Reviewerly/exHarmony huggingface dataset:
Automobile Dashboard Object Recognition for Interested Vehicle in ITS

This paper proposes an algorithm to describe the distribution of automobile dashboard object (ADO) . In this algorithm, we propose ADO encoding and similarity computation algorithm SDEA. We encode every automobile dashboard object into dashboard cell to assign them specified anti-noise ability even if the ADO is interfered by environment light, dust, shadow and so on. Usually the ADO of every vehicle is different so that every vehicle has its own different presentation code. The encoding method present the ADO with accurate cells to decrease noises, and the difference between source and candidates can then be computed effectively. In this paper, we proposed a novel Synthesized Distance of Mahalanobis and Euclidean distance for Encoded ADO (SDEA) algorithm to minimize the correlation between cells of the ADO and increase the robustness of recognition. The tiny difference of the ADO is then enlarged and the recognition of an interested candidate is easier with strong robustness. In special vehicle detection, the algorithm has more effective performance compared with only Euclidian or Mahalanobis distance. Over 500 vehicle images are utilized in our experiments and the result verifies that the ADO cells encoding SDEA algorithm has distinguished recognition ability even if the images are contaminated by strong noise and the ADO has shape transformation and position shift so that this algorithm presents the ADO mathematically and recognizes a specified vehicle accurately in huge amount of automobile images and is effective for interested vehicle recognition in big data with an excellent noise tolerant capability. This algorithm overcomes the shortcoming of other category methods when they recognize a special vehicle with the serious transformed object structure and strong noise.
APPLYING DEBATE TECHNIQUE WITH REBUTTAL TYPE IN SPEAKING CLASS: PRACTICES AND IMPACTS

This study aimed at investigating the roles of debate technique with rebuttal type in a speaking class setting. It was motivated by the fact that EFL students tend to be passive in speaking activities in the classrooms. This present study applied quasi-experimental design by involving two groups of students: the experimental group and control group in which each group consisted of 35 students of a vocational high school in Malang, Indonesia. The findings suggest that the use of debate technique with rebuttal type empowers the learning activities to be more effective and interesting. It provides the issues in the pubic area and increases the critical thinking of the students. The implications and suggestions were also discussed.&#x0D; Keywords: debate technique, rebuttal type, speaking skills, critical thinking
The Application of “LBL+CBL+PBL+RBL” Four-Track Model in Modern Chinese Teaching

In order to improve the teaching effect in modern Chinese course, this work adopted "LBL+CBL+PBL+RBL" four-track mode.The teaching process is based on teaching materials, guided by problems, assisted by cases and supported by resources.Students' interest in learning is obviously improved and their ability of independent thinking is greatly enhanced.The research shows that the four-track teaching mode not only lays a solid theoretical foundation for students, but also improves their ability to analyze and address Chinese problems.It is an effective comprehensive teaching mode.
Towards the Development of a Navigation System for a Bipedal Robot: Preliminary Analysis of the Literature

In the literature, several works are reported about the gait of bipedal robots.Some papers are associated with stability or automatic learning and navigation, but no paper was found that includes the variables of bipedal gait, stability, navigation and automatic learning, all applied in an indoor bipedal robot.Our objective is, after the analysis of the state of the art, to propose a methodology that will allow the autonomous navigation of a bipedal robot and validate its degree of autonomy with respect to the evaluation metrics reported in the literature.
Educational innovations in Eastern countries. Final Conference of Erasmus + project Fostering Competencies Development in Belarusian Higher Education

Erasmus + KA2 programme project &ldquo;Fostering Competencies Development in Belarusian Higher Education &ndash; FOSTERC&rdquo; consortium partners have met for the Final Conference in Minsk, Belarus, on the 6 th of December 2019. It was an exciting moment. &nbsp;&ldquo;FOSTERC&rdquo; consortium worked together since 2016, many meetings, trainings, common research and creative work have been implemented in a cooperative way. The final &ldquo;FOSTERC&rdquo; event has summarised transformations and novelties that have been created in Belarus higher education institutions during the last three years. This article shortly reviews some insights and solutions that have been discussed during the &ldquo;FOSTERC&rdquo; conference in Minsk.
Integrasi Micro-Apps Individual menjadi One-Stop Services Maranatha Application Suite

Use of information systems at the university, which is the standard management of the university, is required to support all the activities of the academic community. Aside of determining the smooth operations, information technology also maintain the competitiveness of the competitors by constantly updating the information technology so as not to miss. Trends in the development of technology will make a strong connectivity between universities and stakeholders, governments, and partners. Some of the problems that occur when information systems are not yet fully integrated system, separate user management, application is implemented on different platforms and dashboards for management, not integrated. It is important for the university to provide Single and Integrated Application applications that are integrated into each of their services. Single and Integrated Applications are available at the web application / desktop / mobile multi-platform, for which data are available in real time, and there is no duplication of data occurs.
Multi-dimensional Index Construction of Electric Power Multi-source Measurement Data considering Spatio-temporal Correlation

