Reviewerly/exHarmony huggingface dataset:
DCLN: Diverse Curriculum Learning with Neuroevolution in Six-Player Poker Tournament

Recent successes in human-computer gaming AI development have achieved super-human performance in sophisticated confrontation games. Since the Nash equilibrium of multiplayer games is difficult to solve even within only two players, multiplayer poker has always been an challenging problem in the field of Artificial Intelligence. From the perspective of game theory, the pursuing of diverse polices is necessary in non-stationary environment, which is deeply rooted in the non-transitive structure of game. In this paper, in order to provide a tractable solution for multiplayer poker, we first uses the Team-maxmin equilibrium to re-define the solution concept of multiplayer poker without communication in the game playing process. Secondly, we utilize the diverse curriculum learning with neuroevolution (DCLN) method for offline opponent exploitation, try to improve the competitiveness and diversity of the multiplayer poker agent gaming with various opponent. Experimental results show that this kind of agent has the ability to evolve and can defeat different play-styles opponent.
A Survey on Insider Attacks in IAAS-Based Cloud

This chapter provides a literature review and the related work about the insider attacks and solutions in cloud environment. The authors classified solutions into three categories: trusted computing-based approaches, encryption-based approaches, and virtualization-based approaches. The trusted computing approaches use remote attestation, sealed storage, and integrity measurement. Encryption-based approaches use the cryptographic operations along with cloud computing security mechanisms and policies. Virtualization-based approaches use the virtualization technology to solve critical security issues using trusted computing approaches. At the end of this chapter, they compare various solutions and summarize the problems and solutions.
Has Bitcoin Achieved the Characteristics of Money?

Bitcoin is a peer-to-peer network that facilitates transactions between parties minus the proof requirement of an appointed third party (i.e., banks or financial institutions). Accurate understanding into the implementation of bitcoin can be acquired from data of the universal record of bitcoin transactions. Although data from numerous websites show that bitcoin daily transactions count has reached capacities of tens of thousands, it is widely believed that most of these transactions comprise activities between speculators and only a few are actually used for trading of goods and services. The chapter explores if bitcoin has achieved the characteristics of money. For it to survive, bitcoin must overcome the problems of its unconventional pricing mechanism, shortage of vendors who accept it, and the circuitous way of obtaining it.
Social Media, Social Networking, Copyright, and Digital Libraries

Libraries have evolved over time in tandem with the prevailing innovations. This is evidenced by the changes witnessed from the time writing was done on clay tablets to the virtual libraries of today. Technological advancement has been a key pillar of the development of libraries. Indeed, it has given rise to digital libraries that have given access and use of information resources a new focus. Technological developments such as Web 2.0, and specifically social media as well as social networking, have enabled users to access creative works in digital libraries freely. Similarly, they have enabled library users to share the same content freely on social networks. This situation has brought a conflict with copyright laws that require users of creative works to seek permission from their owners before accessing or sharing them. This chapter analyses this conflict and provides recommendations on how it can be managed.
A low-complexity DWT module and CRS minimal instruction set computer architecture for wireless visual sensor networks

Transmitting cost, processing complexity and data security are three important elements in wireless visual sensor networks (WVSNs). This paper presents a complete low-complexity processing system that performs data compression, data correction and data encryption. In this system, the discrete wavelet transform (DWT) first decomposes the original image into DWT coefficients to ensure low transmitting cost, then the coefficients are encrypted using the Cauchy Reed Solomon CRS(20,16) coding scheme to ensure data security. A CRS minimal instruction set computer architecture with a DWT filtering module is proposed to perform the compression, encryption and error correction encoding in a low-complexity processing system. The proposed system is then implemented in a field programmable gated array (FPGA) to demonstrate the feasibility of the proposed system for implementation in WVSNs. Results on a Xilinx Spartan FPGA show that the proposed system requires a lower implementation complexity of 2536 slices as compared to other existing systems such as Crypto-Processor (4828 slices) and SPIHT CRS MISC (5017 slices).
Virtual Communication

The shift of the real communication to the virtual sphere has influenced the nature of interpersonal relations. The article focuses on the characterization of the phenomenon ‘virtual communication', playing the dominating role in the electronic world culture. Drawing from a socio-cultural analysis and the theory of simulacra by J. Baudrillard, the article proposes the classification of the virtual communication types in terms of the nature of human relations and illustrates their peculiarities and features. Using the axiological approach, the author characterizes the phenomenon of the virtual communication and the existential and ethical aspects of the interpersonal relation transfer to the sphere of the information contact. The research resulted in revealing the features and peculiarities of the virtual communication and the benefits and risks for human beings and society.
Research on the application of virtual simulation technology in law experiment teaching under the background of educational informatization

