Reviewerly/exHarmony huggingface dataset:
Improvement of Meteorological Services for Food Security in the Drylands of Nigeria

Well-coordinated efforts are being made in dishing out meteorological services by the NigerianMeteorological Agency to ensure the delivery of timely, accurate and quality weather information for the developmentof agricultural activities in the drylands of Nigeria for sustainable food security. This is through the densification ofthe observations networks, acquisition of the relevant and latest weather instruments and improvement in weatherequipment and training of technical data capturing system, data delivery system, calibration of equipment, installation,routine maintenance of the equipment and training and retraining of staff .This work is aimed at giving an insight tothe efforts being made by NIMET towards the issuance of accurate weather information for agricultural development.The information contained in Seasonal Rainfall Prediction (SRP), 2019 and 2020 is given consideration in this studyespecially as it relates to agriculture for food security in the drylands of Nigeria.
Sign language recognition

There is an absence of communication with deaf people in our society. To overcome this barrier the introduction of Sign Language (SL) took place. To convey meaning to normal people, sign language makes use of patterns that are visually transmitted sign patterns. Sign language is also useful for people suffering with Autism Spectrum Disorder (ASD). Normal people cannot understand the signs used by deaf, as they do not know the meaning of a particular sign. The system proposed here aims at solving this problem. This system uses a camera, which captures various gestures of the hand. Then, processing of the image takes place by using various algorithms. First, pre-processing of the image takes place. Then, determination of edges occurs by using an edge detection algorithm. Finally, a template-matching algorithm identifies the sign and display the text. As the output is text, one can easily interpret the meaning of a particular sign. This also curtails the difficulty to communicate with the deaf. The implementation of the system is by using Python. The system uses various libraries.
Replication of Newport and Aslin (2004) ‘Learning at a distance I. Statistical learning of non-adjacent dependencies.’ Experiment 2

As part of an ongoing project to replicate major findings regarding statistical word segmentation in adults, we replicated Experiment 2 in Newport and Aslin (2004) ‘Learning at a distance I. Statistical learning of non-adjacent dependencies.’ This experiment compared learning of non-adjacent dependencies between syllables with learning of non-adjacent dependencies between segments, reporting below-chance success on the former and above-chance success on the latter. Despite a much larger sample size and more careful experimental controls, we failed to find clear evidence of learning in either condition.
Implementasi Metode Iqra dalam Pemberantasan Buta Baca Alquran Pada Siswa

&#x0D; &#x0D; &#x0D; &#x0D; This study aims to examine: 1) the ability to read the Koran in grade 10 students at SMA N 13 Padang 2) the factors behind the blind reading of the Koran in grade 10 students at SMA N 13 Padang 3) the Implementation of the Iqra method in eradicating blind reading of the Koran in 10th-grade students at SMA N 13 Padang. The method used by researchers in this study is qualitative with a descriptive approach. This research was conducted at SMA Negeri 13 Padang. Data collection techniques used are observation, documentation, and interviews. The data obtained from the study were then analyzed by data reduction, data presentation, and drawing conclusions. The data validation technique used is the triangulation technique, checking the data again with a different technique so as to get valid data. The results showed that the ability to read the Koran of grade 10 students at SMA Negeri 13 Padang was still low due to the lack of interest in reading students. The background of the lack of reading the Koran at SMA Negeri 13 Padang resulted in a lack of encouragement from parents at home. The application of the Iqra method at SMA Negeri 13 Padang is increasing by using this method.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Facial Recognition System with Voice Message Enhancement

Face recognition system has always been a crucial human-computer interaction tool in most security systems, access control, video surveillance, commercial areas, and networks like Facebook as well. After development in the area of artificial intelligence and growing security concerns, face recognition system has attracted attention by providing maximum efficiency in face detection and recognition. Face recognition is considered to be a biometric artificial intelligence-based application. Face recognition is the process of recognizing the face of a person by a vision system. As an extension to the existing face recognition system in this model, a new feature is added i.e., a voice message as output that is produced for each face recognized. The voice message could be anything like a simple hello or even a complex sentence. Keywords: Artificial intelligence, Face recognition, voice message, OpenCV, pyttsx.
FLEXIBLE-EVENT TRIGGERED FILTER-DESIGN WITH RESTRAINT AND CYBER ATTACKS

