Reviewerly/exHarmony huggingface dataset:
Breast Cancer Risk Assessment Model Based on sl-SDAE

In recent years, the incidence of breast cancer among women in China has increased year by year and it has become the most common malignant tumor in women in China. There are already breast cancer risk assessment models for women in Europe and the United States. However, there is no effective breast cancer risk assessment model suitable for women in China. The paper established an effective breast cancer risk assessment model. It selected the survey data of breast cancer population in China as a data set. The paper combines SDAE and LSTM to build a model based on deep learning methods. It uses the roc curve as an indicator of the experimental results. Experiments show that the model has better performance than traditional machine learning algorithms.
Application of K-medoids clustering method for grouping corn plants based on productivity, production, and area of land in East Java

Abstract Corn plants has an important role in compliance national and international food needs after rice and wheat. According to Ir. Pending Dadih Permana, Director General of Food Infrastructure and Facilities (PSP) of the Ministry of Agriculture, East Java is one of province that the biggest corn producers in Indonesia. But it still cannot supply the increasing market needs in Indonesia. Therefore, it requires a grouping corn plants based on productivity, production, and area of land in East Java in order to increase level of procurement of corn plants in East Java. In process of grouping, it use K-Medoids algorithm. K-Medoids algorithm as known as Partitioning Around Medoids (PAM), which is variants of K-Means method. This is based on the use of medoids, not from observation of mean that owned by each cluster, with purpose reduce the sensitivity of partitions due to the extreme values in the dataset. This algorithm is an algorithm that can produce data that is not sensitive to outliers because one of object with some big value. These deviations can occur from data distribution. Based on the result of grouping corn plants based on productivity, production, and area of land which has been done, there are 3 clusters with at most 21 members, and at least 8 members. From this research, it can concluded that K-Medoids algorithm can help determine potential of the location of corn producing plants in East Java.
Using Support Vector Machine to Monitor Behavior of an Object Based WSN System

In this paper, we propose a new method to classify the bound error values of Kullback-Leibler Distance (KLD)-particle filter (PF) based Support Vector Machine (SVM) to reduce the mean number of particles used (sampling) as well as improve the performance of runtime in reality for monitoring an object. In wireless sensor network (WSN) system, the object location is calculated via the collected received signal strength (RSS) variations which are effected by furniture, walls or reflections. Therefore, we propose an architecture diagram to track an object and build the dataset model. By transforming the system state model from the 1D to 2D, the bound error value of KLD resampling can enhance estimation accuracy and convergence rate of declining number of particle used by generating a sample set near the high-likelihood region for ameliorating the effect of the RSS variations. Our proposal considers how to classify and find the bound error values of KLD PF for each iteration. The first iteration, using the observation information via KLD resampling optimal bound error to conduct a resampling on the basis of the initial bound error. From the second to the end iteration, we propose the SVM technique to search the predicted bound error value that fulfills the minimum of mean number of particle used between at the current and the next iteration. Our experiments confirm this technique to apply in reality system.
Intellectual System for Codicological Analysis of Manuscripts

Studying the differences in the graphic writing system, the developed information system provides automated recognition of graphemic variants in different genres of handwritten monuments written in the old Ukrainian literary and Church Slavonic languages. Functionality of the system is based on the cluster analysis method. The user-friendly interface and ease of use provide the ability to apply intelligent systems as socio communicative projects in the libraries and archives.
On Physical Web for Social Networks

The article discusses the use of Physical Web approaches for the expansion of social networks. This implies the presentation of data from social networks in a real (physical) context, as well as the inverse task of using information about a real physical context in querying and analyzing data from social networks. First of all, mobile phones of social network users are considered as real objects that will be used both in data dissemination and in gathering information about the context. In this case, the purpose of consideration is to build a “natural” extension, when the implementation does not require the creation of a special type of social network entries. The general scheme or model of implementation is based on the minimization (or even complete absence) of requesting additional rights to access the social network, the absence of marks in the social network, and the use of basic functionality and standard protocols for mobile devices.
FFT Architecture for Motion Estimation using Phase Correlation

