Reviewerly/exHarmony huggingface dataset:
Fault Tolerant Controller Schemes for Single and Multiple Mobile Robots

In this chapter, an actuator fault tolerant controller is developed for both single and multiple differential drive mobile robots. First, a fault tolerant controller is proposed for loss of effectiveness actuator faults in differential drive mobile robots while tracking the desired trajectory. The actuator loss of effectiveness fault is injected on the kinematic equation of the robot as a multiplicative gain in the left and right wheels angular velocity. Accordingly, the goal is to estimate and tolerate the injected actuator faults. A fault diagnosis method based on joint state and parameter estimation scheme is proposed to estimate the actuator loss of effectiveness gains as well as the states of the system. The estimated values of actuator faults are then used in the controller to compensate their effects on the mobile robots performance. Next, the proposed Fault Tolerant Controller (FTC) method is extended for the leader–follower formation control of mobile robots in the presence of actuator fault in a leader and followers robots. An Extended Kalman Filter (EKF) is utilized for each robot to obtain the parameters and states of the system and as the actuator fault is detected in any of the robots, the corresponding controller compensates the fault. Finally, an obstacle avoidance feature is added to the designed actuator fault tolerant controller, for single mobile robot. Toward this goal, a go-to goal controller is designed for the mobile robot to reach the desired destination. Meanwhile, the robot can detect any obstacle with a given color specification using Kinect sensor mounted on the robot and consequently avoid collisions. The efficacy of the proposed FTC framework is demonstrated for both single and multiple mobile robots by real-time simulation and implementation on Qbot-2 from Quanser.
Web Semantic Technologies in Web Based Educational System Integration

Web based e-Education systems are an important kind of information systems that benefited from Web standards for content, implementation, deployment and integration. An e-Education system requires the collaboration of many actors in a complete ecosystem: public authorities (e.g. Ministry) and knowledge engineers, who build official reference standards; teachers and pedagogical engineers, who build digital pedagogical resources; and IT engineers who build digital platforms for e-Learning. In this article we propose and evaluate a Semantic Web approach to support the features and interoperability of a real industrial e-Education system in production. We show how ontology-based knowledge representation supports the required features, their extension to new ones and the integration of external resources (e.g. official standards) as well as interoperability with other systems and knowledge sharing between different actors. Our proof of concept is entirely based on Semantic Web technologies and complies with the industrial constraints; we qualitatively and quantitatively evaluated it and performed a benchmark of different alternatives on real data and real queries. We present an in-depth evaluation of the quality of service and response time in this industrial context that shows on a real-world testbed that Semantic Web based solutions can meet the industrial requirements, both in terms of services and efficiency compared to existing operational solutions.
Water governance network analysis using graphlet mining

Growing population, urbanization, increased sophistication in life, global warming, and climate change are some of the factors that can worsen the scarcity and quality of water in coming decades. Therefore, a sustainable water governance is essential across the world to face this challenge. In this paper, we study two water governance networks constructed from ground-truth using a new network analysis technique called graphlet analysis. Graphlet is gaining popularity in network analysis due to its power in exposing network structure and functions. To the best of our knowledge, this is the first work to apply graphlet analysis to study water governance network.
On the combination of kernel principal component analysis and neural networks for process indirect control

A new adaptive kernel principal component analysis (KPCA) for non-linear discrete system control is proposed. The proposed approach can be treated as a new proposition for data pre-processing techniques. Indeed, the input vector of neural network controller is pre-processed by the KPCA method. Then, the obtained reduced neural network controller is applied in the indirect adaptive control. The influence of the input data pre-processing on the accuracy of neural network controller results is discussed by using numerical examples of the cases of time-varying parameters of single-input single-output non-linear discrete system and multi-input multi-output system. It is concluded that, using the KPCA method, a significant reduction in the control error and the identification error is obtained. The lowest mean squared error and mean absolute error are shown that the KPCA neural network with the sigmoid kernel function is the best.
Estimating the average diffuse solar radiation based on multiple parameters: a case study of arid climate zone of Pakistan

