Reviewerly/exHarmony huggingface dataset:
A bilingual ontology mapping and enrichment approach for domain ontologies in e-learning

English language is accepted as international language for publishing research in e-learning and Semantic Web area, but people are needed from representing data and knowledge in his native language. This paper proposes an approach for mapping and enrichment of domain ontologies, labeled in two natural languages (bilingual ontologies). Our main goal is to examine how ontology entity labels or comments in two or more natural languages can be used to improve ontology mapping. The proposed approach combines string-based, linguistic, structural and semantic mapping. It also can reuse existing mappings and use learner's or expert's feedback to improve mapping. We discuss the application of our approach for mapping ontologies describing e-learning content.
Revolution by Evolution

This chapter will discuss the development of intelligent tutoring systems (ITSs) for education in the last decade and will trace the challenges they meet. The author will examine the social and cultural impacts of several types of ITSs, from data-driven ITSs, which became the backbone of educational data mining approaches, to model-based adaptive systems. The latter utilizes artificial-intelligence-based tools that can provide dialogue to engage students in the learning process, to provide open learning models in order to promote self-awareness, to adopt meta-cognitive scaffolding, to use social simulation models, and to use cultural models. The layout of the chapter is as follows: the author will describe the technology of various ITSs with a focus on implementation of different techniques and algorithms in ITS modules (e.g., student modeling module, pedagogical module, and interface), followed by a discussion of how these ITSs begin to change the whole spectra of educational paradigms toward open everyone-is-a student models.
AI Affective Conversational Robot with Hybrid Generative-Based and Retrieval-Based Dialogue Models

ChatBot technology has become a widely used in various application fields. An important topic in the research on conversational robots is the improvement of their temperature during operation for enhanced user interaction. In this study, we propose an artificial intelligence affective conversational robot (AIACR), which is an integration of an artificial intelligence deep learning sentiment analysis model and generative-and retrieval-based dialogue models. The sentiment analysis model developed in this study uses three models, namely, multilayer perceptron (MLP), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM). Moreover, word2vec and semantics are utilized as the basis for similarity ranking models. The deep learning dialogue model, sentiment analysis model, and similarity model were integrated and compared as well. The experimental results show that the sentiment analysis model, similarity model, and dialogue model respectively utilize BiLSTM, word2vec, and the retrieval-based model to achieve the best dialogue performance. The major research contributions of this study are the developed AIACR and the proposed affective conversational robot index (ACR Index) as a criterion for evaluating the effectiveness of emotional dialogue robots.
Dataset of mobile learning effectiveness on learning Computer Programming in Community College

While learning through mobile devices, or mobile learning, has been proven feasible [1,2], its effectiveness is still in doubt as contradictory research results were observed [3-5]. In this dataset, the data collected from the experiments on mobile learning effectiveness is presented. The subject Computer Programming was used in the experiments because technical competence is one of the key success factors of mobile learning [6]. Computer Programming is an essential skill for all technical fields. It is therefore a compulsory foundation subject for all technical-related sub-degree programmes in Hong Kong Community College. Instead of comparing immediate pre-test and pro-test results, the entire subject performance of 1434 students in cohorts 2015 to 2017 was evaluated. By having different settings of mobile learning environment for each cohort, the effectiveness of mobile learning could be observed. The data collected was statistically analysed by one-way ANOVA with Turkey HSD post-hoc test. Students' mobile learning experience was also evaluated by survey results using a 5-point Likert Scale questionnaire. The dataset in this paper should provide researchers and educators with further information on how mobile learning could be effectively implemented along with the pedagogical strategies.
Using Answer Set Programming for Commonsense Reasoning in the Winograd Schema Challenge

Abstract The Winograd Schema Challenge (WSC) is a natural language understanding task proposed as an alternative to the Turing test in 2011. In this work we attempt to solve WSC problems by reasoning with additional knowledge. By using an approach built on top of graph-subgraph isomorphism encoded using Answer Set Programming (ASP) we were able to handle 240 out of 291 WSC problems. The ASP encoding allows us to add additional constraints in an elaboration tolerant manner. In the process we present a graph based representation of WSC problems as well as relevant commonsense knowledge.
Databases Security Issues - A Short Analysis on the Emergent Security Problems Generated By NoSQL Databases

