Reviewerly/exHarmony huggingface dataset:
A Parallel Binocular System for 3D Pose Measurement by a Single PTZ Camera

A novel universal parallel binocular (UPB) system by precision linear moving a single PTZ (pan-tilt-zoom) camera is presented here to achieve the accurate pose (position and attitude) measurement in computer vision applications. Unlike traditional parallel binocular system, the optical axes of UPB is not necessarily always perpendicular to its baseline so that it can measure the 3D pose of the object in a wider view. The main sources of error in 3D measurement process using the UPB system are firstly analyzed. The online calibration of intrinsic parameters including the radial lens distortion coefficient of the PTZ camera without any calibration target are then introduced. Moreover, for carrying out the robust PTZ camera online calibration and accurate 3D pose measurement with classic eight-point algorithm, we develop an improved descriptor named as CSCD-SURF(Circular Coordinate Combining Shape-Color Descriptor Under Distortion Based SURF) to extract matching points. Experimental results have been enclosed to show the effectiveness and accuracies of the proposed UPB system.
One out-of-core block solving method of large-scale linear equations

The huge amount of memory is needed for solving the large-scale linear equations. However, many problems can not be calculated due to the limitation of the computer’s physical memory. To solve the above problem, One Out-of-Core solving method using the hard disk instead of memory is proposed in this paper. The data are read and written concentratively by blocks, thus the solving time of the problem can be greatly shortened. The experiments indicate that the method can solve the larger-scale linear equations efficiently, the solving time is only 5.7% more than In-Core method.
Review of Prediction of Chronic Disease Using Different Prediction Methods

In healthcare industry, Disease prediction has been an important goal. There are many algorithms and techniques used to predict disease. This paper explains different techniques used to predict and reviews them in order to find a difference between various parameters.
Evaluation of Real Robot Agent Interface for Spoken Dialogue System

The talk target of spoken dialogue system can be “a robot (real)” or “a screen agent (virtual person/living thing)”. When considering from the viewpoint of easy-to-talk and familiarity, the physical robot could be better because of its physical characteristics. In this research, we constructed a dialogue system with the pet robot “Phyno”, which is a friendly interface as a talk partner, and conducted evaluation experiments compared with a display agent interface in a monitor display or personal data assistant. The experimental results show the benefits of the robot's physicality.
The Research Trend of Picture Book Paratext: An Analysis of Korean Journal Articles

The paratext of picture books is an essential element that makes picture books exist, and it can not be discussed except for the paratext in picture book composition. The purpose of this study was to analyze the recent trends of domestic studies related to paratext of picture books, and specifically, the study was conducted based on the year, research method, and research contents of the papers in domestic journals.<BR> To examine the study questions, 100 papers were analyzed in major journals September 1998-2019. The results of this study can be summarized as follow: First, there are one or two papers in the 1990s and 2000s, and more than 10 papers in 2016. Second, the literature research was the most common type of research, and the method of collecting data was the literature collection. As for the data analysis method, the literature review accounted for more than half of the papers, and all the research methods of the papers related to the paratext of picture books were related to the literature (picture books). Third, the contents of the study were classified into paratext, peritext, and epitext, and the results of analyzing the detailed areas showed that the research using peritext accounted for 90% of the total papers. However, as the significance of the epitext, the standard for selecting picture books for infants, is emphasized, it is necessary to study the picture book paratext that includes peritext as well as epitext. This study is meaningful in that it examined the research trends focusing on domestic journals as a cornerstone for empirical research on the paratext of picture books in the future.
Bangla Speaker Accent Variation Detection by MFCC Using Recurrent Neural Network Algorithm: A Distinct Approach

There are a number of languages accent differential applications that detect the different accents in assorted languages. The studies which have done before most of them are based on the English language and different languages throughout the world. A few researches have been performed in Bangla regional language accent differential applications, which is not conclusive for the system to be able to manage Bangla accented speakers. In this paper, we report regional language accent detection experiments of different types of Bangladesh. We demonstrate a strategy to observe Bangladeshi different accents which exploit Mel frequency cepstral coefficient (MFCC) and recurrent neural network (RNN). Listening from the people of different places in Bangladesh creates an accent differentiation results performed by the speakers. This experimental result shows the adaptation of the people to adapt of the regional languages.
Professional and Educational Standards: Inter-Institutional Matching Issues

