Reviewerly/exHarmony huggingface dataset:
An Investigation the Impact of Knowledge Sharing on Improving Innovation in Companies( Case Study: PERGAS International Consortium)

Creating the success factors of an organization such as organizational creativity, service quality depends on accessibility and efficient use of superior knowledge and better use of knowledge sharing; The purpose of this study is to study the effect of knowledge sharing on the improvement of innovation in companies (Case study: PERGAS International Consortium) which is applied in terms of purpose and is a descriptive survey research. The statistical population of this research is the managers and staff of the PERGAS International Consortium, which is about 635 people based in Kish; Taking into account the error rate of 0.5, the probability of success of 0.5 and the target population of 635 people, the number of samples required for this study was determined to be about 240 tons. That the same amount has been analyzed; The stratified random sampling method was proportional; Data analysis for the hypotheses was linear regression analysis; The results of data analysis showed that knowledge sharing and its types (tacit and explicit knowledge sharing) had a positive and significant effect on innovation and its elements (speed and quality).
Study of Nonequilibrium Structures by the Method of Multifractal Parametrization

Purpose: Investigation of the scale factor on the quantitative characteristics of the microstructure to quantify the structural organization of the material and determine quantitative indicators of microstructures, such as heterogeneity and degree of disorder.Design/methodology/approach: The object of the investigation was microstructures of steel 45. Digital images of the microstructures were obtained using a metallographic microscope imaging processing that included sharpness adjustment, removing a contrast mask, highlighting borders and calibrating images.Findings: Increasing magnification of microstructures images leads to a decrease in the surface area. The smaller objects are revealed, clearly visible the local curvature of the borders and the heterogeneity of the structural organization of the material are more clearly identified. The best information content of a multifractal spectrum is achieved if the area of microstructural objects is used as a measure for its calculation. The larger Δq, when q >> 1, the more ordered is the structure, and the periodic component is more manifested in it. The value of К∞ is finite. It is a measure of disorder in the structural organization of the material, i.e. the larger К∞, the more disorder.Originality/value: The heterogeneity, orderliness, and periodicity of the microstructure of materials in a nonequilibrium state have long been analyzed only at a qualitative level. The use of fractal and multifractal formalism in the description of microstructures opens up wide opportunities for quantitative assessment of the structural organization of the material, clarifies and reveals new aspects of the known mechanisms of structural transformations.
On the Asymptotic Equivalence Between the Radon and the Hough Transforms of Digital Images

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 8 June 2020Accepted: 28 December 2020Published online: 03 May 2021KeywordsRadon transform, Hough transform, image processing, image reconstruction, x-ray tomographyAMS Subject Headings44A12, 94A08Publication DataISSN (online): 1936-4954Publisher: Society for Industrial and Applied MathematicsCODEN: sjisbi
Cooperative Scheduling Strategy of Container Resources Based on Improved Adaptive Differential Evolution Algorithm

The resource scheduling of the container cloud system can be handled as a path planning problem. In response to the need for container resource scheduling that comprehensively considers the interests of users and service providers, this paper combines user quality of service (QoS) models and resource load balancing to study multi-objective container resource scheduling solutions, and proposes an improved Dynamic Adaptive Differential Evolution Algorithm (DADE), which adds adaptive changes to the mutation factor and crossover factor, and optimizes the mutation strategy and selection strategy, so that the algorithm has a broad solution space in the early stage; and a small-scale local search is carried out in the later stage, the resource scheduling strategy based on this algorithm is realized. Perform simulation experiments on the proposed algorithm and scheduling strategy. Experimental results show that the DADE algorithm is superior to mainstream heuristic algorithms in the evaluation of average function evaluation times, solution accuracy, convergence speed and other indicators. The resource scheduling effect has obvious advantages in task completion time, completion cost and resource load balancing.
Unsupervised Learning of Visual and Semantic Features for Video Summarization

