Reviewerly/exHarmony huggingface dataset:
Low-Complexity Koetter-Vardy Decoding of Reed-Solomon Codes using Module Minimization

The Koetter-Vardy (KV) algorithm achieves advanced decoding performance for Reed-Solomon (RS) codes but with a high computational cost. This paper studies the low-complexity KV decoding that utilizes the module minimization (MM) interpolation technique, namely the KV-MM algorithm. A module contains bivariate polynomials that interpolate all the prescribed points with their multiplicity. Presenting the module basis as a matrix over univariate polynomials, row operation further reduces it into the Gr&#x00F6;bner basis, delivering the interpolated polynomial. We will also introduce the re-encoding transformed KV-MM algorithm by giving an explicit construction for the module basis. This research shows MM interpolation yields a remarkably lower complexity for KV decoding than the conventional Koetter's interpolation, especially for high rate codes. This is also true when the re-encoding transform is applied. This finding is a rectification of some earlier results.
Social media and students attitude: A study of accessibility, addictiveness, exposure and communication among college students in coimbatore, Tamil Nadu

Information and Communication Technology has broadened the scope of communication, education and learning. ICT provides a virtual learning space and experience as it would enable technology mediated learning among teachers and students. Mobile phone and social media usage has become inevitable in today's life. The ubiquity of smartphones made easy access of multimedia teaching aids as it is user friendly and convenient (anytime, anywhere learning). Apart from classroom learning, social media provides a platform for innovative and collaborative learning experience. The aim of the present study is to measure and analyze Students’ attitude towards social media. The demographic variables are age, gender and educational qualification. The dependent variables—exposure, communication, addictiveness and accessibility. A survey was conducted among 507 college students from Coimbatore were chosen through multistage stratified random sampling technique. Results states that there is no difference with respect to their age, gender, educational qualification towards exposure, communication, addictiveness and accessibility with respect to social media usage.
Deep Learning Frameworks for Convolutional Neural Networks—A Benchmark Test

Deep neural networks are state of the art for many machine-learning problems. The architecture of deep neural networks is inspired by the hierarchical structure of the brain. Deep neural networks feature a hierarchical, layer-wise arrangement of nonlinear activation functions (neurons) fed by inputs scaled by linear weights (synapses). Deep Learning frameworks simplify model development and training by providing high-level primitives for complex and error-prone mathematical transformations, like gradient descent, back-propagation, and inference. The main goal of this study is to compare the performance of two trending frameworks, Caffe and TensorFlow for Deep Machine Learning. As benchmark for the comparison with other approaches in deep learning was a well-analyzed database for handwritten cipher classification chosen. These two frameworks (Caffe and TensorFlow) were chosen out of nine different frameworks, after checking which ones better fits the proposed selection criteria. Caffe and TensorFlow frameworks were selected after nine different frameworks were analyzed using the following selection criteria: programming language, functionality level, algorithms and network architecture, pretrained models and community activity. As the performance is heavily affected by the number of parameters in the networks, four well-known convolutional neural network (CNN) architectures (LeNet, AlexNet, VGG19 and GoogLeNet) were trained and tested using both frameworks. Using architectures with different deepness will allow investigating how the number of hidden layers improves the accuracy of the CNN. As a CNN requires high computational effort, two computers equipped with different NVIDIA GPU were used to ease the effort and they were used to investigate how the hardware improves the performance of the CNN and if it is worthy to invest on it. As the CNN are widely used for image classification, it was defined that the used architecture was going to be used for classification of handwritten numbers, because this example of classification is very well analyzed and can serve as a benchmark for comparison. Due to this and considering that the training of these networks requires a huge amount of data, MNIST database was set for training and testing them. All the architectures were adapted to the3 MNIST database and they were developed for Caffe and TensorFlow frameworks and analyzed on the named architectures. The biggest differences on our hardware we have got in training the VVG19 and the GoogLeNet architectures in TensorFlow and Caffe.
An Application of Somatosensory Interaction for 3D Virtual Experiments

Interaction based on natural limb movements is the so-called Somatosensory Interaction, which has multi-faceted and continuous features at the information transmission level. The dependence of the keyboard will be significantly weakened, and the learning subject will pay more attention to the corresponding learning tasks in this interactive mode, so that the learning comfort can be significantly improved in the three-dimensional virtual system. Situated in such technical context, more and more teachers and students are actively applying virtual experiment technology in their teaching and learning environment. human-computer interaction mode can directly affect the user’s sense of sensation and immersion. In recent years, with the continuous development of digital media technology, the use of Somatosensory Interaction design and three-dimensional virtual experiment has practical improved the teaching effect and promoted transformation of teaching interaction mode. It not only saves a lot of animation problems involved in traditional virtual experiments, but also save costs significantly, and provide relevant innovation direction for the in-depth application of virtual experimental technology.
Shape Recognition with Recurrent Neural Network