Abstract The purpose of the study is to solve the problem of matching the parameters of inter-institutional identification of social objects in Russian systems of professional and educational standards, which allows optimizing the procedure of employing young professionals based on their competence potential assessment. The methods of comparison, mathematical and computer modeling were used in the paper. A model of a “competence-based converter” is presented. It makes it possible to evaluate the system of professional competencies generated by a graduate on the basis of a combination of levels of educational achievements in academic subjects presented in the diploma. The use of such a model allows employers to optimize the procedure of the selection of young professionals when filling vacancies in accordance with the requirements of professional standards. The results of applying the “competence converter” are given on the example of the specialty 15.03.02 “Technological Machines and Equipment” and the subsystems of the corresponding Russian professional standards (No. 191, 219, 392, 402, 496).
Real-Time Morphing of the Visible Man Liver with Intrahepatic Vasculatures

Computational models for liver deformation are usually performed without considering intrahepatic vasculatures. The major hurdle is the computational cost when deforming the liver and its vessels simultaneously. In this paper we introduce a numerical method containing a combined constrained constructive optimisation (CCO) algorithm and host mesh fitting (HMF) algorithm. While the CCO algorithm is used to generate a large liver vascular network, the HMF algorithm morphs hepatic structure within a host mesh. This technique is applied to the liver of the Visible Man (VM), where total 16,300 vessels are generated to extend the 84 digitised portal and hepatic veins in the VM liver. The liver deformation due to respiration effects and heart beats is simulated in real-time (35 Hz) and matched with the video sequence of an endovascular Trans-Arterial Chemo Embolization (TACE) procedure. In conclusion an efficient method for morphing a virtual liver containing large vasculatures is proposed, and may have applications in chemotherapy and endovascular simulations.
An Update on the ACM Data Science Taskforce

The ACM Data Science Taskforce was established by the ACM Education Council and tasked with articulating the role of computing discipline-specific contributions to this emerging field. This special session seeks to provide an update of the work of the ACM Data Science Taskforce as well as to engage the SIGCSE community in this effort. Members of the taskforce will report on version 2 of a draft report released Fall 2019, and the activities to-date, including a summary of data science curricular efforts to date, as well as the current articulation of computing competencies. This session should be of interest to all SIGCSE attendees, but especially faculty developing college-level curricula in Data Science.
A Privacy Preserving Approach for Home Ownership Prediction

Web service providers have access to private user data such as preferences and behaviors of users, which is used to provide customized or improved services and make predictions. Privacy restrictions such as General Data Protection Regulation (GDPR) mean that such user data should not be traceable back to the original user i.e. the user's privacy should not be compromised. In this paper, we propose a system for predicting home ownership using machine learning, i.e. whether the user is likely to be a homeowner or a renter, on the basis of the user's demographic data, in a way that preserves the user's privacy while making the predictions. Our system uses differential private data perturbation along with homomorphic encryption of the Term Frequency-Inverse Document Frequency (TF-IDF) vectors as the privacy preservation technique to mask the real identities of the users whose home ownership data is predicted. Our trained model is used for prediction on a sample dataset of a few thousand users. We get an accuracy of 69% in the prediction, which is around 4 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> lower than the algorithm performance without the privacy preservation. This shows that it is feasible to implement privacy preservation techniques on demographic prediction without compromising on the prediction accuracy.
Applying RNNs Architecture by Jointly Learning Segmentation and Stemming for Myanmar Language

Due to the powerful development of internet use, the amount of unstructured Myanmar text data has increased excessive. Stemming has been widely used in a variety of search engine to increase the retrieval accuracy. Stemming is a method that reduces morphology similar variant of word into a single term called stems or roots. Stemming also influence in accuracy of text categorization, Information Retrieval and text summarization etc. Many word stemmers are available for the major languages, but they are not existing for Myanmar. Word segmentation for Myanmar Language, like for most Asian Languages, is an important task and extensively studied sequence labeling problem. There is no space between words and segmentation is essential pre-processing requirement for many natural language processing applications. Segmentation error would cause translation mistakes directly. This approach proposes different types of recurrent neural networks and different layers that jointly learn segmentation boundaries and stemming. Joint word segmentation and stemming of this research is aiming to support Information Retrieval and Myanmar natural language processing applications.
Research On Human-Robot Natural Interaction Algorithm Based On Body Potential Perception