Retaining cyber security in a constantly evolving risk panorama is a challenge or all companies. Traditional reactive techniques, wherein sources have been put towards protecting structures against the most important recognized threats, at the same time as lesser-known threats have been undefended, is not a enough tactic. To preserve up with converting security risks, a more proactive and adaptive method is necessary. One of the maximum complex factors of cyber security is the evolving nature of security dangers. As new technologies emerge are used in new or different approaches, new attack avenues are developed. Retaining up with those common changes and advances in assaults, as well as updating practices to protect against them may be challenging. In this project the hybrid cyber-attack is investigated and handled in a way to secure the industrial level prototypes details securing. The new product details are always to be kept secured due to the confidential nature. Even many techniques were used hackers and other malicious persons uses techniques to get those data. If the attack happened in a hybrid way the handling of those things were proposed. Key Words Traditional reactive techniques, security risks, hybrid cyber-attack,
Using Text Mining Techniques for Extracting Information

Nowadays, research in text mining has become one of the widespread fields in analyzing natural language documents. The present study demonstrates a comprehensive overview about text mining and its current research status. As indicated in the literature, there is a limitation in addressing Information Extraction from research articles using Data Mining techniques. The synergy between them helps to discover different interesting text patterns in the retrieved articles. In our study, we collected, and textually analyzed through various text mining techniques, three hundred refereed journal articles in the field of mobile learning from six scientific databases, namely: Springer, Wiley, Science Direct, SAGE, IEEE, and Cambridge. The selection of the collected articles was based on the criteria that all these articles should incorporate mobile learning as the main component in the higher educational context. Keywords:- Text mining ⋅ Information extraction ⋅ Topic identification Scientific databases ⋅ Mobile learning ⋅ Higher education.
Educational animated videos in school education

The article gives the concepts of animation, educational animation and animated video. The relevance of the use of animated videos in the educational process is revealed, in particular, their use is described in cases where it is impossible to show a real object. The advantages of using educational animated videos as one of the ways of visual presentation of educational information are described. The formats for using educational animated videos in the learning process, including the distance format, are described. As an example, an educational animated video "Four-stroke internal combustion engine", created for use in physics lessons, is described, the purpose of its use and some features are considered. Recommendations are given for the effective use of animated videos in the educational process based on the author's personal experience.
Comments on: hybrid semiparametric Bayesian networks

Abstract This note comments on the article of David Atienza, Pedro Larrañaga and Concha Bielza in which they first review recent contributions to Bayesian networks and then introduce a new hybrid version. It combines parametric and nonparametric density estimates for continuous variables by simultaneously allowing for discrete parents. We discuss straightforward extensions of the linear Gaussian parts and potential smoothing over the outcomes of discrete parents and conclude with some minor comments.
Research Into Mental Health Prediction of Community Workers Involved in the Prevention of COVID-19 Epidemic Based on Cloud Model

To get to know the mental status of community workers involved in the prevention of COVID-19 epidemic, provide them with mental counseling and guidance, and predict their mental health status, a cloud model for the mental health prediction of community workers involved in the prevention of COVID-19 epidemic was constructed in this paper. First, the method to collect data about mental health was determined; second, the basic definition of cloud was discussed, the digital features of cloud were analyzed, and then, the cloud theory model was constructed; third, a model to predict the mental health of community workers involved in the prevention of COVID-19 epidemic was constructed based on the cloud theory, and corresponding algorithm was designed. Finally, a community was chosen as the research object to analyze and predict its mental health status. The research results suggest that the model can effectively predict the mental health status of community workers involved in the prevention of COVID-19 epidemic.
An Analysis of Extreme Machine Learning Models

Abstract The full text of this preprint has been withdrawn by the authors due to author disagreement with the posting of the preprint. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
MEAN: Multi-Edge Adaptation Network for Salient Object Detection Refinement

Recent advances in salient object detection adopting deep convolutional neural networks have achieved state-of-the-art performance. Salient object detection is task in computer vision to detect interesting objects. Most of the Convolutional Neural Network (CNN)-based methods produce plausible saliency outputs, yet with extra computational time. However in practical, the low computation algorithm is demanded. One approach to overcome this limitation is to resize the input into a smaller size to reduce the heavy computation in the backbone network. However, this process degrades the performance, and fails to capture the exact details of the saliency boundaries due to the downsampling process. A robust refinement strategy is needed to improve the final result where the refinement computation should be lower than that of the original prediction network. Consequently, a novel approach is proposed in this study using the original image gradient as a guide to detect and refine the saliency result. This approach lowers the computational cost by eliminating the huge computation in the backbone network, enabling flexibility for users in choosing a desired size with a more accurate boundary. The proposed method bridges the benefits of smaller computation and a clear result on the boundary. Extensive experiments have demonstrated that the proposed method is able to maintain the stability of the salient detection performance given a smaller input size with a desired output size and improvise the overall salient object detection result.
EU-Net: Enhanced U-shaped Network for Breast Mass Segmentation

