Reviewerly/exHarmony huggingface dataset:
A PSO Strategy of Finding Relevant Web Documents using a New Similarity Measure

In the world of the Internet and World Wide Web, which offers a tremendous amount of information, an increasing emphasis is being given to searching services and functionality.Currently, a majority of web portals offer their searching utilities, be it better or worse.These can search for the content within the sites, mainly textthe textual content of documents.In this paper a novel similarity measure called SMDR (Similarity Measure for Documents Retrieval) is proposed to help retrieve more similar documents from the repository thus contributing considerably to the effectiveness of Web Information Retrieval (WIR) process.Bio-inspired PSO methodology is used with the intent to reduce the response time of the system and optimizes WIR process, hence contributes to the efficiency of the system.This paper also demonstrates a comparative study of the proposed system with the existing method in terms of accuracy, sensitivity, F-measure and specificity.Finally, extensive experiments are conducted on CACM collections.Better precision-recall rates are achieved than the existing system.Experimental results demonstrate the effectiveness and efficiency of the proposed system.
Demographic Factors in Cyber Security: An Empirical Study

Despite high quality information systems security in place, organizations are vulnerable to cyber-attacks due to lapses in the human behavior. The present paper explores the importance of human factors in cyber security using an online survey data. It uses the work of Parson, Calic, Pattenson, Butavicius, McCormac and Zwaans [23] in measuring the human aspects of cyber security (leaving printouts, links from known source, website access, information in website, password complexity, links from known source, plugging USB in public places) and their linkages with the demographic factors (age, work experience, academic discipline, qualification, and place). ANOVA was used on a sample size of 165. It was found that demographic profile of employees and students significantly differ in their perception towards the cyber security. The paper has suggestions for information security awareness training programmes to handle the inadequacies.
Monolingual backtranslation in a medical speech translation system for diagnostic interviews - a NMT approach

BabelDr is a medical speech to speech translator, where the doctor has to approve the sentence that will be translated for the patient before translation; this step is done using monolingual backtranslation, which converts the speech recognition result into a core sentence. In this work, we model this step as a simplification task and pro-pose to use neural networks to perform the backtranslation by generating and choosing the best core sentence. Results of a task-based evaluation show that neural net-works outperform previous versions of the system.
INCIDENCE OF INDISCIPLINARY BEHAVIOUR AMONG STUDENTS IN SENIOR HIGH SCHOOLS IN BANTAMA SUB-METROPOLITAN SCHOOLS IN THE KUMASI DISTRICT OF GHANA

The study sought to investigate the factors influencing indisciplinary behaviour among students in Senior High School (SHS) and to find means of eradicating such behaviours. The study had the intention of helping teachers, school administrators, students and parents to better understand the problem of indiscipline, its effects and to jointly find solution to them. The simple random sampling was used to select the Bantama Sub-Metropolis. All three SHS, teachers and students in the sub-Metropolis were used for the study. Simple frequency counts and percentages were used to analyze the research questions to come out with the findings. The study indicated that all the respondents saw indiscipline as a major problem among SHS students. It was also clear that the common indisciplinary acts in schools included absenteeism, stealing, bullying, alcoholism, lateness and running away from school to town. Among the recommendations included the revision of outmoded school rules and regulations to suit current modern trends. Article visualizations:
Formation of ICT Competency of Bachelor Students While Studying the Course “Information Technologies” in Education

This paper presents the results of research work on the formation of information and communication technologies (ICT) competency of bachelor students in Teaching who studied the course "Information technology (IT) in education."In this regard, the concepts of "computer literacy", "ICT competence", "ICT competency" are clarified.Also the content and structure of ICT competence are clarified as the basis for the formation of student ICT competency; selection and structuring of the content of the course "Information Technologies in Education" are carried out; information and educational environment of the course are developed and tested.The main aim of the course is formation of ICT competency of students.Monitoring of formation of ICT competency of students during 2018-2019 academic year was carried out on the results of the current and final control of students' progress on the course, input and final test of students for determining the level of formation of ICT competency.Also authors carried out the diagnostics of the orientation of students' motivation.Analysis of the level of students' ICT competency formation showed that it is formed mainly at the average and high levels.Also, it was noted an increase in the level of internal motivation of students after studying the course, due to the student's own activity, and its direct inclusion in the cognitive process.
Does Identity Theft Insurance Undermine Risk Perceptions and Increase Risky Behavioral Intentions?

