Reviewerly/exHarmony huggingface dataset:
SLA - An Asset for Better Cloud Services

Cloud provides service on Pay-as-per-use. With this feature number of online presence have extended at business level to its peak. With increase in business there arise need of providing bridge of assurance between Service Provider (seller) and Service Consumer (customer) for service type and its quality. To achieve these, terminology SLA (Service Level Agreement) has been introduced. Not just withstanding the point of quality of service, SLA also works on monitoring the service and refining the parameters which can increase the level of service. The objective of this paper is to present comprehensive study about how SLA works and what are the parameters that needs to be taken care for measurements.
Towards Datasets Annotation using Semantic Technologies for further Visualization and Business Analytics

This paper proposes an approach to datasets annotation using semantic networks and RDF statements in order to provide a knowledge base of best practices for begineers in business data analytics, who do not know excactly which graphs and charts better suite for visualization of their datasets.
Internet-technologies as tool for personal protection during large-scale virus infections

The article describes how internet-technologies can contribute to the protection of the individual during the spread of viral diseases or natural disasters. Remote work is largely implemented by modern internet-technologies that provide joint data processing and eﬀective communication between members of diﬀerent communities. At the same time, the communication must remain protected from leaks of personal information. Artiﬁcial intelligence actively uses in searching medicines. In this time all of this increase degree of using informational technologies and we have sharpened the range of information security problems.
The Value of Simulation for Learning Project Management

This chapter will explore the value of using computerized simulation case studies to enhance learning in project management education at the undergraduate and graduate college levels. Traditional teaching methods of textbook reading and lectures provide students with a vast number of concepts, processes, tools, and procedures for managing projects. However, it is difficult for students to translate that learning into use in real-life project management situations. Simulations are filling that gap by offering a low-risk environment with lifelike scenarios where students must determine the appropriate project management concept to employ and how to execute it. Simulations allow students to use their newly learned concepts and critical thinking for decision making while receiving immediate feedback which allows the student to make adjustments. This chapter will also discuss the value of these simulations based on the timing of when to introduce them to students during the semester.
Exploring “Hacking,” Digital Public Art, and Implication for Contemporary Governance

This chapter considers the hypothesis that the online (internet) “hacking” concept has applications for community life and processes of sharing memories and identity, and facilitating social engagement and digital inclusion among residents. It specifically presents how the characteristics and practicality of online hacking inspired the design and functionality of a community digital artwork in a disadvantage urban estate in Edinburgh, UK. In addition, the chapter considers the implication of the hacking practices by and among disadvantaged communities for realizing social action, social engagement, and networked society goals of the UK Government's “Big Society” policy. This is significant because the Big Society agenda promotes an interactive networked culture that has transformative potential to connect citizens, build knowledge and continuous learning, and regenerate communities at times of economic austerity in the UK.
Neural Network Modeling for Organizational Psychology

The nature itself of organizational psychology makes the study and modeling of emergence processes the key topic of this science. In this regard we can distinguish between two kinds of emergence: the one related to individual constructs and the other to collective constructs. In the former case the presence of suitable individual and contextual features gives rise to the emergence of suitable individual attitudes of holistic nature. In the latter case the features of single individuals belonging to a group, and reciprocally interacting, give rise to the occurrence of collective features and phenomena. In the last years both kinds of emergence have been studied through computational models. In this chapter we focus on the contribution of Artificial Neural Network (ANN) models to this modeling activity. As regards the emergence of individual constructs there is a consistent number of ANN-based models, most of which formulated in terms of recurrent networks. A review of their successes and failures constitutes a first part of the chapter. Instead, the emergence of collective constructs has been so far modelled by resorting to agent-based models. However, in recent times the ANN models have begun to be used with increasing frequency in this field. Namely, each agent can be modelled in an easier way by representing its structure through a suitable neural network. The final part of the chapter is, therefore, devoted to the problems underlying the use of ANNs as constituents of agent models.
Old