The operation of complex AC/DC power grid changes rapidly and dynamically, which objectively puts forward higher requirements for on-line analysis, and it is urgent to improve the basic data quality of power grid. Because of low quality and poor synchronization of the basic data of power grid, it is impossible to accurately map the actual operation of the power grid. At the same time, the cross-system data matching degree is low and the data correlation is poor, so it can not support the multi-scale data analysis for all kinds of applications. In this paper, the associated method of multi-source heterogeneous data in the power grid is studied. Combined with big data's access characteristics, big data storage, big data retrieval and artificial intelligence technology, the high-speed data storage and index architecture of power big data are constructed, and a multi-dimensional index reflecting the associated relationship of operating data is established from the dimensions of time, space, application, device and so on. It is easier to analyze multi-source data, to improve the basic data quality of power grid, which provides effective support for accurate data analysis and evaluation of power grid.
Short-term prediction of photoelectric power considering the influence of fog and haze

Accurate photovoltaic (PV) power prediction is an important basis for ensuring the safe and stable operation of high-permeability PV power generation systems. There are many environmental factors affecting the output power of PV power generation, and the influence of fog and haze on PV power cannot be ignored. In this paper the main factors of affecting PV power and the necessity of accounting for fog and haze in PV power prediction are described. The Air Quality Index (AQI) is selected as an indicator to measure the severity of fog and haze, and it is included in the historical data source of PV power prediction together with weather conditions. Aiming at the complex nonlinear problem of PV power prediction, a PV power prediction model based on BP neural network algorithm in fog and haze weather is established. In order to ensure the accuracy of PV power prediction, a training sample selection method based on ”similar time” is proposed. The analysis of the example verifies the necessity of considering fog and haze in the PV power prediction and the effectiveness of the proposed method.
The Impact of Analytical Skill of Teacher Book on Teacher’s Competence in Compiling Lesson plan for Elementary Schools in Cicalengka Sub-District

This study aims to determine the influence of the ability to analyze the teacher book on teachers' competence to compile lesson plan for Elementary Schools in Cicalengka Sub-District.The research method employed in this study is quantitative which focuses on survey methods.The sampling technique is saturation sampling with all members of the population as sample; they are eight teachers of the 5th grade of Elementary Schools of 2018/2019 academic year in Cicalengka Sub-District.The independent variable is the analytical skills of teacher book (X) and dependent variable is the ability to compile Lesson Plan (Y).The performed data collection techniques are questionnaires and documentation.The research instruments are tested with validity and reliability tests.The data analysis techniques used are descriptive statistical analysis, prerequisite tests analysis which include normality and linearity tests, and final analysis techniques (hypothesis testing) using simple regression analysis, correlation analysis, and determinant coefficients.The study concludes that (1) The percentage of the answers obtained from the respondents' questionnaire on the analytical skills of teacher book variable is 54.09%;It is categorized as moderate because the score is within 33.33% -66.67% intervals (2) The percentage of the answers obtained from respondents' questionnaire on the ability to compile Lesson Plan variable is 48.18%;It is also categorized as moderate because the score is among 33.33% -66.67% intervals; (3) The analytical skills of teacher book variable has a significant influence on the ability to compile Lesson Plan for Elementary Schools in Cicalengka sub-district.
Characteristic Features of Teaching «Methods of Teaching Special Disciplines»

The relevance of this study is determined by the need to improve the quality of educating students in Master’s programme. Its importance is growing significantly with the introduction of the new major 029 «Information, library and archival work» in Ukraine, which is of a complex nature and provides new methodological and technological approaches to the subject of the course «Methods of teaching special disciplines».Research methodology. The author uses the system-activity approach to describe the purpose, tasks, content, structure, methodology of teaching the discipline «Methods of teaching special disciplines».Results. The article identifies issues arising from the unification of several subject areas into one major 029 «Information, library and archive studies», and from the integration of librarians, information professionals and archivists into one broad subject area.The emphasis is placed on the general information about the discipline «Methods of teaching special disciplines», its place of the discipline in the curriculum, pre-requisites and post-requisites of the discipline, purpose and objectives of the discipline, the name and description of general and professional competences, content and structure of the course. The examples of particular types of classes are given.Novelty. The article is the first attempt to describe innovative pedagogical methods and technologies in teaching the course «Methodology of teaching special disciplines» for students in the master’s programme in 029 «Information, library and archival studies».The practical significance. The main results of the study can be used by higher education institution of Ukraine to further improve the content and methodology of teaching the course «Methods of teaching special disciplines».
AN ANALYSIS OF GRAMMATICAL ERRORS IN WRITING RECOUNT TEXT AT THE FIRST YEAR CLASSES OF ENGLISH DEPARTMENT

