Reviewerly/exHarmony huggingface dataset:
Book review: Applied state estimation and association

The central problem in multiple object or target tracking is the data association problem of partitioning sensor reports into tracks of reports and false or unrelated reports. Given a sequence of reports emanating from the same object, the estimation problem is that of using fusion and estimation techniques to improve the estimate of the state of the object. first-year graduate course. The state estimation is confined to the discrete time models as is appropriate for the intended audience. The book is divided into ten chapters. The first six chapters are devoted to the problem of estimation of a single object with a single sensor. Chapter 7 extends the single sensor to multiple sensors and a single object. Chapters 7–10 then consider the full multitarget and multisensory case which includes the association problem. Throughout the estimation chapters 1 through 6, the authors present the estimation formulation from an optimization viewpoint and develop Cramer-Rao bound (CRB) solutions for each estimation problem addressed. The many illustrations provide a deeper understanding of the more difficult concepts, while the exercises help to confirm an understanding of the chapter content and provide a basis for practical usage on existing and new problems. I highly recommend the book as a textbook or a reference for the expert.
Comparative Study of Classification Approaches for Email Analysis

Illicit messages like threatening and abusive messages affect emotions and psychology of a person. Such messages start exerting influence on mental status, and ultimately physical condition of a person. E-mails are one of the popularly used sources, for communicating personal and official messages. Typically, sentiment analysis of these e-mails includes classifying them into positive, negative and neutral messages. Identifying the sentiments of e-mails using an efficient and effective algorithm is very important and useful step in the domain of e-mail forensics. In this work, support vector machine, k-nearest neighbour, and neural network back-propagation algorithms are used to classify the sentiments of e-mail into positive, negative and neutral categories using self-curated e-mail dataset. This dataset is a combination of Enron's e-mail dataset and publicly available messages converted into e-mails. This paper presents a comparative study of classification approaches for e-mail analysis. Finally, it is concluded that the neural network with the back-propagation training algorithm provides the best results considering the accuracy and the memory requirements with the little compromise on the time required to recognise the sentiment of a given e-mail.
Implementasi Pendekatan Inquiry Tema Aku dan Makanan Sehat untuk Meningkatkan Pemahaman Siswa

The inquiry learning approach is a learning approach that emphasizes the process of searching and finding. Learning material is not given directly, but the role of students in this strategy is to find and find their own subject matter, while the teacher acts as a facilitator and guide for students. This type of research is a Pre-Experimental Design research in the form of One-Group Pretest-Posttest Design. Researchers provide pre-test at the beginning of learning and provide post-test questions at the end of learning by using the inquiry approach. The results of this study indicate that the results of students' understanding were tested using the t test formula in the calculation (to = 7.29) and the magnitude of t listed in the table (tt.ts.5% = 2.45) and (tt.ts.1 % = 3.71), it can be seen that t is greater than tt; which is 7.29> 2.45. So because to is greater than tt it is clear that there is a significant influence on the implementation of the inquiry approach to the theme of “aku dan makanan sehat” to improve students' understanding of third grade OS Bimbel Kotagede Yogyakarta.
The Effects of Job Performance on NCS-based Education in the Airline Service Industry

This study analyzes the job satisfaction and job performance how much they are related to NCS(national competency standards)-based education from university. So this research investigated the impact of NCS(national competency standards)-based education on job satisfaction and job performance(organizational loyalty and customer orientation) based on employees of airline service.BR For this study, this survey was conducted among 80 and 2 hypotheses(sub hypotheses is 5) are summarized as follows. First, NCS(national competency standards)-based education have a significant positive effect on job satisfaction. Second, job satisfaction was significant to have a good effect on job performance of employees. As a result, NCS-based education needs education in university of airline service area.
Handling Unforeseen Failures Using Argumentation-Based Learning.

General Purpose Service Robots operate in different environments of a dynamic nature. Even the robot’s programmer cannot predict what kind of failure conditions a robot may confront in its lifetime. Therefore, general purpose service robots need to efficiently handle unforeseen failure conditions. This requires the capability of handling unforeseen failures while the robot is performing a task. Existing research typically offers special-purpose solutions depending on what has been foreseen at the design time. In this research, we propose a general purpose argumentation-based architecture which is able to autonomously recover from unforeseen failures. We compare the proposed method with existing incremental online learning methods in the literature. The results show that the proposed argumentation-based learning approach is capable of learning complex scenarios faster with a lower number of observations. Moreover, the final precision of the proposed method is higher than other methods.
A Study on Managing Fake Customer Review and Order Claim using Loop holes in e-Commerce Strict Consumer Friendly Policies

