Reviewerly/exHarmony huggingface dataset:
Design of host computer system for induction heating power supply based on LabVIEW

In view of the actual use of induction heating power supply, there are some problems such as the upper computer system is not perfect. Based on the idea of virtual instrument, a host computer system of induction heating power supply based on LabVIEW is designed in this paper. The system improves the shortcomings of traditional measurement and control instruments, and improves the degree of automation of the system. The main tasks of this paper include: the development of serial communication, the establishment of database access, the progress of each functional module of upper computer.
Hybrid Learning Framework for Cybersecurity Courses

As per our observation, Challenge-Based Learning (CBL), despite its excellent outcomes in various scenarios, is insufficient to cover the breadth of topics required by foundational courses. The other challenges faced by CBL are students' lack of background, mismatch of students' course expectations, and conflicting institutional policy requirements. The suggested method in this work combines the strengths of CBL with the traditional methods and can be implemented within a typical 16-week semester course.
Fuzzy filtering‐based fault detection for a class of discrete‐time conic‐type nonlinear systems

The authors investigates the problem of fuzzy fault detection filter (FFDF) design for a class of discrete-time conic-type nonlinear systems. By applying Takagi–Sugeno fuzzy models, the conic-type dynamic FFDF system is established. Then, utilizing the Lyapunov function method to find a sufficient condition which ensures that the conic-type dynamic FFDF system is asymptotically stable. After that, using linear matrix inequalities techniques, the FFDF design problem is transformed into an optimization algorithm. Finally, the simulation results demonstrate that the designed FFDF is effective for detecting the faults.
Integrated Image Processing Modules for Hassle Free Monitoring of Border Area

An automated process is required to reduce the fatigue of operator by extracting the meaningful information from the collected data during surveillance. Integrated image processing modules which combine the image enhancement, panoramic view and automatic change detection are required for hassle free monitoring of border area, thereby, allowing continuous monitoring of the remote border areas without human intervention under prevailing weather conditions. The development and evaluation of integrated image processing module for hassle free monitoring of border area has been presented in this proposed work.
Statistical Feature Based Digital Camera Identification

Digital Forensics has become noted field in the last few years. One of the areas in digital forensics is Digital Camera Identification. This paper proposed a camera identification framework, which is based on the Gray Level Cooccurrence Matrix (GLCM) features. The Photo Response Non-Uniformity (PRNU) noise is a kind of sensor finger impression, which permits to extraordinarily distinguish the camera that took an image. It is estimated from the denoised images using a denoised filter. Then estimate the Gray level Co-occurrence Matrix (GLCM) features of the PRNU noise pattern. A Scaled Conjugate Gradient (SCG) Backpropagation Neural Network is used for classification.
Wireless System for the Detection and Mitigation of Explosions in Tunnels

&lt;p&gt;Physical security challenges from explosions are amplified in confined spaces. The air-blast shock waves reflect and propagate throughout the confined space. This paper describes the process of designing, constructing, and validating a wireless system for identification of explosions in real time. Protection of critical infrastructure requires the creation of a reliable system which provides quick and accurate identification of the hazards and subsequent transmission of the alarm signal to response and rescue services. The proposed wireless system consists of transmitter and receiver modules spaced throughout the tunnel. The transmitter module contains sensors and a microprocessor equipped with blast identification software. The receiver module produces an alarm signal and activation signal for the operation of protecting devices. The experimental validation has been carried out at the underground experimental base of G. Tsulukidze Mining Institute, Tbilisi, Georgia. The results of the testing validated the main characteristics of the system, notably:&lt;/p&gt;&lt;p&gt;No false signals were generated during the series of 20 experiments&lt;/p&gt;&lt;p&gt;After a blast event, the duration for analyzing the potential threat is 2.4 msec and the duration for activating the protection device is 11 msec&lt;/p&gt;&lt;p&gt;The reliable transmission distance is 150 m (492 ft) in a straight tunnel and 50 m (164 ft) in a tunnel with a 90° bend.&lt;/p&gt;
Mobile-Enabled Virtual Reality Visualisation Improves Learning And Training In Health Care

Working in the health care sector required high skill sets and intensive training. Emerging technologies with realistic visualisation and practical such as virtual reality (VR) are being applied to the training process of the health care practitioners. Many researches have done studies in virtual reality and its implementation in education, more specifically in learning and training of a certain skill and enhancing user learning experiences. However, these studies usually remained in the development or research stage and have not widely implemented in real-life training due to various reasons. This research aims to review the effectiveness of virtual reality in the health care sector, its current solutions to the problem within different areas and analyse different VR research processes to encourage more practical implementation of VR. A literature review was performed to review 15 papers that implemented VR technology in learning and training skill for healthcare practitioners, rehabilitation and diagnoses as well as other aspects of virtual reality that can be considered within the health care sector.
Research on Chinese International Education Flip Classroom Based on Ant Colony Algorithm

With the rise of educational technology, the flipped classroom mode is booming. Students watch teaching videos after class, complete homework and internalize knowledge in class, which makes both teachers and students benefit a lot. For students, the autonomy of learning has been greatly strengthened, the learning time after class is flexible, and the limited time is maximized in class; for teachers, although the workload increases, it is conducive to their own strict requirements, and teachers should ensure that they can answer students’ questions at any time. It can be seen that both teachers and students in the flipped classroom are being pushed forward. In order to ensure the efficient learning of students after class, the application of learning management system is essential. Through the combination of flipped classroom teaching mode and network learning management system, the learning time after class is freely grasped by students, which is flexible and efficient. Classroom time is more applied to the interaction and cooperation between teachers and students. In this way, we can increase the classroom capacity and help Chinese teachers to become real preachers, educators and puzzlers. The main research questions are as follows: first, in the flipped classroom based on canvas, what are the students’ attitudes towards language and knowledge acquisition? Second, what learning strategies do students adopt in the learning process? Third, what are the new gains of flipped classroom experience compared with the traditional classroom? The survey results show that the flipped classroom teaching mode can indeed improve the traditional teaching mode - some questions Question. In this mode, students interact with each other more intensively and the classroom is more humanized. Students’ communication skills, self-confidence and knowledge acquisition are all improved. However, different students have different understanding of the validity of this teaching method due to their different understanding of the certainty, authority, source of knowledge, the real meaning of learning content and the measurability and importance of learning results.
Reinforced Evolutionary Algorithms for Game Difficulty Control

