Reviewerly/exHarmony huggingface dataset:
Differential Evolution

Differential evolution is an optimization algorithm with vector-based mutation and crossover. This chapter introduces the principle of differential evolution and its variants. A demo implementation will be provided with discussions.
Comparative and Evaluative Study of Free Learning Management Systems

LMSs are constantly changing; they have transformed the lives of teachers and students. In this chapter, the authors propose a comparative study of free LMSs, from a methodical and systemic approach, that identifies the dimensions, the specifications, and the essential criteria for evaluating LMSs. They evaluate the most recognized LMSs to measure the matching degree between technological possibilities that they offer and the pedagogical assumptions to facilitate the choice of the most suitable LMS to help the users in their choice to benefit the potential offered by technologies in pedagogy.
A Comprehensive Survey on Trust Issue and Its Deployed Models in Computing Environment

Cloud computing is growing with a giant pace in today's world. The speed with which it is growing, the same speed is taken over by the insecure data transfer over the cloud. There are many security issues that are underlying in cloud computing. This chapter presents how a trust is built between any user and a cloud service provider. Various techniques have been adopted to calculate the value of trust and further how it can be strength. This chapter has also explained various trust models based on the necessities of a user. This chapter has also thrown some light over the concept of TTP, i.e., Trusted Third Party which further helps in maintaining trust over the cloud environment.
Hybrid Privacy Preservation Technique Using Neural Networks

A novel Artificial Neural Network (ANN) dimension expansion-based framework that addresses the demand for privacy preservation of low dimensional data in clustering analysis is discussed. A hybrid approach that combines ANN with Linear Discriminant Analysis (LDA) is proposed to preserve the privacy of data in mining. This chapter describes a feasible technique for privacy preserving clustering with the objective of providing superior level of privacy protection without compromising the data utility and mining outcome. The suitability of these techniques for mining has been evaluated by performing clustering on transformed data and the performance of the proposed method is measured in terms of misclassification and privacy level percentage. The methods are further validated by comparing the results with traditional Geometrical Data Transformation Methods (GDTMs). The results arrived at are significant and promising.
Semantic Sampling

A variety of tasks involving social media text require mining rare samples. In text classification, information retrieval, and other NLP tasks, working with very skewed or imbalanced data sets poses many challenges. In such settings, training data sets can be rapidly bootstrapped using highly targeted sampling strategies. This chapter draws on work in active learning, semantic similarity, and sampling strategies to address a variety of social media text mining tasks. The topics involved are particularly well suited for social media analysis. Most tasks surrounding user generated social media text such as content moderation, and recommendations often involve rapid model construction in response to real world events in real time. The methods discussed allow task-specific data sets and models to be constructed rapidly often using just a handful of initial samples. We then explore extensions to sample across languages—allowing powerful pipelines that can transfer resources from well-resourced languages to their low-resource counterparts.
Graph Isomorphism

The concept of graph isomorphism lies (explicitly or implicitly) behind almost any discussion of graphs, to the extent that it can be regarded as the fundamental concept of graph theory. In particular, the automorphism group of a graph provides much information about symmetries in the graph. The related problems of subgraph isomorphism and maximum common subgraph isomorphism generalize pattern matching in strings and trees to graphs, and find application whenever structures described by graphs need to be compared. Combinatorial algorithms are discussed in this chapter for testing graph isomorphism, finding the automorphism group of a graph, finding one or all isomorphisms of a graph as a subgraph of another graph, and finding one or all maximal or maximum common subgraph isomorphisms between two graphs. These algorithms use the techniques given in Chap. 2, and some of them also build upon the algorithms discussed in Chap. 6 for finding maximal and maximum cliques.
Development of Students' Programming Abilities With the Means of Non-Programming Disciplines and Activities

The chapter contains the results of the research dedicated to the topic that has not been given much attention so far in the professional literature – discovering effective ways of developing students' programming abilities with the means of non-programming disciplines and activities. The authors argue that the process of forming capacities of students in programming becomes effective if students participate not only in programming lessons themselves but also dedicate a significant amount of time to other academic disciplines and extracurricular activities such as solving number-theoretic and chess endgame problems. The authors find that these disciplines and activities provide efficient means for developing programming capacities and therefore, their methods are the essential prerequisites for programming course. The significance of the obtained results is that they provide an effective alternative approach to organization of programming teaching process in those educational institutions where the traditional methodology does not bring the desired pedagogical effect.
Security and Privacy for Blockchain: A Systematic Mapping Study

