Reviewerly/exHarmony huggingface dataset:
Forward and Backward Information Retention for Accurate Binary Neural Networks

Weight and activation binarization is an effective approach to deep neural network compression and can accelerate the inference by leveraging bitwise operations. Although many binarization methods have improved the accuracy of the model by minimizing the quantization error in forward propagation, there remains a noticeable performance gap between the binarized model and the full-precision one. Our empirical study indicates that the quantization brings information loss in both forward and backward propagation, which is the bottleneck of training accurate binary neural networks. To address these issues, we propose an Information Retention Network (IR-Net) to retain the information that consists in the forward activations and backward gradients. IR-Net mainly relies on two technical contributions: (1) Libra Parameter Binarization (Libra-PB): simultaneously minimizing both quantization error and information loss of parameters by balanced and standardized weights in forward propagation; (2) Error Decay Estimator (EDE): minimizing the information loss of gradients by gradually approximating the sign function in backward propagation, jointly considering the updating ability and accurate gradients. We are the first to investigate both forward and backward processes of binary networks from the unified information perspective, which provides new insight into the mechanism of network binarization. Comprehensive experiments with various network structures on CIFAR-10 and ImageNet datasets manifest that the proposed IR-Net can consistently outperform state-of-the-art quantization methods.
Oyma Karakterlere Sahip Dairesel Metal Cisimlerin Sınıflandırma Probleminin Görüntü İşleme Yöntemleri İle Çözümü

In this study, two different solution ways have been developed for the problem of classification of industrial small circular metal objects on the surfaces of engraved metal. It is the first proposed solution to perform the pattern matching with XOR operator by extract the character region of the circular metal objects as a pre-process, making the model of the Daugman’s Rubber Sheet Model (DRSM) and performing feature extraction. As a result, obtained that average processing time is 69,72 milliseconds and 0,9398 accuracy rate in the first proposed solution. The second solution is the optical character recognition (OCR) on the circular metal objects that to be realized character region detection and character segmentation as a result of the Maximal Stabil Extremal Region (MSER) and Stroke Width Transform (SWT) algorithms. Character recognition realized by using the model of Convolutional Neural Network (CNN) class which is a deep machine learning approach of artificial intelligence. The character recognition problem of the circular metal objects provided at the same time solved the problem of object classification. As a result, obtained that average processing time is 1,596 second and 0,9719 accuracy rate in the second proposed solution.
Pengaruh Kecerdasan Emosional, Fasilitas Belajar dan Kompetensi Dosen Terhadap Persepsi Prestasi Akademik Mahasiswa Akuntansi

Student academic achievement is a reflection of the quantity and quality of knowledge that has been mastered by students. The purpose of this study, among others, is to determine the effect, emotional intelligence, learning facilities and lecturer competence on the academic achievement of accounting students. The place to do this research is at the Faculty of Economics and Business, Udayana University. Multiple linear regression analysis is an analysis technique used in this study. The sample of this research is the 2016 accounting undergraduate students with the sample determination method using the Slovin formula. The types of data used in this study are qualitative and quantitative data. Data collection techniques using a questionnaire. The results showed that emotional intelligence, learning facilities and lecturer competence had a positive effect on academic achievement of accounting students.&#x0D; Keywords : Emotional Intelligence; Learning Facilities; lecturer Competence; Academic Achievement.
LISTENING DIFFICULTIES FACED BY THE FIRST SEMESTER STUDENTS AT BASIC LISTENING CLASS

This study aimed at describing difficulties faced by students in learning Listening. This study involved seventeen female students and eight male students taking Basic Listening course in one of private university in Malang, East Java. To gain the data, the researcher interviewed the subjects one by one. The findings, then, classified into several classes by referring to previous studies discussing listening difficulties in English as Foreign Language (EFL) classes. The findings showed that there were four difficulties faced by the students. They were the speed and length of the spoken text, vocabulary, pronunciation, and accents. Based on the findings, then, the researchers made several suggestions for further researchers and other language teachers to consider this study in their practice.
Human AI interaction loop training: New approach for interactive reinforcement learning

