Reviewerly/exHarmony huggingface dataset:
A Novel Image Segmentation Based on Clustering and Population-Based Optimisation

Image segmentation is an essential step in image processing and computer vision with many image segmentation algorithms having been proposed in the literature. Among these, clustering is one of the prominent approaches to achieve segmentation. Traditional clustering algorithms have been used extensively for this purpose, although they have disadvantages such as dependence on initialisation conditions and a tendency to find only local optima. To overcome these disadvantages, population-based metaheuristic algorithms can be applied. In this paper, we propose a novel clustering algorithm based on human mental search (HMS) for image segmentation. HMS is a relatively new population-based metaheuristic inspired from the manner of searching in online auctions. HMS comprises three operators: mental search, which explores the neighbourhood of candidate solutions using Levy flight; grouping, which clusters candidate solutions; and moving candidate solutions towards a promising area. To verify the efficacy of the proposed algorithm, we conduct several experiments based on different criteria including mean cost function value, statistical analysis and image segmentation criteria. The obtained results confirm superior performance of our proposed algorithm compared to competitors.
Engineering entanglement, geometric phase and quantum Fisher information of a three-level system with energy dissipation

Quantum Fisher information (QFI) and geometric phase have recently been performed different tasks in quantum information technology.We investigate the QFI and entanglement of a three-level atom in $\Lambda $ configuration interacting with a quantized field mode by using linear entropy.We study the dynamical behavior of the geometric phase based on the engineering of a three-level atomic configuration.We analyze the effect of energy dissipation of the dynamical properties of the geometric phase and the QFI as an entanglement quantifier between the three-level atom and field.We explore the correlation between the engineering geometric phase and QFI in the absence and presence of energy dissipation effect.We have found that the $\mathrm{QFI}$ is very sensitive to the effect of the time dependent coupling and energy dissipation.
Implementation of Classic Image Transformation Algorithm to Quantum State, Boundary Extraction and Transformation of Half-Tone Image to Binary

Abstract The aim of the research is computer simulation of a quantum algorithm to solve the problem of transforming a classical image using quantum computing tools and methods, studying recognition algorithms and creating a recognition model using quantum methods. The method of quantum modeling makes it possible to convert a classical image into a quantum state, select boundaries and convert a grayscale image to a binary one, and shows the possibilities of the quantum information theory in interpreting classical problems. The main results of the article are the developed quantum algorithm that allows recognizing objects, as well as the quantum method aimed at representing/processing a color pixel image. The scientific novelty of the article is expressed in the construction of a quantum system, an exponential increase in the speed of solving computational NP-complete problems, which on classical machines can be solved in unacceptable time. The motivation for writing the work was a high growth interest in quantum computing and the benefits that they guarantee. The development of the theoretical foundations of creating software systems and the design of algorithms for new information technologies and specialized computing systems is a dynamic field, as evidenced by the number of existing works in this direction. The developed algorithms for various problems of complexity classes can give a significant gain in efficiency in comparison with existing classical ones and provide a solution to a number of complex mathematical (including cryptographic) problems.
A study on development of school mathematics contents for artificial intelligence (AI) capability

Artificial intelligence technology, which represents the era of the 4th Industrial Revolution, is now deeply involved in our lives, and future education places great emphasis on building students' capabilities for the principles and uses of artificial intelligence. Therefore, the purpose of this study is to develop the contents of mathematics, the most closely related AI. To this end, I propose the subject that can learn the principle of machine learning, the core technology of artificial intelligence, based on mathematics. In addition, the core contents to be dealt with in 'Basic Mathematics for AI and Data Science' were drawn.
Review of Inpainting Algorithms for Wireless Communication Application

Digital image inpainting is a technique of restoring large removed /damaged regions of an image with the data from the surrounding pixels of the removed region. The issue of image restoration with inpainting techniques occurs commonly in computer vision/image processing when unwanted objects have to be removed from images, for filling cracks in photographs, etc. Digital image inpainting approach is an active field of research in two significant applications of wireless communication: image compression and image recovery from a damaged image due to errors in a wireless channel. This work presents a brief survey of different image inpainting techniques and their contributions to different wireless communication applications.
Semantic Web and Interactive Knowledge Graphs as an Educational Technology

