Reviewerly/exHarmony huggingface dataset:
The causality between Financial Development and Economic Growth in Ethiopia: Supply Leading vs Demand Following Hypothesis

This paper investigates linkage between financial development and economic growth in Ethiopia during the period from 1975 to 2016 using Autoregressive Distributed Lag (ARDL) approach. The paper also schedules Vector Error Correction Model (VECM) in order to observe how fast the cointegrated variables convergence in long-run. Accordingly, the results of bound test confirm existence of the long-run relationship between explanatory variables and economic growth. The empirical results show evidence of long- and short-run positive impacts of financial development on economic growth in Ethiopia which implies that progesses in financial sector contribute to economic growth in both short- and long-run. In consideration of few control variables, the study finds all indicators, except inflation and government expenditure, significantly influence economic growth in the long-run. However, it also reveals that government expenditure, trade openness, human capital, and gross investment are pioneering determinants of the economic growth in Ethiopia in short-run. Moreover, the study employs Granger causality tests in order to show direction of impact is running from financial development to economic growth both in short- and long-run. As a result, it finds that the ‘supply-leading’ hypothesis holds in Ethiopia.
An incremental and distributed inference method for large-scale ontologies over SPARK

The study of the semantic interoperability and the reasoning over big data is today a major challenge for researchers, especially with the birth of semantic web and deep data. The existing solutions are not yet able to satisfy the requirements of the final user especially in terms of the consistency of the results and the request execution time. To do this we need an approach based on an ontology and a distributed and scalable system. Several studies have been done on the reasoning over large-scale ontologies, most are based on Hadoop and MapReduce or non-incremental, i.e., they recalculate the result at the arrival of new data. In this paper we propose an incremental and distributed method of reasoning over very large OWL ontologies based on SPARK, which offers a reduced execution time as it loads the RDF triplets in memory and not in disk. Our method allows creating transfer inference forest (TIF) and effective assertional triples (EAT) to reduce disk space and simplify and accelerate the reasoning process.
Stochastical problem of Helmholtz for Birkhoff systems

Stochastical problem of Helmholtz for Birkhoff systemsThe Helmholtz problem is considered in a probabilistic formulation.By a given stochastic Langevin-Itô equation in an indirect representation, as the equation of the Hamiltonian structure and the equation of the Birkhoffian structure are constructed.The functional that takes a stationary value on solutions of a given stochastic Birkhoff equation, is defined by the method of moment functions.The obtained results are illustrated by two examples: 1) the plane motion of a symmetric satellite in a circular orbit under the action of gravity and aerodynamic forces, and 2) the fluctuation motion of a gyroscope in a gimbal caused by the stochastic fluctuating moment of forces along the suspension axis of the inner ring.
On Wavelet Based Enhancing Possibilities of Fuzzy Classification Methods

If the antecedents of a fuzzy classification method are derived from pictures or measured data, it might have too many dimensions to handle. A classification scheme based on such data has to apply a careful selection or processing of the measured results: either a sampling, re-sampling is necessary or the usage of functions, transformations that reduce the long, high dimensional observed data vector or matrix into a single point or to a low number of points. Wavelet analysis can be useful in such cases in two ways. As the number of resulting points of the wavelet analysis is approximately half at each filters, a consecutive application of wavelet transform can compress the measurement data, thus reducing the dimensionality of the signal, i.e., the antecedent. An SHDSL telecommunication line evaluation is used to demonstrate this type of applicability, wavelets help in this case to overcome the problem of a one dimensional signal sampling. In the case of using statistical functions, like mean, variance, gradient, edge density, Shannon or Rényi entropies for the extraction of the information from a picture or a measured data set, and they don not produce enough information for performing the classification well enough, one or two consecutive steps of wavelet analysis and applying the same functions for the thus resulting data can extend the number of antecedents, and can distill such parameters that were invisible for these functions in the original data set. We give two examples, two fuzzy classification schemes to show the improvement caused by wavelet analysis: a measured surface of a combustion engine cylinder and a colonoscopy picture. In the case of the first example the wear degree is to be determine, in the case of the second one, the roundish polyp content of the picture. In the first case the applied statistical functions are Rényi entropy differences, the structural entropies, in the second case mean, standard deviation, Canny filtered edge density, gradients and the entropies. In all the examples stabilized KH rule interpolation was used to treat sparse rulebases. The preliminary version of this paper was presented at the 3rd Conference on Information Technology, Systems Research and Computational Physics, 2–5 July 2018, Cracow, Poland [1].
The Impact of Agent Definitions and Interactions on Multiagent Learning for Coordination

