Reviewerly/exHarmony huggingface dataset:
Book Maps of Russian Authors of the XIX – early XX centuries as a Source on the Visual History of Kazakhstan: Catalog

The article is carried out within the framework of a scientific project, is devoted to the analysis of book maps of Russian authors of the XIX -early XX centurшуы, cataloging and introduction into scientific circulation of cartographic unique, little-known data in order to study the visual history of Kazakhstan.The source base was the book collections of rare editions with book maps of the National Library named after Alisher Navoi, Tashkent (NBUz) and of the Orenburg Universal Scientific Library named after N.K. Krupskaya (OUSL).During the preparation of the catalog, a single catalog description of the book maps was developed.The methodology of the research is a comprehensive interdisciplinary approach of broad interaction between the humanities and natural sciences, which made it possible to identify, analyze and prepare a bibliography of book maps.A cartographic method is used as a special method, which allows cataloging and description of book maps.The operational and functional advantages of the catalog of cartographic materials are substantiated, where each researcher can get an answer where and under what number the necessary book with a book maps is located.In the description of the book maps using the materials of the book, additional opportunities appear to characterize historical facts more fully and visually show the dynamics of the events taking place.Comparison of the book maps and the text showed that the cartographic material contains information that is not in the text of the book.The historical details of the formation of the border, possible ways of melioration of the Central Asian region unknown pages of military history, the participation of the Kazakhs in the military campaign to Khiva in 1839-1840, the peculiarities of the distribution and nomadic ways of the Kazakhs district were revealed.The authors conclude that Russian scientists, travelers, bureaucrats and the military left a significant book heritage with phenomenal cartographic material for studying the visual history of Kazakhstan.
Person Re-Identification by Siamese Network

Re-Identification of person aims at retrieval of person across multiple non overlapping camera. There was a huge gain in the computer vision community with the advancement of deep learning features and also the number of surveillance in videos increased. The challenges faced by person re-identification is low resolution images, pose variation etc., and convolutional neural networks are supported by a number of state-of-the-art algorithms for person re-identification. In this paper, Siamese network is used to predict the similarity or dissimilarity of a person across two cameras. It's a neural architecture that takes as input a pair of images or videos and the output as the prediction of similar and dissimilar persons along with their prediction scores. The experimentation is done by using datasets iLIDS-VID, PRID 2011 and obtained a recognition accuracy of 79.52% and 85.82% respectively.
Digital Competency of Higher Education Students in the Context of COVID-19

The COVID-19 pandemic has exerted a detrimental impact on students' learning. This is because although online pedagogy has been widely adopted in educational settings, many students have felt unprepared. Therefore, digital competency is crucial for students to cope with the unprecedented situation. It is also critical for international institutions and educators to revise digital competency
Skew Class-Balanced Re-Weighting for Unbiased Scene Graph Generation

An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-Balanced Re-Weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating the deteriorating performances of the minority predicate predictions, showing drastic dropping recall scores, i.e., losing the majority predicate performances. It has not yet correctly analyzed the trade-off between majority and minority predicate performances in the limited SGG datasets. In this paper, to alleviate the issue, the Skew Class-Balanced Re-Weighting (SCR) loss function is considered for the unbiased SGG models. Leveraged by the skewness of biased predicate predictions, the SCR estimates the target predicate weight coefficient and then re-weights more to the biased predicates for better trading-off between the majority predicates and the minority ones. Extensive experiments conducted on the standard Visual Genome dataset and Open Image V4 and V6 show the performances and generality of the SCR with the traditional SGG models.
A Bibliometric Overview of IoT-Based Digital Voting

Abstract: Several studies have empirically explored biometric voting using the IoT to transfer votes to the central system. There aren't many bibliometric studies that categorize the output in this area, though. By keeping an eye on the papers posted on the Scopus platform, this study’s goal is to give research bibliometric analysis on biometric voting utilizing IoT to transfer votes to a central system, classifying trends, the state of the art, and other indications. 267 different materials made up the sample. Using the VOSviewer program, the data was processed and the outcomes graphically represented. According to the study, which examined publications’ simultaneous occurrence by year, trends of keyword, co-citations, coupling bibliographic, and co-authorship analysis, institutions, and countries, the body of knowledge on biometric voting that uses the Internet of Things to transfer votes to a central system is expanding quickly. More than 530 citations were found in just eight works. However, there are other industrious writers. The most significant of the 267 sources used in the review were published in 26.066 percent of the papers. China is the world's leader in this field. This study offers knowledge about the current state of the art and indicates research opportunities and gaps in IoT-based biometric voting.
Research on Strategies to Improve Effective Teaching of English Listening in Rural Junior Middle Schools