Virtual simulation system law teaching system usually adopts multi-level technology and modular technology, which makes the teaching system have good scalability, allows users to carry out practical a operation in actual law teaching, and adds new experimental teaching modules at the same time. The goal of establishing law laboratory is to create a diversified and systematic teaching environment. Based on computer technology, make full use of virtual simulation technology to establish three- dimensional legal model and construct virtual simulation legal experiment system. It can further realize the data analysis of virtual simulation technology and law teaching. The laboratory system has a complete data protection mechanism. All data confidence in the system will be backed up by the system, using incremental backup and stage overall backup model. It can better preserve the data information and operation process generated in the process of law experiment teaching, and Hitler can quickly restore even if data loss occurs.
Computer Forensic Investigation in Cloud of Things

Digital data transformation is most challenging in developing countries. In recent days, all the applications are functioning with the support of internet of things (IoT). Wearable devices involve the most insightful information, which includes individual healthcare data. Health records of patients must be protected. IoT devices could be hacked, and criminals use this information. Smart cities with IoT use information technology to collect, analyze, and integrate information. Smart reduces the network traffic using the ground sensors, micro-radars, and drones monitor traffic to the traffic controller based on that signals are designed. The data collected includes the images and convey information to smart vehicles, which in turn, if data are hacked, may affect many people. Smart city includes important features such as smart buildings, smart technology, smart governance, smart citizen, and smart security. Cyber threat is a challenging problem, and usage of apps may increase malware that affects various customers.
Sampling Approaches

The design of experiments (DoE) is a key process in constructing a surrogate model: DoE methods are used to select the sample points at which simulations are to be conducted.
Tracking Patterns with Particle Swarm Optimization and Genetic Algorithms

The template matching is an important technique used in pattern recognition. The goal is to find a given pattern, of a prescribed model, in a frame sequence. In order to evaluate the similarity of two images, the Pearson's Correlation Coefficient (PCC) is used. This coefficient is calculated for each of the image pixels, which entails an operation that is computationally very expensive. In order to improve the processing time, this paper proposes two implementations for template matching: one using Genetic Algorithms (GA) and the other using Particle Swarm Optimization (PSO) considering two different topologies. The results obtained by the proposed methodologies are compared to those obtained by the exhaustive search in each pixel. The comparison indicates that PSO is up to 236x faster than the brute force exhausted search while GA is only 44x faster, for the same image. Also, PSO based methodology is 5x faster than the one based on GA.
Key-Values

To conclude the description of essential data structures, we need to discuss key-values (kvs), which are the broadest family of structures one can imagine. Unlike arrays and lists, kvs are not concrete structures. In fact, they span, at least in some capacity, all of the popular concrete ones, as well as some obscure.
A Comparative Study of Machine Learning Techniques for Gesture Recognition Using Kinect

Progress and technological innovation achieved in recent years, particularly in the area of entertainment and games, have promoted the creation of more natural and intuitive human-computer interfaces. For example, natural interaction devices such as Microsoft Kinect allow users to explore a more expressive way of human-computer communication by recognizing body gestures. In this context, several Supervised Machine Learning techniques have been proposed to recognize gestures. However, scarce research works have focused on a comparative study of the behavior of these techniques. Therefore, this chapter presents an evaluation of 4 Machine Learning techniques by using the Microsoft Research Cambridge (MSRC-12) Kinect gesture dataset, which involves 30 people performing 12 different gestures. Accuracy was evaluated with different techniques obtaining correct-recognition rates close to 100% in some results. Briefly, the experiments performed in this chapter are likely to provide new insights into the application of Machine Learning technique to facilitate the task of gesture recognition.
Education in Conflict Resolution Using ICT

Education in today's society has meant that new challenges arise, including training on coexistence and a culture of peace in technological media. This article presents the results of a study that aimed to identify how information and communications technology (ICT) can support training in conflict resolution in Colombian public education. To address the use of technology for training in conflict resolution in this experience, the authors took into account the following categories of analysis: peaceful strategies or techniques for conflict resolution; methods and models for conflict resolution; and best practices with ICT. The research results helped identify the relevance of the use of technological tools for education in conflict resolution at an early age and the importance of providing training for teachers in these cross-sectional areas of school contexts.
Clinical Decision Support System for Early Disease Detection and Management