In this study, 50 empirical models from 2 categories, diffuse fraction and diffusion coefficient, were defined with 4 subcategories to estimate monthly mean diffuse solar radiation over the arid climate zone selecting Karachi as a case study. Commonly used statistical performance indicators are analysed to check the accuracy of models. For diffuse fraction models, MAD, MARE, RMSE, RRMSE and R2 ranged from 0.0552 to 0.1789 kWh/m2-day, 0.0272 to 0.0807, 0.0702 to 0.2238 kWh/m2-day, 0.0026 to 0.0081 and 0.90 to 0.99, respectively. Similarly, for diffusion coefficient models, MAD, MARE, RMSE, RRMSE and R2 ranged from 0.0719 to 0.1618 kWh/m2-day, 0.0336 to 0.0724, 0.0847 to 0.2145 kWh/m2-day, 0.0031 to 0.0078 and 0.92 to 0.98, respectively. Results displayed that (1) diffuse fraction models have higher performance than diffusion coefficient models. (2) Sunshine hour fraction models outperformed clearness index, correlated models. (3) Combined models are more accurate than single variable-based models. (4) The proposed models showed better performance with models available in the literature.
Free response evaluation via neural network for an IMathAS system

A fully interactive class with mixed reality and simulation learning should provide many free response types for students to learn beyond numerical answers and multiple choice. Essay and string responses in the IMathAS homework system have to be manually graded, making the free response questions difficult to generate instant feedback. The ability to write questions with automatic feedback during active lecture offer improvements to the current systems and provide an opportunity for critical thinking to occur. The following study provides framework for an interpretive neural network to be implemented into any IMathAS system. These responses can be in the form of equations, words and sentences, or pictures. Findings show that correctly trained networks using manually graded artifacts can be more than 90% accurate in providing feedback to a correct answer in student practice, allowing for lessons that guide students towards correct and well-phrased answers using their own words, and can even assign partial credit. The findings imply that Marzano's taxonomy level of analysis can be reached using the IMathAS system and that critical thinking methods can be directly applied for scoring. When integrated into the existing system, simulation-based or mixed reality homework can have free responses and the grades can be transferred via learning tool interoperability connection into the institutional learning management system for direct scoring in the gradebook.
Approximation algorithms for tours of orientation-varying view cones

This article considers the problem of finding a shortest tour to visit viewing sets of points on a plane. Each viewing set is represented as an inverted view cone with apex angle [Formula: see text] and height [Formula: see text]. The apex of each cone is restricted to lie on the ground plane. Its orientation angle (tilt) [Formula: see text] is the angle difference between the cone bisector and the ground plane normal. This is a novel variant of the 3D Traveling Salesman Problem with Neighborhoods (TSPN) called Cone-TSPN. One application of Cone-TSPN is to compute a trajectory to observe a given set of locations with a camera: for each location, we can generate a set of cones whose apex and orientation angles [Formula: see text] and [Formula: see text] correspond to the camera’s field of view and tilt. The height of each cone [Formula: see text] corresponds to the desired resolution. Recently, Plonski and Isler presented an approximation algorithm for Cone-TSPN for the case where all cones have a uniform orientation angle of [Formula: see text]. We study a new variant of Cone-TSPN where we relax this constraint and allow the cones to have non-uniform orientations. We call this problem Tilted Cone-TSPN and present a polynomial-time approximation algorithm with ratio [Formula: see text], where [Formula: see text] is the set of all cone heights. We demonstrate through simulations that our algorithm can be implemented in a practical way and that by exploiting the structure of the cones we can achieve shorter tours. Finally, we present experimental results from various agriculture applications that show the benefit of considering view angles for path planning.
Website Information Architecture of Latin American Universities in the Rankings

In the 2019 editions of SIR, QS, ARWU and Webometric university rankings, after the process of evaluating and positioning through academic and research quality criteria established in their methodologies, 22 Latin American university institutions located in Brazil, Mexico, Chile, Argentina, and Colombia coincide in their positions in all the rankings considered for the study. This paper intends to characterize the website main menu design of these universities to describe and enlist the information available to users as an alternative to make their academic and scientific processes visible. Results highlight that 18% of the options of the main menu refer to library, academic life, institutional mail services, language translator, search engine, and information to visitors.
Mathematical Modeling of Heart Electrical Instabilities by Using Topology, Convex Analysis, Conceptual Spaces, Graph Theory

