Reviewerly/exHarmony huggingface dataset:
페어로 공기하는 일본어 형용사의 의미관계 고찰 ―반의관계를 중심으로―

There are vocabularies that are inversely related to each other in Japanese adjectives such as ‘大きい↔小さい’ ‘うまい↔まずい/下手’ ‘美しい↔醜い/汚い’This paper examines the meaning and characteristics of semantics based on the semantics of Japanese adjectives appearing in pairs in a sentence in corpus language data.Based on the adjective adjectives of the basic Japanese learning dictionary(『基礎日本語學習辭典』), the anonymous adverbial information of the adjectives in the antonym dictionary is firstly grasped, and the pair relations appearing in the actual text are classified into the opposing semantics and the contrast semantics.The relationship between opposing semantics is again classified into the relationship between polarity opposing semantics and the degree of opposition semantics. The contrasting semantics are classified into the relation between contrasting semantics of color and contrast of semantics of taste, and semantic characteristics Were analyzed.The characteristics of the adjective pair appearing in relation to the polarity opposition tend to have a stable semi-relationship which is generally presented in the comparative antonym dictionary information. We can see that the two adjectives show relatively clear semantics in the door due to complementary and mutually exclusive relationship. On the other hand, in the case of the relationship between degree of opposition, there is a feature that appears as a combination of adjectives indicating the degree of evaluation or the meaning of evaluation attribute.On the other hand, adjectives representing the meaning of colors or tastes are generally not presented in the antonym dictionary. Actually, in the corpus language data, examples that are used as a pair in the relation of color contrast ratio and contrast of taste were examined.
Sequence Clustering Techniques in Educational Data Mining

Digital technology has profound impacts on modern education. Digital technology not only greatly improves access to quality education, but it also can automatically save all the interactions between students and computers in log files. Clustering of log files can help researchers better understand students and improve the learning program. One challenge associated with log file clustering is that log files are sequential in nature, but traditional cluster analysis techniques are designed for cross-sectional data. To overcome this problem, several sequence clustering techniques are proposed recently. There are three major categories of sequence clustering techniques: Markov chain clustering, sequence distance clustering, and sequence feature clustering. The purpose of this chapter is to introduce these sequence clustering techniques and discuss their potential advantages and disadvantages.
Research on Content of User Complaint Classification Based on Data Mining

Aiming at the online complaint content of Chinese websites users, we propose a user complaint classification model based on data mining. In this paper, we first use Bayesian network to segment Chinese words, then extract the emotional semantic features and content features of user complaint content, and finally construct the complaint content classification model by K-means clustering algorithm. Experiments are carried out on the open comment data set, and the validity of the user complaint segmentation and classification algorithm is verified by calculating the precision and recall rate of text segmentation and classification respectively. The experimental results show that this method has good classification effect and practical value.
Deep Recurrent Neural Network with Tanimoto Similarity and MKSIFT Features for Medical Image Search and Retrieval

The innovation of digital medical images has led to the requirement of rich descriptors and efficient retrieval tool. Thus, the Content Based Image Retrieval (CBIR) technique is essential in the domain of image retrieval. Due to the growing medical image data, the searching or retrieving a relevant image from the dataset is a major problem. To address this problem, this paper propose a new medical image retrieval technique, namely Multiple Kernel Scale Invariant Feature Transform-based Deep Recurrent Neural Network (MKSIFT-Deep RNN) using the image contents. The goal is to present an effective tool that can be utilized for effective retrieval of image from huge medical image database. Here, MKSIFT is adapted for extracting the relevant features obtained from acquired input image. Moreover, MKSIFT evaluates the key point descriptor using kernels functions, wherein the weights are allocated to kernels. The feature vectors are employed in the Deep RNN for classifying the images by training the classifier, which is considered as training phase. In testing phase, a set of query images is given to the classifier which adapts Tanimoto similarity for retrieving the images. The proposed MKSIFT-Deep RNN outperformed other methods with maximal precision of 93.723%, maximal recall of 93.652% and maximal F-measure of 93.687%.
Agency Journalism” Laboratory Classes as a Way of Developing Students’ Basic Skills to Work on News Content

