Reviewerly/exHarmony huggingface dataset:
A Generalized Trust Model for Computational Grids &amp; Clouds

In the present era, world is moving very rapidly with internet for sharing of resources and services. There are many heterogeneous and complex applications to be executed within less time. The complex applications require lot of resources such as processing, storage, etc, for execution but due to the constrained resources available within a system, the applications may not execute perfectly. Grid Computing is the technology used to integrate various heterogeneous systems from different boundaries of the world to execute complex applications. This technology creates a virtual organization where huge set of systems are integrated to solve very complicated and critical problems. There are many people who are not able to set up a real organization with lot of infrastructure and other services for execution of applications. Cloud Computing is the technology which provides lot of services like software, hardware, networking and much more for less cost. This technology supports pay-as-you-go model in which the user will pay the amount only for the used services. The major problem is to trust Grid Service Providers, Grid Users, Cloud Service Providers and Cloud Users for doing the transactions, in their respective environment, as there are many systems from various domains participating in the transactions. This paper proposes a generalized Trust Model for Cloud/Grid Computing Environment (T-CGE) to establish very strong trust among the Cloud/Grid Service Providers and Cloud/Grid Users. This paper also demonstrates the reliability statistics by using Cronbach’s Alpha to show the relative consistency of data. ANOVA (Analysis of Variance) is used to show the variations among the service parameters of all service providers, and also variations among the service providers. The T-CGE trust model helps the end users to get better and consistent services from their application developers.
Component based Web Application Firewall for Analyzing and Defending SQL Injection Attack Vectors

Structured query language injection is a top rated vulnerability by open web application security project community. If a web application has structured query language vulnerability in source code, then such application is prone to cyber-attacks, leading to attack on confidentiality, integrity and availability. Attackers are always ready to exploit structured query language injection vulnerabilities by executing various online attack vectors and many times successfully bypass authentication and authorization to gain privilege access on web and database server leading to service interruption, data interception, modification, fabrication and sometime complete deletion of database. The present paper is an attempt to propose an advance component based web application firewall to enhance web application security by mitigating structured query language injection attack vectors by analyzing hypertext transfer protocol request variables through analyzer component and defending injection attack through defender component based on content policy installed on advance web application firewall.
Static and Moving Patterns

This chapter looks at the process whereby the brain segments the world into regions and finds links, structure, and prototypical objects. These are converted into a set of design guidelines for information display. The applications discussed include the following: display of data so that patterns can be perceived, information layout, node-link diagrams, and layered displays. Gestalt laws, vector fields, 2D flow visualization techniques, texture, information density, visual texture, quantitative texture sequences, transparency, multidimensional discrete data, priming, node-link diagrams, expressive motion, animate motion, and pattern finding.
Agent-based Big Data Mining

Big data is the term used to describe a data, which is difficult to process, manage and analyze using traditional databases or data mining algorithms.Useful knowledge can be extracted from this big data with the help of data mining.Due to the volume, variety, and velocity of data, traditional techniques of data mining may be unsuitable to work with big data.As a result, there is a basic need to create powerful and productive enormous information mining methods.Characterization is one of the information mining strategies that is capable of processing a large amount of data and is growing in popularity.It is used to map a data item into one of several predefined classes or categories.Healthcare data is one form of big data not only for its sheer volume, but also for its complexity, diversity, and speed at which it is generated and must be managed.In this paper, we present the problem of mining the big data using software agent.The main goal of this paper is to develop and implement an agent-based big data classification model that can predict the severity of diabetes disease.Results proved that using agent technology in the preprocessing stage saved the memory storage from 8.66 TB to 5 GB memory space.The transfer data time is reduced from about 12 days to about 10 minutes after preprocessing data remotely using the agent.Regarding classification accuracy, the proposed model has proven 87% accuracy and 65% reliability.
Generalizing Results of a Regression

This textbook provides future data analysts with the tools, methods, and skills needed to answer data-focused, real-life questions; to carry out data analysis; and to visualize and interpret results to support better decisions in business, economics, and public policy. Data wrangling and exploration, regression analysis, machine learning, and causal analysis are comprehensively covered, as well as when, why, and how the methods work, and how they relate to each other. As the most effective way to communicate data analysis, running case studies play a central role in this textbook. Each case starts with an industry-relevant question and answers it by using real-world data and applying the tools and methods covered in the textbook. Learning is then consolidated by 360 practice questions and 120 data exercises. Extensive online resources, including raw and cleaned data and codes for all analysis in Stata, R, and Python, can be found at www.gabors-data-analysis.com.
Secure Radio Frequency Transmission for Paperless Voting System