The graph theory, topology, convex analysis have been studied and applied to medicine, cardiology. Heart electrical instability has various causes. The di?erential diagnosis of it is based on qualitative and quantitative electrocardiogram (ECG) assessment. The purpose of this study was to formulate model, an algorithm for making diagnosis of heart electrical instability as oxymoron, fractal and anti-fractal, Moebius strip like processes by using discrete mathematics, graph theory, topology, convex analysis, multiple testing. We used algorithm for diagnosing heart electrical instability, which reduces to qualitative and quantitative analysis of ECG in standard, inverted, 3D (as rotation bodies of ECG's elements) forms; constructing graphs, including "Gift wrapping" algorithm; calculation distances between points, angles between graphs, and others; comparison of qualitative and quantitative characteristics of these graphs by selective multiple testing; formulation of the diagnostic conclusion. Characteristics of ECG by distance between points, perimeters, and angles of polygons of complexes allowed to determine peculiarities of electromagnetic picture, triggers and resonators of heart electrical instabilities. Thus, complex using of discrete mathematics, graph theory, topology, convex analysis, multiple testing allowed us to formulate an algorithm for making diagnosis of heart electrical instability as oxymoron, fractal and anti-fractal, Moebius strip like processes. Analysis of graphs as networks of node-waves with their ribs can be the basis for improving the quality of diagnosis and treatment the patients with heart rhythm and conduction disorders.
Framework for Detecting Control Command Injection Attacks on Industrial Control Systems (ICS)

This paper focuses on the design and development of attack models on the sensory channels and an Intrusion Detection system (IDS) to protect the system from these types of attacks. The encoding/decoding formulas are defined to inject a bit of data into the sensory channel. In addition, a signal sampling technique is utilized for feature extraction. Further, an IDS framework is proposed to reside on the devices that are connected to the sensory channels to actively monitor the signals for anomaly detection. The results obtained based on our experiments have shown that the one-class SVM paired with Fourier transformation was able to detect new or Zero-day attacks.
Global economy and its sustainability in the globalized world

Globalized economy has changed the whole world both in good and in bad. The changes in economy have significant impact on the everyday life, which affect practically everyone. Because the economy, monetary systems and financial markets form the operational platform of the globalized world, it is necessary to understand their role. As the economy is one of the three main pillars of the sustainability, it is impossible to develop the global sustainability without stabile and sustainable economy. The inequality of the distribution of wealth and prosperity is the most critical factor of economic sustainability and the ever-increasing accumulation of wealth and money is one of the most crucial factors jeopardising the global sustainability. People and nations struggling economically are usually having the biggest challenges with both social and environmental sustainability. Wealth works dually: it enables rich people and nations to increase their consumption footprint and they hinder poor people and nations to make consumer decisions and investments needed to improve sustainability. The rich countries have outsourced their unsustainable industrial activities to poor countries having undeveloped legislation and maximized their profits by utilising these socially and ecologically unsustainable labour and production practises, which most are illegal in the rich countries.
Framework for Analysis and Feedback of Evaluation Expressions in Restaurant Reviews

This paper proposes a framework of the cycle for writing and analyzing restaurant reviews. Our method is designed to extract evaluation expressions specific to dishes from the descriptions around the names of dishes in restaurant reviews. It is common to use restaurant reviews for choosing restaurants and dishes. The amount of reputation for foods on the Web has been growing. However, it is difficult for a user to read a large amount of information and select a dish that meets the user's taste. Based on the above background, we believe that some specific expressions evaluating dishes should be easily obtained and given feedback to reviewers; those expressions are reasonable to support the writing and understanding the reviews. We focus on two types of expressions frequently occurred with the name of the dish: common expressions for flavours such as sizzle words, and words related to particular dishes such as cooking methods. Also, the expressions evaluating a dish are acquired by using the dependency analysis and the word importance. In our consideration, the evaluation expression specific to the dish seemed to capture the characteristics of the dish better than just using sizzle words.
Machine translation system modeling based on sentences comparison

Abstract The problem urgency is determined by an increasing need for natural language processing systems especially in the area of construction and civil engineering due to an enormous amount of document in foreign languages to deal with. Author’s purpose is to figure out an approach to machine translation system based on initial sentence and online dictionaries examples comparison. Although online dictionaries are designed to be used by humans, application of some principles based on the theory of graphs and syntactic analysis as well as methods of sets allows to use them as thesaurus for natural processing systems. The author proves the reliability of this approach by using the properties of a relation such as reflexivity, symmetry and transitivity. As a result the author displays a part of a syntactic analysis block of his computer program as well as a block of the program responsible for comparing sentences on the basis of fundamental products. At the end of the paper the author comes to the conclusion that such an approach can be extended not only to the translation problem but also to a broad range of natural language processing issues.
A Study on the Library’s Cultural Value Based on the Perceptions of Users and Librarians in Korea

