Reviewerly/exHarmony huggingface dataset:
Study of Argumentative Causal Relationship between Poverty and Population Growth in Sudan within the period (1980–2019) by using Vector Auto Regressive Model (VAR): دراسة جدلية العلاقة السببية بين نمو السكان والفقر في السودان خلال الفترة (1980- 2019) باستخدام نموذج الانحدار الذاتي (VAR)

This study aimed to determine the form of the relationship and impact that the population growth in Sudan could have increased on poverty rate. To achieve this goal, a standard model for the relationship between the two previous variables was formulated and constructed during the period 1980-2019. Descriptive Analytical method was used in theoretical side of the study and tools of econometrics in analysis was used in practical side (VAR). The study assumed existence of direct relationship having a positive impact statistically between the population growth and poverty in Sudan. A standard model of non-significant impact have been obtained, whereby, the estimation results of Vector Auto Regressive showed that, there is no mutual significant impact between the poverty and population growth during term of the study. However, this result support results of mutual causality relationship both the variables. The study has recommended the necessity of controlling the population growth by legal ways in order to addressing the sources of defection which has been presented by the population growth.
Enhancement for License Plate Recognition using Image Super Resolution technique

License Plate Recognition (LPR) is one of the important applications of AI, however due to image blurring and low resolution, the Optical Character Recognition (OCR) models are unable to recognise the text accurately. Therefore, we propose a deblurring and image super resolution model for image recovery using deep learning and convolutional neural networks. Our results show an improvement of the Peak Signal to Noise Ration (PSNR) from 30.182 to 31.696. It is shown that adding a deblurring and image super resolution model can improve the OCR accuracy up to 62% from 12% and the average error rate from 6.1 to 2.6 per image.
The Influence of Balanced Nutrition Extension Through Poster Media on the Improvement of Knowledge and Change Attitude

The purpose of this study was to determine the Effect of Balanced Nutrition Counseling through Poster Media on Knowledge Enhancement and Attitude Change of School Children in MIN Medan Senembah Kec. Tanjung Morawa. This research is an experimental study using design before and after the intervention using an external comparison group. The population was 346 students in grades 3,4 and 5—the research sample of 78 student samples. Data collected are before and after knowledge, before and after attitude. The method uses a questionnaire. Data collection is done by questionnaire form. Data analysis uses Paired Sample T-Test and Independent T-Test tests. The results obtained by the common knowledge before counseling in the experimental group are 62.87, and in the comparison group is 49.45. The average post-counseling knowledge in the experimental group was 76.17, and in the comparison group was 61.12. The average attitude before in the experimental group was 68.31 and in the comparison group 62.97. The average attitude after the experimental group was 73.85, and the comparison group was 68.95. There were significant differences in knowledge and attitudes before and after counseling (p-value = 0.000> 0.05). There were no differences in knowledge and attitudes between the experimental and comparison groups (p-value = 0.490> 0.05).
Adaptation of Classic Readability Metrics to Czech

We have fitted four classic readability metrics to Czech, using InterCorp (a parallel corpus with manual sentence alignment), CzEng 2.0 (a large parallel corpus of crawled web texts), and the optimize.curve fit algorithm from the SciPy library. The adapted metrics are: Flesch Reading Ease, Flesch-Kincaid Grade Level, Coleman-Liau Index, and Automated Readability Index. We describe the details of the procedure and present satisfactory results. Besides, we discuss the sensitivity of these metrics to text paraphrases and correlation of readability scores with empirically observed reading comprehension, as well as the adaptation of Flesch Reading Ease to Czech from Russian.
Geometric and semantic analysis of road image sequences for traffic scene construction

• A novel and simple geometric analysis network is proposed using heatmaps. • A FCN-based network is developed to perform semantic segmentation. • The scene construction is conducted by combining the geometric and semantic analysis. In this paper, a traffic scene construction framework is proposed based on geometric and semantic analysis of road image sequences using convolutional neural networks (CNNs). For geometric analysis branch of the framework, the image features and heatmaps are extracted to locate the keypoints of road boundaries by CNN. The recurrent module is employed to refine the heatmap prediction to generate more accurate keypoints. The scene layout is then specified according to the keypoints. For semantic segmentation branch of the framework, an encoder-decoder mechanism is utilized to divide the scene layout. A hall module is developed to connect the encoder and decoder parts, which improves the segmentation performance for tiny objects with lower computation cost. Furthermore, the 3D traffic scene models are constructed according to the geometric and semantic analysis results. The traffic simulation can be implemented based on the scene models. The extensive evaluations and comparisons demonstrate the effectiveness of the proposed framework.
On the use of AI as a requirement for improved insolation forecasting accuracy to achieve optimized PV utilization

