Reviewerly/exHarmony huggingface dataset:
Corrigendum: Syllable Complexity and Morphological Synthesis: A Well-Motivated Positive Complexity Correlation Across Subdomains

The authors are thankful to the following experts for sharing their data with the DoReCo project, which was used in the corpus analyses presented in the current paper (see Table 2 of the supplementary materials for details about their data contributions and full bibliographical references): Andrew Cowell (Arapaho, Cowell 2020); Diana Forker (Sanzhi Dargwa, Forker 2020); Alexandro Garcia-Laguia (Northern Alta, Garcia-Laguia 2020); Valentin Gusev, Tiina Klooster, Beáta Wagner-Nagy & Alexandre Arkhipov (Kamas, Gusev et al 2020); Geoffrey Haig, Maria Vollmer & Hanna Thiele (Northern Kurdish, Haig et al 2020); Iren Hartmann (Hoocąk, Hartmann 2013); Birgit Hellwig (Goemai and Katla, Hellwig 2020a, Hellwig 2020b); Olga Kazakevich & Elena Klyachko (Evenki, Kazakevich and Klyachko 2020); Felicity Meakins (Gurindji Kriol, Meakins 2020); Hiram Ring (Pnar, Ring 2020); Françoise Rose (Mojeño Trinitario, Rose 2020); Frank Seifart (Bora, Seifart 2020); Stavros Skopeteas, Violeta Moisidi, Nutsa Tsetereli, Johanna Lorenz & Stefanie Schröter (Urum, Skopeteasa et al 2020); Amos Teo, & H. Salome Kinny (Sümi, Teo and Kinny 2020); Nick Thieberger (Nafsan, Thieberger 2020); Martine Vanhove (Beja, Vanhove 2020); Alexandra Vydrina (Kakabe, Vydrina 2020); Alena Witzlack-Makarevich, Saudah Namyalo, Anatol Kiriggwajjo, Zarina Molochieva & Amos Atuhairwe (Ruuli, Witzlack-Makarevich et al 2020).
Cognitive Internet of Everything (CIoE)

In today's world, we are living in busy metropolitan cities and want our homes to be ambient intelligent enough towards our cognitive requirements for assisted living in smart space environment and an excellent smart home control system should not rely on the users' instructions. The ambient intelligence is a sensational new information technology paradigm in which people are empowered for assisted living through multiple IoTs sensors environment that are aware of inhabitant presence and context and highly sensitive, adaptive and responsive to their needs. A noble ambient intelligent environment are characterized by their ubiquity, transparency and intelligence which seamlessly integrated into the background and invisible to surrounded users/inhabitant. Cognitive IoE (Internet of Everything) is a new type of pervasive computing. As the ambient smart home is into research only from a couple of years, many research outcomes are lacking potentials in ambient intelligence and need to be more dug around for better outcomes. As a result, an effective architecture of CIoE for ambient intelligent space is missing in other researcher's work. An unsupervised and supervised methods of machine learning can be applied in order to classify the varied and complex user activities. In the first step, by using fuzzy set theory, the input dataset value can be fuzzified to obtain degree of membership for context from the physical layer. In the second step, using K-pattern clustering algorithms to discover pattern clusters and make dynamic rules based on identified patterns. This chapter provides an overview, critical evaluation of approaches and research directions to CIoE.
Facility Control and Optimization Problems in Data Centers

Facility design and operation of data centers evolved through a few generations in the last decade, resulting in significant improvement of energy efficiency and operation reliability. The real-time control and optimization problems are challenging because of the heterogeneous architectures, nonlinearity of the systems under control, spatial and temporal variability of the dynamics, and tight coupling of the subsystems. This entry provides a brief introduction of the challenges through discussion about one server enclosure system and one typical data center cooling architecture.
A Framework for Interfacing Unstructured Data Into Business Process From Enterprise Social Networks

The increased number of Enterprise Social Networks (ESN) business applications has had a major impact on organizations' business processes improvements by allowing the involvement of human interactions to these process. However, these applications generate unstructured data which create barriers and challenges to offering the data in the form of web services in a SOA environment, which again impacts negatively the business process. In this context, the authors propose a framework to interface ESN unstructured data into BP using text mining techniques. The Term frequency-inverse document frequency is used as a weighting schema in this framework. After that, the cosine similarity and k-mean are utilized to find similar values from different documents and cluster documents into groups respectively. The result of the evaluation of the framework shows promising results for retrieving social unstructured data. These results can be published into the SOA enterprise service bus using the RESTful web services.
Improvement of Online Game Anti-Cheat System based on Deep Learning