The high redundancy among keyframes is a critical issue for the existing summarizing methods in dealing with user-created videos. To address the critical issue, we present an unsupervised learning method, Spatial Attention Model guided Bi-directional Long Short-term Memory network (Bi-LSTM), on the combination of visual and semantic features. As for the visual feature, we design a Salient-Area- Size-based spatial attention model on the observation that humans tend to focus on sizable and moving objects in videos. Moreover, the Bi-LSTM network is leveraged to exploit the semantic feature. Afterward, the Soft Selected Probability generated from the spatial attention and semantic feature is fused to obtain the final probability for keyframe selection. The reinforcement learning framework, trained by the Deep Deterministic Policy Gradient algorithm, is adopted to do unsupervised training. Extensive experiments on the SumMe and TVSum datasets demonstrate that our method outperforms the state-of-the-art methods in terms of F-score.
A Language-Independent Speech Data Collection and Preprocessing Technique

Virtual assistant or human-like robot has become the most attractive technology nowadays., where we badly need communication. And verbal communication is the most comfortable one. Here, it comes with the necessity of voice recognition system. Recently, researchers are really focusing on developing voice recognition systems with the help of machine learning and deep learning algorithms. And for that, researchers need a large amount of audio data. In this research, we will discuss from collecting audio files from different speakers to represent them into numeric formation so that it becomes possible to apply machine learning or deep learning algorithm on it. Its need some requirement as well as specific formation, instruction to the speaker, organized the speeches and making the scripts are the most important things. And all the description in this work has been described with experience of our own work with speech data. The dataset of this work has exactly 10,992 speech data of 1000 unique words of more than 50 speakers.
Research on a new optimization algorithm simulating multi- states of matter inspired by finite element analysis approach

A new optimization algorithm is proposed, since a huge problem that many algorithms faced was not being able to effectively balance the global and local search ability. Matter exists in three states: solid, liquid, and gas, which presents different motion characteristics. Inspired by multi- states of matter, individuals of optimization algorithm have different motion characteristics of matter, which could present different search ability. The Finite Element Analysis (FEA) approach can simulate multi- states of matter, which can be adopted to effectively balance the global search ability and local search ability in new optimization algorithm. The new algorithm is creative application of Finite Element Analysis at optimization algorithm field. Artificial Physics Optimization (APO) and Gravitational Search Algorithm (GSA) belongs to the algorithm types defined by force and mass. According to FEA approach, node displacement caused by force and stiffness could be equivalent to motion caused by force and mass of APO and GSA. In the new algorithm framework, stiffness replaces mass of APO and GSA algorithm. This paper performs research on two different algorithms based on APO and GSA respectively. The individuals of new optimization algorithm are divided into solid state, liquid state, and gas state. The effects of main parameters on the performance were studied through experiments of 6 static test functions. The performance is compared with PSO, basic APO, or GSA for four complex models which made up of solid individual, liquid individual, and gas individual in iterative process. The reasonable complex model can be confirmed experimentally. Based on the reasonable complex model, the article conducted complete experiments against Enhancing artificial bee colony algorithm with multi-elite guidance (MGABC), Artificial bee colony algorithm with an adaptive greedy position update strategy (AABC), Multi-strategy ensemble artificial bee colony (MEABC), Self-adaptive heterogeneous PSO (fk-PSO), and APO with 28 CEC2013 test problem. Experimental results show that the proposed method achieves a good performance in comparison to its counterparts as a consequence of its better exploration– exploitation balance. The algorithm supplies a new method to improve physics optimization algorithm.
A Promising Hardware Accelerator with PAST Adder

Recent neural network research has demonstrated a significant benefit in machine learning compared to conventional algorithms based on handcrafted models and features. In regions such as video, speech and image recognition, the neural network is now widely adopted. But the high complexity of neural network inference in computation and storage poses great differences on its application. These networks are computer-intensive algorithms that currently require the execution of dedicated hardware. In this case, we point out the difficulty of Adders (MOAs) and their high-resource utilization in a CNN implementation of FPGA .to address these challenge a parallel self-time adder is implemented which mainly aims at minimizing the amount of transistors and estimating different factors for PASTA, i.e. field, power, delay.
ONLINE ACTIVE ENSEMBLE LEARNING FOR ROBOT COLLISION DETECTION IN DYNAMIC ENVIRONMENTS