The objective of this research is to find out and analyze the types of error made by students at the first-year classes in writing a recount text. The methodology of the research used is qualitative methods and the research design is a case study. Moreover, the writers chose the first-year classes students of English Department and observed 40 students. The writers used a written test to gain the data about error in writing a recount text. Based on the analysis of students’ recount text writing, there were several mistakes made by the students, with the total number of percentages was 76%. Of these, 76% errors including 15% errors of irregular verb usage, 12% errors of regular verb, and the type of error most often done by the students was 32% errors of word choice, 2% errors of omission, 3% errors of auxiliary verb, 5% errors of intralingual transfer, 2% errors of adverb, error of misordering was 5%, and error of unclear meaning was only 2%. The number errors of be was 17%, and the use of personal pronoun errors was 7%.&#x0D; &#x0D;
Harnessing GAN with Metric Learning for One-Shot Generation on a Fine-Grained Category

We propose a GAN-based one-shot generation method on a fine-grained category, which represents a subclass of a category, typically with diverse examples. One-shot generation refers to a task of taking an image which belongs to a class not used in the training phase and then generating a set of new images belonging to the same class. Generative Adversarial Network (GAN), which represents a type of deep neural networks with competing generator and discriminator, has proven to be useful in generating realistic images. Especially DAGAN, which maps the input image to a low-dimensional space via an encoder and then back to the example space via a decoder, has been quite effective with datasets such as handwritten character datasets. However, when the class corresponds to a fine-grained category, DAGAN occasionally generates images which are regarded as belonging to other classes due to the rich variety of the examples in the class and the low dissimilarities of the examples among the classes. For example, it accidentally generates facial images of different persons when the class corresponds to a specific person. To circumvent this problem, we introduce a metric learning with a triplet loss to the bottleneck layer of DAGAN to penalize such a generation. We also extend the optimization algorithm of DAGAN to an alternating procedure for two types of loss functions. Our proposed method outperforms DAGAN in the GAN-test task for VGG-Face dataset and CompCars dataset by 5.6% and 4.8% in accuracy, respectively. We also conducted experiments for the data augmentation task and observed 4.5% higher accuracy for our proposed method over DAGAN for VGG-Face dataset.
Algoritma Pemilihan Objek pada Interaksi Antarmuka Berbasis Titik Pandang Mata

Eye tracking provides an alternative input modality to control the interactive contents in computers, particularly in assistive technology and public display. Instead of fixational eye movement, smooth pursuit eye movement has been used for object selection in gaze-based interactive applications. However, some previous works did not consider various similarity measures to match trajectory of eye movement and the presented dynamic stimulus. Furthermore, there was no correction towards the drift of eye movement during object selection. Hence, gaze-based object selection based on smooth pursuit eye movement is still inaccurate and unstable. To fill the research gaps, we proposed a novel approach for accurate gaze-based object selection using linear regression for eye movement’s drifting correction. We measured accuracy, success timing, and stability of object selection to validate performance of the proposed method. From the experimental results, object selection could be performed without the usage of chinrest using Euclidean distance, moving average and linear regression in gaze-based interfaces. The usage of chinrest did not give significant effect on accuracy, success timing and stability of object selection in gaze-based interfaces. The use of linear regression for predicting gaze points and correcting drift of eye movement has improved overall performance of object selection .In future, our results maybe used as a guideline for developing more accurate and more stable interactive applications based on smooth pursuit eye movement.
Penerapan Sistem Pengendalian Intern atas Persediaan Barang pada PT.Mica Jaya Pratama

This study aims to find out how the application of internal control over inventory of goods at PT Mica Jaya Pratama Solo. This research is a qualitative research that is by conducting both structured and unstructured interviews to determine the internal control carried out by the company. In this study the variables are the principles of internal control such as adequate separation of duties (organizational structure), determination of adequate responsibilities (system of authority), design and use of documents and adequate records (healthy practices), physical control of assets and records adequate (healthy practice), independently verify performance (HR). The result of this study is that the inventory control system at PT. Mica Jaya Pratama is in accordance with the SOP, but in an adequate separation of duties there is no division between the purchasing division and the finance division.
Future of Job Market in the Fourth Industrial Revolution