The objective of the article is to represent the experience of conducting the practicals during the study of professional discipline “Agency journalism” by the first year students, as well as to generelaize it and to evaluate its effectiveness with regard to introduction of different teaching methods and techniques that will help future journalists to understand this subject more thoroughly and its significance in the process of creating media product. A number of methods, which are implemented during the classes and which contribute to creative expression of students and approaches are analyzed, the examples of their works performed in the audience are given. The current role of news agencies in the Ukrainian society is difficult to overestimate, since they are in fact considered to be the main “players” in shaping public information flows. Therefore, a wide network of these agencies – world, nationwide and regional – makes it possible to observe the so-called “agency boom” and influences significantly the expansion of their activities. This recovery is caused not only by the huge number of events that take place during an information day, but also by the great capacities of agencies to cover them with the use of various technological “innovations”, in particular audio and video materials, which was previously impossible. In recent years, there have been many changes in our society that have had a significant impact on the development of domestic media, which have a significant impact on the public. During the course of studying the discipline (and this is a whole semester), the students usually work out at least 10-15 events, as well as visit the leading news agencies of the country “Ukrinform”, “UNIAN”, “Interfax”, participate in the events, that take place on the platforms of these agencies; prepare about 25 notes during the semester, which the lecturer analyzes, write two module tests – one theoretical and creative test and one research test.
Nonlinear Activation in Deep Residual Networks

In deep neural network, the Deep Residual Network (ResNets) is very representative, which greatly improves the depth of the network and skillfully avoids the problem of gradient disappearance in the deep network. The network structure has been improved in convergence speed and accuracy. Some papers have analyzed the residual structure, including different branch structure, different function positions, forward propagation process, etc., and proposed preactivation and post-activation. This paper deeply analyzes nonlinear activation in the residual network, put forward the corresponding optimization scheme: New Conv block structure. At the same time this paper also did a lot of experiments, to validate the optimization scheme, the experimental results show that the classification performance of the proposed scheme on CIFAR-10 is improved, and classification accuracy of SlowFast Network on the UCF-101 is greatly improved.
Spam Filtering System Based on Nearest Neighbor Algorithms

In recent years, the email has become the most used way of communication and parcel of our lives due to its efficiency. However, an email is more vulnerable to exploitation, more precisely when we talk about spam. The identification of spam poses challenges. Thus, new algorithms have been investigated lately in order to filter spam. To deal with this problem, we propose a new approach for spam detection based on three Nearest Neighbor (NN) algorithms which are the most simple classifiers in machine learning techniques namely: K-NN, WKNN and K-d tree. To achieve a high performance we pre-processing our emails using some techniques of NLP before extracting features. After that we extract features using Bag-of-words (BOW), N-gram and Term Frequency-Inverse Document Frequency (TF-IDF). In this research paper, we provide a comparison of the three classifiers. The Experimental results have demonstrated that K-NN achieved a high performance based on four measuring factors namely: Precision, Recall, F1-score and Accuracy in both datasets Enron and LingSpam.
Research on Construction Method of Knowledge Graph-Based on Mobile Phone Quality Detection

Knowledge Graph is an effective method for data mining, storage, and display, The introduction of the knowledge graph in the field of mobile phone quality detection can quickly achieve the acquisition of knowledge such as detection methods and detection basis, thereby improving product detection efficiency. The problem of reduced information coverage and high labor costs for limited areas based on predefined relationship types. This paper proposes a relationship extraction method that depends on the semantic structure of the syntax. It analyzes the syntactic features between entities and uses the verb meaning feature as the core to extract the relationship. Finally, the extracted triples are stored in the graph database Neo4j to complete the construction of the mobile phone quality detection knowledge graph. Intelligent support for mobile phone detection process with the knowledge graph.
Hubungan Pemahaman tentang Surat-Menyurat terhadap Kemampuan Menulis Surat Dinas Siswa Kelas VII SMP Negeri 9 Oku

The ability to write student official letters is predicted to be influenced by an initial understanding of the material of the correspondence. For this reason, this study aims to: (1) find out the understanding of correspondence for Grade VII students of OKU 9 Public Middle School, (2) to determine the ability to write official letters for VII grade students of SMP Negeri 9 OKU, and (3) to find out the relationship between understanding of correspondence to the ability of Grade VII students of SMP Negeri 9 OKU to write official letters. The method used in this research is correlation method. The data collection technique used is a test. Data analysis technique used is descriptive statistical analysis techniques. Test results Based on the calculation of the correlation between the test data understanding correspondence with the ability to write official letter VII grade students of SMP Negeri 9 OKU obtained the value of r product moment at a significant level of 5% of 0.381 with rxy of 0.987. When compared to r arithmetic (rxy), then rxy is greater r product moment. That is, the alternative hypothesis (Ha) is accepted and the null hypothesis (Ho) is rejected. Thus, it can be concluded that there is a significant relationship between the understanding of correspondence with the ability to write official letters for eighth grade students of SMP Negeri 9 OKU.
Building Filters for Vietnamese Chatbot Responses

