Reviewerly/exHarmony huggingface dataset:
Performance Analysis of Different Classifiers in Prediction of Breast Cancer

Objectives:The major motivation is to build the prediction model for diagnosis.The fundamental exploration of prediction is to anticipate breast cancer at a prior stage that guarantees a long survival of patients.Methods/Statistical Analysis: In medical field, the classification of tissues surrounding the malicious cancer cells into benign and malignant categories is extremely challenging task to predict.For diagnosis of a disease, Naive Bayesian [NB], Support Vector Machine [SVM] and Artificial Neural Network [ANN] Classification systems are investigated and Fuzzy C-Means Clustering are analyzed to make clusters.Fuzzy C-Means Clustering [FCM] algorithm clusters the data with simulated annealing which is classified using the above mentioned classifiers in furtherance of developing best prediction model with predefined rules.The performance is validated with K-fold cross validation.Findings: The Wisconsin Breast Cancer Dataset [WBCD] from UCI dataset storehouse is utilized to test the execution of classifiers.This dataset holds 10 properties with 699 records.This dataset has been clustered as benign and malignant.In the clusters, to achieve global optima simulated annealing technique is used and the classifiers are applied for clusters.In this examination, Fuzzy C-Means Clustering [FCM] with simulated annealing and Naive Bayesian classifier serves to be the best one with 89.2% accuracy and its F-measure is computed as 0.9417.The various performance metrics are computed for proposed novel model and its results are compared with existing values which indicates, the Naive Bayesian classifier works well for non-dependency data as there is no affinity between attributes and is considered as most noteworthy among them.Application/Improvements: Prediction model can be used for predicting any disease in medical field domain, which can be further improved by using Farthest First Clustering [FFC] algorithm.
Trajectory Stability Control of the lidar tracking robot Based on Improved Particle Swarm Filter Algorithm

Aiming to improve the accuracy of path planning and autonomous positioning of the lidar tracking robot, the accurate tracking control of its moving trajectory is achieved. The trajectory of the lidar tracking robot is affected by the map location offset error, which leads to the low control accuracy. An improved particle swarm filter (PSO) algorithm is proposed to control the trajectory of a lidar tracking robot. The motion environment model of the lidar tracking robot is simulated and constructed, and the space coordinates of the lidar tracking robot are abstracted into the virtual world of particle swarm filter population. The tracking map grid model of the lidar tracking robot is obtained, and the target point is found by Particle Swarm filter evolution and the remote command control is carried out. An integral term of tracking error is designed on the synovial surface of the lidar tracking robot. The simulation results show that the algorithm is used to track the trajectory of the lidar tracking robot. The path offset correction ability is good and the control is stable.
Some decidability results on one-pass reductions

We show second-order decidability results on recognizable tree languages and one-pass reduction sequences with special term rewriting systems. For some classes of term rewriting systems the second-order IO one-pass inclusion problem, the second-order IO one-pass reachability problem, and the IO and OI one-pass common ancestor problems are decidable.
Review of retrieving soil heavy metal by laboratory-based hyper-spectroscopy

Laboratory-based hyper-spectroscopy is a technology based on the ground high resolution spectrometer under laboratory conditions. Owing to effectively controlling the interference level, it plays an important role in the research of soil heavy metal pollution concentration inversion. In this paper, the theoretical basis for the inversion of soil heavy metal content by spectral reflectance characteristics is discussed. The current study of laboratory-based hyper-spectroscopy on soil is analyzed. The common technical methods for the inversion of soil heavy metal concentration by spectral reflectance characteristics are summarized. The problems encountered in the application of laboratory-based hyper-spectroscopy are analyzed.
Privacy preserving solution to prevent classification inference attacks in online social networks

In order to improve their business solutions the data holders often release the social network data and its structure to the third party. This data undergo node and attribute anonymisation before i...
Novel Approaches for Geometrical Model-Based Calculation of Human Body Segment Inertial Parameter Values

In human motion analysis, inverse dynamic solutions are used for the determination of joint reaction forces and net joint torques. This approach requires a full kinematic description, external forces and, most important, accurate anthropometric measures. For the estimation of human body segment parameter values, several methods have been proposed in literature throughout the last 120 years. The paper gives an overview of historical and contemporary methods with a specific focus on approaches based on geometrical models of the human body. Methods for (semi-)automated shape detection, segmentation and calculation of anthropometric dimensions are presented and compared. Body segment parameters of Hatze’s hominoid model can be estimated applying image based or photogrammetric techniques. Results of an evaluation study on 6 subjects (3 male, 3 female) by comparing principal moments of inertia and total body masses are shown. The calculation of body segment inertial parameters from 3D surface scanning techniques has proven to be a fast and accurate method without exposing subjects to radiation. Future developments in application of mobile 3D scanning techniques can be expected to further ease handling and reduce costs.
CRITERIA AND INDICATORS OF PROFESSIONAL PREPAREDNESS OF PERSONS WITH DISABILITIES AND RESTRICTED HEALTH OPPORTUNITIES AS COMPETITIVE SPECIALISTS