Reinforcement Learning (RL) in various decision-making tasks of machine learning provides effective results with an agent learning from a stand-alone reward function. However, it presents unique challenges with large amounts of environment states and action spaces, as well as in the determination of rewards. This complexity, coming from high dimensionality and continuousness of the environments considered herein, calls for a large number of learning trials to learn about the environment through Reinforcement Learning. Imitation Learning (IL) offers a promising solution for those challenges using a teacher. In IL, the learning process can take advantage of human-sourced assistance and/or control over the agent and environment. A human teacher and an agent learner are considered in this study. The teacher takes part in the agent training towards dealing with the environment, tackling a specific objective, and achieving a predefined goal. Within that paradigm, however, existing IL approaches have the drawback of expecting extensive demonstration information in long-horizon problems. This paper proposes a novel approach combining IL with different types of RL methods, namely state action reward state action (SARSA) and asynchronous advantage actor-critic (A3C) agents, to overcome the problems of both stand-alone systems. It is addressed how to effectively leverage the teacher feedback, be it direct binary or indirect detailed for the agent learner to learn sequential decision-making policies. The results of this study on various OpenAI Gym environments show that this algorithmic method can be incorporated with different combinations, significantly decreases both human endeavor and tedious exploration process.
Exploring the Influence of Organizational, Environmental, and Technological Factors on Information Security Policies and Compliance at South African Higher Education Institutions, with a Focus on Implications for Biomedical Research

Globally, concerns over information security vulnerabilities are growing exponentially, fuelled by several headline reports of data breach incidents, which increase in size with each occurrence. On the Africa continent, South Africa is ranked among the most &lsquo;at-risk&rsquo; countries for information security vulnerabilities, having lost approximately fifty billion rands to cybercrime in 2014. South Africa is currently considered to be the most cybercrime-targeted country in Africa. Worldwide, cyber vulnerability incidents greatly affect the education sector, due to the fact that this sector holds more Personal Identifiable Information (PII) than many other sectors. The PII ranges from (but is not limited to) ID numbers and financial account numbers to biomedical research data.&nbsp;In response to growing threats in South Africa, a similar regulation strategy to the European Union General Data Protection Regulation (GDPR), called the Protection of Personal Information Act (POPIA) will be implemented, with a view to mitigating cybercrime and information security vulnerabilities. The extent to which African institutions, and specifically the South African universities, have embraced and respond to these two information security regulations (GDPR and POPIA) is not yet clear and will be a matter of great importance for biomedical researchers. This research study aims to conduct a qualitative exploratory analysis of information security management across three universities in South Africa, by using a Technology, Organizational, and Environmental (TOE) model to investigate the factors which may influence the effectiveness of information security measures. This study is poised to make a significant contribution to the development of a Management Model for security practitioners and a framework for information management of biomedical data.
Cross-Language Plagiarism Detection using Word Embedding and Inverse Document Frequency (IDF)

The purpose of cross-language textual similarity detection is to approximate the similarity of two textual units in different languages.This paper embeds the distributed representation of words in cross-language textual similarity detection using word embedding and IDF.The paper introduces a novel cross-language plagiarism detection approach constructed with the distributed representation of words in sentences.To improve the textual similarity of the approach, a novel method is used called CL-CTS-CBOW.Consequently, adding the syntax feature to the approach is improved by a novel method called CL-WES.Afterward, the approach is improved by the IDF weighting method.The corpora used in this study are four Arabic-English corpora, specifically books, Wikipedia, EAPCOUNT, and MultiUN, which have more than 10,017,106 sentences and uses with supported parallel and comparable assemblages.The proposed method in this paper combines different methods to confirm their complementarity.In the experiment, the proposed system obtains 88% English-Arabic similarity detection at the word level and 82.75% at the sentence level with various corpora.
EAODBT: Efficient Auditing for Outsourced Database with Token Enforced Cloud Storage

Database outsourcing is one of the important utilities in cloud computing in which the Information Proprietor (IP) transfers the database administration to the Cloud Service Provider (CSP) in order to minimize the administration cost and preservation expenses of the database. Inspite of its immense profit, it undergoes few security issues such as privacy of deployed database and provability of search results. In the recent past, few of the studies have been carried out on provability of search results of Outsourced Database (ODB) that affords correctness and completeness of search results. But in the existing schemes, since there is flow of data between the Information Proprietor and the clients frequently, huge communication cost prevails at the Information Proprietor side. To address this challenge, in this paper we propose Efficient Auditing for Outsourced Database with Token Enforced Cloud Storage (EAODBT). The proposed scheme reduces the large communication cost prevailing at the Information Proprietor side and achieves correctness and completeness of search results even if the mischievous CSP knowingly sends a null set. Experimental analysis show that the proposed scheme has totally reduced the huge communication cost prevailing between Information Proprietor and clients, and simultaneously achieves the correctness and completeness of search results.
Automatic Natural Image Colorization

