Reviewerly/exHarmony huggingface dataset:
Neural Architecture Search for a Highly Efficient Network with Random Skip Connections

Regarding the sequence learning of neural networks, there exists a problem of how to capture long-term dependencies and alleviate the gradient vanishing phenomenon. To manage this problem, we proposed a neural network with random connections via a scheme of a neural architecture search. First, a dense network was designed and trained to construct a search space, and then another network was generated by random sampling in the space, whose skip connections could transmit information directly over multiple periods and capture long-term dependencies more efficiently. Moreover, we devised a novel cell structure that required less memory and computational power than the structures of long short-term memories (LSTMs), and finally, we performed a special initialization scheme on the cell parameters, which could permit unhindered gradient propagation on the time axis at the beginning of training. In the experiments, we evaluated four sequential tasks: adding, copying, frequency discrimination, and image classification; we also adopted several state-of-the-art methods for comparison. The experimental results demonstrated that our proposed model achieved the best performance.
Defining gaze tracking metrics by observing a growing divide between 2D and 3D tracking

This work examines the different terminology used for defining gaze tracking technology and explores the different methodologies used for describing their respective accuracy.Through a comparative study of different gaze tracking technologies, such as infrared and webcam-based, and utilising a variety of accuracy metrics, this work shows how the reported accuracy can be misleading.The lack of intersection points between the gaze vectors of different eyes (also known as convergence points) in definitions has a huge impact on accuracy measures and directly impacts the robustness of any accuracy measuring methodology.Different accuracy metrics and tracking definitions have been collected and tabulated to more formally demonstrate the divide in definitions.
Improved Whale Optimization Algorithm Based on Inertia Weights for Solving Global Optimization Problems

Whale Optimization Algorithm (WOA) is a new kind of swarm-based optimization algorithm that mimics the foraging behavior of humpback whales. WOA models the particular hunting behavior with three stages: encircling prey, bubble-net attacking, and search for prey. In this work, we proposed a new linear decreasing inertia weight with a random exploration ability (LDIWR) strategy. It also compared with the other three inertia weight WOA (IWWOA) methods: constant inertia weight (CIW), linear decreasing inertia weight (LDIW), and linear increasing inertia weight (LIIW) by adding fixed or linear inertia weights to the position vector of the reference whale. The four IWWOAs are tested with 23 mathematical and theoretical optimization benchmark functions. Experimental results show that most of IWWOAs outperform the original WOA in terms of solution accuracy and convergence rate when solving global optimization problems. Accordingly, the LDIWR strategy produces a better balance between exploration and exploitation capabilities for multimodal functions.
Preliminary application study of using tele-ultrasound robot system in volunteers

ObjectiveTo explore the accuracy of ultrasonic diagnosis using the tele-ultrasound robot system.MethodsDuring the period from May to June 2018, 42 volunteers were consecutive selected to participate in this study, and their digestive system and urinary system were examined using the robot method manufactured by GI Tech Co., Ltd, Shenzhen and the traditional method. The results including the ultrasound diagnosis and the inspection time of two methods in each patient were compared. The ultrasonographic diagnosis of each volunteer was obtained by two methods and the time was recorded. The ultrasound images of the two methods were mixed together, and the quality of the images was evaluated double-blindly by two senior doctors.ResultsThere were actually 25 positive lesions in 42 volunteers. In the ultrasonic diagnosis of the two methods, 22 lesions were positive detected by robot method, with 5 lesions misdiagnosed. In the traditional method, 24 lesions were positive detected, with 1 lesions misdiagnosed. Using the Wilcoxon signed rank test of paired sample comparison, the score obtained by the robot method was 4.79±0.57, and the score obtained by the traditional method was 4.81±0.54(Z=0.775, P=0.439), the difference was not statistically significant. There was no statistically significant difference in the images′ qulity between the robot method and the traditional method using the chi-square test of the four-grid data(P>0.05). The check time for volunteers with negative result was (8.64±2.95)min in robotic method and (2.55±0.74)min in the traditional method, the difference was statistically significant (t=15.161, P=0.000).ConclusionsThe robot method has high value in common disease diagnosis and high quality in image acquisition, and can be used in real-time diagnosis of the remote areas or community medical.Key words: Ultrasonography; Remote consultation; Telemedicine; Tele-ultrasound Robot System
Indonesian Alphabet Speech Recognition for Early Literacy using Convolutional Neural Network Approach

