Reviewerly/exHarmony huggingface dataset:
상황맥락 제시를 통한 ‘은/는’과 ‘이/가’ 교수 방안 -중국인 초급 학습자를 위한 절충적 접근법으로

A study on a method of teaching ‘eun/neun’ & ‘i/ga’ in situational context: by eclectic approach for Chinese beginner learners. Korean particles ‘eun/neun’ and ‘i/ka’ are the first grammatical forms Korean language learners encounters, which daily usage shows very higher error frequency. In order to resolve Chinese beginner learners'' errors regarding usage of ‘eun/neun’ and ‘i/ka’, this study suggests the situational context must be presented in the method, so that the learners can understand the meaning of the discourse which contains ‘eun/neun’ and ‘i/ga’. To this end, firstly this paper surveyed the theoretical backgrounds of the eclectic approach. And then, from the reviews of the previous study, this paper found various semantic functions of them that need to be taught to beginner learners; such as the markers of topic and contrast by ‘eun/neun’, the markers of subject and option by ‘i/ka’ and the distinctions of semantic function between ‘eun/neun’ and ‘i/ka’ such as uncertainty vs. specificity and old information vs. new information. The significance of this paper is to apply the eclectic approach for presenting situational context proper to teach each semantic functions of ‘eun/neun’ and ‘i/ka’ for Chinese beginner learners.
Efficient data augmentation using graph imputation neural networks

Recently, data augmentation in the semi-supervised regime, where unlabeled data vastly outnumbers labeled data, has received a considerable attention. In this paper, we describe an efficient technique for this task, exploiting a recent framework we proposed for missing data imputation called graph imputation neural network (GINN). The key idea is to leverage both supervised and unsupervised data to build a graph of similarities between points in the dataset. Then, we augment the dataset by severely damaging a few of the nodes (up to 80\% of their features), and reconstructing them using a variation of GINN. On several benchmark datasets, we show that our method can obtain significant improvements compared to a fully-supervised model, and we are able to augment the datasets up to a factor of 10x. This points to the power of graph-based neural networks to represent structural affinities in the samples for tasks of data reconstruction and augmentation.
The shift towards democratic governance: System change versus regime change analysis in The Gambia 2017-2019

Democratic governance has been seen as a catalyst for inclusive growth and meaningful development in many countries. Shifting from authoritarian rule to more democratic rule has also been seen as essential for many governments worldwide. Democratic governance is often a problem for developing countries, including most African countries and particularly The Gambia. This paper analyses the shift towards a more democratic governance style that is heated debated in The Gambia. The debate started when one of the coalition government members asserted that they could achieve regime change; however, a democratic system change remains lacking. This paper has sought to analyze whether the shift toward a more democratic leadership style in The Gambia is one of system change or regime change and ascertain what has democratically changed and bad governance in the new government. This article’s main objective is to create political awareness and enlighten the readers on the change’s misconceptions towards democratic governance. The discussion focuses on democratic governance and the collective action theory of governance to explain the democratic process in New Gambia. This study adopts qualitative case study research methods; the research employed a systematic review of the existing scholarly journal articles, books, newspapers, and television interview recordings. The observation used to identify, understand, and interpret the democratic governance situation in The Gambia from 2017 to 2019. The findings show that the coalition government has succeeded in achieving a democratic regime change; however, the democratic system change itself is too slow, or it is not happening. For the first time in The Gambia’s history, a seating president defeat through the ballot box. In conclusion, the democratic governance system change is far-fetched. It is vital to state that there is little or no difference between Jammeh’s regime and the current regime under President Barrow’s leadership. The same problems continue to persist. Finally, the transparency and accountability mechanisms must be enhanced to address the endemic problem of corruption.
Filtering precipitation through soil

Abstract The article studies the process of filtering precipitation through soil. Two mathematical models are obtained and used, specifically, deterministic and stochastic ones. The basis of these tasks is a diffusion parabolic partial differential equation supplemented by initial-boundary conditions. The findings are used in solving the stochastic problem. The mathematical analytical formulation of the problem using the finite difference method is reduced to an inhomogeneous linear algebraic system of equations, which is implemented in the environment of the Matlab computing complex. Consideration of the task operator linearity and the principle of superposition reduced the amount of computation significantly. A number of conclusions were drawn on the basis of solutions.
Pedagogical conditions for implementing value and sense direction of training mathematics at higher school

