Reviewerly/exHarmony huggingface dataset:
THE MECHANISM OF INTRODUCTION OF CLUSTERS IN THE RUSSIAN FEDERATION

Purpose: The purpose of this article is to reveal distinctive features of clusters and determine whether those territorial economic entities that are being actively implemented in Russia today may be referred to as clusters.&#x0D; Methodology: Our analysis work made it possible to ascertain that clusters as networked (hybrid) entities have important distinctive features. The key to which we believe, along with the local base of formation, is to be considered the fact that being comprised of organizations of different types, they do not provide a single mechanism to coordinate interaction.&#x0D; Result: Study of the territorial entities originating in Russia shows that they cannot be referred to as clusters, mainly because of the administrative voluntarist mechanism of their formation.&#x0D; Applications: This research can be used for Students in mathematic and teachers.&#x0D; Novelty/Originality: In this research, the model of the mechanism of introduction of clusters in the Russian Federation is presented in a comprehensive and complete manner.
Using mathematics education game based ICT: why children like to play game?

Abstract The purpose of this study to create a product of learning media based ICT in the from of mathemmatics education games. Mathematics education games is a software to learn mathematic especially made for kindegarden to help learning process in 2013 curicullum. This research methode use design research with three cycles. In this cycle describe about six young childrens tested wiht small group in research for the second step after literature riview. The result of studyis got that by the issue of improvement in the process of presenting the game so it will improve the design and validity and it will be tested. Interestingly, the student didn’t relize that the process is math learning and the curoisity to solve the game is improve. The student never use this kind of ICT before. Next, by improve skill and knowledge that is got during the game as a strating point that will be continue to the other activity that center in the curiousity earlychildhood.
A Study on Big Data Processing Frameworks: Spark and Storm

Today’s internet world impose a trade-off between Peta-byte to Exa-byte being created in digital computer world attributable enormous volume of unstructured datasets being generating from diverse social sites, IOT, Google, Twitter, Yahoo, monitoring surroundings through sensors, etc., is big data (BD). Because second to second doubles the datasets volume size but the shortage of smooth dynamic processing, analysis and scalability techniques. Because the recent high-speed decade we applied only extant methods and common tools about the gigabyte data process and perform computations on whole world huge data. Apache open free source Hadoop is the latest BD weapon can process zetta-byte dimensions of databases by its most developed and popular components as HDFS and map reduce (MR), to get done excellent storage features magnificent and reliable processing on zetta-byte of datasets. MR likes more famous software, popular framework for handling BD existing issues with full parallel, highly distributed, and most scalable manner. Despite, Hadoop, map and reduces tasks have more limitations like poor allocating custom resources, stream way processing, shortage of latency, the deficit of efficient performance, imperfection of optimization, the real-time trend of computations and diverse logical elucidation. We significant most modern progressive features computing procedures. This examination paper shows Apache fastest spark tool, world latest and fastest tool is apache storm has efficient frameworks to conquer those limitations.
Enhancements for Sliding Window based Stream Classification

In stream mining, there are several limitations on the classification process, since the time and resource are limited. The data is read only once and the whole history of data can not be stored. There are several methods developed so far such as stream based adaptations of decision trees, nearest-neighbor methods and neural network classifiers. This paper presents new enhancements on sliding window based classification methods. As the first modification, we use the traditional kNN (K-Nearest Neighbors) method in a sliding window and include the mean of the previous instances as a nearest neighbor instance. By this, we aim to associate the behaviour pattern coming from the past and current state of data. We call this method as m-kNN (Mean extended kNN). As the second enhancement, we generate an ensemble classifier as the combination of our m-kNN with traditional kNN and Naive Bayes classifier. We call this method CSWB (Combined Sliding Window Based) classifier. We present the accuracy of our methods on several datasets in comparison to the results against the state-of-the-art classifiers MC-NN (Micro Cluster Nearest Neighbor) and VHT (Vertical Hoeffding Tree). The results reveal that the proposed method performs better for several data sets and have potential for further improvement.
Exploring the space of human exploration using Entropy Mastermind

What drives people’s exploration in complex scenarios where they have to actively acquire information? How do people adapt their selection of queries to the environment? We explore these questions using Entropy Mastermind, a novel variant of the Mastermind code-breaking game, in which participants have to guess a secret code by making useful queries. Participants solved games more efficiently if the entropy of the game environment was low; moreover, people adapted their initial queries to the scenario they were in. We also investigated whether it would be possible to predict participants’ queries within the generalized Sharma-Mittal information-theoretic framework. Although predicting individual queries was difficult, the modeling framework offered important insights on human behavior. Entropy Mastermind opens up rich possibilities for modeling and behavioral research.
The Impact of Data Correlation on Identification of Computer-Generated Face Images