With the increase in data, the challenges of complex data, and the financial implications of improved insolation accuracy, it has become necessary to include targeted Artificial Intelligence & Machine Learning (AI/ML) for insolation forecasting. Forecasting is a crucial and cost-effective tool for integrating variable renewable resources into power systems. The ability to accurately forecast irradiance will facilitate increased PV adoption on the grid by making the intermittency less disrupting and allowing for better PV utilization, directly assisting in reduction of carbon energy sources.A key problem in solar forecasting is the intermittency of cloud cover, which often exhibits fractal properties and is still challenging to predict and adversely impacts solar farm output management. The physical models which cover weather can only provide a certain level of predictive accuracy and are particularly challenged by cloud forecasting. The key challenges are: limitations in the physical models, massive data, the need to make substantial simplifying estimations.We propose an approach and methodology which can enhance the predictive capabilities of insolation forecasting based on, and leveraging, a type of "bundled" approach which takes into account both the physical models, as well as the empirical mode determined by AI/ML, and exploiting sensor and satellite inputs. The novel aspect is to expand the AI/ML empirical dimension to achieve improved forecasting where the "non-physical-model" modes provide substantial input. We outline the specific methodology, how this is different from current modes, and how it can improve insolation forecasting. Specific examples will be provided and the benefits discussed.
Non-Markovianity and entanglement detection

We have established a novel method to detect non-Markovian indivisible quantum channels using structural physical approximation. We have shown that this method can be used to detect eternal non-Markovian operations. We have further established that harnessing eternal non-Markovianity, we can device a protocol to detect quantum entanglement.
Form Recognition Based on Lightweight U-Net and Tesseract after Multi-level Retraining

With the rapid development of Internet information technology and the advancement of enterprise digitization, the digitization of paper forms has also received extensive attention. The automatic conversion of paper form documents into electronic form documents mainly faces three problems. The first is that the format of the form file is diverse and the structure is complex. This article uses the XML file of the form to accurately analyze the structure of the file, which is more accurate than the current semantic segmentation method. The second problem is table area detection, this paper uses traditional algorithms to find the contours of the candidate table area, and screens according to the characteristics of the table area to complete the detection and extraction of the table area. The third is that the recognition of the table text is more difficult, not only the interference information such as the table frame will also affect the accuracy of text recognition, and the type of text information in the table is complex, including Chinese, English, numbers, symbols and mixed types, which bring huge challenges to text recognition. This paper uses the lightweight U-Net network model to segment the text area at pixel level, eliminating the interference information of text recognition. The neural network of Tesseract was retrained in a multiple, multi-level manner, and successfully realized the recognition of complex types of text information with an accuracy of about 96%. Based on deep learning and XML table structure analysis algorithm, this paper realizes the recognition of paper version of the form file and the reconstruction of the electronic version of the file.
Smart Distributed DataSets for Stream Processing

There is an ever-increasing amount of devices getting connected to the internet, and so is the volume of data that needs to be processed - the Internet-of-Things (IoT) is a good example of this. Stream processing was created for the sole purpose of dealing with high volumes of data, and it has proven itself time and time again as a successful approach. However, there is still a necessity to further improve scalability and performance on this type of system. This work presents SDD4Streaming, a solution aimed at solving these specific issues of stream processing engines. Although current engines already implement scalability solutions, time has shown those are not enough and that further improvements are needed. SDD4Streaming employs an extension of a system to improve resource usage, so that applications use the resources they need to process data in a timely manner, thus increasing performance and helping other applications that are running in parallel in the same system.
A New Designed Intelligent Weelchair with Elevating Lazyback

