Reviewerly/exHarmony huggingface dataset:
Improved Electronics Medical Records (EMR) Security Using Fuzzy C-Mean and Reversible Data Hiding for Teleradiology

In the medical field, the high range of retrieving patient details embedded information may create more sarcastic issues and unreliability of data. The damage in patient detail embedded data will leads to more ethical and complicated social issues to the patients and it may ruin the trustworthiness of the health care system. In most of the technology assisted in health care system must be take some health records as their reference. Medical images such as MRI scanning images have a great role to identify the diagnosis part of the health care system and also it is used as the input data for technological aspects. Therefore we have to take more preventive measures to these medical images from damages and other issues. The medical imageintegrity, reliability and authenticity will keep up by some of the security mechanisms. Many of the methods derived in this era. But there are lot of limitations too. This paper proposes new method using Fuzzy C means and more efficient cryptographic methodsare embedded in this to develop a more sophisticated way to increase credibility of medical images and also by giving a strong link between images and information without losing the image quality for the end user.
The T-S Particle Filter via Intuitionistic Fuzzy Decision

Aiming at the modeling uncertainty and nonGaussian characteristics of dynamic systems in maneuvering target tracking, a Takagi-Sugeno (T-S) modeling particle filter method via intuitionistic fuzzy decision (IFD-TSPF) is proposed. As for the Structure recognition of T-S model, the membership degree of multiple features are calculated by the ridge reduction distribution function, and then the intuitionistic fuzzy point operator is used to calculate the decision scores corresponding to the features. According to the principle of evidence decision, the appropriate premise variables are selected. Finally, the importance density function is constructed by the estimation results of the proposed T-S model, which can effectively reduce the particle degradation. Through explanation of simulation results, which show that the performance of our algorithm is better than interacting multi-model particle filter(IMMPF) and interacting multi-model Rao-Blackwellized particle filter(IMMRBPF). When the direction of the target is suddenly changed, it can effectively track the target.
Optimize Neutral Framework With Fair Share Resource Allocator For Big Data Processing On Cloud Infrastructure

Data is precious! Data generated from myriad resources are store and treated, extracted for getting knowledge through classical machine learning techniques. The MapReduce is a programming framework that proved its efficiency and reliability for big data processing in a distributed environment. When data are generated in a cloud platform the speed and volume of data are high. MapReduce has its own limitation over processing big data. Spark has also eliminated many boundaries especially dealing with online streaming and cloud generated data. Big Data cluster management is a tedious work that needs specialized algorithms for a fair share approach. Many time resource contention problems have to address with fair-share but the performance was not satisfactory. MESOS and YARN have an implementation of the same. The proposed framework use historical job execution logs for admission control and deadline information is an additional and important part which supplied at the time of submission of the job. The work anticipates different deadline formulation explained in the result section. Finally, a comparison of justice with existing fair share allocation policies is discussed.
Real-Time Vehicle Tracking using Convolutional Neural Networks in Aerial Video

Vehicle tracking based on video images has been widely used in military and civilian fields. The tracking method must robust enough to hand the unexpected situations that may occur during the tracking process. In this paper, a novel vehicle tracking method based on convolutional neural networks (CNNs) is proposed to target the accurate and speed demand of vehicle tracking. The proposed method contains two networks with shared weights. It utilizes the residual block to reduce the train error. Offline training is used to achieve real-time tracking. It also use transfer learning to reduce training time. The experimental results under the real aerial video demonstrate that vehicle tracker achieves an accuracy of 70.8% and the speed of 135fps with GPU. The proposed method is robust enough to handle occlusion and other interference conditions.
MsrFace: Multi-Sphere Radius Loss for Deep Face Recognition

Abstract Loss functions is one of the main challenges in face recognition problems. Recent works focus on designing loss functions that make learned features more discriminative by a larger angular or cosine distance. In this paper, in addition to the method based on additional angle margins, we propose a Multi-Sphere Radius Loss (MsrFace) to add radius constraints. MsrFace pushes learned features to hyperspheres with different spherical radii and the classes can be separated more strictly. We present experiments on several widely used benchmarks to show that MsrFace has a better performance in comparison with some recent state-of-the-art face recognition methods.
Development of a Mathematical Model for Solar Power Estimation Using Regression Analysis

