Reviewerly/exHarmony huggingface dataset:
Technological Concept of Differential Processing of Segments of Video Frame Taking into Account of Key Information

Justification of the approach for forming the core of the technological concept of differentiated processing of video frame segments with taking into account the availability of key information is made. It enables on the one hand to reduce the complexity of the syntactic presentation processes; on the other hand, to provide a given level of reliability of video information, i.e. to implement the processing mode with controlled loss of quality of reconstructed video frames. Unlike the existing methods of processing video data, the proposed concept uses the detection of frame segments containing key information in the region of high-frequency constituents of the transformant, followed by an adaptive reduction of structural-combinatorial redundancy.
Clustered Gaussian Graphical Model Via Symmetric Convex Clustering

Knowledge of functional groupings of neurons can shed light on structures of neural circuits and is valuable in many types of neuroimaging studies. However, accurately determining which neurons carry out similar neurological tasks via controlled experiments is both labor-intensive and prohibitively expensive on a large scale. Thus, it is of great interest to cluster neurons that have similar connectivity profiles into functionally coherent groups in a data-driven manner. In this work, we propose the clustered Gaussian graphical model (GGM) and a novel symmetric convex clustering penalty in an unified convex optimization framework for inferring functional clusters among neurons from neural activity data. A parallelizable multi-block Alternating Direction Method of Multipliers (ADMM) algorithm is used to solve the corresponding convex optimization problem. In addition, we establish convergence guarantees for the proposed ADMM algorithm. Experimental results on both synthetic data and real-world neuroscientific data demonstrate the effectiveness of our approach.
Research on the Influence of National System along "the Belt and Road Initiative" on Investment Area Selection Based on Mult-Motivation Analysis of Intelligent Modeling and Information Processing

Through the multi-task analysis of the intelligent modeling and information processing of the business data of each country over the years, it is possible to distinguish which countries have better business environment. From the perspective of investment motives, this paper studies the influence of national institutional mechanism on the choice of overseas investment regions of Chinese enterprises. In addition, Intelligent Modeling analysis of 65 host countries along "the Belt and Road" and 275 observations in the past 5 years have been conducted in the paper. Base on the information processing, we found that, compared with those enterprises seeking market and strategic assets, Chinese resource-oriented companies always tend to ignore institutional factors in site selection. However, due to the limitations of China's market economy in the development process and the internal problems of those companies, domestic market-oriented and strategic asset-oriented enterprises often pay less attention to certain institutional factors.
Research on Improvement of Information Platform for Local Tourism by Paragraph Vector

InTsuchiya, Takeshi this paper, we discuss and propose the analysisHirose, Hiroo and search method of various informationMiyosawa, Tadashi on tourism in the Suwa area of Nagano Prefecture on the Internet. TheseYamada, Tetsuyasu pieces of information include not only long sentences such as web pages and blogs, but also a lot of content of SNS composed of short sentences of aboutSawano, Hiroaki several words. Therefore, by the conventional search method, basedKoyanagi, Keiichi on the occurrence probability of words in sentences, sufficient accuracy cannot be expected for the search of SNS information composed of several words. In this research, we examined a method using Paragraph Vector for expressing relationships of words included in sentences. By doing this, we aim to acquire the same level of search performance even for SNS content composed of several words.
THE OPINIONS OF PRE-PRIMARY EDUCATION TEACHERS ON THE ADVANTAGES AND DISADVANTAGES OF HETEROGENEOUS AGE GROUPS

The research focuses on the issue of mixed-aged children in nursery schools. The aim of the research was to find out the opinions of the nursery school teachers on the advantages and disadvantages of a heterogeneous age groups based on their practical experiences. In order to find out the opinions of the respondents, a questionnaire was constructed and used. Based on the results, it can be said that 243 respondents consider an advantage to be the better social and emotional development of children, regardless of whether the respondent prefers to work in a homogeneous or heterogeneous age group. However, they did not agree with the disadvantages of heterogeneous age groups. The respondents teaching homogeneous age groups have generally identified far more disadvantages, and the greatest disadvantage being that children in a heterogeneous age group have worse conditions for cognitive development and do not have the opportunity to compete with each other. It has also been confirmed that those respondents with more years of work experience prefer a more heterogeneous age composition of groups than respondents with fewer years of work experience. Keywords: mixed-age groups, nursery school, nursery school teacher, pre-primary education.
Application of Computational Anatomy into Primarily Diagnosing Cardiac Status