The article actualizes the issue of updating the content, methods and means of teaching mathematics at the university within the sociocultural paradigm. A significant contradiction characterizing the crisis situation in the field of higher mathematical education is the mismatch between the traditional organization of the educational process and the powerful developing potential of mathematical disciplines. Being overloaded with a lot of information, altogether with its insufficiently developed anthropological, cultural-like and communicative components, mathematical education hinders the mental development of the learner’s personality in relation to such important qualities as search activity, creativity, and creative thinking. The solution to this problem can be the transformation of the cognitive-information model of learning, the introduction of pedagogical technologies that actualize the sociocultural aspect of mathematical education. The aim of the study is to develop methodological foundations for the implementation of the value-semantic orientation of teaching mathematics at the university, expressed in providing a set of pedagogical conditions related to the selection of content, determination of teaching aids and methods, ways of organizing the interaction of students and a teacher, in which students intelligently master mathematical concepts, and freely operate with them. The didactic conditions for the implementation of the value-semantic orientation of teaching mathematics at the university are: the transformation of mathematical content, expressed in learning from sociocultural experience; the psychodidactic approach, focused on building the students’ self-motivation; the use of teaching methods that provide cognitive and emotional empathy (educational mathematical discourse), the activization of productive mental activity (technology of problematic dialogue); inclusion of non-standard, creative tasks, training cases. Providing these conditions will allow to realize the humanitarian potential of mathematics, to reveal the social, practical and personal significance of the subject matter.
高速かつ省メモリなドライバ姿勢推定

Driver pose estimation is a key component in driver monitoring systems, which is helpful for driver anomaly detection. Compared with traditional human pose estimation, driver pose estimation is required to be fast and compact for embedded systems. We propose fast and compact driver pose estimation that is composed of ShuffleNet V2 and integral regression. ShuffleNet V2 can reduce computational expense, and integral regression reduce quantization error of heat maps. If a driver suddenly gets seriously ill, the head of the driver is out of view. Therefore, in addition to localizing body parts, classifying whether each body part is out of view is also crucial for driver anomaly detection. We also propose a novel model which can localize and detect each body part of the driver at once. Extensive experiments have been conducted on a driver pose estimation dataset recorded with near infrared camera which can capture a driver at night. Our method achieves large improvement compared to the state-of-the-art human pose estimation methods with limited computation resources. Futhermore, We perform an ablation study of our method which composed of ShuffleNet V2, integral regression, and driver body parts detection. Finally, we show experimental results of each driver action for driver monitoring systems.
Study on Microscopic Simulation Technology of Operation Process of Unmanned Vehicle Based on Virtual Track

With the continuous development of unmanned driving technology, more and more unmanned vehicles are on the road to start testing, under this circumstance, how to control these unmanned vehicles has become an urgent issue. Based on the basic idea of virtual track and cellular automata model, this paper researches and designs a microscopic simulation system of the operation process of unmanned vehicles. The aim of this paper is to use the idea of strong controllability of railways to control unmanned vehicles in the form of train diagram to ensure their orderly safety on the road. The stability and correctness of the system are verified through case analysis. At the end of the article, the future research direction is prospected.
Less is More: Data-Efficient Complex Question Answering over Knowledge Bases

Question answering is an effective method for obtaining information from knowledge bases (KB). In this paper, we propose the Neural-Symbolic Complex Question Answering (NS-CQA) model, a data-efficient reinforcement learning framework for complex question answering by using only a modest number of training samples. Our framework consists of a neural generator and a symbolic executor that, respectively, transforms a natural-language question into a sequence of primitive actions, and executes them over the knowledge base to compute the answer. We carefully formulate a set of primitive symbolic actions that allows us to not only simplify our neural network design but also accelerate model convergence. To reduce search space, we employ the copy and masking mechanisms in our encoder-decoder architecture to drastically reduce the decoder output vocabulary and improve model generalizability. We equip our model with a memory buffer that stores high-reward promising programs. Besides, we propose an adaptive reward function. By comparing the generated trial with the trials stored in the memory buffer, we derive the curriculum-guided reward bonus, i.e., the proximity and the novelty. To mitigate the sparse reward problem, we combine the adaptive reward and the reward bonus, reshaping the sparse reward into dense feedback. Also, we encourage the model to generate new trials to avoid imitating the spurious trials while making the model remember the past high-reward trials to improve data efficiency. Our NS-CQA model is evaluated on two datasets: CQA, a recent large-scale complex question answering dataset, and WebQuestionsSP, a multi-hop question answering dataset. On both datasets, our model outperforms the state-of-the-art models. Notably, on CQA, NS-CQA performs well on questions with higher complexity, while only using approximately 1% of the total training samples.
SOME PROBLEMATIC ISSUES OF ENSURING THE ADMISSIBILITY OF ELECTRONIC EVIDENCE IN CRIMINAL PROCEEDINGS

