Reviewerly/exHarmony huggingface dataset:
Experimental study on the influence of temperature and radiation on photovoltaic power generation in summer

Abstract The power generation of photovoltaic systems is random and uncertain, which is the result of a variety of factors. However, there are few studies considering the interaction of two or more factors at the same time. Therefore, the influence of air temperature and radiation on photovoltaic power generation is considered in this paper, and based on the physical system, the experimental data is processed and analyzed through SPSS and DPS data analysis software and the multiple nonlinear regression analysis model. The processing results show that whether it is the quadratic and cubic nonlinear regression equations or the quadratic and cubic trend surface fitting degrees all reach a significant level, it can be used to reveal the law of the influence of two factors on the power generation under the test environment. The analysis results found that the combined effect of temperature and radiation on photovoltaic power generation is more complicated, but the overall impact of solar radiation is significant and greater than the air temperature; low temperature and high radiation, high temperature and high radiation and low radiation conditions have side effects on photovoltaic power generation; when the two are enhanced at the same time, it will help increase the photovoltaic power generation; when the two “one increase and one decrease”, the photovoltaic power generation first increases and then decreases, and there is an optimal air temperature, solar radiation, and maximum photovoltaic power generation.
Dailly Forecasting of Photovoltaic Power Using Non-Linear Auto-Regressive Exogenous Method

Accurate and realistic models of forecasting are very relevant as methods for the successful implementation of renewable energy sources into smart grids. This work, present a short-term forecasting of the power output of building photovoltaic system installed in the Heliopolis university, Cairo, Egypt. The proposed method forecast one day using the autoregressive exogenous (NARX) method, based in neural network times series and based in the exogenous input data of the neural networks. The neural network (NN) is trained with Bayesian algorithm to attain the best performance of the forecasted results. However, the NARX neural networks are efficient to overcome a variety of problems and are common for nonlinear control applications as well as for the forecasting. The NARX model opts for accelerated training and level of convergence and good representation and is characterized by attractive dynamics and sensitivity to interference. This model differs in the use of the exogenous neural network input or exogenous variables. The performance of the NARX approach was analyzed as a function of training data sets, error descriptions based on experimental results of the photovoltaic system. The NARX results is compared with experimental data and neural network method. In addition, a statistical criterion is used to evaluate the effectiveness and performance of NARX model. NARX method performed the neural network in term of the statistical criterion which has a low value. Generally, the efficiency and precision of the results depend heavily on the environmental conditions and the data input.
Malaria Parasite Enumeration and Classification Using Convolutional Neural Networking

Malaria is a migratory and easily transmissible disease transmitted by virus carrier mosquitoes. Visual quantification and classification of parasitemia in emaciated plasma films is a very tiresome, biased, and time-consuming task. This chapter delivers an unambiguous understanding of the quantification and classification of erythrocytes in tainted wafer-thin blood films diseased with plasmodium parasites. The approach mainly includes microscopic imaging of tainted blood glides, amputation of noise and illumination adjustment, erythrocyte segmentation, morphological operations. By means of the segmented illustrations, parasite density estimation and classification of the stage of infection are done. Two different classification techniques ANN and DCNN have been designed and tested to perform the grouping of diseased erythrocytes into their corresponding stages of growth. The traditional neural network ANN approach gave an accuracy of 93% and this was again overwhelmed by using customized Deep Convolutional Network by achieving an accuracy of 95%.
E-learning techniques and technologies analysis for a moroccan virtual university perspective

Since the beginning of the state of health emergency due to the covid-19 pandemic, and with the closure of schools and universities, Morocco has relied on distance education to ensure the continuity of the educational program. The latter therefore continue to follow their courses thanks in particular to the distance learning system commissioned by the Ministry of Education. Morocco can thus rely on a second mean of education and teaching even after the end of the pandemic for the many advantages it has. At present, each Moroccan university has its own platform; it is obvious that students, today, have their own institutional account that allows them to access these platforms, a crossroads for information exchange and document sharing. Students are relatively initiated to this kind of platform such as Moodle or Google Classroom. However, this is not enough. Morocco needs a complete virtual university capable of providing a good quality of education to all those who deserve and demand it. This paper aims to explain the different categories of learners according to learning models, and to make a comparison between the existing virtual universities in order to make our vision of the Moroccan virtual university more concrete by proposing a model for the latter in a forthcoming work.
Information and communication technology, cyber-security and counterterrorism in Africa

