Reviewerly/exHarmony huggingface dataset:
Data Mining: Outlier Detection

Outlier detection techniques aim at singling out the observations that most deviate from the rest of the data at hand. During the years different notions of anomaly have been provided, each one with its peculiarities. Unsupervised methods search for outliers in an unlabelled dataset by assigning to each object a score which reflects its degree of abnormality. In this paper, we provide an overview of unsupervised data mining outlier detection techniques by considering different families of approaches, including statistical-based, distance-based, density-based, isolation-based, angle-based, subspace-based, ensembles, and methods providing explanations. Advantages, drawbacks and potential applicative scenarios are also highlighted.
Integrating Virtual Reality Tools Into Classroom Instruction

Virtual reality (VR) is an emerging possibility for delivering educational content and experiences to students. Virtual Reality's use in education has long been discussed however; one of the main challenges is that VR was unaffordable. Today VR has evolved; the technology is mainstreamed, inexpensive, and more accessible. This chapter will examine the benefits of using VR tools (Google Cardboard), a 3D viewer in conjunction with Smartphone, for educational applications. Related studies suggest that VR tools are effective in subjects where an interactive environment is needed.
Infrastructures for High-Performance Computing: Cloud Computing

This article introduces the basic concepts of cloud computing, which provides an abstraction for remote, infinitely scalable provisioning of computation and storage resources. The article provides a definition of cloud computing, describes the main characteristics of cloud systems, and discusses the main service models (Software as a Service, Platforms as a Service, Infrastructure as a Service) and deployment models (Private Cloud; Community Cloud; Public Cloud) adopted by cloud providers. The article also discusses the most important models for the interconnection of clouds (Hybrid Cloud, Cloud Federation, and Inter-cloud).
Knowing and/or Believing a Think: Deriving Knowledge Using RDF CFL

From the web discussion on a difference between knowing and believing, we have chosen in this paper the statements fulfilling enough our seeing the topic, corresponding to our knowledge level of cognitive science. The aim of paper shows one of the capabilities of our Resource Description Framework Clausal Form Logic (RDF CFL) graph language using as an example a well- known Castaněda’s puzzle. RDF CFL is an appropriate tool that contains a package of inference methods working especially in closed-worlds that have been developed in the clausal form of first order predicate logics.
Kernel Machines: Applications

In this article, we will describe how the kernel approach can be easily implemented for simple and typical knowledge discovery problems, within the context of machine learning. Since the core of this paradigm relies on the so called kernel trick, we will mainly focus on how this fundamental tool can be effectively used, in the design and the application of an inference procedures. In fact, the kernel approach has not only offered to the learning machine community the opportunity of working both with nonlinear predictive models and with different heterogeneous structures, but it has also given a new way to re-design old standard procedures, in order to get more powerful and relative robust models.
بناء برنامج ترويحي مقترح لتحسين بعض سلوكيات التفاعل الاجتماعي لدى الأطفال "مرضى التوحد"

The aim of the study is to build a suggested recreational program to improve some of the social behaviours of the kids who have autism and to recognize the basics for building the suggested recreational program to improve the social interaction behaviors of the kids. The two researchers have used the descriptive method because it`s
Multi-Layer Frame Rate Up-Conversion Using Trilateral Filtering for Multi-View Video

Frame rate up-conversion (FRUC) is a technology for generating a higher frame rate video from one with a lower frame rate. The multi-view plus depth map (MVD) system has been recently considered a promising solution for three-dimensional video systems. Accordingly, an FRUC efficient solution for the MVD system is greatly needed. In this paper, we thus propose an FRUC approach that considers the MVD system characteristics. It divides a frame into multiple layers using a depth map, and it reconstructs occluded regions using neighboring views and depth map information. With reconstructed information, it accurately performs motion estimation when a region occlusion occurs. Experimental results demonstrated that the proposed method significantly improved the quality of interpolated frames, and it can effectively complement other conventional FRUC approaches.
A target detectability assessment metric for the underwater electro-optic imaging system

Compared with the atmospheric imaging, in underwater electro-optic imaging (UEOI), there exist difficulties to describe the target discrimination level within the visibility range, because of the media scattering and inhomogeneous illumination. The large-scale contrast is used to describe the target discrimination traditionally, while it is not linear to human perception. In this paper, the detectability of a UEOI system is firstly defined to quantify the perceptive target discrimination level in full space. And we present a target detectability assessment metric (TDAM), which is used to illustrate the detectability distribution of a UEOI system. Additionally based on the large-scale contrast, an automatic detectability calculation pipeline is proposed, and a trapper-shaped function fits the contrast and detectability well, with the Spearman rank order correlation coefficient (SROCC = 0.8852), Pearson linear correlation coefficient (PLCC = 0.8965) and root-mean-square error (RMSE = 1.6532).
Authenticity and the ‘Authentic City’

