Reviewerly/exHarmony huggingface dataset:
Use of E- Resources by College Students of Arts, Social Science and Science Stream of Raipur city: A Comparative Study

The major objective of this study to compare the use of e -resources by students from Arts, Social Science and Science stream and compare preferences of the Use of e - resources by students of Arts, Social Science and Science stream of Raipur city. The study was based on survey method. Among various techniques of Survey Method, Questionnaire Technique was used for the study. Accordingly a Self structured questionnaire was designed to collect data from the regular UG and P G students of various college of Raipur city. The major findings of the study shows that The calculated value is 2 (df=2) = 5.32 and Table value is 5.991 so Ho Hypothesis is Accepted and Ha Hypothesis is rejected it means there are no Significant difference in Use of e - journals between Students of Arts, social Science and Science Stream. and e -book was found to be the most preferred e-resource by students of Arts, Social Science and Science students of Raipur city.
System of Quality Assurance in the University Education During COVID-19 Crisis

Due to the prevention of the risk of the spread of COVID-19 caused by the new coronavirus SARS-CoV-2, distance education becomes a reality and places new challenges and qualification requirements for teachers of technical subjects and social sciences, as well as the school management. Digital competences correspond to life skills [7, 11, 12] and are therefore considered as a key area for the training of future teachers [1].
Search System for Translation of Al-Qur'an Verses in Indonesian using BM25 and Semantic Query Expansion

Al-Qur'an is a source of life guidance for Muslims. The digital version of the Qur'an is already available on the android platform. The Indonesian Ministry of Religion provides an official application called the Ministry of Religion's Qur'an which has a translation search system in it. However, adjustments need to be made so that Indonesian people can look for translations of verses that have a definite order based on their level of relevance to the keywords entered. This study aims to develop a search system for translations of Indonesian-language Qur'an verses that already exist in the Ministry of Religion's Qur'an application.This study uses the BM25 algorithm with Word2vec as the Semantic Query Expansion method. Data as many as 6236 translated documents in the application are used to create a model of the search system. Tests on hyperparameters are carried out to get the most optimal model. The research results obtained several hyperparameter values in SQE including a window of 7, and a query expansion term of 1. In the BM25 hyperparameter, the optimal condition is obtained when the k1 variable is 1.8 and the b variable is 0.85. The search system was evaluated using the Mean Average Precision and compared with the search system that was previously available in the Ministry of Religion's Qur'an. The MAP score increased with the proposed method, from 0.53718 to 0.66556.
Cyber Security Challenges for Smart Cities

In the new past, keen metropolitan regions was simply conceptualized in sci-fi movies and books of fiction. Today, in any case, that thinking is rapidly changing from creative areas into genuine components. Essentially as savvy metropolitan regions spring up across the globe, they in like manner have made an exceptional security-risk perspective. Along these lines, this is tortured not actually settled forever to cause annihilation at the littlest possibility. Appropriately, industry insiders alongside future city coordinators see the need to address these sort of wellbeing issues. Not at all like the customary security issues of the past, the online assurance essentials of keen metropolitan regions are new and constantly advance around the latest examples in advancement and improvements. Security experts would be particularly served to review a piece of the overall proposed deals with serious consequences regarding perceive the peril scene that might torture the forthcoming keen metropolitan networks. Contraptions, for example, smart energy meters, smart miniature well-being machines, and smart security gadgets give uncommon benefits, hence further working on individuals’ lives. With new inter-connectivity of observation frameworks; robotization in different urban areas; administration arrangements like crisis responders, water, fiasco the board, sterilization and foundation, the extent of efficiencies in the Smart City is steadily growing. Coordinated frameworks, for example, private and public wise transportation will actually want to assemble data about traffic refreshes, just as GPS area and climate conditions. This uses shared information continuously. Along these lines, the advantages should be weighed against the potential perils that accompany such enormous inter-connectivity.
Semi-discriminant cross-modal correlation feature fusion with structure elasticity

Canonical correlation analysis is a classical cross-modal correlation feature fusion method. Its discriminant-guided and structure-based variations have been proposed. However, the variations suffer from insufficient supervisory information and the weak robustness of structure distortion. Thus, we propose a semi-discriminant cross-modal correlation feature fusion method with structure elasticity, i.e. semi-discriminant elasticity canonical correlation analysis. In the method, we construct the structure elasticity by incorporating neighbor geometry manifolds while enforcing the global property of Euclidean distances. Besides, a small number of class labels can be effectively employed to constrain the construction of the structure elasticity and improve the discriminative power of elasticity correlation features. The method solves the problem of insufficient supervisory information and enhances the robustness of structure distortion in correlation fusion theories. Extensive experiments validate the effectiveness of the method in image recognition tasks.
Quantum assisted unsupervised data clustering on the basis of neural networks

