Reviewerly/exHarmony huggingface dataset:
THE EFFECT OF UNIVERSITY AGE ON THE RELATIONSHIP BETWEEN THE SIZE AND FINAL ACCOUNTS' RESULTS IN PUBLIC UNIVERSITIES IN JORDAN

Universities are considered a source of community development due to their important role in developing scientific and practical knowledge, promoting self-development, and equipping the labor market with various skills in different disciplines. To achieve their goals, universities rely on revenues and funding sources to cover their expenses and ensure their sustainability. Therefore, this study identified the impact of university age on the relationship between the size of the university and the results of the final accounts at Jordanian public universities, using a descriptive analytical approach. Financial and non-financial data were collected from public universities for the period from 2009 to 2019. One of the most important findings of the study is the existence of a relationship between the size of the university and the results of the final accounts, as well as the impact of university age on this relationship. The study recommends that policymakers in universities pay attention to maintaining a balance between the age and size of the university in terms of financial and non-financial data, as this has a significant impact on the outcomes of the final accounts. Keywords: Size, Age, Final Accounts’ Result, Public Universities, Jordan DOI： https://doi.org/10.35741/issn.0258-2724.58.2.7
The C-ORAL-BRASIL corpora as a source of spoken language lexical information

In this paper we report a first round of lexicon-based analysis of the C-ORAL-BRASIL spontaneous speech corpora. The C-ORAL-BRASIL corpora currently comprise a corpus of informal spontaneous speech (C-ORAL-BRASIL I) along with media, telephonic and formal speech corpora (C-ORAL-BRASIL II) portraying Brazilian Portuguese (BP). BP lexical corpora-based studies have been carried focusing mostly on written language. Spoken language, given the effort it takes to be properly documented, has received scarce attention. Through our study, we intend to start addressing this gap by providing an overview of results obtained through a series of methodologies that are geared towards profiling the lexical repertoire and variation found in spoken BP. This study is part of a larger project that intends to provide a detailed mapping of lexical phenomena and a basic vocabulary of spoken BP. The corpora .txt files were initially treated through Python coding so that all levels of linguistic and metadata annotation could be removed, but kept accessible for different types of analyses in which text genre, informational and speaker metadata are relevant. Individual word forms were lemmatized and PoS annotated. The quantitative measures presented in this paper were obtained through Python coding in addition to visualization tools available through SketchEngine (KILGARRIFF ET AL., 2014) and Voyant Tools (SINCLAIR &amp; ROCKWELL 2016).
Bert Adapter and Contrastive Learning for Continual Classification of Aspect Sentiment Task Sequences

Task incremental learning, a setting of Continual learning, isan approach to exploit the knowledge from previous tasks for currentlynew task. Task incremental learning aims to solve two big challengesof continual learning: catastrophic forgetting and knowledge transfer orsharing between previous tasks and current task. This paper improveTask incremental learning by (1) transferring the knowledge (not thetraining data) learned from previous tasks to a new task (contrast ofmulti-task learning); (2) to maintain or even improve performance oflearned models from previous tasks with avoid forgetting; (3) to developa continual learning model based on result from (1) and (2) to applyfor aspect sentiment classification. Specifically, we combine two loss baseon contrastive learning modules from Contrastive Knowledge Sharing(CKS) for encouraging knowledge sharing between old and current tasksand improve the performance of the current task by Contrastive Super-vised learning (CSC) module. The experimental results show that ourmethod could get rid of previous learned tasks catastrophic forgettingphenomenon and outperform the previous study for aspect sentimentclassification.
Exploring the Feasibility of a Decentralized Autonomous Organization (DAO) as a New Model for Economic Governance in the Blockchain Era