Over the last few years, the NoSQL (Not only SQL) databases managed to impose themselves as a suitable alternative to their more common relational counterparts.As such, the NoSQL databases have a plethora of practical applications, such as various Web 2.0 solutions, document management but also real-time systems where low latency is critical, embedded systems, storage for sensor networks and finally the IoT (Internet of Things).There are enough differences between the NoSQL and relational databases to presume that the security problems related to each of the two types of databases are at least partially different.This paper tries to review a few of the security aspects associated with the NoSQL databases usage.
Diagnosis of Arthritis Using K-Nearest Neighbor Approach

Disease means ‘shortage of comfort’ or ‘lack of ease’. Disease is also known as illness, sickness, rot, affliction or complaint. Disease is caused by tiny living creatures known as microbes. Pathology is the study of diseases and medical experts means doctors study diseases everyday by using wonderful instrument i.e. microscope which is used to make things look bigger. This paper gives a deep cognition about the diseases, their reasons and the Arthritis. It also provides us a sound noesis about the symptoms of the Arthritis. Basal dendrites referring the receptive from where it’s getting the signals. Let’s suppose the response of the inputs is not that which is desired. So, if the actual response is different to desired. Naturally, we must adjust the internal parameters of nerve cells. This paper gives a novel technique for prediction of arthritis on the bases of the arthritis dataset.
Strategies for Use Case Modeling

A major challenge in teaching use-case modeling (UCM) is to mitigate the difficulties of students that prevent them from producing use-case models with quality. The strategies for UCM are scattered in the literature in several areas, and may not be known to the students, who therefore fail to receive the benefits that would mitigate their difficulties. This paper aims to present a systematic literature review (SLR) to identify, gather and analyze strategies for UCM. During the SLR, two thousand two hundred sixty-six studies published between 2008 and 2018 were returned from 6 bases (ACM, IEEE, Scopus, Science Direct, SpringerLink and Engineering Village), which resulted in the selection of 39 primary studies. These were classified, following the coding procedures of Grounded Theory, into 13 categories of different strategies for UCM. The results can help teachers in the adoption of the most appropriate UCM strategies for their students. Besides, they provide a quick reference for teachers and researchers interested in conducting additional studies on teaching strategies for UCM.
A Model for Naturalistic Programming with Implementation

While the use of natural language for software development has been proposed since the 1960s, it was limited by the inherent ambiguity of natural languages, which people resolve using reasoning in a text or conversation. Programming languages are formal general-purpose or domain-specific alternatives based on mathematical formalism and which are at a remove from natural language. Over the years, various authors have presented studies in which they attempted to use a subset of the English language for solving particular problems. Each author approached the problem by covering particular domains, rather than focusing on describing general elements that would help other authors develop general-purpose languages, instead focusing even more on domain-specific languages. The identification of common elements in these studies reveals characteristics that enable the design and implementation of general-purpose naturalistic languages, which requires the establishment of a programming model. This article presents a conceptual model which describes the elements required for designing general-purpose programming languages and which integrates abstraction, temporal elements and indirect references into its grammar. Moreover, as its grammar resembles natural language, thus reducing the gap between problem and solution domains, a naturalistic language prototype is presented, as are three test scenarios which demonstrate its characteristics.
Constraint Learning: An Appetizer

Constraints are ubiquitous in artificial intelligence and operations research. They appear in logical problems like propositional satisfiability, in discrete problems like constraint satisfaction, and in full-fledged mathematical optimization tasks. Constraint learning enters the picture when the structure or the parameters of the constraint satisfaction/optimization problem to be solved are (partially) unknown and must be inferred from data. The required supervision may come from offline sources or gathered by interacting with human domain experts and decision makers. With these lecture notes, we offer a brief but self-contained introduction to the core concepts of constraint learning, while sampling from the diverse spectrum of constraint learning methods, covering classic strategies and more recent advances. We will also discuss links to other areas of AI and machine learning, including concept learning, learning from queries, structured-output prediction, (statistical) relational learning, preference elicitation, and inverse optimization.
Recognition of Military and Civilian ships in SAR Images based on Ellipse Fitting Similarity