Fast Fourier Transform(FFT) is one of the widely used algorithms in almost all signal processing applications in various contexts. Efficient hardware architectures to implement FFT tailored for a typical application scenario is always challenging and requires a lot of analysis. In this paper an architecture for a 64×64 FFT is proposed for a real time video motion estimation application. Global motion estimation for video data employing phase correlation has many advantages such as its ability to estimate larger motions with low computational complexity in addition to spectrum insensitivity and immunity to noise and illumination variation. The major computational block that decides the efficiency of the phase correlation algorithm is FFT. The proposed FFT architecture is designed to meet the real-time requirements of a video stabilization module employing phase correlation for the motion estimation where the videos are assumed to be captured at a rate of 30 frames per second or above. The proposed architecture was implemented on a Kintex-7 FPGA board and met the required specifications.
대화가 고등학생의 쓰기 막힘에 미치는 영향 - ‘가전’ 쓰기 중에 나눈 소집단의 대화를 중심으로 -

The purpose of this study was to examine the causes and characteristics of writing blocks of high school students and to suggest conversation as a solution. To this end, a survey was conducted to verify the current situation of participants’ writing blocks. Then, the study examined how small-group conversation affects writing blocks in the Gajeon writing situation.The results showed that, although 80% of the students had already experienced writing blocks their willingness to resolve the problem proved to be strong. The writing blocks of the students had cognitive causes, such as lack of planning before writing, lack of knowledge in writing, and difficulty in creating a writing structure, and emotional causes, including pressure to fill the quantity, situations in which one has to write without having the will, and fear of failure.When experiencing writing blocks, the students attempted to solve the cognitive causes by talking about determining materials, creating and choosing content before writing, and rechecking the given assignment. They also attempted to solve the emotional causes of the blocks by talking about issues such as expressing one’s situation, praising friends, and empathizing with those facing the same situation.Finally, 75.5% of the students said that it is helpful to talk when faced with writing blocks. In addition, students who experienced writing blocks received help in both cognitive and emotional issues while having conversations. Therefore, conversation is an effective way to solve both the cognitive and emotional causes of high school students’ writing blocks.
Utilising the See-and-Follow Method for Enhancing Robot Learning Ability

Abstract The rapid growth and development of robots today can be reflected from variations of research in the robotic field, and one of them that has been flourishing in the area is robot learning. Learning is one of the most important aspect needs to be implemented in a robot that allows the robot to improve its performance through experience-driven knowledge. There are several forms of learning methods and one of the most favourable approach is the see-and-follow method. This paper aims to develop robots that can learn to do the same movements with human movements through the see-and-follow method. Kinect sensor is used as a technology to support the process of recognizing the human body and its movements; which later to be transformed into the robot dimension. The results show that Kinect sensor can be used to measure the distance and recognize the joints of the human body along with the movements produced within a distance of 1 meter to 3.75 meters with an average reading error of 0.04 meter. The average delay time for the whole movement is 2.6 seconds. The average percentage of similarity between robot and human movements for the whole movement scenarios is 93.45%. Hence, high level of similarity in movement concludes that the method of see-and-follow successfully increases the learning skills of the robot.
Interpreting and defining connections in a dependency structure

This paper highlights the advantages of not interpreting connections in a dependency tree as combinations between words but of interpreting them more broadly as sets of combinations between catenae.One of the most important outcomes is the possibility of associating a connection structure to any set of combinations assuming some well-formedness properties and of providing a new way to define dependency trees and other kinds of dependency structures, which are not trees but "bubble graphs".The status of catenae of dependency trees as syntactic units is discussed.
Research on the Overall Attitude Towards Mobile Learning in Social Media: Emotions Mining Approach

In this paper, we address the importance of classification and social media mining of human emotions. We compared different theories about basic emotions and the application of emotion theory in practice. Based on Plutchik’s classification, we suggest creating a specialized lexicon with terms and phrases to identify emotions for research of general attitudes towards mobile learning in social media. The approach can also be applied to other areas of scientific knowledge that aim to explore the emotional attitudes of users in social media. It is based on the Natural Language Processing and more specifically uses text mining classification algorithms. For test purposes, we’ve retrieved a number of tweets on users’ attitudes towards mobile learning.
Research on Application of Private Cloud Platform

Abstract This paper introduces the current development status of cloud computing technology at home and abroad, analyses the history and realistic needs of Enterprise Cloud computing, introduces the IaaS, PaaS and SaaS services in the construction of Enterprise Cloud Computing center, and puts forward seven key technologies of operation and development that need to be solved urgently at present.
Distributions of several infinite families of mesh patterns

Brändén and Claesson introduced mesh patterns to provide explicit expansions for certain permutation statistics as linear combinations of (classical) permutation patterns. The first systematic study of the avoidance of mesh patterns was conducted by Hilmarsson et al., while the first systematic study of the distribution of mesh patterns was conducted by the first two authors. In this paper, we provide far-reaching generalizations for 8 known distribution results and 5 known avoidance results related to mesh patterns by giving distribution or avoidance formulas for certain infinite families of mesh patterns in terms of distribution or avoidance formulas for smaller patterns. Moreover, as a corollary to a general result, we find the distribution of one more mesh pattern of length 2.
Experimental research on the impact of similarity function selection on the quality of keyword spotting in speech signal

