Reviewerly/exHarmony huggingface dataset:
Fault Diagnosis of Industrial Control System With Graph Attention Network on Multi-view Graph

In this paper, a fault diagnosis framework for the industrial control systems is proposed based on graph attention networks (GAT). The proposed method models the complex relationship between sensor signals and fuses multivariate sensor signals with relational information. First, to reveal the different relationships between sensor signals, a graph with multi-view is constructed considering the similarity and correlation properties. Second, for each view of the graph, GAT is used to extract the graph feature and adjust the structure information by introducing an attention weight to each edge. Finally, the multi-view graph features are concatenated to obtain the final fault feature of multivariate sensor signals. In addition, the focal loss is introduced to balance the contribution of easy classification samples to the loss function. The superiority of the proposed method is demonstrated by extensive experiments on the three-phase flow facility data set.
Applied Competences for Students by Using M-Learning Devices in Higher Education

The purpose of this chapter is to present a case study for evaluating the competences (knowledge, skills, and attitudes) applied by students in Higher Education by using m-learning devices in a course taught in an online distance education. The research question was: Which skills do the students need to learn through m-learning devices? Two variables used in this study were: m-learning and learning technology skills. The results shows that m-learning becomes an extension, addition and/or evolution of online studying and that the student requires a set of basic knowledge and skills to handle cell phones and iPods as learning tools. Students also develop skills in computing, communication, productivity, relationships, leadership, self-study while being involved in a study mode such as mobile learning. Students also require competences such as self-management, evaluation and selection of information, creativity, communication and collaborative work.
Thematic Progression from the Perspective of Machine Translation--A Case Study of Culture and Tourism Translation

In the early 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century, Neural Machine Translation sprouted, inspired by linguistics, syntax and semantics, to dig into the connection between lines in multi-dimensions. This method has solved to some extent the inaccurate semantic translation of discourse. A discourse is composed of sentences based on one theme and progressed with Theme and Rheme. In the process of progression, the Theme and Rheme are not chosen randomly. Meanwhile, within a certain semantic frame, not all the sentences are close related with the theme. With the help of Neural Machine Translation research, this paper is aimed to analyze the effective progression of sentences in translated version within a certain semantic frame, and thus improving C-E translation, especially that of culture and tourism discourse.
Quantitative visual attention prediction on webpage images using multiclass SVM

Webpage images---image elements on a webpage---are prominent to draw user attention. Modeling attention on webpage images helps in their synthesis and rendering. This paper presents a visual feature-based attention prediction model for webpage images. Firstly, fixated images were assigned quantitative visual attention based on users' sequential attention allocation on webpages. Subsequently, fixated images' intrinsic visual features were extracted along with position and size on respective webpages. A multiclass support vector machine (multiclass SVM) was learned using the visual features and associated attention. In tandem, a majority-voting-scheme was employed to predict the quantitative visual attention for test webpage images. The proposed approach was analyzed through an eye-tracking experiment conducted on 36 real-world webpages with 42 participants. Our model outperforms (average accuracy of 91.64% and micro F1-score of 79.1%) the existing position and size constrained regression model (average accuracy of 73.92% and micro F1-score of 34.80%).
Data Reduction

An information table comprises a set of data items, presented as tuples (rows), where each tuple includes a set of attributes. Data reductionData reduction refers to redundancy of both data items/instances and attributes, and thus is an important item of study in patternPatterns recognition.
Variance Guided Continual Learning in a Convolutional Neural Network Gaussian Process Single Classifier Approach for Multiple Tasks in Noisy Images

This work provides a continual learning solution in a single-classifier to multiple classification tasks with various data sets. A Gaussian process (GP) is combined with a Convolutional Neural Network (CNN) feature extractor architecture (CNNGP). Post softmax samples are used to estimate the variance. The variance is characterising the impact of uncertainties and is part of the update process for the learning rate parameters. Within the proposed framework two learning approaches are adopted: 1) in the first, the weights of the CNN are deterministic and only the GP learning rate is updated, 2) in the second setting, prior distributions are adopted for the CNN weights. Both the learning rates of the CNN and the GP are updated. The algorithm is trained on two variants of the MNIST dataset, split-MNIST and permuted-MNIST. Results are compared with the Uncertainty Guided Continual Bayesian Networks (UCB) multi-classifier approach [1]. The validation shows that the proposed algorithm in the Bayesian setting outperforms the UCB in tasks subject to Gaussian noise image noises and shows robustness.
Markov Decision Processes