eCommerce has changed the lives of individuals around the globe and its development isn't giving any indications of backing off. In our routine life internet plays an important role. We use web day by day nearly for each and every work. The points of interest offered by e-commerce are online shopping of anything whenever and at wherever, clients can discover the items on web based business sites which is no accessible in physical markets, it decreases cost and time, without venturing out from home it can get our item at home. It have most likely been blamed for selling fake things by a client despite the fact that it can vouch that it got items straightforwardly from the producer or was preferred choice to get from their apparatus when it was made.
Fast Explicit Diffusion Scheme and Fourth-Order Nonlinear PDE for Image Noise Removal

Computing time is an important issue in computational mathematics and computer vision. In this work, fast explicit diffusion (FED) scheme is used to solve a fourth-order nonlinear partial differential equation for noise removal. The numerical results illustrate the effective of the scheme over the traditional methods. Our results have been compared with three popular fourth-order models, You and Kaveh (YK) model, Lysaker, Lundervold and Tai (LLT) model and the mean curvature (MC) model. The proposed strategy has the prevalence in terms of removing noise whereas protecting image features with less computing time.
An Experimental Approach of Applying Boruta and Elastic Net for Variable Selection in Classifying Breast Cancer Datasets

Feature selection identifies the key aspects involved in predicting the outcome. In this study, we propose boruta and elastic net (Enet) feature selection for classifying breast cancer datasets. A comparative study of boruta, Enet along with genetic algorithms (GA) and consistency-based subset feature selections are done, where Enet selected best features for Wisconsin diagnostic breast cancer (WDBC) and breast cancer datasets. To prove the stability of Enet feature selection, variable importance of machine learning algorithms like naive Bayes (NB), multilayer perceptron (MLP) and random forest (RF) is evaluated and compared. It is proved that the features obtained by Enet contain all the common variables selected by tested machine learning algorithms. The proposed Enet feature selection along with MLP for classification yields a better receiver operating characteristic (ROC): 0.990, 0.687 and a reduced root mean squared error (RMSE): 0.159, 0.429 for WDBC and breast cancer datasets, when compared with naive Bayes and RF.
Website Penjualan www.peralatansekolah.com Untuk Sekolah Dasar

Website sales www.peralatansekolah.com sales is a website devoted to elementary school children, in the website designed with the needs of elementary school children now, like school clothes, school supplies, attributes school, and school supplies. Products sold on the website www.peralatansekolah.com obtained from conventional stores, so the products on sale are very diverse, on the website www.peralatansekolah.com the products on sale are already categorized according to the wishes of consumers. In the development of this www.peralatansekolah.com sales website author using form research case studies. The research method used was research and development or better known as the Reasearch and Development. As for the data collection Techniques used are obeservasi, interviews and documentation study. Model design of software used is a model Ravid Application Development. Data collection techniques used are primary and secondary data. Website development on the website www.peralatansekolah.com is built using the PHP programming language and the Java Script as well as a database that uses MySQL. Website authors produce consists of display product page, the display of the product type, the display of the user's main page, page views, how to purchase the shopping cart display page, and display the contact us page. The existence of this sales website is able to introduce and sell products online as well as generate information – information that is sold, enhance citra sehinggan better known by people especially parents who want to buy equipment the school.
Integrating Sensor Platforms to Enhance Information Systems in Public Safety Organizations

Organizations active in the field of public safety are facing new challenges globally due to an increasing number and range of mission types and increasingly intensive missions. This is especially true for alpine search- and rescue (SAR)-missions. A reliable and useful situational overview is thus essential for mission control. At the same time, due to rapid technological developments in the field of sensors, support platforms such as unmanned aerial vehicles (UAVs), as well as the improved availability of data communication infrastructure, new possibilities arise to improve the data and decision base for mission managers. This requires a meaningful and seamless integration of various information sources and systems, however, which are currently often stand alone operated. The work presented in this paper aims to bridge this gap by developing a concept and demonstrates the practical applicability by the integration of various sensor platforms into an existing command and control (C2) system. The provision here from a technical perspective are, detailed insights into this advanced integrated approach supporting the seamless information flow from sensor data generation to data processing (automatic orthogonal picture generation) and visualization via an existing C2 system. In the course of a case study a prototype for such an integrated sensor-based C2 system was developed and tested in a real world mission scenario where feedback from mountain rescuers was gathered during a field test using a qualitative survey. Based on these results implications and topics for future C2 systems are discussed.
Video encryption based on chaotic array system: working with image directly

