Reviewerly/exHarmony huggingface dataset:
Image dehazing algorithm based on improved generative adversarial network

Under foggy conditions, the images and videos collected by the device are blurred and poorly imaged, which greatly impacts the accuracy of subsequent visual tasks such as target detection and recognition. At present, the dehazing methods such as dark channel and AOD- Net based on estimating intermediate variables still have problems such as incomplete dehazing and large color error. Therefore, an image dehazing method based on the generative adversarial network is proposed. The generator adopts a dense block structure connected layer by layer to improve the details of the dehazed image. The discriminator uses PatchGAN to perform block determination and optimize the image resolution. Meanwhile, the generated dehazed image and the real fog-free image are trained and compared with the comparison method. The peak signal-to-noise ratio and structural similarity of the proposed method are improved on synthetic datasets, and the generated images retain better detail and clarity as observed by the human eye.
Development of Competency-Based Approach to Education

The paper deals with competency-based education. Both the development of the category “competence” and application of competences in educational sphere were considered. The purpose is to examine the development of competency-based approach to teaching and learning from the standpoint of modern educational paradigm. &#x0D; The methods of the investigation are analysis and syntheses of the associated researches used to collect and present the findings and draw the conclusion. &#x0D; The results revealed that competency-based education appeared under the influence of the term “competence”; in its development three main stages may be distinguished: 1) 1960-1970 (introduction of the category “competence” in the theory and practice of language learning); 2) 1970-1990 (extension of the studied phenomenon to the field of management and leadership); 3) since the 1990s to the present (comprehensive consideration and analysis of the stated category; finding the blocks of competencies, the principles of systematization; studying the structure of competencies). &#x0D; The conclusion is that in modern educational system competency-based approach is one of the central as existence of the system of competences contributes in description of academic and professional profiles, levels and results of higher education at the international level. Moreover, due to the application of the system of competencies, the process of expanding academic and professional recognition and mobility, as well as increasing the comparability and compatibility of diplomas and qualifications has become possible.
Autostereoscopic 3D Augmented Reality Navigation for Laparoscopic Surgery: A Preliminary Assessment

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Objective:</i> Since Augmented Reality (AR) and 3D visualization have proven to be of great significance to the safety and effectiveness of surgical outcomes, will autostereoscopic 3D AR without glasses bring new opportunities for surgical navigation of laparoscopic surgery? <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> We used the CPD-based deformation registration algorithm and the proposed virtual view generation algorithm, realizing a deformable autostereoscopic 3D AR navigation framework for laparoscopic surgery. The depth perception and user experience of the 3D AR navigation were evaluated compared with the 2D AR display using an in-vitro porcine heart and offline clinical partial nephrectomy laparoscopic images. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> The autostereoscopic 3D AR allowed participants to have a more consistent spatial perception as well as a shorter measuring time than 2D AR with significant difference of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</i> < 0.05. It can also improve relative depth perception for smaller distance separation of objects < = 3.28 mm. However, the autostereoscopic 3D AR perceived a worse experience compared to 2D AR in the user experience. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> Autostereoscopic 3D AR shows a more efficient and robust sense of spatial scale than 2D AR with better potential to shorten the operating time and improve surgical outcomes than 2D AR, but image blur and distortion are issues that must be solved to improve the perception effect. High precise registration and high fluency visualization requirements could make autostereoscopic 3D AR navigation for soft tissue more challenging. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Significance:</i> Our work lays a theoretical foundation for the further development of laparoscopic surgical navigation.
The Relationship of the Philosophy of Education and the History of Philosophy: A Forecast of Educational Trends 2022-2024

Education is a systemic component of the worldview picture of the world. Along with the scientific and pedagogical arsenal, the historical and philosophical component is a fundamental analytical method of educational space research. The purpose of scientific exploration is to predict educational trends in the short term through the prism of historical and philosophical analysis. Synergetic interaction between the philosophy of education and the history of philosophy forms the basis for the study of practical and theoretical dimensions of modern education. The balance of dialectical and synergetic methods in examining educational trends provides a complete analysis in both practical pedagogical and theoretical-philosophical dimensions. The results of the study allow you to analyze the development of education from a cultural and historical perspective, which allows you to predict the optimal ways of further development of this sphere of social activity. The scientific novelty of the work consists in combining the potential of philosophy, history, science, pedagogy, and culture to forecast educational trends in the coming years. Thus, historical and philosophical interpretation of education points to the demand for three key short-term trends in the modern educational space: globalization, technologization, and informatization.
The Silicon Savannah: exploring the promise of cryptocurrency in Africa

