Reviewerly/exHarmony huggingface dataset:
Designing RBFNs Structure Using Similarity-Based and Kernel-Based Fuzzy C-Means Clustering Algorithms

The RBF networks belong to a set of artificial neural network architectures.RBF networks have been successfully applied for solving various data mining tasks including classification, and regression.Successful implementation of the RBF network depends on numerous factors among which, the crucial is its structure.The decision on the network structure has to be taken at the network initialization stage.It requires calculating or inducing the number of centroids, and their respective locations.The above problem is known to be NP-hard, and hence, not easily solvable.The traditional approach for deciding on the number of hidden units is based on applying the k-means algorithm for calculating cluster centroids.Unfortunately, the procedure guarantees neither a satisfactory accuracy nor the required generalization level of the RBF network under development.To alleviate the problem for cluster determination, i.e. number of centroids, we propose the similarity-based algorithm (SCA) for the RBF networks initialization, as well as an alternative method for initializing RBFNs using the kernel-based fuzzy clustering algorithm (KFCM-K).In both cases, the number of resulting centroids and their initial locations are provided automatically.The next step involves applying the optimization procedure resulting in the selection of the final centroids' location.The procedure is integrated with the output weights determination.Since the discussed optimization problem is computationally difficult it has been decided to apply the agent-based population learning algorithm (PLA) which belongs to the class of metaheuristics.A comparative study of approaches based on SCA and KFCM-K is included in the paper.Their effectiveness is demonstrated experimentally using artificial and real benchmark datasets.The results of the computational experiment have shown that both proposed approaches for designing RBFNs perform significantly better than other algorithms used for this task.
Converting MST to TSP Path by Branch Elimination

Travelling salesman problem (TSP) has been widely studied for the classical closed loop variant but less attention has been paid to the open loop variant. Open loop solution has property of being also a spanning tree, although not necessarily the minimum spanning tree (MST). In this paper, we present a simple branch elimination algorithm that removes the branches from MST by cutting one link and then reconnecting the resulting subtrees via selected leaf nodes. The number of iterations equals to the number of branches (b) in the MST. Typically, b &lt;&lt; n where n is the number of nodes. With O-Mopsi and Dots datasets, the algorithm reaches gap of 1.69% and 0.61 %, respectively. The algorithm is suitable especially for educational purposes by showing the connection between MST and TSP, but it can also serve as a quick approximation for more complex metaheuristics whose efficiency relies on quality of the initial solution.
RegionTrack

Atomicity is a correctness criterion to reason about isolated code regions in a multithreaded program when they are executed concurrently. However, dynamic instances of these code regions, called transactions , may fail to behave atomically, resulting in transactional atomicity violations. Existing dynamic online atomicity checkers incur either false positives or false negatives in detecting transactions experiencing transactional atomicity violations. This article proposes &lt;monospace&gt;RegionTrack&lt;/monospace&gt;. &lt;monospace&gt;RegionTrack&lt;/monospace&gt; tracks cross-thread dependences at the event, dynamic subregion, and transaction levels. It maintains both dynamic subregions within selected transactions and transactional happens-before relations through its novel timestamp propagation approach. We prove that &lt;monospace&gt;RegionTrack&lt;/monospace&gt; is sound and complete in detecting both transactional atomicity violations and non-serializable traces. To the best of our knowledge, it is the first online technique that precisely captures the transitively closed set of happens-before relations over all conflicting events with respect to every running transaction for the above two kinds of issues. We have evaluated &lt;monospace&gt;RegionTrack&lt;/monospace&gt; on 19 subjects of the DaCapo and the Java Grande Forum benchmarks. The empirical results confirm that &lt;monospace&gt;RegionTrack&lt;/monospace&gt; precisely detected all those transactions which experienced transactional atomicity violations and identified all non-serializable traces. The overall results also show that &lt;monospace&gt;RegionTrack&lt;/monospace&gt; incurred 1.10x and 1.08x lower memory and runtime overheads than &lt;monospace&gt;Velodrome&lt;/monospace&gt; and 2.10x and 1.21x lower than &lt;monospace&gt;Aerodrome&lt;/monospace&gt;, respectively. Moreover, it incurred 2.89x lower memory overhead than &lt;monospace&gt;DoubleChecker&lt;/monospace&gt;. On average, &lt;monospace&gt;Velodrome&lt;/monospace&gt; detected about 55% fewer violations than &lt;monospace&gt;RegionTrack&lt;/monospace&gt;, which in turn reported about 3%–70% fewer violations than &lt;monospace&gt;DoubleChecker&lt;/monospace&gt;.
GIS-based Flood Susceptibility Mapping Using Overlay Method in Central Sulawesi