In any democracy, elections play an important role. If the traditional Electronic Voting Machine (EVM) is secured by encryption, it could be made more reliable. Traditional voting process provides security through the use of a paper audit trail which is not environment friendly making it unfit for use in the long run. This paper proposes the use of Blowfish algorithm for encryption along with secure transmission using radio frequency and verification of the cast vote. In this approach, the cast vote is encrypted using Blowfish encryption algorithm and transmitted to the server through radio frequency. At the server, the data is decrypted and sent back to be displayed on the screen of the EVM, eliminating the paper audit trail. This approach will account for a considerable amount of cost reduction without compromising on the security and sanctity of the election process.
Genetic Programming for Automatic Global and Local Feature Extraction to Image Classification

© 2018 IEEE. Feature extraction is an essential process to image classification. Existing feature extraction methods can extract important and discriminative image features but often require domain expert and human intervention. Genetic Programming (GP) can automatically extract features which are more adaptive to different image classification tasks. However, the majority GP-based methods only extract relatively simple features of one type i.e. local or global, which are not effective and efficient for complex image classification. In this paper, a new GP method (GP-GLF) is proposed to achieve automatically and simultaneously global and local feature extraction to image classification. To extract discriminative image features, several effective and well-known feature extraction methods, such as HOG, SIFT and LBP, are employed as GP functions in global and local scenarios. A novel program structure is developed to allow GP-GLF to evolve descriptors that can synthesise feature vectors from the input image and the automatically detected regions using these functions. The performance of the proposed method is evaluated on four different image classification data sets of varying difficulty and compared with seven GP based methods and a set of non-GP methods. Experimental results show that the proposed method achieves significantly better or similar performance than almost all the peer methods. Further analysis on the evolved programs shows the good interpretability of the GP-GLF method.
Validating the Knowledge Acquisition Process Metrics in Content Management Systems

Measuring the processes involved in knowledge engineering for designing and building an intelligent system has taken significant role. Out of the four basic processes involved in knowledge engineering, this paper deals with the knowledge acquisition process and the metrics necessary for measuring the process itself. Three metrics are proposed for the knowledge acquisition process based on the entailment procedures, its length and complexity, and the cohesion and coupling attributes of the collection of knowledge units. These three metrics are formalized based on the Briand’s mathematical properties for validating software metrics. These metrics are indicative in the way it gives an insight on the design and the development of a knowledgebase. In addition to these metrics, newer metrics can also be proposed for knowledge representation and knowledge sharing processes.
Formation of Creative Fantasy and Imagination of Students as A Task of Artistic Pedagogy

The paper reveals one of the key problems of the modern artistic pedagogy, which lies in the application of the potential of creative fantasy and imagination of students in the pedagogical process. It considers the various preconditions for the development of the ability to fantasize, depending on the individual capabilities of students. Reliance on initially individual systems of perception of artistic information with the subsequent development of this perception to the level of creation of full-fledged artistic images and interpretations is proposed as a leading pedagogical strategy. The interrelation between the organized social and aesthetic experience of students and possibilities of creative fantasy and imagination is established. In this process, the role of educational creative teams as a space of social, artistic and aesthetic development of students is highlighted.
Hardware Implementation of Hybrid Model Encryption Algorithm for Secure Transmission of Medical Images

The advancement of medical field needs a more secure function for sharing the medical related images in the present environment. For making secure transmission of medical images the best solution is cryptography algorithm. There are many cryptography algorithm existing in the market out of the entire algorithm we are select the best encryption algorithm which is really suitable for medical images transmission. Transmission of data is easy at the same time secure transmission are meet the different challenges. To full fill the all the challenges we concentrating the encryption algorithm which is highly secure. For making more secure transmission hybrid model encryption algorithm support more compare to single encryption algorithm. Its having capable of providing confidentiality, authenticity and integrity services to medical images exchanged in telemedicine applications. The same hybrid model encryption may implement in real time application using FPGA device. While implementing in hardware the following factors need to be concentrate more such as power, area, throughput, PSNR, Sensitivity etc. keeping all the factors in mind the hybrid model encryption algorithm are developed for secure transmission of medical images. The aim of the research is to encrypt and decrypt medical images efficiently and effectively protect the transmitted data. This research paper presents a model for encrypting transmitted medical image data. This model uses the following encryption algorithm such as Advanced Encryption Standard, Rivest Cipher 4.
Data Driven Enterprises

