Reviewerly/exHarmony huggingface dataset:
Jaringan Syaraf Tiruan Backpropogation dalam Memprediksi Jumlah Tilang di Kejaksaan Negeri Simalungun

This study predicts the number of ticketing by applying Artificial Neural Networks. The application uses the Backpropogation algorithm where the data entered is the number of tickets. Then an Artificial Neural Network is formed by determining the number of units per layer. After the network is formed training is carried out from the data that has been grouped. Experiments are carried out with a network architecture consisting of input units, hidden units, output units and network architecture. Testing is done with Matlab software.
A Contrast of New Network Language Between Arabic and Chinese

Technology is a double-edged sword; the development and application of communication technologies have not only made great contributions to human beings and promoted the development of language, but also brought a rapid spread of language violence that caused great impact on people's lives. With the innovation of Internet technology, this form of language has developed rapidly in the dissemination of Internet media. At present, network language does not meet the requirements of language grammar, so there is a lack of teaching significance. Arabic and Chinese are subordinate to different language families with different network words. A prospective analysis has been performed between Arabic and Chinese network words which is mainly based on the influence of chivalrous network language on society and language. The research was based on 110 questionnaires distributed on social networking sites, and 40 interviews conducted with network users from china and some Arab countries. The ﬁndings suggested participants to use network language because, it is the language of their peers, it is cool and stylish, they have difﬁculties with the Arabic and Chinese language, and network constitutes a secret code, allowing escape from judgements of the older generation. The study concludes that network language is a strong marker of youth identity and group solidarity. Through a comparative analysis of Arabic and Chinese Internet words, Arabic or Chinese learners can clearly understand the similarities and differences between the two network words, and reduce the barriers to Internet communication, and appropriately use Chinese and Arabic Internet words.
Hierarchical Classification Algorithm Based on FastText

At present, methods for automatic assigning labels for literature using the Chinese Library Classification system are mostly based on machine learning methods, and many use the build knowledge base to improve classification effect. These methods are applicable to small-scale dataset, and as the categorical numbers increase, the classification effect will decrease and the time cost will increase dramatically. The paper proposes a hierarchical classification algorithm based on fastText text classification tool to solve classification indexing problem on large-scale literature dataset. The algorithm trains the two-layer classifier from the top-down method and designs an optimization algorithm to reduce the error transmission between the two layers. Experimental results showed that the optimized hierarchical classification technique improved the classification effect compared to the traditional classification technique.
Blockchain Implications for the Monetization of Identity Governed by Individuals

Blockchain technology has been discussed and analyzed throughout numerous practitioner and scholarly applications, but the majority of said analysis has tended to focus on the financial services space. From a blockchain and broader cryptoasset space this seems to make logical sense, but overlooks other potential applications. Specifically, as intellectual assets and intangible property assume greater importance in virtually aspect of the economy, the potential for blockchain and cryptoassets does appear to be increasing. Written in a tone and manner of interest to both practitioner and academic audience, this analysis examines how the Brave browser attempts to rebalance the proverbial scale between who controls the valuable information that continues to dominate the business landscape. In addition this research presents a potential framework for how organizations and individuals can monetize what potentially may be the most value resource going forward – digital information - and do so in a manner that is both equitable as well as leveraging the emerging technology of blockchain.
Incremental transfer learning for video annotation via grouped heterogeneous sources

Here, the authors focus on incrementally acquiring heterogeneous knowledge from both internet and publicly available datasets to reduce the tedious and expensive labelling efforts required in video annotation. An incremental transfer learning framework is presented to integrate heterogeneous source knowledge and update the annotation model incrementally during the transfer learning process. Under this framework, web images and existing action videos form the source domain to provide labelled static and motion information of the target domain videos, respectively. Moreover, according to the semantic of the source domain data, all the source domain data are partitioned into several groups. Different from traditional methods, which compare the entire target domain videos with each source group from the source domain, the authors treat the group weights as sample‐specific variables and optimise them along with new adding data. Two regularisers are used to prevent the incremental learning process from negative transfer. Experimental results on the two large‐scale consumer video datasets (i.e. multimedia event detection (MED) and Columbia consumer video (CCV)) show the effectiveness of the proposed method.
Anti-occlusion Video Target Tracking Based on Double Threshold Judgment

