Reviewerly/exHarmony huggingface dataset:
Using Micro-Processor Vector Instructions to Optimize Unsupervised Machine Learning K-Means Algorithm

The K-Means clustering algorithm is a widely used unsupervised learning technique for data analysis that is still relevant decades after it was originally published. The algorithm is capable of splitting a given multi dimensional input of data points into a given number of clusters based on a measure of proximity. The iterative algorithm creates k partitions by finding their cluster centers and then assigning each of the points to the nearest center from a given input data set. This method of unsupervised clustering has become widely utilized in applications like data mining and statistical data analysis were large data sets are processed in order to extract key information about trends in the data. Apache SPARK is one of the most used big data and data analytics frameworks and it includes an implementation of the K-Means algorithm. In this work we characterize the SPARK implementation of K-Means, analyze its performance bottlenecks and suggest improvements that can help it achieve up to 33x higher throughput by using vector capabilities available on many modern server processors.
Multi-angled Face Segmentation and Identification Using Limited Data

This paper introduces a different approach to face segmentation that aims to improve face recognition when given large pose angles and limited training data. Face segmentation is achieved by extracting landmarks which are manipulated in such a way as to normalize unseen data with a classification model. The approach is compared with related systems, followed by further tests that show consistent results across other datasets. Experiments include frontal and non-frontal training images for classification of various face pose angles. The proposed system is a promising contribution, and especially shows the importance of face segmentation. The results are achieved using minimal training data, such that both accurate and practical face recognition systems can be constructed.
Soil moisture clustering using the k-means clustering method in the UNS’s agricultural laboratory at Jumantono

The UNS Agricultural Laboratory is a location used by the Faculty of Agriculture to conduct experiments/testing and practice. There are many technologies used there, but there is no device to detect soil moisture. The aim of this study is to make a device to detect soil moisture using an Arduino microcontroller and FC-28 soil moisture sensor. The data in this study are the results of the humidity sensor readings from the devices made that are installed in the UNS Agricultural Laboratory soil. The humidity sensors that are used are four pieces, and the data obtained from the sensor reading will update every 10 minutes. The data is then processed to classify the soil based on its humidity using the K-Means Clustering method. The results of this study indicate that the results of soil research based on the level of humidity can be divided into three types. The first is soil with a humidity level below 20%. The second is soil with humidity level of 20 – 40%. Finally there is soil with humidity level of 95 – 100 %.
Proposal of a new method for image encryption and decryption technique

The proposed paper is reported image encryption and decryption using a new random number generator technique named Seam's Random Number (SRN) generator. The generated random sequences perform XOR operation with gray scale image pixels to acquire the encrypted image. Implement of the mentioned instance is inspected by evaluating histogram, Mean Square Error (MSE), entropy and cross correlation between the original image and encrypted image. Here, the instance has been put to the test of two different gray scale images pixels. Histogram results of encrypted image using mentioned intention is horizontal and evenly concentrated comparable with encryption instance using produced sequence of SRN generating technique. Moreover, high MSE is found which is measured between main image and encrypted image. In addition, low correlation is found between neighboring pixels of encrypted image. This proposed method will be a potential candidate in image encryption applications.
Insurance ratemaking using the Exponential-Lognormal regression model

This paper is concerned with presenting the Exponential-Lognormal (ELN) regression model as a competitive alternative to the Pareto, or Exponential-Inverse Gamma, regression model that has been used in a wide range of areas, including insurance ratemaking. This is the first time that the ELN regression model is used in a statistical or actuarial context. The main contribution of the study is that we illustrate how maximum likelihood estimation of the ELN regression model, which does not have a density in closed form, can be accomplished relatively easily via an Expectation-Maximisation type algorithm. A real data application based on motor insurance data is examined in order to emphasise the versatility of the proposed algorithm. Finally, assuming that the number of claims is distributed according to the classic Negative Binomial and Poisson-Inverse Gaussian regression models, both the a priori and a posteriori, or Bonus–Malus, premium rates resulting from the ELN regression model are calculated via the net premium principle and compared to those determined by the Pareto regression model that has been traditionally used for modelling claim sizes.
Research And Analysis of Predicate Encryption Based on Hierarchical Permission