&lt;p&gt;Segmentation of breast masses in digital mammograms is very challenging due to its complexity. The recent U-shaped encoder-decoder networks achieved remarkable performance in medical image segmentation. However, these networks have some limitations: a) The multi-scale context information is required to accurately segment mass but is not effectively extracted and utilized. b) The global context information is often ignored by the skip connection. To overcome these limitations and achieve better segmentation, we propose an Enhanced U-shaped Network (EU-Net). The proposed EU-Net comprises of 3 novel components: 1) dense block, which is employed in the encoder and the decoder in place of convolutional layers to achieve the multi-scale features. 2) Multi-Scale Feature Extraction and Fusion, which is used in the junction between the encoder and the decoder for further extracting and fusing the multi-scale context information. 3) Skip Connection Reconstruction, which is inserted between the encoder and the decoder at each stage, to redesign the skip connection and emphasize the global context information. Extensive experimental results under different settings show that the proposed EU-Net achieves superior performances than the previous state-of-the-art segmentation models, and other existing approaches on IN-Breast and CBIS-DDSM mammogram datasets. The generalization ability of the proposed EU-Net is evidenced through crossdataset and ternary dataset evaluation performance. In the ternary dataset evaluation, the model is trained and evaluated on the UDIAT breast ultrasound dataset without finetuning. The EU-Net achieves higher generalization performance in both evaluation experiments. These experiments collectively indicate the efficiency and high generalization ability of the proposed EU-Net.&lt;/p&gt;
On the Selection of Wavelet Models in the Simulation of Seismic Accelerograms through Evolutionary Optimization

A numerical procedure, based on an evolutionary optimization algorithm, has been proposed by the authorsfor the simultaneous generation of the three components of the seismic ground acceleration. The methodology allowsthe determination of a train of seismic waves modeled by three different waveforms, for the generation of groundseismic acceleration components. The parameters of each wave, i.e., amplitude, frequency, duration, arrival time anddirection, are determined using an evolutionary optimization algorithm. Although no theoretical justification is knownby the authors for the generation, at the seismic source, of specific initial waveforms, both in case of fracture or ofsliding with friction, waveform acceleration components that satisfy the condition of zero final velocity should inprinciple be preferred. The latter is a physical restriction that is automatically satisfied by anti-symmetrical functions,thus eliminating the need to correct the baseline of simulated accelerograms. The error of fit of simulated accelerogramsgenerated by three different waveforms proposed in the literature was herein determined by comparison with actualseismic records. On that basis, estimations of the expected error of the evolutionary optimization algorithm inengineering applications are presented.
Smartphone: IOS Vs Android

The growing popularity of the smartphone has caused intense competition between technologies such as Symbian, Google, Microsoft and Apple. This Page presents a brief review and comparison of the Android app from Google and the iPhone app from Apple. Comparisons are made on the basis of their platform, their performance and the growth of the mobile world. New Key Features introduced on Android and IOS are also explained. KEYWORDS — Android; An apple; Applications; Google Android; iPhone; Java; Jelly Bean; Mobile Application; Objective C; Smartphone.
FAKE PRODUCT IDENTIFICATION USING BLOCK CHAIN

In the current supply chain, there are several phoney items. It is vital to have a system in place that allows end users to examine all data about the goods they are purchasing in order to determine whether the product is authentic or not. Blockchain technology is a network of peer-to- peer nodes that keep a record of transactions, also known as blocks, and commonly referred to as "chains" in various databases. This type of storage is sometimes referred to as "digital ledge." It has a distributed file system and immutable properties. IPFS (Inter Planetary File System), a Distributed Web File System, is used by our system to track product ownership. IPFS is more helpful than http because it can efficiently distribute large amounts of data and does not allow duplication. IPFS and the Blockchain have a lot in common. IPFS allows you to handle large amounts of data and embed immutable, persistent IPFS links in blockchain transactions. This protects and time stamps your content without having to place the data on the chain. The system keeps track of the product's maker, how ownership changes, and allows customers to see who is the current owner of the goods. Customers will be given a Graphical user interface (GUI) to scan QR codes associated with products, and information about those products will be shown. KEYWORDS – Inter Planetary File System (IPFS), Blockchain, supply chain, QR code.
AUTO-ASSISTANCE SYSTEM FOR VISUALLY IMPAIRED PEOPLE USING DEEP LEARNING