Medical error is an adverse event of a failure in healthcare management, causing unintended injuries. Proper clinical care can be provided by employing a suitable clinical decision support system (CDSS) for healthcare management. CDSS assists the clinicians in identifying the severity of disease at the time of admission and predicting its progression. In this chapter, CDSS was developed with the help of statistical techniques. Modified cascade neural network (ModCNN) was built upon the architecture of cascade-correlation neural network (CCNN). ModCNN first identifies the independent factors associated with disease and using that factor; it predicts its progression. A case progressing towards severity can be given better care, avoiding later stage complications. Performance of ModCNN was evaluated and compared with artificial neural network (ANN) and CCNN. ModCNN showed better accuracy than other statistical techniques. Thus, CDSS developed in this chapter is aimed at providing better treatment planning by reducing medical error.
EDUCATIONAL AND METHODOLOGICAL SUPPORT OF THE EDUCATIONAL PROCESS OF THE UNIVERSITY

The elements of the educational and methodological support of the educational process in terms of the formation of competencies have been supplemented, systematized and unified.
A Modal Characterization of Visibility and Focus in Granular Reasoning

This chapter proposes two key concepts-focus and visibility-as modalities of modal logic. Scott-Montague models that we have proposed represent properties of visibility and focus and the concept that p is visible as modal sentence $$\mathrm{V}p$$ and p is clearly visible-or is in focus-as modal sentence $$\mathrm{C}p$$ .
Possibility Clustering Algorithm for Incomplete Data Based on a Deep Computing Model

Clustering is an essential part of data analytics and in Wireless Sensor Networks (WSN). It becomes a problem for causes such as insufficient, unavailable, or compromised data in the face of uncertainties. A solution to tackle the instability of clusters due to missed values has been proposed. The fundamental theory determines whether to incorporate an entity into a group if it is not clear and probable. One of the main issues is identifying requirements for three forms of decision definition, including an entity in a cluster, removing an object from a group, or delaying a decision (defer) to involve or rule out a group. Current studies do not adequately discuss threshold identification and use their fixed values implicitly. This work explores using the game theory-based Possibility Clustering Algorithm for Incomplete Data (PCA-ID) framework to address this problem. In specific, a game theory will be described in which thresholds are determined based on a balance between the groups’ precision and generic characteristics. The points calculated are used to elicit judgments for the grouping of unknown objects. Experimental findings on the deep learning datasets show that the PCA-ID increases the overall quality considerably while maintaining comparable precision levels in competition with similar systems.
Beyond Takedown: Expanding the Toolkit for Responding to Online Hate

The current preoccupation with ‘fake news’ has spurred a renewed emphasis in popular discourse on the potential harms of speech. In the world of international law, however, ‘fake news’ is far from new. Propaganda of various sorts is a well-worn tactic of governments, and in its most insidious form, it has played an instrumental role in inciting and enabling some of the worst atrocities of our time. Yet as familiar as propaganda might be in theory, it is raising new issues as it has migrated to the digital realm. Technological developments have largely outpaced existing legal and political tools for responding to the use of mass communications devices to instigate or perpetrate human rights violations. This chapter evaluates the current practices of social media companies for responding to online hate, arguing that they are inevitably both overbroad and under-inclusive. Using the example of the role played by Facebook in the recent genocide against the minority Muslim Rohingya population in Myanmar, the chapter illustrates the failure of platform hate speech policies to address pervasive and coordinated online speech, often state-sponsored or state-aligned, denigrating a particular group that is used to justify or foster impunity for violence against that group. Addressing this “conditioning speech” requires a more tailored response that includes remedies other than content removal and account suspensions. The chapter concludes by surveying a range of innovative responses to harmful online content that would give social media platforms the flexibly to intervene earlier, but with a much lighter touch.
Theory and history of traditional applied art. Didactic materials for self-extracurricular work of students

Reference book is addressed to students studying the theory and history of traditional applied art. It is a set of materials for self-extracurricular work. There are of the questions and tasks for research, artistic and creative activities, information and reference materials, dictionaries and references. The presented theoretical and practical material reveals the main aspects of the existence of traditional applied art in Russia, the logic of development, successive artistic and stylistic features of the language of its specific types.
Question Answering System in the Domain of Early Childhood Education in Bahasa Indonesia