A new three-dimensional discrete chaotic system is proposed according to the Marotto theorem in this paper. On this basis, a coupled array chaotic system is given as the drive system. Moreover, with the help of the bidirectional generalised synchronisation theorem, the response system is constructed and proved to be chaotic. According to the chaotic matrix generated by the above systems, a special video encryption scheme was proposed. While the experiment shows that the encryption scheme occupies a large key space and enjoys an approximately uniform distribution of the ciphertext entropy already, the main contribution of the new constructed system is the higher speed than the vector system since once the encryption starts, the chaotic matrix generated by the array systems and the image with the same size to the matrix can be operated directly.
Bayesian feature construction for the improvement of classification performance

In this paper we are going to talk about the problem of the increase in validity, concerning the process of classification, but not through approaches having to do with the improvement of the ability to construct a precise classification model using any algorithm of machine learning. On the contrary, we approach this important matter by the view of a wider encoding of the training data and more specifically under the perspective of the creation of more features so that the hidden angles of the subject areas, which model the available data, are revealed to a higher degree. We suggest the use of a novel feature construction algorithm, which is based on the ability of the Bayesian networks to re-enact the conditional independence assumptions of features, bringing forth properties concerning their interrelation that are not clear when a classifier provides the data in their initial form. The results from the increase of the features are shown through the experimental measurement in a wide domain area and after the use of a large number of classification algorithms, where the improvement of the performance of classification is evident.
A privacy preservation model for big data in map-reduced framework based on k-anonymisation and swarm-based algorithms

In recent years, two mainstream technologies have become the centre of IT world, big data and cloud computing. Both these fields are fundamentally different but used together generally. The big-data deals with huge scales of data however cloud-computing is majorly about the infrastructure. Together these fields are beneficial for enterprises varying from the government sector to social sites, from academic to medical sectors, etc. Thus, it becomes important to safeguard the datasets so that the end-users of data may not access the information delivered by the users. This paper presents a hybrid k-anonymisation model for the map-reduce framework which guarantees the preservation of privacy in the cloud database using the combination of swarm-based algorithms. The proposed model focuses on deriving fitness function which will give high value of privacy and low information loss. The simulation and comparison with other algorithms shows better privacy and utility when working with proposed model.
WEBSITE IMPLEMENTATION FOR MAKING SCHOLARSHIP ACCEPTANCE DECISIONS AT SMK N 1 TALANGPADANG USING THE ANALYTICAL HIEARARCHY PROCESS METHOD

Processing of scholarship data in SMK 1 Talangpadang generally still uses Microsoft Excel, while for data processing takes a long time. The problem that often arises is the scattering of the file for distributing scholarship receipts every year. This scholarship consists of Academic Achievement, Nonacademic Achievement, Personality, Mileage, Report Card, Organizations, Dependent Parents, Parent's Income, and Parent's Status. The method used in this study is the Analytical Hierarchy Process can also help in determining several criteria by using a different analysis from each of the specified sources.
THE CONTRIBUTION OF THE AORTIC BRANCHES IN THE VASCULARIZATION OF CERVICAL REGIONS, DURING THE DEVELOPMENT OF THE NINE BANDED ARMADILLO (Dasypus Novemcinctus, L. 1758)

The work describes the origin, branching and distribution of aortic branches, during the development of the armadillo, experimental model in the study of human leprosy. Using contrast injection, the branches of the subclavial, common carotid and omocervical aa. were identified as to its cooperation in irrigation of the ventral, lateral, dorsal, costal cervicals regions, encephalic and the thyroid gland
PERKEMBANGAN BAHASA ANAK: STUDI KASUS KOMUNIKASI ANTARSISWA DI SEKOLAH DASAR

Language is essentially speech of human thoughts and feelings on a regular basis, which uses sound as an intermediary in delivering messages. Language skills are human feelings through appropriate sounds, which are used together, interact, and identify in good conversations. In the process of language development, prior language skills have always been the basis for the development of the next language about such language development, discussion of language development in elementary school children becomes interesting to explore, especially how the language concept is. This article will discuss the analysis of children's language development in communication at school. The purpose in writing this article is to improve children's ability in language. In this data collection using the method of obsevation. Keywords. Language, Children's Language, Development, Communications
WikiWars-UA : Ukrainian corpus annotated with temporal expressions