In order to improve the accuracy and precision of online learning-based collision detection methods, an online active ensemble learning for robot collision detection (OAELRCD) is proposed in this paper. The OAELRCD consists of two key components: (1) an ensemble learning method to combine several base classifiers in order to improve the accuracy and precision of collision detection, (2) an active learning algorithm to reduce the number of training samples in order to realize online training and learning when the environment changes. We evaluate the proposed OAELRCD on one robot arm in dynamic environments with moving workspace obstacles, showing that the proposed OAELRCD outperforms state-of-the-art online learning-based method and geometric collision checkers. Compared to the state-of-the-art online learning-based method for robot collision detection in dynamic environments, the proposed OAELRCD provides noticeable improvements in TPR, AUC, Accuracy and TNR. Compared to state-of-the-art geometric collision checkers, with the proposed OAELRCD, collision checks are faster.
Corrections to “Binary Masks of Concentric Rings as a Method to Approximate Identification of Diatoms Using Images”

In the above article <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , although references <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> – <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref4" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref5" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"/> <xref ref-type="bibr" rid="ref6" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[6]</xref> to equations (1)–(11) presented in Section II-B “Concentric Ring Binary Masks” under Section II “Methods” are adequate to understand the framework related to image processing; the methodology of concentric ring binary masks was developed in <xref ref-type="bibr" rid="ref7" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[7]</xref> and <xref ref-type="bibr" rid="ref8" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[8]</xref> , the following changes should be considered in <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> :
Digital education as a method of reducing pedagogical goals and objectives

The article focuses on a comparative characteristic of predigital and digital education. The study methodology deploys the differentiation of philosophical and scientific paradigms. Pre-digital education is associated with the classical paradigm. Accordingly, the parameters of pre-digital education include the notions of finite and true knowledge, a singular type of learning algorithms, a balanced learning process, and the deterministic influence of educational programs. The indicators of digital education are identified within the framework of the non-classical and post-non-classical paradigms. The non-classical form of digital education is characterized by the presence of alternative educational programs, the polysemantic object of study, infinite knowledge, and pragmatic orientation. This form of education accentuates the subjective and personal context, the opportunities for plural explanations, and the technologies for studying the singular, the local, and the fragmental. Digital education contributes to modeling infinite discourses and intersubjective practices. All educational innovations within digital education appear as a way of ontological and gnoseological construction. Digital education enhances the role of interdisciplinary education. The competencies of “cross-cutting” and extensive understanding of a complex subject appear. Students should be focused on the results of teaching in various disciplines. A consequence of the expanding learning opportunities is the necessary reduction of pedagogical goals and objectives.
Support System for creating SQL Queries using Natural Language

Nowadays, all the people have their own personal devices that are connected to the web. Each one of these people try to get the knowledge that they need via the internet. Much of the knowledge is within some sort of database. A person that needs to retrieve the data via a database and has very little or no knowledge about the database languages faces a problematic scenario. Therefore, there is requirement for a means and a system that permits the average person to access natural language within a table. This paper looks to make a system like the one mentioned using NLP by giving natural language question as input and outputting SQL query, to access the related info via a Vehicle database table with no difficulty. The various steps that are there during these processes are lemmatization, tokenization, parts of speech tagging, chunking and mapping the values. The dataset being used to implement the proposed system has a group of structured natural questions on car details. This research would be an overall view of the usage of Natural Language Processing (NLP) and use of regular expressions to map the query in English to SQL.
Structure-preserving extremely low light image enhancement with fractional order differential mask guidance

Low visibility and high-level noise are two challenges for low-light image enhancement. In this paper, by introducing fractional order differential, we propose an end-to-end conditional generative adversarial network(GAN) to solve those two problems. For the problem of low visibility, we set up a global discriminator to improve the overall reconstruction quality and restore brightness information. For the high-level noise problem, we introduce fractional order differentiation into both the generator and the discriminator. Compared with conventional end-to-end methods, fractional order can better distinguish noise and high-frequency details, thereby achieving superior noise reduction effects while maintaining details. Finally, experimental results show that the proposed model obtains superior visual effects in low-light image enhancement. By introducing fractional order differential, we anticipate that our framework will enable high quality and detailed image recovery not only in the field of low-light enhancement but also in other fields that require details.
Mean Decision Rules Method with Smart Sampling for Fast Large-Scale Binary SVM Classification