In recent years, great progress has been made in moving target tracking methods, but there is still no effective solution to the problem that affects the tracking accuracy, especially the problem that the target is occluded in the tracking process. To solve this problem, this paper proposes an anti-occlusion video target tracking method based on double threshold judgment, which combines Discriminative Scale Space Tracking (DSST) algorithm and Kalman filtering algorithm, according to the change of correlation coefficient P between adjacent templates. Firstly, the target is tracked by DSST algorithm, and the location information and scale information of the target are obtained. In the process of tracking the target, the occlusion threshold \( T_{1} \) is set to determine whether occlusion occurs. If not, the tracking of DSST algorithm is continued. Otherwise, the Kalman tracking algorithm is invoked to track the target during occlusion. At the same time, according to the change of correlation coefficient, the threshold \( T_{2} \) is set to judge whether the occlusion is over or not. If the occlusion is over, the DSST algorithm is returned. Otherwise, Kalman filter algorithm is used to track the occlusion until the end of the tracking. The simulation results show that this method can effectively solve the problem of tracking moving objects when they are occluded.
INTERFEROMETRIC REFRACTION STATICS FOR SEISMIC REFLECTION DATA FROM CHEONGJU GRANITIC BODIES OF KOREA

Rapid variations in the thickness of the refractor and low velocities affect greatly the imaging of the reflectors of land seismic data.Conventional solutions to obtain the weathering models employ the first break picking method, which requires time consuming steps and causes the human error in picking the first arrivals.Interferometric approach(IRS: interferometric refraction statics) which utilizes the first arrival signal instead of first break picking, is tested in this study to the synthetic data from the velocity structure provided by surface geophysics and borehole geophysics for the Cheongju granitic bodies of South Korea.The unresolved long-wavelength statics are much removed by IRS weathering correction and the overlying refractors are incidentally delineated in the refraction convolution stack(RCS).
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension

In conversational machine comprehension, it has become one of the research hotspots integrating conversational history information through question reformulation for obtaining better answers.However, the existing question reformulation models are trained only using supervised question labels annotated by annotators without considering any feedback information from answers.In this paper, we propose a novel Answer-Supervised Question Reformulation (ASQR) model for enhancing conversational machine comprehension with reinforcement learning technology.ASQR utilizes a pointer-copy-based question reformulation model as an agent, takes an action to predict the next word, and observes a reward for the whole sentence state after generating the end-of-sequence token.The experimental results on QuAC dataset prove that our ASQR model is more effective in conversational machine comprehension.Moreover, pretraining is essential in reinforcement learning models, so we provide a high-quality annotated dataset for question reformulation by sampling a part of QuAC dataset.
Dynamic Estimation of Significant Factors of Forming a University Ranking in the Context of Transition to an Information Society

The paper draws attention to the comparative dynamics of university ranking factors for the last five years (2013-2018). The period under review is characterized by an active transition to the information society. The article deals with a relevant problem of identifying factors in the educational organization ranking formation. These factors are significant in the period of transition to an information society. The aim of the research is development and pilot approbation of the author’s algorithm of dynamic subject-oriented analysis on factors forming a ranking of an educational institution. The basic methods of this research include: factor analysis for the university ranking formation, theoretical and experimental substantiation of the author’s algorithm of dynamic subject-oriented analysis, statistical methods of comparison of teachers and heads of educational organizations, a uniform experimental basis of interpretation and prediction of dynamics of the ranking reflection in the organization’s information environment. The author’s algorithm is based on the mechanisms of factor analysis of subjective representations. The dynamic basis of the author’s algorithm includes the analysis of stability and sustainability of the considered factors observed in the questionnaire surveys conducted in the period from 2013 to 2018. The poll respondents were 150 leaders and pedagogical staff of technical schools and colleges of the Russian Federation. The subjective focus of the author’s algorithm consists of separate opinions of teachers and managers on the educational organization ranking. The significance of a number of factors in the ranking structure, which remain relevant for five years, is confirmed. Among such factors are: the developed information and educational environment, the faculty capacity, the number of accredited specialties and the issuance of degree certificates. The ranking structure revealed the most dynamic positions with a high significance and output beyond the limits of the previously established statistical deviation. In addition, there are factors, the priority of which has been identified recently, for example, the factor of demand by entrants, recommended by the management, and the factor of research activity that was recommended by teachers. The predicted changes for the near and distant future were also revealed. We draw a conclusion about the applicability of the proposed algorithm to identify common factors. The role of education system actors in the formation of university rankings is determined. In the open information environment the algorithm contributes to dynamic forecasting for the near and distant future.
The Feasibility of Interactive Multimedia Validation Data Results on Folk Poetry Texts Learning in MTS Nurul Amaliyah Tanjung Morawa