With the popularity of video games and the increase of game players, cheating has become more common. Thus, game operators have designed and applied various anti-cheating systems. However, they each has some problems, the most general of which is the lag of penalty. In this article, AI models will be applied to improve anti-cheating systems. The paper first discusses the existing anti-cheating systems and their drawbacks, referring to some relevant literature. After that, two main scenarios are proposed, and one of them is actually modeled, trained and tested. This part is mainly done with python coding and the help of YOLOv5 program. The model still has room for improvement, but it does have substantiated the feasibility of above schemes.
HPC Ranking of Big Performance Tableaux

The sparse outranking digraph, introduced in Chap. 9 , is suitable for tackling the ranking of big multiple-criteria performance tableaux with thousands or millions of records. To effectively compute rankings from performance tableaux of these sizes, we propose in this chapter a collection of C-compiled and optimised Digraph3 modules that may be run on HPC equipment as available, for instance, at the University of Luxembourg.
Usability of CAPTCHA in Online Communities and Its Link to User Satisfaction

Completely automated public turning test to tell computers and humans apart (CAPTCHA) is a security mechanism that is used by online communities to block spam and hacking. Today, there are a large number of sophisticated CAPTCHAs that are robust; however, most of them are unusable. This chapter describes a study to examine types and characteristics of Text CAPTCHAs used by top 50 online communities. Furthermore, the chapter is the first of its type to develop a scale to measure usability of text CAPTCHA and to examine the relationships between dimensions of CAPTCHA and perception of usability and satisfaction with registration process in online communities. Factor analysis and equation modeling study suggests that text-based CAPTCHA can be measured on four reflective dimensions of (1) content, (2) visual layout, (3) distortion, and (4) service in which correlate significantly to users' sanctification with online community.
Parameters Estimation of Photovoltaic Models Using a Novel Hybrid Seagull Optimization Algorithm

Estimating parameters and establishing high-accuracy and high-reliability models of photovoltaic (PV) modules by using the actual current-voltage data is important to simulate, model, and optimize the PV systems. Several meta-heuristic optimization techniques have been developed to estimate the parameters of the solar PV models. However, it is still a challenging task to accurately, reliably, and quickly estimate the unknown parameters of PV models. This paper proposes a novel hybrid seagull optimization algorithm (HSOA) for estimating the unknown parameters of PV models effectively and accurately. In proposed HSOA, the personal historical best information is embedded into position search equation to improve the solution precision. A novel nonlinear escaping energy factor based on cosine function is presented for balancing global exploration and local exploitation. The differential mutation strategy is introduced to escape from the local optima. We firstly select twelve classical benchmark test functions to investigate the feasibility of HSOA, and experimental results show that HSOA is superior to most compared methods. Then, HSOA is used for solving parameters estimation problem of three benchmark solar PV models. The comparison results demonstrate that HSOA is superior to BOA, GWO, WOA, HHO, SOA, EEGWO, and ISCA on solution quality, convergence and reliability.
From Ground to Cloud

As technology has become an agent of immense change, it has forced upon the education system Cloud Computing, which in the form of School on the Cloud will have significant future ripple effects. In moving from ground (present conditions) to Cloud (future conditions), there is a need to examine the educational enhancements and the future ramifications of this technology. The description of these effects represents the goal of this chapter, by reviewing the major aspects of the future of Cloud Based Education and by presenting the approach and the results of a foresight exercise, which have led to the development of a series of future scenarios related to the future roles of the major education stakeholders.
Dynamic Edge Association in Hierarchical Federated Learning Networks

Federated Learning (FL) is a promising privacy-preserving distributed machine learning paradigm. However, communication inefficiency remains the key bottleneck that impedes its large-scale implementation. Recently, hierarchical FL (HFL) has been proposed in which data owners, i.e., workers, can first transmit their updated model parameters to edge servers for intermediate aggregation. This reduces the instances of global communication and straggling workers. To enable efficient HFL, it is important to address the issues of edge association in the context of non-cooperative players, i.e., workers, edge servers, and model owner. However, the existing studies merely focus on static approaches and do not consider the dynamic interactions and bounded rationalities of the players. In this paper, we propose the edge association strategies of the workers to be modelled using an evolutionary game. Then, we provide numerical results to validate that our proposed framework captures the HFL system dynamics under varying sources of network heterogeneity.
An Adaptive Multi-pairwise Ranking with Implicit Feedback for Recommendation