This paper relies on the Mean Decision Rule (MDR) method for solving large-scale binary SVM problems. It consists in taking small random samples of the full dataset and separate training for each of them with consecutive averaging the respective individual decision rules to obtain a final one. This paper proposes two new approaches to improve it. The first proposed approach is a new sampling technique that exploits SVM and MDR properties to fast form so-called smart samples by selecting only the objects, that are candidates to be the support ones. The proposed technique essentially increases MDR convergence and allows to reach the highest quality in less time. In the case of kernel-based MDR (KMDR) the proposed sampling technique allows additionally to reduce the number of support objects in the final decision rule and, as a result, to decrease the recognition time. The second proposed approach is a new data strategy to accelerate random access to large datasets stored in the traditional libsvm format. The proposed strategy allows to quickly extract random subsets of objects from a file and load them into RAM and is also suitable for any sampling-based methods, including stochastic gradient methods. Joint use of the proposed approaches with (K)MDR allows to obtain the best (or near the best) decision of large-scale binary SVM problems faster, compared to the existing SVM solvers.
The Impact of Strategic Management Accounting Techniques on Achieving Competitive Advantage in the Jordanian Public Industrial Companies

This study aimed to figure out the impact of the Strategic Management Accounting Techniques (SMATs) on achieving the Competitive Advantage (CA) in the Jordanian Public Industrial Companies. To achieve the objectives of the study, the descriptive analytical approach and social surveys were used and questionnaires were prepared and distributed to all the financial managers in the Jordanian public industrial companies. They were sent to (80) managers where (75) of them were subject to study by 93.8% after being chosen through the simple, random method. The study concluded many results, most important of which are: The level of the SMATs in the Jordanian public companies was high. The level of the competitive advantage dimensions in the Jordanian public industrial companies was high. There was an impact for the SMATs on the achievement of the competitive advantage, where it explains (66.1%) of the variance in achieving the competitive advantage. The study provided many recommendations, most important of which was the need to provide a modern technological environment for linking the modern SMATs with the different accounting systems in the Jordanian industrial companies, and conducting studies and research in the advanced methods and modern systems of the strategic management accounting and trying to link them to the accounting systems in general in order to accommodate the environmental variables.
Incorporating multi-level positive feedback to session-based nearest-neighbor

User feedback such as clicks, likes and follows is widely considered positive signal, as users infer their preference through these signals. However, a click feedback that is intended for a purchase indicates that the user's interest level is greater than that of a user who clicks just to view a page. Thus, compared with the unary positive feedback, some other types of user feedback are considered more reliable because of their ability to indicate the different preference levels of users. Some recommender systems focus on using such multiple-level positive feedback to develop model-based methods. In this paper, we take them into account to implement a nearest-neighbor method that demonstrates competitive performance for session-based recommendation. Herein, we present experimental evaluations on different domain datasets to demonstrate the benefits of our proposed method. Our results show that incorporating such multiple-level preferences of users with the neighbor-based model leads to an improvement in performance for session-based recommendation.
Expression Recognition of Dual Channels Model System Based on Mini_Xception_SE

As an important attribute of human face, expression is widely used in human-computer interaction, artificial intelligence and other fields. As an important biometric recognition technology, expression recognition has become a hot research topic in the field of computer vision. With the continuous development of deep learning, appropriate neural network and a large number of preprocessed data can train a high-performance model, so as to improve the accuracy and convenience of expression recognition. Because the local details of the face determine the expression information, this paper not only uses the face detection technology in the image preprocessing, but also uses Difference of Gaussian pyramid to obtain the detailed information of the facial expression image to enhance the image detail features. Because of the large amount of parameters in Xception model, this paper proposes a dual channels model based on mini_Xception_SE for facial expression classification. The experimental results on fer2013 and CK+ dataset show that the feature information of facial expressions in different facial regions can be effectively extracted by applying detail enhanced image to the dual channels model based on Mini Xception SE, so as to improve the accuracy of facial expression recognition.
Image Super-Resolution Using Deformable Convolutional Network

Social media network is inseparable from image recognition, and image super-resolution (SR) reconstruction plays an important role in image recognition. The changes of scale and geometry are rarely considered in the image super-resolution reconstruction based on deep learning over the years, we introduce a super-resolution reconstruction network based on deformable convolutional network. We replace the ordinary convolution with the deformable convolution to pretend the geometric deformation and extract abundant local features. The image super-resolution reconstruction is usually based on the conventional convolutional neural network (CNN). Most CNN-based SR models do not utilize the features of the original low resolution (LR) image as much as possible, resulting in lower performance. After introducing the idea of deformable convolution, though the complexity is increased, the recognition accuracy is obviously raised.