Decentralized autonomous organizations (DAOs) are a new type of organization that are run using blockchain technology and are designed to operate without the need for traditional management structures. In this research project, the authors explored the feasibility of using DAOs as a new model for economic governance in the blockchain era. The authors examined the key features and functions of DAOs, assessed the potential benefits and challenges of using these organizations as a model for economic governance, and considered the legal and regulatory implications of DAOs. The authors also examined case studies of existing DAOs and analyzed their successes and failures in order to identify best practices for implementing and operating DAOs effectively. The findings suggest that DAOs have the potential to disrupt traditional models of economic governance by providing a new way for organizations to operate and make decisions in a transparent and decentralized manner. However, there are also challenges and limitations to using DAOs as a model for economic governance.
Addition and Subtraction of the Numbers up to 1000 without Crossing Using Scratch

The article is devoted to the application of information technologies in the creation of educational games for the acquisition of new knowledge in primary school. Specialized software Scratch for introducing computer culture to children is reviewed. The basis for the developed animated module is the mathematics textbook for the third grade, authored by Prof. Dr. Vladimira Angelova and Zhana Koleva, “Prosveta plus” publishing house, Sofia, 2017. An animated Scratch module is presented to acquire and practice competence on the topic of Addition and subtraction of numbers up to 1000 without crossing. The game can be used as part of a lesson for new knowledge, for exercise, as well as for independent work.
Blockchain Technology In Healthcare Industry

Since blockchain was originated through Bitcoin, study has been conducted to expand its use cases beyond finance. One sector where blockchain is anticipated to have a big influence is healthcare. Blockchain technology has several uses in the healthcare industry, including the evolution of smart health applications, monitoring tools, the distribution and retention of electronic health records, drug trial data, and the storage of insurance details. Despite the paucity of research on blockchain and healthcare, it is clear that this technology is poised to revolutionize the industry. Thanks to its decentralized principles, blockchain can enhance patient information security and accessibility, which has the potential to undermine the current healthcare structure and create a new one where patients are in charge of their own care. By using blockchain as a model, we examine both the recent and current changes in the healthcare industry in this article. Along with the difficulties encountered and potential outcomes, we also talk about blockchain’s uses.
A Route Planning for Oil Sample Transportation Based on Improved A Star Algorithm

Aiming at the problems of sharp path turning points, weak search directionality, and excessive number of calculation nodes in the traditional star algorithm when planning paths, the angle constraint, the way guide strategy, and the path search step size of the A star algorithm are improved. Firstly, the angle constraint of the evaluation function is introduced, and the right angle is changed to an obtuse angle, and the path smoothness, path search time and path length are optimized. Secondly, on the basis of deright-angled paths, the introduction strategy of party guidance is introduced to enhance the directionality of path search and further shorten the path search time. Finally, on the basis of the above two improved algorithms, a method of improving the step size is proposed to simplify the number of nodes of the path obtained by improving the star algorithm. The simulation experiment results show that the improved star algorithm can quickly plan a smooth path with short distance and less planning time.
BlockTicket: A framework for electronic tickets based on smart contract

As the use of digital subscription services like electronic tickets (E-ticketing) has grown in the age of e-commerce, so too have instances of copyright and violation. Because it is dependent on the centralized authority administration of authoritative institutions, the traditional E-ticketing system has a significant cost associated with it. Blockchain, which is a distributed system, has the characteristics of decentralization, anonymity, auditability, security, and persistency. These attributes allow it to address the problems that are currently being experienced by the E-ticketing system. In this study, we present a framework for E-ticketing that makes use of blockchain technology. The blockchain-based electronic ticketing model eliminates the involvement of third parties while also lowering the potential of data leaks and improving users' levels of privacy. This is accomplished by separating the credential information of users from the financial transactions. In the meanwhile, a blockchain implementation of the existing E-ticketing architecture has the potential to improve throughput, reduce the amount of redundant work, and boost the efficiency of consensus. An examination of the experimental data shows that the framework has a number of advantages, some of which are a high throughput, flexible scalability, and efficient ticket holding times.
Algorithms for Finding Duplicate Conferences and Conference Groups in Scientometric Systems