The article is devoted to the actual problem of training people with disabilities and disabilities as competitive specialists in adaptive physical culture. The authors substantiate the relevance of the study; reveal the essence of competitiveness as the ability of persons with disabilities to compete in the labor market on a par with specialists who do not have limited health opportunities. Based on the theoretical analysis and personal experience of pedagogical activity, the authors develop criteria and indicators for the level of preparedness of students with disabilities as future specialists in adaptive physical culture. As the main criteria, the authors consider the levels of theoretical, practical and personal readiness of students to implement future professional activities. According to the authors, each criterion includes a number of individual criteria that reflect the knowledge, skills, skills, professionally important qualities of their personality and the ways of their implementation, which constitute the content of general cultural, professional and professional competencies required by trainers-methodologists and trainers-teachers adaptive physical culture to successfully perform their work functions. The results of the study can serve as a basis for the development of diagnostic tools for pedagogical monitoring of the level of professional preparedness of persons with disabilities and disabilities as competitive specialists in adaptive physical culture.
Coarse-to-fine road scene segmentation via hierarchical graphical models

The road scene segmentation is an important problem which is helpful for a higher level of the scene understanding. This article presents a novel approach for image semantic segmentation of road scenes via a hierarchical graph-based inference. A deep encoder–decoder network is first applied for a fast pixel-wise classification. Then, hierarchical graph-based inference is performed to get an accurate segmentation result. In the inference process, all the object classes are grouped into fewer categories which contains at least one class. The category labels are assigned to image superpixels using Markov random field model. For each category, a pixel-level labeling based on fully connected conditional random fields is performed to divide image into different classes. After the inference for all categories, the results are integrated together to get the final segmentation. In additional to low-level affinity functions, the feature maps from network are integrated in pairwise potentials of the graphical models. This hierarchical inference scheme can alleviate the confusion of classes belonging to different categories. It performs well for small objects without adding more computational burden. Both qualitative and quantitative assessments are adopted to evaluate the proposed method. The results on benchmark data sets prove the effectiveness of the proposed hierarchical scheme, and the performance is competitive with the state-of-the-art methods.
Self-calibrating phase shifting shadow moiré technique

The phase shifting shadow moiré is an efficient technique for 3-D shape measurement. Our recent research focuses on improving its measurement accuracy without addition the complication of the experimental set-up. Considering that the existing measurement process of shifting shadow moiré technique is complex, we had been looking for ways to make it easier. In this paper we propose some effective techniques to fulfil the aim. In the proposed method, the binocular stereovision technique is used to calibrate the geometric parameters of our setup. Then a method is developed to determine the grating translation difference. After that an iterative self-tuning algorithm is use to retrieve the accurate phase. The proposed method is fast and can be implemented easily in many applications. Optical experiments are implemented to verify the feasibility of this method.
A Fuzzy Logic Model for Evaluating Customer Loyalty in e-Commerce

This research proposes a model for customer’s loyalty by sentiment analysis of ecommerce products. The purpose behind this research is to evaluate the response of customers in the shortest time. It practices sentiment analysis that tends to understand the user’s feedback about the product and services on ecommerce sites. The data is openly available on these sites in the form of reviews, comments and appraisals. This data focus on the customer’s opinions and helps business to take proficient decisions in the limited time. It takes subjective reviews because objective part contains emotion symbols. Many people do not know the proper use of emotions. It also prefers to use Stanford POS (Parts-of-Speech) tagger from Stanford Core NLP toolkit. This tagger assigns part of speech to every word of the reviews as we extract adjectives to measure the scores. This paper also used these techniques: tokenization, Lemmatization and stop words removal. By the use of soft computing approach- Fuzzy logic, it will able to design a customer loyalty model by its membership functions and truth values between 0 and 1. It uses SentiWordNet software to measure the P-N polarity scores. This proposed model reduces the problems from the related past researches. This research collects results from the reviews from Amazon.com which shows 72% customers are loyal towards ecommerce products. The outcomes that can allow business organization improve customer loyalty techniques to gain profitable results.
A Deep Architecture for Chinese Semantic Matching with Pairwise Comparisons and Attention-Pooling