Junior high school is a critical period of English learning. According to the English curriculum standards in China, junior high school English should not only pay attention to the imparting of language knowledge but also pay attention to the cultivation of language skills. The junior high school stage is the critical period for students to learn. English listening is the first link of language skills and dominant position on speaking, reading and writing and other aspects of far-reaching impact. Different from the rich teaching resources and activities of urban junior middle schools, the resources and facilities in rural areas are relatively weak, so the effective teaching of junior middle school English listening needs to be paid more attention, which meets the requirements of the new curriculum standards of junior middle school English and the needs of curriculum reform in China. Based on this situation, this paper investigates and studies the effective teaching of junior middle school English listening. From the perspective of teachers and students, it combines quantitative research with qualitative research to find out the phenomenon of low efficiency and ineffective teaching in the current rural junior middle school English listening teaching, analyzes its causes, and puts forward effective countermeasures. In this study, 185 students were surveyed and 10 students and 15 teachers were interviewed by questionnaire and interview. Based on the relevant theory of effectiveness, combined with the interpretation of the specific requirements and basic spirit of English ability in the new curriculum standards, as well as the analysis and research of test results, this paper summarizes the inefficiencies existing in English listening teaching in rural junior high schools at this stage, and makes an in-depth study of the causes of the problems, including misleading effects of examination results and the professional level of teachers. In view of these problems, the author proposed to improve and enhance the effective teaching strategies of rural junior middle school English students, hoping to be helpful to the majority of front-line grassroots teachers.
Strategic Management Process and Performance of Construction and Engineering Company in Rwanda: A Case of NPD Ltd

The main purpose of this research is to investigate the effect of strategic management process on organizational performance of NDP Ltd construction and engineering company in Rwanda. The research used descriptive research design with a mixed approach of qualitative and quantitative data. The results determined the sample size of 217 respondents from 500 people using Krejcie and Morgan (1970). However, only 188 respondents participated in this research with response rate of 86.6% of 217 respondents. The results of the first objective revealed that environmental scanning plays a significant and a positive effect on organizational performance in Rwanda with overall mean of 1.621 tending to high score of to a very great extent. The results of the second objective revealed that strategy formulation plays a significant and a positive effect on organizational performance in Rwanda with overall mean of 1.324 tending to high score of to a very great extent. The results of the third objective revealed that a big number of respondents strongly agreed that strategy implementation plays a significant effect in organizational performance. Lastly, the results of the fourth objective revealed that strategy evaluation plays a positive and significant effect in performance of NPD Ltd. Hence, these are supported by correlation analysis results which revealed that there is a positive and significant relationship between environmental scanning and budgetary performance (r=0.717 and sig=0.00&lt;0.01), and quality performance (r= 0.783 and sig=0.00&lt;0.01). The strategy formulation and timely performance (r=0.647 and sig=0.00&lt;0.01), and quality performance (r= 0.696 and sig=0.00&lt;0.01). Another positive and significant relationship between strategy implementation and timely performance (r=0.650 and sig=0.00&lt;0.01), strategy evaluation and quality performance (r= 0.707 and sig=0.00&lt;0.01) level of significance. Hence, in conclusion since all the predictors of strategic management process have a positive and significant relationship with the measures of organizational performance, this implies that there is a positive and significant relationship between strategic management process and organizational performance of NPD Ltd in Kigali City of Rwanda. It is in this regard the research would like to recommend to managers, evaluators, and strategy formulators as well as implementers to ensure that strategies are well formulated, implemented and evaluated to ensure organizational performance. This recommendation is made because the results of the study indicated that strategic management process has a positive effect on organizational performance. Keywords: Strategic Management, Organizational Performance, Construction and Engineering Company Performance, NPD ltd, Rwanda
Transfer knowledge for punctuation prediction via adversarial training

