Reviewerly/exHarmony huggingface dataset:
Techniques for Named Entity Recognition on Arabic-English Code-Mixed Data

As a result of globalization and better quality of education, a signifcant percentage of the population in Arab countries have become bilingual/multilingual. This has raised the frequency of code-switching and code-mixing among Arabs in daily communication. Consequently, huge amount of Code-Mixed (CM) content can be found on different social media platforms. Such data could be analyzed and used in different Natural Language Processing (NLP) tasks to tackle the challenges emerging due to this multilingual phenomenon. Named-Entity Recognition (NER) is one of the major tasks for several NLP systems. It is the process of identifying named entities in text. However, there is a lack of annotated CM data and resources for such task. This work aims at collecting and building the first annotated CM Arabic-English corpus for NER. Furthermore, we constructed a baseline NER system using deep neural networks and word embeddings for Arabic-English CM text. Moreover, we investigated the usage of different types of classical and contextual pre-trained word embeddings on our system. The highest NER system achieved an F1-score of 77.69% by combining classical and contextual word embeddings.
Analysis on Misclassification in Existing Contraction of Fuzzy Min–Max Models

Fuzzy min–max (FMM) neural network is one of the most useful models for pattern classification. Various models have been introduced based on FMM model to improve the classification performance. However, the misclassification of the contraction process is a crucial issue that has to be handled in FMM models to improve classification accuracy. Hence, this research aims to analyse the existence and execution procedure of addressing the misclassification of the contraction in the current FMM models. In this manner, practitioners and researchers are aided in selecting the convenient model that can address the misclassification of the contraction and improve the performance of models in producing accurate classification results. A total of 15 existing FMM models are identified and analysed in terms of the contraction problem. Results reveal that only five models can address the contraction misclassification problem. However, these models suffer from serious limitations, including the inability to detect all overlap cases, and increasing the network structure complexity. A new model is thus needed to address the specified limitations for increasing the pattern classification accuracy.
Advanced Stroke Labeling Technique Based on Directions Features for Arabic Character Segmentation

Offline Character segmentation of text images is an important step in many document image analysis and recognition (DIAR) applications. However, the character segmentation of both writing styles (printed and handwritten) remains an open problem. Moreover, the manual segmentation is time-consuming and impractical for large numbers of documents. Based on the unconstraint-cursive handwritten perspective, the auto character segmentation is more challenging and complex. The Arabic script writing style suffers from many common problems, such as sub-words overlapping, characters overlapping, and missed characters. These challenging issues have attracted the attention of researchers in the field of DIAR for Arabic character segmentation. The proposed method combines a new advanced Stroke Labelling based on Direction Features (SLDF2) technique and a modified vertical projection histogram (MVPH) technique. This technique extracts the relationship between each text stroke pixel and its 8 neighboring foreground pixels and labels it with the proper value before identify the possible segmentation points. The text preparation for the segmentation process was achieved using multiple preprocessing steps and developing an advanced stroke labelling technique based on direction features. Several Arabic language structural-rules were made to detect the candidate segmentation points (CSP), detect many character overlapping cases, solve the missed characters problem that appears as a result of using the text skeleton in VPH, and validate the CSP. All techniques and methods are tested on the ACDAR benchmark database. The validation method used to measure segmentation accuracy was a quantitative analysis that includes Recall, Precision, and F-measurement tests. The average accuracy of the proposed segmentation method was 92.44%, which outperforms the state-of-the-art method.
Layanan Pendidikan Bagi Anak Tunagrahita Dengan Tipe Down Syndrome

One of abnormalities in children in terms of mental retardation is down syndrome. Down syndrome is a mental retardation disorder in which the IQ is below normal average. Children with down syndrome have mental retardation function of intellectuals in the range of middle inability with the lateral range of IQ is 40-45. The cause of down syndrome is the abnormalities of chromosome number 21 which is not properly composed of two chromosomes. In fact, there are three chromosomes (trisomy 21) that cause the children to experience the physical and intellectual malformation. Mental retardation children with down syndrome experience some barriers to achieve the developmental tasks of cognitive, affective, and psychomotor aspects. However, they can still develop a non-academic potency such as taking care of themselves, making friends, and appreciating othersâ€™ property rights. Children who grow up with the abnormalities like as down syndrome have the same rights to obtain adequate educational services. Therefore, some educational services programs that can be arranged for the children with mental retardation of down syndrome are classroom-supports and guidance for them through visual and graphic approaches, cues-prompts and scaffolding, technology-based learning, and music-based learning.&#x0D;
Answer Categorization Method Using K-Means for Indonesian Language Automatic Short Answer Grading System Based on Latent Semantic Analysis

