Reviewerly/exHarmony huggingface dataset:
Underwater Image Enhancement Algorithm for Real Time Monitoring and Surveillance

Underwater Images are rarely being taken into consideration and capturing such images is big challenge. Due to attenuating and scattering properties of water, underwater images will be affected. Observing collected database leads to realization of high amount of Haze in an image which was the dominant degrading factor. Hence an enhancement and restoration of images captured is a need for accurate analysis. The proposed algorithm applies Single Image Dehazing to resolve the issue related to underwater image degradation. Firstly, calculation of dark channel to give low intensity value image is performed followed by finding the atmospheric light for underwater images. Further, the transmission image is obtained by using Gaussian Low Pass Filter, which reconstructs image in very less time by removing a good amount of haze as compared to Soft-Matting approach. Refined & Reconstructed image shows improvement in an image contrast according to the SNR values. Also the brightness values are well preserved than input image. Real time monitoring and surveillance in an underwater domain are the potential application areas of this method
Improving the Organization Technology competitiveness through Effective Management of Conflict-Risk within Agile Teams

Whenever, teams get together to work towards a common goal, conflict risk is always inherent. This applies to Agile development teams as well. Some researchers even argue it as necessary for project success. Agile development environments is no exception. The main purpose for adopting Agile is to improve the organization technology competitiveness. This is because Agile enables organizations to create software of quality standard, that allows for quicker response to the continuously changing market needs of today' world. Agile allows companies to address the market requirements while still being adaptive to constant change. Conflict-risk has been identified as one of the hindrances to realizing this purpose by the current literature, making conflict-risk a frontier towards successful Agile development project delivery. This paper explores the impact of an effectively managed conflict-risk in Agile teams, on the organization technology competitiveness. The research methodology adopted was the quantitative research approach that employed the use of purposive sampling technique. The study collected data using a survey, through an online questionnaire that gathered data from Agile professionals across SA. The findings reveal an extreme and vital role of conflict existence towards achieving agile project goals. Moreover, the findings show the impact of an effective conflict-risk mitigation and management with regards to an organization ability to maintain an effective technology competitiveness; which assist in organizations' overall success.
Applicational Achievement of K-Means Algorithm among Apache Spark and Map Reduce

Tremendous data all around the globe have been an enthusiastic subject in computer science to explore and analyze that has raised the prominence of information. Blast incoming data through online networking,explorationin big organizations to get more access to intelligent research has become a great demand.MapReduce and its discrepancy have been very worthwhile in accomplishingenormouscalibratereports with robust applications on specialty groups. Therefore, a substantial quantity of the particular schemes is assembled over a non-cyclic intelligence flow and is not suitable to demonstrate for some other influential applications. An unbending architecture design was exclusively introduced using MapReduce that evaluates each job in a straightforward approach. Major steps in MapReduce such as a map, shuffle and reduce are allowed to change, synchronize and combine the outputs that are collected from every node cluster. Subsequently,to overwhelm the system to manual and recede, this paper proposes Apache Spark a manipulating form to split the tremendous information. The prime adversary for “successor to MapReduce” is Apache Spark. Similar to a broadly significant engine MapReduce, Spark has been designed to run distinctadditional workloads and to perform in that space witha greatlyaccelerated speedadapted framework. In this paper conflict between these two systems altogetherutilized with execution exploration by considering its information computation in a specified machine. Clustering process (K-Means) and asserting different criteria essentially, speed up the system, energy consumption of the system,scheduling delay of the jobthan the current systems.
Rethinking Sparse Gaussian Processes: Bayesian Approaches to Inducing-Variable Approximations.

Variational inference techniques based on inducing variables provide anelegant framework for scalable posterior estimation in Gaussian process (GP)models. Most previous works treat the locations of the inducing variables, i.e.the inducing inputs, as variational hyperparameters, and these are thenoptimized together with GP covariance hyper-parameters. While some approachespoint to the benefits of a Bayesian treatment of GP hyper-parameters, this hasbeen largely overlooked for the inducing inputs. In this work, we show thattreating both inducing locations and GP hyper-parameters in a Bayesian way, byinferring their full posterior, further significantly improves performance.Based on stochastic gradient Hamiltonian Monte Carlo, we develop a fullyBayesian approach to scalable GP and deep GP models, and demonstrate itscompetitive performance through an extensive experimental campaign acrossseveral regression and classification problems.
SIMULATION OF IMAGE DATA TO SUPPORT THE TRAINING OF CONVOLUTIONAL NEURAL NETWORKS FOR OBJECTS RECOGNITION