In order to improve storage utilization and reduce communication bandwidth, data deduplication technology is widely used in cloud storage service system. However, there is a conflict between data security and data deduplication brought by encryption technology. This paper introduces a predicate encryption method based on hierarchical authority, which mainly includes system model, attack model definition and implementation scheme. The scheme is an encryption algorithm based on two-way vector space, which mainly contains six sub algorithms of polynomial computing time. At the same time, the performance and security of the scheme are analyzed. Theoretical analysis and experimental results show that the scheme is a safe, effective and efficient scheme.
Possibilistic Art (PoArt), an Approach based on Mind Geometry for Digital Media

This paper, in the domain of digital media, introduces the theoretical basis of possibilistic art. It models the bases of visual art in the atmosphere of possibilistic thought and the fuzzy geometry by introducing meaningful forms and introduces a way for recording and displaying emotional-behavioral responses of artist in the visualcomputational space. Finally, as a function of presented concepts, the paper introduces a semi-algorithm for meaningful deformation. This article, by representation of a method based on the eastern thinking and a computational thinking of the west, make a step in the way of eliminating the theoretic and instrumental shortages of visual arts of Iran in the grounds of computational arts.
Deep learning for recognition of Javanese batik patterns

Batik is one of the cultural heritages of a special Indonesian nation. Because of its diversity and uniqueness on October 2, 2009, Batik was first established as Masterpieces of the Oral and Intangible Heritage Humanity by UNESCO. To maintain sustainability, continuous research is needed. Although the topic of research on batik is already common, the introduction of batik patterns still has challenges that need to be resolved. One of the challenges of pattern recognition is in terms of classifying batik motifs. To simplify the work of computers in classifying, in this case the implementation of deep learning is needed by using the convolutional neural network (CNN) method. The convolutional neural network (CNN) method is one of the architectures in deep learning, this method is more effective for classifying images such as batik patterns because the convolutional neural network method has a convolution operation. In this operation the image will be extracted every feature so that it can produce patterns that can facilitate classification. In the process of training the convolutional neural network method requires heavy computation and not a short amount of time, therefore the use of GPU performance is needed to speed up the training time. The experimental process begins by compiling five classes of data sets of batik images, the class consisting of batik parang rusak, batik kawung, batik nitik, batik ceplok, and batik lereng with a total of 750 batik images as data sets. The data set was then trained using the Python programming language and GPU CUDA. The test results using cross-validation can achieve an accuracy of 90.14%. So that the results of the above tests can be concluded that deep learning using the CNN method can be used to classify batik patterns well.
An Online Feasibility Calculator for Building the Photovoltaic Systems

The paper presents an online web-oriented system named SOLARS, which is aimed at calculating the feasibility of building the photovoltaic (PV) systems. SOLARS currently enables potential investors to calculate the technical and financial feasibility of building the PV systems in the Republic of Srpska (Bosnia and Herzegovina). Very intuitive GUI design enables investors to obtain feasibility calculations in three simple steps: (i) selection of a geographical location, (ii) specification of technical parameters, and (iii) specification of financial parameters. A usage scenario is illustrated by a real feasibility calculation example.
Extrapolation, Design and Implementation of a Bangla Web Document Amendable Text Synopsis

With nearly 200 million speakers, Bengali is one of the ten most widely spoken languages in the globe. Growing internet resources show a definite need for apps in the Bengali language, recovery systems and automatic text summary. It is hard to design a scheme for producing summaries of human quality, so many scientists have concentrated on the extraction of sentences or paragraphs, which is a kind of summary. With the assistance of machine-supported technology, an smart method is implemented in this study job to summarize the Bangla texts. This scheme can be commonly used to effectively summarize Bangla text that helps to quickly obtain the focal or central components of the papers. A excellent instance of a summary scheme is the convention search engine such as Google, representing a compressed search results description. Other examples include summarizing news for Bangla SMS or WAP, news subscriptions addressed by keywords, etc. An algorithm is suggested in this work Bangla Summarization System to extract the Bangla overview that operates on four passes. On the input Bangla phrases and word frequency calculation, the first two passes conduct tokenization and the next two passes conduct phrase scoring and summary generation.
Kham Dialect Speech Synthesis Based on Deep Learning

In this paper, we constructed speech synthesis corpus of Kham dialect. At the same time, we designed SAMP-Kham machine-readable phonetic label of Kham dialect, and proposed a framework of Kham dialect speech synthesis based on deep learning. The deep learning architecture includes deep neural network (DNN), hybrid long short-term memory (LSTM) and hybrid Bidirectional long short-term memory (BLSTM). The objective and subjective evaluations test showed that quality of synthesized speech of Kham dialect was satisfied.
A naive learning algorithm for class‐bridge‐decomposable multidimensional Bayesian network classifiers