This Chapter presents a conceptualisation of Information and communication technology (ICT), Cyber Security and Cyber Terrorism to guide the understanding of their rising significance in the 21st century. It provides a background on ICT, Cyber Security and Cyber Terrorism development in Africa by discussing the emerging ICT and cybersecurity ecosystem in Africa comparing existing infrastructures, architecture, gaps, and geographic maturity to developed countries. The Chapter reviews existing trends and implications of ICT and Cyber Security in a violent world considering the various instruments of mobilisation as well as the role of different digital platforms in various global security crises. It focuses on the deficits of Science, Technology, ICT and Cyber Security in Africa, highlighting existing whitespaces, anticipated growth and the way forward for Africa’s ICT, Cyber Security and ICT-Driven Counterterrorism in a violent world. It offers some policy blueprints for transforming and improving Africa’s preparedness for counterterrorism and counterinsurgency through ICT and cyber-security.
Research on Text Emotion Analysis and Product Performance based on NLP and VAR Model

Abstract Customers’ evaluations, in terms of text or ratings, are important sources of information for online shops to decide products and marketing strategies. In this paper, we discuss methods to conclude and predict merchants’ performance and characteristics of customers’ behavior of target products based on Amazon dataset. First, we design a Natural Language Processing (NLP) model, Amazon-BERT, to predict text to rating based on transfer learning. We improve the model accuracy by 20% compared to the original pre-trained BERT, and we use the predicted rating in our following steps of modeling. Second, we use PCA to extract 5 principle components, which estimate review’ s popularity, product’s reputation and so on. And then, for pacifier, we find that vector auto regression (VAR) model fits better, we apply it to the regression and doing a Granger causality test. Finally, we looked at the impulse response and variance decomposition. All 3 factors included do have a small impact.
SOCIAL MEDIA FOR SCHOLARLY COMMUNICATION IN CENTRAL ASIA

Social Media Platforms (SMPs) have emerged as the new frontiers for academic engagement, more so during the pandemic. Cultural barriers, close censorship, and language restrictions may limit the participation of Central Asian scholars in the global scientific communication. This article explores the patterns of Social Media (SoMe) use in Central Asia and outlines probable deterrents of academic engagement in the region. Some suggestions are formulated to offer digital and socio-cultural solutions aimed to improve Central Asian scholars’ activities on SoMe platforms and bridge the divide for fruitful academic partnerships.
MR Brain Image Segmentation to Detect White Matter, Gray Matter, and Cerebro Spinal Fluid Using TLBO Algorithm

This paper proposes an advanced and precise technique for the segmentation of Magnetic Resonance Image (MRI) of the brain. Brain MRI segmentation is to be familiar with the anatomical structure, to recognize the deformities, and to distinguish different tissues which help in treatment planning and diagnosis. Nature’s inspired population-based evolutionary algorithms are extremely popular for a wide range of applications due to their best solutions. Teaching Learning Based Optimization (TLBO) is an advanced population-based evolutionary algorithm designed based on Teaching and Learning process of a classroom. TLBO uses common controlling parameters and it won’t require algorithm-specific parameters. TLBO is more appropriate to optimize the real variables which are fuzzy valued, computationally efficient, and does not require parameter tuning. In this work, the pixels of the brain image are automatically grouped into three distinct homogeneous tissues such as White Matter (WM), Gray Matter (GM), and Cerebro Spinal Fluid (CSF) using the TLBO algorithm. The methodology includes skull stripping and filtering in the pre-processing stage. The outcomes for 10 MR brain images acquired by utilizing the proposed strategy proved that the three brain tissues are segmented accurately. The segmentation outputs are compared with the ground truth images and high values are obtained for the measure’s sensitivity, specificity, and segmentation accuracy. Four different approaches, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Bacterial Foraging Algorithm (BFA), and Electromagnetic Optimization (EMO) are likewise implemented to compare with the results of the proposed methodology. From the results, it can be proved that the proposed method performed effectively than the other.
A Performance Analysis of Deep Convolutional Neural Networks using Kuzushiji Character Recognition