No AccessHandbuch Data ScienceJun 2019Data Driven EnterprisesMario Meir-Huber, Stefan PappMario Meir-HuberSearch for more papers by this author, Stefan PappSearch for more papers by this authorhttps://doi.org/10.3139/9783446459755.008SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2019Pages: 215-230Print ISBN: 978-3-446-45710-2eISBN: 978-3-446-45975-5 Copyright & Permissions© 2019 Carl Hanser Verlag GmbH & Co. KGPDF downloadLoading ...
Global Development Perspective, Economic Realignment and its Challenges-A Synoptic view

Economic development of a country cannot be achieved without the global orientation.The global development is multidimensional and multilateral and it transformed many countries.Emerging Market and Development Economies shifted the geographical landscape and new growth centers have come up and now the paradox is whether these developments are identical with the countries and people.The literature shows that the global economy was expanding with falling unemployment and brought inequality among countries.International cooperation, world tax and migration governance is increasing and it brought new challenges.India and a few developing countries have unique position as a global player in the knowledge based sectors.Technology has changed its trajectory and the robotics and machine intelligence would significantly replace labour in the future.The countries like India and China have grown faster than the OECD average.However, impending ricochet in global GDP growth is doubtful as slow growth and trade tensions are observed in the world.Such global trade tensions may be avoided if the countries respect multilateralism.The current condition of the global manufacturing activity, lower business spending, lesser consumer purchases of durable goods, passive final demand, muffled inflation and trade tensions needs to be addressed.The world output discussed in this paper shows that for the periods 2020 and 2021 it is not favourable for the emerging market and developing economies.The assessment of the global economic situation portrays that the global economic realignment is unfavourable to the emerging market and developing economies in particular and other economies in general.This necessitates for an appropriate policy formulation to contain the declining trend otherwise global equality cannot be achieved and results in furthering of geopolitical and bilateral and multilateral trade tensions in the future.
Improving Decision Tree Forest using Preprocessed Data

Random forest is one of the best techniques in data mining for classification. It not only improves accuracy of classification but performing best for various data types. Data mining researchers concentrated on improving random tree forest by constructing trees by using various methods. In this paper, we are improving decision forest by applying various preprocessing techniques. Decision tree forest is created by using bootstrapped samples. Trees created using preprocessed data improves not only accuracy of classification but also improves time required to construct forest. Experiments are carried out on various UCI data sets to show better performance of our proposed system.
Optimizing Cost of Irregularly Structured Problems in Cloud

This paper deals with maximizing the cost of parallel applications in a cloud-based environment. The cost belongs to monetary cost and cost of efficiency. The core argument seems to be that the parallel program robustness should affect the current monetary cost. Dynamic method of optimization is used to minimize the cost of computation. In order to measure the overall monetary cost of parallel computation, a cost model is used to evaluate the cost of parallel infrastructure as well as the cost of delayed performance. The main purpose of this cost model is to identify the necessary resources for performing this type of operation. Different methods have been used in the cloud environment. But these solutions do not take into account the uncertainties in the scheduling system, namely task start / perform / finish time, the unpredictable data transfer period between tasks, the unexpected arrival of new tasks. Such factors contribute to the breach of the task deadline and increase the cost of renting the service of executing the task, this effect will increase the monetary cost. Will boost the output by reducing the ambiguity in the scheduling process that requires time for execution of tasks and time for data transfer. In order to be precise a scheduling algorithm, uncertainty-Aware Scheduling Algorithm (ASA) is built to schedule complex and multiple tasks. When a task has been accomplished, its beginning / prosecution / target time is accessible that implies the ambiguity are no longer visible and therefore does not impact its related pending task.
Use of Social Web and Perceptions of Professional Behaviours Regarding Social Web Postings among Physiotherapy Students