One of the primary motivations behind Bitcoin and other blockchain-based cryptocurrencies has been to disrupt the monetary status quo by providing individuals with faster, cheaper, and more secure forms of money. The potential benefits of providing superior private monies as an alternative to national currency monopolies are especially pronounced in the developing world, where citizens often suffer under poor monetary institutions and unreliable - and often inaccessible - financial services. No region has suffered more from poor quality monetary institutions than Africa. This chapter examines the potentially bright future of cryptocurrency in Africa. Over the past decade, Africa has been the epicenter of the "mobile money revolution." Although its cryptocurrency uptake has lagged behind other regions, I outline why Africa is perhaps the most promising frontier for the widespread adoption of cryptocurrency. I also discuss the opportunities and challenges that crypto startups face in the region.
One datum and many values for sustainable Industry 4.0: a prognostic and health management use case

&lt;span&gt;Industrial context of today, driven by the Industry 4.0 paradigm, is overwhelmed by data. Decreasing cost of innovative technologies, and recent market dynamics have pushed and pulled respectively for those architectures and practices in which data are the masters. While advancing, we have to take care of waste, even though intangibility of data makes them hardly connected to waste. In this paper we are going to reflect on data intensive context of today, focusing on the industrial sector. A smart approach for fully exploiting data collecting infrastructures is proposed, and its declination in a prognostic and health management (PHM) use case set inside an automatic painting system is presented. The contributions of this papers are mainly two: first of all, the general conceptual take-away of "data re-use" is presented and discussed. Moreover, a PHM solution for painting system's number plates, based on optical character recognition (OCR), is proposed and tested as a proof-of-concept for the "data re-use" concept. Summarizing, the already-in-use data sharing principle for achieving transparency and integration inside Industry 4.0, is presented as complementary with the proposed "data re-use", in order to develop a really sustainable shift toward the future.&lt;/span&gt;
Deep Generative Models Under GAN: Variants, Applications, and Privacy Issues

Deep learning has lately acquired a lot of attention in machine learning because of its capacity to train features and classifiers at the same time, resulting in a significant boost in accuracy. To attain a high level of accuracy, the models require huge amounts of data and processing capacity, both of which are now available due to the advancements in big data, Internet of Things, and cloud computing. Even though, some applications like medical diagnosis, image recognition, and biometric authentication faces the problem of data scarcity which affects the predictive analytics of deep learning. In order to tackle the issue, deep generative models like Generative Adversarial Networks (GAN) come into existence that are capable of artificially generating synthetic data for specific problems. In this article, various models of GAN and their applications were explored and a comparison of the models were also given. As the data increases, another issue faced by the applications is of data privacy. With rising privacy concerns, more priority has to be given for privacy issues while developing intelligent applications. GAN and its variants are nowadays used as an attacker as well as a defender against various privacy risks which were also presented in this review. As a future work, GANs potential to solve the issues of data privacy and security has to be deeply explored.
An Adaptable and Agnostic Flow Scheduling Approach for Data Center Networks

Cloud applications have reshaped the model of services and infrastructure of the Internet. Search engines, social networks, content delivery and retail and e-commerce sites belong to this group of applications. An important element in the architecture of data centers where these applications run is the communication infrastructure, commonly known as data center networks (DCNs). A critical challenge DCNs have to address is the processing of the traffic of cloud applications, which due to its properties is essentially different to the traffic of other Internet applications. In order to improve the responsiveness and throughput of applications, DCNs should be able to prioritize short flows (a few kB) over long flows (several MB). However, given the time and space variations the traffic presents, the information about flow sizes is not available in advance in order to plan the flow scheduling. There has been a wealth of solutions developed in this space, and prior work includes flow scheduling mechanisms optimizing for a specific workload but fall short when workloads are not known in advance, or comprise a collection of applications changing dynamically. In this paper, we present an adaptable mechanism called Adaptable Workload-Agnostic Flow Scheduling (AWAFS). It is an adaptable approach that can adjust in an agnostic way the scheduling configuration of DCN forwarding devices. This agnostic adjustment contributes to reduce the Flow Completion Time (FCT) of those short flows, representing around 85% of the traffic handled by cloud applications. AWAFS operates by observing the traffic and detecting statistical properties that provide a hint to adapt the scheduling parameters. Our evaluation results based on simulation show that AWAFS can reduce the average FCT of short flows between 16.9% and 45.2% when compared to the best existing agnostic non-adaptable solution, without inducing starvation on long flows. Indeed, it can provide improvements as high as 39% for long flows. Additionally, AWAFS can improve the FCT for short flows in scenarios with high heterogeneity in the traffic present in the network, with a reduction up to 5% for the average FCT and 15% for the tail FCT.
Designing a supervised feature selection technique for mixed attribute data analysis