The state-action space of an individual agent in a multiagent team fundamentally dictates how the individual interacts with the rest of the team. Thus, how an agent is defined in the context of its domain has a significant effect on team performance when learning to coordinate. In this work we explore the trade-offs associated with these design choices, for example, having fewer agents in the team that individually are able to process and act on a wider scope of information about the world versus a larger team of agents where each agent observes and acts in a more local region of the domain. We focus our study on a traffic management domain and highlight the trends in learning performance when applying different agent definitions.
Cluster-based Unsupervised Automatic Keyphrases Extraction algorithms: experimentations on Cultural Heritage datasets

Automatic keyword extraction is the process of identifying key terms and key phrases from documents that can appropriately represent the subject of the documents. We present here a work-in-progress, an experimentation done on unsupervised keyword extraction, with the aim of automatically associating scored keyphrases to texts, using (standard or innovative) cluster based methods, and integrating word embedding to enhance semantic relatedness of keyphrases. In the paper we present the datasets used, the state-of-theart for unsupervised automatic extraction algorithms, based on cluster methods, and we describe in details the algorithms implemented and preliminary results obtained. The results obtained are discussed, commented, and compared with those obtained, in previous experimentations, using TextRank, RAKE and Tf-idf.
SENTIMENT ANALYSIS WITH WORD EMBEDDING-THE CASE OF DOUBLE-TRACK EDUCATION SYSTEM IN GHANA

This paper applies the concept of sentiment analysis for the determination of polarities (positivity, neutrality or negativity) of sentiments borne in the views expressed by Ghanaians regarding the newly introduced double track system in Second Cycle Schools in Ghana. These views are sourced from tweets (twitter posts). Accurate analysis of sentiments depends largely on the context of word usage. Most sentiment analysis approaches however ignore context when predicting sentiments; thereby leading to loss of context. In this paper, the loss of context is avoided with the use of the concept of Word embedding. Word embedding is a context-preserving technique which embeds the contextual information of data in the form of vectors before analysis of sentiment is done. An overall model accuracy of 76% was achieved using this technique. Our model’s accuracy outdoes similar works such as Garg’s (2016) work with an accuracy of 72%. The results from this work may help the Ghana government to get well informed on how the citizenry reacted to the reform of the educational system as well as help those at the helm of affairs to know how to roll out policies in the near future.
To Build Corpus of Sindhi Language

The present day state of Sindhi corpus construction is elaborated in detail in this paper.The issues like corpus acquisition, tokenization and preprocessing have been analyzed and discussed minutely for Sindhi corpus enhancement.Initial observations and results are included for letter unigram, bigram and trigram frequencies.There has been discussed the present status of Sindhi corpus in perspective of restriction and future work.Orthography and script were also explored in this paper with reference to corpus development.Basically the word corpus was used first time by German Scholar (Das Corpus).The plural of corpus is corpora, which is used for huge text data consists of millions and billions of text data.The task of Natural Language Processing was very challenging because there was the scarcity of resources for computational linguistics and research.Different text corpora have been made in different languages of different countries, after reviewing the corpora of different languages of various countries, we are trying to make the corpus for Sindhi language.
Hard-Decision Quantization Algorithm Based on Deep Learning in Intra Video Coding

In video encoder, hard-decision quantization (HDQ) is well-suited for parallel processing, but suffers from non-negligible coding performance degradation compared with soft-decision quantization (SDQ). In this paper, by fully simulating the behavior of SDQ, a coefficient-adaptive offset model constructed by the deep learning approach is proposed to adjust the output of HDQ. Experiment results show that the proposed algorithm achieves promising RD performance and well-suited for hardware encoder implementation design.
O16. Distinct Neural Networks Predict Opioid Versus Cocaine Abstinence in Polysubstance Users

Opioid use disorder (OUD) is a major public health crisis; while effective treatments are available, it has proven difficult to predict treatment outcomes. Using a novel machine learning approach, connectome-based predictive modelling (CPM), we previously identified patterns of neural network connectivity that predict abstinence from cocaine during treatment and replicated these findings in an independent sample. Understanding neural mechanisms of treatment response in OUD may facilitate the development of personalized and/or novel treatment approaches.
AN ANALYSIS OF FACTORS THAT AFFECTING STUDENT’S INTEREST IN FOLLOWING THE STUDENT ACTIVITY UNITS OF STMIK JAYANUSA