The growing energy demand and the need for clean energy source have created a major impact on the renewable energy sector. One such renewable energy source, solar energy, is gaining momentum in various applications like electric power systems, water heaters and many more. Solar power estimation is important because it helps the grid engineers to make decisions like voltage and frequency control that is integral to the working of the grid. This paper presents a mathematical model to estimate solar power that can be generated by photovoltaic panel. It is difficult to estimate solar power that can be generated using a solar panel because it depends on a lot of weather factors which are variable. It is very much essential to have an accurate model for solar power estimation to ensure reliable and economic operation of power grid. The major factors taken into account for solar power estimation are: ambient temperature, cell temperature and solar irradiance. The relation between the above-mentioned factors is modelled by performing regression analysis and is tested for accuracy.
Leveraging Textural Features for Mammogram Classification

Cancer is the body' s tissue cells that continue to grow beyond normal and out of control so that cancer cells push normal cells and cause death in normal cells. One type of cancer is cancer that attacks breast tissue or is called breast cancer. The sooner breast cancer is detected, it will increase the chance the patient will survive. One of the techniques in the early detection of breast cancer is mammography screening. To minimize human error in checking the results of mammography, a CAD system is needed in checking the results of mammography. Therefore, in this research, a system that can classify breast tissue from mammogram into three classes, namely normal, benign, and malignant has been built. The performance of the system reaches F1-Score 74.02%, Recall 76.15% and Precision 74.02%. The system achieves this performance by combining the Uniform Local Binary Pattern and GLCM features and the Random Forest classification method.
Bayes-OS-ELM :An Novel Ensemble Method For Classification Application

Online Sequential Extreme Learning Machine (OS-ELM) has high accuracy and fast update speed in the areas of classification, such as fault diagnosis and anomaly detection. However, OS-ELM selects hidden layer parameters randomly leads to unstable output, which reduces the reliability of OS-ELM seriously. In this paper, a ensemble method based on OS-ELM and Naive Bayes(Bayes-OS-ELM) has been developed. The ensemble model establishes parallel sub-classifiers with OS-ELM and a secondary classifier with Naive Bayes to fuse the results of the former sub-classifiers. Because of the parallel structure, the ensemble model can greatly reduce the disturbance caused by the random set of hidden layer parameters of OS-ELM and make the classification result more stable. Besides, as an accurate and stable algorithm, Naive Bayes effectively promote the accuracy and stability of the classification model. Several UCI data sets have been involved to verify the proposed classification model. Experimental results show that this method has high accuracy, stable result and great generalization performance compared with the existing approach.
Mining Temporal Association Rules of an Attributed Graph Sequence at High Conceptual Levels

To find more informative knowledge from a transactional database, some methods for mining association rules at different conceptual hierarchy have been proposed. For a given attributed graph sequence, how to mine temporal association rules at high conceptual level is the task of this paper, which has not been ever studied. In order to formalize the conceptual hierarchy of an attributed graph sequence, we do the clustering of the vertexes of each attributed graph in the given attributed graph sequence by the k-means algorithm. Viewing each cluster as a vertex, a non-attributed graph sequence can be formed. A new mining algorithm designed based on the Apriori algorithm is able to mining the temporal association rules at high conceptual level for the given attributed graph sequence. Experiments presented in this paper show the good performance of the proposed algorithm.
Procedural Generation of Roads with Conditional Generative Adversarial Networks

Procedural terrain generation refers to the generation of terrain features, such as landscaping, rivers or road networks, through the use of algorithms, with minimal input required from the user. In the process of game development, generating terrain is often an important part of the game development process. Traditional generation methods are often too time consuming especially with larger terrain maps. On the other hand, procedural methods that generate terrain automatically often do not have much user control over the output. We explore the usage of conditional generative adversarial networks in the creation of road maps, as well as the application of such road maps in the creation of game levels in game development engines such as Unreal Engine 4.
An Event-Centric Scientific Literature Information Organization Method

Massive scientific and technical literature has led to information overload for researchers. To obtain scientific and technical information efficiently, this paper proposes a method to organize scientific literature information with events. It aims to promoting the discovery and management of relations among scientific literature for intelligent applications. To construct the semantic link network of scientific research events, the first step is to analyze external characteristics and internal textual contents of scientific literature, and then extract information as the elements of scientific research events. Publication events, cooperation events and citation events formulate the semantic link network of scientific research events. We investigate data sources for the construction of scientific research events, and then we verify the possibility of the proposed method in the case study.
Testing Problems. Testing as a Coding of Knowledge System