Games are considered capable of being used as a learning medium that can help teachers to teach children how to pronounce the Indonesian alphabet in early literacy, we try to build one aspect of the game in this study. The approach we use is a speech recognition approach that uses the convolutional neural network method. The results of this study indicate that CNN can recognize speech, with input data is in the form of sound. We use the MFCC feature vector sound feature to make a 3-dimensional matrix of input sound into CNN input. We also use the Sequential CNN architecture made from a simple 10 layer neural network, which produces a model with a small size, approximately only about 6 MB, with high accuracy (84%) and an F-Measure of 0.91.
Review on Speech Recognition using Deep Learning

Speech recognition is the method of translating spoken words into text.The speech recognition process digitizes the sound waves into basic language units.Speech recognition is one of the most used technologies in today's life.This technology can be seen everywhere around a person, for example in phones, games, etc.The main purpose of the paper is to know the knowledge and the technology behind this superb invention.
A Study on Event-Oriented Text Retrieval with Deep Neural Network

In recent years, more and more data have been generated via news media on Internet. Especially, when tremendous notable events happen at home and abroad, which cases a large demand for event retrieval. Text retrieval is the fundamental and important issue to search information under the scenario of Big Data Some recent works have proposed neural information retrieval, which has a significant improvement in terms of some problems (e.g., semantic gap). However, such retrieval takes a general-purposed query rather than event-oriented query as the retrieval object, which fails to search events in the event-oriented text retrieval scenaiio. As such, in this paper, we first analyze the difficulties and investigate a large of recent deep neural methods that may be used for event-oriented text retrieval. After that, we propose a pipeline based on deep neural networks. Finally, we present several tasks at different pipeline stages for evaluating event oriented text retrieval.
PEMANFAATAN SAMPAH KERTAS SEBAGAI BAHAN BAKU PAPERBOARD UNTUK MEMPRODUKSI BENDA FUNGSI DAN ESTETIK

The direction of the implementation of this study is to reduce dependence on natural resources and preserve them from extinction, especially timber natural resources. Green design content is a systematic implementation in each stage. An initial idea was to find an alternative building material that was environmentally friendly. One of the implementations is conducting experiments on panel production from paper waste materials. Paper panels that have the same capabilities as pre-existing panel products. The aim is to make it an interior design material for building, both for construction and aesthetic functions. The consideration of the selection of panel materials is the wide scope of utilization, flexibility, easy treatment, and factors of people who are used to it. In full, this type of paper waste contains forms that are fatty, limp and fragile. This character strongly supports the implementation of that perception. Furthermore, this character becomes a stimulus to bring up the manufacturing method, which is a vertical cross-configuration, which uses limp and fragile sheet paper and then arranged into a panel area with a thickness of 20 mm which requires hard, sturdy, and strong properties.
The Communication Competence of Visual Reader at Visually Impaired's Cinema

Bioskop Harewos is a cinema that specialized in visually impaired film viewers in Bandung, Indonesia. The cinema runs with a traditional and distinct watching process. Some important concepts used in this research are related to communication competences; Visually impaired; and film. Using a qualitative method with constructivism paradigm and case study approach, this research tries to uncover several aspects, such as the communication capabilities of Visual Readers in Bioskop Harewos; the management effort of Bioskop Harewos in enhancing the communication effectiveness between Visual Readers and visually impaired viewers; also the competency of communication skills that required by Visual Readers. The result of this study revealed that the communication skills of Visual Readers in terms of being a prompter to visually impaired viewers were still under expectation. In terms of improving communication skills, the manager of the Bioskop Harewos has made several introductions and pre-viewing activities, yet the result is not as expected. This research also found three key competencies that are a necessity to be possessed by Visual Readers.
Educational innovation studies as a complex system

Educational innovations are deﬁned as a complex, self-renewing systemic formation, dependent on changing social and educa-tional priorities. The system of educational innovations has been deve-loped taking into account the methodological and historical bases of itsformation, deﬁning the object and subject, classiﬁcations of innovationsin education, criteria for selection and implementation of innovative expe-rience, quality of innovative activities. Based on the analysis of scientiﬁcand theoretical approaches, the object of pedagogical innovation studies isthe innovation process, conditions, methods and results of its implemen-tation, the subject is the relationship between the eﬃciency of innovationprocesses and its determining factors, as well as ways of inﬂuencing thesefactors in order to increase the eﬀectiveness of changes. The conceptualand categorical apparatus of educational innovations has been deﬁned.The paper demonstrates diverse scientiﬁc views in the interpretation of itsessence leading to the coexistence of diﬀerent approaches in typologizationand classiﬁcation of pedagogical innovations. The scope, innovative poten-tial, and scale of transformations are considered the main peculiarities ofinnovations. The empirical part presents the results of the scientiﬁc con-textual Master’s project “Innovative Experimental Educational Systems”implemented at the National Pedagogical Dragomanov University (Kyiv,Ukraine). The “quality of innovation activities” is considered a promisingtrend in the further study of educational innovation problems. Pedagogi-cal universities are deﬁned as the centres of forming students” ability toanalyse, summarize, select innovative pedagogical experience, design andpersistently introduce it into educational practice.
Application of Leading and Innovative 3D Technologies for Crime Investigation