As a typical representative of service robots for the elderly and the disabled, intelligent wheelchair have strong environmental adaptability, safe and stable motion performance, and intelligent interaction methods. They are an indispensable tool in social rehabilitation projects. For ordinary wheelchairs, most of them allow the rider to sit down slowly, but for those riders who have a back disease or need assistance to sit down, ordinary wheelchairs are not convenient to ride. Therefore, this article aims to improve the portability of wheelchairs for people with back problems. We propose a novel wheelchair based on the elevating lazyback, a new structure that can adjust lazyback angle quickly and flexibly. Different with the traditional wheelchair, the rider has the ability to get into the wheelchair from bed and sofa without others helped. we carry out the modeling and simulation of a new structure with the help of two kind of software, i.e. the MATLAB and the ANSYS. The results show that the proposed wheelchair has a good kinematic performance and well-feasibility, which means that the wheelchair can be used in the daily life. These simulated results will be significant for the development of intelligent wheelchair.
DDocE: Deep Document Enhancement with Multi-scale Feature Aggregation and Pixel-Wise Adjustments

Digitizing a document with a smartphone might be a difficult task when there are suboptimal environment lighting conditions. The presence of shadows and insufficient illumination may reduce the quality of the content, such as its readability, colors or other aesthetic aspect. In this work, we propose a lightweight neural network to enhance photographed document images using a feature extraction that aggregates multi-scale features and a pixel-wise adjustment refinement step. We also provide a comparison with different methods, including methods not originally proposed for document enhancement. We focused on aesthetics aspects of the images, for which we used traditional image quality assessment (IQA) metrics and others based on deep learning models of human quality perception of natural images. Our deep document enhancement (DDocE) method was able to lessen the negative effects of different artifacts, such as shadows and insufficient illumination, while also maintaining a good color consistency, resulting in a better final enhanced image than the ones obtained with other methods.
SMART Learning as New Educational Model through the Eyes of Teachers and Students

The paper analyzes teachers’ and students’ attitude to a newly accepted SMART learning model, reveals peculiarities of SMART education. Relying on the conducted analysis, the authors identify how well teachers and students are familiar with SMART learning conception, discover specificity of introducing SMART technologies in educational institutions of Belgorod region. Scientific originality of the study involves revealing the essence of SMART learning as a new educational model recently implemented in educational institutions of different levels. The conducted research allows drawing the following conclusions: teachers accept importance of SMART technologies and recognize the potential of SMART learning environment in improving the educational process quality, however, they failed to develop a subjective position on the problem of SMART technologies introduction.
Corruption and Growth in the Economic and Monetary Community of Central Africa (EMCCA): The Role of Democracy

The objective of this paper is to analyze the role of democracy in the relationship between corruption and economic growth in EMCCA member countries over the period 2002-2020.The results obtained from this study, using the dynamic panel of the generalized method of moments (GMM), confirm the "grain of sand in the economy's wheels" hypothesis in the EMCCA countries.Furthermore, taking into account the interactive influence of democracy in the corruption-growth relationship reveals that democracy could reduce the negative effects of corruption on economic growth.The results of the study suggest that the promotion of democratic norms is crucial to limiting the level of corruption and stimulating economic growth in the EMCCA sub-region.
Query Complexity of Heavy Hitter Estimation

We consider the problem of identifying the subset <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">γ</sup> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</inf> of elements in the support of an underlying distribution <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P$</tex> whose probability value is larger than a given threshold γ, by actively querying an oracle to gain information about a sequence <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> , <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$X$</tex> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> , … of i.i.d. samples drawn from P. We consider two query models: (a) each query is an index <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$i$</tex> and the oracle return the value X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> and (b) each query is a pair of indices (i, j) and the oracle gives a binary answer confirming if X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> = X <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</inf> or not. For each of these query models, we design sequential estimation algorithms which at each round, either decide what query to send to the oracle depending on the entire history of responses, or decide to stop and output an estimate of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">γ</sup> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</inf> , which is required to be correct with some prespecified large probability. We provide upper bounds on the query complexity of the algorithms for any distribution <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{P}$</tex> and also derive lower bounds on the optimal query complexity under the two query models. We also consider noisy versions of the two query models and propose robust estimators which can effectively counter the noise in the oracle responses. A full version of this paper is accessible at: https://arxiv.org/pdf/2005.14425.pdf
Gesture-Based Media Controlling Using Haar Cascade