The article discusses the methods developed by the author for assessing the proximity of the description of conferences in order to detect duplicates, as well as building groups of conferences. An overview of the existing catalogs of conferences on the Internet is given. Their advantages and disadvantages are analyzed. The necessity of developing methods for more thorough verification of input data about conferences is substantiated. The description of the algorithms developed by the author and their software implementation and testing on big data is given on the example of the scientometric system IAS ISTINA. The developed algorithms make it possible to search for similar conferences by primary descriptions when registering a conference, search for duplicates in the database of the scientometric system, and combine conferences of different years into groups. The described methods can be used in the development of conference catalogs and scientometric systems to improve the quality of initial data verification.
Improving the spatial resolution of digital holograms using one-dimensional subpixel scanning

The article considers the issue of increasing the spatial resolution during the registration of digital holograms. In practice, discretization is carried out by measuring signal samples using sensors with a certain finite aperture. The resolution is determined by the size of the aperture area over which the averaging takes place. The resolution enhancement method is based on the sampling equation for signals obtained by the subpixel shift method using generalized functions. A subpixel shift is carried out using a spatial shift by a value less than the element of resolution. Apertures of various shapes are used, for example, elliptical, diamond-shaped, hexagonal, but rectangular apertures are most commonly used. The discretization equation involves the use of a two-dimensional aperture function. Below is a way to increase the resolution when using a one-dimensional function. This can be done based on the structure of the holographic signal. In this case, you can use a subpixel shift in only one direction. Digital holography is distinguished by the fact that photodetector matrices which have a spatial resolution much lower than photographic media used in traditional holography methods are used to register the signal,. Therefore, digital holography uses optical schemes with small angles between interfering beams. However, certain restrictions are imposed on the shape of the objects under study. If the objects have a shape that is significantly different from a flat one, it is necessary to use traditional schemes. The article presents mathematical modeling of the resolution enhancement method based on the recovery of signals from a real hologram obtained in the usual way. The increase in resolution is achieved using a one-dimensional subpixel shift. The use of a one-dimensional subpixel shift makes it possible to significantly simplify the optical scheme of the holographic setup.
An approach to pseudocoloring of grey scale image using deep learning technique

Abstract Image pseudo colorization is the process of adding RGB colours to grayscale images to make them more appealing. Deep learning technology has made progress in the field of automatic colouring. In general, we divide automatic colouring methods into three groups based on where the colour information comes from: colouring based on what you already know and on reference pictures. The colouring method can meet the needs of most users, but there are some drawbacks. For example, users can’t colour different reference graphs for the different things in a picture. In order to solve this problem by recognising several objects and background regions in a picture and combine the final colouring results, the proposed method uses the deep learning approach that regional mixed colours be used more and the method be mastered by using deep learning. Qualitative results (visual perception) validate the effectiveness of pseudocolorisation which split into foreground colour based on a reference picture and background colour based on prior knowledge. Quantitative results such as Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR), Image Matching Error and Entropy validates the effectiveness of strong edge strength, visually appealing quality and retention of maximum information without disturbing quality of image.
Single shot multibox detector object detection based on attention mechanism and feature fusion

The single shot multibox detector (SSD) is one of the most important algorithms in single-stage target detection, having the characteristics of multiscale detection and rapid detection speed. However, the effective SSD feature layers are independent of one another, which can lead to object detection difficulties. To address this problem, we proposed an improved SSD object detection algorithm. First, the global attention mechanism (GAM)—which can enhance spatial and channel information—was introduced into the multiscale feature layer. The channel attention module of the GAM was improved. Second, a feature fusion module was introduced to strengthen the relationship between feature layers. Finally, the cross stage partial structure was introduced into the feature fusion module, and used to improve the model’s learning ability. For model training and detection based on the PASCAL VOC dataset, the mean average precision and frames per second obtained by the improved SSD algorithm were 84.67% and 18.67, respectively, which could effectively detect difficult targets.
3D Measurement Based on Structured Light Digital Micromirror Projection System

