Reviewerly/exHarmony huggingface dataset:
Integrated Navigation Algorithm for Intelligent USVs with Unstable GPS

In complex marine or unpredictable combat environments, there may be unstable and even loss of GPS for the integrated navigation system of unmanned surface vehicles (USVs). This paper analyzes the error model of loose combination navigation, and proposes a method of the recurrent neural network (RNN) aided inertial navigation system (INS). When the GPS signal is interrupted, the RNN is used to correct the navigation error of the INS. Simulation results show that in case of unstable GPS, the RNN-aided navigation system can ensure rapid convergence of error, and the east position error can be kept within a satisfactory scope. Compared with the single inertial navigation system, the navigation accuracy is effectively improved.
An improved pattern-based prediction model for a class of industrial processes

For the problem of simplifying pattern-based modeling procedures, an improved pattern-based modeling method is put forward via pattern classification for a class of complex processes. It is a pure data-driven modeling method using statistical attributes of the processes. At the beginning of the paper, a method of system dynamics description based on pattern moving is introduced. Then, an improved method of pattern-moving-based prediction modeling is put forward, and it simplifies the pattern-moving-based modeling method by integrating an initial model and a classification mapping. It consists of two parts: system pattern construction and pattern classification. And a constructive classification neural network (CCNN) is designed to describe system dynamics by classifying the system pattern, and its generalization is discussed. Finally, simulations using data of an actual production process demonstrate the feasibility of the proposed modeling method, and the effectiveness of the CCNN is verified using comparison experiments.
Design Of High Flexibility Visual Search And Analysis Engine System

With the rapid development of the Internet, access logs grow explosively and contain rich important information. There are various requirements for temporary log analysis, such as fault location, business characteristics analysis, and system operation. This kind of requirements are temporary, sudden and diverse. In order to meet this kind of requirements quickly, a distributed visual search and analysis engine system is designed, which has high flexibility and low threshold. The flexibility of the system is enhanced by abstracting the general search and analysis engine module, supporting the configurable extension of statistical dimension and providing user-friendly visual interface. At the same time, for unified management of configuration files, abstract matrix extended framework, which can improve the loading automation of configuration file and descriptibility of dependency relationship. Finally, two case studies verify the feasibility and flexibility of the system.
Semantic-Aligned Attention With Refining Feature Embedding for Few-Shot Image Classification

Autonomous driving relies on trusty visual recognition of surrounding objects. Few-shot image classification is used in autonomous driving to help recognize objects that are rarely seen. Successful embedding and metric-learning approaches to this task normally learn a feature comparison framework between an unseen image and the labeled images. However, these approaches usually have problems with ambiguous feature embedding because they tend to ignore important local visual and semantic information when extracting intra-class common features from the images. In this paper, we introduce a Semantic-Aligned Attention (SAA) mechanism to refine feature embedding and it can be applied to most of the existing embedding and metric-learning approaches. The mechanism highlights pivotal local visual information with attention mechanism and aligns the attentive map with semantic information to refine the extracted features. Incorporating the proposed mechanism into the prototypical network, evaluation results reveal competitive improvements in both few-shot and zero-shot classification tasks on various benchmark datasets.
Computer Context Analysis of Macao's Public Administration Education: A case study of Macao Polytechnic Institute

In evaluating public administration education in Macao, this paper applies the case study method to the example of Macao Polytechnic Institute and the computer context analysis method to the textual information of the Official Bulletin of the Macao SAR Government. This paper maintains that Macao Polytechnic Institute is the main body of public administration training in Macao, providing both degree programs for students and non-degree programs for public servants. Furthermore, this paper recommends setting up in the future a flexible training center for public servants, open training programs within the Greater Bay Area, and strengthening cultural education for overseas Chinese.
Random Number Generation Algorithms for Performance Testing