This study is aimed to know the feasibility of interactive multimedia validation data results on folk poetry texts learning. The development of interactive multimedia is focused on folk poetry text material. The students are expected to learn independently through interactive multimedia that has been adjusted by the developer. The research and development population is VII grade students of MTS Nurul Amaliyah Tanjung Morawa. The research was using the 4D method, which involves four stages of research: define, design, development, and disseminate. The product of this research results in the form of interactive multimedia development of folk poetry texts is Adobe Flash CS5 media. The results shows that the feasibility of the media and material scored an average of 88.8 % with the criteria of "good" and 86.4% with the criteria of "good" in the development of interactive multimedia on folk poetry text learning.
Deep, spatially coherent Inverse Sensor Models with Uncertainty Incorporation using the evidential Framework.

To perform high speed tasks, sensors of autonomous cars have to provide as much information in as few time steps as possible. However, radars, one of the sensor modalities autonomous cars heavily rely on, often only provide sparse, noisy detections. These have to be accumulated over time to reach a high enough confidence about the static parts of the environment. For radars, the state is typically estimated by accumulating inverse detection models (IDMs). We employ the recently proposed evidential convolutional neural networks which, in contrast to IDMs, compute dense, spatially coherent inference of the environment state. Moreover, these networks are able to incorporate sensor noise in a principled way which we further extend to also incorporate model uncertainty. We present experimental results which show that this approach leads to a denser environment perception in only one time step while at the same time reducing the false positive and negative rates.
The Impact of Executive Management on Internal Auditing Quality Under the Agency's Problem: Evidence From Egypt

The study provides a framework for determinants of an impact of Executive Management on internal auditing quality according to eight dimensions (1) leadership style of executive management, (2) Organizational Culture, (3) operation cost of internal auditing, (4) providing information and disclosure for internal auditor (5) cooperation and facilitation from executive management (6) the attitude of executive management of earnings management, (7) the attitude of executive management for creative accounting and (8) professional ethics of executive management. The population of the study is Egyptian manufacturing firms listing at EGX and their employees. Accordingly, the number of received questionnaires was 392 from 28 firms in the first half of 2019 but the financial reports for 2018. The study found the impact of executive management on internal auditing quality, and it is significant at 0.01 level. According to adjusted R-squared; executive management interprets (97.1%) of an internal auditing quality level. Finally; the current framework of corporate governance need to take into organizational characteristics, as well as the ethical and behavioural variables, with than further controls on the Board of Directors, as well as ensuring the independence of internal audit to maximize the quality of internal audit.
The Development Process of Higher Order Thinking Skills Based Poetry Text Assessment Tools on VIII Grade Students of Junior High School Brigjend Katamso II Medan

This study is aimed describe the process of developing higher order thinking skills based poetry text assessment tools. Students need to be trained in their thinking skills by giving students the type of Higher Order Thinking Skills (HOTS) that can be used to improve students' thinking skills. This research was conducted in Junior High School Brigjend Katamso II Medan. The result shows that Preliminary studies were research and gathering preliminary information, namely needs analysis. The needs analysis in this research development began by distributing questionnaires to the needs of eighth grade students of Junior High School Brigjen Katamso II Medan, amounted to 35 students and 2 Indonesian language teachers of Junior High School Brigjen Katamso II Medan. The results of the needs analysis were as follows: That 100% of the teachers stated that they knew the assessment tools and 70% knew the steps in preparing the assessment tools. However, only 0% of teachers test their assessment tools before use so that 100% of teachers need a valid, effective and practical assessment tool.
Modeling dangerous driving events based on in-vehicle data using Random Forest and Recurrent Neural Network.

Modern vehicles produce big data with a wide variety of formats due to missing open standards. Thus, abstractions of such data in the form of descriptive labels are desired to facilitate the development of applications in the automotive domain. We propose an approach to reduce vehicle sensor data into semantic outcomes of dangerous driving events based on aggressive maneuvers. The supervised time-series classification is implemented with Random Forest and Recurrent Neural Network separately. Our approach works with signals of a real vehicle obtained through a back-end solution, with the challenge of low and variable sampling rates. We introduce the idea of having a dangerous driving classifier as the first discriminant of relevant instances for further enrichment (e.g., type of maneuver). Additionally, we suggest a method to increment the number of driving samples for training machine learning models by weighting the window instances based on the portion of the labeled event they include. We show that a dangerous driving classifier can be used as a first discriminant to enable data integration and that transitions in driving events are relevant to consider when the dataset is limited, and sensor data has a low and unreliable frequency.
АКТИВІЗАЦІЯ САМООСВІТНЬОЇ ПІДГОТОВКИ ВИКЛАДАЧІВ ГУМАНІТАРНИХ ДИСЦИПЛІН ВВНЗ ІЗ ВИКОРИСТАННЯМ КЕЙС-ТЕХНОЛОГІЇ

