Reviewerly/exHarmony huggingface dataset:
Deep learning Architectures is a class of Machine Learning Algorithms

The human brain is consisted of billions of neurons which are unified by a mixture of synapses. If “enough” synaptic inputs to the neuron fires, then the neuron will also shoot. This progression is known as “accepted wisdom”. To replicate this procedure of philosophy on computers, we need to have a combination of machine learning and neural networks. This wide-ranging know-how of putting artificial brain into machine is in veracity fascinating, we humans for the reason that of our imaginative brain power has this natural instincts to go ahead of what seems not viable to craft tools and proficiency which becomes an additional room to our everyday verve, which can make decisions on our behalf and make our living tremendous well-organized. It is with this persevere on to make humans super productive the new paradigm shift have been started putting artificial intelligence to the computer machines and now time has come to go long mode to build machines which can practically believe like human brains.
High Order N-gram Model Construction and Application Based on Natural Annotation

The language model based on the n-gram grammar plays an important role in NLP tasks. In this paper, language models based on language boundary are proposed to conquer the challenge of the very big language data: intra-sentence boundary model and inter-sentence boundary model. We developed a training tool on the Hadoop platform based on MapReduce programming, and conducted the prefix tree to compress and store the model. We implemented our model in identifying the boundary in the syntactic parsing, achieving a good result.
Triggered attention model for scene text recognition

Scene text recognition has attracted the attention of many researchers owing to its widely application. Numerous methods have been proposed in this field and achieved unprecedented success. However, most of the datasets and algorithms are designed for English scene text recognition while only a few researches focus on the Chinese scene text recognition. Furthermore, despite the higher processing speed and ability of working as language model, attention-based mechanism suffers from the misalignment problem. Triggered attention model is proposed to tackle these problems. Taking character image as input, A triggered attention-based encoder-decoder network that outputs character sequence is proposed in this paper. Furthermore, an encoder network that takes colorful images as input is implemented to extract deep representations of input images. Experiments on various datasets show that the Triggered attention method substantially outperforms the existing attention based methods.
IMPLEMENTASI METODE SUPPORT VECTOR MACHINE DALAM PREDIKSI PERSEBARAN DEMAM BERDARAH DI KOTA BANDAR LAMPUNG

Dengue fever is a one of the dangerous diseases and very often causes casualties every year, especially in the tropics or subtropics countries. Dengue fever cases increase during the rainy season, many factors affect the spread of dengue fever, such as vegetation, population and landfills. The aim of this research is to predict the number of cases of Dengue fever using support vector machine. The data used are dengue data in Bandar Lampung City, weather data, population data and distance matrix data between dengue fever events with each other. The amount of data used is 1,080 data with 3 kernels: linear, gaussian and polynomial. In this study four experiments were carried out, the first two experiments were carried out without Feature Selection and the next two experiments were carried out with Feature Selection. After the experiment was found The best performance in the experiment with Feature Selection with 44 Variables. From the experiments conducted, Gaussian kernel achieved the highest R 2 value which is 75.52%, while the Linear kernel and Polynomial Kernel achieved R 2 value of 74.61% and 75.15%, respectively
The Optimal Time Typing to Identifying Users at the Keyboard Handwriting

This article deals with the problem of ensuring protection against unauthorized access to data by identifying users according to biometric characteristics. The article deals with the issue of choosing the optimal time of typing. This question is relevant when identifying the user by keyboard handwriting. Typing too long can be tiring for the user. Too short a time can lead to incorrect identification and errors of the first and second kind. The article presents the results of experiments in which users type text 10, 20, 30 seconds. The results show that the optimal set time is 20 seconds. This time does not cause significant fatigue, at the same time in 20 seconds the handwriting becomes stable enough to identify the user.
Knowledge Graph Embedding Based On Multi-information Fusion

The knowledge graph embedding aims to represent the elements in the knowledge graph as low-dimensional dense distributed representation vectors in the continuous semantic space, which has achieved good results in the knowledge graph completion. However, the existing embedding methods do not make full use of and extract the existing information in the knowledge graph, which will directly affect the quality of knowledge graph embedding. In order to make the embedded model can fuse more reasonable constraints and rich semantic information, in this paper, the entities in the traditional triples are divided into concepts and entities, and through the conceptual constraint information generated by the entity, the structural information of the triple and the entity description text information encoded by the deep learning, the hidden relationship between the existing entities in the knowledge graph is mined, and a more accurate semantic space representation is obtained. Experiments show that the embedded model proposed in this paper has better performance.
MEASURING SIMILARITY BETWEEN BIOMEDICAL DATA BY USING FURIA ENSEMBLES RULE-BASED CLASSIFICATION