Computational anatomy refers to the study field of anatomical geometry and the visible scale of the form and structure of organisms. It greatly benefits cardiac experts by exposing the visual structure of the heart: by modelling internal organ in its three-dimension image, general conclusions can be drawn from observing its shape, taking its measurements, calculating its volume, and so on. This method has minimized the time taken as well as the cost needed to diagnose accurately. The goal of this paper is to create a software which allows to construct a 3D cardiac image from patient’s CT scan or MRI. Through this model, several data can be extracted and used to make the first-stage deduction whether the heart is in its healthy state. The state is determined by applying computer graphics and data analysis methods for modelling and computing.
Modifying Bit-Level Data Compression Scheme based on Adaptive Hamming Code Data Compression Algorithm

This paper is directed towards the development of an improved HCDC(k) to minimize the possibility of inflation. An improved adaptive hamming code data compression has been developed by reducing the number of bits used when substituting a non-valid codeword. The proposed algorithm is expected to provide greater compression ratio, space savings, and compression speed. It is also expected to minimize the possibility of inflation when compressing random texts. To evaluate the efficiency of the modified algorithm, simulations were conducted to compress sample texts from COCA corpus and random texts using the original HCDC(k) and modified HCDC(k). Compressing sample texts from COCA corpus lead to a significant difference of 7.5% in space savings and 0.11 in compression ratio. The simulation conducted with random texts lead to a significant difference of 10.45% in space savings and 0.1 in compression ratio. This means that the modified algorithm is more effective when solving inflation.
Cubo: Communication System for Children with Autism Spectrum Disorders

The current research, focusing on Social Innovation and Inclusive Design, seeks to understand and explore the interpersonal communication of children with autism spectrum disorders. The main objective is to contribute to the development of the cognitive and social skills of these children, improving and facilitating their difficulties in three domains: verbal language and communication; interpersonal relationships and in the field of thought and behavior. In order to help solving these problems, Cubo has been developing, an innovative system of universal and inclusive communication, composed of a new universal and alternative/augmentative alphabet and digital object that promotes autonomy, social integration, personal development and interpersonal relationships. This first paper aims to inform and promote the discussion of the process and results of the ongoing research, describing the first phases of the design process itinerary and methods applied, as well as the description of the following phases that cause multidisciplinarity and co-creation.
A New Cluster-Aware Regularization of Neural Networks

Inherent clusters formed by observations used for the training of a classification model is a frequently encountered case. These clusters differ in certain characteristics, however in classical modelling techniques no information on these differences is fed into the model. Differentiations in purchasing styles of e-commerce customers may be a good example for this case. While some customers like to do research and comparisons on price, functionalities and comments, some others may need a shorter examination to decide on their purchase. In a similar manner, purchasing journey of a deal seeker customer would differ from a luxury buyer customer. In this paper, we propose a neural network model which incorporates different cluster information in its hidden nodes. Within the forward propagation and backpropagation calculations of the network, we use a non-randomized Boolean matrix to assign hidden nodes to different observation clusters. This Boolean matrix shuts down a hidden node for observations which do not belong to the cluster that the node is assigned to. We performed experiments for different settings and network architectures. Also, analyses are conducted to study the influence of alternative application patterns of the Boolean matrix on the results – expressed in terms of iterations and epochs for an Adam (adaptive moment estimation) optimization. Empirical results demonstrate that our proposed method works well in practice and compares favorably to fully randomized alternatives.
Short Term PV Power Forecasting Using ELM and Probabilistic Prediction Interval Formation

Accurate prediction of solar power is important to the grid operator for ensuring energy management from multiple sources without jeopardizing stability and to the PV plant owner for scheduling plant maintenance periods and avoiding power imbalance costs. It is evident that meteorological data like solar irradiance is more readily available than the historical PV power output series with hourly samples. In this case, indirect forecasting can be utilized where PV output predictions are obtained using the solar irradiance forecasts. Decisions solely based on point forecasts can be risky considering the sharp variations in solar irradiance patterns. The inherent uncertainty in the point forecasts can be quantified by associating them with a probability distribution to form prediction intervals (PIs) which is a more interpretable representation of uncertainty. This paper presents a probabilistic forecasting approach using a nonparametric PI formation method based on Extreme Learning Machine. No prior assumption on the error distribution is required for the PI formation. Solar irradiance data from NUS geography weather station, Singapore, is analyzed and assembled into two separate sets for better model performance. Coverage probability and interval scores are evaluated for the resulting PIs which show promising results.
An Improved Compressive Tracking Approach Using Multiple Random Feature Extraction Algorithm

