Reviewerly/exHarmony huggingface dataset:
Model formation of future teachers readiness to innovative activity

The relevance of the study is justified by the new requirements for the personality of a specialist in the field of education. Thus, an obligatory component of professional and pedagogical competence is the innovative competence of the teacher. The formation of this competence in future teachers in the system of their professional training in higher education requires special methodological system of innovative competence formation. The purpose of this article is to reveal the essence and to describe the model of readiness to innovative activity of future teachers. The purpose of the article is realized by applying theoretical (spectral analysis, generalization) and empirical (modeling, methods of pedagogical diagnostics) methods of scientific knowledge in the research. On the basis of the application of these methods in the study, the essence, features and structure of pedagogical innovative activity are determined. The necessity of innovative activity readiness formation of future teachers is substantiated. The components of the structure of teacher's readiness for innovative activity are described. The essence of pedagogical formation model of future teachers readiness to innovative activity is described. Its efficiency on the basis of quantitative and qualitative results is proved. The elements of scientific novelty in the work are represented by the creation of an experimental pedagogical model of the formation of the future teachers’ readiness to innovative activity, the substantiation of methodological and methodical aspects of its implementation, the development of the criteria base for determining the level of formation of the future teachers’ readiness to innovative activity.
CONSULTING AS A PEDAGOGICAL GUIDANCE TECHNOLOGY IN THE CONTINUOUS EDUCATION OF A PEDAGOGUE

The present day tendency of rapid constant changes of the informational and technological space surrounding a pedagogue, suggests the necessity of continuous education. The necessity of constant renovation of knowledge leads a pedagogue to obtain new competences, to the emergence of new educational practices. As a result of high competition in all spheres of society the client-orientated course of modern system of professional education makes a pedagogue search for new forms of getting competences, which are not set in the frames of certain disciplines and thus provide the continuity of educational processes. Here we suggest consulting, having been worked out as a pedagogical technology that guaranties the achievement of the set goals, reproduction and replication. The purpose of our research is to substantiate the expediency of using the pedagogical technology of consulting to support the process of continuous professional training of a teacher directly at the workplace. The article deals with the theoretical stage results of our research in working out a consulting model as a pedagogical technology of continuous pedagogue’s educational guidance as well as the analysis of pedagogue-participants’ opinions concerning the pilot approbation of this model.
Multiple Path Prediction for Traffic Scenes using LSTMs and Mixture Density Models

This work presents an analysis of predicting multiple future paths of moving objects in traffic scenes by leveraging Long Short-Term Memory architectures (LSTMs) and Mixture Density Networks (MDNs) in a single-shot manner.Path prediction allows estimating the future positions of objects.This is useful in important applications such as security monitoring systems, Autonomous Driver Assistance Systems and assistive technologies.Normal approaches use observed positions (tracklets) of objects in video frames to predict their future paths as a sequence of position values.This can be treated as a time series.LSTMs have achieved good performance when dealing with time series.However, LSTMs have the limitation of only predicting a single path per tracklet.Path prediction is not a deterministic task and requires predicting with a level of uncertainty.Predicting multiple paths instead of a single one is therefore a more realistic manner of approaching this task.In this work, predicting a set of future paths with associated uncertainty was archived by combining LSTMs and MDNs.The evaluation was made on the KITTI and the CityFlow datasets on three type of objects, four prediction horizons and two different points of view (image coordinates and birds-eye view).
One-Pixel Attack: Understanding and Improving Deep Neural Networks with Evolutionary Computation

Recently, the one-pixel attack showed that deep neural networks (DNNs) can misclassify by changing only one pixel. Beyond a vulnerability, by demonstrating how easy it is to cause a change in classes, it revealed that DNNs are not learning the expected high-level features but rather less robust ones. In this chapter, recent findings further confirming the affirmations above will be presented together with an overview of current attacks and defenses. Moreover, it will be shown the promises of evolutionary computation as both a way to investigate the robustness of DNNs as well as a way to improve their robustness through hybrid systems, evolution of architectures, among others.
Deep-Shallow Occlusion Parallelism Network for Person Re-Identification

Abstract In recent years, the progress of person re-identification has advanced significantly, but person in real scenes are often obscured by various objects. This problem has been ignored or solved based on an incomplete assumption by the previous person re-ID methods. This paper proposes a new training mechanism, Deep-Shallow Occlusion Parallelism network, which responds to multi-scale occlusion in a more universal case and alleviate the negative impact of occlusion effectively. Specifically, the proposed consists of shallow occlusion and deep occlusion, to which are applied with shielding simulation to enhance the difficulty of training samples. What’s more, channel-spatial attention is applied to various positions in each branch to concentrate on discriminative features afteqr occlusion. In the end, the weighted fusion of the two branches not only informs the network of the information of changes before and after occlusion, but also complements the deep and shallow information effectively. It makes the network more robust. This method has excellent performance on the three basic re-ID datasets and the largest partial re-ID dataset, with the state-of-the-art reached to or even surpassed.
Aggregation-Based Attributed Graph Summarization