In the fourth industrial revolution era, artificial intelligence (AI) technologies are rapidly developed and will influence our lives and societies in many ways.In addition, AI technologies can work together with other technologies such as the internet of things, 3D printing, block-chain, and quantum computing, that will make dramatic and rapid changes in society, which people may not be fully prepared for.With these new technologies, millions of jobs will likely be replaced, but there is also an excellent opportunity for new types of jobs to be created.For this reason, this paper will discuss the changes and challenges of the job markets in the future.This paper will particularly explore what kinds of jobs, in which robotics and AI technologies could replace human workers.Besides, this paper will discover some of the soon available AI-related jobs and explore what skills that humans need to develops to become a more attractive candidate to employers in future job markets.
Pengukuran Pertumbuhan Volume Data Pada Sistem Informasi Pondok Pesantren

Islamic boarding school information system is a system that can launch a variety of boarding school management processes with available features. The boarding school system also facilitates all aspects involved in the estimation and management of the pesantren. Which aims to find out ways and get important information related to a boarding school. Growth related to the problem of growth measurement will change a volume of data to the growth of large changes in number and size. Regression is a statistical analysis method used to see the effect between two or more variables. These variables are connected with functional properties that are realized in a mathematical model. This paper explains in detail how to measure the growth of the volume of data available in Islamic boarding schools. Measurement of data volume growth in the Islamic boarding school information system consists of metric complexity, number of transactions and the size of the data. The three components are then processed through multiple linear regression and will then produce a picture in the form of modeling that can later show predictions of data volume growth.
A Note on Three Approaches to Connexivity

In this paper I explore whether three different approaches to connexivity -namely, Lewis and Langford's, Wansing's and Egré and Politzer's -can be non-trivially related.Lewis and Langford's approach consists of the definition of the conditional in terms of consistency via the definition of possibility in terms of consistency and Lewis' definition of the (strict) conditional in terms of possibility; Wansing's approach is based on his definition of the falsity condition for the conditional as "if A is true then B is false"; Egré and Politzer's approach relies on the modalization of the falsity condition for the conditional in its conditional form (A→◇~B).With this background in mind, I build upon Estrada-González's work relating connexivism and possibilism, the thesis according to which everything is possible.Estrada-González's work relates Lewis and Langford's connexive approach with Nelson's idea that every proposition is self-consistent.He does so using a fragment of LP augmented with a special conditional.This relation has resulted in the study and postulation of several truth-functional modalities, some of which have already been discussed elsewhere.I employ several truth-functional modalities to evaluate Egré and Politzer's falsity conditions for the conditional.I show that in using Béziau's truth-functional modality ◇ B , definable in Estrada-González's framework, one can retain in a single framework the three different approaches.More specifically, distinguishing the notion of logical equivalence from the notion of interderivability and these two from the notion of coimplication, it is possible to relate ~(A→B) and (A→◇~B) as interderivable schemas and in a valid biconditional.I also show some consequences of this for Béziau's negative appraisal of three-valued truthfunctional modalities.
The Impact of High Involvement Management on Employee Retention

The purpose of this study is to explore the impact of high involvement management (power, information, rewards, and knowledge) on employee retention in Syrian Private Financial Institutions. A sample of 238 employees participated in this study. The research concluded that there is a significant positive impact of rewards and knowledge practices on employee's retention, without any significant impact from power and information practices on employee's retention.
Gibbs Sampling Subjectively Interesting Tiles

The local pattern mining literature has long struggled with the so-called pattern explosion problem: the size of the set of patterns found exceeds the size of the original data. This causes computational problems (enumerating a large set of patterns will inevitably take a substantial amount of time) as well as problems for interpretation and usability (trawling through a large set of patterns is often impractical). Two complementary research lines aim to address this problem. The first aims to develop better measures of interestingness, in order to reduce the number of uninteresting patterns that are returned [6, 10]. The second aims to avoid an exhaustive enumeration of all ‘interesting’ patterns (where interestingness is quantified in a more traditional way, e.g. frequency), by directly sampling from this set in a way that more ‘interesting’ patterns are sampled with higher probability [2]. Unfortunately, the first research line does not reduce computational cost, while the second may miss out on the most interesting patterns. In this paper, we combine the best of both worlds for mining interesting tiles [8] from binary databases. Specifically, we propose a new pattern sampling approach based on Gibbs sampling, where the probability of sampling a pattern is proportional to their subjective interestingness [6]—an interestingness measure reported to better represent true interestingness. The experimental evaluation confirms the theory, but also reveals an important weakness of the proposed approach which we speculate is shared with any other pattern sampling approach. We thus conclude with a broader discussion of this issue, and a forward look.