Reviewerly/exHarmony huggingface dataset:
WorldView-2 Satellite Image Classification using U-Net Deep Learning Model

Land cover maps are important documents for local governments to perform urban planning and management. A field survey using measuring instruments can produce an accurate land cover map. However, this method is time-consuming, expensive, and labor-intensive. A number of researchers have proposed using remote sensing, which generates land cover maps using an optical satellite image with various statistical classification procedures. Recently, artificial intelligence (AI) technology, such as deep learning, has been used in multiple fields, including satellite image classification, with satisfactory results. In this study, a WorldView-2 image of Terangun in Aceh Province, which was acquired on Aug 2, 2016, was classified using a commonly used deep-learning-based classification, namely, U-net. There were eight classes used in the experiment: building, road, open land (such as green open space, bare land, grass, or low vegetation), river, farm, field, aquaculture pond, and garden. For comparison, three classification methods: maximum-likelihood, random forest, and support vector machine, were performed compared to U-Net. A land cover map provided by the government was used as a reference to evaluate the accuracy of land cover maps generated using two classification methods. The results with 100 randomly selected pixels revealed that U-Net was able to obtain a 72% and 0.585 for overall and kappa accuracy, respectively; whereas, overall accuracy and kappa accuracy for the maximum likelihood, random forest and support vector machine methods were 49% and 0.148; 59% and 0.392; and 67% and 0. 511; respectively. Therefore, U-Net outperformed those three of classification methods in classifying the image.&#x0D;
Face Recognition using Probabilistic Model for Locally Changed Face

Face recognition is an attention-grabbing area in research field due to various challenges like aging, pose variation, facial expression, and illumination problem. Now-a-days, plastic surgery is a standout amongst the above mentioned exciting issues of face recognition. Local plastic surgery is a type of plastic surgery in which any one feature of the face is changed instead of all features of face. In this paper, the face recognition on local plastic surgical faces using probabilistic approach is presented, where a probabilistic approach like Naive Bayes Classifier, Neural Network Classifier are used to recognize the faces with local plastic surgery from the database. Naive Bayes classifier is fused with Expectation Maximization Algorithm (EMA) for better recognition of the faces from the database. Finally, Results of Naive Bayes Classifier, Naive Bayes Classifier with EMA is evaluated on standard Plastic Surgery Database(PSD). Similarly, Neural network classifier is also been tested on PSD database, which will aid to decide which classifier is efficient for recognizing plastic surgical faces. The motive of this paper is give increase in recognition rate with the help of effective classifier.
Robot Path Planning Based on Artificial Intelligence Algorithm

Path planning is the most important part in the process of mobile robot research. A good path planning algorithm can greatly improve the efficiency of the robot, and can be accurately and effectively applied to the designated and scheduled tasks. The path planning of a mobile robot can be regarded as an optimization problem with constraints. The optimization algorithm can be divided into global path planning and local path planning. Local planning enhances the real-time control of the robot and makes the planning path smoother. The combination of global planning and local planning can make the goal of local planning more clear, thus avoiding the blindness of robot motion caused by simple local path planning.
Information Processing Methods of Electronic Warfare Events Based on Communication Technology

Traditional electronic countermeasure incident intelligence processing has problems such as low accuracy and stability and long processing time. A method of electronic countermeasure incident intelligence processing based on communication technology is proposed. First, use the integrated digital signal receiver to identify various modulation methods in the complex signal environment to facilitate the processing and transmission of communication signals, then establish an electronic countermeasure intelligence processing framework with Esper as the core, and flow the situation to the processing conclusion through the PROTOBUF interactive format Redis cache. The data can realize the intelligent processing of electronic countermeasure incidents. The experimental results show that the method proposed in this paper increases the recall rate by 5 to 20% compared with other methods. This method has high accuracy and stability for electronic countermeasure incident intelligence processing and can effectively shorten the time for electronic countermeasure incident intelligence processing.
Monitoring library and information services at Central Scientific Agricultural Library

