Reviewerly/exHarmony huggingface dataset:
Semantic Similarity Computation Among Hindi Words Using Hindi Lexical Ontology

The measurement of semantic similarity between two concepts/words has always been a challenge in the field of document retrieval. As per current literature, there is no method to compute semantic similarity for Hindi words. Therefore, a new computational method is proposed to measure semantic similarity between Hindi words using lexical ontology. In this approach, Hindi WordNet is used which gives the relational information among various Hindi words/concepts. The performance is tested on translated Miller and Charles’s benchmark dataset. In this paper, the three different semantic similarity measuring methods are used to compute semantic similarity for 20 words pairs. The accuracy of the results is also measured in this work using the correlation coefficient. The proposed method in this paper is focusing on the study and analysis of the results obtained from semantic similarity measuring methods on Hindi concepts/words.
Fast Image Defogging Algorithm Design Based on Dark Channel Prior

Foggy weather brings unfavorable influences to transportation, visual sensing, security and so on. Although some traditional algorithms have excellent processing effects on computers, it is difficult to implement on platforms with high real-time requirements such as self-driving and aerial photography. In this paper, one simple but efficient algorithm has been proposed to meet the requirement of real-time image defogging. Instead of dividing by squares, continuous plane detection strategy has been adopted so that one foggy image can be divided into many continuous parts marked by one independent label, avoiding the blocking effect of image processing. Based on Dark Channel Prior (DCP), estimate of atmospheric light and transmission function seems no longer troublesome. Meanwhile, downsampling has been used suitably to decrease the computational complexity of intermediate processes while avoiding loss of precision. Finally, we achieved much faster single image defogging speed as well as better PSNR and SSIM.
Research on Computer Information Retrieval Based on Deep Learning

Abstract With the growth of digital information on the Internet, online stores, online music, video and photo libraries, search engines and recommendation systems have become the main way people are looking for information quickly.Recently, the development of deep learning has achieved many results in the fields of speech recognition, image processing and natural language processing.In addition, deep learning is also applied in the field of recommendation systems and information retrieval.This paper mainly studies a real-time recommendation algorithm for smart TV based on deep learning.The recommendation of TV program based on dnn and rnn is applied to the content-based method in the recommendation system. Experiments show that the model can effectively solve the problem of data sparsity and can alleviate the cold start in collaborative filtering to some extent.
Comparison of Encoding and Decoding Methods for Binary Files

Abstract The main problem to be an obstacle in communication between platforms, networks such as local networks or the Internet is a problem in data transfer. The problem often found in a hardware which is cannot receive data that is processed by other hardware. So to change data between two computer systems, it is necessary to convert data from one form to another. One solution to this problem is transfered data or file, previously converted to the plaintext form first. Conversion to the plaintext form can be supported by most computer systems. In this study, a software was designed using Visual Basic programming language that can convert binary files to plaintext using the Base 64 encoding or decoding method, UUEncode / UUDecode, XXEncode / XXDecode, BinHex 4.0, Hexadecimal, and Quoted-Printable. The results of the design is produce a software that can be used as an alternative program to convert files into text files, besides that the program can also display a benchmark comparison graph (speed) of each method and comparison of overhead (the ratio of the size of the file to the file before encoding).
Analysis between GPA and TOEFL Score For Postgraduate Student of Sports Education using Bivariate Logistic Regression

Abstract Due to the globalization era, there is the challenge of competition in all fields including finding work in sports education. To meet the stakeholder’s needs, it is necessary to have some criteria for prospective workers and also a well-planned process in academic. Grade Point Average (GPA), is one of the most important achievements during the postgraduate programme. Meanwhile, another academic component is English as a Foreign Language Test Score (TOEFL) which also has a very significant impact on seeing the job. This paper will analyze valuable factors that influence GPA and TOEFL using a bivariate approach. In this case, the GPA will be defined as two levels, i.e low and high. TOEFL is also able to define as two categories, i.e more and less than 450. Hence, bivariate logistic regression is the most appropriate approach. The result shows that the coefficient of age has strong evidence to reject the null hypothesis at a significant level of 0.05. It means that the variable of age has a huge contribution to influence the score of the TOEFL for the postgraduate programme in sports education.
One-Verifier Signature Scheme and Its Applications

