Reviewerly/exHarmony huggingface dataset:
Selection of Cloud Delivery and Deployment Models

Research interests on cloud computing adoption and its effectiveness in terms of cost and time has been increasing. However, one of the challenging decisions facing management in adopting cloud services is taking on the right combinations of cloud service delivery and deployment models. A comprehensive review of literature revealed a lack of research addressing this selection decision problem. To fill this research gap, this article proposes an expert system approach for managers to decide on the right combination of service delivery and deployment model selection. The article first proposes a rule-based expert system prototype, which provides advice based on a set of factors that represent the organizational conditions and requirements pertaining to cloud computing adoption. Next, the authors evaluate the system prototype. Lastly, the article concludes with a discussion of the results, its practical implications, limitations, and further research directions.
Hairball Buster: A Graph Triage Method for Viewing and Comparing Graphs

Abstract Hairball buster (HB) (also called node-neighbor centrality or NNC) is an approach to graph analytic triage that uses simple calculations and visualization to quickly understand and compare graphs. Rather than displaying highly interconnected graphs as ‘hairballs’ that are difficult to understand, HB provides a simple standard visual representation of a graph and its metrics, combining a monotonically decreasing curve of node metrics with indicators of each node’s neighbors’ metrics. The HB visual is canonical, in the sense that it provides a standard output for each node-link graph. It helps analysts quickly identify areas for further investigation, and also allows for easy comparison between graphs of different data sets. The calculations required for creating an HB display is order M plus N log N , where N is the number of nodes and M is the number of edges. This paper includes examples of the HB approach applied to four real-world data sets. It also compares HB to similar visual approaches such as degree histograms, adjacency matrices, blockmodeling, and force-based layout techniques. HB presents greater information density than other algorithms at lower or equal calculation cost, efficiently presenting information in a single display that is not available in any other single display.
Introduction

The COVID-19 pandemic has forced society and individuals to change their behavior in many drastic ways. Responses to this new reality can be digitally enabled. We propose three important dimensions that help us to classify digitally enabled responses: class of response, type of knowledge contribution, and level of analysis. This threefold perspective helps us position the contributions of the following nine chapters in this book.
M-Readiness Assessment Model Development and Validation

It is important for distance learning institutions to be well prepared before designing and implementing any new technology based learning system to justify the investment and minimize failure risk. It can be achieved by systematically assessing the readiness of all stakeholders. This paper first proposes an m-readiness assessment process and subsequently describes different activities required to develop a m-readiness assessment model. The model was validated and applied to measure m-readiness index of Allama Iqbal Open University (AIOU), Pakistan. Primary data (N=612) were collected. Factor analysis was applied. Extracted factors were confirmed with confirmatory factor analysis. Based on the extracted factors, m-readiness index was calculated. Furthermore, m-readiness index association with age, gender, program of study, income, locality and province were also analyzed by applying one way ANOVA and Kruskal Wallis. The results reveal that there is a significant difference in m-readiness with respect to income, locality, age, gender, program of study and province.
VR User Preference Recommendation Strategy Based on Improved BP Neural Network Algorithm

Digital twin technology and virtual augmented reality technology have been developed rapidly in recent years, and in order to make users get a good immersive experience, predicting user preferences and realizing integrated human-computer interaction are urgent problems. The application model of deep learning is a research hotspot in recent years, especially in some more complex pattern recognition problems, which has produced a large number of relevant research results, and the study of user preference strategy based on user line analysis, as a multidimensional data analysis task, is suitable for building multidimensional models to solve. Therefore, firstly, using the behavioral characteristics data of users in virtual reality, the Kmeans algorithm is used to categorize the scenes in virtual reality according to the obtained multidimensional data, and the data is enhanced to overcome the problem of unknown data characteristics and improve the robustness of the model. Again, according to the high-dimensional characteristics of users' behavioral characteristics, Improved BP neural network preference recommendation model was studied, and compared with the traditional clustering model for analysis. The experiments show that the model improves on the accuracy of user characteristics analysis in the virtual environment while ensuring the response time, and can effectively provide preference prediction to users, thus improving their immersive experience.
Design of Interactive Platform for Northeast Folk Handicraft Inheritance and Development Based on VR