The findings of monitoring of repertoire of and demand for library and information services (document, reference, bibliographic awareness, cultural and educational services) are discussed. The mentioned services are available through the CSAL website. The types and forms of services are analyzed, their place and demand for them are discussed. The focus is made on the online services providing access of in-house information resources, e. g. e-libraries, e-catalogs, virtual exhibitions, etc. The terminological analysis of services titles as compared to the new national standard of library information services was executed. It was revealed that the services titles meet the standardized terms and definitions. The degrees of demand for difference services are estimated as active, fluctuating and decreasing. To improve library and bibliographic services and information support of science for agro-industrial complex, the authors recommend: to study products and services of unstable and decreasing demand, to load explicit list of services in the Information Services section, to design scientist personal accounts, to make it possible for the users to take part in selecting documents for digitization and to receive e-newsletter on new library acquisitions, etc. The authors conclude that CSAL provide the wide range of library information services which is essential to increase accessibility and friendliness of Library-generated information products, to improve information support of research in agricultural industries. The findings will be used in planning user library and information services. It is imperative that user services are monitored and user demands are analyzed.
Research and Implementation of Secure Multicast Communication Algorithm Based on Chaos Neural Network

With the increasing demand for broadband service, the multicast communication industry is increasing in order to increase speed and save related resources. In order to save resources and improve the speed of transmission, multicast communication is proposed. Although researchers have begun to focus on the field of multicast communication, key security problems have not been effectively solved. With the development of technology and cryptographic processing technology, the traditional secret algorithm ensures security by increasing the length of keys, but also increases the difficulty of calculation. Therefore, finding a more perfect encryption algorithm is an effective method to ensure multicast communication.
Research on Fast Compensation Algorithm for Interframe Motion of Multimedia Video Based on Manhattan Distance

To improve the video quality, aiming at the problems of low peak signal-to-noise ratio, poor visual effect, and low bit rate of traditional methods, this paper proposes a fast compensation algorithm for the interframe motion of multimedia video based on Manhattan distance. The absolute median difference based on wavelet transform is used to estimate the multimedia video noise. According to the Gaussian noise variance estimation result, the active noise mixing forensics algorithm is used to preprocess the original video for noise mixing, and the fuzzy C-means clustering method is used to smoothly process the noisy multimedia video and obtain significant information from the multimedia video. The block-based motion idea is to divide each frame of the video sequence into nonoverlapping macroblocks, find the best position of the block corresponding to the current frame in the reference frame according to the specific search range and specific rules, and obtain the relative Manhattan distance between the current frame and the background of multimedia video using the Manhattan distance calculation formula. Then, the motion between the multimedia video frames is compensated. The experimental results show that the algorithm in this paper has a high peak signal-to-noise ratio and a high bit rate, which effectively improves the visual effect of the video.
The universal exponentiable arrow

We show that the essentially algebraic theory of generalized algebraic theories, regarded as a category with finite limits, has a universal exponentiable arrow in the sense that any exponentiable arrow in any category with finite limits is the image of the universal exponentiable arrow by an essentially unique functor.
Reference Clock Signal Distribution for Quantum Key Distribution

Quantum key distribution (QKD) requires high synchronization accuracy, specially in systems that use gated detectors at the receiver station. In this work, we present a reference clock distribution method to support long-term QKD. An average quantum bit error rate (QBER) of 1.8% was measured, which is suitable for different QKD implementations. This method is suitable for longdistance and long-term QKD implementations and does not require additional transmitted qubits for synchronization.
Verified compilation of C programs with a nominal memory model

Memory models play an important role in verified compilation of imperative programming languages. A representative one is the block-based memory model of CompCert---the state-of-the-art verified C compiler. Despite its success, the abstraction over memory space provided by CompCert's memory model is still primitive and inflexible. In essence, it uses a fixed representation for identifying memory blocks in a global memory space and uses a globally shared state for distinguishing between used and unused blocks. Therefore, any reasoning about memory must work uniformly for the global memory; it is impossible to individually reason about different sub-regions of memory (i.e., the stack and global definitions). This not only incurs unnecessary complexity in compiler verification, but also poses significant difficulty for supporting verified compilation of open or concurrent programs which need to work with contextual memory, as manifested in many previous extensions of CompCert. To remove the above limitations, we propose an enhancement to the block-based memory model based on nominal techniques; we call it the nominal memory model. By adopting the key concepts of nominal techniques such as atomic names and supports to model the memory space, we are able to 1) generalize the representation of memory blocks to any types satisfying the properties of atomic names and 2) remove the global constraints for managing memory blocks, enabling flexible memory structures for open and concurrent programs. To demonstrate the effectiveness of the nominal memory model, we develop a series of extensions of CompCert based on it. These extensions show that the nominal memory model 1) supports a general framework for verified compilation of C programs, 2) enables intuitive reasoning of compiler transformations on partial memory; and 3) enables modular reasoning about programs working with contextual memory. We also demonstrate that these extensions require limited changes to the original CompCert, making the verification techniques based on the nominal memory model easy to adopt.
Design of Sports Image Contour Feature Acquisition System Based on the Background Subtraction Method