There are numerous areas relied on random numbers. As one knows, in Cryptography, randomness plays a vital role from key generation to encrypting the systems. If randomness is not created effectively, the whole system is vulnerable to security threats where an outsider can easily predict the algorithm used to generate the random numbers in the system. Another main application where one would not touch is the role of random numbers in different devices mainly storage-related like Solid State Drives, Universal Serial Bus (USB), Secure Digital (SD) cards random performance testing. This paper focuses on various novel algorithms to generate random numbers for efficient performance evaluation of different drives. The main metrics for performance testing is random read and write performance. Here, the biggest challenge to test the random performance of the drive is not only the extent to which randomness is created but also the testing should cover the entire device (say complete NAND, NOR, etc.). So, the random number generator should generate in such a way that the random numbers should not be able to be predicted and must generate the numbers covering the entire range. This paper proposes different methods for such generators and towards the end, discusses the implementation in Field Programmable Gate Array (FPGA).
Graph structures for data visualizations

Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be noticed.
Payment Application Design Using Cart (Classification And Regression Tree) Method

This payroll application is used to calculate the amount of salary that will be received by the Lion Parcel Blitar courier. This study aims to help find solutions for admins to complete payroll tasks quickly and accurately by developing a payroll system that is easy to implement. This research uses the RND (Research and Development) method according to Sugiyono (2012), because this method is used to generate and test the effectiveness of a product. To facilitate the grouping of couriers delivering the most packages and delivering the least packages, the method used by the author in this study is the CART method with the final result being a confusion matrix class a ( &lt;= 600 ) of 5 data and a confusion matrix of class b (&gt; 600 ) of 3 data. In addition, Black Box testing is also used with a percentage of 92.3% and questionnaires to validators of 86.1%, as well as prospective users as testing applications with a percentage of 90.4%. So this application is declared very feasible.
Making and Trusting Decisions in Visual Analytics

Decision making and trust have both become rising topics in the research community of Visual Analytics (VA). Many efforts have been made to understand and facilitate making decisions with VA, as well as build and calibrate trust. However, previous research largely took VA as a tool to facilitate decision making, but did not explore the possibility to dissect each analytical step in VA as decision making and discuss how decision making theories can be utilized to improve the trustworthiness of decisions in VA. Therefore, this paper instead proposes such alternative take on the relation between decision making and VA, inspects the processes of visually analyzing data as decision making, and discusses how to leverage decision making theories to facilitate trustworthy decision making in VA.
نموذج تعليم مهارة الكلام عبر الإنترنت فى الجامعات الإسلامية الحكومية

Learning interaction is an essential element during the online learning process. Therefore, this study offers an internet-based speaking skill learning model. This study aims to describe the process, the problem, and the learning model of learning internet-based speaking skills. This study uses a descriptive qualitative approach with case studies, focusing on learning at state Islamic universities in East Java, Indonesia, The State Islamic University of Maulana Malik Ibrahim Malang, and The State Islamic University of KH. Ahmad Shiddiq Jember. For data collection, the researcher used three methods: first, observation of online speaking learning activities conducted at two universities to find out the steps of learning, secondly interviews were conducted with lecturers who teach speaking courses and also students in the class to find out the obstacles faced. and thirdly documentation in the form of photos of activities and platforms used in the learning process to complete the data. For data analysis, the researcher used the Miles and Huberman method: data collection, data classification, data presentation, and concluding. The findings of the research : (1) that the internet-based speaking skill learning process is based on Personal Learning, (2) There are two problems in learning internet-based speaking skills: Internal Problems that is from the lecturer side cannot explain the whole theme and from the student side; bored and External Problems: electronic constraints related to internet signals and data, (3) internet-based speaking skill learning model That is the interaction between students - students and interactions between lecturers - students. The formal findings of this study are: learning maharah kalam with an internet-based personal learning model based on interaction with synchronous media shows its effectiveness in Islamic universities.
Solving sparse reward games using deep Q-learning with demonstration and partial training

Games that has a sparse reward space are considered a challenge in the field of deep reinforcement learning. Such games are impossible to tackle with a random exploration commonly used in early stages of learning process. We present a modified Deep Q-learning architecture, as well as some small modifications to perform better in this sparse reward games. We also introduced partial training, a policy improvement technique for neural network that kickstarts an agent to get rewards faster in games with sparse rewards. We apply our methods to 2 games with varying difficulty. The results indicate that out agent learns faster and performs significantly better compared to classical Deep Q-Learning. We further shows that combined with partial training, Deep Q-learning is viable to even solve games with really sparse reward.
Synchronization of Reserved Information in Large-Scale Control Systems for APCS of NPP

The task of synchronizing reserved information in the form of data coming to the Top-level subsystem (TLS) from Low-level subsystems (LLS) of the Automated Process control system (APCS) of a Nuclear power plant (NPP) is considering. The method of solving this problem and the algorithm implemented in APCS NPP software are proposing.
Is the Virtual Classroom a Window Into the Future During COVID-19?