With the development of SAR imaging technology, detailed information of ship targets is displayed in high resolution SAR, which facilitates target recognition. In this paper, the differences between the structures of military and civilian ships are investigated. The paper utilizes the characteristics of military ships that have structures like towering bridges, which make them have a protruding structure in SAR images compared with civilian ships. As a result, the similarity between the ship contour and the fitting ellipse is used to recognition the military and civilian ships. Hence, a method for recognizing military and civilian ships in high-resolution SAR images based on ellipse fitting is proposed. This method can be divided into two steps, first extracting the edge of the ship target in the image, and then judging the similarity between the edge and the ellipse. The high-resolution images obtained by GF-3 satellite are used for experimental verification. The experimental results show that this method can effectively classify military and civilian ships.
FPGA and GPU-based acceleration of ML workloads on Amazon cloud - A case study using gradient boosted decision tree library

Cloud vendors such as Amazon (AWS) have started to offer FPGAs in addition to GPUs and CPU in their computing on-demand services. In this work we explore design space trade-offs of implementing a state-of-the-art machine learning library for Gradient-boosted decision trees (GBDT) on Amazon cloud and compare the scalability, performance, cost and accuracy with best known CPU and GPU implementations from literature. Our evaluation indicates that depending on the dataset, an FPGA-based implementation of the bottleneck computation kernels yields a speed-up anywhere from 3X to 10X over a GPU and 5X to 33X over a CPU. We show that smaller bin size results in better performance on a FPGA, but even with a bin size of 16 and a fixed point implementation the degradation in terms of accuracy on a FPGA is relatively small, around 1.3%–3.3% compared to a floating point implementation with 256 bins on a CPU or GPU.
On the Need for Large Quantum Depth

Near-term quantum computers are likely to have small depths due to short coherence time and noisy gates. A natural approach to leverage these quantum computers is interleaving them with classical computers. Understanding the capabilities and limits of this hybrid approach is an essential topic in quantum computation. Most notably, the quantum Fourier transform can be implemented by a hybrid of logarithmic-depth quantum circuits and a classical polynomial-time algorithm. Therefore, it seems possible that quantum polylogarithmic depth is as powerful as quantum polynomial depth in the presence of classical computation. Indeed, Jozsa conjectured that “ Any quantum polynomial-time algorithm can be implemented with only O (log n ) quantum depth interspersed with polynomial-time classical computations. ” This can be formalized as asserting the equivalence of BQP and “ BQNC BPP .” However, Aaronson conjectured that “ there exists an oracle separation between BQP and BPP BQNC . ” BQNC BPP and BPP BQNC are two natural and seemingly incomparable ways of hybrid classical-quantum computation. In this work, we manage to prove Aaronson’s conjecture and in the meantime prove that Jozsa’s conjecture, relative to an oracle, is false. In fact, we prove a stronger statement that for any depth parameter d , there exists an oracle that separates quantum depth d and 2 d +1 in the presence of classical computation. Thus, our results show that relative to oracles, doubling the quantum circuit depth does make the hybrid model more powerful, and this cannot be traded by classical computation.
Optimizing Training Data and Hyperparameters of Support Vector Machines Using a Memetic Algorithm

Support vector machine (SVM) is a well-known machine learning algorithm widely used for classification and regression problems. Despite the high prediction rate of this technique in a wide range of real applications, the efficiency of SVM and its classification performance highly depends on the hyperparameters setting as well as the selection of feature subset. Moreover, high memory and computational complexity of SVM training can be a limiting factor for its application on huge dataset. In this work we propose a novel memetic algorithm for support vector machine called SE-SVM to address mentioned problems. We use evolutionary techniques that optimize hyperparameters and select features and training set simultaneously. The algorithm is applied to seven datasets. All of that datasets are binary classification problem. We compare the SE-SVM to different evolutionary algorithms, random search techniques and other well-established classifiers. The experimental results show that the end result obtained by SE-SVM achieves high classification performance with a shorter training and classification time.
Data driven recognition of interleaved and concurrent human activities with nonlinear characteristics

