilistic logic for reasoning about preconditions, postconditions and execution of actions in time. The language of our logic allows statements like “precondition of the action A will hold in the next moment” and uncertain information like “probability that the precondition of A will hold in the next moment is at least one half.” We axiomatize this logic, provide corresponding semantics built on branching-time temporal models, and prove that the axiomatization is sound and strongly complete.
Read Afrikaans Normal/ Read Afrikaans Fast

The corpus contains speech of 127 mother tongue speakers of Afrikaans. Every speaker was asked to read a text fragment from a book or a newspaper (about 20-40 seconds of speech per speaker) at a normal speed (RAN), followed by reading a shorter portion of the same text (about 15 seconds) at a faster speed (RAF). The collection is intended to become a primary national language resource, with a view of creating a resource for phonetic researchers, as well as for developers of acoustic models and lexicons for automatic speech recognition (ASR) in Afrikaans. In its creation there was a close collaboration with ELIS-UGent (Belgium).
Application of Deep Neural Network for the Vision System of Mobile Service Robot

The solution of object detection task is valuable in many fields of robotics. However, application of neural networks for mobile robots requires the use of high – performance architectures with low power consumption. In search of suitable model, a comparative analysis of the YOLO and SqueezeDet architectures was conducted. The task of detecting wooden cubes by mobile robot with the camera with the aim of collecting them was solved. A specific dataset was constructed for the training purposes. Applied SqueezeDet neural network has reached precision 89% and recall 82% for IOU ≥ 0.5.
The Effect of Parallelism on Data Reduction

In this paper, we investigate the effect of parallelism on two data reduction algorithms that use k-Means clustering in order to find homogeneous clusters in the training set. By homogeneous, we refer to clusters where all instances belong to the same class label. Our approach divides the training set into subsets and applies the data reduction algorithm on each separate subset in parallel. Then, the reduced subsets are merged back to the final reduced set. In our experimental study, we split the datasets into 8, 16, 32 and 64 subsets. The results obtained reveal that parallelism can achieve very low preprocessing costs. Also, when the number of subsets is high, in some datasets the accuracy of k-NN classification is almost equal (if not better) to the one achieved when using the standard execution of the reduction algorithms, with a small loss in the reduction rate.
The study on 2015 Revised Music Subject Curriculum Implementation Monitoring Tools Development Validation

2015 개정 음악과 교육과정이 개정되고 실행되면서 교실수업에서 개정된 음악 교과 내용이 어느 정도 실행되고 있는지 여부를 요구를 조사 할 필요성이 대두되고 있다. 이에 2015 개정 음악과 교육과정이 교실수업에서 어느 정도 실행할 수 있었는지 여부를 모니터링 할 수 있는 타당한 도구개발이 필요하다. 이 연구에서는 2015 개정 음악과 교육과정 실행 모니터링도구를 개발하여 음악과 교육과정이 음악 수업 속에서 어떻게 운영되고 있는지 모니터링 할 수 있는 도구(안)를 제시하고자 한다. 이와 같은 연구의 목적에 도달하고자 음악과 교육과정 문서상에 음악교과내용을 교과 역량, 성취기준, 생활화 영역 등을 연구의 기초에 정리하였다. 이를 기반으로 교육과정 문서상의 내용이 교실수업에서 실행할 수 있는지 여부를 모니터링 하는 질문과 교사-자기 점검 차원의 체크리스트 질문을 도구로 개발하고, 개발한 도구의 질문 내용이 타당한 여부를 반복하여 검증하였다. 이와 같은 모니터링 도구 개발 과정에서 현장 적합성을 검증받아 논의한 자문의견에서 음악과 교육과정 실행 및 개선에 관한 시사점을 도출하여 제시하였다.As the 2015 Revised Music Subject Curriculum is implemented, there is a need to investigate the extent to which the revised music contents are being implemented in the classroom class. It is necessary to develop appropriate tools to monitor the extent to which the 2015 Revised Music Subject Curriculum could be implemented in classroom instruction. Therefore, this study purpose to provide tools to monitor how music and curriculum are operated in music class by developing monitoring tool of the 2015 Revised Music Subject Curriculum. In order to reach the goal of this research, music instructional contents were compiled on the theoretical background in music and curriculum documents. The questionnaires that monitor whether the contents of the document can be executed in the classroom were designed and developed, and it was repeatedly verified whether or not the questions of the developed tools were valid. In this advisory opinion, the implication of Music Subject Curriculum and improvement were suggested and presented.
An Urgency for Change in Roles