A survey was conducted to study the impact of identity theft insurance on consumer risk perceptions and their risky behavioral intentions. By using the data collected from this survey, we were able to elicit and estimate subjects’ risk perceptions toward identity theft and also their intentions to engage into risky online shopping activities which may endanger their personal identity. The results show that identity theft insurance, as a curative remedy, does undermine individual risk perceptions of being victimized by the identity theft. Additionally, we also demonstrate that having identity theft insurance surprisingly increases consumer intentions to involve into the activities that could put personal identity at risk. Identity theft insurance, which is believed by consumers to protect their personal information, does have unintended impact with negative consequences on consumer welfare. This research provides valuable and deep insights for consumers, marketers, and government agencies to understand the identity theft insurance.
Usage of Library Websites in Promoting Academic Library Services: A Survey with Special Reference to the Selected College Libraries of Upper Assam

This paper tries to portrait the usage of library website as a tool for promotion of library services in the selected college libraries affiliated to Dibrugarh University. A pre-structured questionnaire has been used for the collection of primary data under the scope of the study. A total 12 colleges has been selected randomly for the study. This study helps in identifying the current status of library websites & ICT infrastructure as a whole in the colleges of Upper Assam.
Machine Learning: Why Do Simple Algorithms Work So Well?

Author(s): Jin, Chi | Advisor(s): Jordan, Michael I | Abstract: While state-of-the-art machine learning models are deep, large-scale, sequential and highly nonconvex, the backbone of modern learning algorithms are simple algorithms such as stochastic gradient descent, gradient descent with momentum or Q-learning (in the case of reinforcement learning tasks). A basic question endures---why do simple algorithms work so well even in these challenging settings?To answer above question, this thesis focuses on four concrete and fundamental questions:- In nonconvex optimization, can (stochastic) gradient descent or its variants escape saddle points efficiently?- Is gradient descent with momentum provably faster than gradient descent in the general nonconvex setting?- In nonconvex-nonconcave minmax optimization, what is a proper definition of local optima and is gradient descent ascent game-theoretically meaningful?- In reinforcement learning, is Q-learning sample efficient?This thesis provides the first line of provably positive answers to all above questions. In particular, this thesis will show that although the standard versions of these classical algorithms do not enjoy good theoretical properties in the worst case, simple modifications are sufficient to grant them desirable behaviors, which explain the underlying mechanisms behind their favorable performance in practice.
Deep Universal Graph Embedding Neural Network.

Learning powerful data embeddings has become a center piece in machine learning, especially in natural language processing and computer vision domains. The crux of these embeddings is that they are pretrained on huge corpus of data in a unsupervised fashion, sometimes aided with transfer learning. However currently in the graph learning domain, embeddings learned through existing graph neural networks (GNNs) are task dependent and thus cannot be shared across different datasets. In this paper, we present a first powerful and theoretically guaranteed graph neural network that is designed to learn task-independent graph embeddings, thereafter referred to as deep universal graph embedding (DUGNN). Our DUGNN model incorporates a novel graph neural network (as a universal graph encoder) and leverages rich Graph Kernels (as a multi-task graph decoder) for both unsupervised learning and (task-specific) adaptive supervised learning. By learning task-independent graph embeddings across diverse datasets, DUGNN also reaps the benefits of transfer learning. Through extensive experiments and ablation studies, we show that the proposed DUGNN model consistently outperforms both the existing state-of-art GNN models and Graph Kernels by an increased accuracy of 3% - 8% on graph classification benchmark datasets.
Accelerating Multivariate Cryptography with Constructive Affine Stream Transformations

On December 20th, 2016, the National Institute of Standards and Technology (NIST) formally initiated a competition to solicit, evaluate, and standardize one or more quantumresistant cryptographic algorithms.Among the current candidates is a cryptographic primitive which has shown much promise in the post-quantum age, Multivariate Cryptography.These schemes compose two affine bijections S and T with a system of multivariate polynomials.However, this composition of S and T becomes costly as the data encrypted grows in size.Here we present Constructive Affine Stream (CAS) Transformations, a set of algorithms which enable specialized, large-scale, affine transformations in O(n) space and O(n log n) time, without compromising security.The goal of this paper is to address the practical problems related to affine transformations common among almost all multivariate cryptographic schemes.
ANALISIS TEKSTUR PADA CITRA IRIS MATA MENGGUNAKAN ALGORITMA GRAY LEVEL CO-OCCURENCY MATRIX