In the traditional digital signature scheme, anyone can check the validity of a signer's signature. However, in some practical applications, such as e-Voting, e-Health, e-Payment, e-Bidding where the privacy of the signer is required, it is necessary that there is only one responsible person who is able to check the validity of the signer's signature. There exists a few solutions for this problem such as combining the traditional digital signature scheme with an encryption scheme or using the strong designated verifier signature scheme with judgment. In this paper, we propose another solution for this problem, we name one-verifier signature scheme. Compare among three solutions, our proposed solution is the best one in term of efficiency.
Adaptive Multiple Peak and Histogram based Reversible Data Hiding

The aim of this study is to propose a histogram shifting-based reversible data hiding scheme for anatomical magnetic resonance images (MRI) of human brain. In this study, a human brain MRI dataset was employed for experimental purposes, which were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) open dataset. In experimental design, only healthy subjects are included in order to avoid any bias due to morphological brain abnormalities. The dataset contained three-dimensional 16-bit-depth T1-weighted brain MRIs of 25 healthy people. The proposed data hiding scheme for an MRI volume started with an atlas-based partitioning procedure to segment the volume into several region-of- interests, called as chunks in this paper. Then, the well-known histogram-shifting method was applied with some modifications to each chunk, separately. Finally, the total capacity of chunks in bit-per-voxel (BPV) and the stego-image quality in peak signal-to-noise ratio (PSNR) were calculated. The average PSNR and BPV values of the cohort were reported in several experimental conditions including different segmentation regions and several numbers of peak-zero bin-pairs per chunk. The main contribution of this study is the modifications on the traditional histogram-shifting-based reversible data hiding scheme that are considering the characteristics of the T1-weighted MRI data. Furthermore, the distinguishing feature of this study is that, on contrary to similar studies in the literature, this study employed three-dimensional MRI scans of real cohort. The experimental results of this study revealed the applicability of data hiding schemas on neuroimaging data.
Research on Data Model Application of Localized Display Software

Abstract This paper combines the domestic display software research and development work, the display process and main technologies are described, the model design and verification under kirin operating system was carried out, the standardization of data representation model and the generalized control display data interface are studied, and the data driven mechanism and control model has carried on the preliminary study and practive application, the rationality of the test of verify the feasibility of technology and framework.The application results show that the design of the new software is feasible and efficient, which reduces the difficulty of control development, improves the flexibility and scope of application of the control, greatly reduces the workload of software maintenance, and plays a good role in the follow-up research and development of the software.
Develop a Model to Secure and Optimize Distributed File Systems for ISP Log Management

Distributed file system (DFS) is a transformation of traditional file systems to perform file read, write and execution of petabyte or larger-sized datasets with high-velocity and different structures. In order to process these large amounts of data in an inexpensive and efficient way, Hadoop Distributed File System (HDFS) is used and designed to scale up from a single server to hundreds of servers, with a very high degree of fault tolerance. Secured and optimized distributed file system for ISP user log management is a centralized log analytics platform that supports collaboration in a distributed environment. DFS for ISP log provides a uniform name space for all ISP along with location independence and location transparency. Any user's internet access log can be accessed and analyzed using apache sparks data brick file system on stored log in DFS. In this paper, at first, limitation of popular DFS like NFS and CIFS is explored. ISP log management, DFS file architecture and security is considered for designing proposed ISP log management system. Finally, simulation on proposed distributed file system and query processing is performed to obtain the desired result related to optimization and security of the proposed distributed log analysis system.
The Project of Intellectual System for Rating of Educational Institutions

The main principles of choosing of educational or pre-school institutions with the help of the developed intellectual information system are considered in the paper. The analysis of main stages and means of system development, its functions for ranking educational institutions are presented. The advantages of this project is the ability to evaluate not only educational, but also pre-school institutions, that will embrace more users of different ages.
Deterministic Analysis and Research on Reasonable Purchase Price Range Based on Multi - Objective Fuzzy Optimization Model

Abstract Reasonable purchase price has multiple objectives, it is necessary to take into account the increase in food and industry income, but also take into account the development of the entire grain industry, and even to consider improving production efficiency. This paper chooses the minimum purchase price of wheat, the market purchase price, the output and the grain income and the production efficiency as variables, and takes the lower limit of the grain and farmers’ income, the upper limit of the market price and the minimum purchase price, the supply and demand relationship, the inventory restriction, the financial support ability And the scope of international food price as constraints, establishing multi-objective fuzzy optimization model to explore the government’s optimal minimum purchase price decision-making mechanism. Through analysis, it was found that the minimum purchase price developed by the state did not increase the area of grain cultivation, but it effectively inhibited the reduction of grain planting area. Finally, we use this model to predict the minimum purchase price for food for 2017, with a reasonable range of: (115.52, 121.54).
Media Development on the Concept Sentence Learning Model based Android for Students with Hearing Impairment