How might the analytical article and the creative endeavour find consonance? I consider a possible approach of combining extracts from a screenplay with my own reflections on my purpose in writing the screenplay. The content applies to a global attitude towards an “-ism” that sits low on the radar of public comment or concern: age-ism. I am more direct in the title of this article: “Old.” The screenplay When Granny Went on the Internet explores the notion that acquiring digital technological expertise can lead to an expanded definition of self and can empower the individual to break free from expectations around their role in the family and in society. The storyline offers a challenge to stereotypes about age and gender and suggests that New Media may provide a portal through which definitions of self can be re-imagined. Granny’s story disrupts the traditional alignment between technology, patriarchy, sexism and age-ism and re-imagines it.
Why One Should Learn Ethical Hacking

This chapter sets the stage for the rest of the book by presenting the importance of learning hacking techniques by each and every person dealing with cyber operations. The chapter explains various basic terminologies used in the ethical hacking domain and also provides step-by-step instructions for setting up an ethical hacking lab in order to carry out the attacks mentioned in further chapters of this book. The chapter also reveals the legal issues with the ethical hacking domain by providing details of existing cyber laws, acts, and regulations framed by various countries in order to deal with the harmful hacking activities and cybercrimes.
Cryptographic Games

The term “game” has substantially different meanings within the security area, depending on whether we speak about cryptographic security in particular, or system security in a more general setting that includes quantitative security with help of game theory. Game theory and cryptography are, however, of mutual value for each other, since game theory can help designing self-enforcing security of cryptographic protocols, and cryptography contributes invaluable mechanisms to implement games for security. This chapter introduces both ideas, being rational cryptography for the design of protocols that use rationality to incentivize players to follow faithfully, but also addresses the classical security goals like confidentiality, integrity, availability and authenticity by describing security games with quantitative and unconditional security guarantees. The chapter closes with a connection between network design for security and the P/NP question whose discovery is made with help from game theory.
Securely Communicating With an Optimal Cloud for Intelligently Enhancing a Cloud's Elasticity

One of the principle features on which cloud environments operate is the scaling up and down of resources based on users' needs, called elasticity. This feature is limited to the cloud's physical resources. This article proposes to enhance the elasticity of a cloud in an intelligent manner by communicating with an optimal external cloud (EC) and borrowing additional resources from it when the cloud runs out of resources. This inter-cloud communication is secured by a model whose structure is similar to the Kerberos protocol. To choose the optimal EC for a particular request of a user, a list of parameters, collectively termed as RePVoCRaD, are enumerated. Once chosen, trust is established with the chosen EC and inter-cloud communication begins. While existing works deal with third parties to establish or secure inter-cloud communication, this work is novel in that there is absence of third parties in the entire process, thereby reducing security threats and additional costs involved. Evaluating this work based on turnaround time and transaction success rate, in a real-time cloud environment, it is seen that the cloud's elasticity is so enhanced that it successfully accommodates its users' additional demands by the fastest means possible.
App Evaluation Based on Random Forest Algorithm

This paper proposes an App evaluation and analysis method based on machine learning algorithm for the problem of App evaluation without distinguishing categories. Firstly, the random forest model is used to predict App evaluation and detect the importance of sample attributes. Then combined with statistical methods, the word cloud map is used for further analysis and verification. Finally, characteristics of App products and users from multiple perspectives were analyzed, providing guidance and suggestions for App developers' future product development and users' reasonable choice of products, which effectively promotes the good development of commercial App market.
An Effective Data Access Method for Public Cloud Data Storage

These days attribute-based encryption has been gotten as a promising strategy to give versatile and secure data to oversee the conveyed storage in the cloud registering environment. In the attribute-based encryption plan, the single property pro should execute the customer legitimacy check and mystery key flow, and thusly, its outcomes. Customers may be stuck in the believing that at a stretch get their mystery keys and choose to get the required data from Cloud. So as to maintain a strategic distance from the single-point execution bottleneck, we are proposing multi-master access control plan which will assess the issue such way that it gives increasingly successful access control on cloud data to the clients.
In-container Robot Palletizing with 3D vision-based Datum Point Positioning

Palletizing inside the container faces inefficiency of manual palletizing and positioning inprecision of robot. To solve these problems, it is necessary to consider two important procedures: robot palletizing scheme analysis and datum point positioning inside the container. A 3D vision-based positioning algorithm, which was combined with robot kinematics was proposed and then executed. A D-H table was built for robot RS050N to achieve the homogeneous transformation matrix; then, whole corner positioning algorithm based on 3D vision was designed, which included segmentation, down sampling, filter and an algorithm designed for boundary fitting in point cloud. It can reduce data volume, improve efficiency, and remove the point cloud which interferes positioning precision, such as stiffeners and bumps at corners. In the experiment, this algorithm can realize precise positioning for industrial robot palletizing in the container, meeting MAE of 1.715 mm and RMSE of 1.976 mm.
Application of Fuzzy Clustering Maximum Tree Algorithm in Teaching Quality Evaluation