This chapter introduces sequential decision problems, in particular Markov decision processes (MDPs). A formal definition of an MDP is given, and the two most common solution techniques are described: value iteration and policy iteration. Then, factored MDPs are described, which provide a representation based on graphical models to solve very large MDPs. Abstraction and Decomposition of MDPs are introduced, as alternatives to simplify large problems. The chapter concludes by describing two applications of MDPs: power plant control and service robot task coordination.
Leveraging the Arduino Platform to Develop Information Technology Devices

Arduino is a famous board, which incorporates serial communication interfaces, including universal serial bus (USB) and an integrated development environment (IDE) based on Processing, a programming language that supports C and C++. It consists of a microcontroller with several other components that provide easy interconnections with other devices. Arduino and its components have been studied during the class of Computer Architecture for the degree in Computer Science at the University of Cagliari in 2016. At the end of the class, seven groups of students have been selected and chosen to carry out a device prototype on top of Arduino and show their methodology, the sensors they embedded on top, how data could be extracted, collected, stored in database for further processing and analytics. The development has been performed following the open source best practices; documentation and codes of these projects have been made online for free downloading and sharing in order to further contribute to the advancement and widespread usage of the Arduino platform.
AI Graphical Programming Learning Platform for Children

In this era of rapid development of artificial intelligence, the truth of competition in the future is the talent. Countries all over the world attach great importance to the programming and artificial intelligence education of children. In the future, the theoretical development and practical application of this emerging field will usher in a period of rapid development. This paper presents an artificial intelligence graphical programming learning platform based on Jetson Nano, which combines artificial intelligence education with modular software programming, provides students with a more open, intelligent, and interesting programming environment through the cross-platform development mode of "cloud service + intelligent hardware", and adopts visual programming to reduce the difficulty of artificial intelligence programming. The platform can fully tap learning resources, reduce education costs, and make artificial intelligence education more intuitive and humanized.
The model prediction-based two-stage state estimation algorithm

For nonlinear coupled systems, the traditional filter algorithm has the disadvantages of large amount of calculation and insufficient estimation accuracy. In order to reduce computational complexity and improve estimation accuracy, this paper proposes a model prediction-based two- stage state estimation algorithm. Firstly, a two-level subsystem is established through model decoupling and reconstruction. Then the linear state estimation is obtained base on first-level linear subsystem by Kalman Filter (KF). Secondly, the predictive model is obtained by substituted the first-level state estimation into the second-level subsystem. The second-level nonlinear state estimation is obtained base on prediction model and KF. Finally, the state estimation of the nonlinear system is transformed into the state estimation of the linear system by the proposed two-stage state estimation filter. And in the end, the effectiveness of the filtering algorithm is verified by simulations.
Near-duplicate Video Retrieval Based on Deep Unsupervised Key Frame Hashing

In recent years, original videos are often re-edited, modified and redistributed, which not only cause copyright problems, but also deteriorate users’ experience. Near-duplicate video retrieval based on learning to hash has been widely concerned by people. However, there are still two major defects with existing methods. Firstly, the information capacity of hash code needs to be maximized. Secondly, the retrieval efficiency of partially repeated video is insufficient. In this paper, we propose a near-duplicate video retrieval method based on key frame hashing to improve retrieval performance. We design the semi-distributed hash layer to force the distribution of the continuous key frame hash code to approach the optimal distribution, i.e., the half-half distribution. By minimizing the semantic loss, quantization loss, and bit uncorrelated loss, we train our model to generate compact binary hash codes. To retrieve partially repeated videos, the proposed video subsequence matching method can accurately locate the near-duplicate fragments between the queried video and the target video. Experiments on two public datasets present that the mean average precision (MAP) of our hashing method is 0.63, which effectively improves the accuracy of video retrieval.
CROSS-CULTURAL COMPETENCIES IN MODERN EDUCATIONAL ENVIRONMENT

The article touches upon one of the most urgent problems of modern professional education - the training of a future specialist in multicultural EE (educational environment). At present, social development requires a thorough study of the notion of ‘multicultural education’ given the necessity to educate future professionals ready to interact in a cross-cultural team. Based on such core methods of the scientific research including analysis, comparison, abstraction and generalization, the authors analyze the basic pedagogical categories that underlie this process and clarify the key concept of the research, concluding that the multicultural educational environment is a complex pedagogical phenomenon that involves the comprehensive development of the individual with a special emphasis on his/her competencies to act in a multinational society effectively.
Access