Recent progress in machine learning has affected almost all areas of the modern economy. The use of quantum protocols to speed up classical machine learning approaches may have further profound effects on society in the future. Here, we developed a hybrid quantum-assisted self-organizing feature map, a type of artificial neural network, and apply it to the data clustering problem in an unsupervised manner. We show that it allows us to reduce the number of calculations in a number of clusters. It is believed that similar types of hybrid quantum classical algorithms can be the main test bed to achieve practical quantum supremacy on Noisy Intermediate Scale Quantum devices.
Convergence and gradient algorithm of a class of neural networks based on the polygonal fuzzy numbers representation

Abstract As a special case of general fuzzy numbers, the polygonal fuzzy number can describe a fuzzy object by means of an ordered representation of finite real numbers. Different from general fuzzy numbers, the polygonal fuzzy numbers overcome the shortcoming of complex operations based on Zadeh’s traditional expansion principle, and can maintain the closeness of arithmetic operation. Hence, it is feasible to use a polygonal fuzzy number to approximate a general fuzzy number. First, an extension theorem of continuous functions on a real compact set is given according to open set construction theorem. Then using Weierstrass approximation theorem and ordered representation of the polygonal fuzzy numbers, the convergence of a single hidden layer feedforward polygonal fuzzy neural network is proved. Secondly, the gradient vector of the approximation error function and the optimization parameter vector of the network are given by using the ordered representation of polygonal fuzzy numbers, and then the gradient descent algorithm is used to train the optimal parameters of the polygonal fuzzy neural network iteratively. Finally, two simulation examples are given to verify the approximation ability of the network. Simulation result shows that the proposed network and the gradient descent algorithm are effective, and the single hidden layer feedforward network have good abilities in learning and generalization.
Despeckling of Synthetic Aperture Radar Image using Deep-Learning Model

This paper mainly studies despeckling method for Synthetic Aperture Radar (SAR) image. The denoising method based on deep learning usually needs a lot of samples and noiseless reference images to realize data regression. However, speckle noise is very different from random additive noise such as white Gaussian noise in that its distribution is affected by the background target and it is hard to obtain completely noiseless reference images. Therefore, we proposed a local learning method and constructed a deep learning model to overcome this challenge, which named Speckle-Denoise-Net (SD-Net). In proposed method, the speckle noise can be effectively suppressed without the need for noiseless reference images. It can not only solve the problem of lacking training samples and having excessive image size, but also take advantage of unsupervised learning for inherent noise to make a good balance between smoothing and structure-preserving compared With other methods. At present, the test results of this method (Including denoising effect, original information retention, and algorithm speed) have exceeded most of the existing traditional denoising algorithms and deep learning methods, which allow it to bring new inspirations to solve the problem of speckle noise.
Different CNNs for Enhancing Post-Hurricane Damage Classification

Over the past decades, post-hurricane measurements are usually done by humans driving around the affected region to note down manually, which is mostly correct but time-consuming. In this case, more effective methods with CNN models could be proposed since they are advanced at processing and classifying image data. This paper proposed several different CNN models to tackle this issue, which can be utilized to enhance post-hurricane damage measurement by classifying images of buildings from the affected region as damaged or not. Firstly, we got the image data from the affected region with labels of whether being damaged. Five models were trained by these image data, which were basic CNN model, CNN model with Batch Normalization, CNN model with Residual Blocks, pre-trained VGG-19 with frozen weights, and pre-trained VGG-19 with fine tuning. The experimental results demonstrated that Batch Normalization, Residual Blocks, and Learning Rate Decay could greatly improve the basic CNN model performances. The CNN model with Residual Blocks and Learning Rate Decay can reach 99.65%, which is the best model in our project. Furthermore, we note that fine-tuned pre-trained VGG-19 model can also reach 99.6%. This paper can help the rescuers measure the post-hurricane damage in a faster and more efficient way with our classification models based on CNN.
Privacy-Preserving Friend Matching for Mobile Social Networks