Semantic sentence matching is a fundamental technology in natural language processing. In the previous work, neural networks with attention mechanism have been successfully extended to semantic matching. However, existing deep models often simply use some operations such as summation and max-pooling to represent the whole sentence to a single distributed representation. We present a deep architecture to match two Chinese sentences, which only relies on alignment instead of recurrent neural network after attention mechanism used to get interaction information between sentence-pairs, it becomes more lightweight and simple. In order to capture original features enough, we employ a pooling operation named attention-pooling to convergence information from the whole sentence. We also explore several excellent performance English models on Chinese data. The experimental results show that our method can achieve better results than other models on Chinese dataset.
Multitree-like Graph Layering Crossing Optimization

We improve a method of multitree-like graph visualization using a spanning tree-driven layout technique with constraints specified by layers and the ordering of groups of nodes within layers. We propose a new method of how the order of subtrees selected by the driving spanning tree can be derived from the actual edge crossings. Such a subtree order leads to additional decreasing of total edge crossings from 1% to 50%. This depends on the shape of the processed graph, ranging from a pure tree to a general acyclic graph. Our achievements are demonstrated using several datasets containing up to millions of people, species, or services. The proposed subtree ordering method of layered graphs that are similar to acyclic multitrees retains the generating of acceptable layouts in almost linear time.
Modern file transfer protocol using lossless compression, lattice-based encryption, and a data integrity hashing function

With the recent advances in technology, the creation and access to voluminous amounts of data has become more and more common. From consumer computers and smartphones to big data, the transfer of data entailed a longer download and upload time as the size gets bigger, thus taking a significant amount of time for data to reach its destination. Moreover, data can be corrupted as it is being transferred over the network and most file transfer protocols do not have a file integrity check. Lastly, most file transfer protocols employ the RSA cryptosystem which can be broken through the use of a quantum algorithm known as Shor's algorithm. The proponents have implemented a file transfer protocol that integrates file hashing, compression, and a quantum-resilient cryptosystem. The results show that the proposed file transfer protocol is faster than plain FTP and even faster against FTP over TLS.
The Implementation of Chinese Acoustic Model Efficiency Optimization Based on Convolutional Neural Network

In the speech recognition system, the traditional convolution method of convolutional neural network (CNN) model has the problem of low efficiency, especially when processing a large number of speech feature map data. In this paper, a new convolution method for the CNN model has been proposed, which makes the efficiency of CNN model about five times faster than the traditional method. And at the same time, the recognition accuracy of the new method is comparable with traditional method.
Design and testing of a chromatic dispersion system for displacement application by using a spatial-bandpass-filter

Chromatic confocal displacement measurement is a newly developed technology for linear precision positioning with a sub-micron meter accuracy over a millimeter order range. In this technique, the chromatic spectrum of a white light source is divided by an optical dispersion system. The encoded spectrum disperses along the axial direction, with which the axial position can be detected by a spectrometer at a high precision. Thus, the optical dispersion system that determines the final measurement precision is required to own a large dispersion capacity and a small spherical aberration. A chromatic dispersion lens (CDL) module and a spatial-bandpass-filter (SBF) were proposed in this research to improve the dispersion performance. Based on the geometrical imaging principle, the CDL and SBF parameters were preliminarily designed and further tested by the optical design software ZEMAX. The testing results verified that with SBF and CDL, the spherical aberration was largely reduced and the imaging quality was greatly modified. The focus beam spot of the central wavelength 550 nm can be as small as 10μm. Under such a condition, the measurement error is no larger than &plusmn;0.7&mu;m over a 1700&mu;m measurement range.
A Decision Support System for a Surgeon in Preoperative Planning of Mini-laparotomy Gallbladder Surgery

The article is devoted to a developed decision support system for a surgeon in preoperative planning of mini-laparotomy gallbladder surgery. The structure of the decision support system is presented. The sets of indicators are defined that a surgeon should take into account when performing the selection of surgical approach localization and quantitative assessment of a surgical approach. A developed algorithm of decision-making support of a surgeon in preoperative planning of mini-laparotomy gallbladder surgery is presented that takes into account individual characteristics of a patient. The algorithm was implemented in the SurgEXPERT software package.
Improving the Dictionary Construction in Sparse Representation using PCANet for Face Recognition