In this paper, I argue that the benefits that smart cities purport to provide cohere poorly with a number of our shared phenomenological intuitions about the relationships(s) between authentic experience and technologised society. While many of these intuitions are, strictly speaking, pseudo-problems, they deserve our attention. These issues will only grow more pressing as our ‘dumb cities’, already so opaque to experience, give way to hyper-technologised ‘smart cities’. However, it is possible to design our way out of these pseudo-problems. Assuming we accept my argument that the distinction between authenticity and the device paradigm is premised upon a certain kind of category error, there is no categorical or definitional reason why it is not possible for urbanised, technologised spaces to feel authentic, whether by virtue of their aesthetic properties, or because they facilitate ‘authentic’ behaviour. Indeed, I argue that ‘inauthenticity’ is an aesthetic rather than an ontological category (much like ‘ugliness’, or ‘boring-ness’), with feelings of inauthenticity serving as evidence of a basic failure of design. Redressing these failures of design requires that we adopt a novel approach to the design and use of technical objects. Consequently, in the concluding analysis of the chapter I outline how the feeling of authenticity can be invoked in the smart city and, consequently, how these failures of design can be avoided.
Unique Digitized Activity Signature for Human Authentication

Activity recognition of human is one of the most important structure behind many implementations on devices like smart phones such as medical approaches, health tracing, context-awake mobile, human observe device, etc. This paper relates a robust device for activity recognition of human by smart phones. independent from other task, we explored the collection of instance selection and feature selection to decrease dimensionality of dataset in order to increase the presentation. Nowadays people give a little amount of time for any particular task. In this particular time nobody wants to waste his or her time for giving signature for any kind of security matching. In this paper, we are approaching a highly secured and time saving process for activity recognition called, `Activity Recognition by Digitized Method using Machine Learning Technique'. Our system consists of a smart phone which receives the details from its inbuilt sensors. Our concerned server received the sensors data from the smartphone. A unique map for an individual user is being generated by the server. The smartphone itself again sends the sensors details to the server and then again the server generates a unique map using those data and if the newly generated map is matched with previous one then the activity is recognized. This modern system is very useful and effective as there is less chances of fraud as the human own is the authentication signature.
CASE STUDIES OF NUCLEAR RESEARCH SOFTWARE DEVELOPMENT

Experimental and operational data are valuable assets for the field of nuclear science and technology. It is very important to develop software tools to assist scientists to manage the data effectively and to conveniently access and share the data. This paper presents 5 case studies on software development pertaining to topical areas such as nuclear fuel performance, waste management, biological research, reactor physics, and chemistry analysis at Canadian Nuclear Laboratories (CNL). Each case study illustrates the design and development of the database and user interface for its target research area and end users. While datasets based on flat files are still used in reactor physics studies, full-scale relational databases are developed for most of the other studies. The user interfaces developed for these areas include web applications, desktop applications, and mobile applications. These software tools have become essential parts of the research activities at CNL.
Visualization of Biological Pathways

Biological pathway are represented by means of graph diagrams. Graphs are a classical way to visualize interaction among element such as social interactions, protein-protein interaction, proteins and small molecules interactions as well as linked cascade of biochemical reactions. To help the researchers to understand more easily the interactions in biological pathways where several layout algorithms have been developed. Layout algorithms allow the user to automatically convey pathways, highlighting connections between nodes, changing visual style parameters to obtain an easy to understand pathway diagram. Clearly, the design of appropriate drawings is a complex and costly task where automation is required.
Building of Virtual Multidocuments Mapping to Real Sources of Data in Situation-Oriented Databases

Within the framework of the Polyglot Persistence approach, a situationally-oriented database is considered - an information processor within a web application that processes XML/JSON documents based on the HSM hierarchical situational model. A flexibility task in HSM of mapping virtual documents for real data from various physical storages is discussed. In order to embed new data warehouses, it is suggested to follow the principle of invariance, the model does not change when embedding a new mapping. An approach mapping embedding is proposed, which provides for each new type of storage the development of three modules: manipulation, loading and saving, implemented as callback functions. To embed three modules working with the repository, a common plug-in technology is used. The approach is illustrated by the example of mapping to the table of the relational database SQLite. The advantages of the approach and practical implementation on the PHP platform are discussed and demonstrated on SQLite database.
STANDARD BAYES LOGIC IS NOT FINITELY AXIOMATIZABLE