Abstract This study was conducted to assess the cultural value of the library by performing a perception survey for public library librarians and users. We have undertaken the processes of deriving a preliminary evaluation index based on domestic and foreign research results, determining the final evaluation index, and assessing the library’s cultural value within the scope of librarians and users, among others. As a result of conducting this research, we have divided the evaluation areas around cultural values into four areas of development: local culture, succession and promotion of cultural heritage, contribution to local residents’ cultural enjoyment, and formation of community culture in general. As a result of analyzing by evaluation area, the area of the formation of community culture turned out to have the highest average of 3.92, followed by an average of 3.84 for the area of contribution to the local residents’ cultural enjoyment, 3.82 for the area of development of local culture, and 3.53 for the area of the formation of community culture. Furthermore, respondents demonstrated the highest level of agreement to the evaluation item, stating that libraries contribute to resolving the cultural gap, within the area of formation of community culture.
The HyperBagGraph DataEdron: An Enriched Browsing Experience of Datasets

Traditional verbatim browsers give back information linearly according to a ranking performed by a search engine that may not be optimal for the surfer. The latter may need to assess the pertinence of the information retrieved, particularly when s$$\cdot $$he wants to explore other facets of a multi-facetted information space. Simultaneous facet visualisation can help to gain insights into the information retrieved and call for further refined searches. Facets are potentially heterogeneous co-occurrence networks, built choosing at least one reference type, and modeled by HyperBag-Graphs—families of multisets on a given universe. References allow to navigate inside the dataset and perform visual queries. The approach is illustrated on Arxiv scientific pre-prints searches.
In-App Activity Recognition from Wi-Fi Encrypted Traffic

In today’s world, mobile applications have been widely used, which bring great convenience to people’s lives. However, at the same time user privacy is potentially threatened. This paper shows that a passive eavesdropper can identify fine grained user activities (known as in-app activities) by analysing encrypted traffic collected by sniffing a wireless network. Even though encryption protocols are used to secure communications over the Internet, side channel data such as frame length, inter arrival time and direction are still leaked from encrypted traffic. To identify in-app activities from this side channel data machine learning techniques are used. Furthermore, we show that just by observing only a small subset of encrypted traffic (rather than observing the entire transaction), one can identify in-app activities accurately. The proposed solution was evaluated with 51 in-app activities from three popular social networking apps and obtained high detection accuracy, 95.4% when Bayes Net algorithm is used.
A System for Analysis, Visualization and Retrieval of Crime Documents

In this paper, we present the demonstration of a system that helps in analytics and visualization of crime information extracted from large text repositories. Extraction of crime indicators is performed using a CNN-BiLSTM based multi-classification network. The system is equipped with a query and retrieval system that provides a user with insights about the crime patterns and statistics extracted from the underlying repository. It allows a user to browse through functionally related articles linked automatically by the system. The system also allows the user to deep dive into the repository and view several aggregated statistic along various dimensions.
Swarm Intelligence Based Feature Clustering for Continuous Speech Recognition Under Noisy Environments

Swarm Intelligence based Feature Clustering using Artificial Bee Colony (ABC) technique is proposed and implemented in this research work. It is used to group and label the features of continuous speech sentence. This algorithm is unsupervised classification which classifies the feature vectors into different clusters and it will enhance the quality of clustering. Simulation is carried out for various number of clusters for different speech recognition algorithms under clean and noisy environmental conditions of NOIZEUS database signals. Experimental results reveal that the proposed hybrid clustering provides substantial enhancements in various performance measures compared with the existing algorithms and found as number of cluster as 5 produces the optimal result. For this optimal value, ABC clustering technique provides optimal enhancement in the recognition accuracy of Continuous Speech Recognition compared with the existing clustering techniques under different speech signal environments.
Application of a Novel Subject Classification Scheme for a Bibliographic Database Using a Data-Driven Correspondence

A novel subject classification scheme should often be applied to a preclassified bibliographic database for the research evaluation task. Generally, adopting a new subject classification scheme is labor intensive and time consuming, and an effective and efficient approach is necessary. Hence, we propose an approach to apply a new subject classification scheme for a subject-classified database using a data-driven correspondence between the new and present ones. In this paper, we define a subject classification model of the bibliographic database comprising a topological space. Then, we show our approach based on this model, wherein forming a compact topological space is required for a novel subject classification scheme. To form the space, a correspondence between two subject classification schemes using a research project database is utilized as data. As a case study, we applied our approach to a practical example. It is a tool used as world proprietary benchmarking for research evaluation based on a citation database. We tried to add a novel subject classification of a research project database.