With the continuous development of science and technology, the research on the protection and inheritance of Northeast folk handicrafts is also constantly innovating and developing. These studies not only innovate and broaden the existence of Northeast folk handicrafts, but also attract more and more people to pay attention to and understand the Northeast folk handicraft. Augmented reality and virtual reality technologies (ARVR for short), as today's cutting-edge technologies, are also used in the restoration of historical sites and the protection of digital cultural heritage. At the same time, modern scientific and technological civilization has brought new crafts and new materials to the traditional development, and has brought the possibility of expansion. In an era when challenges and opportunities coexist, Northeast folk handicrafts must seize the opportunity to get out of the predicament. The purpose of this article is to design an interactive platform for the inheritance and development of Northeast folk handicrafts based on VR. This article analyzes the four aspects of research time, research author, research institution, and research keywords. It understands the current main research forces of folk crafts in Northeast China and the cooperation status between the research forces, and interprets the inheritance of folk crafts in Northeast China based on the knowledge map. The status quo. According to the survey, the media has the greatest influence, accounting for 65.1%; followed by friend promotion and museum exhibitions, accounting for 35.6% and 38% respectively; people who actively learn about themselves through their hobbies and interests account for the least, accounting for only 13.6%, with passive acceptance as the Lord.
A layer-crossing multi-factor and dynamic security model over a moving target defence

Moving target defence mainly focus on the single parameter hopping and rarely refer to the hopping of multiple parameters in multiple layers. With the background of database security, this paper constructs a layer-crossing, multi-parameter and dynamic security model over moving target defence. Seven parameters which belong to different layers in the database application are defined, and two mathematical functions of successful attack probability and the reconnection time of the legitimate users are proposed. Then through mathematical analysis, this paper comes to the conclusion that it is impossible to let the successful attack probability and the average reconnection time take the minimum values at the same time. Finally, under specific scenarios, the specific expressions of the two functions and the optimal hopping interval of each parameter are present. The model proposed is not only applicable to the security of database system, but also to other information systems.
Dynamic Electronic Textbooks

“The digital age has spawned an e-publishing revolution and cultivated the growing prevalence of e-books” (Carreiro, 2010, p. 232). Breakthroughs in technology allow e-books to be downloaded, customized, printed, or sent instantly anywhere. A rapidly evolving culture of information-seeking students have embraced these new technologies as they increasingly expect the immediacy and familiarity of digital content. The academic publishing industry must respond to this growing demand. “How individuals access digital content will depend largely on how academic publishers adapt to the new digital environment” (Janke, 2011, p. 153). This chapter examines how contemporary education theory could be used to support certain e-textbook design features, enhancing the student's learning and fostering educational growth.
Distributed Broad Learning System With Quantized and Censored Communications

As a novel neural network model, the broad learning system (BLS) has been used extensively due to its simple structure and few model hyperparameters. This paper presents a distributed BLS algorithm with quantized and censored communications (DQC-BLS). The DQC-BLS algorithm is based on the alternating direction method of multipliers (ADMM). In order to reduce the high communication cost, the DQC-BLS algorithm improves the communication mechanism between the in-networks agents by introducing quantization and communication-censoring strategies. Quantization reduces the number of bits for each transmission whereas communication censoring reduces the number of communications. The experimental results verify that the DQC-BLS algorithm can reduce communication costs while maintaining similar performance on the test set.
Neural Combinatorial Optimization for Coverage Planning in UGV Reconnaissance

Urban combat environment is affected by buildings and traffic network with certain spatial characteristics. A single unmanned combat vehicle carries out the coverage reconnaissance task will be constrained by the limited information and low efficiency, so multiple UGVs(Unmanned Ground Vehicles) cooperative coverage becomes an effective means to solve the problem. In this paper, the urban coverage reconnaissance task is formulated as a vehicle routing problem, and a new traversal model is proposed, which takes account the connectivity characteristics of the road. At the same time, different from the traditional routing planning, which takes the total cost of executing tasks as the optimization objective. We consider the time efficiency of the battlefield, and takes the coverage reconnaissance time as the optimization objective function (in most cases of multiple UGVs coverage tasks, the longest task execution time is taken as the optimization objective). And we proposed a neural combinatorial optimization technique to solve the problem. Through simulation experiments, the feasibility and effectiveness of the proposed model is verified.
Modification and Performance Improvement of Paillier Homomorphic Cryptosystem