Currently, blockchain is an cutting-edge technology in the field of security and privacy. As an interdisciplinary technology, blockchain has attracted the attention of academia and industry. Essentially, blockchain is a tamper-proof distributed database with cryptography to achieve security and privacy through decentralization. Blockchain has the characteristics of anonymity and openness. In most blockchain platforms, any node can access all the da-ta on the blockchain. Blockchain provides security for the untrusted network environment. However, it also faces severe challenges in security and privacy. To systematically understand the state-of-the-art of blockchain security and privacy, we screened 25 papers to make a systematic mapping study. Then we summarized and analyzed the research status of this field. Firstly, we summarize the blockchain applications related to security and privacy. Secondly, the challenges of blockchain security and privacy are analyzed. Finally, we discuss the development trend of blockchain security and privacy.
A Cross-Cultural Comparison of the Academic Engagement of Students

Higher education around the world is undergoing serious transformations as a result of technological, social, and economic processes. Universities from various countries are trying to secure a competitive position for themselves on the international market for educational services. Currently thousands of universities from different countries are trying to enter the race to join the race for international rankings. In their quest to reach the top of the rankings some countries seek to follow the experience of other, more successful nations by adopting their “best” practices. It is not surprising that all countries wish to secure a prosperous future for themselves and take efforts to avoid remaining on the sidelines of world development. However, a factor such as culture or national mentality may undermine the strategies and transformation processes that are being developed.The goal of this study is to conduct a cross-cultural analysis of the academic engagement of students from Russian, Chinese, Japanese, American, and British universities and determine the role that cultural differences play in existing educational systems. The study sample consisted of 26,648 Bachelor’s students who were enrolled at universities in the United States, United Kingdom, China, Japan, and Russia during the 2016–2017 academic year. We used data from the “Student Experience in the Research University” (SERU) international research project to construct an index of student class involvement on the basis of a factor analysis. The results of our univariate analysis of variance (ANOVA) showed that students from Japanese, Chinese, American, British, and Russian universities demonstrate qualitatively different levels of academic involvement, which depends on the organization of the educational process as well as the cultural traditions of these countries. Thus, the Chinese students turned out to be the least engaged, since they were not required to actively participate in class discussions or to discuss additional questions related to the course with the instructor. Russian and American students demonstrated the highest level of engagement compared with other students from countries in the survey.The author emphasizes the need to take into account existing cultural aspects when developing educational strategies, interacting with foreign students in the classroom, as well as conducting comparative studies.
Hybrid Methods

Depending on the type of the random search method, it may happen that the procedure finds rather fast a local extremum $$x_{loc}^{\ast }$$ of the objective function f under consideration, but get then stuck in this point. On the other hand, there are procedures having features to omit this behavior. For example, a special property of simulated annealing methods is that also non-improving steps are possible with decreasing probability (cooling). Thus, steps out of the neighborhood of a local extremum are possible with a certain probability.
Digital Forensics in Distributed Environment

This chapter is about an ongoing implementation of a digital forensic framework that could be used with standalone systems as well as in distributed environments, including cloud systems. It is oriented towards combining concepts of cyber forensics and security frameworks in operating systems. The framework consists of kernel mechanisms for data and event monitoring. The system monitoring is done in kernel mode by various kernel modules and forensic model mapping is done in user mode using the data collected by those kernel modules. Further, the authors propose a crime model mapping mechanism that makes use of rule sets that are derived from common cyber/digital crime patterns. The decision-making algorithm can be easily extended from a node in a computing cluster, to a cloud. The authors discuss the challenges to digital forensics in distributed environment and cloud extensions and provide some case studies where the proposed framework is applied.
Recognition and Change Point Detection of Dogs' Activities of Daily Living Using Wearable Devices