We introduce a technique to automatically colorize natural grayscale images that combines both local and global features. Automatic colorization is a hard problem of computer vision and usually requires user interactions such as human-labelled color scribbles or reference images to achieve proper results. Based on Convolutional Neural Networks (CNN), our model is trained in an end-to-end fashion and can process images of any resolution. We improve the model of Iizuka et al. [1] by adding edge detection network and adjusting the input of the loss function. We also compare our model with the state of the art and show some improvements. Furthermore, we try colorizing ink wash paintings and achieve a special style.
Analisis Algoritma Datamining pada Kasus Daerah Pelaku Kejahatan Pencurian Berdasarkan Provinsi

Theft is a behavior that causes harm to victims who are targeted and can cause victims. The level of theft behavior is increasing in each region due to the increasing number of unemployment and lazy nature of work that makes a person commit theft to make ends meet. The purpose of this study was to analyze using the technique of datamining in the area of perpetrators of theft crimes by province. The technique used is clustering with the K-means method. Data sourced from the Indonesian Central Statistics Agency with the url address: https://www.bps.go.id/. The results of the study using this technique are clustered in areas in Indonesia which have the highest crime theft rates. From the results of the study using the K-means technique, that there are 17 provinces out of 34 provinces that have the highest crime theft (C1) areas, namely: Aceh, North Sumatra, West Sumatra, Riau, Jambi, South Sumatra, Lampung, DKI Jakarta, West Java, Central Java, East Java, Banten, West Nusa Tenggara, East Nusa Tenggara, South Kalimantan, South Sulawesi, Papua. The results of the study are expected to be information for the government in conducting policies to reduce the crime crime rate in Indonesia which is very high (50%).
The Libra Project: A Transnational Monetary Dystopia – Analysis of the Disruption Generated by the Facebook-Led Stable Coin

Since the suspension of the dollar convertibility into gold, the international monetary system has not been short of imperfections, and preoccupying sources of disorder. In this article, we reflect on Facebook’s announcement in June 2019, of a digital currency, called Libra, underpinned by blockchain technology, managed by a consortium of private and non-state actors, backed by an unspecified basket of fiat currencies. Since the consolidation of the nation state, sovereignty and money have been intertwined notions. One merit of the Libra project is to question the former notion in the light of the latter. While it is initially dressed in the utopian clothes of cheap, fast, convenient and secure Internet payments, backed by a stable reserve of value relying on cryptographic tools, we show that through the chaotic disruption it causes to the present system, Libra is indeed a corporate oligarchy masterplan conducive to a transnational dystopia.
MODELS OF DECISION MAKING BY A NAVIGATOR UNDER IMPLICIT AGREEMENTS WITH COLREG RULES

The purpose of the article is the model of perception of difficult situations by the navigator where the rules of COLREG are inconsistent.To build these models, a formal analysis of the situations was carried out, which allowed to design a decision-making support system to reduce risks and accidents related to sea transport.The article presents formal approaches that take into account the factors of vessel speed, qualification of a navigator, and the situations that influence the formation of maneuvering strategies.The illustrations show difficulties and ambiguous situations from the point of view of the rules.An important factor for decision-making is the ability of an adequate perception of the situation by a navigator, and the conditions in which this process is considerably difficult are given.The arguments are made in favor of the use of ECDIS and AIS navigation information systems, and the examples that indicate the difficulties of making decisions at the time of a large number of vessels are given.A relationship between the perception of service information by the navigator and the choice of maneuvering strategies is made.The recommendations on the development of a decision-making support system for navigators in difficult navigation situations are given.The approaches to apply a decision-making support system, as well as the formation of data about the navigator are proposed.
Controlling of Wind Turbine Generator System based on Genetic Fuzzy-PID Controller

In this research, Genetic Algorithm (GA) was used as a feasible solution for optimization/tuning of a simulated Fuzzy Proportional Integral Derivative (FPID) controller for a variable speed wind turbines (WTs).The aim of this research is to tune the membership function (MF) of a FPID controller using genetic programming and investigating the performance of the system.Firstly, Mamdani's Fuzzy Inference System (FIS) is implemented to control the WTs.The MF is encoded to formulate the chromosome by choosing some parameters like centre and left-right base, and then a proper fitness function is designed to minimize mean square error.Finally, the GA is applied and simulated on MATLAB by choosing different number of generation.The GA works by slowly "evolving" a population of chromosomes that represent better and better solutions to the problem.The simulation results show that the proposed method, Genetic FPID (GFPID), has better performance compared to the other controlling techniques discussed in this paper.GFPID system has better transient response and steady state response than the other systems with lower value of overshoot and steady state error.
EHP:Entity Hyperplane Projection for Knowledge Graph Embedding with Entity Descriptions