Until recently, 3D scanning, modeling and virtual reality technologies were associated exclusively with a spectacular picture on the PC screens.However, today each of us is faced with this kind of technology in our everyday lives.In particular, such leading technologies can be used to restore the situation in which the crime was committed, to recreate the crime model, and also open up new opportunities and take to a whole new level the process of detecting, fixing and studying traces, and conducting various expert studies.The purpose of this paper is to study, based on the available information and practical results, not only the essence of 3D technologies and 3D scanners, in particular, but also the possibilities of their application for the purpose of disclosing and investigating crimes.The classification of existing models of 3D scanners allowed us to determine their advantages and disadvantages which can be somehow used in forensics and forensics.
Improving data quality using a deep learning network

&lt;p&gt;IoT data is collected in real time and is treated as highly reliable data because of its high precision. However, it often exhibits incomplete values for reasons such as sensor aging and failure, poor operating environment, and communication problems. The characteristics of IoT data transmitted with high precision and time series are suitable to use LSTM, which is one kind of RNN. In this paper, when applying LSTM to data quality improvement in IoT environment where data are collected simultaneously from several sensors, it is suggested that it is effective to construct LSTM individually for each sensor accuracy.&lt;/p&gt;
The Implementation of Demonstration Method in Area of A Triangle Learning at SDN 20 Delta Pawan

The research used Classroom Action Research. Data analysis was carried out by counting teacher activities during the learning process by peers as well as calculating student test result data, which was calculated using the formula of percentage scores and grade averages. The results showed that the average test in each cycle increased, namely in the first cycle 65.5 increased in the second cycle to 89.0. In the first cycle, around 45% of students scored below the KKM, but in the second cycle, there were no more students who scored below the KKM. It showed that the wide triangle flat learning using the demonstration method could improve the learning outcomes of IV grade SDN 20 Delta Pawan students need to be implemented as good learning, which can optimize student abilities. Learning planning arranged in each cycle, namely (1) competencies and learning outcomes to be achieved, (2) learning indicators, (3) Principal material, (4) stages of learning scenarios (5) media and learning resources, (6) time allocation, and (7) learning evaluation. Keywords: Demonstration Method, Area of Triangle, Learning Outcomes
HOW TO DEVELOP THE STANDARDIZED INSTRUMENTS OF MULTIPLE INTELLIGENCES IN ELEMENTARY SCHOOL: A QUALITATIVE-DESCRIPTIVE APPROACH

It was rarely the academicians, and practitioners knew how to make the standardized instruments to analyze the student's multiple intelligences. The purpose of this study was to describe the development of a standardized instrument of multiple intelligences for children in elementary school. The research method used was descriptive qualitative. In qualitative research, the research instrument was the researcher himself. The research instrument was not external or objective, but internal or subjective, namely the researcher himself without using tests, questionnaires, or experiments. Therefore the direct presence of researchers was a necessity in qualitative research. The data collection techniques used were observation and documentation. The results obtained in this study were to identify multiple intelligences in 7-12 years old children, we could use standardized instruments based on the classification of children's multiple intelligences. To analyze the development of multiple intelligences of 7-12 years old children, we could use the major theory of Gardner and Armstrong. The development of standard instruments used to measure multiple intelligences of 7-12 years old children, we could use 13 steps of Jamaris theory. This development step unravels the theory, determines the basis, then turns it into a standardized instrument that can be used to measure the type of multiple intelligences in 7-12 years old children. How to use the standardized instruments and its recommendations for teachers are discussed.Keywords:multiple intelligences; standardized instrument; development
Analyzing Resume using Natural Language Processing Machine Learning and Django

Parse details from a resume using natural language clarifying, find the keywords, assemble them onto sectors based on their keywords and lastly show the most relevant resume to the manageress based on keyword matching. Initially, the user transmits a resume to the web platform. The parser parses all the crucial information from the resume and auto fills a form for the user to analyze. Once the user confirms, the resume is displayed to the employers. Also, the user gets their resume in both JSON format and GUI view. Parsed data include name, email address, social profiles, years of work experience, work experiences, years of edification, education experiences, publications, certifications, volunteer experiences, keywords and finally the cluster of the resume , Computer science, human resource are the best examples.
The molecular entities in linked data dataset