One of the greatest strengths of e-learning resources depends on audio and video files. The video file makes users more attentive and enthusiastic due to balanced picture and sound quality. During the pandemic situation that arose in twentieth century, it has made parents more awakened for video- and audio-based learning. The paper proposes the face-controlled application that handles media. The combined approach of face detection, iris detection and media control is an innovative way to solve the problem. Proposed face-controlling system pauses and resumes the media by identifying whether the user’s iris looking at screen or not. Face detection is achieved from Haar cascade classification, and iris detection is done through the Hough transform classifier. The media pauses if the iris of the person using the system is not toward screen for about 8 ms. The important advantage of system lies in power consumption since the video stops for the period if the user is distracted while watching the video. Despite powerful visual aesthetics in media, there is a need to handle distractions which occur when watching media. We tend to miss some important parts in video, and we need to rewind back the video till the last viewed content. People have an inclination to make notes while listening to video lectures, and this requires to start and stop the video enormous times. This leads to wasting time, and also learning is not that effective.
Efficient Maintenance of Hospital Records by Entrusted Proof of Venture Algorithm In Block Chain Technology

Recent days hospital record is being used for several purposes and the same set of records have been maintained securely by using many technologies in concern of security constraints and issues. The technologies are cloud computing, Data mining and Artificial intelligence etc. but still trusted record maintenance is major concern in hospitals. We have chosen block chain technology to resolve above cited issue in efficient and secured hospital record maintenance. There are three key parameters of block chain technology namely decentralization of node, openness concept and security. In which we have concentrated on third security key parameter by using efficient entrusted proof of venture algorithm used in our work to maintain proper usages of data related to hospital records. We have analyzed and compared the performance of data uses with the help of solidity command line compiler (solc) in our simulation.
Virtual Reality For Android Based Parturition Simulation

The purpose of making this application is to facilitate the practice of learning the parturition process (childbirth) for midwifery academics. The method used is in the form of virtual reality applications, namely technology that has the potential to produce real conditions in the form of 3D objects and virtual environments to the user, in this case the simulation of parturition (childbirth). The results of making this application are used to make it easier for academics in the learning process, because for now what we know for the learning process and practice is still with aids in the form of visual aids that can only be found in hospitals or medical and midwifery schools. The benefits of using this application are to make it easier for midwifery academics in the learning process for parturition(childbirth).
Post competency evaluation model of English Majors Based on Data Mining

The evaluation results of traditional English professional competency model are only reflected in a single dimension, and lack of description of characteristics. Therefore, a post competency evaluation model for English Majors Based on data mining is designed. Firstly, the evaluation index system of English professional post competency is established. After the specific indicators are determined according to the selection principles, the importance test is completed by calculating the concentration degree, dispersion degree and coordination degree. By establishing the hierarchical structure model and 1–9 scale evaluation table, the relevant weight of each index in the actual situation can be calculated. Finally, based on the data mining algorithm, a certain member is completed At this point, we have completed the construction of the post competency evaluation model of English Majors Based on data mining. The experimental results show that the final competency evaluation results of the designed model are similar to those of the traditional model, indicating that the accuracy of the evaluation is reliable.
Compositional Rule of Inference with a Complex Rule Using Lukasiewicz <i>t</i>-Norm

Inference systems are intelligent software performed generally to help people take appropriate decisions and solve problems in specific domains. Fuzzy inference systems are a kind of these systems that are based on fuzzy knowledge. To handle the fuzziness in the inference, the compositional rule of inference is used, which has two parameters: a t-norm and an implication operator. However, most of the combinations of t-norm/implication do not give an adequate inference result that coincides with human intuitions. This was the motivation for several works to study these combinations and to identify those that are compatible, in order to guarantee a performance close to that of humans. We are interested in this paper to a more general form of rules, which is complex rules, whose premise is a conjunction of propositions. To obtain the consequence in a fuzzy inference system using the compositional rule of inference with a complex rule, we study, in this work, Lukasiewicz t-norm which was not investigated before in this context. We combine it with known implications, and we verify the satisfaction of some criteria that model human intuitions.