This paper presents an object-tracking algorithm with multiple randomly-generated features. We intent to improve the compressive tracking method whose results are fluctuated between good and bad. Because the compressive tracking method generates the image features randomly, the resulting image features varies from time to time. The object tracker might fail for missing some significant features. Therefore, the results of traditional compressive tracking are unstable. To solve the problem, the proposed approach generates multiple features randomly and chooses the best tracking results by measuring the similarity for each candidate. In this paper, we use the Bhattacharyya coefficient as the similarity measurement. The experimental results show that the proposed tracking algorithm can greatly reduce the tracking errors. The best performance improvements in terms of center location error, bounding box overlap ratio, and success rate are from 63.62 pixels to 15.45 pixels, from 31.75% to 64.48%, and from 38.51% to 82.58%, respectively.
Modeling and simulation of a dual-axis solar tracker for PV modules

Abstract The paper deals with the modeling and simulation in virtual prototyping environment of a mechatronic solar tracker used for photovoltaic systems, with the aim to increase the energetic efficiency, by maximizing the rate of incoming incident solar radiation. The solar tracker in study is an equatorial dual-axis mechanism, which allows the adjustment of the diurnal and seasonal angles of the PV module in accordance with a predefined tracking program, the actuating sources being linear actuators. The modeling and simulation of the tracking system is carried out by using the MBS (Multi-Body System) commercial software solution ADAMS (Automatic Dynamic Analysis of the Mechanical Systems).
Multi‐objective genetic algorithm on hexagonal search for fast motion estimation

Motion estimation (ME) is a process of correlating the frames by pointing the blocks in the candidate frame similar to those in the previous frame. Hexagonal Search (HS) reduces 10–15% search points over Diamond Search (DS) with negligible Peak Signal to Noise Ratio (PSNR) loss. Adapting an optimization will achieve a massive improvement on the search process. The proposed HS-GA method combines the HS with genetic algorithm (GA) to optimize ME. The HS-GA method reduces 72% on average search points pre block (ASPB) with PSNR loss of 2.5 dB and single generation. If the GA is executed with 10 generations the PSNR loss is reduced to 0.48 dB, but ASPB drastically increase to ∼316%. The proposed work aims at multi objective on selection of better chromosomes and on control of generations to avoid the unnecessary generations to improve the GA. This process is named HSIGA(HS with Intelligent GA). The proposed HSIGA adapts five intelligent techniques like Histogram to decide new generations, Region based chromosome selection, Mutation on inner search gene, second rank valley point stopping criteria and interlaced HS to improve search process with negligible PSNR loss with in affordable search.
SPLide: An Integrated Development Environment Supporting Software Product Lines

SPLide is an integrated development environment for applying software product line to Korean weapon system software development. SPLide supports feature-oriented software development lifecycle. Development of the weapon system software has been made based on a single software development process. Engineers reuse some of the code, common modules and algorithms from previously developed software to increase productivity. However, legacy software is not designed for reuse and cannot be effectively reused. This necessitated strategic reuse, and we developed a tool to support systematic software reuse by applying software product line engineering. In this paper, we propose an integrated development environment that can apply feature-oriented software product line engineering to efficiently develop various weapon system software.
Evolutionary Optimization of Intruder Interception Plans for Mobile Robot Groups

The task of automated intruder detection and interception is often considered as a suitable application for groups of mobile robots. Realistic versions of the problem include representing uncertainty, which turns it into NP-hard optimization tasks. In this paper we define the problem of indoor intruder interception with probabilistic intruder motion model and uncertainty of intruder detection. We define a model for representing the problem and propose an algorithm for optimizing plans for groups of mobile robots patrolling the building. The proposed evolutionary multi-agent algorithm uses a novel representation of solutions. The algorithm has been evaluated using different problem sizes and compared with other methods.
Some Statistical and CI Models to Predict Chaotic High-Frequency Financial Data

Forecasting of financial time series data is a complex problem, which has benefited from recent advancements and research in machine learning. To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARMA models. As a competitive tool to statistical forecasting models, we use the popular classic neural network of perceptron type. To train neural networks, the BP algorithm and heuristics like genetic and micro-genetic algorithm are implemented. A comparative analysis of selected learning methods is also performed and evaluated.
Recommender System Based on Unstructured Data