Summary Multidimensional Bayesian network classifier (MBC) has become a popular classification model because of their intuitive graphical representation ability among class variables. But learning MBC and performing multidimensional classification based on the MBC can be very computationally demanding. For the tractability of performing multidimensional classification, a class‐bridge‐decomposable (CB‐decomposable) MBC model is proposed and it alleviates the computation complexity. But there are few works to efficiently and systematically learn the CB‐decomposable MBC model. Thus, we focus on addressing a naive learning algorithm of CB‐decomposable MBCs. Briefly, we learn the CB‐decomposable MBC model by dividing it into three components: class subgraph, bridge subgraph, and feature subgraph. First, we analyze why the class subgraph can be learned based on general Bayesian network learning methods. Second, we give how to learn bridge subgraph based on information gain ratio. Third, to make the CB‐decomposable MBC model effective and simple, we also study the learning and updating strategies of feature subgraph. Further, we propose the naive learning algorithm of the CB‐decomposable MBC. Finally, by comparing with other methods on several benchmark datasets, experimental results illustrate that our naive learning algorithm not only has higher accuracies, lower learning, and classification times but also has simple and intuitive representation ability.
TRACTION CONCEPT «REHABILITATION» IN THE FRENCH MEDICINE SYSTEM

&#x0D; &#x0D; &#x0D; Given the increase on the planet of the number of people with congenital birth defects, technocratization of society, an increase in the number of persons with disabilities was expected at the beginning of the 21st century to one billion consequently, disability is a worldwide social phenomenon, which should be the focus of every country, its bodies of state power and administration, scientists and physicians, psychologists and educators, specialists in physical culture and sports. So, we see that under the concept «réadaptation» (f) in France, a set of tools and methods used by the relevant specialists to rehabilitate persons with disabilities, as well as simply patients, in order to return them to the maximum possible conditions of everyday life. The twentieth century in France is characterized by the creation of a number of organizations whose activities are aimed at working with such people.&#x0D; &#x0D; &#x0D;
The future networks—a cognitive approach

In the internet the routing of data has been increased from hierarchical and statistical approaches to highly energetic, sentient approach with a highly incorporated intelligence. This acquired knowledge helps in the execution of actions and decision-making applied here while routing. Current routing involves the property of self-organization and self-adaption. Addressing these properties has been a challenge when we focus on the dependability aspect. Today’s awareness of the network involves more of ad hoc infrastructures and dynamic connections along with their setup, the reason being the humongous increase in the number of users’ on the internet in the past decade. Here, in this work the above aspects are dealt in detail with an exhaustive literature survey to show the promising scope of cognitive routing in the future networks.
A method for predicting software reliability using object oriented design metrics

Object-oriented software metrics are important in software engineering field for measurement of the software quality, size estimation, complexity and effort. Object oriented software development is the most popular and best practice in software development. Object oriented software metrics can be used for measuring extend of object orientedness of a program. These quantities are important information indicators, that can be helpful for a software developer in take decisions for designing good software. It is proven that the software metrics provide quantitative information about the structure of the code, which can be used to determine the reliability of software. The design of the software is highly recommended to be object oriented in the software development environment. Software metrics specific to programs that have object oriented design can be used to measure the quality of the software like inheritance, assessing the effects of coupling, the complexity of the code, size aspects, etc, of the software developed in such an environment. Software measurement is a way for assessing the quality of software. Many attributes can define quality of a software, internal (size, complexity) as well as external (reliability)[14]. Often internal attributes are contributing factors to external attributes. Reliability as a term means the degree to which a component, product or system executes specified functions for a specific period of time under specified conditions. Software reliability is the software being trustworthy of the specifications. The aim of the work is to model the relationship between reliability of object oriented software to the object oriented design metrics and predict reliability of software.
Mapping Networks and Trees with Multidimensional Scaling of Proximities

Network methodology typically has two separate stages: (1) constructing a graph from relational data, and (2) drawing the graph on a map to comprehend its structure. Multidimensional scaling (MDS) is discussed as a distance-driven graph drawing method. It is shown that popular drawing methods in computer science that minimize the potential energy of a spring model are equivalent to a simple form of MDS without optimal transformation of the graphical distance. They share a weighted least squares loss function (Kruskal’s stress). The best way to minimize Stress (Guttman’s algorithm) is shown to be a particular force-directed updating scheme in terms of a spring model. With several analyses of two examples (a simple graph and an additive tree), it is shown that using shortest path distances in the graph as input to MDS gives better drawing results than just using its adjacency matrix. Inclusion of data weights in Stress to emphasize good fit of small distances turns out not to be essential. It may even be detrimental to correct representation of line length in a weighted graph.
A novel method for performance analysis of classifiers in haze detection