According to data from the ministry of health, with the high intensity of use the gadget nowadays, therefore the number of people with eye disease is increasing. To overcome increase suffers of eye disease, it takes need early detection for who suffers potentially eye disease so that handling and prevention of blindness from eye disease effect can be immediately. The process detection of eye disease can be see in iris, there are several disease can be seen in iris among there are diabetic retinopathy and glaucoma. This research present texture analysis for iris images, the method is used GLCM (Gray Level Co-occurency Matrix) which is implemented using Matlab, and using 5 parameters namely contrast, correlation, energy, homogeneity and entropy. Process analysis texture is developed with preprocessing technique, the result of texture in images data iris can be recognized and produce the dataset of result from feature extraction with GLCM (Gray Level Co-occurency Matrix).
Application of Wavelet Transform for Speech Signal Segmentation

The article discusses the possibility of using the wavelet transform for speech signal segmentation. To identify the attributes of phonemes it is proposed to use the Daubechies multiscale wavelet transform. Taking into account the characteristics of the speech signal frequency range, the required number of levels of decomposition is selected. The proposed speech signal processing algorithm and the results of the segmentation program for chosen significant characteristic words are given in Matlab environment.
Which aspects of size and distance for real objects are coded through the hierarchy of visual areas?

The visual system must transform two-dimensional retinal information into estimates of real-world distance and physical size based on pictorial and binocular cues. Although numerous human neuroimaging studies have investigated how various brain regions represent retinal size, distance, familiar size, physical size, these studies have often used visually impoverished stimuli, typically images, with conflicting cues, and studied factors in isolation. We investigated how brain activation levels (and patterns) in ventral- and dorsal-stream regions depend upon multiple factors (retinal size, physical size, familiar size, distance) that co-occur in the real world. We presented real objects at real distances during functional magnetic resonance imaging (fMRI). We manufactured MR-compatible Rubik’s cubes and dice at their typical sizes (5.7 cm and 1.6 cm, respectively) and each other’s typical sizes. We oriented them obliquely at two distances (25 cm, within reach, vs 91 cm, out of reach), such that two combinations subtended the same retinal angle (4.7° for small, near and large, far) while other combinations yielded smaller (1.3°; small, far) or larger (15.9°; large, near) retinal angles. Univariate contrasts revealed that the dorsal-stream regions -- left superior parietal occipital cortex (SPOC) and bilateral anterior intraparietal sulci (aIPS) – showed higher activation for objects in near (vs. far) space, even when retinal angles were matched, and for physically large (vs. small) objects in near space. Higher-order perceptual visual areas – lateral occipital cortex (LOC) and the parahippocampal place area (PPA) – distinguished between the two objects, showing higher activation for the Rubik’s cube (vs. die). Activation in bilateral V1 and PPA increased with retinal size. Taken together, our results suggest that distance and physical size are processed in the dorsal stream, where those features are critical for actions such as reaching and grasping; whereas, object features are more important in the ventral stream, where they are relevant for perception.
Picketing the Virtual Storefront: Content Moderation and Political Criticism of Businesses on Yelp

This article examines incidents in which business owners incur criticism on the consumer review platform Yelp based on political ideology. I analyze two case studies from the summer of 2018 by considering the sentiments expressed in the review texts, the application of Yelp’s relevant policies, and the tactical adaptations of reviewers. The case studies evince a normative conflict over how the platform should treat viral criticism of this sort. While Yelp clearly cannot truly function as a laissez-faire public forum, its moderation criteria can be gamed, and its efforts evidently exclude a range of sentiments that some users find meaningful. The article provides an in-depth exploration of a platform that has received somewhat less attention in the growing literature on the role of private intermediaries in shaping what kinds of speech attain visibility in the digital public sphere.
Detection of Steel Materials and Bolts from Point-Clouds of Power Transmission Pylon

Shape grammar implementation tools play an important role in the generation of designs.Most of the available tools were created to allow the application of shape grammar rules without restrictions.This is largely because shape grammars are context free, as such, the application of a rule is not controlled during derivation.This is also the reason some generate designs that have parts that are too small for the human eye to see.This work presents a tool that allows the addition of bag context to shape grammar rules such that it automatically allows when a rule should be applied based on a defined range.Bag context represents information that is not part of a developing design but instead evolves separately during a derivation.This helps to generate an infinite number of images that are similar but not identical.This tool could offer a wide range of application areas such as aiding the teaching of shape grammar or bag context shape grammar implementation in formal language classes or higher learning in general, a framework to support designers for the development of an improved bag context shape grammar interpreter tool, and others.
A Sample DApp

In this chapter, we will build a full DApp from scratch. While we will not be going in-depth on the steps involved, this chapter will help you identify all the components involved in the construction of a decentralized application. In the upcoming chapters, we will focus on each of the different parts, but you can always refer back to these pages to understand how each section fits within the bigger picture.
Development of a Configuration Management Course for Operations Students