The recognition of logistics objects is an essential prerequisite for the optimization of operational logistics processes and can be performed among others via image-based methods. However, the lack of available data for training domain-specific recognition models remains a practical problem. For this reason, we present an approach to solving this problem. The core principle of our approach is the automated generation of image data from 3D models, in which the appearance of the objects varies through variations of different parameters. The first results are promising: Without any real image data, we have created a neural network for recognition of real objects with a recall quality of 86%.
Dynamic Service Negotiation

This document defines the Connectivity Provisioning NegotiationProtocol (CPNP) which is designed to facilitate the dynamicnegotiation of service parameters. CPNP is a generic protocol thatcan be used for various negotiation purposes that include (but are notnecessarily limited to) connectivity provisioning services, storagefacilities, Content Delivery Networks, etc.
Trust Computing Model Based on Meta-Heuristic Approach for Collaborative Cloud Environment

Collaborative cloud environment required trustworthiness from and to consumer and provides services associated with it. Operations related to consumer’s side are able to send task and crucial data to the cloud data center, that operation raises high trust between both the parties. Large flow of user request becomes complicated and heavy to mange in collaborative computation offered by the cloud. So, inter-service bed must provide and tackle large number of consumer request efficiently and provide or allocated trustworthy resources to associate the request. In this context, precision-based manageable service provider platform with respect to trust is emerging as a vital problem. In this paper, we have proposed a precise and collaborative trust computing mechanism by using meta-heuristic approach for trustworthy cloud computing. In initial module, inter-service platform relay response and request for managing and logging of allocated virtual machine. In the second module, resource selection by using nonlinear objective functions with the help of meta-heuristic approach to improve trust evaluation. All those operation depend on quality parameter. Therefore, in the third module, we proposed QoS prediction by appling learning approach, i.e., neural network to understand historical information in collaborative cloud computing environments. Performance analysis and experimental results verified the feasibility and effectiveness of the proposed scheme.
Land Mark Recognition Through Image Classification

Land mark classification and detection is useful in many social sharing websites and search engines to extract relevant data on given input image. Recent research in object recognition has used such sites as a source of image data, but the test images have been selected and labeled by hand, yielding relatively small validation sets. In this paper we study image classification on a much larger dataset of 30 million images, including nearly 2 million of which have been labeled into one of 500 categories. The dataset and categories are formed automatically from geotagged photos from Flickr, by looking for peaks in the spatial geotag distribution corresponding to frequently-photographed landmarks. We learn models for these landmarks with cloud vision API.
A influência da educação na saúde: da antiga à nova geração

In the course of its activities, Medical Clinic Pilares da Saude found that the majority of its clients were men and women over the age of forty-five. This was contrary to what they had initially envisaged, since they expected ladies over thirty years of age, hence a younger population. In order to better understand this reality, they carried out an investigation using quantitative methodologies, performing a survey on health education and health concerns. The aim of the study was to analyse the relationship between education and health as well as their effects on the new generation as opposed to the older generation. The results showed that education has been a driving force for greater literacy in health in the new generation, allowing to conclude that it concerns itself more with health when in comparison with the older generation due to the education they have received. JEL Codes: I10 - General Health (Health, Education and Welfare
Analysis of NSP(New Southern Policy) Countries' Edutech Market for Overseas Expansion of Korean ICT-based Educational Services

신남방국가는 성장하는 새로운 시장으로서 또한 생산기지로 각광을 받으면서 해외직접투자를 통한 산업화가 급격히 진행되면서 고등교육과 직업 교육에 대한 수요가 증가하고 있다. 이에 따라 대학 입 시, 영어, 수학, 소프트웨어 교육의 중요성이 높아지고 있으며, 보다 효과적이고 효율적인 교육을 위하 여 이러닝을 포함하여 에듀테크를 도입하기 시작하였다. 신남방국가 11개국 중에서 베트남, 인도네시아와 태국은 시장성이 가장 높은 국가들로서, 우리나라 의 ODA 집중 지원 대상국이면서, 에듀테크 시장은 물론 ICT 기반 교육 서비스 시장의 성장 잠재력이 매우 높기 때문에 한국의 에듀테크 기업이 진출을 선호하는 국가이다. 대학 입학 수요가 급증하고 있 는 베트남과 태국의 경우에는 한국과 유사한 형태의 대학입시 사교육과 영어 교육 분야에 대한 수요 가 매우 많다. 베트남과 인도네시아에는 한국 기업들이 다수 진출하여 양질의 취업 기회를 제공함에 따라 한국어 및 직업 교육 수요가 증가하고 있다. 더욱이 질 높은 교육 서비스를 위하여 강사양성 시 장도 성장하고 있다. 에듀테크 수요자는 학습자, 교수자, 교육기관 및 정부로 구성되므로 기업의 독자 적인 현지 진출을 추진하면서도 정부의 ODA 사업과 연계하여 진출하는 방안도 모색하여야 한다.As the NSP countries are emerging as a growing new market and base of production, the demand for higher education and vocational education is increasing due to the rapid progress of industrialization through foreign direct investment. Accordingly, the importance of college entrance exams, English, mathematics, and software education is increasing, and Edutech, including e-learning, has been introduced for more effective and efficient education. Of the 11 NSP countries, Vietnam, Indonesia and Thailand are the target countries for Korea s ODA intensive support and as the growth potential of the Edutech market as well as the ICT-based education services market is very high, Korea s Edutech companies prefer to enter the local market. In Vietnam and Thailand, where the demand for university admission is rapidly increasing, there is a great demand for private education and English education similar to Korean private institutions. Many Korean companies have entered Vietnam and Indonesia, providing many employment opportunities. Accordingly, there is a great demand for Korean language and vocational education for employment in Korean companies. Moreover, the market for instructor training is growing for high quality education services. The Edutech consumer consists of learners, instructors, educational institutions and the government, so it is also necessary to seek ways to enter in conjunction with the government s ODA project while promoting the company s own local advancement.
STRENGTHENING CHARACTER EDUCATION IN ELEMENTARY SCHOOL THROUGH ECRANISATION-BASED LITERACY