At present, human-computer interaction has become a research hotspot in the field of computer. In the field of vision of human-computer interaction, identifying and tracking human activities is still a highly concerned task for researchers. Sensors and communication technologies are being used to capture human motion and provide an interactive way for human-computer cooperation. However, using intelligent computing method to identify human activities in different scenes is still a field rarely studied by researchers. In this paper, we make full use of an intelligent calculation method to recognize other people's activities and carry out human-computer interaction. By detecting the information of human joints, we calculate the movement rate of joints, and then effectively identify simple human actions. Through experiments, paper robot can accurately respond to human actions.
Application of Ecosystem Modelling Methodology on Rural Areas of Crimea—Systematic Approach

The possibility of adapting agricultural ecosystems and modelling their condition under changing economic conditions is determined by agricultural status indicators under current conditions compared to retrospective indicators characterizing the socio-economic and ecological status of rural areas and the agricultural ecosystem as a whole. The analysis of their development involves a systematic approach, the first phase of which is the development of a verbal model taking into account statistical data on the economic, social and environmental situation of agricultural areas. For this purpose, three districts of the Republic of Crimea (Krasnogvardeysky, Dzhankoysky and Saksky) were selected as pilot regions. The indicators were calculated to identify strong and weak development trends and the ability to adapt the regions to the conditions of changes in economic activity. It can be seen that at this stage the Krasnogvardeysky region occupies the best position (integral coefficient: 0.3), where the economic and social coefficients are highest; the position of the Dzhankoysky and Saksky regions is approximately the same (integral coefficients: 0.19 and 0.18, respectively). The development of a methodology for ecosystem modelling initially involves the assessment of individual elements. First simulations of winter crops were carried out, thereafter an agro-ecological evaluation of soils based on soil parameter modelling. Further development steps of the methodology are (1) the analysis of the status of all regions of the Republic of Crimea (RC) and (2) the identification of optimization and sustainability regions using logical-mathematical models and fuzzy cognitive maps (FCM). In particular, they will address the questions of how rational solutions can be found for their development and how emerging problems can be eliminated.
Hierarchical Encoding of Sequential Data With Compact and Sub-Linear Storage Cost

Snapshot-based visual localization is an important problem in several computer vision and robotics applications such as Simultaneous Localization And Mapping (SLAM). To achieve real-time performance in very large-scale environments with massive amounts of training and map data, techniques such as approximate nearest neighbor search (ANN) algorithms are used. While several state-of-the-art variants of quantization and indexing techniques have demonstrated to be efficient in practice, their theoretical memory cost still scales at least linearly with the training data (i.e., O(n) where n is the number of instances in the database), since each data point must be associated with at least one code vector. To address these limitations, in this paper we present a totally new hierarchical encoding approach that enables a sub-linear storage scale. The algorithm exploits the widespread sequential nature of sensor information streams in robotics and autonomous vehicle applications and achieves, both theoretically and experimentally, sub-linear scalability in storage required for a given environment size. Furthermore, the associated query time of our algorithm is also of sub-linear complexity. We benchmark the performance of the proposed algorithm on several real-world benchmark datasets and experimentally validate the theoretical sub-linearity of our approach, while also showing that our approach yields competitive absolute storage performance as well.
Distributed Secure File Storage System Using Cryptography

In recent times, cloud storage and distributed system have risen to great success owing to their easy use and availability to users. These have helped users to gain access to their data anywhere, anytime solely with the help of internet. But all these features come with greater risks and security threats. Traditional data security methods cannot be considered fully reliable given the advancements in cryptanalysis techniques and sophistication of cyber-attacks. Encryption alone is insufficient to ensure data security and integrity. In this paper, we provide an approach to create a secure distributed file system. We propose a two-layer architecture where the data will first involve interaction with the server. Once we ensure authenticity and integrity of the data, the next step involves storing it securely on the cloud which involves interaction of server with the distributed system which will be completely abstract for the user. The first layer makes the use of digital signature for client authentication and data integrity. The second layer will make use of attribute-based distributed storage of data in encrypted form with hashing techniques to remove redundancy in the data stored in the system.
Extraction of Degradation Parameters for Transparency of an Image Restoration Network

Many image restoration processors based on convolutional neural network (CNN) has been proposed because of its high performance. However, it is well known that restoration by these networks is not robust against perturbations on a degradation model. If restoration fails, it is difficult for users to find the cause because no explanation is given by the network. In this paper, we propose an additional network to extract internal parameters for an image restoration network to supply users explaining information on restoration process. Experimental results show that the proposed network successfully extracts estimated degradation attributes and gives helpful information to assist users to find a root cause of a restoration failure.
Quantum Inspired Evolutionary Algorithm for Web Document Retrieval