Abstract This study aims to find out development of android based learning media that adopts the Concept Sentence model for students with hearing impairment, so that their learning outcomes, especially in understanding a social science’s learning materials would increase. The subjects of the study were 30 students from one of the special schools for students with hearing impairment in Surakarta City. The present study was conducted using a research and development method. The data were collected using a questionnaire while Miles and Huberman model of Analysis was employed for the data analysis. The results show that most of the students consider that they had difficulty in understanding the board material of social science, due to the inadequateness of the previous media. The students need a media that come with the required competence to be achieved, clearly and briefly delivered using words and punctuations that can be easily understood by the students. The students wish for an interesting media display and most of them assent on the use of “Belajar IPS itu Asyik dan Menyenangkan” as the title on the learning media’s homepage.
Electrodynamic processes models in atmospheric surface layer

Abstract The paper outlines the methodological approaches to the problem of mathematical modeling of electrical processes taking place in the surface layer of the atmosphere. A comparative analysis of various problem statements was carried out in terms of the possibilities of qualitative and quantitative analysis, as well as a set of hypotheses taken into account in the models regarding the properties of the studied processes.
Detecting negativity in user comments using emotional maps and convolutional neural networks

In this paper we present a new approach to negativity detection in online user comments - an emotional image model. This model mimics image processing paradigm, where a comment is represented as a sentiment map retracing the sequence and proportions of various emotions in the text extract. We use 1D convolutional neural networks (CNN) to process 1D multichannel emotional maps which represent the emotional/sentiment image of a comment. The results show that our approach is capable of modeling and processing complex emotional patterns and detecting specific sentiments within the text image (negativity in our case) in a way similar to a classical CNN in object detection/image classification tasks.
INNOVATION AS A DISTINGUISHING FEATURE IN THE MODEL OF FUNCTIONING OF A MODERN MANAGER

The present article shows how important is the role of innovations in companies, their impact on managing companies as well as how innovations affect companies’ competitiveness in the market. The choice of the topic was influenced, to a large extent, by the author’s interests and insights that determine management innovations in contemporary enterprises. The aim of the article is to show steps must be undertaken to implement management innovations in managerial staff. The main intention is to describe the level of innovation of managerial staff, measured by implementation of management innovations in modern enterprises. The theoretical part of the article describes managerial staff, i.e. factors shaping the level of innovation, features and tasks of managers. In the empirical part of the work the author presents the results of the research on correlations between the level of innovation in the area of management and the features affecting the environment. There are no significant relationships between characteristics of managerial staff and the intensity of management innovations. These relationships concern age, seniority in managerial positions and education. The statistical analysis also did not demonstrate any significant correlations between company’s components such as the type of activity carried out, capital structure, age and size of an enterprise. The research has shown that implementation of innovations in the area of management and their direct relationship with management styles is an important phenomenon for the functioning of enterprises. Relationships in the level of innovation as well as the impact of external and internal factors on innovations implemented in enterprises have been studied. The methods and techniques that can be applied to study the level of innovations are described. The author presents the assessment of innovation in the area of management as a factor influencing the development and financial and non-financial results of an enterprise. Then the author does research concerning identification of the level of innovation in the surveyed enterprises. An important source of information on management innovations in the surveyed enterprises are direct interviews in enterprises of production, service and trade profiles in the municipality and the town of Ostróda.
A New Complex Wavelet Relative Phase for Osteoporosis Diagnosis

Trabecular bone (TB) characterization for osteoporosis diagnosis on plain radiographic images presents a challenging task in medical imaging. The goal of this paper is to study the information of complex wavelet coefficients to extract descriptors of the trabecular bone. These descriptors are extracted using some statistics of a new relative phase coefficients. The relative phase modeling is performed using two well-known circular models, called Von Mises and Wrapped Cauchy. Both models have the advantages of simple feature extraction using maximum likelihood estimators and similarity measurement of the Kullback-Leibler divergence, which is very helpful in the runtime task. Our proposed approach is used to recognize the osteoporosis patients from a population composed of both osteoporosis patients and control cases. Experimental results on a TB radiograph database show that considering the proposed approach improves the classification performances over the state-of-the-art methods.
PENERAPAN METODE DRILL UNTUK MENINGKATKAN KEMAMPUAN MENULIS EJAAN BAHASA INDONESIA