How to use the unrated items information in implicit feedback of the recommendation system has gained momentum. Some existing methods regard unrated items as negative feedback. However, unrated items may not be observed or may not be the user's genuine dislike. The paper solves the problem of classifying unrated items as single feedback in most implicit feedback recommendation scenarios. We proposed an adaptive multi-pairwise ranking with implicit feedback for a recommendation based on Multiple Pairwise Ranking with Implicit Feedback (MPR), namely MPR+. Our method divides unrated items into unknown preference items and negative feedback items according to the popularity ranking of the number of items rating by all users. Based on the MRP algorithm, by reducing the number of models, optimizing model selection, introducing dynamic parameters to reconstruct the target function. Experiments on four datasets show that the MPR+ model achieves satisfactory performance by dynamically adjusting parameters and outperforms the state-of-the-art implicit feedback approaches by using different evaluation metrics.
Sentiment Analysis of Comment Texts Based on CNN-BiGRU-Attention

In recent years, deep neural network models have become more and more widely used in the field of sentiment analysis. Compared with traditional machine learning algorithm-s, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Convolutional Recurrent Neural Networks (CRNN), etc are more stable and accurate. However, these algorithms are prone to overfitting during the experiment, resulting in lower accuracy and precision. Therefore, in this paper, we propose a feature extraction method based on convolutional neural network (CNN) and bidirectional GRU model, the two features are fused in the full connection layer, which contains both the text features and the semantic relationship between the front and back of the text, then we output the results through a layer of attention mechanism and softmax function. Finally, after many experiments, it has been proved that our model works better than baselines in the accuracy, precision, recall rate, and F value by 3 to 5 percentage points, which proves the feasibility of the proposed model.
Towards Neural-Symbolic Learning to support Human-Agent Operations

This paper investigates neural-symbolic policy learning for information fusion in distributed human-agent operations. The architecture integrates a pre-trained neural network for feature extraction, with a state-of-the-art symbolic Inductive Logic Programming (ILP) system to learn policies, expressed as a set of logical rules. We firstly outline the challenge of policy learning within a military environment, by investigating the accuracy and confidence of neural network predictions given data outside the training distribution. Secondly, we introduce a neural-symbolic integration for policy learning and demonstrate that the symbolic ILP component, when considering the length of the learned policy rules, can generalise and learn a robust policy despite unstructured data observed at policy learning time originating from a different distribution than observed during training.
Service Level Agreements for Smart Healthcare in Cloud

With the advancement of Cloud computing, the adoption of cloud service in various industries is fast increasing. This is evident in the healthcare domain where the adoption is on the rise recently. However, the research contribution in this domain has been limited to certain functions. While cloud can increase availability, reachability of services, it is critical to design the healthcare service before provisioning. Besides, it is important to formulate Service Level Agreements (SLAs) to ensure that consumers can get guaranteed service from the service provider. The objective of this paper is to design the cloud based smart services for patient diagnostics. This research specifically defines service architecture for patients, physicians and diagnostic centers. In order to measure the proposed services, metrics of each SLA parameter is described with its functional and non-functional requirements. This paper also explains a case study implementation of a basic patient service using Google App Engine.
Critical Infrastructure Protection in Developing Countries

Not all infrastructures are critical. In most countries' definitions, a critical infrastructure (CI) is a collection of indispensable assets that provide an essential support for economic and social well-being, for public safety and for the functioning of key government responsibilities. CI assets can be classified into three broad categories: Physical, Cybernetic and Human. In the present era, Information and Communication Technology sector (e.g., Cloud Computing, Big Data, Internet of Things) can be regarded as the backbone of the economies of developed and the developing countries worldwide since they provide basic services to all segments of a society. Critical infrastructure protection (CIP) is a concept du jour in many developed countries. The present chapter discusses the method of protecting critical infrastructures in developing countries. It observes many developing countries experiencing massive growth in Internet capacity and the use of Internet-based technologies. Attacks on the information infrastructure can severely affect the ability of a country to function effectively.
Confidence Learning from Noisy Labels for Arabic Dialect Identification

In this paper, we propose a new deep learning network for Arabic Dialect Identification (ADI) that addresses the incorrect label problem using confidence information. The recently released dataset for the MGB-5 ADI challenge includes a small amount of verified labels but a large amount of noisy labels, which makes the ADI task very challenging. We propose a confidence learning network (CLN) that utilizes a multi-task learning strategy to handle confidence information by leveraging the verified and noisy label sets. The proposed CLN employs a confidence refinement module using a Gumbel-softmax sampler that generates representations with more discriminative capabilities. Experimental results demonstrate that the proposed CLN model shows higher classification accuracy than conventional state-of-the-art ADI systems.
PEMBUATAN APLIKASI PEMBELAJARAN AKOR DASAR GITAR