Moving target detection (MTD) is one of the emphases and difficulties in the field of computer vision and image processing. It is the basis of moving target tracking and behavior recognition. We propose two methods are improved and fused, respectively, and the fusion algorithm is applied to the complex scene for MTD, so as to improve the accuracy of MTD in complex and hybrid scenes. Using the main idea of the three-frame difference image method, the background difference method and the interframe difference method are combined to make their advantages complementary to overcome each other’s weaknesses. The experimental results show that the method can be well adapted to the situation of periodic motion interference in the background, and it can adapt to the situation of sudden background changes.
Hybrid CTC-Attention Network-Based End-to-End Speech Recognition System for Korean Language

In this study, an automatic end-to-end speech recognition system based on hybrid CTC-attention network for Korean language is proposed. Deep neural network/hidden Markov model (DNN/HMM)-based speech recognition system has driven dramatic improvement in this area. However, it is difficult for non-experts to develop speech recognition for new applications. End-to-end approaches have simplified speech recognition system into a single-network architecture. These approaches can develop speech recognition system that does not require expert knowledge. In this paper, we propose hybrid CTC-attention network as end-to-end speech recognition model for Korean language. This model effectively utilizes a CTC objective function during attention model training. This approach improves the performance in terms of speech recognition accuracy as well as training speed. In most languages, end-to-end speech recognition uses characters as output labels. However, for Korean, character-based end-to-end speech recognition is not an efficient approach because Korean language has 11,172 possible numbers of characters. The number is relatively large compared to other languages. For example, English has 26 characters, and Japanese has 50 characters. To address this problem, we utilize Korean 49 graphemes as output labels. Experimental result shows 10.02% character error rate (CER) when 740 hours of Korean training data are used.
Quantum Walk-Based Vehicle Routing Optimisation

This paper demonstrates the applicability of the Quantum Walk-based Optimisation Algorithm (QWOA) to the Capacitated Vehicle Routing Problem (CVRP). Efficient algorithms are developed for the indexing and unindexing of the solution space and for implementing the required alternating phase-walk unitaries, which are the core components of QWOA. Results of numerical simulation demonstrate that the QWOA is capable of producing convergence to near-optimal solutions for a randomly generated eight location CVRP. Preparation of the amplified quantum state in this example problem is demonstrated to produce higher-quality solutions than expected from classical random sampling of equivalent computational effort.
A Study on the Difference of Information Acquisition Ability among Students in Higher Vocational Colleges

The explosive growth of all kinds of online information brought by mobile Internet has brought challenges to the information acquisition ability of students in higher education institutions.In order to explore the differences, influencing factors and ways to improve the information acquisition of college students in higher education institutions, 306 questionnaires from students in five higher vocational colleges were collected and analyzed.It was found that there was no significant difference in the information acquisition ability of students by gender.However, the majors of higher vocational college students have a significant impact on their information acquisition ability.It was suggested to taking comprehensive measures to improve the information acquisition ability of students in higher vocational colleges according to their characteristics.
Farklı Çiçek Türlerini Derin Öğrenme Yöntemi İle Tanıma

In this study, an algorithm that can recognize flower species using deep learning techniques has been developed.Differentiating plants and flowers is important for researchers, agricultural engineers, forestry engineers, farmers and botanists.In order to distinguish flower types, features and forms specific to each flower need to be extracted.Classifying flower images is a challenging task because of the complex background of flowers, the similarity between different flower types, and the differences between the same flower types.For this reason, the recognition and grouping of flower images in computer environment increases the performance by providing various facilities.In this study, a system has been developed that predicts the type of flower when the image of a flower is loaded into the system.The Oxford 102 data set was used in the training and testing of the system.This data set contains 7370 images of 102 different flower species.Due to its achievements in image processing in recent years, ResNet152 deep learning architecture is used in this study to classify flower species.The system, which showed 99% classification success for test images, was found to be more successful than other flower classification methods.
Training Capacity of the Kahoot! Testing Technology in Teaching Legal English

The article focuses on the issue of using the modern testing system Kahoot! to achieve the goals of scaffolding in the developing digital environment and online education. It summarizes the results observed during the system-based practice, especially in the unprecedented situation of an abrupt and extensive shift from face-to-face training to a remote type of knowledge formation and assessment at the beginning of the COVID-19 pandemic. The article considers three types of tasks in an interactive testing system: from assessment ones to educational ones with extended functionality. Special emphasis has been placed on the active role of a student in personalizing his goal setting. The methodological approach proposed aims at consolidating the training capacity of the testing system: it contributes to the development of logical, critical, and creative thinking, stimulates high-order thinking, combines teamwork and competition. It maintains students’ motivation, their self-awareness as team contributors, and intensifies autonomous learning.
Research on the Effectiveness of Hybrid Teaching of General Mechanical Equipment Maintenance Course