Graph summarization has a wide range of applications that involve many large-scale graph data processing problems. However, most of existing approaches for graph summarization neglect attributes and relationships residing in nodes and edges in graphs. In this paper, an aggregation-based attributed graph summarization approach is proposed by fully taking attributes and relationships into account during graph summarization. We present a new approach to quantitatively calculate node merge error, and a heuristic measure to dynamically determine the threshold of node merge error per iteration. We implemented our aggregation-based attributed graph summarization approach. Extensive experiments were made for illustrating effectiveness and efficiency of our approach in comparison with the state-of-the-art approach based on graph topology.
Algorithmic Enforcement Online

Artificial Intelligence (AI) is increasingly and pervasively affecting society. AI and algorithms play a relevant role in the Intellectual Property (IP) discourse as well. Tomorrow, algorithms will of course come as creators and innovators but today they are already here as enforcers. Content sanitization online will soon — and inevitably — become the sole domain of algorithmic enforcement tools. This process towards algorithmic enforcement online has been characterized by a few important recent developments. First, emerging regulatory framework, new legislative obligations and judicial trends would like to force online intermediaries to implement architectural changes leading to sanitization of allegedly infringing content — possibly legit freedom of expression — by design. Second, as a consequence, algorithmic enforcement brings about privatization of enforcement and delegation of public authority. Third, in online enforcement of intellectual property — as well as any other content sanitization — algorithms take decisions reflecting policy’s assumptions and interests that have very significant consequences to society at large, yet there is limited understanding of these processes. Finally, but critically for the focus of this investigation, Online Service Providers’ (OSPs) regulatory choices — occurring through algorithmic tools — can affect profoundly enjoyment of users’ fundamental rights, such as freedom of expression, freedom of information and right to privacy and data protection. This Chapter will map the complex conundrum triggered by algorithmic enforcement in the IP domain with special emphasis on copyright and trademark enforcement online. In doing so, this chapter, first, briefly explains what algorithmic enforcement online actually is. Second, this chapter will dig into voluntary, judicial and legislative measures that have been promoting the emergence of algorithmic enforcement online. Third, this chapter will assess tensions between algorithmic enforcement and human rights, including due process, freedom of information, freedom expression, privacy and freedom of business. In conclusion, this chapter will discuss solutions for protecting human rights from algorithmic enforcement negative externalities.
Research on Consistency Enhancement of Wireless Channel Key Against RF Fingerprint

Abstract The development of the physical layer key extraction technology provides a new type of encryption mechanism that uses the reciprocity and time varying of wireless channels to generate keys. This encryption mechanism can achieve informational security. However, the wireless channels in the real environment are not completely reciprocal, which results in low consistency in generating the keys. In this paper, we study the influence of RF fingerprint on channel reciprocity, and propose a calibration matrix algorithm resist the RF fingerprint effect. Based on the RF fingerprinting model established previously, we propose two calibration methods from the perspective of RF gain and I/Q gain. The superiority of the two calibration methods is proved by the average system capacity and the linear quantification error rate. Next, we extend the calibration matrix algorithm of narrowband systems to wideband OFDM systems. The applicability of the calibration matrix algorithm in OFDM systems is proved by the linear quantification error rate of the uplink and downlink channels. In order to prove the reliability of the algorithm in the real environment, we use a USRP to collect multiple sets of measured data, and analyse the data through the calibration algorithm. The final result shows that this algorithm makes the uplink and downlink channel reciprocity compensated.
Predicting the Empirical Distribution of Video Quality Scores Using Recurrent Neural Networks

Video quality assessment is a crucial routine in the broadcasting industry. Due to the duration and the excessive number of video files, a computer-based video quality assessment mechanism is the only solution. While it is common to measure the quality of a video file at the compression stage by comparing it against the raw data, at later stages, no reference video is available for comparison. Therefore, a no-reference (Blind) video quality assessment (NR-VQA) technique is essential. The current NR-VQA methods predict only the mean opinion score (MOS) and do not provide further information about the distribution of people score. However, this distribution is informative for the evaluation of QoE. In this paper, we propose a method for predicting the empirical distribution of human opinion scores in the assessment of video quality. To this end, we extract some frame-level features, and next, we feed these features to a recurrent neural network. Finally, the distribution of opinion score is predicted in the last layer of the RNN. The experiments show that averages of predicted distributions have comparable or better results with previous methods on the KonVid-1k dataset.
Developing An Intelligent Logistics and Distribution System For A Large Number of Retail Outlets: A Big Data Analytics Approach