The Automatic Short Answer Grading (Simple-O) has been created for grading short answer with Bahasa Indonesia using K-Means and Latent Semantic Analysis (LSA) method. In this experiment, the text document feature will be extracted using Term Frequency-Inverse Document Frequency (TF-IDF) and then classified using K-Means. From the experiment, 149 documents are expected to be clustered into five classes. The result of the clustering using K-Means is 100% matched with clustering using human rater. The result of grading with LSA is 74%.
Prior-based Hierarchical Segmentation Highlighting Structures of Interest

Abstract Image segmentation is the process of partitioning an image into a set of meaningful regions according to some criteria. Hierarchical segmentation has emerged as a major trend in this regard as it favors the emergence of important regions at different scales. On the other hand, many methods allow us to have prior information on the position of structures of interest in the images. In this paper, we present a versatile hierarchical segmentation method that takes into account any prior spatial information and outputs a hierarchical segmentation that emphasizes the contours or regions of interest while preserving the important structures in the image. Several applications are presented that illustrate the method versatility and efficiency.
Anisotropic Diffusion-Based Unsharp Masking for Sharpness Improvement in Digital Images

Various available imaging systems are capturing images with deficient sharpness due to numerous unavoidable shortcomings. Perceiving and extracting information from such images is uneasy. Hence, it is required to process these images properly to produce sharper and clearer details. Many methods exist that can be used to increase the sharpness of digital images. Among such, the unsharp mask has gained high popularity due to its rapidness and simplicity. Still, this filter usually degrades the processed images by an overshoot effect, which appears around the edges as white shades. In this study, an anisotropic diffusion-based unsharp mask filter, so-called ADUSM, is proposed, in that the degraded image is filtered using an amended anisotropic diffusion filter rather than processing it only by a low-pass Gaussian filter. This modification permitted the attenuation of the overshoot artefact which yielded to obtain better quality results. The ADUSM is tested with several types of images and assessed with two adequate quality metrics. Many experiments indicated that the proposed filter can outperform different existing methods and produce satisfactory results with reasonable application time.
Available techniques in hadoop small file issue

Hadoop is an optimal solution for big data processing and storing since being released in the late of 2006, hadoop data processing stands on master-slaves manner [1] that’s splits the large file job into several small files in order to process them separately, this technique was adopted instead of pushing one large file into a costly super machine to insights some useful information. Hadoop runs very good with large file of big data, but when it comes to big data in small files it could facing some problems in performance, processing slow down, data access delay, high latency and up to a completely cluster shutting down [2]. In this paper we will high light on one of hadoop’s limitations, that’s affects the data processing performance, one of these limits called “big data in small files” accrued when a massive number of small files pushed into a hadoop cluster which will rides the cluster to shut down totally. This paper also high light on some native and proposed solutions for big data in small files, how do they work to reduce the negative effects on hadoop cluster, and add extra performance on storing and accessing mechanism.
Swarm Intelligence on Color-Embedded-Grayscale Image

The recovery process of color-embedded-grayscale image becomes challenging task since of its ill-posed nature. The recovered color image produced from the color-embedded-grayscale image exhibits unpleasant result. Some important color information are lost in this stage. To overcome this issue, this paper presents a new technique on improving the quality of recovered secret image by means of optimization process. The proposed method exploits the effectiveness of swarm intelligence on finding the optimum adjustment weight. As reported in Experimental Section, the proposed method offers promising result for the color recovery task in terms of subjective and objective measurements. The proposed method yields the mean squared error about $3.447\mathbf{E}+08$ over training and testing dataset.
Markerless Multi-View-based Multi-User Head Tracking System for Virtual Reality Applications

In this paper, we propose a markerless multi-view-based multi-user head tracking system for VR/AR/MR applications. In the Virtual Reality domain, the user typically interacts with the virtual world through his movements. The point-of-view of the user's head mounted display (HMD) is based on the user's head pose. Most systems track the head of the user based on the tracking of the HMD itself and are typically constrained to a single user. Aiming to improve the user's experience in complex dynamic environments, we propose a head tracking method based on a multi-camera scenario, able to deal with the interaction of multiple users while coping with the existence of dynamic objects. The method relies on the voxelization of dense point cloud data, which is segmented to detect elements in the scene. Each element is holistically tracked along time and each detected user has his head's position tracked using a dedicated Particle Filter. The system was evaluated using an annotated ground-truth, according to several metrics, and demonstrated a mean 3D positioning error below 18 mm. Furthermore, tests with multiple users were performed that successfully validate the proposed head tracking approach. The aforementioned tests also showed that the proposed approach is computationally efficient (running time below 20 ms for 4 users).
Overview of modern technology: VR, AR, MBAR, MR.

