Reviewerly/exHarmony huggingface dataset:
Transformation of Education: Threat or Limitless Opportunities?

In recent decades there have been many disputes about the viability of Russian education. Supporters of radical changes argue that it is ineffective in the new conditions of the global transition to digital. Schools and universities are intimidated by a generation of “zetas” who “do not succumb” to standard approaches, spending most of their free time online, with clip thinking, inability to concentrate for a long time, and other difficulties and peculiarities. Opponents of digitalization are trying to protect teenagers from unlimited use of gadgets. There are discussions about how to use digital devices wisely and without harming their health. In the spring of 2020 the situation develops in such a way that the “harm” from the long-term communication of young people with gadgets becomes incomparable with the possibility of stopping the learning process. Educational institutions are urgently moving to work in a remote format. In the course of this situation, this is the only way out. The transition reveals many problems that, if possible, are solved during the process. It becomes obvious that it is impossible to use distance learning as the only possible format of work. What the future will be like depends on many factors. Will it remain universal with a sign of quality of traditional Russian education or will it undergo a radical transformation? Employers rely on creative employees who are efficient in multitasking and capable of continuous self-education throughout their lives. The concept of life-long learning continues to be a trend, without giving up its positions. The purpose of this article is to find out whether the new generation of young people is so different that all approaches of the traditional education system are broken down about their “armor”. Or under the flag of Generation Z the idea of a radical transition to a completely new education is being promoted.
Emerging Case Studies of Ambient Intelligence Services

Analysis of currently dominating and emerging use cases provide extremely valuable information for understanding of the key drivers of a technology development. In this chapter, the authors particularly focus on introducing and making an overview of the emerging case studies on top of the ambient intelligence technology. They discuss several case studies that illustrate opportunities and design details for development of highly personalized smart services. The chapter provides definition of the key enablers of the service smartness (e.g., location-awareness, design principles, and restrictions, etc.). The discussion on provided definitions and presented enablers is supported by a few use case examples in the field of e-tourism and e-healthcare. In addition, the chapter introduces the general principles of ZeroUI concept and role of virtual and augmented reality in delivering the new user experience. The chapter is targeted to clearly ground the book scope to the real-life use cases and most relevant trends.
FedIM: An Anti-attack Federated Learning Based on Agent Importance Aggregation

Federated learning (FL) is a distributed framework for machine learning (ML) model training. Training agents upload local model parameters rather than original training data, and the central server performs parameter aggregation. FL can protect user data privacy and break the information island when training the ML model. Federated Average (FedAvg) is an aggregation method commonly used in the FL training task. The central server calculates the mean value of the local model parameters to obtain the new global parameters. FedAvg assumes that all the training agents are honest, which means the central server lacks terminal agents' knowability. When there are attackers in the training agents, the global model's performance may be deeply affected, and the training task cannot be completed normally. To solve this problem, we propose a Federated Learning method with aggregation based on the Importance of training agent (FedIM), in which the central server performs pre-evaluation on the agent parameters before aggregation, calculates the weights of parameters according to the historical behavior records of training terminals and performs federated aggregation to improve the anti-poisoning ability of learning task. Experiments show that our method can effectively improve the global model's anti-poisoning ability and accelerate the training speed compared with the FedAvg method when malicious agents are involved.
Influential Researcher Identification in Academic Network Using Rough Set Based Selection of Time-Weighted Academic and Social Network Features

Researchers entering into a new research area are interested in knowing the current research trends, popular publications and influential (popular) researchers in that area in order to initiate their research. In this work, we attempt to determine the influential researcher for a specific topic. The active participation of the researchers in both the academic and social network activities signifies the researchers' influence level across time. The content and frequency of social interaction to a researcher reflects his or her influence. In our system, appropriate time-based social and academic features are selected using entropy based feature selection approach of rough set theory. A three layer model comprising semantically related concepts, researcher and social relations is developed based on the appropriate (influential) features. The researchers' topic trajectories are identified and recommended using Spreading activation algorithm. To cope up with the scalable academic network, map reduce paradigm has been employed in the spreading activation algorithm.
An Approach of Role Updating in Context-Aware Role Mining