Previous studies demonstrate that part-of-speech (POS) tags are helpful for punctuation restoration tasks. However, extra computation cost will be needed during decoding, due to POS tags are provided by an external POS tagger. This paper proposes to transfer knowledge via adversarial training and orthogonality constraints to fill in the gap. Adversarial multi-task learning is introduced to learn task invariant knowledge from the extra POS tagging task for a punctuation prediction task. Furthermore, orthogonality constraints are used to make private and shared features dissimilar. Only the punctuation predicting task is used during decoding. So extra computation is not needed. Experiments are conducted on IWSLT2011 datasets. The results show that the punctuation predicting models trained with adversarial learning obtain performance gains over the baseline models on test sets. The results also demonstrate that the models trained with orthogonality constraints further obtain performance improvement.
Reading Card and Index Card Match as a Learning Media in Mathematics Subject Materials of Fractions

The application of Reading Card and Index Card Matching strategies in Mathematics is done in a fun way. The implementation repeats the material that has been given before. emphasis on numeracy skills. The focus of this research is how to apply the Reading Card and Index Card Match strategies in Mathematics in class IV MI Islamiyah Jatirogo, Jatirogo District, Tuban Regency. This research sample uses 15 students. The method in this research is qualitative. Based on the research conducted, it can be concluded that the application of the Reading Card and Index Card Match strategies applied in learning Mathematics has been carried out well. The teacher has been able to make lesson plans well, namely by making lesson plans and preparing media in the form of well-matched cards. In the implementation of learning the teacher has been able to carry out learning steps with the Reading Card and Index Card Match strategies. In the evaluation activities, the teacher carried out the evaluation well so that it was seen that with the Reading Card Match and Index Card Match strategies the student learning outcomes were satisfactory so that this strategy was used again in subsequent learning.
Effectiveness of using ludic activities to enhance students’ speaking skills

This study aims to find out the effectiveness of using ludic activities to develop English-speaking skills. Speaking is vital for interaction and for communicating messages. Based on some researchers’ experience, speaking is the most challenging skill to develop in the English language teaching-learning process. For that reason, this research was conducted to solve the problem in students' speaking skills thus they can better communicate their thoughts or ideas. Moreover, the research participants were seventeen students from the second year. This study used an action research method by collecting quantitative data that was analyzed to provide a background on the topic under study. The findings reveal that there was a significant improvement in the student’s scores since students gather 4.56 points on the pretest and 8.12 points on the posttest, this demonstrates the effectiveness of using ludic activities to improve students’ speaking skills. To sum up, ludic activities enhance students speaking skills in an efficient way. Because of this, students practice the speaking components which help them to speak clearly. This study provides more information for future research about the incorporation of ludic activities to improve students’ speaking skills.
A new intuitionistic fuzzy implication and the negation, conjunctions and disjunctions generated by it

A new – the 207-th – intuitionistic fuzzy implication is introduced over intuitionistic fuzzy sets. Over its basis, new intuitionistic fuzzy negation, conjunctions and disjunctions are constructed. For the first time, it is shown that all three conjunctions generated by the same implication coincide, whence their respective disjunctions are different. Some properties of the newly constructed operations are studied.
Space–Time Adaptive Processing Using Deep Neural Network-Based Shrinkage Algorithm Under Small Training Samples

This correspondence discusses a space–time adaptive processing (STAP) method using deep neural network (DNN) and shrinkage algorithm (SA) under small training samples. A DNN-based SA is developed to estimate the clutter-plus-noise covariance matrix (CNCM) from the sample covariance matrix. This technique establishes a learning-based nonlinear mapping between the sample eigenvalues and the optimal shrunk eigenvalues and, thus, provides accurate CNCM estimation. Specifically, a DNN consisting of cascaded convolutional neural network (CNN) and fully connected network (FCN) is designed, where the CNN performs the feature extraction and the FCN derives the estimation of the shrunk eigenvalues. To improve the CNN performance, a deep residual learning strategy is introduced into CNN. Due to the lack of labeled data, an airborne radar system is simulated for the construction of dataset consisting of sample eigenvalues and optimal shrunk eigenvalues. Numerical results demonstrate that the proposed DNN-based SA STAP has superior performance compared with the state-of-the-art SA STAP methods.
GeauxTrace: A Scalable Privacy-Protecting Contact Tracing App Design Using Blockchain