Reliability of tools and reproducibility of study results are important features of modern Natural Language Processing (NLP) tools and methods. The scientific research is indeed increasingly coming under criticism for the lack of reproducibility of results. First step towards the reproducibility is related to the availability of freely usable tools and corpora. In our work, we are interested in automatic processing of unstructured documents for the extraction of temporal information. Our main objective is to create reference annotated corpus with temporal information related to dates (absolute and relative), periods, time, etc. in Ukrainian, and to their normalization. The approach relies on the adaptation of existing application, automatic pre-annotation of WikiWars corpus in Ukrainian and its manual correction. The reference corpus permits to reliably evaluate the current version of the automatic temporal annotator and to prepare future work on this topics. The corpus is freely available for the research on https://github.com/thhamon/WikiWarsUA
Occlusion handling strategies for multiple moving object classification

A framework has been designed for detection and classification of multiple moving vehicles. Background subtraction is used for detection of multiple moving objects like vehicles using Gaussian mixture model (MOG). Classification for multiple moving vehicles using K-nearest neighbour is done based on different features in this research. The method used in this research also improves the value of accuracy and occlusion rate for multiple moving vehicles in video frames. In this paper, we also learn a single detector for different types of multiple moving vehicles such as buses, trucks, and cars. This detector uses a special kind of function that is known as occlusion metric function. The main goal of this research is to build a function that is used to calculate the performance of detector between number of false positives and hit rate in heavy traffic (high activity) and small traffic (low activity) region.
Can Internal Marketing Activities Save Small and Medium Industrial Enterprises in a Tough Economy Back to the Basics Approach

Recently, and due to the economic pressure in Jordan, several small and medium enterprises have filed for bankruptcy, or downsized their business leading to the layout of hundreds of employees, which will deepen the economic problems in Jordan and increase the circle of effect. Small and medium enterprises can follow other courses of actions to address such issue. This paper aims to understand the possible impact of internal marketing practices and activities during economic problems on the employees' productivity in small and medium industrial enterprises. In order to achieve this aim, a descriptive analytical methodology was used, quantitative approach through survey strategy was applied and electronically administrated questionnaires were utilised to collect the primary data from a representative stratified random sample of 490 small and medium industrial enterprises out of 2,291 registered local small and medium industrial enterprises. 345 filtered and screened questionnaires were analysed using SPSS, the main results of simple and multiple regression analyses indicate that internal marketing mix has a strong positive direct effect on employees' productivity, where internal promotion has the most effect followed by workplace, internal product and internal price respectively. Discussion, conclusion, and recommendations are provided based on the results of the study.
POLAR: Posture-level Action Recognition Dataset

Action category definitions in existing datasets are inconsistent since actions can be labeled at different levels. In this paper, the authors focus on the less studied posture-level actions and introduce a new image dataset, “POsture-Level Action Recognition” (POLAR). The POLAR dataset has a total of 35,324 images and covers 99% of posture-level human actions based on our analysis on the VOC dataset. For action recognition on the POLAR dataset, irrelevant objects or backgrounds could mislead human action predictions. The authors propose the Human-centric Contextual Regions Network (HCRN) that extracts pose related contextual features according to the person in question to address this problem. HCRN outperforms the prior best method in the VOC action classification competition on the POLAR dataset, also reports the new state-of-the-art result, 92.2% with 2.0% relative improvement on the PASCAL VOC action dataset and achieves a promising result on the Stanford 40 dataset.
Multi-document summarisation using feature distribution analysis

Recently, opinion documents have been growing rapidly in an environment where anyone can express an opinion on the internet or SNS. This situation requires an automatic summarisation technique in order to understand the contents of large-scale opinion documents. However, it is not easy to summarise the opinion documents with previous text summarisation technologies since the opinion documents include subject expressions, as well as features of targets objects. In this paper, a method to identify and extract the representative documents with a large amount of opinion documents is proposed. In addition, experiments show that the proposed method successfully extracts representative opinion documents.
Breast cancer detection in mammogram image with segmentation of tumour region

In our proposed breast cancer malignant detection study are performed with the aid of fuzzy min max neural network technique. Majority of women's are affected in this breast cancer at a early stage the mammogram images are mostly play in a vital role. Initially the input mammogram image smoothened with the aid of adaptive median filer from that smoothened image we are segmenting tissues with the aid of Histon based fuzzy c-means clustering. We are extracting features from that segmented image the features are statistical and semantic features. Then we can identify the malignant region with the aid of these features. The segmented region is maligned or benign using an optimal fuzzy min max neural network with grey wolf optimisation algorithm with the aid of these we will identify a breast cancer region.