Automatic Chatbot is a developing trend in the world because of its convenience, efficiency, applicability in many fields such as E-commerce, customer care, and so on. Through Chatbot, we can cut down human resources at the place that Chatbot can replace, increase the convenience for both customer and company. Some good chatbot systems in the world primarily used in English. With Vietnamese, however, the outputs of these models are often not so good due to different structures and grammar. Some output (i.e. chatbot's responses) is incorrect or even misleading to the user. This can cause some serious problems in some legal or financial applications. In this paper, we proposed an approach to reevaluate outputs of general chatbot to reduce incorrect responses. Specifically, we firstly deployed well-known approaches in English chatbot into Vietnamese chatbot. After that, we build filters to assess chatbot responses based on the characteristics of the Vietnamese language. If the response is not suitable, the system will not give back to the user. In this case, the system can require more information to find a better response or at least return announcement instead of an incorrect response. We also developed a web-based application with chatbot integration to evaluate our approach in real life scenario.
Validation of audio-based solar system visual aid for special school students

Abstract Making and testing of miniature solar systems intended for students has been carried out and specifically for special school students. The purpose of making this tool is to help the learning process in students with special needs for science materials, especially in the solar system. The solar system visual aid consists of sun with its planets, including from Mercury to Neptune. Planets were designed to move automatically. In addition, this visual aid was also made audio containing explanations for each planet and also the sun. Validation of tools is carried out by material and media experts. The results of the material validation are good. While the media validation is sufficient and received well. This audio-based solar system visual aid is good enough to be used by special school students.
Faceted Exploration of Cultural Heritage

The richness of Cultural Heritage (CH) sites exposes tourists to an information overload which makes it difficult to efficiently select the items that they like and can practically visit within a tour.
MG-CMF: A Multi-granularity Capture Matching Features Model for Text Matching

There is a problem of lacking attention to the matching information between texts in text matching. In order to improve the accuracy, we propose multi-granularity capture matching features (MG-CMF) model to capture matching features from multiple granularities. The model uses convolution operations to construct the representation of text under multiple granularities, uses max pooling operations to filter more reasonable text representations, and builds matching matrices at different granularity. We use a convolution neural network (CNN) to capture matching features at the different granularity and input the captured matching features into the fully connected neural network to obtain the matching similarity. By making some experiments, we can get that the accuracy and $$F_{1}$$ values of the optimal experimental results on MSRP corpus are 73.60 and 82.20%, the mean average precision (MAP) and mean reciprocal rank (MRR) of the optimal experimental results on the WIKIQA corpus are 0.6821 and 0.6924. We draw a conclusion that the MG-CMF model is better than that of the other text matching models on accuracy.
Advances in earthquake-swarms monitoring networks WEBNET and REYKJANET

&amp;lt;p&amp;gt;The IG CAS in cooperation with IRSM CAS operates two local seismic networks deployed to monitor the seismic swarms in West Bohemia/Vogtland, Czechia and Reykjanes Peninsula, Iceland.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;WEBNET monitors the region of West Bohemia since 1991 developing from 4 short period stations to 24 broadband stations today. The seismoactive region West Bohemia/Vogtland lies in the border area between Czechia and Germany in the western part of Bohemian Massif. It is an intra-continental area with persistent swarm-like seismicity but rarely also main-shock after-shock sequences may occur.&amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;REYKJANET local seismic network is situated in Reykjanes Peninsula on Southwest Iceland. The area is an onshore part of the mid-Atlantic plate boundary between the North America and Eurasia Plates. The seismic activity of Reykjanes peninsula is represented by typical main-shock after-shock sequences as well as earthquake swarms. The REYKJANET network was built in 2013 and it consists of 15 stations placed around the epicentral area.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Both networks have been substantially upgraded during the last years. In case of REYKJANET the replacement of old sensors and digitizers with new ones made the operation easier and ready for near future plan to stream the waveform files in real time. WEBNET network which was long years divided into two subnets &amp;amp;#8211; on-line permanent stations and off-line autonomous stations, was recently homogenized by eco-powering and 4G LTE data connecting of the off-line stations. Additonally, the micro network HORNET was deployed within the WEBNET epicentral zone to monitor Horka water dam.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Data from both above mentioned networks are automatically searched for seismic events by the neural-network-based detector designed by Doubravov&amp;amp;#225; et al. (2016, 2019) providing event list with completeness magnitude Mc=0 for REYKJANET and Mc=-0.5 for WEBNET. The main difference of sensitivity is given by different noise levels of the two networks.&amp;lt;/p&amp;gt;
Effectiveness of the Blended Learning Approach in Teaching and Learning Selected EFL Grammar Structures at a University Level – A Case Study