Every person is now easier to express themselves through music. Many music-themed applications can be found on smartphones, including those based on Android. With the rapid development of smartphone technology nowadays, more and more applications are used as learning media. With the rapid development of technology, an application that can help a beginner guitarist learn basic guitar chords, their shape and tone, is needed. Therefore, an application was developed to facilitate beginner guitarists to learn basic guitar chords. In making this application, the software used consisted of Adobe Flash CS5, Adobe Air, Android Emulator, Android SDK, Adobe Photoshop CS6, and Sound Recorder application. This application had been tested and successfully implemented to facilitate beginner guitarists to learn basic guitar chords, along with their shape and tone.
NATURE OF HEURISTIC OR THE FIRST PRIMER ON RESEARCH: A Study Guide Module for Mind Control and Related Matters

The study of research and the nature in which it resides is a modern reality among scholars. There are various research materials available now on the market. However, the most interesting in the study of research is how researchers aspire and are inspired to do their works. What gives them the ideas and the inspiration to land on a certain topic that otherwise gives them the intellectual satisfaction or the pure love for doing it. In this guide book, essential elements are included for the purpose of disseminating knowledge. The editorial work is used only for education purposes of the activity last August 2021 under the title Gawad Paham, or literally a collection of works that penetrates essential learning competencies and papers that exhibits the ideas of what the nature of research is.
Introduction of Software Engineering Concepts for Electrical and Computer Engineering Students and Applications to Senior Projects

This paper describes results of incorporating basic software engineering principles into the senior design curriculum for electrical and computer engineering students who have no prior software engineering exposure.Software engineering concepts are introduced to computer and electrical engineering students in the fall of the senior year using lectures, books, and guided application to a year long project.As the electrical and computer engineering fields have broadened, introducing software engineering topics to all of the students before graduation has become increasingly valuable to both students and faculty.The senior design course itself is described as it is currently along with its evolution over the course of the program's history.Student perspectives are analyzed using comments from the course evaluation as well as taking a closer look at how one project team applied software engineering basics in their project toward greater success and satisfaction in their senior design experience.Several further improvement possibilities for the course are identified from this student feedback, especially regarding the response of the more hardware oriented electrical engineering students to software engineering topics.
Atomic Qubits

In this chapter we treat quantum computing with atomic qubits. Atoms are a very natural setting for encoding qubits. Although they intrinsically have more than two energy levels it is possible to prepare, measure, and control a two-dimensional subspace of atomic states using optical techniques that have been developed over the last 50 years. The most precise instrument developed by humankind, the optical atomic clock, exploits the remarkable coherence properties of atoms. The availability of well developed techniques for cooling and trapping both neutral atoms and ions using electromagnetic fields, together with the availability of long coherence times, motivates their use as qubits for quantum computation and simulation. Images of arrays of neutral atom and trapped ion qubits are shown in Fig. 13.1. Open image in new window Fig. 13.1 Arrays of atomic qubits. (a) Fluorescence image of two-dimensional array of optically trapped Cs atom qubits. The atomic spacing is 3 μm. Image courtesy of Minho Kwon and Trent Graham. (b) Linear array of trapped Ca ions in a Paul trap. The spacing of the central ions is about 5 μm. Image courtesy of Rainer Blatt.
Introduction

This chapter provides the framework for the contents of this book. This includes a brief introduction to the problem of geospatial analysis of compositional data and approaches to the solution. Additionally, the data sets and the R packages used throughout the book are presented.
Neural identification using recurrent high-order neural networks for discrete nonlinear systems with unknown time delays

This chapter addresses fundamental aspects about the identification of uncertain nonlinear systems with unknown time delays using recurrent high-order neural networks trained with an algorithm based on the EKF.
Educational XRealities

XRealities is a recently approved pedagogic fellowship project that will run for two years, during 2019 and 2020. This paper will introduce the project plan, some early experiments and expectations. XRealities relates to the many real and digital situations that we encounter in our digitally expanded life experiences. In contemporary education, we notice a problematic situation with abundant screens. People sit next to each other and stare at screens, looking for contact and content. We describe the current dichotomy between latent online learning materials and potential real-world experiences in campus education. The XRealities project is focused on finding alternative approaches for educational settings in which both real- world social interactions and virtual content provide new forms of intertwined learning experiences. We assume there is huge educational potential in the right juxtaposition of visual digital and real-world experiences and the embodied interaction within this augmented reality.
Tailored recommendations

Many popular internet platforms use so-called collaborative filtering systems to give personalized recommendations to their users, based on other users who provided similar ratings for some items. We propose a novel approach to such recommendation systems by viewing a recommendation as a way to extend an agent’s expressed preferences, which are typically incomplete, through some aggregate of other agents’ expressed preferences. These extension and aggregation requirements are expressed by an Acceptance and a Pareto principle, respectively. We characterize the recommendation systems satisfying these two principles and contrast them with collaborative filtering systems, which typically violate the Pareto principle.