This study aims (1) To determine the factors that influence student interest in participating in Student activity units in STMIK Jayanusa and (2) The most dominant factors affecting the interest of STMIK Jayanusa students in participating in Student activity units.This research was carried out on the STMIK Jayanusa campus with a total response of 52 students who participated in the UKM. Data collected using questionnaires were analyzed by factor analysis through Statistical Program Social Scence (SPSS) 17.0 for Windows. The results of this study indicate that there are 3 factors that influence the participation of STMIK Jayanusa students in participating in Student activity units namely (a) The heredity factor has an eigenvalue of 2,284 with a variance value of 76.125%. Intelligence factor has an eigenvalue of 0.415 with a variance value of 13.833%. While environmental factors have an eigenvalue value of 0.301 with a variance value of 10.042%. and (2) The most dominant factor influencing student participation in participating in Student activity units is a factor that originates within students, namely the heredity factor of 76.125%
A Structured Approach to Guide the Development of Incident Management Capability for Security and Privacy

The growth and evolution of threats, vulnerabilities, and cyber-attacks increase security incidents and generate adverse impacts on organizations. Nowadays, organizations have been strengthened in aspects of information security and information through the implementation of various technological solutions. Nevertheless, defined processes for the proper handling and coordinated management of security incidents should be established. In this paper, we propose an incident management framework that is adaptable to educational organizations and allows them to improve their management processes in the face of computer incidents. We introduce a coordination network with three levels of decision-making that defines interfaces and communication channels with supporting policies and procedures for coordination across processes and process actors. It enables different organizations to maintain focus on different objectives, to work jointly on common objectives, and to share information that supports them all in case of security incidents. Our model enables the examination of incident management processes that cross organizational boundaries, both internally and externally. This can help CSIRTs improve their ability to collaborate with other business units and other organizations when responding to incidents.
The Scenario Approach to Establishing GR Communications in Higher Education Institutions

Education and professional training are at the basis of human development and social progress. This strategic significance of education is enhanced manifold in the present-day age of information and economics of knowledge. A rapid development and technological upgrading of Russian higher education suggests the need for the implementation of modern information and communication technologies in higher education institutions and for the development of a communications system complete with a set of structures that influence their activities in this or that way. Considering that the quality and efficiency of Russian educational organizations largely depend on the policies pursued by public and local authorities, there is a need in the organization and implementation of an effective way of interacting with them. This article focuses on the nature of GR (Government Relations) communications in higher education institutions and carries out an analysis of the prospects and practical application of one of the GR technologies, namely, corporate social responsibility of educational institutions using the example of foreign universities. Furthermore, the study highlights the need and prospects for using the scenario approach when planning and implementing GR communications in educational institutions.
Graph Cutting in Image Processing Handling with Biological Data Analysis

In this contribution we present graph theoretical approach to image processing focus on biological data. We use the graph cut algorithms and extend them for obtaining segmentation of biological cells. We introduce completely new algorithm for analysis of the resulting data and sorting them into three main categories, which correspond to the certain type of biological death of cells, based on the mathematical properties of segmented elements.
Self-Driving Car Using Artificial Intelligence

Self-driving autonomous vehicles are the solution for enhancing mobility intelligence related to driving. This project presents effective ways for implementation of a self-driving car. Proposed work is based on Artificial Intelligence, Computer Vision and Neural Networks. The proposed technology is implemented on a mini-robot car that was built from scratch, which uses Raspberry Pi and a camera as its core. The system built runs a script for complex task handling and sending appropriate commands to the vehicle. Image Processing techniques are also issued in the proposed system to identify various objects and traffic lights on the way. The system learns to autonomously navigate through reinforcement learning. Tensor Board is used to keep track of the working and efficiency of the trained Neural Network. The efficiency of the system is recorded at 96% as of now.
An Immutable Algorithm Approach to Improve the Information Security of a Process for a Public Organization of Ecuador

Currently, information security is among the main characteristics that must be achieved within the security of private and public organizations worldwide.For this reason, globally recognized algorithms such as the AES, IDEA, RC5, DES, RSA are researched with the aim of identifying the most suitable and obtaining a greater degree of security and speed of encryption in order to mitigate the information vulnerabilities between processes and be applied as a feasible alternative in an electoral process.The deductive method was used to analyze the information obtained in the references.After the study it is possible to conclude that to improve security in the processes of public organizations in Ecuador it is necessary to implement cryptographic mechanisms.
Distinguishing Narration and Speech in Prose Fiction Dialogues

This paper presents a supervised method for a novel task, namely, detecting elements of narration in passages of dialogue in prose fiction. The method achieves an F1-score of 80.8%, exceeding the b ...
A Control-Model-Based Approach for Reinforcement Learning.