Similarity measures are essential to solve many pattern recognition problems such as classification, clustering, and retrieval problems. Various distance/similarity measures that is applicable to compare two probability density functions. Data comparison is widely used field in our society nowadays, and it is a very import part. To compare two objects is a common task that people from all walks of life would do. People always want or need to find the similarity between two different objects or the difference between two similar objects. Some different data may share some similarity in some given attribute(s). To compare with two datasets based on attributes by classification algorithms, for the attributes, we need to select them out by rules and the system is known as rule-based reasoning system or expert system which classifies a given test instance into a particular outcome from the learned rules. The test instance carries multiple attributes, which are usually the values of diagnostic tests. In this article, we are proposing a classifier ensemble-based method for comparison of two datasets or one dataset with different features. The ensemble data mining learning methods are applied for rule generation, and a multi-criterion evaluation approach is used for selecting reliable rules over the results of the ensemble methods. The efficacy of the proposed methodology is illustrated via an example of two disease datasets; it is a combined dataset with the same instances and normal attributes but the class in strictly speaking. This article introduces a fuzzy rule-based classification method called FURIA, to get the relationship between two datasets by FURIA rules. And find the similarity between these two datasets.
The Effect of Project based Learning using Cyber Science Museum on Elementary School Students’ motivation for Science Learning and Attitude toward Science

The purpose of this study is to investigate the effect of project learning using cyber science museum on elementary school students" motivation for science learning and attitude toward science. For the study, 30 students from G grade 4 elementary school in B Metropolitan city were selected for the study using "National Science Museum", "National Gwacheon Science Museum", "Busan Marine Natural History Museum", "National Geological Museum" The course was held in about 2 months. In this study, the following conclusions were obtained. First, project learning using the Cyber Science Museum was found to be effective in enhancing elementary school students" motivation to learn science. Second, project learning using the Cyber science museum was found to be effective for elementary school students" attitude toward science. Third, elementary school students showed a positive response to project learning using Cyber Science Museum. It is considered that the project learning using cyber science museum positively influenced elementary school students" motivation to learn science and attitude toward science.
Research on a Blended Teaching Method by OBE Model

Recently OBE theory has been adopted in the educational reform to overcome the current problems in classroom teaching.The aim is to help students learn as much as possible through various methods, such as the combination of online tasks with classroom activities.This survey is to prove the effect of online learning on English majors in China.A goal must be designed before the course is set.Besides classroom teaching, the exercise of words and grammar, listening, reading, writing and translation can be turned into online tasks for students to practice.And the extra tasks are welcome in that cellphone is available to all and is our best friend.Owing to the high standard of the online tasks, once students are able to complete them, they will get high grades.After two months of learning, they become accustomed to a new way of teaching.Moreover, their abilities have been promoted to a higher level.To a large extent, they tend to actively participate in classroom activities.Even without the aid of cellphones, they aren't indifferent to learning and teaching any longer.Only in this way are they involved in classroom activities.In a word, the blended teaching method is meaningful and constructive for students to achieve the goal.Therefore, it is expected that this research can arouse lecturers' inspiration to make up more appropriate tasks and facilitate students to recapture their love in learning.
EUPHEMIZATION AS PART OF RUSSIAN MEDIA OPPOSITION DISCOURSE

Opposition discourse does not have a long history or established traditions in Russia as it does in the West where it has long become an integral part of political culture and plays a special role in modern mass communications. This paper raises the problem of risks associated with the danger of critical journalism being branded extremism on the one hand and violating the law prohibiting journalist using vocabulary not part of the Russian literary standard, on the other. It presents the results of content analysis of media texts that use a variety of expressive means of veiling sharp criticism of Russian government and mitigating emotions such as euphemisms. Considerable attention is paid to queering as an aspect of the verbal picture of an anti-world. A case study of Novaya Gazeta, the most consistent critic of Russian government, illustrates the impact recent legislation on extremism and the use of the Russian language has had on journalism in Russia and examines the ways opposition media have employed to comply with the regulations.
PRAGMATIC TASKS AND THEIR RESULTS OF LANGUAGE SELECTION, CODE-EXCHANGING, AND NON- EXCHANGING OF SOCIATAL ENCODING AND DECODING OBSERVATION