Abstract The evaluation process of school teaching quality is more complicated. The traditional schemes mainly based on manual evaluation or expert evaluation. Due to the strong subjectivity, there is a big deviation between the evaluation results and the real results. To this end, a teaching quality evaluation program based on the largest tree of fuzzy clustering is proposed. After raw data collection, preprocessing, and data mining, accurate and objective evaluation results are finally obtained. Taking the original evaluation data set as the object, construct the fuzzy clustering maximum tree model, and obtain the importance matrix of the data set and the similarity coefficient between the input data variables. With the help of the fuzzy clustering maximum tree model, the importance of each factor in the evaluation of teaching quality can be objectively evaluated. Application examples also verify the superiority of the proposed evaluation algorithm.
Data Backup and Recovery With a Minimum Replica Plan in a Multi-Cloud Environment

Cloud computing has become a desirable choice to store and share large amounts of data among several users. The two main concerns with cloud storage are data recovery and cost of storage. This article discusses the issue of data recovery in case of a disaster in a multi-cloud environment. This research proposes a preventive approach for data backup and recovery aiming at minimizing the number of replicas and ensuring high data reliability during disasters. This approach named Preventive Disaster Recovery Plan with Minimum Replica (PDRPMR) aims at reducing the number of replications in the cloud without compromising the data reliability. PDRPMR means preventive action checking of the availability of replicas and monitoring of denial of service attacks to maintain data reliability. Several experiments were conducted to evaluate the effectiveness of PDRPMR and the results demonstrated that the storage space used one-third to two-thirds compared to typical 3-replicas replication strategies.
Technical framework and model of artificial intelligence for boosting the revitalization of rural education

This paper mainly explores the transformation of Al technology model and framework to promote the revitalization of rural education. Give full play to the application of data model establishment, data information analysis and data management in education based on artificial intelligence, and promote the transformation of the structure of rural education system. Artificial intelligence technology for education designed professional curriculum evaluation system, and used the hierarchical analysis method to establish the importance of index scale table, make the education system can be compatible with various computer algorithms, using comprehensive analysis method selection evaluation index, through the application of artificial intelligence, can greatly help rural education to further improve teaching informatization.
A machine learning approach for data protection in virtual reality therapy applications

Health information is a protected asset that should be kept private. The tradability of personal data brings risks to the health information shared by users online. Authentication is a crucial first step when working to keep personal information private. Virtual Reality applications usually bypass application-specific authentication in favor of provider-specific authentication (e.g. Steam, etc). This approach is not ideal for health applications. Virtual Reality secure authentication can be difficult because most methodologies are not user-friendly. Previously proposed Virtual Reality authentication systems use PINs, Patterns, or 3D object sequences. We propose an authentication method based on dynamic movements (i.e. dance moves). Machine learning-based models are employed to determine if the received movement matches the user’s previously chosen movement. The proposed method thus balances security with a better versatility than the one provided by traditional methods.
Classification of Sentiment of Reviews using Supervised Machine Learning Techniques

Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.
Ancient World

The features of depicting space in the reliefs and murals of Ancient Egypt are considered. Attention is drawn to the preservation of the connection of ancient Egyptian art with primitive art in sacred paintings and to the evolution of the ways of depicting space in secular scenes. There is enough material to reconstruct the ancient Egyptian version of the World Tree myth and to establish links with other archaic myths and ideas about the World Tree in the synchronous cultures of the Middle East. When analyzing markers of evolutionary changes, the most active channels were established and the forecast of the self-organization scenario was checked. The results are presented in the form of generalized psychological portraits and behavior patterns of representatives of the main estates.
Introduction

We motivate this book with statistics on the growth of social media platforms in various communities across the globe and provide an outline of the included methods.
Secure and Robust Telemedicine using ECC on Radix-8 with Formal Verification