Discussion of information retrieval (IR) systems thus far has focused on the provision of retrieval mechanisms to access online content. But there are a host of larger issues related to access of information, such libraries, access to content, copyright and intellectual property (IP), preservation of digital materials, and open-access publishing with the emerging larger open science. This chapter expands the perspective on IR systems to look at their role in the context of access to information.
Analysis and Assessment of Cross-Language Question Answering Systems

Within the sphere of the web, the overload of information is more notable than in other contexts. Question answering systems (QAS) are presented as an alternative to the traditional information retrieval (IR) systems seeking to offer precise and understandable answers to factual questions instead of showing the user a list of documents related to a given search. Given that the QAS is presented as a substantial advance in the improvement of IR, it becomes necessary to determine its effectiveness for the final user. With this aim, seven studies were undertaken to evaluate: 1) in the first two, the linguistic resources and tools used in these systems for multilingual retrieval (Research 1, Research 2), and 2) the performance and quality of the answers of the main monolingual and multilingual QA of general domain and specialized domain in the web in response to different types of questions and subjects, so that different evaluation means can be applied (Research 3, Research 4, Research 5, Research 6, Research 7).
Symmetric Star-convex Shape Tracking With Wishart Filter

In stochastic estimation problems, we aim to estimate an unknown state of interest given a set of measurements received from noisy sensory devices, such as radar, Light Detection and Ranging (LiDAR), etc. A common model of the measurements’ random error is white Gaussian noise. This noise model is usually used to derive an estimator of the unknown state; accordingly, the state is described as a random variable with a Gaussian density. In some applications, the unknown state is known to take only positive values, e.g. estimating size or dimension. In such a case the classical approaches based on Gaussian densities might fail, i.e., produce a negative value. In this work, a recursive Bayesian filter is proposed based on modelling the unknown state as Wishart distributed random matrix. This model ensures that the probability densities of the random variables are restricted to positive real values, even though the measurement’s noise is still modeled as white Gaussian noise. The feasibility of the proposed Bayesian Wishart filter is demonstrated within the framework of Extended Target Tracking (ETT). A target contour measurement model of a symmetric star-convex shape is presented and integrated in the proposed filter to track the target’s extent.
Building a voting system with using blockchain technologies in the example of Hyperledger

It discusses the characteristics and features of work in the Hyperledger Fabric system and the characteristic problems of the implementation of transactions. The registers and principles of their work in the respective systems are considered, in particular, the connection of the system to voting and the check of Blockchain blocks. Defined mechanisms for the protection of relevant systems and characteristic vulnerabilities. Considered public key infrastructure in the system.
Prerequisites to Design a Collision Free Trajectory in a 3D Dynamic Environment for an UAV

Abstract This research presents the main steps needed for designing a piece-wise linear trajectory which grants an unmanned aerial vehicle (UAV) to reach a destination pose in a workspace with dynamic obstacles. The first objective is characterized by the examination of kinematics and dynamics of a quadcopter. For this purpose, a nonlinear mathematical model was developed. The validation of the mathematical model was confirmed by MATLAB and real time experiments. The second step aims to properly map the 3D environment. For this objective, an algorithm for a cuboid rectangular decomposition was developed and implemented, by extending a 2D decomposition technique. The evaluation of the proposed path planning algorithm occurred for different scenarios and the validation of the results was established through numerical simulations. In the end, a comparison for two path planning scenarios is shown: for a 3D static environment and for a 3D dynamic environment, where the movement of a dynamic obstacle is known.
Graph Traversal

Most algorithms on graphs require a systematic method of visiting the vertices of a graph. Two basic and common methods of exploring a graph are the generalization to graphs of the preorder traversal and the top-down traversal of trees. In a depth-first traversal of a graph, also known as depth-first search, the vertices reachable from a given, initial vertex are visited before their adjacent vertices, and those vertices which a visited vertex is adjacent with are visited in first-to-last order. In a breadth-first traversal of a graph, also known as breadth-first search, the vertices reachable from a given initial vertex are visited in order of non-decreasing distance from the initial vertex. These systematic methods of visiting the vertices of a graph are the subject of this chapter.
The Benefits of Cloud Computing