With the rapid development of Internet of Things (IoT) and mobile technologies, the service offerings available in the IoT and mobile environments are increasing dramatically. How to provide intelligent and personalized services for users becomes a challenging issue. Several context aware service recommendation approaches have been reported to leverage roles to represent common knowledge within user communities, based on which services can be recommended for users. Prior studies on context aware role mining mainly focus on mining roles from a fixed data set of user behavior patterns, while most of them neglect the dynamic change of the input data. The frequent change of the user data will result in the change of extracted roles, and how to efficiently update extracted roles according to change of the input user data remains a challenging issue. In this paper, towards this issue, the authors introduce a novel role updating approach in context aware role mining. In the apporach, several algorithms are presented towards various scenarios such as new users and new contexts are removed from and added into the input data. Experiments show that compared with existing solutions, the proposed algorithms can guarantee the completeness of updating results while keeping good updating efficiency.
A Semiotic Examination of the Security Policy Lifecycle

Major security breaches continue to plague organizations decades after best practices, standards, and technical safeguards have become commonplace. This worrying trend clearly demonstrates that information systems security remains a significant issue within organizations. As policy forms the basis for practice, a major contributor to this ongoing security problem is a faulty security policy lifecycle. This can lead to an insufficient or worse, a failed policy. This chapter is aimed at understanding the lifecycle by analyzing the meanings that are attributed to policy formulation and implementation by the stakeholders involved in the process. A case study was carried out and a “snapshot in time” of the lifecycle of IS security policy lifecycle at the organization revealed that a disconnect is evident in the security policy lifecycle.
Data Confidentiality

When medical data are collected and disseminated for research purposes, the organization which releases the data has an ethical, and in most cases a legal, responsibility to maintain the confidentiality of the data relating to individuals involved. Striking a balance between getting data to researchers and maintaining this confidentiality is becoming an increasingly tricky proposition. Methods developed in the field of statistical disclosure control aim to thwart potential disclosures of private information while still allowing researchers the ability to use the data. This chapter presents a survey of the main types of potential disclosure risks, an overview of the widely used disclosure control methods, and the most common techniques for measuring privacy.
Governing the Internet of Everything

The Internet of Everything takes the notion of IoT a step further by including not only the physical infrastructure of smart devices, but also its impacts on people, business, and society. Our world is getting more connected, if not smarter, but to date governance regimes have struggled to keep pace with this dynamic rate of innovation. Yet it is an open question whether security and privacy protections can or will scale within this dynamic and complex global digital ecosystem, and whether law and policy can keep up with these developments? The natural question, then, is whether our approach to governing the Internet of Everything is, well, smart? This chapter explores what lessons the Institutional Analysis and Development (IAD) and Governing Knowledge Commons (GKC) Frameworks hold for promoting security, and privacy, in an Internet of Everything, with special treatment regarding the promise and peril of blockchain technology to build trust in such a massively distributed network. Particular attention is paid to governance gaps in this evolving ecosystem, and what state, federal, and international policies are needed to better address security and privacy failings.
Searching

The problem of searching is basic in the computer science field and vast amount of literature is devoted to many fascinating aspects of this problem. Starting with searching for a given key in a pre-processed set to the more recent techniques developed for searching documents, the modern civilization forges ahead using Google Search. Discussing the latter techniques is outside the scope of this chapter, so we focus on the more traditional framework. Knuth [83] is one of the most comprehensive sources of the earlier techniques; all textbooks on data structures address common techniques like binary search and balanced tree-based dictionaries like AVL (Adelson-Velsky and Landis) trees, red–black trees, B-trees, etc. We expect the reader to be familiar with such basic methods. Instead, we focus on some of the simpler and lesser known alternatives to the traditional data structures. Many of these rely on innovative use of randomized techniques, and are easier to generalize for a variety of applications. They are driven by a somewhat different perspective of the problem of searching that enables us to get a better understanding including practical scenarios where the universe is much smaller. The underlying assumption in comparison-based searching is that the universe may be infinite, that is, we can be searching real numbers. While this is a powerful framework, we miss out on many opportunities to develop faster alternatives based on hashing in a bounded universe. We will address both these frameworks so that the reader can make an informed choice for a specific application.
Computer Crimes