Central Sulawesi is the largest province on the Sulawesi island with a dominant sloping topographic condition and has a variety of soil types, flow density, land use and rainfall that makes this region vulnerable to flooding. Flooding is a hydrometeorological disaster that will adversely affect aspects of human life such as social and economic activities in an area because it can cause environmental damage, casualties and disrupt economic activity. Because of its enormous impact, the purpose of this research study is to find out areas in the province of Central Sulawesi that are suscept to flooding. The method used in this research study is scoring the classification of flood hazard parameters such as slope level, elevation, soil type, rainfall, land use and flow density which are then overlay using ArcGis 10.2.2. The results obtained that the province of Central Sulawesi has three categories of areas suscept to flooding with a low category 6630.3 km2, moderate 46081.9 km2 and high category 7104.7 km2. Based on the results and discussion, it can be concluded that Central Sulawesi province has a dominant level of vulnerability which is moderate.
Usability Evaluation using Eye-Tracking on E-Commerce and Education Domains

The rapid consumption of content on the web and the creation of online businesses and educational platforms require improved usability for the user. It also calls for concerted effort to understand users' interactions with these digital products and services. This study was carried out to investigate the search and scan patterns employed by users interacting with the selected web interface from the e-commerce and education domains and also to establish the possibility of a localized interface design patterns in Nigeria. Usability evaluation was conducted using OGAMA software as an eye-tracking tool, to record as well as to analyze eye and mouse tracking data from slideshow eye-tracking experiments concurrently. During the evaluation period, fixation count, fixation length and saccade length of each website were taken into the consideration. Findings revealed that educational based site delivers users’ need than that of e-commerce due to less level of distraction as a result of fewer images and pictures. The study recommends that interface designs should be void of the display of excessive ad contents and include proper formatting of navigation items to contribute towards an enhanced user experience.&#x0D;
IMPROVING LIFELONG LEARNING: CLUSTER AND ECOSYSTEM APPROACHES

The qualitative changes of the modern economy modify the demands of the labor market that necessitates the qualitative transformation of the infrastructural basis of the continuous education system (lifelong learning). The study aims to propose the improvement of the system of lifelong learning based on the study of the education system environment and the development of the corresponding infrastructure, based on the cluster and ecosystem approaches. The methodology of the study includes comparative analysis, systematic approach and analysis of foreign and Russian scientific publications on the current state of the problem of lifelong learning, as well as the legislative and regulatory acts of the Russian Federation, including the national program “Digital Economy of the Russian Federation”, the National projects “Education” and “Science” that contribute to the development of the system of continues education. The results of the study show key features of the development of the infrastructural basis of the continuous education system (lifelong learning) in Russia, including clustering of the scientific, educational and research environment, that requires implementation of the ecosystem approach. These features ensure diversity, maximum productivity and resources circulation of the participants within clusters, as well as adaptability and scalability of the lifelong learning system. This paper presents the trends of structural changes in demand for lifelong learning, caused by the establishment and development of new markets related to the implementation of the National Technology Initiative and introduction of digital technologies in all sectors of the economy; considers the impact of ongoing National projects on the establishment and development of the infrastructure for the system of continuous education (lifelong learning) in Russia; presents the impact of lifelong learning on the quality of life of an individual; shows the influence of the systemic professional development of teachers and lecturers at various levels of the education system on the efficiency of the continuous education of an individual, and substantiates the introduction of cluster and ecosystem approaches to implementation of the concept of lifelong learning.
Hybrid load balance based on genetic algorithm in cloud environment

Load balancing is an efficient mechanism to distribute loads over cloud resources in a way that maximizes resource utilization and minimizes response time. Metaheuristic techniques are powerful techniques for solving the load balancing problems. However, these techniques suffer from efficiency degradation in large scale problems. This paper proposes three main contributions to solve this load balancing problem. First, it proposes a heterogeneous initialized load balancing (HILB) algorithm to perform a good task scheduling process that improves the makespan in the case of homogeneous or heterogeneous resources and provides a direction to reach optimal load deviation. Second, it proposes a hybrid load balance based on genetic algorithm (HLBGA) as a combination of HILB and genetic algorithm (GA). Third, a newly formulated fitness function that minimizes the load deviation is used for GA. The simulation of the proposed algorithm is implemented in the cases of homogeneous and heterogeneous resources in cloud resources. The simulation results show that the proposed hybrid algorithm outperforms other competitor algorithms in terms of makespan, resource utilization, and load deviation.
The Role of Organizational Environment in Enhancing Managerial Empowerment in Al-Aqsa Network for Media and Art Production