The paper describes an evaluation of the application of selected similarity functions in the task of keyword spotting. Experiments were carried out in the Polish language. The research results can be used to improve already existing keyword spotting methods, or to develop new ones.
Low Resource Named Entity Recognition Using Contextual Word Representation and Neural Cross-Lingual Knowledge Transfer

Low resource Named Entity Recognition can be solved by transferring knowledge from a high to a low-resource language with shared multilingual embedding spaces. In this paper, we focus on the extreme low-resource NER scenario of unsupervised cross-lingual knowledge transfer, where no labelled training data or parallel corpus is available. We apply word-alignment with the contextualised word embedding and propose an efficient cross-lingual centroid-based space translation mechanism for contextual embedding. We found that the proposed alignment mechanism works well between different languages, compared to current state-of-the-art models. Moreover, word order differences is another problem to be resolved in cross-lingual NER. We alleviate this issue by incorporating a transformer, which relies entirely on an attention mechanism to draw global dependency between input and output. Our method was evaluated against state-of-the-art results, and it indicate that our approach was better in terms of the performance and the amount of resources.
New approach for modeling and developing multi-agent systems based on case based reasoning

In this paper, we present a multi agent architecture based on Incremental Dynamic Case-Based Reasoning (IDCBR). Our approach inherits from Model Driven Architecture (MDA [11]), which aims to design, develop and implement models or meta-models of multi-agent systems that we build from AUML. We have designed a generic and scalable class diagram to develop complex multi-agent systems [3] for Decision Support System based on IDCBR to predict and anticipate a dynamic situation. The source code of the models is generated by an open source tool called AndroMDA [13]. The model and source code will be used to design and develop applications to implement and simulate multi-agent models for Management of Common Renewable Resources [4].
The use of modern information technologies applying in implementing course and diploma projects by students in the specialty in “devices and systems of orientation, stabilization and navigation”

The article is devoted to teaching senior students the design, calculations and synthesis of instruments and systems of orientation, stabilization and navigation using modern information technologies. The methodology is considered of using a range of software products by students, covering most of the design stages. The experience gained can be considered positive, both from the educational and methodological point of view - according to the feedback of teachers conducting course and diploma projects, and from the point of view of learning outcomes analysis according to assessments of employers and graduates of the department.
Three-Dimensional Text Applications with OpenGL

Abstract Text is one object that can be manipulated into three-dimensional shapes. The main effects in displaying three-dimensional text include the shadow effect, the depth of 3D effects, the type of bevel on the edges of the text or the addition of light effects and transformations such as rotating and flipping. In addition to the above its effects, it can also do 3D text rotation by doing animation on the text. One way to make text into 3D objects can use OpenGL which is an engine that is widely used for 3D applications. In addition to 3D effects OpenGL also provides features to give color, apply texture to objects, lighting, rotation, and scaling. The results of this study are software that can convert a text into three-dimensional text in the form of images by providing shadow effects and adding background graphics in the form of textures with the addition of several features such as rotation, scaling, and moving. The software also can print the results of 3D text and save the results in a bitmap image format.
The face recognition based on ensemble method

In this paper, we study the performance of different deep convolutional networks in 1:N face recognition and process the feature comparison in order to obtain a higher face recognition rate. The improved feature comparison method is proposed: using Basis Pursuit De-Noising (BPDN) method combined with Homotopy algorithm to replace the nearest neighbor analysis in 1:N face recognition. Based on the Facenet model, we also compare the performance of different loss functions in face verification and recognition. Furthermore, the ensemble method and voting classifier are used to integrate Facenet, InsightFace and BPDN method to realize 1:N face recognition. It should be noted that the 1:N dataset is transformed from Labeled Faces in the Wild (LFW) dataset, including the 1 dataset and the N dataset. The 1 dataset and the N dataset contain 1680 faces and 5749 faces respectively, one for each person. The final experiments show that the ensemble method's accuracy is 82.98%, which is higher than the above methods, and indicate the ensemble method we proposed can improve the face recognition rate.
Operational control methods of wastewater turbidity

Abstract The article identifies the optimal method of operational control of wastewater turbidity generated during the production of water-dispersion paint material.
Image Correlation Technique for Slope and Curvature of a Cantilever Beam Using Light Intensity