Computer Crimes survey the history and examples of computer crimes, their types, costs to society, and strategies of detection and prevention. In the discussion, it is noted that a great number of computer attacks fall into two categories: penetration and denial-of-service attacks. And these are discussed in depth. Attack motives are also discussed. Are nations, businesses, and individuals prepared for computer attacks? Are they ready to pay the price? We look for answers to these questions as we ponder the costs and consequences of computer crimes. We note also that although it is difficult to estimate the actual costs of e-attacks on physical system resources, progress is being made for better and more accurate estimates. An in-depth discussion of the social and ethical consequences that include psychological effects, moral decay, loss of privacy, and loss of trust follows. We end the chapter with recommendations for educating the computing device users in computer ethics. The need to educate the user to be aware of possible sources of computer crimes and what to do if and when one becomes a victim of these crimes is stressed. It is noted that education can go a long way in reducing computer crimes if the users take crime preventive steps every time they use the computer and computer-related technologies.
Mammogram Classification Using Support Vector Machine

Among the objectives of artificial intelligence techniques, we find computer-aided diagnosis systems that support preventive medical check-ups and perform detection, recognition, and classification patterns. Recently these techniques are emerged in different areas particularly in medical imaging. Medical image is an important source of information, and a golden tool for the diagnosis and assessment of a pathological analysis process. In this chapter Computer-Aided Diagnosis (CAD) system is proposed in detection and diagnosis of breast cancer, it is mainly composed of the following steps: preprocessing mammographic image, segmentation of suspect region on the mammographic image using Chan Vese model, extraction of global and local descriptors and then image classification into malignant and benign mammograms using Support Vector Machine (SVM) classifier. The analysis of mammographic images proposed system with a choice of the subset of local descriptors after tumor segmentation leads to a classification of malignant and benign mammograms. System proposed achieves 92% for accuracy.
Image Presentation

This chapter deals with image presentationImage presentation, a technique to present images to user after images are retrieved from an image database. Variety of ways of presenting images to users or visualizing an image database are demonstrated to readers in this chapter. The chapter begins with simple caption browsingCaption browsing to category browsingCategory browsing which is very common on the web. More powerful and sophisticated content browsingContent browsing techniques are introduced in the next, including content browsingContent browsing in 3D, with focus and force-directed content browsingForce-directed content browsing. The chapter is highlighted and concluded with Google image search which is a sophisticated application of variety of image presentationImage presentation techniques discussed earlier in the chapter.
MSD-Kmeans: A Hybrid Algorithm for Efficient Detection of Global and Local Outliers

Existing outlier detection algorithms exhibit different sensitivity to noisy data such as extreme values. In this paper, we propose a novel cluster-based outlier detection algorithm named MSD-Kmeans that combines the statistical method of Mean and Standard Deviation (MSD) and the machine learning clustering algorithm K-means to detect outliers more accurately with the better control of extreme values. There are two phases in this combination method of MSD-Kmeans: (1) applying MSD algorithm to eliminate as many noisy data to minimize the interference on clusters, and (2) applying K-means algorithm to obtain local optimal clusters. We evaluate our algorithm and demonstrate its effectiveness in the context of detecting possible overcharging of taxi fares. We compare the performance indicators of MSD-Kmeans with those of other outlier detection algorithms both from statistical and machine learning-based methods and prove that the proposed algorithm achieves the highest measure of precision, accuracy, and F-measure. This illustrates that our MSD-Kmeans can be used for effective and efficient outlier detection on data of varying quality on IoT devices.
Introduction to Some Other Nature-Inspired Algorithms

In order to solve any problem through the use of computation, algorithms are required. These days, algorithms are inspired from the working of nature. These algorithms are becoming popular among researchers. Many real-world solutions are being obtained from them. Nature-inspired algorithms are powerful, flexible, find better results within a small period of time, and can be used to search optimal values for the problems. This chapter introduces some of the popular nature-inspired algorithms other than genetic algorithms (GAs), which were studied earlier.
TDNCIB: Target-Driven Navigation with Convolutional Information Branch Based on Deep Reinforcement Learning

Target-driven navigation has been a significant application field of reinforcement learning. However, sparse reward and lack of useful environment information are still existing problems. Therefore, in this paper, we propose a novel design to the target-driven visual navigation based on deep reinforcement learning: Target-Driven Navigation with Convolutional Information Branch (TDNCIB), which enriches the high-dimensional information learned by the network from the environment. It is an improvement of A2C algorithm. CIB can help make full use of environmental information and serve as a complement to the baseline. Besides, a novel reward design using similarity is introduced to relieve sparse reward. The ablation study is implemented in 4 different scenes of AI2-THOR framework comparing the baseline and our TDNCIB. According to our evaluation, TDNCIB converges faster than the baseline and achieves better performance in 5 common metrics with shorter trajectory length, less collision and higher success rate.
Introduction