As the time constraints for completing customer satisfaction surveys are becoming tighter, there emerges a need for developing a new format of surveying customers. The idea is to limit the number of score benchmark questions, and let customers express their opinions in a free format. As a result the collected data will mainly contain open-ended text comments. This chapter presents a strategy to modify the existing recommender system built based on both numerical (structured) and text data (unstructured) to work on unstructured data only to make it work with the new survey format.
[Paper] Usefulness of Tactile Scatter Plots

The goal of making a scatter plot is to visually identify the type of relationship between two quantitative variables quickly. To explore whether a scatter plot can achieve this goal when it is made in the form of a tactile graph and presented to blind people, we conducted an experiment in which x-y data sets were presented to blind participants in three data representations: tactile graph, tactile table, and electronic table, and the participants were asked to identify the type of relationship between two variables. Under all presentation conditions, eight participants or more achieved 100% correct rate. Tactile graphs were understood with the shortest time, tactile tables with the second shortest, and electronic tables needed the longest time. This time differences were due to the different strategies for identifying the relationships. Both tactile graph and tactile table conditions gained significantly higher subjective ratings than the electronic table condition. These results show the usefulness of tactile scatter plots for blind people although it is limited to the present experimental condition.
A study on the possibility of cooperation among Open Data using predicate vector

近年，多くの行政・団体がオープンデータの公開，活用に取り組んでいるが政府の推進する「機械判読可能で人手を多くかけずにデータの2次利用が可能である」というデータ活用までには至っていない．政府は，2012年の「電子行政オープンデータ戦略」策定や2014年にデータカタログサイト「DATA.GO.JP」[1] 運用を行いオープンデータの推進をしてきている．地方自治体のオープンデータは，データ形式・フォーマットの違いにより開示されても積極的な利活用まで至っていないのが現状である．当研究では，地方自治体のオープンデータの可能性を探るために開示されているCSVデータの項目名，項目値をベクトル化することによりデータの類似度を測る述語ベクトル法を提案する．またその述語ベクトル法を用いて，各データ間の連携可能性について調査した．
Interface Information Visualization of Intelligent Control System Based on Visual Cognitive Behavior

A typical intelligent control system, Trina Solar MES production line, is studied in this paper. To begin with, the system information characterization and encoding method are determined through the behavior experiment. Then, the layout of interface graphic elements and system information is found through eye movement experiment. The result indicated that, visual design model of intelligent control systems is set up based on cognitive behavior process and the best layout, which provides enterprises with realizable and optimized design strategy. That is applied to the MES production line of information control system in Trina Solar Technology Company. In conclusion, it provides theoretical support, physiological analysis methods and technical means for the research of information interface design from the perspective of behavior and physiological science so as to guarantee the enterprise’s safe and efficient production.
The Use of Dictogloss Technique in Teaching Writing of Procedure Text

Writing is a skill which is consider as difficult skill in teaching learning process because it need an effort to collect ideas and words on the paper. Most of student said that writing process is very difficult and often make them lazy and bored to write and to make student motivated and enthusiastic to study the teachers probably have the strategy, method or a technique to teach their students and this research attempt to describe the Dictogloss technique that is implemented by the teacher in teaching writing of procedure text in the ninth grade of SMPI Bahrul-Huda sumber anyar. From this research, the researcher want to know the process of dictogloss technique and the advantage of that technique in writing material. In this research, the resercher use qualitative approach. Data collection procedures that used in this research are observation, interview, and documentation. The result show that implementation of Dictogloss technique that is implemented by the teacher in the class room is run well and teaching learning process is successful by using the technique. Students get progress to write than before and they get many vocabularies as guided writing, they also know how to arrange words to be sentence then write it to be one paragraph but in the other side the technique is need much time and sometimes it affect student in learning.
Intuitionistic Fuzzy Density Based Spatial Clustering of Applications with Noise: IFDBSCAN

Density-Based Spatial Clustering of Application with Noise (DBSCAN) is a well-known, unsupervised machine learning tool for clustering. DBSCAN creates clusters based on dense regions while marking points that lie alone in low-density regions as outliers. In DBSCAN, the density is detected via core points which are quite sensitive to input parameters: ϵ is radius of the neighborhood and MinPts is minimum number of points constraint within ϵ radius. In contrast to crisp core point definition, intuitionistic fuzzy core point definition makes DBSCAN algorithm capable to detect different patterns of density by two different combinations of input parameters. In this study, a DBSCAN extension is proposed based on this idea: IFDBSCAN. The proposed extension is then illustrated by a computational experiment on a synthetic dataset. Results show that, IFDBSCAN can establish fine-tuned clusters relatively to classical DBSCAN and provide users more insight on the estimation of input parameters.