The purpose of this study is to create a media for teaching staff in early childhood education schools, which is one of the features of the education robot answering system. Speech is used as input and output data from the system. The question answering system is a conversation data in the domain of early childhood education that is collected. Preprocessing stages are performed in the dataset to produce data that can be processed by the system. The question answering system uses the RNN algorithm with the Seq2Seq model. The highest results of the training process are 89.5% accuracy, precision 99.02%, and 70.5% recall. The response generation test also obtained an accuracy of 75%. The results of testing the response to the Questions according to the dataset produce maximum value.
Scale-Aware Guided and Structure-Preserved Texture Filter

In this letter, a new texture filter with scale-aware gradients and structural preservation is proposed. The proposed filter uses a hybrid <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H^{-1} $ </tex-math></inline-formula> variational model via measuring sparsity with scale-aware gradients by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{0}$ </tex-math></inline-formula> norm and structural fidelity by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H^{-1}$ </tex-math></inline-formula> norm with the embedded Laplacian operator. Extensive qualitative and quantitative experimental results demonstrate that the proposed method 1) smooths small-scale low/high contrast textures and intensive noise while preserving sharp and prominent structures simultaneously; 2) significantly outperforms state-of-the-art texture filtering methods; and 3) has fast convergence.
Genetic Algorithms (GAs) and Their Mathematical Foundations

In this chapter, the authors back GA procedures using old mathematical facts. More rigorous working of mathematical facts about GAs are raised in this chapter. In fact, there are a large number of similarities in the population of strings. The authors see how GA exploits these similarities to generate good solutions. So, in this whole procedure they show which schema or pattern will grow and which pattern will die or be lost as generation passes by due to the effect of selection, crossover, and mutation operator. The study of this building block hypothesis, leads to better understanding of GA. It will also help us to reach optimal solutions in much less time.
Computational photography

Computer vision is taking an increasingly important place in processing images that we get from cameras attached to embedded compute platforms. Applications running in real time on mobile and automotive compute systems, such as digital image zoom, high dynamic range, and panoramic imaging, became important for several industries. In this chapter, we will consider one of the use cases, image stitching, implemented with OpenVX.
A Universal Image Forensics of Smoothing Filtering

Digital image smoothing filtering operations, including the average filtering, Gaussian filtering and median filtering are always used to beautify the forged images. The detection of these smoothing operations is important in the image forensics field. In this article, the authors propose a universal detection algorithm which can simultaneously detect the average filtering, Gaussian low-pass filtering and median filtering. Firstly, the high-frequency residuals are used as being the feature extraction domain, and then the feature extraction is established on the local binary pattern (LBP) and the autoregressive model (AR). For the LBP model, the authors exploit that both of the relationships between the central pixel and its neighboring pixels and the relationships among the neighboring pixels are differentiated for the original images and smoothing filtered images. A method is further developed to reduce the high dimensionality of LBP-based features. Experimental results show that the proposed detector is effective in the smoothing forensics, and achieves better performance than the previous works, especially on the JPEG images.
The Role of the Game of Balanced Nutrition Ladder Snake in Influencing Changes in Knowledge and Attitudes of Children in Muhammadiyah Tanjung Morawa Elementary School

This study aims to determine the effect of the educational game of balanced nutrition ladder snakes on changes in knowledge and attitudes and actions in elementary school children in Muhammadyah school, Tanjung Morawa district. This type of research is a Quasi-Experimental study (quasi-experimental design) with the design of One Group Pretest- Posttest. The sample in this study was 75 elementary school children Muhammadiyah Kec. Tanjung Morawa. Data was collected using a questionnaire that was filled in by the respondent himself. If the data is normally distributed then the type of test used is the dependent T-test (in pairs). If the data is not normally distributed then the test used is the ranking test. The statistical test results obtained p-value = 0,000 &lt; 0.05, which shows that the influence of snake ladder education game balanced nutrition to changes in knowledge and attitudes in school children. The conclusion in this study is the influence of the educational game of balanced nutrition ladder snakes on changes in knowledge and attitudes in school children.
Incorporating LINE Smartphone Affordances

This study used MALL technology to mediate a collaborative learning environment focused on cross-cultural understanding. Research questions addressed the participants' perceptions about the role of the English language today, the use of technology to assist language learning, their attitudes about studying English via cross-cultural interaction, and their perceptions leading to instructional technology design best practices for English learning activities using LINE. Students from Japan and Taiwan wrote collaborative 700 word essays, collaborating via the LINE smartphone app. Data collection used a survey, open-ended questions at the conclusion of the study, and analysis of the actual essays. The instructional design was shown to be successful in fostering beneficial responses by the participants and a strong willingness to engage in future international communication. The affordances provided by LINE are analyzed, and best practices offered for using LINE as a platform for learning.