Grammar seems to be the backbone of any foreign language learning, including English as a foreign language (EFL). Currently, the most common approach to teaching EFL, especially at a university level, appears to be a blended learning approach, which is a combination of traditional, face-to-face teaching and online learning. The purpose of this study is to evaluate the effectiveness of the use of the blended learning approach to teaching and learning selected grammar structures in a university Course of Practical English Language. The blended learning approach in this study reflects the use of traditional, face-to-face teaching and mobile learning, more specifically, the use of a mobile application, outside the classroom settings. The research methods of this article include a literature review of available studies on the research topic and statistical methods of the analysis and evaluation of the results of students’ pre- and post-tests, which focused on the selected grammar structures. The findings of both literature review and the statistical analysis indicate that the blended learning approach contributes to the effectiveness of student’s learning of the selected grammar structures since the results confirm that the success of teaching and learning depends on a variety of methods used.
Performance Analysis of Apache Spark MLlib Clustering on Batch Data Stored in Cassandra

With the tremendous increase in the amount of data being generated from variety of sources there is a need of efficient data storage and processing techniques. Some of the sources generating this large amount of data are Weather Sensors, Scientific experiments, etc. This huge voluminous data is termed as BigData. Due to ever-increasing amount of data there is a demand for faster data ingestion and processing. Apache Spark, a dominant processing tool is a publicly available platform for processing outsized data and is mostly intended for iterative machine learning jobs. In this study, an integrated approach i.e., Spark MLlib Clustering on batch weather data stored in Cassandra database is proposed. This helps to analyze our data into number of Clusters which is required and useful for further examination of data. The main idea of this study is to evaluate Batch Processing performance of an integrated approach with two popular clustering algorithms.
Extracting and Reconstructing Human Behavior Video Feature in High Voltage Exclusion Zone

Abstract A method is presented to the discrimination, extraction and reconstruction of human behavior in the area forbidden by high voltage, in particular, a method of scene reproduction of the accident scene when a high-voltage area endangers the power equipment injury or causes a personal property accident due to human behavior. To validate this method, an experiment was conducted on a small lake with high voltage transmission line over through. The Server video after the proposed method can be used to recognize human behavior, and transmission efficiency has been improved up to 35%.
SVMIRE - An Open Source SVM Image Retrieval with Relevance Feedback System For Earth Observation Data Classification

The continuous increase of Earth Observation image acquisitions requires new weakly supervised algorithms for classification and image retrieval. In this paper, we present the architecture of SVMIRE (SVM Image REtrieval with Relevance Feedback) which is a flexible, modular, and fast data mining system based on a relevance feedback approach that increases the performance of the Support Vector Machine (SVM) classifiers. The proposed system has the capability of storing and reusing the obtained classification model, and results. The functionalities of the SVMIRE system are tested on two datasets: one Landsat 8 OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) and one Sentinel-2 MSI (Multi-Spectral Instrument).
Application Research of Blockchain Technology in Electricity Information Collection

Data security is particularly important in the field of electricity information collection. This paper proposes a electricity information collection model that considers secure access and interaction based on blockchain technology. Base on this model, a strategy for safe access of electricity collection equipment and secure transmission of electricity measurement data is proposed. Finally, experimental analysis verified that the model and security transmission strategy proposed in this paper can be applied to the field of electricity information collection, which can effectively improve the reliability of electricity information data and collection equipment, and ensure the safety of the power grid.
Complex Hadamard diagonalisable graphs

In light of recent interest in Hadamard diagonalisable graphs (graphs whose Laplacian matrix is diagonalisable by a Hadamard matrix), we generalise this notion from real to complex Hadamard matrices. We give some basic properties and methods of constructing such graphs. We show that a large class of complex Hadamard diagonalisable graphs have vertex sets forming an equitable partition, and that the Laplacian eigenvalues must be even integers. We provide a number of examples and constructions of complex Hadamard diagonalisable graphs, including two special classes of graphs: the Cayley graphs over Zrd, and the non–complete extended p–sum (NEPS). We discuss necessary and sufficient conditions for (α,β)–Laplacian fractional revival and perfect state transfer on continuous–time quantum walks described by complex Hadamard diagonalisable graphs and provide examples of such quantum state transfer.