This study aims at identifying the role of organizational environment? ?in enhancing the ? managerial ?empowerment of employees at Al-Aqsa Network for Media and Art ?Production. To achieve the objectives of the present study, the researcher used the ?analytical descriptive approach, and a survey was used as the main tool for collecting ?the necessary data. The study sample consisted of (67) employees holding supervisory ?positions who were selected in a complete census method for all employees in ?supervisory positions at Al-Aqsa network for Media and Art production. 67 ?questionnaires were distributed, while the number of recovered questionnaires was ??(61) valid for the purposes of statistical analysis at a response rate of (91%). The ?results showed that there is a statistically significant role at the level of (??0.05) ?between the organizational environment in the following areas (organizational culture, ?organizational structure and organizational resources) and the enhancing the ?administrative empowerment. The organizational structure field was the strongest area ?in association with the organizational environment. In addition, the study concluded ?that the dimensions of organizational environment have a statistically positive and ?significant effect at the level of (??0.05) in enhancing the administrative ?empowerment. ?
Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases

Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i.e. a set of triples.Two main challenges still face the current crop of encoder-decoder-based methods, especially on small subgraphs: (1) low diversity and poor fluency due to the limited information contained in the subgraphs, and (2) semantic drift due to the decoder's oblivion of the semantics of the answer entity.We propose an innovative knowledgeenriched, type-constrained and grammar-guided KBQG model, named KTG, to addresses the above challenges.In our model, the encoder is equipped with auxiliary information from the KB, and the decoder is constrained with word types during QG.Specifically, entity domain and description, as well as relation hierarchy information are considered to construct question contexts, while a conditional copy mechanism is incorporated to modulate question semantics according to current word types.Besides, a novel reward function featuring grammatical similarity is designed to improve both generative richness and syntactic correctness via reinforcement learning.Extensive experiments show that our proposed model outperforms existing methods by a significant margin on two widely-used benchmark datasets SimpleQuestion and PathQuestion.
A double design-science perspective of entrepreneurship – the example of smart contracts in the insurance market

Purpose Following the call for strengthening the third pillar of knowledge in entrepreneurship as well as work-applied management contexts constituted by pragmatic design principles, we present a case study on an insurtech for insurance firms specialized in smart contract insurance solutions such as flight delay or ski resort insurance. Design/methodology/approach Design science. Findings This not only serves as a pointer for how insurances may master their digital transformation while remaining competitive. But moreover, on the meta level, we find that the adoption of entrepreneurial design principles by the students, whose experiential project represents our case study, does not necessarily require continuous support or foundational knowledge to be delivered beforehand. However, for a deeper or more holistic assessment of the case sketched in their project, it makes sense to introduce them to newer developments such as the simple, practical framework of the Entrepreneur's Question Index. Originality/value Innovative teaching method on innovative topics.
Crowding and attention in a framework of neural network model

In this article, I present a framework that would accommodate the classic ideas of visual information processing together with more recent computational approaches. I used the current knowledge about visual crowding, capacity limitations, attention, and saliency to place these phenomena within a standard neural network model. I suggest some revisions to traditional mechanisms of attention and feature integration that are required to fit better into this framework. The results allow us to explain some apparent theoretical controversies in vision research, suggesting a rationale for the limited spatial extent of crowding, a role of saliency in crowding experiments, and several amendments to the feature integration theory. The scheme can be elaborated or modified by future research.
Development of Pedagogical Creativity of Future Teachers of Primary School By Means of Innovative Education Technologies

In the higher education system, the development of students' creative abilities is one of the most pressing problems. The problem of pedagogical creativity of future teachers of primary school is examined in the article. The pedagogical creativity of the elementary school teacher as an integral personal and professional quality, which consists in the ability to pedagogical creativity, original solution of pedagogical problems, active use and improvement of innovative teaching technologies is characterized. The article identifies the features of pedagogical creativity and characterizes the specific pedagogical creativity of elementary school teachers. The experience of interconnecting pedagogical creativity and innovative processes in education has allowed us to determine the basis of the benefits of innovative learning for the development of students' personal and professional qualities. Therefore, in the opinion of the author, the development of pedagogical creativity will be effective provided that innovative educational technologies are implemented into the educational process. Mathematical data processing methods and Statistica software package were used to analyze the results obtained and to objectively consider the dynamics of changes in active, cognitive, motivational, emotional and personal components. The conducted research showed the effectiveness of the use of innovative teaching technologies for the development of all components of pedagogical creativity of future primary school teachers. The author came to the conclusion that the following innovative technologies have the greatest didactic potential for the development of pedagogical creativity: situational learning technology; game technology; project technology; problem-based learning technology; information and communication technology; inventive problem solving technology, portfolio.
Automated Clustering of High-dimensional Data with a Feature Weighted Mean Shift Algorithm