BACKGROUND:Social media websites which facilitate exchange of user-generated content has enormous potential in health care which allows Physiotherapists to post educational information for patients also to share ideas as it acts as a great source of information for research and learning.OBJECTIVE: To survey students enrolled in professional physical therapy programs regarding their use of social web and perceptions of professional behaviors regarding social web postings.MATERIALS AND METHODS: A non-experimental observational type of study has been done with 100 Physiotherapy students of three different years and were selected according to the selection criteria.The survey instrument was provided to participate in the study addressing students use of social web.RESULTS AND CONCLUSONS Students are unaware about the social media policy and its importance.Students should learn about the privacy concern and professional boundaries.There is a need for social media policies and education regarding the proper use of social media
An automatic region detection and processing approach in genetic programming for binary image classification

© 2017 IEEE. In image classification, region detection is an effective approach to reducing the dimensionality of the image data but requires human intervention. Genetic Programming (GP) as an evolutionary computation technique can automatically identify important regions, and conduct feature extraction, feature construction and classification simultaneously. In this paper, an automatic region detection and processing approach in GP (GP-RDP) method is proposed for image classification. This approach is able to evolve important image operators to deal with detected regions for facilitating feature extraction and construction. To evaluate the performance of the proposed method, five recent GP methods and seven non-GP methods based on three types of image features are used for comparison on four image data sets. The results reveal that the proposed method can achieve comparable performance on easy data sets and significantly better performance on difficult data sets than the other comparable methods. To further demonstrate the interpretability and understandability of the proposed method, two evolved programs are analysed. The analysis shows the good interpretability of the GP-RDP method and proves that the GP-RDP method is able to identify prominent regions, evolve effective image operators to process these regions, extract and construct good features for efficient image classification.
Moderating effects of related experience and firm size: will multinationals opt for acquisitions in high informal institutional settings

This study examines the influence of informal institutional distance on the establishment mode strategies of foreign multinational enterprises (MNEs) in Ghana with the moderation role of experiential knowledge (related experience) and parent firm size. The empirical analysis is based on a sample of 223 green-field and acquisition market entries over the period 2001-2016, drawn from 20 countries. The study employs binomial logistic regression to establish the relationships among the variables, and the results reveal that high informal institutional distance leads to the preference for acquisitions over green-field investments. The study also finds that high level of experiential knowledge (related experience) and large parent firm size have positive relationship with acquisition establishment as compared to green-field investment. We therefore argue that strong related experience and large parent firm size strengthen the choice for acquisitions in relation to high informal institutional distance.
Advanced Strings

This chapter will cover more advanced string algorithms than the previous chapters have discussed. They should be easier to understand now that you have learned about some other data structures. Specifically, this chapter will focus on searching for string patterns.
Sentiment Analysis of Movie using Artificial Neural Network

Semantics is the investigation of the importance of phonetic expressions. For their need everybody is normally restless to realize other individuals’ surveys be that as it may, charitable measure of audits are accessible in every single page. We propose for computerized wistful examination of motion picture on the grounds that it’s extremely troublesome for an ordinary individual to seek in every single site to separate valuable audits and dynamic the importance out of the content. Regular Language Processing (NLP) and Text Analysis are utilized in Sentimental Analysis which recognizes the feelings in a given setting of content information. Preceding making accord on whether to watch a motion picture individuals request counsel. It would be astounding on the off chance that we had a robotized framework to give results dependent on the client’s audits. Conclusions communicated by the general population in their audits can be comprehended by utilizing a wistful examination of motion picture surveys. Thus, wistful examination assumes a urgent job in the everyday life. We propose to utilize Sentimental Classifier utilizing Artificial Neural Network (ANN) as our essence of the paper is to give data whether the motion picture is delicate or shocking
Why Python is Most Famous

We now have a plethora of programming languages to meet our requirements, but the most pressing issue is how to teach programming to beginners. We recommend Python for this job in this article since it is a programming language with a well-organized syntax and strong capabilities for solving any problem. Furthermore, it is extremely similar to basic math reasoning. In most top institutions, Python is selected as the main programming language for freshmen. Python makes it simple to write code. In this article, we provide several computer code samples developed in Java, C++, and Python, as well as a comparison of the three languages.To begin, this article discusses the benefits of Python over C++ and Java. The results of a comparison of brief program codes written in three distinct languages are then shown, followed by a discussion of how pupils comprehend programming. Finally, the experimental findings of students' programming course success are shown.
smarter medicine: la liste Top-5 de la SSORL

smarter medicine»: la liste «Top-5» de la SSORL Organisation responsable «smarter medicine -Choosing Wisely Switzerland»