The purpose of this study was to develop a model of character education at elementary schools through ecranisation-based school literacy movement. The model was compiled and developed through four stages following Plomp (1997). The research began with the identification of the main values of the characters in the film from the exposition of the Laskar Pelangi novel. Realization of the model through the determination of syntax components, social systems, support systems, inst­ructional impacts and the accompanying effects. The validation and revision of the model by education experts and teachers at elementary school. The results showed that the character education model developed, can be realized in the form of modules and the instruments in the form of interactive media with the application of the Adobe Flas program that is valid, practical and effective to be applied in schools through literacy activities. The module contains (1) background, understanding, goals, scope, goals, and targets; (2) the stages of character education through the school literacy movement in elementary schools; (3) Development of the main values of character through ecranisation; (4) Closing. This developed model has been tested in Pengkol 1 Public Elementary School, and succeeded in improving the quality of character education etrengthening in local schools.
Penerapan Model Student Teams Achiement Divisions untuk Meningkatkan Hasil Belajar Matematika Kelas IV  SD 8 Kandangmas

This study aims to improve student learning outcomes using the Student Teams Achiement Divisions learning model for fourth grade students of SD 8 Kandangmas. This study uses Classroom Action Research, consisting of four stages, namely: planning, implementation, observation, and reflection. The results of this study show that the results of observation of learning activities in the first cycle reached 69% with the criteria of Enough and the second cycle increased to 83% with the criteria of Good. The observation results of the management of teacher learning in the first cycle reached 78% with the criteria of Good and in the second cycle increased to 88% with the criteria Very Good. The results of student evaluation tests in the pre-cycle with an average value of 57, in the first cycle increased to 74 and in the second cycle to 86 with the criteria complete. the average learning outcomes achieved 85.3 with classical completeness reaching 90%. The conclusion of this study is through the use of the Student Teams Achiement Divisions learning model can improve the learning outcomes of fourth grade students of SD 8 Kandangmas.
Data Privacy Preservation Based on Multitenant Isolation in Cloud

Cloud computing is a whole new paradigm that offers a non-traditional computing model for organizations to adopt Information Technology. In Cloud Computing systems, the data is stored on remote server’s access through internet. And the main reason behind its rapid growth is its capability to share resources at various remote location, its flexibility, low cost, scalability etc. which has also helped in its major development but with its growth, the security issues like confidentiality, availability, and integrity becomes a major concern. The security problem related to data’s get amplified under the cloud model.as new dimensions are introduced. This paper provides a secure cloud computing architecture based on multi-tenant isolation using Universal Onaway Hash function (SHA-256) and Universal Unique identifier (UUID) ensuring authentication, confidentiality and integrity. In this paper, it use access control mechanism for authenticating the users, then encrypted hash function for secure transition between client’s side to the server and unique identifier that provide isolation to the file in the server. The scope of the paper is basically for PAAS model of Cloud Computing and Public Cloud.
SECCSL: Security concurrent separation logic

© The Author(s) 2019. We present SecCSL, a concurrent separation logic for proving expressive, data-dependent information flow security properties of low-level programs. SecCSL is considerably more expressive, while being simpler, than recent compositional information flow logics that cannot reason about pointers, arrays etc. To capture security concerns, SecCSL adopts a relational semantics for its assertions. At the same time it inherits the structure of traditional concurrent separation logics; thus SecCSL reasoning can be automated via symbolic execution. We demonstrate this by implementing SecC, an automatic verifier for a subset of the C programming language, which we apply to a range of benchmarks.
Clustering Model of False Positive Elimination in Moroccan Fiscal Fraud Detection