Based on practice of hybrid teaching in the course of general mechanical equipment maintenance, this paper studied factors affecting the effectiveness of traditional teaching and hybrid teaching.It was considered that the effectiveness of teaching was affected by many factors, and traditional curriculum teaching was affected by orientation of curriculum objectives, students' learning enthusiasm, teachers' teaching ability, teaching methods and teaching software and hardware resources.In addition to above factors, hybrid teaching was also affected by construction quality of MOOC or SPOC and related application ability.With hybrid teaching of general mechanical equipment maintenance, the average scores of teaching classes were significantly improved.The improvement range of hybrid teaching was 8.1% -26.3%.The results of students who were enrolled from secondary vocational schools were improved by 8.1% -15.3% after adopting hybrid teaching.The scores of students enrolled in the college entrance examination increased by 13.1% -26.3%.
Electronic repeat dispensing

Journal of Prescribing PracticeVol. 2, No. 6 Calculation SkillsElectronic repeat dispensingJanice DaviesJanice DaviesSearch for more papers by this authorJanice DaviesPublished Online:10 Jun 2020https://doi.org/10.12968/jprp.2020.2.6.310AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 2 June 2020Volume 2Issue 6ISSN (print): 2631-8385ISSN (online): 2631-8393 Metrics History Published online 10 June 2020 Published in print 2 June 2020 Information© MA Healthcare LimitedPDF download
Queue Waiting Time Estimation Using Person Re-identification by Upper Body

In this paper, we propose a new approach to estimating waiting time in queue based on object tracking and person re-identification by upper body. The task we are considering is practically important in video analysis, because this data can be used for predictive analytics and improvement of customer services. The main idea of the proposed method is to use upper body detections instead of full body detections. This decision is due to the following fact: in queues, the upper bodies are more visible. Using re-identification allows us to perform video analytics on sparse frames and thereby increase the computational efficiency of the estimation algorithm. Also in this work, we introduce a novel upper body regression by head, upper body random size augmentation to improve re-identification performance in real-world scenarios and a method for calculating metrics for queue waiting time estimation algorithms. Our experimental evaluation showed that the proposed algorithm has a high accuracy of queue waiting time estimation.
OO-LSTM: A trusted medical transfers prediction model with on-chain and off-chain data fusion

When the medical services surrounding patients cannot meet the needs of patients, transfer treatment has become an unavoidable part in the current medical environment. Initially, people choose the transfer path within their own cognitive range. With the development of the Internet, people can obtain the transfer paths of patients similar to them from all over the country through the Internet for reference, further combine machine learning models such as RNN, LSTM, and CNN to recommend the best transfer path. However, the treatment behaviors usually span multiple medical institutions, and it is difficult to comprehensively and efficiently share the cases, examination results and treatment process between the institutions. In addition, the source of traditional prediction model’s data set is opaque, and the integrity of data needs to be verified, which all affect the accuracy of prediction result. In order to solve the above problems, we improve the traditional LSTM, and propose OO-LSTM which integrates data sets on the blockchain and off the blockchain, uses the analysis method of transfer path based on blockchain association and traceability mechanism, and supplements and cross-validates the data on the chain and off the chain. Based on OO-LSTM, we can obtain a reliable and complete prediction data set and provide more accurate and credible recommendations for patients according to the conditions of patients in the current medical institutions. Experiments have proved that our method has higher credibility and more accurate results than traditional prediction models at the same cost.
Evaluation in Virtual Heritage

Evaluation in virtual heritage is concerned with learning about and assessing the extent to which an interactive system offers a satisfactory user experience (UX) and meets user goals and expectations. Evaluation in virtual heritage is an empirical process of research, which reaches for conclusions about the quality of a system by observing, measuring (aspects of), and interpreting the UX. It is inherently a complex activity that requires careful planning and selection of methods. It does not rely on underlying technology; however, adaptations of process and methods must be made to allow for results and feedback in context. Therefore, it must be designed so that it is useful, reliable, valid, and productive. Evaluation methods and processes are of interest to both cultural heritage (CH) professionals and technology designers, who aim to provide systems that address the widest range of potential users. This chapter discusses basic concepts, processes, and empirical evaluation methods in virtual heritage, with examples.