In this chapter the following basic question is considered: The test system is a measurement system through assessments of students’ knowledge. The main issues here are: the question of assessing the adequacy of the results of measuring the real level of knowledge and skills of students,the question of a comprehensive assessment of the level of assimilation of the system of knowledge that connects quantitative and qualitative indicators.The main issues here are the question of assessing the adequacy of the measurement results to the real level of knowledge, skills of students. In this regard we shall consider two models of the algebraic formalization of the representation of the knowledge system—in the form of a free group of factors that determine the knowledge system, and in the form of a semantic network, which we represent using finite Boolean algebra. These models allow us to show that all binary tests i.e. tests involving answers in the form of either “yes” or “no”, can be solved true, not knowing the specific nature of the proposed questions. Then we shall show that probability measure can be used as a measure of the level of assimilation of the knowledge system represented by the semantic network.
On the Automatic Assessment of Learning Outcome in Programming Techniques

With the introduction of digital media and the rapid spreading of the digitalization process in our society, there is an increased need in the use of online methods for the automatic evaluation of learning outcomes. Such kinds of electronic assessments (abbr. E-Assessment) are of particular importance in the areas of science and engineering education, where students have to learn and exercise on programming techniques. In this work, we have reviewed and analyzed existing approaches aiming at automatic verification of computer programs for teaching and learning purposes. Based on the capabilities and characteristics of these systems, they have been clustered into three categories. After the study of the strengths and limitations of these approaches, we put forward our view on several aspects which are relevant for an E-Assessment system. Our further contribution lies in the discussion of relevant research questions as well as the potential impacts of E-Assessment in future academic teaching.
A Matching-Integration-Verification Model for Multiple-Choice Reading Comprehension

Multiple-choice reading comprehension is a challenging task requiring a machine to select the correct answer from a candidate answers set. In this paper, we propose a model following a matching-integration-verification-prediction framework, which explicitly employs a verification module inspired by the human being and generates judgment of each option simultaneously according to the evidence information and the verified information. The verification module, which is responsible for recheck information from matching, can selectively combine matched information from the passage and option instead of transmitting them equally to prediction. Experimental results demonstrate that our proposed model achieves significant improvement on several multiple-choice reading comprehension benchmark datasets.
Three-Stream Convolutional Networks for Video-based Person Re-Identification

This paper aims to develop a new architecture that can make full use of the feature maps of convolutional networks. To this end, we study a number of methods for video-based person re-identification and make the following findings: 1) Maxpooling only focuses on the maximum value of a receptive field, wasting a lot of information. 2) Networks with different streams even including the one with the worst performance work better than networks with same streams, where each one has the best performance alone. 3) A fully connection layer at the end of convolutional networks is not necessary. Based on these studies, we propose a new convolutional architecture termed Three-Stream Convolutional Networks (TSCN). It first uses different streams to learn different aspects of feature maps for attentive spatio-temporal fusion of video, and then merges them together to study some union features. To further utilize the feature maps, two architectures are designed by using the strategies of multi-scale and upsampling. Comparative experiments on iLIDS-VID, PRID-2011 and MARS datasets illustrate that the proposed architectures are significantly better for feature extraction than the state-of-the-art models.
Fusion Particle Filter for Nonlinear Systems Based on Segmental Gauss-Hermite Approximation

Multisensor fusion estimators play an important role in modern information processing. Weighted measurement fusion (WMF) algorithm has widely been applied to data compression of multisensor linear systems. Due to the complexity and uncertainty of nonlinear systems, the application of WMF algorithms is limited in multisensor nonlinear systems. In this article, an approximate linear relationship is established by using the segmental Gauss-Hermite approximation method for multisensor nonlinear systems. Based on the relationship and weighted least squares (WLS) method, a WMF algorithm is presented to compress the data for multisensor nonlinear systems. By combining the WMF algorithm with Particle Filter (PF), a weighted measurement fusion Particle Filter (WMF-PF) is presented for multisensor nonlinear systems. Compared with the centralized fusion PF, the proposed WMF-PF has a fair accuracy and less computational cost. It has a potential application in navigation, GPS, target tracking, communications, big data and so on. An example is given to show the effectiveness of the proposed algorithms.
Simple Classification Models for Coarse-Grained Problem

There exist datasets which cannot achieve high accuracy for all the classification models in the current application. In view of this problem, this paper proposed a high precision coarse-grained classification model. The main idea of coarse-grained model is to predict a set of labels that might contain the gold label. Experiments show that the model is effective in small data sets and can help people to make decisions. We summarized the model and discussed possible future improvements.
Criminal Conviction Classification Based on Multiple Learning Methods

The application of artificial intelligence in the legal field can save a lot of the time for legal professionals. In particular, in this paper we propose a method for predicting what kind of conviction a suspect has according to the facts of the crime of the suspect. Specifically, we first pre-process the data and then use multiple classification methods to classify the crime facts, and finally combine the results of each model to gain a more accurate of conviction classification.
Establishment of corrosion big data standard acquisition platform for refining process