This research is motivated by many mistakes in Indonesian spelling usage that cause the law of writing Indonesia spelling skills. Based on these problems, researchers solve it by implementation drill method. The purpose of research were (1) to describe the implementation of learning the drill method application in writing spelling Indonesian skillsand (2) describe an increase in the ability to write spelling Indonesian with the adoption drill. The method used is classroom action research. The reaseacrh design use the model Kemmis and Mc. Taggart of planning, implementation, observation and reflection with 3 cycles. The results show that (1) the teacher carry out all stages of the drill method based on the stage that has been planned, (2) Student ability to write Indonesian spelling each cycle increases. In cycle I percentage of learning completeness only reached 38%, in cycle II increased to 83% an in cycle III increased to 93%. So implementation of drill method can increased Indonesia spelling skills.
A knowledge based system for diagnosing heart diseases

Abstract The purpose of this study was to design a knowledge based expert system for diagnosing heart diseases by using forward chaining as one of inference technique and analyse the software functionality through black box testing. The heart organ chosen because many mortality rates are caused by sudden heart attacks. The research method consists of seven stages: preliminary studies, data collection, knowledge acquisition, knowledge representation, knowledge implementation, system evaluation, and the last, drawing conclusions. The software application recognizes the type of heart disease after consultation by answering a few questions about the symptoms of disease that displayed by the system, and can infer the type of heart diseases suffered by the patient. Based on the results of black box testing, the expert system application has been running with good functionality. The knowledge based expert system is designed to provide solutions or initial actions that can be performed by patient in relation to heart disease he suffered (early detection).
Frame selection to accelerate Depth from Small Motion on smartphones

Depth from Small Motion (DfSM) is particularly interesting for smartphone devices because it makes it possible to get depth information with minimal user effort and cooperation. The state of art method requires about 30 images for the optimization to converge fast and produce accurate depth-map. As the use of high number of frames contribute to long execution time and huge memory allocation, we propose a frame selection strategy using Inertial Measurement Unit (IMU) and image based analysis. As a result, only 5 frames with appropriate viewpoint from the reference one are used for the depth-map generation. Full experiment is done on an Android platform using optimized version of the proposed method with CPU-GPU co-processing under OpenCL. We are able to provide accurate camera parameters and depth-map estimates using only these selected frames.
The analysis method and application of automobile driving direction deviation

Abstract When the automobile body deviates, being one of the hidden dangers of road traffic safety, it affects not only the automobile operation stability, but also the accuracy of headlight detection results. The driving directions analyzing method based on binocular stereo vision was presented. The shape center of the front license plate was extracted as automobile body feature point based on mathematical morphology and Hough transform. The sequence of the feature points was fitted into a straight line, which was the driving direction of the automobile. The measurement error of the headlight beam irradiation was corrected. The experiment results show that that this method can effectively detect the deviation angle of the automobile driving direction, and improve the accuracy of headlight detection.
Chess Game Software Design

Abstract Along with the development of information technology (IT) which is rapidly increase. There is development of technological and electronic tools that are able to create efficient time. In the Design of the Chess Game Software, users use computerization to find solutions to chess game by using the method (Alfha-Beta). Alfha-Beta method is one of the first discoveries in reducing the number of positions that must be searched so as to increase the depth level of the search that can be achieved in one game. The idea is in a large part of the tree we are not looking for values exact of a position, but only interested if it is better or worse than what we found before only the value of a position that fulfills the main conditions must be determined (the main provisions include the change between the player’s best move and the opponent’s best step from a root into a tree). And the Programming Language used in Chess Game Software Design is Visual Bbasic 6.
Contrast Enhancement using Mode-Separate Histogram Equalization

Due to the inferior quality of image capturing devices and acute environmental condition, digital images may suffer from poor contrast. Several histogram equalizing methods have been proposed to enhance these images but most of them either create over enhancement with artifacts or cannot fulfill consumers' desired contrast. However, due to having improper handling of the pixels, histogram equalization (HE) based techniques often fail. Hence, a new image enhancement approach has been introduced here named as Mode-Separate Histogram Equalization (MSHE) which finds a separation point using most frequent intensity of an image and afterwards HE is applied. In experiments qualitative and quantitative assessments exhibits that the performance of MSHE is better than other contrast enhancement with brightness preservation techniques.