The purpose of this study is to monitor dogs' daily activities using wearable devices for capturing their movement data, which are utilized to detect the change points and recognize the activities. Two small and light devices are attached to the dog's collar and back to collect acceleration data and gyro data of Activity of Daily Living (ADL), including sitting, standing, lie down, walking, and running. In this research, we recognize ADL and detect change points using multiple wearable devices for evaluation of recognition performance in terms of devices' locations and combinations. The classifier is constructed by a deep learning algorithm of convolutional neural network (CNN), and the differences between the accuracy of the window size, sliding window size, the type of data, and the mounting position of the device were compared. As a result, when the window size was 3000ms, the average of five activity classifications is 92.6%, which is higher than the previous research using LDA and QDA. In the change point detection, we achieve an F1-score of 80.3% when the sliding window size is 900ms. It was also found that the performance in both activity classification and change point detection is more accurate when using both the acceleration data and the gyro data as compared with only using one of them. Further, the accuracy of the device is higher when both the back and the neck were used.
Introduction

In this chapter, we first introduce concepts and implementations of Control-Flow Integrity [], which is a fundamental approach to mitigating control-flow hijacking attacks, and then present practical issues of previous CFI systems and summarize how we address those problems.
Multi adaptive semi-supervised learning based on Evidence Theory

Semi-supervised classification(SSC) trains few labeled samples and a great many of unlabeled samples to obtain a good classification model. Graph semi-supervised learning is one of the main research directions of SSC. It uses unlabeled samples and the few limited labeled samples to build a more reliable graph model and spreads the label information on the graph model. However, the traditional graph semi-supervised learning algorithms are time-consuming and can not well reflect the underlying data distribution of high-dimensional data. To deal with these problems, we propose an Multi adaptive semi-supervised learning based on Evidence Theory (MASSL). MASSL uses some multiple feature subspaces of original data to train different adaptive semi-supervised learning models, and uses Fuzzy Cautious ordered weighted average reasoning method and Evidence theory to combine the different results to obtain a better recognition effect. Experimental results show that the proposed approach can effectively exploit unlabeled data and perform better compared with prevailing related approaches.
CodeCity: On-Screen or in Virtual Reality?

The ninth IEEE Working Conference on Software Visualization (VISSOFT 2021) builds upon the success of the previous eight editions of VISSOFT, which in turn followed after six editions of the IEEE International Workshop on Visualizing Software for Understanding and Analysis (VISSOFT) and five editions of the ACM Sympo0sium on Software Visualization (SOFTVIS). In 2021, VISSOFT will take place and be co-located with ICSME.
A Game-Based Approach for Simulation and Design of Supply Chains

This chapter reviews the potential benefits and challenges of knowledge-based computer game simulation as means of understanding the dynamics of global procurement and manufacturing supply chains. In particular the chapter focuses on the use of software agents to assist decision making across the supply chain, for example in raw material procurement. The chapter describes a framework for supply chain scenarios in multi-agent based simulation games. The agents' behaviour is governed by business rules, based on the concept of normative knowledge representation and its reasoning mechanism (known as rule-based reasoning, RBR) and that also come closer to the task that confronts the supply chain operational manager – the analysis of current case in hand in terms of previously decided business problem solutions, known as case-based reasoning (CBR). The aim is to introduce more realistic behavior of the supply chain actors and improve understanding in operational management of supply chains.
Cloud Computing Adoption

The purpose of this article is to develop a cloud computing adoption scale for Indian manufacturing, service, and process Industries. The scale development procedure has been followed from the previous studies and is refined further for clear understanding to adopt easily. In the first step, we have conducted a qualitative study for item selection. The second step includes pilot testing of 110 responses for measurement scale purification and finally, we are validating the scale with 660 sample respondents through convergent validity and discriminant validity. The initial result showed very poor threshold values for the items “Top management support” and “Marketplace establishment” which are having a strong literature support. This measurement scale will help managers to evaluate the level of cloud adoption to increase the business performance. The study is a first time attempt to develop a validated scale for cloud adoption that can be used in Indian Industries.
Marketing and Marketing Plan for Information Services

Marketing is a social and economic process through which individuals and groups meet their needs and desires by creating and exchanging products among themselves. Initially, marketing was practiced by companies in selling sectors of products and services and did not seem to fit the noncommercial side. In recent decades, almost all types of organizations adopted the methods and practices of marketing, including information services. It is necessary to develop a program to properly plan all the actions and resources needed to achieve these objectives, and effectively control the deviations relative to them, allowing correcting the actions that do not get the expected results in order to constantly improve the system implemented. For this, it is necessary to devise a marketing plan that focuses on the planning of all activities and objectives we want to achieve and the means necessary to do so. The marketing plan is a strategic document that will serve to identify the position occupied in the market, set goals and how they will be achieved, and expected results.
The Method of Aerobics Remote Sensing Image Target Recognition Based on BP Neural Network