Recently, sparse representation has attracted increasing interest in computer vision. Sparse representation based methods, such as sparse representation classification (SRC), have produced promising results in face recognition, while the dictionary used for sparse representation plays a key role in it. How to improve the dictionary construction in sparse representation is still an open question. Principal component analysis network (PCANet), as a newly proposed deep learning method, has the advantage of simple network architecture and competitive performance for feature learning. In this paper, we have studied how to use the PCANet to improve the dictionary construction in sparse representation, and proposed a new method for face recognition. The PCANet is used to learn new features from face images, and the learned features are used as dictionary atoms to code the query face images, and then the reconstruction errors after sparse coding are used to classify the face images. It is shown that the proposed method can achieve better performance than the other five state-of-art methods for face recognition.
All Colors Motion Estimation for the Frame Sequential Video with Pastel Color

Currently, Y, Cb, Cr 4:2:0 system is widely used as the color format for the video system. We have proposed the frame sequential system as an alternative method which has one color plane in a frame. In this system, the conversion to usual RGB requires a motion compensated color interpolation. However, it is not easy in the case of high saturation color images, because of its difference between color planes. To solve this problem, we have proposed the color format which has dependent colors. The primary colors (RGB) are changed to pastel colors. To address the conversion to RGB, motion estimation should be used, some problem of motion vector still remains. To correct motion vectors, the process of motion estimation should be enhanced. In this paper, all colors motion estimation is added as new motion estimation after the previous motion estimation. We use these processes as two stages motion estimation. Two stages motion estimation is compare to the previous only. From experimental results, all colors motion estimation gives higher PSNR. The maximum of PSNR gain is about 1dB.
A Flexible Framework for Program Evolution and Verification

We propose a flexible framework for modeling of distributed systems, supporting evolution by means of unrestricted modifications in such systems, and with support of verification and re-verification. We focus on the setting of concurrent and object-oriented programs, and consider a core high-level modeling language supporting active, concurrent objects. We show that our framework can deal with verification of software changes that are not possible to verify in comparable frameworks. We demonstrate the approach by variations over a simple example.
Tagging Assistant for Scientific Articles

With the advent of World Wide Web (WWW), world is being overloaded with huge data. This huge data carries potential information that once extracted, can be used for betterment of humanity. Information from this data can be extracted using manual and automatic analysis. Manual analysis is not scalable and efficient, whereas, the automatic analysis involves computing mechanisms that aid in automatic information extraction over huge amount of data. WWW has also affected overall growth in scientific literature that makes the process of literature review quite laborious, time consuming and cumbersome job for researchers. Hence a dire need is felt to automatically extract potential information out of immense set of scientific articles in order to automate the process of literature review. Such service would require machine learning models to train. Whereas, such model in turn require training dataset. To construct a quality dataset often involves employment of annotation tools. There exist wide variety of annotation tools, but none are tailored to assist annotation of scientific articles. Hence in this study, web-based annotation tool for scientific articles is developed using Python language. The developed assistant employs state of the art machine learning models to extract metadata from scientific articles as well as to process article’s text. It provides various filters in order to assist annotators. An article is divided into various textual constructs including sections, paragraphs, sentences, tokens and lemmas. This division can help annotators by addressing their information need in an efficient manner. Hence, this annotation tool can significantly reduce time while preparing dataset for full-text scientific articles.
Checkers Player Next Move Aided System

Main aim of the paper was to create checkers player move aided system. For this purpose two different approaches were used. First one was well known searching algorithm—Negamax and second one was Reinforcement Learning algorithm—SARSA. For the purpose of making experiments on algorithms’ performance special environment was created. It was checkers program which main goal was to give its user possibility of launching the game between two different agents. One of its constraints was also to make creating and adding new agent easy for the future use in other research.
Segmentation of the Wear Area of Planar Shoeprint Using Mean Shift

In the field of forensic science, the wear area of planar shoeprint is a useful information for analysis of criminal suspects. However, the identification of shoeprint wear areas is often subjective to the expert's personal experience. In this paper, considering the characteristics of planar shoeprint images, a segmentation algorithm based on mean-shift is proposed to process planar shoeprint image to get the wear area. Firstly, the multiplicative intrinsic component optimization method(MICO) is employed to pre-segment the image. This method combines the level set with the offset field correction method to segment the outline of the unevenly printed shoeprint image. Secondly, we use mean shift to segment the image. Finally, we compare the actual wear area with the segmented wear area. It is found that the segmentation effect is good. By pattern recognition method, the wear area of the shoeprint is segmented to provide more objective technical support for detecting cases.