Завидняк В.І., к.ю.н., доцент, доцент кафедри
An Efficient Blurring-Reconstruction Model to Defend Against Adversarial Attacks

Although deep neural networks have been widely applied in many fields, they can be easily fooled by adversarial examples which are generated by adding imperceptible perturbations to natural images. Intuitively, traditional denoising methods can be used to remove the added noise but the original useful information is eliminated inevitably when denoising. Inspired by image super-resolution, we propose a novel blurring-reconstruction method to defend against adversarial attacks which consists of two period, blurring and reconstruction. When blurring, the improved bilateral filter, which we call it Other Channels Assisted Bilateral Filter (OCABF), is firstly used to remove the perturbations, followed by a bilinear interpolation based downsampling to resize the image into a quarter size. Then, in the reconstruction period, we design a deep super-resolution neural network called SrDefense-Net to recover the natural details. It enlarges the downsampled image after blurring to the same size as the original one and complements the lost information. Plenty of experiments show that the proposed method outperforms the state-of-the-art defense methods as well as less training images demanded.
Mechanism for improving non-formal education in the Republic of Sakha (Yakutia)

This article deals with the problem of non-formal education and analyzes the results of implementing the concept of a mechanism for improving non-formal education in the Republic of Sakha (Yakutia). based on the traditions of the titular people. The author considers the state and prospects of development of the project of the strategic development plan of the Republic, which has three directions: education, training and health improvement.
A Cognitive Model of Morphological Neural Network

In this paper, a morphological neural network (MNN) cognitive tree model related to multi disciplines is proposed. The model has four layers: soil layer, primary layer, growth layer and presentation layer. Through the study of MNN at different levels, the cognitive function and mechanism of MNN are profoundly revealed, and the theoretical framework of MNN cognition is established. This paper can be seen as an example of multi-disciplinary crossover and fusion research, which not only helps to improve the understanding of MNN itself, but also brings some inspirations to promote the interdisciplinary research and coordinated development of computer science, artificial intelligence, neurobiology, cognitive psychology and so on.
Teachers Ability on Information and Communication Technology in Industry 4.0 Era

Being a teacher in industrial 4.0 is demanded to adaptable to technological developments.This study aims to describe the understanding and ability of teachers of Vocational High Schools to use ICT in learning.This research is survey research.Research respondents were 142 Vocational Teacher of Computer and Information Technology expertise programs in South Sulawesi Province.The study was conducted from April to July 2019.Data were collected using a questionnaire that had previously passed the Validity and Reliability test.Data analysis was performed using descriptive analysis.The results showed that the teacher's understanding of hardware in the form of inputs, processes, and outputs was very well understood.However, they still lacked knowledge of artificial intelligence.The ability of teachers to utilize offline technology in learning is quietly better, but it still needs to be improved in terms of making mind maps, instructional videos, and graphic designs.The teacher is also familiar with the use of search engines, hotspots, e-mail, and LAN connected online.However, teachers have not optimized the use of databases, online learning, online video, online data storage and sharing.
PERANCANGAN ALAT BANTU SULAMAN DENGAN MENGGUNAKAN METODE DESIGN THINKING DI INDUSTRI RUMAH TANGGA SULAMAN KAPALO SAMEK YUSNETTI

The home industry of Kapalo Samek Yusnetti embroidery has been established since 2012, this home industry produces embroidery fabrics consisting of 3 products, salendang, baju muslim and jilbab. The are production activities carried out, one of these activities has problems in the process of printing the patterns, this problem lies in the part of the length of time for the printing of the patterns, especially in salendang products which can take 2-3 hours / piece with the total working time per day of 5 -6 hours/day. Activities that take a lot of time to cause disorders in the organs of. This work is categorized as dangerous, this work is included in the dangerous category, that is needed to solve this problem by designing a tool to eliminate the process of printing patterns To start the design process, the design thinking method is used. So for this design thinking method, the first step starts from the empathy stage, which is then continued with define, ideas, prototypes and trials. By going through the stages of the design thinking method, a tool in the form of a portable patterns mall is designed to solve problems that arise where the work of printing motifs is no longer done.
Finding key points in a hand image using heatmaps

Abstract This paper presents an approach to finding key points in the image of a hand using heat maps. For this, a convolutional model of a neural network and a training method on heatmaps were combined. An open bank of palm photos, supplemented by a set of own images and synthetic data, was used as a dataset. Direct and inverse transformations of two-dimensional coordinates on the plane into heat maps with a 2D Gaussian function centered at coordinate points were applied, data was prepared, the model was trained and tested. As a result of this approach, a neural network model able to recognise real world images was obtained.
IDENTIFYING THE POINT PLEASANT FORMATION’S CONDENSED INTERVALS WITH XRF CHEMOSTRATIGRAPHY