Identifying optimal features is critical for increasing the overall performance of data classification. This paper introduces a supervised feature selection technique for analyzing mixed attribute data. It measures data classification performances of features with a user-defined performance criterion and determines optimal features to boost the overall data analysis performance. A performance evaluation is managed to highlight the usefulness of the technique with existing feature selection techniques such as analysis of variance test, chi-square test, principal component analysis, and mutual information. Visualization is also utilized to understand the differences in classifying instances with different features. From a comparative performance testing and evaluation, we found 5 ∼ 10% performance improvements with the proposed technique. Overall, evaluation results showed the usefulness of our proposed feature selection technique in mixed attribute data analysis.
An integrated framework for abnormal event detection and video summarization using deep learning

Video surveillance plays an emerging role for achieving intelligent transportation system for information extraction derived from the environment.The information extracted from real time scenario can be quite significant for detecting, classifying and summarization of abnormal events that includes investigations in video surveillance [1,2].A number of researchers over the decades concentrated on the objective of detection, classification and summarization of abnormal events in reduced computational complexities and maximized event frame [3,4].
Multiple face mask wearer detection based on YOLOv3 approach

&lt;div align="left"&gt;&lt;a name="_Hlk108683337"&gt;&lt;/a&gt;&lt;span lang="EN-US"&gt;The coronavirus disease 2019 (COVID-19) is a highly infectious disease caused by the SARS-CoV-2 coronavirus. In breaking the transmission chain of SARS-CoV-2, the government has made it compulsory for the people to wear a mask in public places to prevent COVID-19 transmission. Hence, an automated face mask detection is crucial to facilitate the monitoring process in ensuring people to wear a face mask in public. This project aims to develop an automated face and face mask detection for multiple people by applying deep learning-based object detection algorithm you only look once version 3 (YOLOv3). YOLOv3 object detection algorithm was concatenated with different backbones including ResNet-50 and Darknet-53 to develop the face and face mask detection model. Datasets were collected from online resources including Kaggle and Github and the images were filtered and labelled accordingly. The models were trained on 4393 images and evaluated based on precision, recall, mean average precision and the detection time. In conclusion, DarkNet53_YOLOv3 was chosen as the better model compared to ResNet50_YOLOv3 model with its good performance on accuracy with a mAP of 95.94% and a fast detection speed with a detection time of 50 seconds on 776 images. &lt;/span&gt;&lt;/div&gt;
ASSESSMENT OF INVESTMENT D ECISIONS AND FINANCIAL PERFORMANCE OF SMALL AND MEDIUM ENTERPRISES IN FEDERAL CAPITAL TERRITORY, NIGERIA

This study evaluated the Assessment of Investment Decisions and Financial Performance of Small and Medium Enterprises in the Federal Capital Territory, Nigeria. Multi-stage sampling technique was adopted and used. Data were collected through the use of structured questionnaires from 400 sampled SMEs. Data were analyzed using, Descriptive Statistics, Correlation Matrix and Logit Regression Model. The results show there is a positive correlation between annual return i.e. financial performance and new property acquisition and a negative correlation existed with new plant and acquisition. The result of the logit model shows that the factors influencing investment decisions among SMEs were the coefficient of education (P&lt;0.01) probability level. The coefficient of the competition level was negative and statistically significant at (P &lt;0.05). Furthermore, the coefficient of the initial investment capital (P&lt;0.05). The coefficient of the infrastructure was positive and statistically significant at (P&lt;0.05). Therefore, the study recommends that Policy should be formulated towards educating and training SMEs owners to have more knowledge about business enterprise operations in order to be able to assess their financial performance, external funding and capital should be provided to SMEs at a lower and affordable interest rate, SMEs owners should be encouraged to be willing to invest more into their business to expand their business for more profit, local polices and local infrastructure to enhance SMEs participation in Federal Capital Territory AMAC should be provided. Media blitz could also be harnessed to augment policy awareness among SMEs.
ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN INTERACTION IN THE CONTEXT OF INCLUSIVE EDUCATION