Sensor-based human activity recognition gained a lot of research interest within the field of pervasive computing due to its wide range of application domains. Recognition of complex human activities is a challenging task due to the tendency of humans to perform activities in an interleaved and con current scenario. In this paper, we address the problem of complex activities recognition using a combination of the discriminative features called Strong Jumping Emerging Patterns (SJEPs) and the fuzzy sets theory. The proposed approach is designed to fit the challenges of multi-label classification, nonlinear separation, and recognition of multiple overlaps of interleaved and concurrent activities. Besides the need for a training dataset of complex activities that is difficult to obtain. The proposed approach uses a training dataset of simple activities to extract two sets of SJEPs for linear and nonlinear activities. Then, a novel SJEP-based recognition approach is presented to recognize simple and complex activities. We evaluate our approach using two datasets collected from two different labs. Experimental results show the efficiency of our approach in recognizing simple and complex human activities, besides the superiority of our approach against other competing approaches with regard to recognition accuracy.
Enneagram Approach for Intelligent Agile Integration

Agile integration is a practice used to stabilize the process in software development by reducing the number of cycles and encourages simplicity in code releases. The Enneagram approach is a methodology which can help distributed teams improve and establish faster Agile integration. It helps to create a personal profile containing habitual patterns and behavioral relations.
Adaptive Object Selection Based on Multi-Threshold Image Processing

Adaptive object selection technique based on the results of multi-threshold image processing is considered. The keynote feature of the proposed approach is the use of information about the properties of the selected object(s) at multiple threshold values a posteriori to find an optimal threshold based on the criterion that takes into account the geometric properties of the object. Multi-threshold object selection algorithms based on several scale-invariant geometric criteria such as the object perimeter elongation coefficient and the elongation coefficient of the circumscribed ellipsoid main axis are implemented and validated. The suggested characteristics help to distinguish between compact and elongated objects while remaining invariant to the image scale. The results for both synthetic and observational imagery obtained by remote sensing equipment are used for validation purposes.
Example-Based Feature Tweaking Using Random Forests

In certain application areas when using predictive models, it is not enough to make an accurate prediction for an example, instead it might be more important to change a prediction from an undesired class into a desired class. In this paper we investigate methods for changing predictions of examples. To this end, we introduce a novel algorithm for changing predictions of examples and we compare this novel method to an existing method and a baseline method. In an empirical evaluation we compare the three methods on a total of 22 datasets. The results show that the novel method and the baseline method can change an example from an undesired class into a desired class in more cases than the competitor method (and in some cases this difference is statistically significant). We also show that the distance, as measured by the euclidean norm, is higher for the novel and baseline methods (and in some cases this difference is statistically significantly) than for state-of-the-art. The methods and their proposed changes are also evaluated subjectively in a medical domain with interesting results.
A direct method to Frobenius norm-based matrix regression

Regression analysis has been widely used for face recognition. This paper mainly discuss the following regularized matrix regression problem: Given a set of k image matrices A1,A2,…,Ak∈Rm×n and an image matrix B∈Rm×n, find x=(x1,x2,…,xk)T ∈Rk such that minx∈Rk∥x1A1+x2A2+⋯+xkAk−B∥F2+λ2∥x∥22, where x1,x2,…,xk are also a set of representation coefficients, λ is the model parameter, and ∥A∥F represents the Frobenius norm of matrix A.Yuan and Liao [S.F. Yuan, A.P. Liao, Least squares Hermitian solution of the complex matrix equation AXB + CXD = E with the least norm. J. Frankl. Inst. 351 (2014), pp. 4978–4997] introduced a new product for matrices and vectors, and solved the least squares Hermitian problem of complex matrix equation AXB+CXD=E. In this paper, we deeply investigate this product and its relative properties about matrix trace, norm, and determinant. We then provide a direct method to get the close form solution for solving the regularized image matrix regression problem in face recognition.
Abnormal electricity consumption detection based on ensemble learning

Abnormal electricity consumption (AEC) seriously affects the management of the power grid marketing department, causing huge economic losses to the power supply enterprises. An accurate abnormal electricity consumption detection system is thus vital to reducing the non-technical losses and the operating cost of state Grid. Comparing to the normal case, the amount of abnormal electricity consumptions is relatively small, leading to a typical imbalance problem. In this paper, an ensemble learning algorithm based on an artificial neural network (ANN) is developed for the abnormal electricity consumption detection. We studied 6 typical AECs, and an monthly amount of electricity consumptions is firstly analyzed for feature extraction and classifier learning. Then, the ensemble learning scheme built on the extreme learning machine (ELM) algorithm and the majority voting method is proposed for the classifier training and AEC classification. Experiments on the electricity consumption data of State Grid Zhejiang Electric Power Corporation are conducted to demonstrate the effectiveness of the proposed algorithm.