In order to achieve high precision 3d morphology reduction, this paper adopts the projection method with digital grating as the structured light generator, builds an experimental platform for 3D measurement based on digital micromirror, and combines the coded complementary Gray code for phase unwrapping operation in the algorithm of restoring the 3D shape of the object. The experimental results show that clear 3D object shape restoration is achieved in the time domain, and the measurement uncertainty reaches <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm \ 2.45\ \mu\mathrm{m}$</tex> as verified by repeatability experiments.
Adaptive Distributed Fusion Estimation for Multi-sensor Nonlinear System with Bounded Noise

For the state estimation problem of multi-sensor nonlinear systems with unknown parameters and unknown-but-bounded noise, a distributed fusion estimation is proposed. The method firstly converted the nonlinear system to a new linear system by Taylor series expansion. Then an adaptive set-membership filter (ASMF) is designed as the local optimal filter for each sensor system based on set membership estimation. Subsequently, a global optimal filter is obtained by using the union geometry method of the Minkowski sum. Finally, the simulation results verify the feasibility and effectiveness of the proposed adaptive distributed fusion estimation algorithm.
Exploring the Performance of Farasa and CAMeL Taggers for Arabic Dialect Tweets

In Natural Language Processing (NLP), Part Of Speech (POS) tagging is an important step; it is a fundamental requirement for many applications, such as information extraction, machine translation, and grammar checking. Successful POS taggers have been developed for many languages, including Arabic. Currently, the spread of social media has increased the diversity of dialects as people use them in their online communications. Therefore, it has become more difficult for researchers to classify some words that are understood by humans but not computers. In addition, most Arabic POS research focuses on Modern Standard Arabic (MSA), while Dialect Arabic (DA) receives less attention. This paper aims to evaluate the performance of two Arabic taggers when used on dialect Arabic tweets and determine which tagger is the appropriate one, which will accordingly help to improve the existent taggers for dialect Arabic tweets. We used the Farasa and CAMeL taggers, which are commonly used to analyze Arabic texts and are considered the best taggers for Arabic. The results indicate that CAMeL tagger performed better than Farasa tagger, with accuracies of 92% and 83% respectively. In other words, a hybrid POS tagger trained with MSA and DA returns better results than the one trained on MSA.
Deep Hierarchical Semantic Model for Text Matching

In recent years, some effective textual matching models are proposed for solving NLP-related tasks. However, these models have the following issues: they cannot extract semantic information at different levels from the words of text pairs; they cannot integrate the low-level information to fine-tune the high-level information. To address these, this paper proposes a novel deep learning neural network, namely deep hierarchical semantic model (DHSM), for text matching, which consists of multiple semantic processing layers, a pooling layer, and a prediction layer. Specifically, each semantic processing layer consists of three parts: encoding part, interaction part and fusion part; and it can well represent the semantic information and enable the information interaction at different levels through attention mechanism. Moreover, the pooling layer uses pooling method to extract key information of the text pairs, based on which the prediction layer determines the relationship between text pairs.
Incorporating Triple Attention and Multi-scale Pyramid Network for Underwater Image Enhancement

: Clear images are a prerequisite of high-level underwater vision tasks, but images captured underwater are often degraded due to absorption and scattering of light. To solve this issue, traditional methods have shown some success, but often generate unwanted artifacts for knowledge priori dependency. In contrast, learning-based approaches can produce more refined results. Most popular methods are based on an encoder-decoder configuration for simply learning the nonlinear transformation of input and output images, so their ability to capture details is limited. In addition, the significant pixel-level features and multi-scale features are often overlooked. Accordingly, we propose a novel and efficient network that incorporates triple attention and a multi-scale pyramid with an encoder-decoder architecture. Specifically, a triple attention module that captures the channel-pixel-spatial features is used as the transformation of the encoder-decoder module to focus on the fog region; then, a multi-scale pyramid module designed for refining the preliminary defog results are used to improve the restoration visibility. Extensive experiments on the EUVP and UFO-120 datasets corroborate that the proposed method outperforms the state-of-the-art methods in quantitative metrics Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Patch-based Contrast Quality Index (PCQI) and visual quality.
Segmentation of Mammogram Abnormalities Using Ant System based Contour Clustering Algorithm