The traditional image discriminating methods can accurately identify forged pictures generated by splicing, tampering, etc. But most methods cannot identify the forged pictures generated by the GAN models. In this paper, we specially explore to identify forged face created with the GAN models. Our target is to analyze the effect of data correlation on identification of computer created face images. In this work, we mainly test on false face datasets generated by StyleGAN and DCGAN. Both datasets are divided into two experimental control groups. We use the convolutional neural network models such as ResNet-18, VGG, and GoogLeNet to perform classification experiments on the control experimental groups. The results show that the models used in this paper can accurately distinguish the real faces and the forged faces generated with GAN. The validation analysis shows that the data correlation has a low influence on identification of forged faces with specific models.
Cropout: A General Mechanism for Reducing Overfitting on Convolutional Neural Networks

Recently, a lot of Convolutional Neural Networks (CNNs) have been proposed for computer vision applications. However, how to improve the generalization ability of them remains challenging. In this paper, we propose a novel mechanism, namely, Cropout, to further reduce overfitting on Convolutional Neural Networks. The proposed Cropout is able to enlarge the diversity of the feature-map produced by convolutional layer, and further improve the generalization ability of deep CNNs. It mainly consists of three operations: grouping, cropping, and concatenating. Specifically, we first divide the feature-map produced by convolutional layer into different groups, and each group is considered as one transformation path. Next, each transformation path is assigned with a random crop transformation. Finally, all the transformation paths are concatenated into a new feature-map for further training. Extensive experiments on two benchmark datasets CIFAR-10/100 validate the effectiveness and generality of Cropout. Specially, the ResNeXt-29, 8×64d (with 34M parameters) with our proposed Cropout achieve the error rate of 3.38% and 16.89% on CIFAR-10/100 dataset, and surpass the standard ResNeXt-29, 16×64d (with 68M parameters) which is twice larger in model size. In addition, the proposed Cropout is able to be applied to different modern deep networks (e.g., ResNeXt, ResNet and DenseNet) to further boost the performance on image classification tasks.
Measuring Context Change to Detect Statements Violating the Overton Window

The so-called Overton window describes the phenomenon that political discourse takes place in a narrow window of terms that reflect the public consensus of acceptable opinions on some topic. In this paper we present a novel NLP approach to identify statements in a collection of newspaper articles that shift the borders of the Overton window at some period of time, and apply it on German newspaper texts detecting extreme statements about the refugee crisis in Germany.
Approximation Ratios of $$\mathsf {RePair}$$ , $$\mathsf {LongestMatch}$$ and $$\mathsf {Greedy}$$ on Unary Strings

A grammar-based compressor computes for a given input w a context-free grammar that produces only w. So-called global grammar-based compressors ( $$\mathsf {RePair}$$ , $$\mathsf {LongestMatch}$$ and $$\mathsf {Greedy}$$ ) achieve impressive practical compression results, but the recursive character of those algorithms makes it hard to achieve strong theoretical results. To this end, this paper studies the approximation ratio of those algorithms for unary input strings, which is strongly related to the field of addition chains. We show that in this setting, $$\mathsf {RePair}$$ and $$\mathsf {LongestMatch}$$ produce equal size grammars that are by a factor of at most $$\log _2(3)$$ larger than a smallest grammar. We also provide a matching lower bound. The main result of this paper is a new lower bound for $$\mathsf {Greedy}$$ of 1.348..., which improves the best known lower bound for arbitrary (not necessarily unary) input strings.
Aggregation Connection Network For Tiny Face Detection

Face detection has been greatly developed in recent years. Despite the remarkable progress, finding tiny faces in the wild is still a challenge due to the vastly scales, blur, occlusion and low resolution. This paper proposes an Aggregation Connection Network (ACN) which robustly solves these problems in tiny face detection. ACN utilizes the features from different convolution layers and performs superiorly on finding multi-scale faces in a single shot, especially for tiny faces. Specially, there are two novel modules in ACN that play significant roles: an aggregation connection module and a context module. First, by integrating efficient aggregation connection module, our ACN can effectively reduce the feature disappearance caused by image scaling. Second, the elaborately designed context module can make full use of the rich contextual cues without adding extra parameters. As a consequence, our ACN achieves state-of-the-art detection performance among several popular face detection benchmarks i.e. WIDER FACE, FDDB and Pascal Face.
Investigating Image Registration Impact on Preterm Birth Classification: An Interpretable Deep Learning Approach

Deep learning algorithms have recently become the dominant trend in medical image classification. However, the decision-making rationale of convolutional neural network (CNN) classifiers can be obscure. Interpretable machine learning techniques, such as layer-wise relevance propagation (LRP), can provide a visual interpretation of these decisions. In this work, we build a 3D CNN model to classify neonatal $$T_2$$ -weighted magnetic resonance (MR) scans into term or preterm. Additionally, we investigate the impact of different registration techniques applied to the image dataset on the classifier’s predictions. Finally, we compute LRP ‘relevance maps’, which indicate each voxel’s importance to the outcome of the decision. Our resulting LRP heatmaps show no visually striking differences between the different registration techniques, while also revealing anatomically plausible features for term and preterm birth.
Public Group Announcements and Trust in Doxastic Logic