Shape recognition is a fundamental problem in the field of computer vision and is important to various applications. A number of methods based on deep CNN has acquired state-of-the-art performance in shape recognition. Among them, model-based methods perform convolutions with 3D filters on the voxels or point cloud in continuous 3D space, and the volumetric representation makes them exploit complete structure information. Unfortunately, in order to train the deep network with available samples in a reasonable amount of time, these methods have to use a coarse representation, typically 30 × 30 × 30 grid, which will inevitably sacrifice much discriminate detail information. This paper presents a novel approached based on recurrent neural network to solve this problem. In each step, the model selects the location of the subvolume from where the local 3D CNN feature is extracted, and the hypothesis is formulated by merging the features of subvolumes of each step. In this way, the proposed approach can explore the shape with high resolutions and exploit the fine-grained 3D structure information. Primary experimental results on the public dataset verify the effectiveness of the proposed method.
DEMOCRATIZATION AS FREEDOM IN CHOOSE AN EDUCATIONAL PROCESS MODEL

The article considers that such a choice of democratization, as freedom in choosing an educational process model, presupposes liberalization of the whole system of the pedagogical activity, domination of the atmosphere of creativity, cooperation, innovation, and constructive dialogue among all participants in the educational process in an educational institution. At the same time, in a democratic society, the choice of an educational model by a teacher should take into account the characteristics, priorities, and inclinations of students. It is stated that the implementation of such an approach should unfold in the context of the development of specific methods of education and upbringing.
PENINGKATAN KOMPETENSI GURU MENGGUNAKAN TEKNOLOGI INFORMASI DAN KOMUNIKASI (TIK) DALAM PEMBELAJARAN DI SEKOLAH DASAR MELALUI SUPERVISI KLINIS

This research is motivated by the demands of an era full of technology so that the learning process must shift from conventional learning to active learning that utilizes ICT in the learning process. The facts found in the field indicate that teachers do not use ICT in the learning process for difficult reasons, teachers do not have the competence to use ICT, and facilities that are less available. The purpose of this study is to improve teacher competence using information and communication technology in learning through clinical supervision. This research is a classroom action research which includes the stages of planning, implementation, observation, and reflection. This research was carried out in the target schools of Pasir Penyu sub-district starting March 13 to April 30 2019 against 10 elementary school teachers. Data analysis using observation sheets that observe the actions of teachers using ICT in the learning process. The results showed an increase in teacher competency using ICT in the learning process with the percentage of the first cycle amounting to 57% with the medium category being 83% which included a very high category.
Fact-Checking Journalism sebagai Platform Kolaborasi Human and Machine pada Jurnalisme Digital

Fact-checking journalism becomes a new trend in journalism studies. The peculiarities of fact-checking journalism are the collaborations which developed beyond boundaries of the editorial room and conventions of journalism. Human and machine disruptions within fact-checking journalism are unavoidably developed into hypermedia organization. Collaboration is built amongst journalist, user, and digital media application in order to describe the whole factual context This paper reviews this phenomenon as main concept of hypermedia organization manifestation in fact-checking journalism. Keywords: digital journalism, fact-checking, hypermedia organization, collaboration
About Scientific Computing within Python and Jupyter Notebook

Summary Jupyter Notebook is a web-based application that allows you to write and comment on Python code interactively. This is a valuable mean to experiment, to do research, and to share your results with others. Increasingly, many researchers use this computing environment in their researches. In a brief second form highlights the key reasons for the growing popularity of the Python programming language and project Jupyter. According to the company TIOBE, which collects monthly statistics of search queries and, based on the data obtained, compiles its own visualized ratings of programming language, the Python ranks 3rd grade in popularity among programming languages. He was chosen as the language of the year in 2007, 2010 and 2018. Considered aspects of installing programs, libraries and packages in the Windows operating system. It is recommended to download and install libraries from the whl-file repository on the webpage by Christoph Gohlke Laboratory for Fluor escence Dynamics University of California. Demonstrated simplicity and efficiency of scientific computing in Jupyter Notebook. In particular, it is shown that the code for calculating the matrix size 5000x5000 takes only a few lines.
Multiple Obstacle Detection for Assistance Driver System Using Deep Neural Networks