The article is devoted to justification of scientific approaches toward the organization of self-education training of humanitarian subjects’ teachers in a higher military school as part of continuous professional education. It has been proven that case technology application contributes to the enhancement of self-educatory training competence of humanitarian subjects’ teachers due to the performance of various kinds of preemptive tasks (search and provision of information on Internet, creating reference list of Internet information resources, development of multimedia presentation in MS Power Point program, using electronic mail, chat message exchange, creation and placement of information in blogs and so on), promotes research activity of humanitarian subjects’ teachers at a higher educational establishment, establishes proper environment for practical application of acquired knowledge when performing different kinds of creative tasks.
COMPARATING ANALYSIS OF COORDINATE SEARCH METHODS EFFICIENCY FOR THE OPTIMAL SOLAR PANEL POSITION FINDING

The work is devoted to the investigation of the effectiveness of the coordinate search methods for solving the problem of finding the position of the solar panels, in which the greatest power of the produced current is achieved. The existing solutions in the market of sun surveillance systems (solar trackers) are analyzed. The advantages and disadvantages of such systems are presented in comparison with fixed panels. It is proposed to improve the hardware and software for research of the solar panels efficiency, developed by the authors and highlighted in the previous works, by integrating the program realization of the algorithm of coordinate search of the maximum power of solar panels into the existing. For this purpose, the efficiency of three algorithms for coordinate search of the maximum, namely, the method of coordinate ascending, the Huck-Jeeves method and the Rosenbrock method, was studied in three parameters. Experiments were carried out on data obtained both experimentally using the lab stand for solar panel efficiency research, and by generation using mathematical model of the solar panel efficiency dependance on the angle of radiation, described in previous works. TThe results of experiments are analyzed, which showed a fundamental difference between the work of coordinatewise search algorithms on a mathematical model and experimental data. The main indicators of the efficiency of algorithms are substantiated on the basis of the meteorological conditions in which the measurement was carried out for the formation of experimental data. Conclusions are drawn regarding the efficiency of using these coordinate-based search methods for solving the problem of finding the optimal position of solar panels. Further prospects for research on this topic and the possibility of using coordinate-wise search methods in software of solar panels with a biaxial orientation on the position of the Sun are given.
Teaching descriptive-text writing through flannel doll for eight grade students of Junior High School

&lt;p&gt;The purposes of this research are to describe the procedure and the strengths and weaknesses of teaching descriptive-text writing through flannel doll for eight grade students of SMPN 1 Maospati. The research uses qualitative as the approach. The design of this research is descriptive research. This research covers of eight grade students of SMPN 1 Maospati. The sample of the research is 30 students of 8I grade which consist of fifteen male and fifteen female. This sample is chosen by purposive sampling. The technique of analyzing data is compiling data, disassembling, reassembling, interpreting, and concluding. The result of the research are: (1) The procedure of “Teaching Descriptive-Text Writing through Flannel Doll for Eight Grade Students of SMPN 1 Maospati” consists of preparation and presentation done by the teacher. These preparation activities includes preparing the Syllabus, RPP, lesson plan, flannel doll, then preparing the classroom into five groups in break time. The presentation activities include pre-activities, whilst-activities, and post-activities. Pre-activities include greeting, checking attendance, and giving apperception and motivation by showing flannel doll. Whilst-activities include giving explanation (modeling) about descriptive text, joint construction of descriptive text about flannel doll (show how to planning and drafting with students), independent construction of descriptive text about each group’ flannel doll (the steps are planning, drafting (writing), revising, editing, and final version (publishing). Post-activities include review and conclude the material. (2) The strengths are flannel doll make the students are enthusiastic, interested in English lesson, happy, active, enjoy, helpful, and easier in writing descriptive text, flannel doll increase the students’ vocabulary and understanding in writing descriptive text, the using of flannel doll in descriptive text writing is suitable for eight grade students of SMPN 1 Maospati, guiding and explaining writing step and the using of editing and revising checklist is helpful for the students in writing descriptive text activity. While the weaknesses are the process of making six flannel dolls need more time, flannel doll will easily dirty if it is not in good hands, some students still crowded in the class, and some students confused to describe the unfamiliar color and the size of the doll’s clothes.&lt;/p&gt;