We present a doxastic logic for multi-agent systems with public group announcements. Beliefs are represented using belief bases and a dynamic of trust is introduced in order to handle belief change under contradictory announcements. We provide a complete axiomatization for this logic and illustrate its expressive power with a simple example.
Location Sensitive Regression Algorithm for Multi-oriented Scene Text Detection with Focal Loss

Scene text detection methods based on direct regression are always efficient and accuracy. However, the previous methods also have obvious shortcomings. The central text pixels always constrained the text box in the process of direct regression, which made the predicted results inaccurate. To solve this problem, we propose a novel Location Sensitive Regression algorithm for multi-oriented scene Text Detection (LSRTD), which assigns different weights to text pixels according to their distance to text box vertexes. At the same time, we apply Focal Loss to pixel-wise text/non-text classification task, the number of false positive cases in results is decreased because of Focal Loss balances the loss weights of easy and hard samples during training. Our method LSRTD is validated on benchmark datasets. The F-measure of ICDAR2015 is 0.84 on single-scale testing.
Unsupervised Moving Object Segmentation from Stationary or Moving Camera based on Multi-frame Homography Constraints

Moving object segmentation is the most fundamental task for many vision-based applications. In the past decade, it has been performed on the stationary camera, or moving camera, respectively. In this paper, we show that the moving object segmentation can be addressed in a unified framework for both type of cameras. The proposed method consists of two stages: (1) In the first stage, a novel multi-frame homography model is generated to describe the background motion. Then, the inliers and outliers of that model are classified as background trajectories and moving object trajectories by the designed cumulative acknowledgment strategy. (2) In the second stage, a super-pixel-based Markov Random Fields model is used to refine the spatial accuracy of initial segmentation and obtain final pixel level labeling, which has integrated trajectory classification information, a dynamic appearance model, and spatial temporal cues. The proposed method overcomes the limitations of existing object segmentation algorithms and resolves the difference between stationary and moving cameras. The algorithm is tested on several challenging open datasets. Experiments show that the proposed method presents significant performance improvement over state-of-the-art techniques quantitatively and qualitatively.
Order differences to enhance the delineation of rock units boundaries by using aerospectrometric data of Wadi Al Mushash area, Central Eastern Desert, Egypt: A new approach

The present work utilizes airborne gamma-ray spectrometric data, in a trial to refine the surface geological mapping of sedimentary rocks at Wadi Al Mushash, Central Eastern Desert of Egypt. The acquired aerospectrometric data were supported by the surface geology concerning the study area. This area is mainly covered by sedimentary rocks of Tertiary to upper Cretaceous as Nubian Sandstone Formations (Quseir Clastics and Tarif Sandstone), Duwi Phosphates Formation, Dakhla Shale Formation, Esna Shale Formation, Tarawan Formation, Thebes Formation, and Quaternary deposits. High-resolution airborne gamma-ray spectrometry can be very helpful in mapping the surface geology. The method provides estimation of the apparent surface concentrations of the most common naturally occurring radioactive elements, such as potassium (K), equivalent uranium (eU) and equivalent thorium (eTh). This work is based on the assumption that, the absolute and relative concentrations of these radioelements vary measurably and significantly with lithology. The composite image technique is used to display and simulate three parameters of the radioelements concentrations. The technique offered much in terms of lithologic discrimination, based on color differences and showed efficiency in defining areas, where different lithofacies occur within areas mapped as one continuous lithology. The Differences technique is used to delineate contacts of the rock units and the mainly structures of the study area. It is based mainly on varying in change of concentration of successive total count measurements within each line in the study area. Then, a suitable difference order is selected and matched to original measured line. Moreover, plotting the suitable order as grid and applied grid peaks on this grid. Then, these ridge grid peaks are symbolized over surface geological information and prepared radioelement composite map. This provides more detail about the surface geology, and addition more contacts occurred within original rock units. These subdivisions of rock units may be related to one formation that consists of more than one member having a different composition.
Learning mean progressive scattering using binomial truncated loss for image dehazing

In this study, the authors propose a novel progressive dehazing network to address the single image haze removal problem based on a new mean progressive scattering model. Different from methods that learn atmosphere light and transmission maps with different networks, these two variables are optimised in a unified network. Following the methodology of traditional prior-based methods that estimate a coarse transmission map first, a progressive refinement branch in the decoder has been designed to restore the fine-scale transmission map. To improve the prediction accuracy of the transmission map, a novel binomial truncated loss that assigns weights to error values according to the probabilities of error occurrences has been proposed. An ablation study is conducted to verify the effectiveness of the components in the proposed method. Experiments in the synthetic datasets and real images demonstrate that the proposed method outperforms other state-of-the-art methods.
Variation under a psycholinguistic perspective: number agreement in Brazilian Portuguese and its impact for the schooling