Nowadays quality of outdoor as well as indoor images are very important. But outdoor images are deteriorated by haze, fog, rain etc. These atmosphere conditions adversely affect the visibility of the images and reduces the contrast. It is very much adverse in applications especially in military. Image de-hazing algorithm are commonly used to restore the original image which is affected by haze. Fine details in the images are lost due to haze. But if we are applying haze removal algorithms for non-hazy images then the image may get blurred. So haze detection algorithms are necessary before the application of haze removal algorithms. Herein, an algorithm for detection of haze using different classifiers has been implemented and the performance of classifiers are measured and tabulated. In this work initially a number of image features are estimated to train different classifiers such as support vector machine, logistic regression naïve Bayes Classifiers and Decision Tree.
Sentiment analysis of Indonesian presisential election 2019 on the twitter with lexicon-based and support vector machine (SVM)

The development of social media is currently very rapid, and Twitter is a social media that is widely used by people in disseminating information, even in the presidential election process of social media twitter has an important role in the dissemination of good or evil information to drop the image of one presidential candidate. This study aims to analyze all information obtained from Twitter to obtain positive and negative values to obtain the electability predictions of presidential candidates. In the process of classification, this research uses the lexicon-base method and support vector machine (SVM). The preprocessing data stage uses part-of-speech tagging, chi-square test, and aggregating opinion on the entity (NN & NNP) which aims to avoid low recall from the lexicon-based method. The data used takes tweets both in Indonesian and english with several datasets of 2000 tweets with the separation of two opinions from each presidential candidate. From the polarity accuracy, get the value with the jokowi keyword to get a positive value of 24.10%, negative 38.30% and neutral 38.30% while for the keyword prabowo it gets a positive value of 0.20%, negative 0.10% and neutral 0.70%.
An analysis on the current researches in encryption algorithms based on neural networks

Abstract To perform an in-depth analysis on the current researches concerning the application of neural networks in the encryption algorithms, this paper used some research methods such as literature review and algorithm analysis to review the proposal and application of neural network technologies as well as the proposal and research and development of encryption algorithms through. The importance of neural networks in the encryption algorithms is highlighted. The current researches on encryption algorithms based on neural networks are reviewed from three dimensions, namely, algorithm framework, algorithm model and algorithm application. Through the research, it shows that the neural network technology has become the preferred choice for developing the encryption algorithms. This paper also proposed the direction of improvement through fusion application of multiple information processing technologies. This work lays the basis for improving the encryption algorithms based on neural network.
Auto-Selection of Cluster Number in MMMs-Induced Fuzzy Co-Clustering

Fuzzy co-clustering induced by multinomial mixture models (FCCMM) is an effective method for analyzing such cooccurrence information data as document-keyword frequencies, but often suffers from the cluster validation problem due to a priori selection of cluster numbers. In this paper, a modified model of robust cluster number selection in Gaussian mixture models is proposed, where the optimal number of clusters are automatically extracted in FCCMM through rejection of unnecessary clusters considering a novel penalty on cluster volumes.
A Computer Aided Detection System for Breast Cancer in the MammogramsBased on Particle Swarm Optimization Algorithm

The majority cancer mortality among women is due to breast cancer over the world wide. Recent researches have shown the effectiveness of x-ray mammography in early detection of breast cancer. Unfortunately, the present systems for early detection are expensive and needs extremely complex algorithms. The crucial challenge in designing a computer-aided detection (CAD) systems for breast cancer are the segmentation phase, which requires highly complex computation. Hence, this paper proposes a CAD system to be utilized for breast cancer detection in mammographic datasets. The segmentation step is performed by a Particle Swarm Optimization Algorithm (PSO). Statistical, textural and shape feature are calculated over the segmented region. A non linear support vector machine (SVM) is exploited in the next phase in order to analyze the extracted features and classify the mammograms into normal, benign or malignant. For the sack of evaluating the performance, the experiment is performed on Mini-MIAS database. The obtained accuracy rates based on 10-folds cross validation are 85.4% for classifying normal from abnormal, 89.5% for classifying malignant from benign. The experiment shows that the classification accuracy is 81% when classifying normal, malignant or benign. The result compromises with recent researches concurs that the proposed algorithm compromises between the achieved accuracy to complexity cost.