The scalar multiplication techniques used in Elliptic curve cryptography (ECC) are having the scope for gaining the computation efficiency. This is possible through the reduction of precomputed operations. Finding the more efficient technique compares to the most recent or efficient one is a research gap for all schemes. The manuscript presents an application oriented work for Telemedicine using ECC. It is based on robust application on reduced computational complexity. The methodology we apply for the same is Scalar Multiplication without precomputation on Radix-8. Introduced software and the hardware performance are reporting a big advantage over all the related proposed techniques. The reason to cover this problem is to provide a path on a fascinating area of ECC on a smaller key size be applicable for all applications on a same level of security strengths. The smaller length key gives the higher speed and shorter clock cycle to initiate the operation.
Evaluation of Reliable Data Storage in Cloud Using an Efficient Encryption Technique

Cloud-based reliable and protected data storage technique is proposed in this chapter. The proposed technique encrypts and protects data with less time consumption. Power consumption of storage is dependent upon capacity of storage and physical size of storage. Time analysis is presented graphically in this chapter. Reliable data storage is represented in cloud based proposed approach. Data is encrypted with minimum time complexity due to usage of proposed cloud-based reliable data storage. The competent ratio of time complexity is graphically observed in proposed data storage technique. Power consumption of storage has been typically dependent on the basis of capacity of storage and amount of storage. A ratio of power consumption and capacity of storage is presented in cloud-based approach. An efficient usage of energy is shown depending on current consumption and voltage in proposed reliable approach.
Research Methodology

In general, the research methodology is divided into several sections. First, the reaction behavior of β-Gal-catalyzed conversion of lactose was studied in order to validate obtained information from the literature. Subsequently, a depth study on the reaction was carried out in the enzymatic hollow fiber membrane reactor. The study mostly emphasizes the effect of transmembrane pressure (TMP) on the β-Gal-catalyzed conversion of lactose. Before that, an appropriate method for the immobilization of β-Gal on the membrane surface was developed. The hydrodynamic study was also carried out in order to choose the most suitable configuration for immobilization and reaction on the hollow fiber membrane. The significance of the effect of TMP on the reaction was analyzed assisted by the coupled kinetics reaction and mass transfer model. The details on the research methodology are described accordingly with the chapter.
Artificial Neural Network

ThisArtificial neural network chapter covers one of the hottest topics and one of the most powerful machines in computing world, i.e., CNNConvolutional Neural Network (CNN) . The chapter begins with the introduction of artificial neuronsArtificial neurons and a review of perceptronPerceptron . A simple ANN and nonlinear ANN are formally introduced in the next. After describing activationActivation and inhibitionInhibition techniques, a very important BP algorithmBP Algorithm is formulated and illustrated in great details. The second part of the chapter dedicates to CNNConvolutional Neural Network (CNN) which is a breakthrough development on ANN technology. This part begins with the introduction of CNNConvolutional Neural Network (CNN) architectureArchitecture followed by step-by-step anatomy and description of the CNN convolutionConvolution layers. The chapter concludes with the visualization and MatlabMatlab implementation of a real CNN architectureArchitecture . Readers are shown with the insight view of the actual CNNConvolutional Neural Network (CNN) with great details.
Immersive Virtualizations

Immersive gamified environments hold immense potential to deliver value to the learning process of the content in a training and developmentTraining and development” \b \i settings. However, it requires the adoption of new methodologies and approaches. The purpose of such virtual apps may differ greatly and thus training needs for immersive virtualizations in gamification experiences also differ. This chapter explains immersive virtualized experiences in the context of gamification, their role and functions in various learning settings, the technologies behind extended realities like augmented realityAugmented reality” \b \i , mixed realityMixed reality” \b \i , and virtual realityVirtual reality” \b \i as well as their interrelatedness.
Research on Recommender System Based on Curiosity Guided Identity Modification

Faced with the problem of information overload of big data, multi-factor fusion is the key technology of recommendation systems. How to provide personalized products for users accurately is the demand of recommendation system. Therefore, a new nearest neighbor algorithm is proposed to fuse the two kinds of identity and use curiosity as guidance to mining hidden information more efficiently, although the algorithm of curiosity modified identification degree swings in a small range, other evaluation indexes are improved. The improvement of the Receiver Operating Characteristic (ROC) curve shows that the robustness and improvement degree of the sub-algorithm is more significant.