Mean shift is a simple interactive procedure that gradually shifts data points towards the mode which denotes the highest density of data points in the region. Mean shift algorithms have been effectively used for data denoising, mode seeking, and finding the number of clusters in a dataset in an automated fashion. However, the merits of mean shift quickly fade away as the data dimensions increase and only a handful of features contain useful information about the cluster structure of the data. We propose a simple yet elegant feature-weighted variant of mean shift to efficiently learn the feature importance and thus, extending the merits of mean shift to high-dimensional data. The resulting algorithm not only outperforms the conventional mean shift clustering procedure but also preserves its computational simplicity. In addition, the proposed method comes with rigorous theoretical convergence guarantees and a convergence rate of at least a cubic order. The efficacy of our proposal is thoroughly assessed through experimental comparison against baseline and state-of-the-art clustering methods on synthetic as well as real-world datasets.
Semantic Deep Learning to Translate Dynamic Sign Language

Dynamic Sign Language Recognition aims to recognize hand gestures of any person. Dynamic Sign Language Recognition systems have challenges in recognizing the semantic of hand gestures. These challenges come from the personal differences in hand signs from one person to another. Real-life video gesture frames couldn’t be treated as frame-level as a static sign. This research proposes a semantic translation system for dynamic hand gestures using deep learning and ontology. We used the proposed MSLO (Multi Sign Language Ontology) in the semantic translation step. Also, any user can retrain the system to be a personal one. We used Three-dimensional Convolutional Neural Networks followed by Convolutional long short-term memory to improve the recognition accuracy in Dynamic sign language recognition. We applied the proposed system on three dynamic gesture datasets from color videos. The recognition accuracy average was 97.4%. We did all the training and testing processes on the Graphics Processing Unit with the support of Google Colab. Using "Google Colab" in the training process decreases the average run time by about 87.9%. In addition to adding semantic in dynamic sign language translation, the proposed system achieves good results compared to some dynamic sign language recognition systems.
Multiple Sensor Linear Multi-Target Integrated Probabilistic Data Association for Ultra-Wide Band Radar

Ultra-Wide Band (UWB) radar has a number of advantages of resolving multipath, exceptional spatial resolution, and ranging performance. However, several difficulties are confronted for multiple target tracking using UWB radars such as clutter signals which contaminate target signals, and unidentified number and behavior of the targets. Hence, this paper presents to develop a multiple moving target tracking algorithm, consisting of preprocessing and multiple target tracking steps. In the preprocessing step, static clutter reduction and constant false alarm rate (CFAR) detection extract the target candidate range measurements from each UWB radar. Then, two multiple target tracking (MTT) steps are developed: range- based MTT and position-based MTT. The range-based MTT is mainly based on existing linear multi-target integrated probabilistic data association (LM-IPDA) from each UWB radar measurement. Then the outputs of each LM-IPDA are gathered in the positioning center to estimate the position of multiple targets. On the other hands, the position-based MTT is based on multiple sensor LM-IPDA (msLM-IPDA) as an accurate target tracking method for various uncertainties by improving the probabilistic model of LM-IPDA. The tracking performance of two MTT methods is investigated with both numerical simulation and experiments.
Unsupervised Fact Checking by Counter-Weighted Positive and Negative Evidential Paths in A Knowledge Graph

Misinformation spreads across media, community, and knowledge graphs in the Web by not only human agents but also information extraction algorithms that extract factual statements from unstructured textual data to populate the existing knowledge graphs.Traditional fact checking by experts or crowds is increasingly difficult to keep pace with the volume of newly created misinformation in the Web.Therefore, it is important and necessary to enhance the computational ability to determine whether a given factual statement is truthful or not.We view this problem as a truth scoring task in a knowledge graph.We present a novel rule-based approach that finds positive and negative evidential paths in a knowledge graph for a given factual statement, and calculates a truth score for the given statement by unsupervised ensemble of the found positive and negative evidential paths.For example, we can determine the factual statement "United States is the birth place of Barack Obama" as truthful if there is the positive evidential path (Barack Obama, birthPlace, Hawaii) ∧ (Hawaii, country, United States) in a knowledge graph.For another example, we can determine the factual statement "Canada is the nationality of Barack Obama" as untruthful if there is the negative evidential path (Barack Obama, nationality, United States) ∧ (United States, ≠ , Canada) in a knowledge graph.For evaluating on a real-world situation, we constructed an evaluation dataset by labeling truth or untruth label on factual statements that were extracted from Wikipedia texts by using the state-of-the-art BERT-based information extraction system.Our evaluation results show that our approach outperforms the state-of-the-art unsupervised approaches significantly by up to 0.12 AUC-ROC and even outperforms the supervised approach by up to 0.05 AUC-ROC not only in our dataset but also in the two different standard datasets.