ARTIFICIAL INTELLIGENCE AND BLOCKCHAIN INTERACTION IN THE CONTEXT OF INCLUSIVE EDUCATION Anton Dziatkovskii PhD in education CEO of PLATINUM software development company, Australia, Queensland &nbsp; ABSTRACT The article talks about
A novel multi-view X-ray digital imaging stitching algorithm

BACKGROUND: In fan beam X-ray imaging applications, several X-ray images sometimes need to be stitched together into a panoramic image because of the size limitations of the detector. OBJECTIVE: This study aims to propose a novel multi-view X-ray digital imaging stitching algorithm (MVS) based on the CdZnTe photon counting linear array detectors to solve the problem of fan beam X-ray stitching deformation. METHODS: The panoramic image is generated in four steps including (1) multi-view projection data acquisition, (2) overlapping positioning, (3) weighted fusion and (4) projected pixel value calculation. Images of a globe and foot are scanned by fan beam X-rays and a CdZnTe detector. The proposed method is applied to stitch together the scanned images of the globe. Three other methods are also used for comparison. Finally, this MVS algorithm is also used in the stitching of scanned images of the foot. RESULTS: Compared with the 50% stitching accuracy of other methods, the new MVS algorithm reached a stitching accuracy of 94.4%. Image distortion on the globe and feet is also eliminated and thus image quality is significantly improved. CONCLUSIONS: This study proposes a new multi-view X-ray digital imaging stitching algorithm. Study results demonstrate the superiority of this new algorithm and its feasibility in practical applications.
Towards an approach for cloud service composition in Multi-Cloud environment based QoS using deep Q-learning

Cloud Computing is internet-based computing where resources are offered over the internet on-demand and based on Pay-as-you-go. The advantages of Cloud computing have encouraged many enterprises to migrate to it and adapt its services. Sometimes users’ requests are not met from only one cloud and to better satisfy users’ complex requirements in front of the huge number of cloud services, multiple services from the various cloud can be combined by using Cloud Service Composition (SC). SC is an NP-Hard problem divided into services selection and service composition. Few works have combined these two sub-problem for service composition in a Multi-cloud environment. In this paper, we propose an approach to Service Composition in a Multi-Cloud environment based on cooperative Agents to merge these two sub-problems. Such us, our approach combines DQL for obtaining the optimal composition with locale selection of atomic service for each abstract service.
ERP Low-Code Cloud Development

Constructing ERP business process as required has always been focused by manufacturing enterprises. Nowadays the low-code development is expected to provide an ability to develop ERP systems with high efficiency, using well-established component/service and cloud platform technologies. And nonprofessional staff who understand business can easily participate in ERP development. Any change of ERP process is always driven by that of business requirements. So, requirement-driven business process design was discussed in this paper to implement the ERP low-code development. And a framework was proposed to demonstrate the implementation of such development, in which any change of business requirement was decomposed into that of composing parts, process nodes and the involved cloud services/components.
Baby Learning with Vision Transformer for Face Recognition

Computer vision and natural language processing (NLP) are the two active machine learning research areas. In the recent decades, the integration of these two areas gives rise to a new interdisciplinary field, which is currently attracting more attention of researchers. Transformer-based model, an approach which had great success in NLP tasks, is now being widely utilized in computer vision projects relating to image classification and object detection. However, there are not many studies exploiting this technique in face recognition tasks. Besides, when applying a deep learning model in real-world applications such as face recognition task, it is hard to have a sufficient number of samples like standard datasets, especially for very important persons. To simulate the practical condition, we build a new face dataset named VFD, which contains 3000 images of 1000 famous Vietnamese people. Moreover, to tackle face recognition in the context of limited training samples, we adopt the baby learning approach to train the Vision Transformer (ViT). The method trains the ViT models via iterations that gradually increase the number of parameters to enhance the generalization. Finally, we validate the method with our dataset with various configurations of face tasks to demonstrate the robustness of the model. The experiments show that our method has comparative results in general and outperforms in the context of scarce data compared to CNN-based methods such as ResNet.