The logistics and distribution system in the retail industry in Indonesia has its own complexity.The growth and productivity of the retail outlets in Indonesia have been increasing from year to year.Distribution activities in this study are related to the formation of salesman visit routes involving around 38,900 customer base retail outlets, which are quite numerous in number, calling for a challenging approach to find the optimized solution.Therefore, the case study in this research will be discussed on the concept of the Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP), considering the work balance and visit pattern constraints.The methods used in this research are the balanced K-means and the Minimum Span Tree -Kruskal's Walk algorithm, which are proven to solve the problem with a shorter computation time and a more balanced daily route than the current conditions as the results.
AN ANALYSIS OF STUDENTS’ INTEREST OF READING ENGLISH NOVEL IN FACULTY OF LETTERS MUSLIM NUSANTARA AL-WASHLIYAH UNIVERSITY

This research was aimed to find out how the condition of students’ interest in reading English novels in Faculty of Letters, Muslim Nusantara University Al-Washliyah. As a student majoring in English Literature, reading novels is one of the activities that must be carried out because most of the subjects given are closely related to centrality, one of which is an English-language novel. This research was conducted at the fifth semester students of the Faculty of Literature at Muslim Nusantara University Al-Washliyah. This research used a qualitative descriptive method. The instruments used were observation and interviews. Based on the results of the study it was found that the condition of students’ interest of reading English novels in Faculty of Letters, Muslim Nusantara Al-Washliyah University is very poor. Keywords: Analysis, Reading, Novel
Recognization of Cloth Pattern and Colour for Blind People

As we know that recognizing cloth pattern and colour of a cloth is easy for a person but, it is a difficult task for visually impaired people i.e blind people.This Recognization system will help the visually impaired people to recognize the pattern and colour of the cloth.But this is also a challenging research problem due to rotation, scaling, illumination, and especially large intra-class pattern variations.We have developed a system that recognizes clothing patterns in four main categories (plaid, striped, pattern-less, and irregular) and identifies 8 clothing colors.By using algorithms like SIFT and Classifier like SVM and Discrete Wavelet Transform, Radon Transform to detect the color and Pattern respectively.To evaluate the effectiveness of the proposed approach, we used the CCNY Clothing Pattern dataset.
Translation Quality Assessment of Proverbs from English into Arabic: The Case Study of One Thousand and One English Proverbs Translated into Arabic

Proverbs are very important in every language and culture.However, translators sometimes mistranslate them.Thus, this study sheds light on the translation quality assessment of proverbs.These proverbs are collected from One thousand and one English proverbs translated into Arabic by Omar Jabak.This study aims at pointing out the most frequently used strategies for translating proverbs, and investigating how far Na Pham's error analysis model is appropriate for the description and assessment of the strategies used in translating these proverbs.Na Pham's error analysis model is used to identify comprehension, linguistic and translation errors.Moreover, this study aims at detecting the most common errors under each strategy used.The findings of this study show that Na Pham's error analysis model is appropriate for the assessment.It also reveals that there are certain types of errors which are committed more often than others.In addition, it uncovers that the types of errors detected when translating proverbs using partial equivalence and paraphrasing are very similar.Comprehension errors, giving an inaccurate meaning, and distorting the meaning are the most frequently detected errors when translating proverbs using partial equivalents and paraphrasing.On the other hand, wrong lexical choice and too-literal translation are the most frequently detected errors when using literal translation.Finally, this study suggests solutions for improving the quality of Arabic translations of proverbs.In addition, some recommendations for further studies are suggested Cite as: Alfaleh, B.
Investigating Trusted Records for Employment and Education

Employment uses trusted records such as award certificates as a part of hiring procedures.Some countries practices for employment history reference; either a service certificate or systems based on pension records which are based on number of years of employment that are also can be used for pension eligibility.However, there is an increase on the number of issued award certificates which are, in most cases, do not follow an agreed standard, which increases challenges for an authenticated record sharing.Moreover, employment systems are not required to share employment history nor to accommodate record requests educational institutes.Additionally, public employment systems are by design isolated from interaction with private sector.The research suggest that distributed systems would reduce costs for verifying and authenticating records while being flexible on allocating trusted records such as employment history, awarded certificates and resumes. INTRODUCTIONThe general practice, to verify an awarded certificate, candidates are required to provide their official documents to validate any claim on their personal resume.Also, employees must get some type of certificate to verify the length of the job with job duties and experienced gained.This means any employment experience outside the organisation is hardly recognised.Which consequently makes it difficult for individuals to build a resume without a proper managed employment history.At the same-time employers can not verify any job seeker claims.The existing practice to verify employment record requires different departments to manually and physically provide references.Such records are either: (1) authenticated records issued through official organisations or agencies, or (2) non-authenticated records that are provided by individuals who are responsible for the accuracy of records such as resumes.Unfortunately, some individuals provide inaccurate information which create a burden on hiring companies to verify the provided information.The challenge presented here is how to improve information sharing between education and employment.
Innovative Training in Digital Transformation