Student dependence on technology, higher education institution efforts of digitalization in teaching and learning, along with the faculty member hesitancy to adopt innovative technologies all contribute to current challenges in higher education. The focus of this chapter is on exploring the gap between student and faculty perceptions about utilizing technology in teaching and learning, in addition to enlighten institutional roles and strategies to sustain efforts for managing such environments and faculty development. Multiple research methods are used to derive insights. The findings point towards the need to increase knowledge base and to expand faculty development around digital teaching and learning strategies as well as fostering student faculty collaboration to improve learner engagement and performance. As a result of changing roles, the pedagogical methods and technological tools used in digital teaching and learning process should not be a sole decision of the institution, but as a result of a collaborative mindset of institution, faculty, and students.
EMD and KPCA-based Speckle Suppression in SAR Images

Abstract This paper proposes a speckle suppression method for synthetic aperture radar (SAR) images based on empirical mode decomposition (EMD) and kernel principal component analysis (KPCA) following three steps: first, SAR image after logarithmic transformation is decomposed by EMD; second, noise in each intrinsic mode function (IMF) is further removed by KPCA; finally, the denoised SAR image is obtained by accumulating the IMFs processed by KPCA. In the second step, IMF, decomposed by KPCA, is reconstructed by the selection of appropriate principle components according to noise energy proportion, which is approximately calculated based on the statistical properties of speckle noise and energy distribution model of EMD-decomposed Gaussian white noise (GWN). Experimental results show that, compared with traditional EMD-based image denoising algorithms, the proposed method is superior in both speckle suppression and detail information retention.
DEVELOPMENT OF COMPUTERIZED MONITORING AND CONTROL SYSTEM OF THE MOBILE ROBOT’S POSITIONING ON LARGE FERROMAGNETIC SURFACES BASED ON INTELLIGENT TECHNIQUES. AUTOMATION OF THE MONITORING AND CONTROL PROCESSES OF MOBILE ROBOT FOR PROCESSING OF LARGE INCLINED SURFACES

The article renders the special features of the design of a tracked mobile robot (MR) for moving over inclined ferromagnetic surfaces while performing specified technological operations. There is conducted a synthesis of the functional structure and selective technological parameters (such as control coordinates) of the computerized monitoring and control system (CMCS) intended for use with this MR. Application of the CMCS with the proposed functional structure allows substantially increasing the accuracy of the MR monitoring and control, which in turn provides for a considerable enhancement in the quality and economic efficiency of the operations on processing of large ferromagnetic surfaces.
Intrinsically musical game worlds

This poster submission explores the concept of nuanced dynamic music generation as a core game mechanic. The world in which this game is set is intrinsically musical. All events and actions in the world add to the music rather than being sound effects that are layered over top of a soundtrack. The music that is generated is often more of a rhythmic soundscape than a song in this game.
An Optimization Algorithm Based on Multi-free Dynamic Schema of Chromosomes

In this work, continuing the line of research from our previous papers [1–4], we further explore the notion of a schema in evolutionary algorithms and its role in finding global optima in numerical optimization problems. We present another optimization algorithm called Multi-Free Dynamic Schema (MFDS) which differs from the Free Dynamic Schema (FDS) algorithm introduced in [6] because in the MFDS the free dynamic schema operator is applied six times to different groups of chromosomes. The results of numerical experiments show that this change speeds up the search for a global optimum for most of the test problems.
Efficient and Fast Expression Recognition with Deep Learning CNN-ELM

Facial expression recognition is a significant direction in facial computer version. Although convolutional neural networks (CNNs) have received great attention in recognition task especially for images, they require considerable time in computation and are easily to be trapped in over-fitting due to kinds of reasons. This paper suggests a fast and efficient network for expression recognition, which takes full advantages of CNN and ELM (Extreme Learning Machine). Facial expressions can be learned well and calculated fast with satisfying accuracy through it. Experimental results on real-life expression database prove that our proposed approach can effectively reduce the calculation time and improve the performance.
Bayesian neural tree models for nonparametric regression

Summary Frequentist and Bayesian methods differ in many aspects but share some basic optimal properties. In real‐life prediction problems, situations exist in which a model based on one of the above paradigms is preferable depending on some subjective criteria. Nonparametric classification and regression techniques, such as decision trees and neural networks, have both frequentist (classification and regression trees (CARTs) and artificial neural networks) as well as Bayesian counterparts (Bayesian CART and Bayesian neural networks) to learning from data. In this paper, we present two hybrid models combining the Bayesian and frequentist versions of CART and neural networks, which we call the Bayesian neural tree (BNT) models. BNT models can simultaneously perform feature selection and prediction, are highly flexible, and generalise well in settings with limited training observations. We study the statistical consistency of the proposed approaches and derive the optimal value of a vital model parameter. The excellent performance of the newly proposed BNT models is shown using simulation studies. We also provide some illustrative examples using a wide variety of standard regression datasets from a public available machine learning repository to show the superiority of the proposed models in comparison to popularly used Bayesian CART and Bayesian neural network models.