Pragmatics is a branch of Semiotics which interprets the relation between symbols and signs.People's utterances whereas interacting have completely different meanings in step with the circumstances and therefore the intentions that speakers try and convey to a observer, whereas the same person might interpret and even get affronted due to the dearth of knowledge concerning the topic.It's a field studied by linguistics however, the majority study of this paper elucidates the indispensible effort that ought to be created by speakers once conveyance a concept by adding a lot of details doable so as to stop insulting.The paper deals with all options of linguistics together with speech acts, reference and reasoning, supposable and illation, cooperation and implication, additionally discourse and culture.Varied samples are given on every feature within the most applicable manner doable to gift the explanation for insults that will occur while the individuals are interacting.It also deals with the six functions of the language towards the coding scheme.The six functions involved are: the referential function, the directive function, the expressive function, the phatic function, the metalinguistic function and the poetic function.It also elucidates the factors influencing language choice and code-switching.
Improving The Students’ Listening Skills Of Eleventh Grade Students Of Madrayah Aliyah Tahfizhil Quran Yayasan Islamic Centre Sumatera Utara Through Computer Assisted Language Learning (Call) Method.

The aim of this research was to improve the students’ listening skill of Eleventh Grade students at Madrasah Aliyah Tahfizhil Quran Sumatera Utara in the Academic year of 2019 using CALL method. This research was classified into an action research. It was conducted in two cycles with two meetings for each cycle. The main subject of this research were 27 students of class Eleventh IPA I at Madrasah Aliyah Tahfizhil Quran Sumatera Utara. The data obtained were qualitative and quantitative. The qualitative data were gained from the observation, interviews and discussion with the collaborator. The data were transformed into interview transcript and field notes. Meanwhile, the quantitative data were gained from the listening tests that were conducted before the implementation and after the implementation in Cycle I and Cycle II. The action implemented in this research were using CALL method, giving more explanation in English, using LCD projector, laptop, speaker and providing activities that enable the students to enrich their vocabulary. The result of the research showed that the use of CALL method in the English teaching and learning process was effective to improve the students’ listening skill. There was an increase in the means score from 11,1 % in Pre-Test, in Post-Test I 59,9 % and Post-Test II 88,9 %. There were some good improvements in some aspects such as their familiarly in listening to English words and their vocabulary. Moreover, CALL method successfully caught the students attention.
EXPLORING STUDENTS’ INTEREST IN LEARNING ENGLISH AT SMP NEGERI 8 KOTA JAMBI

The purpose of this survey method research is to discover the type of the students’ interest and the factors that influence the students’ interest during learning English. A questionnaire was used as the instrument to 79 second graders at SMP Negeri 8 Kota Jambi from random classes. The result of the distributed questionnaire was computed and described into a presentation and diagram total score descriptively. The finding of this research revealed that 30 students as 37.53% population the students’ interest was categorized into Medium which internal factors dominated the students’ interest. It is found that joy, psychology and attention are the dominant factors that influence the students’ interest. Which is in the internal factors, the interest is influenced from the inside. Based on this research the students are indicated have lack of enjoyment during the class. While the teachers’ methodologies and activities also were indicated as the dominant factors of external factors.
Factors Affecting Library Anxiety of Graduate School Students in Gadjah Mada University

Background of Study: The library has an important role in supporting the process of education and research in a university. Students need a library as a place to study, discuss, work on assignments, or do research. But sometimes students who visit the library feel uncomfortable and confused about the condition they are facing that is usually known as library anxiety.Purpose: This study aims at identifying library anxiety among Graduate School Students in Gadjah Mada University, and examine several factors considered to affect library anxiety, namely barriers to service by librarians, affective, library comfort, knowledge of the library, mechanical/technological, information retrieval, regulation, resources.Method: This is aquantitative deductive research, using a survey approach. The population were Graduate School Students at Gadjah Mada University, with a total sample of 60 students taken by purposive sampling technique. Data were collected using questionnaires. The data were analyzed using smartPLS 3.0.Finding: The results showed that 95% of Graduate School students at Gadjah Mada University experienced library anxiety. There are 8 factors that affecting library anxiety of Graduate School Students in Gadjah Mada University, namely barriers to service by librarians, affective, library comfort, knowledge of the library, mechanic/technology, information retrieval, regulation, resources.Conclution: The library anxiety experienced by Graduate School students in Gadjah Mada University is 100% influenced by those 8 factors tested in this study.
Semantic and Morphological Information Guided Chinese Text Classification