The advent of the internet of things and big data era has brought new ideas for corrosion analysis and prediction of refining units. With the establishment and application of all kinds of information systems, refining enterprises have accumulated a large number of structured, unstructured, corrosion influencing factors and corrosion result data sources, but all kinds of system data are independent, and the traditional storage methods are limited in calculation, processing and analysis and mining ability, which do not have the conditions of big data analysis and utilization, so it is urgent to study the standardized collection method of corrosion data and establish a unified data center. Based on the whole life process of equipment, this paper studies the data content, data characteristics and current main data acquisition and management forms of corrosion related data in each link of the equipment through-life. In addition, this paper puts forward the overall structure of corrosion big data standard acquisition system for refining process in the enterprise as a whole. The big data acquisition system, which is suitable for corrosion big data comprehensive collection, efficient storage and open interface, is established to carry on the data acquisition step by step, and lays a foundation for the acquisition and analysis and utilization of corrosion big data from refining process.
BPCEX: Towards Blockchain-based Privacy-preserving Currency Exchange

Privacy-preserving currency exchange between different cryptocurrencies on blockchain remains an open problem as the existing currency exchange schemes cannot provide anonymity of traders or confidentiality of exchange amount. To solve this problem, we introduce BPCEX: a privacy-preserving currency exchange scheme which protects traders' identities and the exchange amount, by usage of techniques including linkable ring signature, range proof, Diffie-Hellman key exchange, Pedersen commitment and UTXO swap. In BPCEX, the traders' identities are hidden to verifiers and dealmakers, while the exchange amounts are hidden to the verifiers. BPCEX supports floating exchange rate, partial deal and public verification, without additional confirmation of traders, which improves the success rate and shortens the waiting time of the deal. Moreover, BPCEX is compatible with the auditable privacy-preserving blockchain system, which realizes the traceability of traders' identities and the exchange amount to prevent money laundering and illegal exchange, making BPCEX suitable in real-life applications, including currency market and stock market.
Use of PCA Solution in Hierarchy and Case of Two Classes of Data Sets to Initialize Parameters for Clustering Function

In literature, the initial parameters are critical for K-means function. By seedling randomly or ad hoc approach, the results are not optimal. This chapter details an estimation method using the principal component analysis (PCA) solution based on the connection between PCA solution and membership of clusters from K-means from research. All the mathematical justification is provided. The evaluation has been done empirically with a comparative study. The validation results show the significant feasibility of the proposed method to initialize parameters.
A Survey on Electronic Health Records Using Cloud Computing Environment

One of the serious problems existing in the cloud is to oversee or to verify the information’s as of unapproved people. Here in the restorative field, the patient-driven model depicts the patient’s personal health record, wherever health data is to be verified from the outside servers. Electronic health record difficulties incorporate expensive programming bundles, framework security, tolerant secrecy, and obscure prospect government guidelines. Impending advancements via electronic health records incorporate bar coding, radio-recurrence ID, also discourse acknowledgment. We have provided a study on electronic health records in cloud computing with various technical aspects. Also, there is a detailed description of their applications. Load balancing is also the most significant aspect of cloud computing.
Lung Cancer Analysis and Diagnosis by Coalition of Photo Metric and Quality Metric Parameters

Lung cancer still seems to be a very common cause of death among the people all over the world. This paper proposes a method which is a coalition of two statistical methods Photo Metric and Quality Metric enacted for lung cancer analysis and diagnosis. Suggested methodology is established on the basis of identified statistical and mathematical parameters exploring the light variation and quality features of the lung image to carry out the evaluation for cancerous and non-cancerous types. The distinguishing Photo Metric parameters are Brightness, Luminance, Contrast and LC index, whereas the Quality Metric parameters are VIF, MSE, VSNR and PSNR. These eight identified parameters are applied one by one on the cancerous and noncancerous lung images using image processing technique using MATLAB to get an individual parameter statistical range through number of iterations in real time. If the test image falls in the particular calculated statistical range for cancerous lung images, then the test image is infected or else normal. But practically some of the values of the statistical range for cancerous and noncancerous lungs overlaps due to which the final decision is taken by an image classifier. The ANN is used as an image classifier to sort out this problem resulting in an efficient lung cancer diagnosis. The microscopic lung images are used to implement the proposed methodology. The performance analysis of each identified parameter is the heart of this paper. The authentication of the proposed methods is tested successfully using Standard Diagnostic Test.