Breast cancer is the most widespread cancer that affects females all over the world. The Computer-aided Detection Systems (CADs) could assist radiologists’ in locating and classifying the breast tissues into normal and abnormal, however the absolute decisions are still made by the radiologist. In general, CAD system consists of four stages: Pre-processing, segmentation, feature extraction, and classification. This research work focuses on the segmentation step, where the abnormal tissues are segmented from the normal tissues. There are numerous approaches presented in the literature for mammogram segmentation. The major limitation of these methods is that they have to test each and every pixel of the image at least once, which is computationally expensive. This research work focuses on detection of microcalcifications from the digital mammograms using a novel segmentation approach based on novel Ant Clustering approach called Ant System based Contour Clustering (ASCC) that simulates the ants’ foraging behavior. The performance of the ASCC based segmentation algorithm is investigated with the mammogram images received from Mammographic Image Analysis Society (MIAS) database.
On the rationality of generating functions of certain hypersurfaces over finite fields

&lt;abstract&gt;&lt;p&gt;Let $ a, n $ be positive integers and let $ p $ be a prime number. Let $ \mathbb F_q $ be the finite field with $ q = p^a $ elements. Let $ \{a_i\}_{i = 1}^\infty $ be an arbitrary given infinite sequence of elements in $ \mathbb F_q $ and $ a_1\neq 0 $. For each positive integer $ i $, let $ \{d_{i+j, i}\}_{j = 0}^\infty $ be an arbitrary given sequence of positive integers with $ d_{ii} $ coprime to $ q-1 $. For each integer $ n\ge 1 $, let $ N_n $, $ \bar N_n $ and $ \widetilde{N}_n $ denote the number of $ \mathbb F_q $-rational points of the hypersurfaces defined by the following three equations:&lt;/p&gt; &lt;p&gt;&lt;disp-formula&gt; &lt;label/&gt; &lt;tex-math id="FE1"&gt; \begin{document}$ a_1x_1+\cdots+a_nx_n = b, $\end{document} &lt;/tex-math&gt;&lt;/disp-formula&gt;&lt;/p&gt; &lt;p&gt;&lt;disp-formula&gt; &lt;label/&gt; &lt;tex-math id="FE2"&gt; \begin{document}$ x_1^2+\cdots+x_n^2 = b $\end{document} &lt;/tex-math&gt;&lt;/disp-formula&gt;&lt;/p&gt; &lt;p&gt;and&lt;/p&gt; &lt;p&gt;&lt;disp-formula&gt; &lt;label/&gt; &lt;tex-math id="FE3"&gt; \begin{document}$ a_1 x_1^{d_{11}}+a_2 x_1^{d_{21}}x_2^{d_{22}}+ \cdots+a_n x_1^{d_{n1}}x_2^{d_{n2}} \cdots x_n^{d_{nn}} = b, $\end{document} &lt;/tex-math&gt;&lt;/disp-formula&gt;&lt;/p&gt; &lt;p&gt;respectively. In this paper, we show that the generating function $ \sum_{n = 1}^{\infty}N_nt^n $ is a rational function in $ t $. Moreover, we show that if $ p $ is an odd prime, then the generating functions $ \sum_{n = 1}^{\infty}\bar N_nt^n $ and $ \sum_{n = 1}^{\infty}\widetilde{N}_nt^n $ are both rational functions in $ t $. Moreover, we present the explicit rational expressions of $ \sum_{n = 1}^{\infty}N_nt^n $, $ \sum_{n = 1}^{\infty}\bar N_nt^n $ and $ \sum_{n = 1}^{\infty}\widetilde{N}_nt^n $, respectively.&lt;/p&gt;&lt;/abstract&gt;