Technologies of knowledge representation, inductive reasoning, and semantic annotation methods are considered in relation to knowledge graphs that are focused on the domain of nuclear physics and nuclear power engineering. Interactive visual navigation and inductive reasoning in knowledge graphs are performed using special search widgets and an intelligent RDF browser. As a toolkit for ontologies refinement and enrichment, a software agent for the context-sensitive searching for new knowledge in the WWW is presented. In order to evaluate the measure of compliance of the found content with respect to a specific domain, the binary Pareto relation and Levenshtein metrics are used. The proposed semantic annotation methods allow the knowledge engineer to calculate the measure of the proximity of an arbitrary network resource in relation to classes and objects of specific knowledge graphs. Operations with remote semantic repositories are implemented on cloud platforms using SPARQL queries and RESTful services. The proposed software solutions are based on cloud computing using DBaaS and PaaS service models to ensure scalability of data warehouses and network services. Examples of using the proposed technologies and software are given.
The Use of Augmented Reality for Solving Arithmetic Problems for Preschool Children

Preschool children are required to acquire problem-solving ability and related time and sequence concepts to solve mathematical story problems. The maturation and pervasion of disruptive technologies, such as augmented reality (AR), may help preschool children to better acquire this knowledge and skills. However, it is still unknown how preschoolers would make use of AR as a learning tool for tackling arithmetic story problems with the involvement of the concepts of time and sequence. Consequently, the present study attempted to employ direct observation and interview methods to compare and gain insights into young children’s learning behaviors under traditional 2D pictorial and AR contexts. In line with the early development trajectories of a normal child aged 4–6 years old, a series of planned arithmetic problems which primarily comprised seriation (e.g. first, second, third) and scheduling (e.g. arriving, leaving) concepts were structured in scenario-based stories and designed specifically for preschool children. The findings of the current study reveal that AR intervention may well develop the problem-solving and independent mathematical thinking ability for preschool children by encouraging them to consider all information involved in the story problems, rather than simply guessing the answer from a 2D pictorial mode. Finally, based on the fact that the majority of preschool children still rely on a concrete counting method, the recommendation is to integrate AR technology into the traditional pictorial scenarios for the purpose of supporting the development of children’s ability to solve arithmetic story problems.
A simulation study on investigation the optical and structural properties of polyethylene fiber

Multiple-beam Fizeau Interference technique, in transmission, is one of the precise techniques used in the examination of fibers. The modeling equations to simulate multiple-beam Fizeau interferograms, which express the fringe shift or phase modulation of the drawn polyethylene (PE) fibers in the case of using an immersion liquid with a refractive index greater than and less than of PE fiber, were presented. The simulated interferograms were analyzed by our previously published algorithm, which built on the use of the Fourier transform technique for fringe analysis. The simulated interferograms were used to study the effect of the drawing process on the structural properties of PE fibers. Some physical structural parameters, such as the refractive indices, the birefringence, the orientation function, and others, are significantly affected by the drawing process. The density, the degree of crystallinity, the mass fraction crystallinity, the mean square density fluctuation, the molar refractivity, and others are not affected or slightly affected by the drawing process. A new simple approach to determine the isotropic refractive index (μiso) and the principal refractive indices (ΔμoII&Δμo⊥) of fully oriented PE fiber is presented. The maximum draw ratio (Dmax) and some constants for PE fiber were obtained. The results obtained are in fair agreement with the published results of PE fiber, which indicates the validity of the simulation equations presented. To illustrate the idea of the work, convenient interferograms, figures, and tables were presented.
Are primary education teachers from Catalonia (Spain) trained on the ICT and disability?

In this work, we present the results of a study that had as an objective to understand the perception of key informants (directors, heads of studies and ICT coordinators from education centers, as well as well as directors and technology managers from teacher’s training centers), on the levels of technological training and knowledge possessed by the Primary Education teachers from the Autonomous Community of Catalonia, with respect to the use of the ICT with students with disabilities. For the methodology, a qualitative research design was used. The information was obtained from an analysis of 68 interviews conducted to professional from the education sector in Catalonia. The results obtained showed that the professionals perceived a low level of training and knowledge of the teachers with respect to the use of the ICT with people with disabilities. The findings showed the need to design and implement didactic training that empowers the teachers.
Active feature acquisition on data streams under feature drift

Abstract Traditional active learning tries to identify instances for which the acquisition of the label increases model performance under budget constraints. Less research has been devoted to the task of actively acquiring feature values, whereupon both the instance and the feature must be selected intelligently and even less to a scenario where the instances arrive in a stream with feature drift. We propose an active feature acquisition strategy for data streams with feature drift, as well as an active feature acquisition evaluation framework. We also implement a baseline that chooses features randomly and compare the random approach against eight different methods in a scenario where we can acquire at most one feature at the time per instance and where all features are considered to cost the same. Our initial experiments on 9 different data sets, with 7 different degrees of missing features and 8 different budgets show that our developed methods outperform the random acquisition on 7 data sets and have a comparable performance on the remaining two.
Fast Real-time Counterfactual Explanations