In this paper, we propose an efficient private set intersection protocol, named LL-PSI, to enable two parties (where each party has an individual set) to obtain the intersection of their sets without leaking other information about their sets to each other. Compared with existing protocols, LL-PSI reduces the computational latency of the intersection between two sets significantly at the expense of communication costs between the parties. Based on LL-PSI, we propose a privacy-preserving friend matching scheme for mobile social networks, dubbed PAIRING. PAIRING allows users to match with those who have common interests while preserving users&#x0027; private information against the semi-honest server and curious users. We analyze the security of PAIRING and conduct a comprehensive performance evaluation, which demonstrates that PAIRING is secure and efficient.
Motivational aspects of a military leader’s managerial culture development

The purpose of the article is to reveal the main prerequisites for the development of the motivational component of the managerial culture of the military leader. The following research methods were used in the work: analysis and generalization of scientific results on research of the nature of professional activity of the personality, – for clarification of motivational aspects of development of managerial culture of the military leader. In the content of military and professional activity it is necessary to single out motivational phenomena related to the managerial culture of a military leader, which are as follows: values about the military profession, assessment of the significance of professional activity, correlation of military and professional values with the system of meaningful life orientations of servicemen and other personal goals and values, the place of professional activity in the structure of “life essences”, etc. Awareness of the military leader's own ability to realize professional intentions is the sphere of realization of the most important personal values, which provides a motivational attitude to the professional activity. The managerial culture of a military leader depends on the assessment of the significance of professional activity taking into account personal individual needs and goals, which may or may not coincide with the goals of the service. The highly developed managerial culture is closely related to the general concept of professional aspirations and professional self-determination (from gaining of professional education to the stage of mastery), which are the properties that allow you to effectively and reliably perform complex activities in a variety of conditions. An important condition for the professional culture of a military leader, which ensures his full existence in professional activities, should be considered the tendency to develop the managerial potential as a set of ideas about work based on self-understanding, self-perception and personal growth. The basis of this movement is the professional and personal activity of military leaders – persistence in achieving goals, self-confidence, confidence in the professional experience and certainly a positive attitude towards others. These leaders are determined, do not stop there. Their personal characteristics are manifested at the level of self-awareness and are revealed in the process of self-regulation of behavior in professional activities. This is what helps a military leader to orient himself well in life, to be free in choosing his professional path and responsible for the consequences of the choice.
SSD Based Target Detection Algorithm for the Autonomous Driving Vision System

Target detection algorithm takes a central role in autonomous driving vision system, participating in a wide rang of applications in the obstacle detection, vehicle detection, traffic state detection, and others. In recent years, accurate and efficient deep learning models have been proposed for real-time applications. However, the most accurate approaches tend to employ efficient feature fusion method and improved training strategy, such as spatial pyramid pooling. This practice limits real-time performance due to intense extra computational cost. In this paper, we avoid the traditional feature fusion method by proposing an efficient feature fusion method based on feature bi-directional fusion technique and introduce a training method with noise based on confidence loss sequence. We demonstrate the efficiency of the proposal on the PASCAL VOC2007 and KITTI sets, which include the common target detection task and typical autonomous driving target recognition task. The experimental results show that our approach significantly improve the accuracy for complex background samples and reduce the false detection rate of small targets by keeping real-time performance.
A Lightweight Embedding Probability Estimation Algorithm Based on LBP for Adaptive Steganalysis

Adaptive steganography is the most advanced steganography currently, an important method to detect it is to integrate the embedding probability into feature extraction of adaptive steganalysis. Unfortunately, most of the existing methods directly use the true embedding probability maps, which are generated by prior knowledge: the specific steganographic strategies and embedding payloads. However, these cannot be known in advance for steganalysis tasks in the real world. To overcome this difficulty, we propose an embedding probability estimation algorithm based on the local binary pattern (LBP) for adaptive steganalysis. The algorithm we proposed has the advantage of not relying on prior knowledge. Meanwhile, for the first time, LBP operator is introduced into embedding probability estimation. As a non-machine learning method, it has a lighter-weight architecture because it does not need large-scale data sets for training. Experimental results show that the algorithm can better reduce the impact of embedding payloads mismatch than the existing methods, especially when the embedding payload is small.
Research and project activities of students: social and psychological aspects