In the recent past only a few of the "Unicorn" companies such as Facebook, Amazon, and Google were concerned about being able to deploy software into production on distributed computing architectures multiple times per day. To facilitate their ability to do this they developed a series of practices such as Continuous Integration/Continuous Deployment (CI/CD), Test Driven Development, Agile Development and DevOps. The goal of these techniques was to be able to get the work of their thousands of developers into production as quickly, safely, reliably and consistently as possible. Most other large-scale companies have since adopted as least some of these practices and they are considered standard procedure for most large organizations.
Polynomial Regression and Response Surface Methodology: Theoretical Non-Linearity, Tutorial and Applications for Information Systems Research

Information systems (IS) studies regularly assume linearity of the variables and often disregard the potential non-linear theoretical interrelationships among the variables. The application of polynomial regression and response surface methodology can observe such non-linear theoretical assumptions among variables. This methodology enables to examine the extent to which two predictor variables relate to an outcome variable simultaneously. This paper utilizes the expectation confirmation theory as an example and provides a methodological commentary that illustrates a step-wise process for conducting a polynomial regression and response surface methodology.
Application of Blockchain in International Trade: An Overview

The purpose of this paper is to present the potential advantages of using blockchain technology in the field of international trade. Blockchain, known as one of the disruptive technologies of the moment, will bring important changes to the process of specific foreign trade operations. In this paper, there are presented elements specific to the blockchain technology, its underlying principles, but also the areas in which this technology can be applied. Referring to the academic literature and various online sources, we identified blockchain-based applications in the international trade field (logistics and financial scope) and we highlighted the potential benefits of this technology.
Idiomaticity Prediction of Chinese Noun Compounds and Its Applications

Idiomaticity refers to the situation where the meaning of a lexical unit cannot be derived from the usual meanings of its constituents. As a ubiquitous phenomenon in languages, the existence of idioms often causes significant challenges for semantic NLP tasks. While previous research mostly focuses on the idiomatic usage detection of English verb-noun combinations and the semantic analysis of Noun Compounds (NCs), the idiomaticity issues of Chinese NCs have been rarely studied. In this work, we aim at classifying Chinese NCs into four idiomaticity degrees. Each idiomaticity degree refers to a specific paradigm of how the NCs should be interpreted. To address this task, a Relational and Compositional Representation Learning model (RCRL) is proposed, which considers the relational textual patterns and the compositionality levels of Chinese NCs. RCRL learns relational representations of NCs to capture the semantic relations between two nouns within an NC, expressed by textual patterns and their statistical signals in the corpus. It further employs compositional representations to model the compositionality levels of NCs via network embeddings. Both loss functions of idiomaticity degree classification and representation learning are jointly optimized in an integrated neural network. Experiments over two datasets illustrate the effectiveness of RCRL, outperforming state-of-the-art approaches. Three applicational studies are further conducted to show the usefulness of RCRL and the roles of idiomaticity prediction of Chinese NCs in the fields of NLP.
Coarse to Fine Framework for Kidney Tumor Segmentation

Accurate segmentation of kidney tumor is a key step in image-guided radiation therapy.However, shapes, scales and appearance vary greatly from patient to patient, which pose a serious challenge to segment targets correctly.In this work, we proposed a coarse-to-fine framework to automatically segment kidney and tumor computed tomography (CT) images.Specifically, we adopt two resolutions and propose a improved 3D U-Net network for kidney tumor segmentation.The model in the coarse resolution can robustly localize the kidney, while the model in the fine resolution can accurately refine the boundary of kidney and tumor.
Human Gait Feature Extraction based-on Silhouette and Center of Mass

When someone walks, there is a repetitive movement or coordinated cycle that forms a gait.Gait is different, unique and difficult to imitate.This characteristic makes gait one of the biometrics to find out one's identity.Gait analysis is needed in the development of biometric technology, such as in the field of security surveillance and the health sector to monitor gait abnormalities.The center of mass is the unique point of every object that has a role in the study of humans walking.Each person has a different center of mass.In this research, through a series of processes in image processing such as video acquisition, segmentation, silhouette formation, and feature extraction, the center of mass of the human body can be identified using a webcam with the resolution of 640 x 480 pixels and the frame rate of 30 frames/second.The results obtained from this research were gait frames of 510 frames from 17 pedestrian videos.Segmentation process using background subtraction separates the pedestrian object image from the background.Silhouette gait was produced from a series of image enhancement processes to eliminate noise that interferes the image quality.Based on the silhouette, feature extraction provides the center of mass to distinguish each individual's gait.The sequence of center of mass can be further processed for characterizing human gait cycle for various purposes.