The recent increase in computational power and data available to the general public has given rise to the development of a plethora of highly performant deep neural network architectures. A popular and successful application of these has been in the field of image recognition, in which many yield promising results. Yet, there are differences and caveats in each, and knowing the right one to choose for a particular task can save a lot of money, time, and computational resources. In order to take a closer look at the differences in their performance, we have compared 5 of the many top architectures, namely the VGG16, VGG19, DenseNet121, ResNet50, and ResNet152V2 in terms of their error percentage, class-balanced error percentage, standard deviation of class-balanced accuracy, and training time. To achieve this, we have used 2 datasets, the Kuzushiji MNIST or KMNIST dataset, which is a balanced dataset with 10 classes and the Kuzushiji 49 or K49 dataset, an imbalanced dataset with 49 classes, both of which comprise cursive characters from the Japanese Hiragana script. All of the results were computed and verified using 10 fold cross validation. For the Kuzushiji MNIST dataset, the VGG16 showed the best results in all of the aspects. For the Kuzushiji 49 dataset, the DenseNet121 showed the highest performance in all aspects other than training time, and the latter was least for the VGG16.
Socio-Economic Aspects of Digital Transformation of Higher Education

Aim . The presented study aims to assess the socio-economic aspects of higher education transformation based on the widespread use of digital technologies. Tasks . The author describes the essence of digital inequality (digital divide), determines the place and role of teachers and students in the transformation of education driven by digital technologies, and highlights the specific features of virtual reality. Methods . This study uses the methods of the systems approach, comparative and structural analysis, analytical and theoretical generalization. Results . The author outlines the concept of a new paradigm of education based on the widespread use of digital technologies and transformed roles of teachers and students. Conclusions . Digitalization of education has made it possible to form an individual educational trajectory based on different forms of presentation of educational content and its communication from the teacher to the student, including using such technologies as virtual reality and artificial intelligence.
An overview of the different methods for optimizing the virtual resources placement in the Cloud Computing

Abstract The emergence of Cloud Computing has driven users of new technologies to adopt it. This is made possible by the used technologies, namely the virtualization, which is the key element in Cloud Computing. The management and orchestration of virtual resources (the virtual machine placement and VNFs) in the Cloud remains a complex task, which, recently, attracted the interest of many researchers. This paper provides an overview of the techniques adopted to optimize the virtual machines placement and the virtual networks functions. Focused on different resources (CPU, memory, bandwidth and storage)applied in a virtualized environment. These techniques always target several approaches including the improvement of the QoS defined in the service level agreements (SLA), the good management of the energy used by physical resources, the allocation of resources and the management tasks in the data center.
Identification of Gray Sheep Using Different Clustering Algorithms

Nowadays, with the growth of Internet and the uncontrolled increases in amount of data, the user face problems to retrieve the accurate information. To overcome the difficulty of a user, recommender systems are designed that helps the user to find the relevant results. Recommender system is predication-based tools that suggest the items according to behavior of user. This paper is focused on the limitation of gray sheep user problem in collaborating filtering. The gray sheep users are those whose behavior is not similar with any other user. The existence of gray sheep user effects the performance and accuracy of recommender system. To recognize the gray sheep users problem, the clustering techniques can be used. In this paper, we define about various clustering techniques that can be used to categorize the gray sheep users in the system and improve the performance of recommender system.
Multifactor Pattern Implicit Authentication

Text and pattern passwords are vulnerable to different attacks like shoulder surfing attacks, guessing attacks, and smudge attacks. To resist these attacks, this article proposes an OP-Grid technique; an implicit graphical authentication scheme having different patterns on every Login session. With a one-time Login pointer, the user creates his password pattern. A legitimate user is authenticated not only through the user-created pattern but also the way he performs it using finger velocity and stroke time features. OP-Grid is implemented on android cell phones, gathered samples from 33 volunteers, and the experimental study carried out to evaluate its accuracy and usability.
Estimating Task Efforts in Hardware Development Projects in a Scrum Context

Hardware developers started experimenting with Scrum to accelerate their product development. However, it is not possible to implement Scrum in the same way as it was done for software systems, where the approach is already well established. One of the processes required in Scrum is the estimate of task efforts when creating a backlog for an Agile Sprint. This article presents a pilot validation experiment of a novel Agile framework for the development of hardware systems, including a parametric tool to estimate task effort in a more rigorous way than traditional confidence votes. This article presents the validation of electronic hardware design task estimation and overall project performance. The validation is performed through experimental work with teams of junior engineering students. The validation experiment showed an improvement from a minimum of eight to a maximum of eighteen percent when employing the presented tool during planning phases of the development.
Shared Collection Development, Digitization, and Owned Digital Collections