This paper discusses the impact of schooling in the production of number agreement in Brazilian Portuguese (BP) by 6th graders in Rio de Janeiro. Number agreement in BP can vary between: (a) redundant, the standard variety or (b) non-redundant, non-standard forms. Such variation is influenced by speakers’ socioeconomic status (SES) and educational level (Naro, 1981; Scherre &amp; Naro, 1998 among many others). Results of an elicited production experiment carried out with 6th graders suggest influence of SES/ type of school and academic performance in language production; the effects of schooling and metalinguistic awareness are discussed. An account of language production is presented by conciliating L1 and L2 models of speech production, considering the possibility of the coexistence of varieties in a single speaker to be akin to bilingualism.
Accelerated ML-Assisted Tumor Detection in High-Resolution Histopathology Images

Color normalization is one of the main tasks in the processing pipeline of computer-aided diagnosis (CAD) systems in histopathology. This task reduces the color and intensity variations that are typically present in stained whole-slide images (WSI) due to, e.g., non-standardization of staining protocols. Moreover, it increases the accuracy of machine learning (ML) based CAD systems. Given the vast amount of gigapixel-sized WSI data, and the need to reduce the time-to-insight, there is an increasing demand for efficient ML systems. In this work, we present a high-performance pipeline that enables big data analytics for WSIs in histopathology. As an exemplary ML inference pipeline, we employ a convolutional neural network (CNN), used to detect prostate cancer in WSIs, with stain normalization preprocessing. We introduce a set of optimizations across the whole pipeline: (i) we parallelize and optimize the stain normalization process, (ii) we introduce a multi-threaded I/O framework optimized for fast non-volatile memory (NVM) storage, and (iii) we integrate the stain normalization optimizations and the enhanced I/O framework in the ML pipeline to minimize the data transfer overheads and the overall prediction time. Our combined optimizations accelerate the end-to-end ML pipeline by $$7.2{\times }$$ and $$21.2{\times }$$ , on average, for low and high resolution levels of WSIs, respectively. Significantly, it allows for a seamless integration of the ML-assisted diagnosis with state-of-the-art whole slide scanners, by reducing the prediction time for high-resolution histopathology images from $$\sim $$ 30 min to under 80 s.
Calibration of Stereo Radiography System for Radiostereometric Analysis Application

This paper describes a new alternative to conventional radiography system currently used for radiostereometric analysis studies. Instead of using two non-calibrated X-ray sources with a cumbersome calibration cage, we propose to use the biplanar radiography EOS system. Its fixed configuration provides a preliminary calibration and a much simpler acquisition protocol. A flexible and accurate calibration method is presented to optimize EOS default calibration using a simple object and a self-calibration method. To validate our system, we calculate the 3D reconstruction error of a known object. Results showed an accuracy of 70±11μm and 0.05±0.02° for translation and rotation respectively, and an average epipolar error of 23±03μm.
Eyelid Movement Command Classification Using Machine Learning

The Eyelid Drive System (EDS) is an assistive technology device intended to allow users to wirelessly control other devices, such as power wheelchairs and personal computers, using commands consisting only of blinking and winking. In this paper, four machine learning classifiers are trained on data taken from one subject and validated offline on the training subject plus two additional subjects. The classifiers are assessed for accuracy, computational and memory requirements, and transferability from the "training" subject to the other two subjects. A support vector machine (SVM) achieved the highest level of accuracy (97.5%) while using a potentially prohibitive level of computational and memory resources. A logistic regression classifier also achieved excellent accuracy (96.5%) while using two to three orders of magnitude fewer computational and memory resources than the SVM.
Face Image Analysis in Mobile Biometric Accessibility Evaluations

Smartphones cameras are widely used for biometric authentication purposes. This enables more and more users experience face recognition in different common scenarios (e.g., unlocking phones, banking, access controls). One of its advantages is that face recognition requires low interaction with the systems (by simply looking at the smartphone's screen). Thus, it may be useful for people affected by mobility concerns. For this reason, researchers recently started to conduct mobile biometric evaluations recruiting accessibility populations. The aim is to analyse all those factors that, depending on the users' capabilities, influence the biometrics recognition process. In this paper we focus our attention on sample quality, analysing the face images collected during a mobile biometric accessibility study. Results obtained enable us to understand how the users' accessibility concerns influence the biometric sample quality and discuss possible solutions for eradicating this inconvenience. This assessment had been conducted following the recommendations of the ISO/IEC TR 29794-5.