Cloud computing is gaining ground in the global ICT market and day by day a significant number of Small and Medium Enterprises (SMEs) are adopting cloud services with sole purpose to improve their business environment and become more efficient, competitive and productive. Migrating a business IT infrastructure to the cloud offers reduction on server and storage costs, software maintenance expenditures, network and energy expenses as well as costs associated with disaster recovery. Since the cloud computing model works on a “pay-as-you-go” basis, it provides the option to pay for what is used. Thus, its adoption can offer slow start-up or expansion costs, creating an environment for rapid innovation and development. Into that context, this article presents the Greek side of cloud evolution through two representative case studies, the migration of an IT system of a Greek industry, from an in-house data center to Google Cloud and a study of the “in-house” IT infrastructure of the National Confederation of Hellenic Commerce. Findings from the Greek industry, indicate that the cloud proposal could cost 50%, or 24% less per month (depending on the solution). As far as the National Confederation of Hellenic Commerce is concerned, the article proposes only a new measure of security using Cloud services for reasons that will be discussed at the case study. Both case studies take into account the present costs of the IT system (energy consumption, third party contracts and maintenance) and propose alternatives through cloud migration. Results indicate that cloud computing offers benefits and significant cost savings for both studied cases, showing promising ways for the successful adoption of the cloud.
End-to-End Automated Machine Learning System for Supervised Learning Problems

Automated machine learning (AutoML) represents one of the fastest-growing fields in both industry and academia. Its popularity has increased over the last few years due to the rising demand for machine learning systems usable by non-experts. Existing open-source systems fail to fully automate the machine learning tasks, being more like a black box that generates non-reusable pipelines. We propose an end-to-end system accessible to non-specialists and data scientists who wish to effortlessly create reusable and easy to understand pipelines for any regression and classification problems. The proposed solution increases the automation level by performing an advanced data cleaning followed by a complete feature engineering process without any domain knowledge about the data set. These processes are integrated into a search that generates for each model the optimum instance of preprocessed data. Bayesian optimization (BO) with a Gaussian process (GP) maximizes the performance of these models that will be ensembled using standard and probability-based stacking. We prove that the system performs better than 43.47% of practitioners on real-world data sets, being robust to any variation of tabular data. The true potential is revealed by surpassing at least one similar tool in 50% of performed benchmarks. These results were achieved for any tested data set, regardless of its dimensionality, after a maximum of 30 minutes.
Solving Non-Convex Shape-from-Shading Problem Throughneural-Network Based Shape and Depth Jointly Optimization

Shape-From-Shading (SFS) is one unique and long-standing problem aiming to reconstruct shape/depth from only one image. The SFS problem can be regarded as an inverse process of image rendering, i.e., solving the Bi-directional Reflection Distribution Function (BRDF) model with the given image. Among various BRDF models, physically based perspective Cook-Torrance BRDF model is generally used because of its universality. However, the solution of Cook-Torrance BRDF model based SFS problem has not been properly addressed because the model is very complicated and non-convex. In this paper, we present a shape and depth jointly optimization method under the neural network framework to solve this kind of SFS problem. Our method has three aspects of contributions. Firstly, we remove the unrealistic surface smoothness constraint imposed on the solution of SFS. Secondly, the design of shape and depth jointly optimization provides the method with a higher probability of bypassing local optimum and finding the global optimum. Thirdly, the neural network framework enables our method with the ability of performing point-wise optimization, that is, the optimization does not subject to the approximation on the discrete domain. Through experiments, we prove that our method can solve Cook-Torrance BRDF model based SFS problem with good accuracy.
Introduction

This chapter introduces optimization algorithms and their details, including the inspiration, classification, and development. The problems that optimization algorithms are facing are emphasized.
Usability of Digital Resources

The chapter basically examined usability of digital library resources in Francis Sulemanu Idachaba Library University of Agriculture Makurdi, Nigeria. The population for the study comprised of the entire library staff of the Francis Sulemanu Idachaba Library University of Agriculture Makurdi. Sample for the study was 118 respondents drew from population of the library staff. Data was collected using structured question. The data collected was analysed using descriptive statistics; precisely arithmetic mean. Findings revealed that there are different types of library resources digitized in Francis Sulemanu Idachaba library. There are also criteria used for the selection of digitized library resources while there are challenges associated with the process of digitization of library resources of Francis Sulemanu Idachaba Library of University of Agriculture Makurdi. The chapter concluded that latest advancement in the field of information technology have compelled libraries to embrace automation because the facilities provided by automated libraries go far beyond the activities of traditional libraries. It was recommended that the library management should provide effective solution to the problems faced in the digitization unit by purchasing more equipment for the services such as digital camera, Photoshop software, a standby generating set, standardization on the supplied paper size and CD-ROMs, upgrading of the systems in other to meet the needs of modern day's technologies.