Тенденции развития науки и образования
Information Science in Technical Education Process in Czech Republic

Information and Communication Technologies are one of the basic educa-tions of areas in the Framework Educational Programme for Basic Education to enable students to acquire a basic level of information literacy. The aim is to equip students with elementary skills in operating of computer equipment and modern information and communication technologies, but also to navi-gate in the world of information, creatively work with information and use them in further education and in practical life.&#x0D; The paper deals with the analysis of the situation and draws conclusions on the system of education in the Czech Republic in the context of the chal-lenges of INDUSTRY 4.0 in response to the capture, the robotics industry and services.
Multiple watermarking technique using optimal threshold

&lt;span&gt;The multiple watermarking technique has drawn more attention due to high demand for embedding more than one copyright. This paper proposes a multiple watermarking scheme using DWT-SVD by utilizing HVS characteristics. Our scheme embeds multiple watermarks in red and blue colors. The proposed scheme examines the coefficients of U orthogonal matrix for embedding and extracting watermarks. Watermarks are scrambled by Arnold transform before embedded in the host image in order provide additional security. The optimal thresholds for red and blue colors are revealed by finding a trade-off between normalized-cross correlation and imperceptibility from quantization steps. The experimental results demonstrate that our scheme achieves high resistant under JPEG and JPEG2000 compression for both inserted watermarks. &lt;/span&gt;
A note on the Gao et al. (2019) uniform mixture model in the case of regression

Abstract We extend the uniform mixture model of Gao et al. (Ann Oper Res, 2019. 10.1007/s10479-019-03236-9 ) to the case of linear regression. Gao et al. (Ann Oper Res, 2019. 10.1007/s10479-019-03236-9 ) proposed that to characterize the probability distributions of multimodal and irregular data observed in engineering, a uniform mixture model can be used. This model is a weighted combination of multiple uniform distribution components. This case is of empirical interest since, in many instances, the distribution of the error term in a linear regression model cannot be assumed unimodal. Bayesian methods of inference organized around Markov chain Monte Carlo are proposed. In a Monte Carlo experiment, significant efficiency gains are found in comparison to least squares justifying the use of the uniform mixture model.
Improving the role of language model in statistical machine translation (Indonesian-Javanese)

The statistical machine translation (SMT) is widely used by researchers and practitioners in recent years. SMT works with quality that is determined by several important factors, two of which are language and translation model. Research on improving the translation model has been done quite a lot, but the problem of optimizing the language model for use on machine translators has not received much attention. On translator machines, language models usually use trigram models as standard. In this paper, we conducted experiments with four strategies to analyze the role of the language model used in the Indonesian-Javanese translation machine and show improvement compared to the baseline system with the standard language model. The results of this research indicate that the use of 3-gram language models is highly recommended in SMT.
Towards Scalable Gaussian Process Modeling

Abstract Numerous engineering problems of interest to the industry are often characterized by expensive black-box objective function evaluations. These objective functions could be physical experiments or computer simulations. Obtaining a comprehensive idea of the problem and/or performing subsequent optimizations generally requires hundreds of thousands of evaluations of the objective function which is most often a practically unachievable task. Gaussian Process (GP) surrogate modeling replaces the expensive function with a cheap-to-evaluate data-driven probabilistic model. While the GP does not assume a functional form of the problem, it is defined by a set of parameters, called hyper-parameters, that need to be learned from the data. The hyperparameters define the characteristics of the objective function, such as smoothness, magnitude, periodicity, etc. Accurately estimating these hyperparameters is a key ingredient in developing a reliable and generalizable surrogate model. Markov chain Monte Carlo (MCMC) is a ubiquitously used Bayesian method to estimate these hyperparameters. At GEs Global Research Center, a customized industry-strength Bayesian hybrid modeling framework utilizing the GP, called GEBHM, has been employed and validated over many years. GEBHM is very effective on problems of small and medium size, typically less than 1000 training points. However, the GP does not scale well in time with a growing dataset and problem dimensionality which can be a major impediment in such problems. For some challenging industry applications, the predictive capability of the GP is required but each second during the training of the GP costs thousands of dollars. In this work, we apply a scalable MCMC-based methodology enabling the modeling of large-scale industry problems. Towards this, we extend and implement in GEBHM an Adaptive Sequential Monte Carlo (ASMC) methodology for training the GP. This implementation saves computational time (especially for large-scale problems) while not sacrificing predictability over the current MCMC implementation. We demonstrate the effectiveness and accuracy of GEBHM with ASMC on four mathematical problems and on two challenging industry applications of varying complexity.
Maybe Look Closer? Detecting Trolling Prone Images on Instagram