Recently proposed models such as BERT, perform well in many text processing tasks. They get context-sensitive features, which is a good semantic for word sense disambiguation, through deeper layer and a large number of texts. But, for Chinese text classification, majority of datasets are crawled from social networking sites, these datasets are semantically complex and variable. How much data is needed to pre-train these models in order for them to grasp semantic features and understand context is a question. In this paper, we propose a novel shallow layer language model, which uses sememe information to guide model to grasp semantic information without a large number of pre-trained data. Then, we use the Chinese character representations generated from this model to do text classification. Furthermore, in order to make Chinese as easy to initialize as English, we employ convolution neural networks over Chinese strokes to get Chinese character structure initialization for our model. This model pre-trains on a part of the Chinese Wikipedia dataset, and we use the representations generated by this pre-trained model to do text classification. Experiments on text classification datasets show our model outperforms other state-of-arts models by a large margin. Also, our model is superior in terms of interpretability due to the introduction of semantic and morphological information.
Research on the Hidden ‘De’ in Basic Noun Compounds Based on the Large-Scale Corpus

Basic noun compounds refer to phrases with nominal functions composed of two nouns. The study on the concealment of “de” in basic noun compounds is helpful to discover the implicit knowledge of noun and noun combinations, and the transformational rules between “N-N phrases” and “N-de-N phrases”. Scholars have described the hidden “de” from different perspectives, but no statistical analysis has been carried out from a large-scale corpus. This paper takes newspapers in Dynamic Circulation Corpus (DCC) for ten years as the corpus source and extracts basic noun compounds from it. We find out part without “de” in corpus by searching corpus for verification and after that, summarize types of basic noun compounds from two levels of syntactic structure and semantic relationship. We also analyze the reason for this phenomenon.
Development of massage learning support system using a pneumatic soft device with position sensors

Massage is used for various purpose. But recently, the deterioration of the quality of the massager is regarded as a problem. Therefore, quantification of massage technique is important. In this research, the purpose is development of the massage learning support system using a pneumatic soft device with position sensors. We use pneumatic soft device as an arm model. We use pressure sensor and position sensor for sensing of force and position. And we use “OpenGL” for displaying visually easy to understand. The Basic experiment of the massage device shows effective possibility. In this paper, we will describe the system configuration.
TG Network: A Model that More Effectively Identifies the Use of the Auxiliary Word “DE”

In the knowledge base of function word usage of “trinity”, the auxiliary word “DE” has the characteristics of high frequency and flexible usage. In this paper, a neural network model (TG network) is proposed to automatically recognize the usage of “DE”. In this network, the self-attention mechanism is firstly adopted as the first-layer feature encoder and GRU (gated recurrent unit) as the second-layer semantic extractor, and the recognition accuracy rate reaches 82.8%. Experiments show that the recognition effect of TG network is better than that of previous methods. In further experiments, the larger the window, the better the effect of the model is proved by setting different windows. At the same time, the fine-grained analysis of each usage category is carried out. In the future, it is expected that this model will automatically recognize more function words and the recognition results can be applied to other natural language processing tasks.
Video Signal Formats for HDTV and UHDTV

From the 1950s up until about 2002, analog and digital TV had a resolution of 625 or 525 lines. This format is also called Standard Definition Television or SDTV. Many countries, however, are already broadcasting in High Definition Television - HDTV - format. This increases the number of visible lines to 1080 and the number of visible pixels to 1920 per line. And it’s not over yet with HDTV: Ultra High Definition, providing an even sharper TV screen picture, is already coming.
The Use of Assessment Based on Curriculum to Increase Reading and Writing Skills for Blind Children in Inclusive School

This study aims to determine the effectiveness of the use of Assessment Based (on) Curriculum (ABC) in the intervention of reading and writing Braille for blind children in inclusive schools.This paper used Single Subject Research (SSR) with Multiple Baseline Cross Variable design as the research method.The result of data analysis shows the mean after giving of Assessment Based Curriculum in the intervention phase of writing ability, writing sentence, reading the word and reading the subject sentence is 89.27%, 89.89%, 89.34% and 81.77% significant increase.
Simulated Annealing for Timeliness and Energy aware Deep Learning Job Assignment

In this paper, we propose a simulated annealing method for timeliness and energy aware deep learning (DL) job assignment. In the proposed method, we make a decision of server position for DL training jobs in order to consider both the timeliness of deep neural network (DNN) model updating and the associated energy consumption. For timeliness management, we design three penalty functions; step, linear, and exponential functions, so as to practically penalize the service quality degradation of DL inference due to delay of DL training. For energy management, we formulate the operation cost optimization problem considering energy consumption for DL training. By using these management schemes, our method is able to find the trade-off of the timeliness and energy consumption of DL training. Especially, in order to overcome the non-smoothness of the defined problem, we design a simulated annealing (SA) based metaheuristic which finds approximated optimal solution for DL training job assignment. For the performance evaluation, we show the preliminary experimental results of DL training jobs with AlexNet, Inception-V3, and ResNet on NVIDIA GPU devices.