While library models already exist for sharing physical materials and joint licensing, this paper envisions an aspect of future collections involving a national digital collection owned, not licensed, by libraries. Collaborative collection development, digitization, and digital object management of owned collections can benefit societies in multiple ways, from expanding access to users otherwise unable to reach these materials, to preserving content even when disaster strikes, to reducing duplication of effort and expense in collection or digitization. This article will explore both the benefits of and the challenges to this type of collaboration.
Cryptography Techniques Within SCADA System-Asurvey

SCADA is abbreviation of “Supervisory Control and Data Acquisition”, the system occupies an important and fundamental aspect today, given the technologies and capabilities in managing and acquisition the most important infrastructures, companies and institutions. These systems were operating in a relatively isolated environment, but due to the evolution in communication systems and the needed to manage resources in real time, the necessity to connect these systems with other communications systems emerged. This trend opened a new aspect that threatens the security of these systems, so these systems are faced numerous and varied attacks from time to time. The main factor in SCADA system is the data and the information, which are exposed to hacking or changing the content of information that transmitted (changing the content of information sometimes creates real risks for the system, and sometimes these risks may be disastrous). Therefore, most of the researches and studies are focus on finding solutions to protect this information. One of these solutions is the cryptography that used in order to encrypt the data that exchanged between channels. The two types of cryptography algorithms which symmetric and asymmetric are used within SCADA system, also the new technologies emerged to integrate more than one crypto algorithm, which the hybrid crypto and cross-crypto algorithm that merge two algorithms in order to get the best features of both algorithms. This paper presents some of important researches highlighted on cryptography techniques that presented in order to get the best security in SCADA communications. A brief summary of the most common researches and studies that deals with the topic of cryptography in SCADA systems, with an explanation of the characteristics, advantages and disadvantage of each one.
A Novel Criterion of Reconstruction-based Anomaly Detection for Sparse-binary Data

Computer usage behaviour information can be used by anomaly detection algorithms to identify the current user of the computer system for security reasons. However, the data collected in this setup can be binary and very sparse, resulting in poor performance for some widely used anomaly detection methods. In this study, we propose a novel reconstruction criterion inspired by the F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score and the cross-entropy loss, that tackles the class imbalance problem introduced by binary and sparse data distribution with effectively merging reconstruction criterion calculated from vector elements of both positive and negative classes. Our experiments show that the proposed criterion can effectively improve the performance of reconstruction based anomaly detection methods, including both the PCA and the autoencoder.
Drug Audit Based on Different Models and Different Feature Extraction Methods

The audit data has the characteristics of large amount and complex form, and the current drug audit mostly rely on database methods, resulting in low audit efficiency. Although k-means and other clustering algorithms have been studied in the field of auditing, there are problems of poor clustering effect and low accuracy. To accelerate the intellectualization of drug audit in the era of big data, this paper proposes six experimental schemes based on k-means clustering model, Random Forest classification model, Multi-Layer Perceptron classification model and two feature extraction methods to conduct drug expiration audit on a hospital's drug delivery data in 2019. Then the most suitable model and feature extraction method for drug expiration audit are found by comparing relevant performance evaluation indexes. This paper first verifies the feasibility of experimental schemes by using the Titanic dataset, because it is also a binary-classification problem and contains both character and numeric features, which is very similar to the drug delivery data, which will enlighten the processing and feature extraction of drug delivery data. Finally, the effectiveness and feasibility of the models proposed in this paper are validated through specific experimental schemes.
An Analytical Study on Importance of SLA for VM Migration Algorithm and Start-Ups in Cloud

Cloud computing is a part and parcel of any small and large-scale business. Recently, cloud start-ups have increased a lot as their scale of business is taken to the next level. Start-ups are easily made possible and their profits get realized easily and quickly because of cloud advantage. Today’s start-ups will be tomorrow’s private incorporation which adds value and business to the cloud. Service level agreement (SLA) plays a major and crucial role in binding the business. This paper does a feasibility study on service level agreement compliance for especially start-ups in cloud computing, as start-ups get a major share in the number of business growing in cloud.