Quantum computing is a growing area of research dedicated to applying the rules of quantum mechanics to the development of more efficient algorithms. In this chapter, we give historical background and discuss the main motivations to start studying quantum computing today.
Role of Open Source Software in Big Data Storage

Today, the world is revolving around Big Data. Each organization is trying hard to explore ways for deriving value out of huge pile of data we are generating each moment. Open Source Software are widely being adopted by most academicians, researchers and industrialists to handle various Big Data needs because of their easy availability, flexibility, affordability and interoperability. As a result, several open source Big Data tools have been developed. This chapter discusses the role of Open Source Software in Big Data Storage and how various organizations have benefitted from its use. It provides an overview of popular Open Source Big Data Storage technologies existing today. Distributed File Systems and NoSQL databases meant for storing Big Data have been discussed with their features, applications and comparison.
Identity theft

Identity (ID) theft is a crime where a thief steals your personal information, such as your name, your social security number, or your credit card information to commit fraud. The ID thief can use your information for fraudulent purchases or to fraudulently apply for credit, file taxes, or get medical services. When we developed this chapter, we wanted the reader to know everything possible about this major problem.
Mobile application to improve reading habits using Virtual Reality

Education is changing rapidly, so emerging technologies are being used to improve this process. One of these technologies is the Virtual Reality (VR), whose field of action is increasingly broad, so it has been incorporating new methods in teaching having a great positive impact in recent years, but the applicability in the area of communication is minimal. Seeing the challenges faced by the Ministry of Education in the development of reading skills and strengthening the capabilities of students in our country. In the this research a new alternative is proposed, to improve the beginnings of the habit of reading in students of second grade of Elementary School having to use of a mobile application with VR named Diverticuentos that generates scenes of the readings in 360o besides being con-nected to a databases like firebase that allows us to see the progress of each stu-dent verifying that it is possible to integrate this new technology in the sessions and to generate a beginning of habit of reading of the students.
Blockchain Utopia and its Governance Shortfalls

This chapter sows seeds of scepticism about blockchain governance and liberation narratives. It starts off by locating these narratives within a long standing history of utopian thought on technology and law and the recurrent promises of science-driven, technological replacements of law and the State, on and off since the Enlightenment. The discussion then explores the peculiar features of blockchain technology upon which its utopian credentials are said to be grounded. First, it argues that blockchain utopias are, much like their internet predecessors, guilty of technological essentialism by drawing determinist conclusions from an underlying architectural design, and by failing to allow for the functional and technological latencies within that design. It is in their interactions with social domains that those latencies are ‘resolved’ in favour of certain configurations and not others. Such utopian narratives are not harmless when they generate blindfolds about the actual societal concretisation of the technology and its oppressive rather than emancipatory effects. Second, the discussion problematises the claimed virtue of blockchain applications as trustless systems against various conceptions of trust, and argues that blockchain does not and cannot speak to the trust questions at the interface between information and reality, for which society will continue to require trusted third parties to deliver ‘trusted’ value judgements. Furthermore, it is not a foregone conclusion that a society in which trust becomes redundant or less important would in fact be an ideal society. Third, the chapter questions the virtues that lie behind blockchain’s decentralisation theme by arguing that the corporate structure has rather successfully formalised collective action and invariably opted for centralised decision-making for coordination and functional specialisation. Moreover, the decentralisation that blockchain enables within an organisation does not touch upon economic concentration within a market, but it is the latter that presents the real 21st century challenge to the marketplace and democracy.
Fast Gates

Off-resonant excitation: For shorter gate pulses errors due to off-resonant excitation increase. The sharp edges of the pulse have a broad frequency spectrum. These different frequency components can drive the second motional mode, higher orders and counter-rotating modes of the main motional mode and will cause an AC Stark shift coupling to the carrier depending on the un-stabilised relative phase of the Raman beams $$\phi _0$$. Shaping the edge of the pulse on the timescale of a few motional periods of the ion’s motion reduces the pulse bandwidth and strongly suppresses errors due to off-resonant excitation [1]. However this method starts to fail once the total gate length is on the same order of magnitude as the ideal pulse shaping length.