The article examines social pedagogical and psychological aspects of the management of research and project activities of students. The role of the concepts of "interest", "creativity", "motivation" and "oversituational activity" in these types of activities, which reveal the essence of project and research activities, is considered. The aim of the study is to research the influence of the role of students' interest on the effectiveness of project and research activities. The novelty of the research topic is expressed by an indication of the high role of students' interest in project and research activities, which in both cases significantly affects the efficiency of the results of projects or research work. The theoretical significance of the study is represented by the considered structural relationships in implementation of project and research activities. The practical significance is expressed by generalized conclusions about the importance of organizing a psychological environment focused on the interests of each student, in which these types of activities are carried out. The methodological basis of the research is a literature review, structural and comparative analysis. The results of the study indicate that the effectiveness of the implementation of project and research activities is largely determined by the flexibility of the approach to learning and the created conditions of the educational environment, contributing to the formation of a positive attitude of students to the proposed educational topics, activities and projects.
Construction of Domain-specific Knowledge Graph of Forest Diseases based on Deep Learning

In order to effectively organize and manage the text knowledge of forest diseases, this paper proposes a knowledge graph construction method based on deep learning, which uses the knowledge graph to describe the complex relationship between entities in a structured form. Firstly, the general method of constructing forest disease knowledge graph is described. Secondly, the characteristics of forest disease text are analyzed, preprocessed and labeled. And the data set is constructed. Then, for the unstructured text in the data set, the innovative deep learning method of Bi-gated Recurrent Unit (BiGRU) and Conditional Random Field(CRF) model is used to identify the named entities of forest disease names and therapeutic agents. Experiments on forest disease attribute text corpus show that the precison is 93.15%, which can be effectively applied to the named entity recognition task of forest disease text. Finally, the extracted knowledge is stored in the neo4j map database to intuitively reflect the internal structure of the knowledge graph and realize the visualization and reasoning based on forest disease knowledge.
Machine Learning Framework for Detecting Offensive Swahili Messages in Social Networks with Apache Spark Implementation

Languages morphological context varies by community. The linguistic analysis became more complex due to grammatical variations, cultural, traditional, slang, misspellings, and language variance. Many studies in sentimental analysis have focused on natural language processing and peoples opinions. Text language processing takes time, requires lots of storage space, and a fast computer to work in distributed networks. Many developers choose Hadoop and Map Reduce to process Big Data. This study developed a methodology that employs Apache Spark as a text classification processing engine since it is faster in cluster computing systems. African libraries and packages for language lemmatization and stemming are still lacking. The proposed approach was utilized to detect offensive Swahili texts in social networks. Swahili is the third most widely spoken language in Africa. Four different machine learning techniques were tested as benchmarks, with the multinomial logistic model proving to be the most effective. The evaluation measures show that the proposed machine learning framework is versatile and suitable for usage in centralized and distributed systems.
Structure-Preserved Heterogeneous Graph Representation

Heterogeneous graph (HG) contains various types of nodes or links, which are highly correlated and present intricate structures due to different links. These structures reflect the crucial factors of topology. Therefore encoding meaningful structures is a basic requirement to obtain node representations with high quality. So far, some representative structures have been studied in an HG, from one-hop edges to high-order local structures, such as meta-paths and network schema. In this chapter, we will introduce several works focusing on structure preservation. By capturing respective structures, they successfully depict the rich semantics and complex heterogeneity, and effectively support downstream tasks.
Searching Efficient Models for Human Activity Recognition

Human Activity Recognition (HAR) can be measured in various ways in a new era of growing technologies. This paper studies different classification and data processing tasks. This paper proposes using HAR to monitor the elderly while being power-efficient and respecting an individual’s privacy, allowing it to be run on mobile devices like smartphones or smartwatches. Upon reviewing other methods of HAR by sensor data, we realized that they severely lacked in the areas mentioned earlier. Moreover, they either used older classification techniques or made too complex and over-the-top models for the same. We tested a total of nine methods to find the best model/method, from simple support vector machines (SVM) and convolutional neural networks (CNN) to hybrid models. The best results were produced by a simple, fully connected network (multi-layer perceptron) with the data condensed using Fisher’s linear discriminant analysis (FLDA) that gave us 98.6% accuracy. Our final model satisfies both the requirements we had set; it is simplified and produces benchmark results.
Module Development with Boost.Python and PyBind

In the previous chapter, we saw how to create a basic Python extension module. We added code to expose functionality from the underlying C++ library of statistical functions. We saw how to perform the conversion between PyObject pointers and native C++ types. While not especially difficult, we saw that it is potentially error prone. In this chapter, we consider two frameworks – Boost.Python and PyBind – that overcome these difficulties, making the development of Python extension modules easier. We build two quite similar wrapper components, the first based on Boost.Python and the second on PyBind. The intention here is to compare the two frameworks. Following this, we look at a typical Python client and develop a script to measure the relative performance of the extension modules. We end the chapter with a simple Flask app that demonstrates using our PyBind module as part of a (limited) statistics service.