Abstract In the article [2] a hierarchy of modal logics has been defined to capture the logical features of Bayesian belief revision. Elements in that hierarchy were distinguished by the cardinality of the set of elementary propositions. By linking the modal logics in the hierarchy to the modal logics of Medvedev frames it has been shown that the modal logic of Bayesian belief revision determined by probabilities on a finite set of elementary propositions is not finitely axiomatizable. However, the infinite case remained open. In this article we prove that the modal logic of Bayesian belief revision determined by standard Borel spaces (these cover probability spaces that occur in most of the applications) is also not finitely axiomatizable.
Comment on: “RETRACTED: Rift-related active fault-system and a direction of maximum horizontal stress in the Cairo-Suez district, northeastern Egypt: A new approach from EMR-Technique and Cerescope data” by

Xie recently extended nullnorms and uninorms to fuzzy truth values and investigated whether the extended nullnorms and extended uninorms form respectively type-2 nullnorms and type-2 uninorms on the algebra of fuzzy truth values. In this note, we show by counterexamples that there exist some flaws in a previous paper by Xie [Fuzzy Sets Syst. 352 (2018) 92-118], such as Corollaries 2.1, 3.1, 4.1 and 4.2, Lemmas 3.3, 3.4, 3.5, 3.6, 3.7 and 3.8, Propositions 3.4, 3.5, 3.10, 3.11, 4.1 and 4.4, Theorems 3.1, 3.3, 4.1 and 4.3, and then we provide the correct versions. In addition, Proposition 3.7 in that paper is correct, but its proof contains a flaw. We point out the flaw and give a more general result than Proposition 3.7 in that paper.
Crossing Over Settings, Practices and Experiences: Connecting Learning in Museums and Classrooms

This chapter considers the potential of social and mobile technologies to support connecting formal aspects of learning with informal learning experiences that occur during museum visits with an aim to address one specific challenge, namely the need to understand and promote learning across school and out-of-school contexts. It draws on the concepts of ‘mobile pedagogy’ (Kukulska-Hulme et al., in Mobile pedagogy for English language teaching: A guide for teachers. British Council, London, 2015) and ‘crossover learning’ (Sharples et al., in Innovating Pedagogy 2015. Innovation Report 4, The Open University, 2015) to frame an empirical investigation that draws on the use of a microblogging technology (Twitter) by a Year 9 History class (13–14 s) from a secondary school in Milton Keynes during a trip to the Museum of London ( http://www.museumoflondon.org.uk/) . Through specific examples it provides, the chapter emphasises the processes that may enable learning to occur across contexts and calls for pedagogical innovation where the design of seamless learning environments for crossover learning experiences should not be device- or tool-specific, rather be based on firm pedagogical understandings and blend of traditional and mobile pedagogies.
Clustering and semi‐supervised classification for clickstream data via mixture models

Abstract Finite mixture models have been used for unsupervised learning for some time, and their use within the semisupervised paradigm is becoming more commonplace. Clickstream data are one of the various emerging data types that demand particular attention because there is a notable paucity of statistical learning approaches currently available. A mixture of first‐order continuous‐time Markov models is introduced for unsupervised and semisupervised learning of clickstream data. This approach assumes continuous time, which distinguishes it from existing mixture model‐based approaches; practically, this allows account to be taken of the amount of time each user spends on each webpage. The approach is evaluated and compared with the discrete‐time approach, using simulated and real data.
A Note on Comparison between Statistical Cluster and Neural Network Cluster

Background: Cluster analysis is a data reduction technique in rows of the data matrix. This technique is widely used in engineering, biology, society, pattern recognition, and image processing. Objective: In this paper, self organized map (SOM) using the artificial neural network and different statistical techniques of cluster analysis are used on Population data of 33 districts of Rajasthan with 9 variables for comparison purpose. Methods: The goal of this work is to identify the most suitable technique for clustering the data by using the artificial neural network and different statistical clustering techniques. We received all patents regarding artificial neural network and k-means cluster method. Conclusion: The k-means cluster analysis is found as good as Neural Network cluster analysis, whereas Hierarchical cluster analysis and two steps cluster analysis provide some variation from the neural network cluster analysis.
A Compliance-Driven Framework for Privacy and Security in Highly Regulated Socio-Technical Environments