With the application of high-end technology in the fields of face recognition, target tracking, autonomous driving, remote sensing image analysis, etc., leapfrog development has also been achieved, and the target recognition algorithms proposed in different fields have also shown that they can match human capabilities. What this article puts forward is the research of target recognition method in aerobics teaching activities based on machine algorithm and improved BP neural network algorithm. As a hotspot in the field of machine learning, deep learning algorithms have been widely introduced into remote sensing (Remote Sensing, RS) applications. The data generated by hundreds of remote sensing sensors every day and the dramatic increase in the amount of calculation that it brings and the complex surface environment Severely improve the efficiency and accuracy of computer data analysis algorithms. This article builds aerobics exercise type recognition data set. At the same time, in order to enable the BP neural network algorithm to further improve the recognition accuracy, based on the BP neural network recognition. The experimental results show that the high-precision aerobics remote sensing image target recognition method proposed in this paper can accurately identify the target in the remote sensing image, and then establish a relevant aerobics type recognition data set through artificial enhancement, which effectively improves the efficiency.
A Trust Case-Based Model Applied to Agents Collaboration

This chapter shows an approach for developing a method that uses case-based reasoning (CBR) for calculating trust levels in agents' collaboration. The proposed development foresees all interactions between agents are considered in the updating process of general trust level. These collaborations are also stored in a CBR database. Each new interaction calculates a situational trust level based on similar cases. This trust level will be weighed against the global trust level, creating an indicator based on the requested collaboration without excluding the collaboration history.
Design and implementation of university performance Appraisal System based on responsibility center

This paper mainly puts forward corresponding solutions for the design and implementation of university performance compensation a management system. The steps of computer university performance compensation management system mainly include system analysis, system design, system implementation and system maintenance. In the design of the computer system, the various modules, the required database table and the table field menu design are shown in detail. In the implementation of the system, the event, code and menu implementation of the corresponding functional controls in the form are presented. This paper analyzes the design and implementation of the university performance compensation management system of computer technology with data input, data storage, information calculation and modification book removal. Its data analysis technology has promoted the further development of performance compensation management system in universities.
Novel First Responder Digital Forensics Tool as a Support to Law Enforcement

There are many freeware and commercial tools which can be used to provide forensics information based on dead and live forensics acquisition. The main problem with these tools is that in many cases the investigator cannot explain the script functionality and generated results and information during the trial. Because of this reason there is an increased need for developing and using script which can be easy explained and adapted to any analysis which should be made by the examiners. The chapter presents a novel developed First Responder script which can be used to perform a live and dead forensics analysis in support of Law Enforcement during the investigation process.
Last-Position Elimination-Based Fireworks Algorithm for Function Optimization

Fireworks algorithm (FWA) searches the global optimum by the cooperation between the firework with the best fitness named as core firework (CF) and the other non-CFs. Loser-out tournament-based fireworks algorithm (LoTFWA) uses competition as a new manner of interaction. If the fitness of a firework cannot catch up with the best one, it is considered a loser and will be reinitialized. However, its independent selection operator may prevent non-CFs from aggregating to CF in the late search phase if they fall into different local optima. This chapter proposes a last-position, elimination-based fireworks algorithm which allocates more fireworks in the initial process to search. Then for every fixed number of generations, the firework with the worst fitness is eliminated and its sparks is reallocated to other fireworks. In the final stage of search, only CF survives with all the budget of sparks and thus the aggregation of non-CFs to CF is ensured. Experimental results performed show that the proposed algorithm significantly outperforms most of the state-of-the-art FWA variants.
Seeing Information Cultures

Information cultures affect an organization's ability to define and implement a practical and effective information strategy. Just as business culture supports or impedes business strategy, so does information culture support or impede information strategy. Organizations typically have many information cultures, some of which may be contradictory. Unmanaged information cultures can present a significant risk in the knowledge economy. Information culture is reflected in the organization's values, norms, and practices concerning the management and use of information. The information science literature provides insufficient treatment to help us understand the day-to-day impacts of information cultures on business. It also fails to provide an inclusive description of the range of cultures extant in the information management profession and practice. Just like business cultures, information cultures exist and may vary by level of the organization. The authors call out and describe dynamic factors that affect information cultures at all five levels.