Counterfactual explanations are considered, which is to answer {\it why the prediction is class A but not B.} Different from previous optimization based methods, an optimization-free Fast ReAl-time Counterfactual Explanation (FRACE) algorithm is proposed benefiting from the development of multi-domain image to image translation algorithms. Built from starGAN, a transformer is trained as a residual generator conditional on a classifier constrained under a proposal perturbation loss which maintains the content information of the query image, but just the class-specific semantic information is changed. The transformer can transfer the query image to any counterfactual class, and during inference, our explanation can be generated by it only within a forward time. It is fast and can satisfy the real-time practical application. Because of the adversarial training of GAN, our explanation is also more realistic compared to other counterparts. The experimental results demonstrate that our proposal is better than the existing state of the art in terms of quality and speed.
Effects of exercise information using social network service(SNS) on physical activity and psychological variables among inactive college students

Purpose The current study investigated the effects of exercise information using social network service(SNS) to identify changes of physical activity and psychological variables among inactive college students. Methods Inactive college students(30 experimental group, 30 control group) were voluntarily participated in the 12-weeks intervention. During this period, the experimental group received exercise information through SNS. And all study participants’ physical activity, stages of physical activity, self-efficacy, motivation, and perceived benefits and barriers were measured at the pre, mid and post intervention. Frequency analysis, chi-square test, 2-way ANOVA RM were conducted to analyze data obtained in the study. All procedures were performed by using SPSS 23.0. Results The exercise information intervention using SNS during 12 weeks had a positive effect on the stages of physical activity of inactive college students, and there were statistically significant differences. In addition, physical activity, perceived benefits and barriers, self-efficacy, motivation positively improved after the intervention, but there were no statistically significant differences between experimental and control group. Conclusions The present study suggests that psychological strategies using various SNS programs have positive effects for inactive college students to increase physical activity and its related psychological variables.
Principal Word Vectors

We generalize principal component analysis for embedding words into a vector space. The generalization is made in two major levels. The first is to generalize the concept of the corpus as a counting process which is defined by three key elements vocabulary set, feature (annotation) set, and context. This generalization enables the principal word embedding method to generate word vectors with regard to different types of contexts and different types of annotations provided for a corpus. The second is to generalize the transformation step used in most of the word embedding methods. To this end, we define two levels of transformations. The first is a quadratic transformation, which accounts for different types of weighting over the vocabulary units and contextual features. Second is an adaptive non-linear transformation, which reshapes the data distribution to be meaningful to principal component analysis. The effect of these generalizations on the word vectors is intrinsically studied with regard to the spread and the discriminability of the word vectors. We also provide an extrinsic evaluation of the contribution of the principal word vectors on a word similarity benchmark and the task of dependency parsing. Our experiments are finalized by a comparison between the principal word vectors and other sets of word vectors generated with popular word embedding methods. The results obtained from our intrinsic evaluation metrics show that the spread and the discriminability of the principal word vectors are higher than that of other word embedding methods. The results obtained from the extrinsic evaluation metrics show that the principal word vectors are better than some of the word embedding methods and on par with popular methods of word embedding.
GraphCL: Contrastive Self-Supervised Learning of Graph Representations

We propose Graph Contrastive Learning (GraphCL), a general framework for learning node representations in a self supervised manner. GraphCL learns node embeddings by maximizing the similarity between the representations of two randomly perturbed versions of the intrinsic features and link structure of the same node's local subgraph. We use graph neural networks to produce two representations of the same node and leverage a contrastive learning loss to maximize agreement between them. In both transductive and inductive learning setups, we demonstrate that our approach significantly outperforms the state-of-the-art in unsupervised learning on a number of node classification benchmarks.
Explainable Artificial Intelligence for Falls Prediction

With a rapidly ageing population, it is likely that we will encounter an older adult falling. Falls can cause death, serious injury or harm, loss of confidence and loss of independence. Falling can happen to any of us, however those over 65 years of age can be classified as a group of adults who are more vulnerable and at increased risk of falling. This paper focuses on applying explainable artificial intelligence techniques, in the form of decision trees, to healthcare data in order to predict the risk of falling in older adults. These decision trees could potentially be introduced for health and social care professionals to help aid their judgements when making decisions.
The role of mathematics teacher in the digital era