The Molecular Entities in Linked Data (MEiLD) dataset comprises data of distinct atoms, molecules, ions, ion pairs, radicals, radical ions, and others that can be identifiable as separately distinguishable chemical entities. The dataset is provided in a JSON-LD format and was generated by the SDFEater, a tool that allows parsing atoms, bonds, and other molecule data. MEiLD contains 349,960 of ‘small’ chemical entities. Our dataset is based on the SDF files and is enriched with additional ontologies and line notation data. As a basis, the Molecular Entities in Linked Data dataset uses the Resource Description Framework (RDF) data model. Saving the data in such a model allows preserving the semantic relations, like hierarchical and associative, between them. To describe chemical molecules, vocabularies such as Chemical Vocabulary for Molecular Entities (CVME) and Simple Knowledge Organization System (SKOS) are used. The dataset can be beneficial, among others, for people concerned with research and development tools for cheminformatics and bioinformatics. In this paper, we describe various methods of access to our dataset. In addition to the MEiLD dataset, we publish the Shapes Constraint Language (SHACL) schema of our dataset and the CVME ontology. The data is available in Mendeley Data.
Sentiment Analysis of News Articles for Financial Signal Prediction

The task point is to break down stock forecast based on news stories and this should be possible by nostalgic examination by having three parameters and this undertaking will give a brief of any news (feelings). The parameters on which our venture will produce results are Positive, Negative or Neutral. This will help the proficiency of any association in light of the fact that previous manual methodology was taken a stab at utilizing a human to peruse the articles and grouping the notion, yet this framework will make a programmed approach for advertise developments (forecast). In this undertaking, we will utilize two innovations for example AI and Natural Language Processing. Because of the unpredictability of the securities exchange, value variances dependent on opinion and news reports are normal. Brokers draw upon a wide assortment of freely accessible data to advise their market choices. For this task, we concentrated on the examination of freely accessible news reports with the utilization of PCs to give guidance to stock exchanging. We execute a model that predicts the following days' stock development by fusing pertinent monetary data, for example, late stock value development and income shock, and literary data from these money related reports. We exhibit that the model which incorporates printed data performs altogether better than a model with budgetary data alone.
The Role of Computational Stylometry in Identifying (Misogynistic) Aggression in English Social Media Texts

In this paper, we describe UniOr_ExpSys team participation in TRAC-2 (Trolling, Aggression and Cyberbullying) shared task, a workshop organized as part of LREC 2020. TRAC-2 shared task is organized in two sub-tasks: Aggression Identification (a 3-way classification between “Overtly Aggressive”, “Covertly Aggressive” and “Non-aggressive” text data) and Misogynistic Aggression Identification (a binary classifier for classifying the texts as “gendered” or “non-gendered”). Our approach is based on linguistic rules, stylistic features extraction through stylometric analysis and Sequential Minimal Optimization algorithm in building the two classifiers.
Leading Forensic and Sociological Aspects in Investigating Computer Crimes

Our paper discusses the leading features of the formation and functioning of a criminal group when committing crimes with the help of electronic payment systems.Currently, there is an increase in the number of computer crimes around the world.According to the Prosecutor General of the Russian Federation, the number of crimes in the IT sphere has increased.The most common are illegal access to computer information and malicious computer programs.At the same time, the number of investigated crimes is reduced.Most of these crimes, due to their technological complexity, are committed by organized groups that have their own internal connections, features, hierarchy, motives.This paper scrutinizes by what methods, methods and means these groups (members of groups, relations between them, social roles, etc.) can be investigated to build practical recommendations for law enforcement agencies to identify such groups and curb their activities.Our comparative analysis revealed the general characteristics of such groups, both in sociological theory and in criminology.Based on our own observations and analysis, it was concluded that an organized criminal group, being an independent category, and considered as an element of the forensic characteristics in forensics, meets all the characteristics of the group as a sociological phenomenon.Consequently, the methods of sociology can be used in two ways: first, to identify such groups, and second, to identify relationships and roles within groups in identifying and investigating computer crimes.
Continuously Tracking Core Items in Data Streams with Probabilistic Decays

The sheer scale of big data causes the information overload issue and there is an urgent need for tools that can draw valuable insights from massive data. This paper investigates the core items tracking (CIT) problem where the goal is to continuously track representative items, called core items, in a data stream so to best represent/summarize the stream. In order to simultaneously satisfy the recency and continuity requirements, we consider CIT over probabilistic-decaying streams where items in the stream are forgotten gradually in a probabilistic manner. We first introduce an algorithm, called PNDCIT, to find core items in a special kind of probabilistic non-decaying streams. Furthermore, using PNDCIT as a building block, we design two novel algorithms, namely PDCIT and PDCIT+, to maintain core items over probabilistic-decaying streams with constant approximation ratios. Finally, extensive experiments on real data demonstrate that PDCIT+ achieves a speedup of up to one order of magnitude over a batch algorithm while providing solutions with comparable quality.