Contact tracing is the approach to identifying physical contact between human beings using a variety of data such as personal details and locations to discover the potential infection of diseases. Since the outbreak of the COVID-19 pandemic, contact tracing has been used extensively to quarantine the people at risk to stop the spread. Moreover, the data collected during contact tracing are typical spatiotemporal data, which can be used to study the disease and discover the spread pattern. However, both traditional labor-intensive and modern digital-based approaches have limitations in terms of cost and privacy concerns. In this paper, we proposed GeauxTrace, a Blockchain-based privacy-protecting contact tracing platform, which separates private data from proof of contact. Sensitive data collected by the front-end app via Bluetooth-based methods are stored locally, and only the proofs of contacts are uploaded onto the immutable private blockchain, which forms a global contact graph at the backend. Our approach not only enables multi-hop risky users to be notified but also reveals the infection patterns via the global graph, which could help study diseases and assist the policymaker. Our implementation shows the feasibility of the proposed platform in real-world scenarios and achieves the performance of 20-30 user requests per second.
Analysis on Blockchain Effectiveness Towards Protecting Renewable-Based Smart Power Grids

Non-renewable energies have been increasingly destructive to the environment, and as a result, society has been seeking to replace these energies at a growing rate. Converting current non-renewable-based power grids to environmentally friendly smart energy grids has been identified as one of the most powerful ways to remove the reliance on non-renewable energies. However, failing to keep these new power grids efficient and secure will cause this concept to fail to become a reality. With a new energy management system, a new security system is also required. Some developing technologies such as blockchain and artificial intelligence have been identified as candidates for strong and efficient security protocols for smart grids. Analysis shows that blockchain technology can provide incredible defense against malicious tampering and data protection. Artificial intelligence can be trained to identify attacks before they become destructive. Along with these new technologies, concepts such as the firewall should be included due to their general effectiveness and efficiency. By utilizing both old and new security protocols, a safer, more efficient, and more reliable energy grid for the future can be created.
An autonomous path planning method considering terrain aided navigation constraints

In order to solve the autonomous path planning problem with terrain aided navigation constraints, a navigation-first path planning method was adopted, that is, the distribution of terrain matching regions and the drift of navigation errors were given priority, and the path planning problem was transformed into the shortest path problem of single source directed graph to determine the nodes on the path. Secondly, the path planning between adjacent nodes is transformed into a path planning problem with initial direction constraint and terminal direction freedom/constraint, and the lateral path is smoothed. Finally, for the local path passing through the no-fly zone, the obstacle avoidance path planning with initial and terminal direction constraints is carried out based on convex optimization method. The simulation results show that the autonomous path planning method considering terrain aided navigation constraints can plan the shortest flight path under the constraints of navigation accuracy, maneuverability and no-fly zone.
Receding Horizon Optimization-based Integrated Path Planning and Tracking Control of eVTOL UAV

In this paper, we present an integrated dynamic path planning and trajectory tracking control strategy for electric vertical take-off and landing (eVTOL) unmanned aerial vehicles (UAVs) by using a receding horizon optimization (RHO) approach. Considering that the effective sensing range of onboard sensors is practically short, we formulate the path planning into minimum curvature-based RHO problems with a polynomial path template. By adding the contraction constraint derived from the backstepping controller, the proposed nonlinear model predictive control (NMPC), as an online optimization scheme, guarantees the closed-loop stability. It also achieves the optimal control performance by minimizing the tracking error and power consumption. The experimental results of the dynamic path planning and trajectory tracking demonstrate the effectiveness of the proposed control algorithm.
An improved RRT* algorithm for mobile robots path planning

Rapidly-exploring Random Tree Star(RRT*) is a path-planning algorithm based on Rapidly-exploring Random Tree(RRT). RRT* is applicable to complex and high-dimensional problems. Although RRT* algorithm is asymptotically optimal, its slow convergence rate makes it less efficient. To address this problem, an efficient optimal path-planning algorithm based on RRT* is proposed in this paper, which combines the advantages of Tropistic RRT* and Quick-RRT*. Firstly, an adaptive sampling strategy based on the tree growth is proposed to search an initial path in restricted space. Secondly, the path is optimized by node rejection after initial path is found. Finally, the improved algorithm expands the elements of possible parent vertices in ChooseParent and Rewire procedures. The simulation results demonstrate the feasibility of the proposed algorithm.
Design and Implementation of State Detection System Based on Big Data

With the development of technology, demands of safety operation get more and more higher. In order to avoid system failure and reduce the maintenance cost, applying state detection technology to monitor system’s operation state in real time becomes more and more important. A big data state detection method which based on multi-scale analysis was proposed. It can realize the operation monitoring of equipment. Besides, at the hardware level, edge computing platforms with different performance were designed, such as raspberry computing module, F1C100S and STM32F series, which could collect equipment operation data via 4G or 5G networks, compute operation status. At present, the condition monitoring platform runs stably and has achieved good results.