Multiple obstacle detection is a challenging problem and has many important applications including video tracking, intelligence surveillance, robot navigation and autonomous driving. In existing methods, individual obstacle’s detection and contextual visual patterns are modeled separately and interactions from obstacles and their surroundings are mostly considered in a symmetric way, which we argue is not an optimal strategy. To tackle these difficulties, in this paper, we propose a deep convolutional networks for solving the online multiple obstacles detection problem. The method consists of deep visual information extraction and visual pattern learning. They are modeled as deep Convolution Neural Networks, which are able to learn discriminative visual features for obstacle detection and model inter-object relations in an asymmetric way and give the orientation extraction for the moving obstacles. The deep learning framework is trained in an end-to-end manner for better adapting the influences of visual information as well as inter-object relations and orientation information. Extensive experimental comparisons with state-of-the-arts as well as detailed component analysis of the proposed method on the benchmarks demonstrate the effectiveness of our proposed framework.
The Impact of Inquiry-Based Virtual Laboratory on Students' Inquiring Abilities

This study aimed to determine the ability of inquiring in the class using an inquirybased virtual laboratory. The research design of this study was based on quasiexperimental research using the pre-test post-test non-equivalent control group design. The results showed that the inquiring abilities for the experimental class had increased as a result of the learning process. A significant value was obtained based on the t-test for the experimental class. In the control class, there was an increase in the process of asking questions, making hypotheses and conclusions and communicating. A significant difference was found in the inquiring abilities of students in the classroom using the inquiry-based virtual laboratories as compared to students who had not used the virtual laboratory.
Empathy Tool Design-Eye Disease Simulator Based on Mixed-Reality Technology

People who are suffering from oculopathy have a tons of inconveniences in daily life due to cognitive difficulties. Our research results - eye disease simulator embrace inclusive design philosophy may make some changes. However, a kind of empathy tools aimed at helping designer experience eye disease has been lacking for a long time. Our project brings out an eye disease simulator based on mixed reality technology (Microsoft Hololens) enable designers understand the eye disease patient better, and verify & optimize visual design through serious expert interviews, patient shadowing and other design methods. Compared with the previously existed oculopathy simulator, our research result covers a variety of eye disease’s traits, even more truly reveal the world of visually impaired groups can see. Getting the benefit from wearability and interactivity of Microsoft Hololens, visual designers can see world through transparent front glass simultaneously in an acceptable way, while recording issues on previous guide system through various interaction (including Leapmotion, eye tacking). Our research results can be widely used to guide system design in public space, web design and so on. In conclusion, our research project validates the effectiveness of eye disease simulator through actual design cases, and explores new approach that combines cutting-edge technologies with orthodox empathy design tools.
Artificial intelligence retrieval algorithm for text data from multiple data sources

The current artificial intelligence retrieval algorithm ignores the diversity of the text data from multiple data sources, resulting in poor retrieval accuracy, and the problem of low retrieval efficiency is caused by the diversity of the text data from multiple data sources. In this paper, an artificial intelligence retrieval algorithm based on correlation computation is proposed. The documents with different length in the text data from multiple data sources are normalized, the format markers, invalid characters, disused words and affixes in the text data are eliminated, and the words with strong representation ability are selected as the feature items to realize the feature selection and dimension reduction of the text data from multiple data sources. This method improves the inefficient retrieval efficiency caused by different length of text data. According to the descriptive query statements of text data from multiple data sources, the text correlation degree is calculated, and the retrieval result is obtained based on the mean value of correlation degree. The experimental results show that the proposed algorithm can not only realize the diversified query of the input text data from multiple data sources, but also can retrieve the data associated with the input descriptive query words accurately and quickly, and get a better comprehensive evaluation value.
Revisiting Visual Grounding

We revisit a particular visual grounding method: the “Image Retrieval Using Scene Graphs” (IRSG) system of Johnson et al. Our experiments indicate that the system does not effectively use its learned object-relationship models. We also look closely at the IRSG dataset, as well as the widely used Visual Relationship Dataset (VRD) that is adapted from it. We find that these datasets exhibit bias that allows methods that ignore relationships to perform relatively well. We also describe several other problems with the IRSG dataset, and report on experiments using a subset of the dataset in which the biases and other problems are removed. Our studies contribute to a more general effort: that of better understanding what machine-learning methods that combine language and vision actually learn and what popular datasets actually test.
From News to Medical: Cross-domain Discourse Segmentation