We consider a new form of model-based reinforcement learning methods that directly learns the optimal control parameters, instead of learning the underlying dynamical system. This includes a form of exploration and exploitation in learning and applying the optimal control parameters over time. This also includes a general framework that manages a collection of such control-model-based reinforcement learning methods running in parallel and that selects the best decision from among these parallel methods with the different methods interactively learning together. We derive theoretical results for the optimal control of linear and nonlinear instances of the new control-model-based reinforcement learning methods. Our empirical results demonstrate and quantify the significant benefits of our approach.
Cross-Domain Text Sentiment Classification Based on Wasserstein Distance

Text sentiment analysis is mainly to detect the sentiment polarity implicit in text data. Most existing supervised learning algorithms are difficult to solve the domain adaptation problem in text sentiment analysis. The key of cross-domain text sentiment analysis is how to extract the domain shared features of different domains in the deep feature space. The proposed method uses denosing autoencoder to extract the deeper shared features with better robustness. In addition, Wasserstein distance-based domain adversarial and orthogonal constraints are combined for better extracting the deep shared features of the different domain. Finally, the deep shared features are used for cross domain sentiment classification. The experimental results on the real data sets show that the proposed method can better adapt to domain differences and achieve higher accuracy.
Development of prototype educational software for children with hearing disabilities for kids

Abstract At present, there are different factors that lead to creating this auditory difficulty as a medical diagnosis. Becoming an impediment to the integral development of the people since a greater effort must be made for their formation and communication in the daily life. In this way, that in the process of academic training for these people, who have this type of difficulty should be based on the use of new methodological strategies, generating new learning scenarios for the best class development. It will contribute in that space given that its condition of absolute deficiency of listening makes it difficult to understand a specific context, in this case in Spanish, this fundamental matter being a communication mechanism in their daily lives and in the framework of interpretive and argumentative competences. For this subject and given the condition of the student, is considered important to accompany the learning with ICT tools that allow the process to be more dynamic, from the virtual context where the student will directly interact with a learning content in a didactic way understanding in a better context this information that is taught in a classroom with a traditional methodology. Among the main advantages of using these computing resources in class is: having a homogeneous group with respect to the knowledge that corresponds to the degree they are studying and improving their lexicon ability for communication in their daily lives as they go to enrich their vocabulary and understand some words they did not understand. One of the many reasons of the distractions today, are the new technologies, the mobile devices like cellphones, videogames, audio and video players, among other things which don’t allow keep the needed focus in class. Through the use of the developed web application it will dynamically offer a virtual space to interact with the contents of the Spanish subject to the student improving their communication and understanding the context where they live in their daily lives. Applying technological tools searching to solve this problem. The development of this research starts by generating a tool in this case a web application that, in a dynamic way, provides spaces and training activities in the basic contents of primary in Spanish, offering the student a variety of levels in those who can improve knowledge and apply the acquired notions in their training process. Additionally, you can keep track of your progress and score in the levels you are surpassing. By De la Torre S [1].
Memory Categorization: Separating Attacker-Controlled Data

Memory corruption attacks against software written in C or C++ are still prevalent and remain a significant cause of security breaches. Defenses providing full memory safety remain expensive, and leaner defenses only addressing control-flow data are insufficient. We introduce memory categorization, an approach to separate data based on attacker control to mitigate the exploitation of memory corruption vulnerabilities such as use-after-free and use-after-return. MemCat implements this approach by: (i) providing separate memory allocators for different data categories, (ii) categorizing the use of memory allocations, (iii) changing allocations to take advantage of the categorization. We demonstrate the effectiveness of MemCat in a case study on actual vulnerabilities in real-world programs. We further show that, although our prototype implementation causes a high overhead in two edge cases, in most cases the performance hit remains negligible, with a median overhead of less than 3% on the SPEC benchmark suite.
Blockchain technology: Challenges and opportunities for banks

This paper explores blockchain technology's potential implications for banks. This study is based on qualitative-based interviews with three professional bankers in different European banks that are dealing with the challenges of blockchain. The results indicate that blockchain technology is both a threat to and an opportunity for banks, providing support to the argument that blockchain is a disruptive technology that will create new opportunities for banks and new risks to their business. Nonetheless, the current lack of regulation and some technical limitations of the technology mean that blockchain is an opportunity for rather than a risk to banks. Blockchain technology may vastly improve banking processes' efficiency and therefore the products and services banks offer customers, particularly in areas such as lending, payments and capital markets.