Improvement in network infrastructure and smartphones have made images based social media platforms like Instagram and Flickr popular. The visual medium of communication has also led to an alarming increase in trolling incidents on social media. Though it is crucial to automatically detect trolling incidents on social media, in this paper, we look at the problem from the eye of prevention rather than detection. A system that can recognize trolling prone images can issue a warning to users before the content is posted online and prevent potential trolling incidents. We attempt to make a supervised classifier to detect trolling prone images and discuss why the conventional state-of-the-art image classification method does not work well for this task. We also provide an extensive analysis of trolling patterns in images from Instagram, discuss challenges and possible future paths in detail.
Your Flutter App Can Work with Files

At this point in our journey, you’ve learned how to create a Flutter app and precisely control how it looks and lays out in any orientation and on any device. That’s pretty cool! You know how to have it maintain data with Form fields. But how do we get it to save that data? How do we get it to read that data in the first place?
Enhancement of Capacity, Detectability and Distortion of BMP, GIF and JPEG images with Distributed Steganography

The advance of Big Data and Internet growth has driven the need for more abundant storage to hold and share data.People are sending more messages to one another and paying attention to the aspects of privacy and security as opposed to previous decades.One of the types of files that are widely shared and instantaneous available over the web are images.They can become available as soon as a shot is taken and keep this closely related to the owner; it is not easy.It has been proposed here to use Steganography to embed information of the author, image description, license of usage and any other secrete information related to it.Thinking of this, an analysis of the best file types, considering capacity, detectability, and distortion was necessary to determine the best solution to tackle current algorithm weaknesses.The performance of BMP, GIF, and JPEG initialises the process of addressing current weaknesses of Steganographic algorithms.The main weaknesses are capacity, detectability and distortion to secure copyright images.Distributed Steganography technique also plays a crucial part in this experiment.It enhances all the file formats analysed.It provided better capacity and less detectability and distortion, especially with BMP.BMP has found to be the better image file format.The unique combination of Distributed Steganography and the use of the best file format approach to address the weaknesses of previous algorithms, especially increasing the capacity.It will undoubtedly be beneficial for the day to day user, social media creators and artists looking to protect their work with copyright.
Classification of Mouth Gestures in German Sign Language using 3D Convolutional Neural Networks

Automatic recognition of sign language gestures is becoming necessary with an increased interest into human-computer interaction in sign language as well as automatic translation from sign language. Most of the research on sign language recognition focuses on hand gesture recognition. However, there are also non-manual signals in sign language. Mouth gestures represent mouth shapes that add information to the hand gestures not related to spoken language visemes. For German Sign Language, mouth gesture recognition would be an important addition to manual gesture recognition. This research work evaluates the method 3D convolutional neural networks for recognising mouth gestures in German Sign Language. For the recognition of certain mouth gestures, temporal information is mandatory and the extraction of both spatial and temporal features by 3D convolutional networks makes the classification of all gestures easier. Our research work compares how different initialisations affect learning and classification by the network. We achieve an accuracy of around 68% on testing 10 classes of mouth gestures in German Sign Language.
Target-Embedding Autoencoders for Supervised Representation Learning

Autoencoder-based learning has emerged as a staple for disciplining representations in unsupervised and semi-supervised settings. This paper analyzes a framework for improving generalization in a purely supervised setting, where the target space is high-dimensional. We motivate and formalize the notion of target-embedding autoencoders (TEA) for supervised prediction, designed to learn intermediate latent representations jointly optimized to be both predictable from features as well as predictive of targets---encoding the prior that variations in targets are driven by a compact set of underlying factors. As our theoretical contribution, we provide a guarantee of generalization for linear TEAs by demonstrating uniform stability, interpreting the benefit of the auxiliary reconstruction task as a form of regularization. As our empirical contribution, we extend validation of this approach beyond the commonly-studied static domain to multivariate sequence forecasting, investigating the advantage that TEAs confer on both linear and nonlinear architectures.