Data security and privacy have become an important problem while big data systems are growing dramatically fast in various application fields. Paillier additive homomorphic cryptosystem is widely used in information security fields such as big data security, communication security, cloud computing security, and artificial intelligence security. However, how to improve its computational performance is one of the most critical problems in practice. In this paper, we propose two modifications to improve the performance of the Paillier cryptosystem. Firstly, we introduce a key generation method to generate the private key with low Hamming weight, and this can be used to accelerate the decryption computation of the Paillier cryptosystem. Secondly, we propose an acceleration method based on Hensel lifting in the Paillier cryptosystem. This method can obtain a faster and improved decryption process by showing the mathematical analysis of the decryption algorithm.
A Hybrid Concept of Cryptography and Dual Watermarking (LSB_DCT) for Data Security

Now a days, in communication technology safety of digital data in the form of text, image, and video, audio is a biggest problem. With the rapid development of the network multimedia systems, security is the biggest issue. Digital watermarking is one of the solutions to these problems. It hides some secret data into the original image and this information is use for image authentication and security. This paper Focus an application using Hybrid approach of Cryptography technique and dual watermarking for the purpose of Providing highly security and authentication of digital data. This paper use cryptography and QR Code in combined approach of LSB and DCT Digital image water marking technique. The Experimental results are given in the form of table and graph. This algorithm provides more security and data authentication compare to other image data security approach.
Supply Chain Coordination Based on Web Service

The importance of integrating and coordinating supply chain business partners have been appreciated in many industries. In the global manufacturing industry, supply chain business partners' information integration is technically a daunting task due to highly disconnected infrastructures and operations. Information, software applications, and services are loosely distributed among participant business partners with heterogeneous operating infrastructures. A secure, and flexible information exchange architecture that can interconnect distributed information and share that information across global service provision applications is, therefore, immensely advantageous. This chapter describes the main features of an ontology-based web service framework for integrating distributed business processes in a global supply chain. A Scalable Web Service Discovery Framework (SWSDF) for material procurement systems of a manufacturing supply chain is described. Description Logic (DL) is used to represent and explain SWSDF. The framework uses a hybrid knowledge-based system, which consists of Case-Based Reasoning (CBR) and Rule-Based Reasoning (RBR). SWSDF includes: (1) a collection of web service descriptions in Ontology Web Language-Based Service (OWL-S), (2) service advertisement using complex concepts, and (3) a service concept similarity assessment algorithm. Finally, a business scenario is used to demonstrate functionalities of the described system.
Creating and Analyzing Induced Decision Trees From Online Learning Data

Decision trees may be created in various ways. They may be manually drawn based on data, or they may be induced directly from data using supervised machine learning. Decision trees induced from online learning data may evoke insights that may benefit teaching and learning. This work introduces a method for inducing decision trees and addresses how to set the parameters for the trees based on particular decision making and research question making. This work uses online learning data to create decision trees and to enable practical insights from the resulting data visualizations.
Segmentation

Image segmentation is the process of partitioning an image into non-overlapping regions of homogeneity, where homogeneity is defined in terms of an image feature such as intensity and certain textures. In the material image analysis, the segmentation task is a necessary pre-processing step to extract important information concerning material structures from images. Material researchers would typically like to first separate the foreground regions occupied by materials of interest from the remainder of an image and then conduct subsequent analyses on the segmented images, especially the material foreground. The subsequent analyses include, for instance, the morphology analysis for extracting the structures of the materials or the dispersion analysis for quantifying the spatial arrangement of certain material elements. In this chapter, we focus mainly on the major development in image segmentation for material images, as the subsequent image analyses after segmentation are to be discussed in the latter chapters. In the first section of this chapter, we describe the unique characteristics of material images and the challenges that they cause in image segmentation. In the second section, we present a sequence of analysis steps to address these challenges for material image segmentation. In the remaining sections, we describe different approaches that can be used to carry out the analysis steps in material image segmentation.
Research on the Construction of Ontology Knowledge System for Internet Financial Risk Management

In the context of the Internet era, how to effectively express and store Internet financial risk information is a problem that needs to be solved urgently. With the maturity of information technology, the use of ontology technology to build an Internet financial risk management ontology knowledge system model can effectively solve this problem. First of all, confirm the scope of the Internet financial risk management ontology knowledge system. Then, by communicating with domain experts, construct the ontology knowledge system model of Internet financial risk management. Finally, use knowledge acquisition to fill the Internet financial risk management ontology knowledge system model, including the definition of the categories and concepts, the relationship between the concepts and so on. The ontology knowledge system model provides a basis for knowledge expression and knowledge sharing of Internet financial risk information, thereby effectively satisfying Internet financial risk management decision support and improving the efficiency of Internet risk management.
2P-AGRCFN: Two Phase Attention Gated Recurrent Context Filtering Network for Sequential Recommender Systems