World Health Organization (WHO) outlined that there are two eighty-five million visually disabled worldwide. Among them thirty-nine million people are completely blind. One of the difficult activities that could be conducted by visually impaired is object detection which could be implemented using Machine Learning. It is an approach towards Artificial Intelligence that provides system the capacity for natural learning and development from experience without specifically programmed. It provides computer vision to the system which make decisions based on training algorithms. The chief goal of this research paper is to develop an object detection system to assist totally blind individual to manage their activities independently. Paper also compares different object detection algorithms like Haar Cascade and Convolutional Neural Network with yolov5(CNN). Haar Cascade classifier is a basic face detection algorithm which could also be trained to detect different objects whereas convolutional neural network falls under deep learning approach which could be employed for object recognition. The custom dataset is created with 2300 images consisting of 3 different classes. This comparison is being executed to find the yolov5 as a suitable algorithm for this system from the aspect of accuracy for real time scenario. Keywords- Object Detection, Computer Vision, Deep learning, Feature Extraction and Recognition , Convolution neural network.
Determinants of Capital Market Development in Kenya

This study sought to determine the determinants of capital market development in Kenya. The empirical study was conducted using time series data for the period 2001- 2020. This study adopted quantitative approach and employed secondary data of twenty years; Pearson correlation test was used to evaluate the relationship between the variables. Furthermore, this study used multiple regression analysis by applying Ordinary Least Square (OLS) method. The macroeconomic variable data involved were stock market liquidity, investment, and foreign direct investment. For capital market development indicators, market capitalization, listed companies, value traded and turnover ratio were considered. The regression results demonstrate that all the variables, stock market liquidity, investment and foreign direct investment are important determinants of capital market development in Kenya. The findings from Ordinary Least Square (OLS) indicated that the model is significant in a whole and the three independents explain 91% changes in the dependent variable. The study recommends the following policies among others; the government should regulate and control financial sector in order to promote the capital market development. Policy maker should cut off restriction for the foreigner investors and to create strategies to increase the foreign direct investment and offer incentives.
Understanding 3D vision as a policy network (preprint)

A 'policy network' is a term used in reinforcement learning to describe the set of actions that are generated by an agent depending on its current state. This is an untypical starting point for describing 3D vision, but a policy network can serve as a useful representation both for the 3D layout of a scene and the location of the observer within it. It avoids 3D reconstruction of the type used in computer vision but is similar to recent representations for navigation generated through reinforcement learning. A policy network for saccades (pure rotations of the camera/eye) is a logical starting point for understanding (i) an ego-centric representation of space (e.g. Marr's 2.5D sketch and (ii) a hierarchical, compositional representation for navigation. The neural implementation of policy networks in general is uncontroversial whereas possible implementations of 3D coordinate transformations in the brain have yet to be developed. Hence, if the representation underlying 3D vision can be described as a policy network (in which the actions are either saccades or head translations), this would be a significant step towards a neurally plausible model of 3D vision.
Online Sequential Extreme Learning Machine-Based Active Interference Activity Prediction for Cognitive Radar

For anti-active-interference-oriented cognitive radar systems, the mismatch between the acquired and actual interference information may result in serious degradation of cognitive anti-active-interference performance. To yield more effective knowledge of the electromagnetic environment and eliminate the mismatch effect, the electromagnetic activity prediction technique, which deduces future electromagnetic behaviors based on current observations, has received increasing attention. However, high computational complexities limit the application of conventional electromagnetic activity prediction methods in dynamic active interference prediction with high real-time requirements. In this paper, the online sequential extreme learning machine (OS-ELM)-based method, which is dedicated to high-efficiency active interference activity prediction, is proposed. The advancement includes two aspects. First, benefiting from the single-hidden-layer network structure and recursive-formula-based output weight updating, the proposed OS-ELM-based frequency prediction (OS-ELM-FP) and OS-ELM-based angle prediction (OS-ELM-AP) models can predict the interference state and update the prediction model parameters with much higher computational efficiency. Second, the better generalization performance enables the proposed method to achieve smaller interference activity prediction errors compared with conventional methods. Numerical examples and prediction results based on measured jamming data demonstrate the advantages of the proposed method.