Education in today’s epidemic time is possible only through virtual learning platforms. COVID-19 has changed the mode of learning and shown the power of learning in virtual mode. In this paper a self-designed questionnaire on virtual learning was developed to analyse the impact of virtual learning on undergraduate and postgraduate university students. The self-developed questionnaire containing 25 items is tested for validity and reliability. 25 items are further segregated into 5 factors using exploratory factor analysis. These five factors are: Virtual Learning Environment; Virtual Learning and Health Related Issues; Virtual Learning and Interpersonal Issues; Virtual Learning and Technological Issues; Virtual Learning Deliverables. To have the comparative study of undergraduate and postgraduate students for the use of virtual learning platforms, one-way ANOVA is implemented. One-way ANOVA is implemented for each factor and it was found that virtual learning effectiveness increases when students move from Under graduation (UG) to Post Graduation (PG) in India.
EEG Signal Processing for Survey of Dynamic Auditory Verbal Learning and Memory Formation in Brain by Fractal Analysis

Learning and memory involve a complex cognitive process to acquire, retain, and retrieve information in the central nervous system. However, the brain mechanism still needs to be well understood. This study aimed to examine the dynamic auditory verbal learning model of the brain mechanism involved in cognitive learning using the scale-free approach by the fractal analysis of electroencephalogram (EEG) data. This illustrates how the complexity of information processing in the brain changes while auditory and verbal learning occurs. Therefore, a standard verbal-auditory cognitive assessment test was used to create a learning paradigm. Eighteen healthy male volunteers (19–23[Formula: see text]years old) were recruited and their verbal memories were assessed using the Rey auditory verbal learning test. Fifteen unrelated words were sequentially presented to the subjects and they were asked to recall the presented words as many as possible. The experiment was repeated five times with no stop in between. EEG recording was performed before, during and after each stage. Subsequently, the Hurst exponents of EEG were calculated and their associations with the recalled words and the learning rate were estimated. The approximate entropy was intended to confirm the Hurst exponent variations of signals. The statistical analysis of the data showed that the increase in the number of the recalled words was positively correlated with an increase in the Hurst exponents of EEG signals (more significant at the temporal channels) and a decrease in the approximate entropy of EEG signals during the learning of trials. These results denoted a reduced complexity pattern in EEG signals while rehearsing auditory and verbal memories.
The Innovation of Multimedia Art Teaching Curriculum Design Based on Computer Technology

With the continuous development of global informatization, multimedia teaching is an important teaching method in the field of art teaching. However, due to the existence of multimedia, multimedia teaching has become an important teaching method. In multimedia teaching, there is a lack of selectivity, the emphasis on teaching-related technology and strict use has severely restricted multimedia teaching teachers. The development and application of large-scale data technology provides strong technical support for multimedia teaching. Multimedia teaching extends the teaching time and provides students with richer and better learning tools, which will have a positive impact on improving the quality of teaching. This article aims to study the application of BD research methods in the field of multimedia art teaching curriculum design. This article mainly uses literature research method, practical research method, questionnaire survey method, interview method, etc., through comprehensive collection, sorting, classification, research and summary of the literature in this area, to provide important knowledge, perspectives and perspectives for this topic. The accumulation of methodology. And try to organize the computer BD method into a scientific research method helpful to multimedia art teaching courses. Experimental research shows that most art teaching in colleges and universities are equipped with multimedia equipment, but through research on teachers, we can notice that the utilization rate of multimedia equipment in multimedia classrooms in colleges and universities is not high. In order to promote the innovation of multimedia teaching plan design, teachers should increase research And the development of the application of BD in the design of learning plans.
GM-PHD Filter based Multi-target Tracking Method for Radar and Monocular Camera