The recent trends in recommender systems have focused on modeling long-term tastes as well as short-term preferences. The various recurrent architectures have used for sequence modeling in recommender systems, since each state is a combination of current and previous layer output recurrently. Although the Recurrent Neural Networks (RNNs) have the ability for modeling both long-term and short-term dependency to some extent, the monotonic nature of temporal dependency of RNN reduces the effect of short-term interests of the user. Thus final interests of the users can’t be predicted from the hidden states. We propose a Two Phase- Attention Gated Recurrent Context Filtering Network (2P-AGRCF) for dealing with user’s long-term dependency as well as short-term preferences. The first phase of 2P-AGRCFN is performed in the input level by constructing a contextual input using certain number of recent input contexts for handling user’s short-term interests. This can handle the correlation among recent inputs and leads to strong hidden states. In the second phase, the contextual-hidden state is computed by fusing the attention mechanism and the hidden state at current time step, which leads to the effective modeling of overall interest of the user on recommendation. We experiment our model with YooChoose DataSet and it shows efficacy in generating personalized as well as ranked recommendations.
FFT-Based Identification of Gilbert–Elliott Data Loss Models

Nowadays, radio- or Internet-based communication is gaining popularity in various fields, e.g., in signal processing. As these are not reliable real-time communications, some of the data are lost during the transmission. There are several stochastic models allowing the analysis of this phenomenon. The spectral properties of these models result in specific distortion in the signals' spectra. As the spectrum of the signal can easily be calculated via the fast Fourier transform (FFT), FFT-based identification methods of the data loss can be developed. In this article, a new identification method is proposed for the simpler cases of the Gilbert-Elliott model class. The article summarizes the mathematical description of data loss and introduces the Gilbert-Elliott model family. The novelty of the article is the identification method that is based on the autocorrelation function of the Gilbert-Elliott model. The proposed method is compared with classical procedures based on the Baum-Welch algorithm and the novel ones based on global optimization. The theoretical results are supported by extensive simulations.
Leader and breakaway detection in racing sports videos

This paper addresses the important problem of leader detection in racing sports videos (e.g., cycling, boating and car racing events), as his/her proper framing is a pivotal issue in racing sports cinematography, where the events have a linear spatial deployment. Over the last few years, as autonomous drone vision and cinematography emerged, new challenges appeared in drone vision. While, until recently, most computer vision methods typically addressed still camera AV footage, drone sports cinematography typically employs moving cameras. In this paper, we solve the problem of leader detection in a group of similarly moving targets in sports videos, e.g. the leader of a sports cyclist group and his/her breakaway during a cycling event. This is very useful in drone sports cinematography, as it is important that the drone camera automatically centers on such a leader. We demonstrate that the novel method described in this paper can effectively solve the problem of leader detection in sports videos.
Identification of Wireless Devices From Their Physical Layer Radio-Frequency Fingerprints

Extensive research has been performed in recent years for the identification of wireless devices from their radio frequency (RF) emissions. The main idea of identifying a wireless device through its RF emissions is that the electronic circuits and the RF components have specific characteristics determined by the production and manufacturing processes. These characteristics, which result in unique differences, can be used to distinguish a wireless device from another because they appear as subtle modification of the RF signal in space even if the wireless device generates a signal conformant to the standard. This chapter describes the main techniques for the fingerprinting of wireless devices using their RF transmission. There are still some key challenges to overcome. This chapter tries to identify them in this context as well as providing possible approaches to solve them. Further research work is needed to investigate the portability issues between fingerprints taken using different receivers, as well as to identify and remove potential other sources of bias.
Data Simulations Using Cosine and Sigmoid Higher Order Neural Networks

A new open box and nonlinear model of cosine and sigmoid higher order neural network (CS-HONN) is presented in this chapter. A new learning algorithm for CS-HONN is also developed in this chapter. In addition, a time series data simulation and analysis system, CS-HONN simulator, is built based on the CS-HONN models. Test results show that the average error of CS-HONN models are from 2.3436% to 4.6857%, and the average error of polynomial higher order neural network (PHONN), trigonometric higher order neural network (THONN), and sigmoid polynomial higher order neural network (SPHONN) models range from 2.8128% to 4.9077%. This suggests that CS-HONN models are 0.1174% to 0.4917% better than PHONN, THONN, and SPHONN models.