Global digitalization affects all areas of socio-economic activity.It radically changes many business processes, which in turn affects the professional requirements for staff.The role and importance of digital competencies, which an employee of an enterprise should possess, is growing.The article analyzes the changes in the labor market that are caused by the influence of digitalization.It is shown that the need for innovative personnel who own modern information and communication technologies is growing.The dynamics of training innovative personnel in Russia is considered.The relationship between professional standards and educational programs has been studied.
Technologies of tutor activity in the educational environment of higher school

The relevance of the issue of students' adaptation to the educational environment of the university is caused by the aggravated contradiction between the need of society and the state for high quality higher education and the insufficient willingness of applicants to enter higher education to receive professional education in the new conditions of interaction with the educational environment of higher education. The authors set a goal to consider the factors of successful adaptation of students to the educational environment of higher education, one of which is the technology of tutoring. Turning to scientific sources allowed the authors to identify the provisions adopted as a regularity of the adaptation process, and to define adaptation as an intensive and dynamic, multilateral and complex life process associated with the development of an individual's sustainable skills to meet the requirements for him during training and education in higher education based on appropriate adaptive responses.
Synthesis of the BCAC4ND2 Ionophore from p-t-Butylcalix[4]arene Ethylester

A bstrak Penelitian tentang sintesis ionofor BCAC4ND2 dari p-t-butilkaliks[4]arena etilester telah dilakukan. Penelitian ini bertujuan untuk mensintesis ionofor BCAC4ND2 dari p-t-butilkaliks[4]arena etilester. Ionofor BCAC4ND2 disintesis dengan cara menghidrolisis p-t-butilkaliks[4]arena etilester dalam larutan KOH-etanol 95%. Reaksi hidrolisis ester terpromosi basa ini direfluks selama 28 jam pada suhu 78 o C. Ionofor BCAC4ND2 diidentifikasi dan dikarakterisasi dengan titik leleh, kromatografi lapis tipis (KLT), spektrometer FTIR, 1 H-NMR, dan 13 C-NMR. Ionofor BCAC4ND2 yang diperoleh berupa padatan putih (perolehan 62,38%), titik leleh 326-328 o C, dan KLT (SiO 2 , etanol : etilasetat = 1 : 1 v/v , R f = 0,9 3). A bstract Research on the synthesis of the BCAC4ND2 ionophore from p-t-butylcalix[4]arene ethylester has been carried out. This study aims to synthesize the BCAC4ND2 ionophor from p-t-butylcalix[4]arene ethylester. The BCAC4ND2 ionophore is synthesized by hydrolyzing p-t-butylcalix[4]arene ethylester in aqueous KOH-ethanol of 95%. The hydrolysis reaction of these base-promoted esters are refluxed for 28 hours at 78 o C. The BCAC4ND2 ionophore was identified and characterized by melting point, thin layer chromatography (TLC), FTIR, 1 H-NMR, and 13 C-NMR spectrometers. The BCAC4ND2 ionophore obtained was in the form of white solid (62.38% recovery ), a melting point 326-328 o C, and TLC (SiO 2 , ethanol : ethylacetate = 1 : 1 v/v, R f = 0.93).
Automatic Clustering Crime Region Prediction Model using Statistical Method in Data Mining

The most common social problem faced today all over the world are crimes.Quality of life, economic growth and reputation of nation are greatly affected by crimes.There has been increase in the crime rate since last few years and in order to reduce this crime rate government needs to take the preventive measures.In order to secure the society from such crimes there is a need to adopt new advanced system and approaches to improve crime analytics for protecting the community.Accurate real time crime predictions help up to a greater extent to reduce crime rate but it is a challenging problem as occurrences of crime depends on many factors.Various visualizing techniques and machine learning algorithms are followed for predicting the crime distribution in an area.The raw data sets were processed and visualized based on needs In the first step and later machine learning algorithms were used to extract knowledge out of the large datasets and find the hidden relationships among data which is further used to for reporting and discovering the crime patterns which is very important source of information for crime analyst to analyze these crime networks by means of various interactive visualizations for crime prediction and thus is very supportive in preventing crime.