Target tracking is one of the most important technologies in intelligent vehicle environment perception. Because of complicated environment and the unknown interference, there are many problems in practical application, such as, the single sensor tracking method cannot effectively meet the application requirements, so the millimeter wave radar and camera fusion method is an effective method. But considering that large targets may correspond to multiple radar echoes, existing data association methods can't effectively address this situation. An improvement Gaussian mixture probability hypothesis density (GM-PHD) multi-target tracking algorithm based on monocular camera and radar is proposed. The camera target is converted to polar coordinates and associated based on the angle. Associating the target of radar and camera in polar coordinate by angle range of camera target, and then divide the measurement set into several levels, and set the confidence for each level. Combining the radial range of the millimeter-wave radar with the azimuth of the camera to estimate the position of the target more accurately, then the fusion measurement data is introduced into the improved GM-PHD multi-target tracking algorithm to update the multi-target state. The real target data are used to verify the tracking algorithm. The experimental results show that the proposed tracking algorithm can effectively improve the tracking accuracy and robustness.
Corpus-Based Statistical Analysis of TEM-4 Reading Comprehension

TEM-4 reading comprehension is one of the key and difficult points to the students. This paper creates the small corpus based on TEM-4 reading comprehension during 2009 to 2019 and then adopts corpus analytic software to make statistical analysis from the textual, lexical and syntactic aspects in order to provide some reference in reading teaching and preparing for the exam.
A Systematic Approach to Define Semantics for Prioritised Logic Programs

We propose in this paper a framework of prioritised logic programs (\(\textit{PLP}\)) to represent priority information explicitly in a program. Differently of others approaches, we do not restrain the preference relation only to literals, but to sets of literals. As consequence, we can express in \(\textit{PLP}\)s sophisticated forms of preferences without changing the programs or introducing new atoms to obtain artificially the intended preferences. Besides, inspired on various developments in the literature on preference, we present a comprehensive and systematic treatment to deal with preferences in logic programming. In fact, we introduced 32 different criteria (semantics) to establish preference between partial stable models as well as those semantics whose definition depends on partial stable models. We show some properties of our framework; in particular, we guarantee these semantics for \(\textit{PLP}\) generalise their counterparts for logic programs without preferences.
The Application and Evaluation of Mobile Learning of Systematic English Grammar Teaching in Social Software Weixin

With the development of the world, people rely on their mobile phone to get information in spite of that most of the information are fragmented. So many people can not get the whole picture. This research aims to develop a mobile learning system in English Grammar teaching as well as make an evaluation system on this learning. The research was carried in Geely University of China in Chengdu, Sichuang Province and the samples are university freshmen.
Teaching physical chemistry: Developing and using an electronic course in the discipline

An electronic course (EC) “Physical Chemistry” was created on the basis of the Hypermethod platform for full-time bachelors of the Chemical and Technological Institute of the Ural Federal University (180 people in 2019). The developed EC refers to mixed (hybrid) courses. It combines the classroom work of students in lectures and laboratory work and work on the Hypermode platform to fulfill educational tasks. The choice of the course model is due to both the specifics of the training discipline and the peculiarities of the content of the curriculum of the discipline. Most of the course is e-Learning. It includes the performance of 4 calculation works in physical chemistry. Each design work contains 2 to 4 tasks. Each task provides for arbitrary variation of the option number for each student, and the total number of options for one task is 20-22 options.
Secure Selections on Encrypted Multi-writer Streams

Performing searches over encrypted data is a very current and active area. Several efficient solutions have been provided for the single-writer scenario in which all sensitive data originate with one party (the Data Owner ) that encrypts and uploads the data to a public repository. Subsequently, the Data Owner accesses the encrypted data through a Query Processor , which has direct access to the public encrypted repository. Motivated by the recent trend in pervasive data collection, we depart from this model and consider a multi-writer scenario in which the data originate with several and mutually untrusted parties, the Data Sources . In this new scenario, the Data Owner provides public parameters so that each Data Source can add encrypted items to the public encrypted stream; moreover, the Data Owner keeps some related secret information needed to generate tokens so that different Query Sources can decrypt different subsets of the encrypted stream, as specified by corresponding access policies. We propose security model for this problem that we call Secure Selective Stream ( SSS ) and give a secure construction for it based on hard problems in Pairing-Based Cryptography. The cryptographic core of our construction is a new primitive, Amortized Orthogonality Encryption , that is crucial for the efficiency of the proposed implementation for SSS .