The Point Pleasant Formation is a major unconventional drilling target in eastern Ohio; however, obstacles arise from its highly variable total organic carbon (TOC). This research focuses on mapping the organic-rich condensed intervals (CI) of seven wells, using a sequence stratigraphic framework. The Point Pleasant Formation is problematic for traditional methods of sequence stratigraphy, due to the complex nature of mixed carbonate-siliciclastic systems and a lack of distinct facies changes within the monotonous section. These issues have been circumvented with the use of geochemical analyses. Relative elemental abundances are measured with a handheld X-ray fluorescence (pXRF) spectrometer. Strata displaying unique geochemical trends have been grouped into chemofacies. The trends observed come from proxies for terrigenous sedimentation (Ti, Zr and Rb), marine sedimentation (Ca and Sr), clay sedimentation (Al and K), upwelling (P), pyrite minerals (Fe and S) and biogenic quartz (Si/Al). The chemofacies present in the core suggest the presence of Lowstand Systems Tract (LST), Transgressive Systems Tract (TST) and the Highstand Systems Tract (HST). Condensed intervals are associated with the end of the Transgressive Systems Tract (TST) and capped by the Maximum Flooding Surface (MFS). Two cores and wireline logs of seven wells from eastern Ohio, donated by EMF Geoscience Inc. and Artex Energy Group LLC, are used to demonstrate that this pattern is mappable and laterally reproducible. Identifying sequence stratigraphic surfaces, along with the log and core analysis, will create a framework to identify the CI in the two wells with core and to predict the CI in the remaining five wells without core.
A Corpus for Argumentative Writing Support in German

In this paper, we present a novel annotation approach to capture claims and premises of arguments and their relations in student-written persuasive peer reviews on business models in German language. We propose an annotation scheme based on annotation guidelines that allows to model claims and premises as well as support and attack relations for capturing the structure of argumentative discourse in student-written peer reviews. We conduct an annotation study with three annotators on 50 persuasive essays to evaluate our annotation scheme. The obtained inter-rater agreement of $\alpha=0.57$ for argument components and $\alpha=0.49$ for argumentative relations indicates that the proposed annotation scheme successfully guides annotators to moderate agreement. Finally, we present our freely available corpus of 1,000 persuasive student-written peer reviews on business models and our annotation guidelines to encourage future research on the design and development of argumentative writing support systems for students.
The Satisfaction of Library Users Services at UPT Balai Informasi Teknologi (Research and Development Division for Technology Information)-LIPI

The big challenge facing the library is how to provide excellent services to meet the users'needs. In facing this challenge, the UPT Balai Informasi Teknologi LIPI library has madeimprovements in library services. To determine the effectiveness of these improvements, inSeptember 2018, a study was conducted. The research objective was to find out the users'satisfaction in receiving library services; the method used was descriptive research. Thenumber of respondents was 23 people. The research results showed that the services providedwere good enough, this can be seen from the respondents' answers that most respondentsexpressed satisfaction with the quality of the library collection, completeness of thecollection, easy access to the collection, service of library staff, and libraryfacilities/infrastructure. However, there were a small number of respondents who stated thatthey were dissatisfied with the ease of access to the collection and the services of librarystaff. Therefore, these two service groups should be of particular concern to the UPT BalaiInformasi Teknologi LIPI library manager to improve them, so that user satisfaction can beachieved.Keywords: Library, Library services, User satisfaction, UPT Balai Informasi Teknologi (Research and Development Division for Technology Information) LIPI.
EXPLORATORY DATAANALYSIS AND TOPIC MODELLING ON TED TALKS

TED (Technology, Entertainment, Design) is a non-profit organization that influences the audience across the globe to deep dive into thinking .The short, powerful talks in more than 100 languages, from great inspired achievers engage the curious people and change their way of perception about issues on science, entertainment, business, technology, global concerns and various other topics. Why do some TED Talks get more views, go viral? What makes a TED talk the change maker in outlook, attitude and behaviour? What intrigues and influences people? This paper aims to analyze the various drivers behind maximum view count of certain TED talks from the start of 2006 till the end of June 2020. The analysis takes into consideration various parameters such as the speaker’s profession, chosen topic, his/her transcript, number of views, comments, tags to name a few. This analysis will help an aspiring TED Talker identify the drivers and plan a video that will attract more view counts. This research paper uses Exploratory Data Analysis with a special emphasis on Natural Language Processing using the native Latent Dirichlet Allocation model from Gensim and the LDA Mallet. Analysis of the TED Talk data suggests that the content is a prime driving factor rather than the public speaking abilities thereby making the view count less predictable.