Regulatory compliance is a top priority for organizations in highly regulated ecosystems. As most operations are automated, the compliance efforts focus on the information systems supporting the business processes of the organizations and, to a lesser extent, on the humans using, managing, and maintaining them. Yet, the human factor is an unpredictable and challenging component of a secure system development and should be considered throughout the development process as both a legitimate user and a threat. In this chapter, the authors propose COMPARCH as a compliance-driven system engineering framework for privacy and security in socio-technical systems. It consists of (1) a risk-based requirement management process, (2) a test-driven security and privacy modeling framework, and (3) a simulation-based validation approach. The satisfaction of the regulatory requirements is evaluated through the simulation traces analysis. The authors use as a running example an E-CITY system providing municipality services to local communities.
Technology and Mastery: Exploring Design Sensitivities for Technology in Mountaineering

The idea of man’s ‘mastery over nature’ is ubiquitous in western philosophy and in western thinking. Technology has been widely used in support of this end. Given the growing interaction design opportunities for personal digital technologies in supporting outdoor and recreational nature activities such as mountaineering, it is timely to unpack the role that technology can play in such activities. In doing so, it is important to consider the intrinsic and extrinsic motivations at play for the individual and the accepted social norms or ‘rules’ that are associated with the activity through its community and passed on through its community of practice. Technologies that may be considered as a form of ‘cheating’ when first introduced (such as handheld GPS) can later become accepted through common practice, although the rules are often nuanced. For example, it is widely regarded that GPS should not replace the skill of map reading and navigation. In this chapter, we consider different forms of mastery over nature that technology can support and reflect on the design sensitivities that these provide.
Simplifying Secure Cloud Computing Environments with Cloud Data Centers

This chapter aims to dive into the particular security and risk aspects of using a cloud data center and how a cloud customer can evaluate and benchmark the security of his chosen cloud data center provider. Cloud data center providers can offer many advantages to their customers compared to traditional in-house or on premises information technology (IT) operation. Most cloud data centers are more secure than their on premise counterparts especially in companies that do not have IT security as their primary skill or focus. The wide range of cloud customers and their respective industry- or country-specific requirements demands that cloud data center fulfills comprehensive set of certifications and attestations to be allowed to work with such industries or in such countries. A cloud data center customer profits from such conditions directly by receiving the same low infrastructure costs. The chapter outlines the needed considerations and security measures that should be applied by using a cloud data center service provider.
HarpCI, Empowering Performers to Control and Transform Harp Sounds in Live Performance

The goal of our research is to provide harpists with the tools to control and transform the sounds of their instrument in a natural and musical way. We consider the development of music with live electronics, with particular reference to the harp repertoire, and include interviews with six harpists that use technology in their professional performance practice. We then present HarpCI, a case study that explores how gestures can be used to control and transform sound and light projection in live performance with the electric harp. HarpCI draws on research from the areas Human Computer Interaction (HCI) and Music Interaction Design (MiXD) to extend the creative possibilities available to the performer, and demonstrates our approach to bridging the gap between the performer/composer and the harp on one side, and the technology on the other. We discuss the use of guitar pedals with the electric harp, and the limitations they impose, and then introduce the MyoSpat system as a potential solution to this issue. MyoSpat aims to give musicians control over auditory and visual aspects of the performance through easy to learn, intuitive and natural hand gestures. It also aims to enhance the compositional process for instrument and live electronics, through a new way of music notation for gesturally controlled interactive systems. The system uses the Myo® armband gestural controller, a device to control live sound processing that is non-invasive to instrumental technique and performer. The combination of these elements allows the performer to experience a tangible connection between gesture and sound production. Finally, we describe the experience of Eleanor Turner, who composed and performed The Wood and the Water (Turner 2016) using MyoSpat, and we conclude by presenting the outcomes from HarpCI workshops delivered at Cardiff Metropolitan University for Camac Harp Weekend, Royal Birmingham Conservatoire’s Integra Lab and Southampton University.
Short-Term Time Series Modelling Forecasting Using Genetic Algorithm

The prediction analysis of a network traffic time series dataset in order to obtain a reliable forecast is a very important task to any organizations. A time series data can be defined as an ordered sequence of values of a variable at equally spaced time intervals. By analyzing these time series data, one will be able to obtain an understanding of the underlying forces and structure that produced the observed data and apply this knowledge in modelling for forecasting and monitoring. The techniques used to analyze time series data can be categorized into statistical and machine learning techniques. It is easy to apply a statistical technique [e.g., Autoregressive Integrated Moving Average (ARIMA)] in order to analyze time series data. However, applying a genetic algorithm in learning a time series dataset is not an easy and straightforward task. This paper outlines and presents the development of genetic algorithms (GA) that are used for analyzing and predicting short-term network traffic datasets. In this development, the mean squared error (MSE) is taken and computed as the fitness function of the proposed GA based prediction task. The results obtained will be compared with the performance of one of the statistical techniques called ARIMA. This paper is concluded by recommending some future works that can be applied in order to improve the prediction accuracy.