Abstract The complex characteristics of the digital era requires more than just conceptual knowledge. In addition to a so called 21 st century skills, ‘digital literacy’ has been raised to highlight a set of important competencies to cope with the demands of the future era. Digital literacy comprises practical and functional skills, critical thinking and evaluation, creativity, e-safety, proficient communication, collaboration, cultural and social understanding, and information processing. Developing such competencies since early education requires a big role of teacher. By putting the idea of technological pedagogical content knowledge (TPACK) into practice, there are three important areas of consideration. First, preparation stage for which teacher should design or prepare technology-based or technology-assisted tasks that focus on exploratory activities. Second, during the implementation stage teachers needs to give proper scaffolding to bring from technical exploration to mathematical exploration. Lastly, the assessment stage for which teachers could use technology to improve efficiency and accessibility.
Basic Classes in Conceptual Modeling: Theory and Practical Guidelines

Since the 1970s, many approaches to representing domains have been suggested.Each approach maintains the assumption that the information about the objects represented in the information system (IS) is specified and verified by domain experts and potential users.Yet, as more IS are developed to support a larger diversity of users such as customers, suppliers, and members of the general public (e.g., in the case of many multiuser online systems), analysts can no longer rely on a stable single group of people for the complete specification of domains; therefore, prior research has questioned the efficacy of conceptual modeling in these heterogeneous settings.This paper aims to address this problem by providing theoretical foundations rooted in psychology research supporting the existence and importance of special classes that are termed basic-level categories.Based on this research, we formulate principles for identifying basic classes in a domain.These classes can guide conceptual modeling, database design, and user interface development in a wide variety of traditional and emergent domains.
Seismic moment tensor and dynamic parameters of earthquakes in the Central Tien Shan

In the study, seismic moment tensors (SMT) of 177 earthquakes in the Central Tien Shan with K ≥ 10.5 (M ≥ 3.6) occurring from 2007 to 2017 are determined on the basis of the wave inversion method and data from the KNET seismic network.The 177 obtained solutions have been added to an SMT catalogue, which includes 284 events with 2.8 ≤ M ≤ 6 that have occurred from 1996 to 2017.Some characteristics of the SMT catalogue are discussed along with constructed principal stress axes azimuth diagrams and dip angle distribution graphs.For the most part of events, the compression axis of the seismic events has a north-northwest direction and a sub-horizontal orientation; the direction of the tension axis does not have a pronounced maximum, while for most events it has a subvertical orientation.In addition to the scalar seismic moment, the dynamic parameters (DP) of the 150 events from the SMT catalogue that have occurred from 1999 to 2014 were computed: the source radius (Brune radius) and tangential stress drop.Studied correlations between the DP and magnitude show the link between the stress drop and earthquake magnitude to be the weakest.The Lode-Nadai factor distribution on the grounds of the seismic moment tensors from the SMT catalogue was constructed and the deformation types typical for the studied area identified.A comparison between deformation regimes and stress drop values is presented.
Cross-Depicted Historical Motif Categorization and Retrieval with Deep Learning

In this paper, we tackle the problem of categorizing and identifying cross-depicted historical motifs using recent deep learning techniques, with aim of developing a content-based image retrieval system. As cross-depiction, we understand the problem that the same object can be represented (depicted) in various ways. The objects of interest in this research are watermarks, which are crucial for dating manuscripts. For watermarks, cross-depiction arises due to two reasons: (i) there are many similar representations of the same motif, and (ii) there are several ways of capturing the watermarks, i.e., as the watermarks are not visible on a scan or photograph, the watermarks are typically retrieved via hand tracing, rubbing, or special photographic techniques. This leads to different representations of the same (or similar) objects, making it hard for pattern recognition methods to recognize the watermarks. While this is a simple problem for human experts, computer vision techniques have problems generalizing from the various depiction possibilities. In this paper, we present a study where we use deep neural networks for categorization of watermarks with varying levels of detail. The macro-averaged F1-score on an imbalanced 12 category classification task is 88.3 %, the multi-labelling performance (Jaccard Index) on a 622 label task is 79.5 %. To analyze the usefulness of an image-based system for assisting humanities scholars in cataloguing manuscripts, we also measure the performance of similarity matching on expert-crafted test sets of varying sizes (50 and 1000 watermark samples). A significant outcome is that all relevant results belonging to the same super-class are found by our system (Mean Average Precision of 100%), despite the cross-depicted nature of the motifs. This result has not been achieved in the literature so far.