The first step in discourse analysis involves dividing a text into segments. We annotate the first high-quality small-scale medical corpus in English with discourse segments and analyze how well news-trained segmenters perform on this domain. While we expectedly find a drop in performance, the nature of the segmentation errors suggests some problems can be addressed earlier in the pipeline, while others would require expanding the corpus to a trainable size to learn the nuances of the medical domain.
TOWARDS AN IMPLEMENTATION OF BLOCKCHAIN-BASED TRANSCRIPTS WITH NOSQL DATABASES

Blockchain has emerged as a transformative technology, from its beginning as the basis of cryptocurrencies to widerapplications in areas such as property registration and insurance due to its characteristic as a distributed ledger which canremove
Self-optimization in a Hopfield neural network based on the

It has recently been demonstrated that a Hopfield neural network that learns its own attractor configurations, for instance by repeatedly resetting the network to an arbitrary state and applying He...
Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning

To mitigate the negative effect of classification bias caused by overfitting, semi-naive Bayesian techniques seek to mine the implicit dependency relationships in unlabeled testing instances. By redefining some criteria from information theory, Target Learning (TL) proposes to build for each unlabeled testing instance P the Bayesian Network Classifier BNC P , which is independent and complementary to BNC T learned from training data T . In this paper, we extend TL to Universal Target Learning (UTL) to identify redundant correlations between attribute values and maximize the bits encoded in the Bayesian network in terms of log likelihood. We take the k-dependence Bayesian classifier as an example to investigate the effect of UTL on BNC P and BNC T . Our extensive experimental results on 40 UCI datasets show that UTL can help BNC improve the generalization performance.
Smart Sensors and Actors with BACnet<sup>TM</sup> and Mbed OS on Cortex-M Microcontrollers

The increasing use of intelligent sensors and actors in automation proportionally increases the need for suitable interfaces to either interact with, or to provide the means, to participate alongside a common interface. Without appropriate system prerequisites, the integration of sensors or actors, not equipped with such interfaces, into an Ethernet based network, is not possible. As part of a R&D (research and development) project of the University of Wismar, a modified open-source BACnet™1 (Building Automation and Control Networks) stack is used as protocol stack for device-to-device communication between sensors and actors. As hardware, Mbed OS compatible ARM Cortex-M based microcontrollers are used. The target is a, by protocol supported, device-to-device communication for virtually any sensor or actor. The primary tasks of devices utilizing a modified embedded BACnet stack therefore are the extension or provision of Ethernet communication abilities via BACnet/IP. The flexibility of BACnet to project virtually any source of information as a group of standardized objects and services allows integrating a wide range of individual devices into Ethernet networks via a common interface. Combining an established and well-proven, royalty-free protocol with a microcontroller operating system provides all necessary means for a universal interface for the integration of "things" into Ethernet based IP networks via BACnet/IP.
PREPARING MANAGEMENT SCIENCE STUDENTS FOR THE DIGITALIZATION ERA

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 9249-9256ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.2295Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Structural Analysis of Extension-Based Argumentation Semantics with Joint Acceptability

Dung’s abstract argumentation provides us with a general framework to deal with argumentation. For extension-based semantics, the central issue is how to determine the extensions w.r.t. various semantics. Motivated by the acceptability and reinstatement criterion, we propose the notions of J-acceptability and J-reinstatement. Correspondingly, we introduce the J-complete semantics which reveals the gap between complete semantics and preferred semantics. It is shown that acceptability together with J-acceptability forms the foundation of extension-based semantics. For example, any admissible set can be built starting from a conflict-free collection of initial sets by iteratively applying some functions based on acceptability and J-acceptability. This novel idea enables to give a clear description of the extensions of the standard semantics.
Syntactic Interchangeability in Word Embedding Models

Nearest neighbors in word embedding models are commonly observed to be semantically similar, but the relations between them can vary greatly. We investigate the extent to which word embedding models preserve syntactic interchangeability, as reflected by distances between word vectors, and the effect of hyper-parameters—context window size in particular. We use part of speech (POS) as a proxy for syntactic interchangeability, as generally speaking, words with the same POS are syntactically valid in the same contexts. We also investigate the relationship between interchangeability and similarity as judged by commonly-used word similarity benchmarks, and correlate the result with the performance of word embedding models on these benchmarks. Our results will inform future research and applications in the selection of word embedding model, suggesting a principle for an appropriate selection of the context window size parameter depending on the use-case.