Knowledge graph embedding aims to project entities and relations into a low-dimensional and continuous vector space to represent the semantic information of entities and relations. Most existing models of knowledge graph embedding only concentrate on the structured information of the knowledge graph and merely use knowledge triples that can also be called the fact triples to learn the representation of entities and relations, but ignore some useful information that emerge in text descriptions of entities. To this end, this paper proposes the entity hyperplane projection (EHP) model. EHP learns both from knowledge triples and text descriptions, project the entity embedding onto the semantic hyperplane corresponding to entity descriptions to build interactions between the two information sources. Extensive experiments show that EHP model achieves substantial improvements against baselines on the tasks of knowledge graph completion and entity classification.
Warships Formation Identification Based on Neural Network

Abstract Submarine tactical software should be able to identify warships formation. Neural network based pattern recognition method is introduced in submarine tactical software to improve accuracy. This paper discussed how to transform sensor data into training samples by using Matlab, and how to choose training samples. Experiments indicate that GRNN network is capable of this work and suitable for actualizing.
The level of readability of the computer sciences textbook among the eleventh grade students in Jordan

This study aimed to identify readability level of computer science textbooks among the Jordanian eleventh grade students.A cross-sectional, descriptive study was conducted.The study population consisted of all eleventh graders of Al-Mafraq education directorate.The study sample comprised 122 male and female eleventh graders, chosen by intended sampling method from two public schools.However, to answer the research questions, the researchers prepared a close test as an instrument to measure the readability levels using three randomly selected texts, which have never been used, from eleventh grade computer science textbook.After validating the instrument, it was administered to the study participants.The following results were reported: Eleventh grade computer science textbook readability levels were classified based on the scores students attained during the test as follows: the independent level of readability (48.9%), followed by depressed level (35.1%) and finally the educational level with (16.1%); the existence of positive statistically significant relationship at (α≤ 0.05) level between student achievement in computer subject and its readability level; there were statistically significant differences at (α≤ 0.05) level; the eleventh computer sciences textbook readability levels attributable to student's gender, is in favor of females.Based on these results, some recommendations were suggested.
Opinion Mining: How efficient are Online classification tools?

Recently, Online Social Networks (OSNs) are considered as important resource of information, since they provide a huge amount of data that reflects the interactions between users in various fields, such as: politics, sport and business.Opinion mining (or sentiment analysis) is a process that uses natural language processing, and text analysis methods to understand users' feelings or opinions, and detect their polarity, which could be positive, negative or neutral.The outcomes of opinion mining approaches help in extracting useful patterns that enable traders to take critical decisions for business, marketing and politics.In the literature, we have several proposed opinion mining systems, tools and approaches, but in general they are not available on public.Many other online opinion mining tools are simple to use and available for free or as demos.Opinion mining online tools performance need to be evaluated to attract researchers and companies utilizing their advantages.The main purpose of this study is to evaluate how efficient are online opinion mining tools for Arabic language.We used benchmark Arabic opinion collections and classify them using two popular online sentiment analysis tools that support Arabic language; Paralleldots and Repustate.The experiment used prediction quality measurement to evaluate these tools and compare their results with several machine learning classifiers in order to recommend the best available solution for Arabic sentiment analysis.Our results showed that Paralleldots API is highly recommended for Arabic sentiment analysis for both positive and negative reviews.
Research on robust audio zero watermarking algorithm based on discrete cosine transform

This paper mainly uses the discrete cosine transform algorithm characteristics, combined with the “zero watermarking” embedding features, to improve the robustness of the watermark algorithm, to balance the uncoordinated relationship between robustness and transparency.
A Calculation Method of Degree of Data Indication Regions in First-Person View Videos for Improvement Transportation

In this paper, we propose a calculation method of degree for detecting regions of various data indication in first-person view videos. In recent years, contents of augmented reality (AR) have been implemented due to advances in computer hardware and interface. By indicating the data on road user's view, intelligent transport systems (ITS) is enhanced. However, since the user's view includes a lot of visually important information which the user should perceive, for indicating the data on the user's view, it is necessary to detect regions that do not include the information. In our method, degree as criteria is obtained based on seam carving and saliency map model. By utilizing the degree, it is possible to adaptively detect the regions of the data indication from different viewpoints. Experimental results show the effectiveness of our method.