Image correlation technique is an optical method based on grey value digital images that can determine the shape, displacement and the strain of an object under load. In this technique, the slope and curvature of a cantilever beam can be obtained based on the intensity values of loaded and unloaded images. According to the conservation principle, for small deformation, the total amount of light reflected for the specified area on the surface would be the same before and after loading when the illumination is unaltered. The method, image correlation technique is obtained on this basis.
Crowd density estimation via multi-scale convolutional neural network in single-image

In the field of video surveillance, the use of artificial intelligence to estimate crowd density in public places has been a popular study. In order to improve the accuracy of crowd density estimates, a multi-scale convolution neural network structure is proposed. And the feature fusion of different receptive field information is performed by using multi-column convolution network, and the hierarchical semantic information with different feature maps at different resolutions is merged to generate a crowd density map with higher quality. The experiment was tested on the Shanghaitech dataset, UCF_CC_50 dataset, and WorldExpo'10 dataset with mean absolute error (MAE) and mean square error (MSE) as the evaluation criteria. The results show that the new network model reduce the value MAE and MSE, improving the accuracy of crowd density estimation.
Information Extraction and Similarity Computation for Semi-/Un-Structured Sentences from the Cyberdata

With the popularization of network and the improvement of network speed, social network application plays an increasingly important role in people’s social life. People express their opinions and ask their own questions on social software, and these huge amounts of data drive researchers to propose various algorithms to extract the information in sentences and classify them. In this paper, we proposed a novel method of sentence similarity computation, which purpose is to extract the syntactic and semantic information of semi-structured and structured sentences and calculate their similarity. We mainly consider the subject predicate and object of sentence pairs, and use Stanford parser to classify Dependency Relation Triples to calculate the syntactic and semantic similarity between two sentences. Extensive simulations demonstrated that our method outperforms the other state-of-the-art methods in terms of correlation coefficient and mean deviation.
Maritime Ecolexicon in Bajo Community of Kolaka Regency Coast and its Implications for Ecology

Abstract This study described deeply about the survival of Bajo language on the coast of Kolaka Regency and its implications for maritime ecology. Data collection was done through: (a) lexicon competency test, (b) observation, (c) interview, and (d) documentation. The results showed that 53.75% of the maritime ecolexicon was no longer survived. It means that the maritime ecolexicon of Bajo language was on the verge of the most extinct loss of maritime flora and fauna. The causes of this inadequacy were: ecological changes, lack use of Bajo language, low environmental awareness of the community, loss of several marine traditions, and transfer of community work from fishermen to other jobs. The recommendation of this study is: Bajo language should be one of the local content materials in schools, government and the community need to run a maritime environment that has an impact on the survival of Bajo language and environmental sustainability.
Live Graphical Computing Logic, Virtual Trees, and Visualization

The chapter is a preliminary overview of a virtual tree computing logic with applications to Virtual Reality. Meta-contextual logic is combined with Morph Gentzen, a new computing logic the author presented in 1997. It has applications towards Virtual Reality (VR) computing since the trees on the languages can carry visual structures via functions. Designated functions define agents, as specific function symbols, to represent languages with only abstract definition known at syntax. The languages are called “Intelligent” in the sense that there are designated function symbols on the syntax with explicit signature annotations, for example having to have 1-1 functions only on a sub signature. Generic diagrams for models are applied for a second order lift from visual context. The techniques allowed us to define a computational linguistics and model theory for “intelligent languages,” i.e., agent augmented languages. Agent JAVA is applied to compute specific transformations for realistic designs.
Classification of News on “Radar” Tarakan Online Using K-Nearest Neighbor Method with N-Gram Features

Abstract Classification or text categorization is one of the most common themes in analysing complex data. The classification in this research aims to define the class of an object that has not been known to the class. The classification process will be performed in the process of learning and testing against a known class dataset object. As an online news information, Radar Tarakan classifies news to ease readers to find the desired news and to ease the work of the administrators to do the automatic classification. The website of Radar Tarakan daily news (www.radartarakan.co.id) publishes approximately 480 local news everyday. Local news is the news in the surrounding areas of Tarakan in North Borneo. The website contains the classification of eight local news including the dimensions of the area surrounding the town of Tarakan. This research uses 720 news to build the system of news classification automatically. A text classification method used in this research is the K-Nearest Neighbor (KNN) with N-grams. The accuracy of matching classes generated by the K-Nearest Neighbor with N-Gram is 85.65% (out of 180 test data) and that of the sub class is 70.35 %. The classification time is around 2.7 second per news.