This paper describes the importance of quantum inspired evolutionary algorithm for web document retrieval. As size of web increases day by day the popular methods for web document retrieval are taking much time thereby making users concerned about it. Our approach takes inspiration from evolutionary algorithms to model a mechanism which returns experimental results with acceptable accuracy. We have seen from trial results that after some time the general capacity of proposed strategy is superior to the best two most well known methods of web document retrieval. The primary bit of leeway of utilizing our proposed calculation is that its presentation doesn’t debase regardless of whether the search space is variable. Irrespective of whether search space is expanding or diminishing the exhibition doesn’t drop for our proposed procedure. We discuss comparative performance of the proposed technique with other popular techniques in this area and conclude the paper with future scope of fine tuning the performance of the proposed mechanism with addition other parameters effecting the performance in the mechanism. In future we intend to refine the propose system to suit diverse web prerequisites like customized web search and furthermore for web document retrieval from centralized server databases as well.
Sentiment Analysis in Movie Reviews Using Document Frequency Difference, Gain Ratio and Kullback-Leibler Divergence as Feature Selection Methods and Multi-layer Perceptron Classifier

Both industries and people are interested in knowing the reviews of the products or movies. If the number of reviews are large, manual classification of reviews into either positive or negative classes, is tedious and time consuming. As an alternative, Sentiment Analysis, a subdomain in Natural Language Processing aims to automate the above process, by training the models by using reviews as the training data. These are popular because this is very essential for making decisions by knowing the other person’s opinion. In this paper, Document Frequency Difference, Gain Ratio and Kullback-Leibler divergence are used for feature selection and the classification is done with Multi-Layer Perceptron classifier. This is done with movie reviews dataset like SAR14, IMDB11, Cornell open source benchmark dataset. The results show that Document Frequency Difference and Gain Ratio and feature selection methods have better sentiment classification performance i.e. with better accuracy and reduced error.
An Adaptive Ensemble Classifier for Handling Recurring Concepts

The assumption with many learning algorithms is that the underlying distribution of the data is static. However, for many real world applications, data is streaming and collected over an extended period of time. Learning in such dynamic and nonstationary environments presents a challenge not common in static domains as the statistical properties of the target variable which the model is trying to predict change over time, a phenomenon known as concept drift. The presence of concept drift can potentially cause a significant accuracy deterioration of an exploiting classifier. Furthermore, previously learnt concepts may reappear and reusing previously learnt models can optimize the learning process in terms of predictive accuracy and processing time. In this paper, we propose handling recurring concepts in time evolving environments with the Diversity Based Ensemble for handling recurring concepts (DERC), a learning algorithm that preserves previously learned diverse models and trains every model preserved with the new data. Empirical studies on one synthetic data set and one real world data set, all associated with concept drift demonstrate that DERC can effectively handle recurring concepts than other two state of the art approaches.
An Effectual Sentiment Analysis for High Classification Rates Using Medical Image Processing

Sentimental data is now a trend can be generally considered into two key types mainly facts and feelings. Facts are unbiased expressions around entities, actions, and their belongings. The thoughts of estimation in terms of sentiments are very extensive. In this paper, the main focus is given on the opinion terminologies that carry positive or negative thoughts. These thoughts are considered as sentiments. Plentiful work is done already using text processing in terms of mining of the information and recovery of the data. It is done using clustering approaches, mining of the text and other various text mining tasks but very less work is in handling of opinions in the medical field. Yet, sentiments are so imperative in the medical field to make decisions. The dataset on which the processing is done is the digital retinal DRIVE dataset was taken with 8-BPC (bits per color level) at 768 × 584 pixels. So this paper put light on the efficient approach for sentiment analysis using normalization and feature extraction for high classification rates and the simulation environment is used as MATLAB for development purpose.
The Library for Hadoop deflate compression based on FPGA accelerator

A Hadoop application will produce lots of intermediate results in the map/reduce process that requires disk I/O and network transmission, which can take a lot of time. By compressing large scale data of intermediate results, it will greatly improve disk access efficiently and reduce program run time. Therefore, Hadoop provides a compression option. Hardware-accelerated solutions have become more desirable. This paper proposes a way that applies a hardware compression accelerator on the Hadoop platform, and the system performance analysis compared with a software-only solution that needs CPU participation. The testing programs are modified the zpipe, TestDFSIO, and Terasort. In contrast with the software solution. The speedup ratio of the zpipe is 6.42X, the speedup ratio of TestDFSIO is 6.28X, the speedup ratio of the Terasort application is up to 3.25X.