Every year government faces tax fraud with many methods. Big data and analytics enable government organizations to improve existing processes of detection and engage in entirely new types of analyses to allow tax authority to prevent tax fraud, reduce the cost of managing taxes and optimize public spending. Fraud detection often includes analyzing large datasets to locate irregularities. Anomaly detection helps by exploring every possible path to find fraudsters but it’s often results in large number of false positive, that is entries wrongly identified as fraud. When a taxpayer is wrongly flagged, the intervention of an audit agent is necessary and so a big time of investigations to finally lose time, effort and money. The purpose of this paper is to propose a model of false positive elimination in Moroccan fiscal fraud detection based on clustering.
A New Embedded Feature Selection Method using IBALO mixed with MRMR criteria

Abstract In order to remove irrelevant data and increase classification accuracy in feature selection, this paper proposed a new Embedded feature selection method with gathering Minimal Redundancy Maximal Relevance (MRMR) criteria, Sequential Forward Selection (SFS) and Improved Binary Ant Lion Optimizer (IBALO) together. Totally, we use three different feature selection methods namely MRMR mixed with Sequential Forward Selection (MS), MS mixed with Binary Ant Lion Optimizer (MS-BALO), an improvement of MS-BALO. Experiments prove that the MS-IBALO method proposed by this paper is efficient compared to MS and MS-BALO methods.
Performance optimization of agricultural products website front-end based on user experience

Abstract With the rapid development of Internet technology, users have higher and higher requirements for the response speed of the website, which makes the performance optimization of the website become extremely important. How to solve this problem is the key to improving the user experience. In this paper, taking the characteristic agricultural product website as an example, we analyze the website front-end performance optimization in CSS optimization, page rendering, HTTP request reduction, Ajax caching and web caching, and offer the corresponding performance optimization methods.
CRYPTO CURRENCY- BLUFF OR REAL

Crypto currency is being adopted across world for various transactions – legal or illegal. A crypto currency is a virtual and digital currency that uses cryptography for security. It has attracted lots of attention in recent years. The first crypto currency was launched in 2009 named as “Bitcoin” by Satoshi Nakamoto. But some people are still not aware about this term and not even heard about it. Crypto currencies gave birth to an incredibly dynamic, fast-growing market for investors and speculators. The present study focuses on the question that whether it is real and mere a virtual myth. It also stresses upon the familiarity and future of crypto currency.
Editor’s note. Special issue algebraic coding theory: New trends and its connections

Dear ColleaguesThe purpose of this special issue of Journal of Algebra,Combinatorics, Discrete Structures and Applications was to collect a sample of papers in active areas of research in algebraic coding theory and its connections to other areas. A number of researchers submitted manuscripts to the special issue. After a thorough review process, six articles have been selected to appear in the special issue. We thank all researchers who submitted an article. Their contributions are sincerely appreciated, regardless of whether they have been accepted for publication or not. We are particularly grateful to our small number of dedicated reviewers who did a meticulous job of reviewing in a short period of time. The articles selected for this special issue are a representative sample of the current research trends in algebraic coding theory. In their article Construction of quasi-twisted codes and enumeration of defining Gulliver and Venkaiah enumerate all twistulant matrices of a given size and use that information to construct quasi-twisted (QT) codes with better parameters and they start new databases over $GF(17)$ and $GF(19)$. QT codes have been studied extensively in coding theory and they continue to yield useful results. In the article Locally recoverable codes from planar graphs Haymaker and O’Pella construct codes that are locally recoverable from 3-connected regular and almost regular graphs. Furthermore, they present methods of constructing regular and almost regular planar graphs. In the paper Constructions of MDS convolutional codes using superregular matrices, Lieb and Pinto show how to obtain MDS convolutional codes from superregular matrices with certain properties. They provide explicit ways of constructing generator matrices of MDS convolutional codes from superregular matrices. In the paper titled over formal power series rings, Korban et al. introduce G-codes over an infinite ring, using tools from group rings. They study the duality properties of these codes and show that their projections are G-codes over finite chain rings. They prove similar results for the lifts of codes over finite chain rings as well. In $Z_q(Z_q+uZ_q)$-linear skew constacyclic codes, Melakhessou et al. consider Zq(Zq+uZq) skew constacyclic codes where q is a prime power and $u^2=0$. They describe the generator polynomials, the minimal spanning sets, and sizes of these codes. They also obtain some new $Z_4$-codes from the Gray images of these codes. In Weight distributions of some constacyclic codes over a finite field and isodual constacyclic codes, Singh describes the weight distribution of a family of constacyclic codes over $F_q$. Singh also constructs a family of non-binary isodual-constacyclic codes of a special length and gives specific examples of the constructions. Algebraic Coding Theory continues to be an active area of research with many theoretical and applied aspects. We believe that this special issue will help disseminate recent results to a broad audience in an open access journal and promote further research.