In the field of game designing, artificial intelligence is used to generate responsive, adaptive, or intelligent behaviors primarily in Non-Player-Characters (NPCs). There is a large demand for controlling game AI since a variety of players expect to be provided NPC opponents with appropriate difficulties to improve their game experience. However, to the best of our knowledge, a few works are focusing on this problem. In this paper, we firstly present a Reinforced Evolutionary Algorithm based on the Difficulty-Difference objective (REA-DD) to the DLAI problem, which combines reinforcement learning and evolutionary algorithms. REA-DD is able to generate the desired difficulty level of game AI accurately. Nonetheless, REA can only obtain a kind of game AI in each run. To improve efficiency, another algorithm based on Multi-objective Optimization is proposed, regarded as RMOEA-DD, which obtains DLAI after one run. Experiments on the game Pong from ALE and apply on a commercial game named The Ghost Story to show that our algorithms provide valid methods to the DLAI problem both in the term of controlling accuracy and efficiency.
Development of a Classifier for the System of Automatic Document Processing with Limited Sampling

The work is aimed at solving the current problem of automatic recognition and processing of scan/photo of documents. To solve this problem, the work uses approaches to artificial intelligence, namely artificial neural networks. The main difference between this work and the existing ones today is the solution of the described problem in conditions of limited sampling. The approaches of neural networks pre-learning using the well-known structure of the autoencoder, data augmentation and minimization of parameters were used to achieve an effective solution at minimal cost.
K2RDF: A Distributed RDF Data Management System on Kudu and Impala

The Resource Description Framework (RDF) has been widely used in various applications or services as a model for displaying, sharing and connecting data. With the increase of RDF data scale, distributed RDF data management system becomes popular, but there are still many problems to be solved. To solve these problems, we proposed a distributed RDF data management system K2RDF based on the Porperty Chain model on the Kudu and Impala platforms. Kudu is a data storage engine that combines OLAP and OLTP scenario and Impala can process SQL queries in real time. The combination of these two platforms provides new options for processing RDF data, making storage more efficient and queries faster. The Property Chain model is derived from the RDF data content. The RDF data is divided into different parts stored in the corresponding attribute table by the class information extracted from RDF schema. In the attribute table, In the attribute table, each column corresponds to a property in the RDF class. This model can increase the data storage density and improve the query processing speed by reducing the number of the join operation. By comparing with the current popular distributed RDF data management systems in some experiments, our system has lower query latency and faster query speed.
Research on information model of urban infrastructures intelligent O&amp;M based on lifecycle

Operation and Maintenance (O&M) management of urban infrastructures is increasingly complex under the rapid development of smart cities in China. The problems of interconnection and interoperability of multi-source, heterogeneous and massive spatio-temporal data will lead to the omission of information changes and the low utilization rate of inaccurate information, which cannot meet the requirements of fine management. This paper focuses on the description, organization, and utilization of intelligent O&M in-formation for urban infrastructures. The description and organization of spatio-temporal data is built based on COBie, according to the information service demands of intelligent O&M for urban infrastructures. Semantic Web theory is used to establish the correlation model and integration method of spatio-temporal data. Finally, these form an information model for urban infrastructures intelligent O&M, and this model has been used in Wenyi Tunnel to verify the feasibility.
Error-Free Lattice-Based Key Encapsulation Mechanism Based on Ring-LWE

As most of the published lattice based encryptions and key encapsulation mechanisms have decryption failure, they are subjected to side-channel attacks. In this paper, we construct an error-free lattice-based key encapsulation mechanism (KEM) which is based on LPR encryption. We also examine some new classes of cyclotomic polynomials for the new error-free lattice-based KEM. The proposed error-free KEM has a reasonable public key size of about 1.472 Kbytes for 128-bit quantum security level. This is comparable to those submitted to the NIST PQC standardization.
Construction of quality evaluation index system of network information resources

Based on literature research and network survey, this paper summarized the current domestic and foreign evaluation methods, evaluation indexes and typical cases of quality evaluation for network information resource. Combining the practice of quality evaluation of network information resource and the progress of information science, the problems and deficiencies in the quality evaluation of network information resource were analyzed. Ideas for constructing evaluation index system were put forward, which takes the academic influence index of network information resources as the core, content index as the key point, form index as auxiliary, and altmetric or webometric index as reference. The research and practice of quality evaluation for network information resource were explored by network bibliographic literature databases, network digital journals and input-output indicators (economic benefit indicators) to ensure the maximum utilization of network information resources.
Space-Time Skeletal Analysis with Jointly Dual-Stream ConvNet for Action Recognition

In this decade, although numerous conventional methods have been introduced for three-dimensional (3D) skeleton-based human action recognition, they have posed a primary limitation of learning a vulnerable recognition model from low-level handcrafted features. This paper proposes an effective deep convolutional neural network (CNN) with a dual-stream architecture to simultaneously learn the geometric-based static pose and dynamic motion features for high-performance action recognition. Each stream consists of several advanced blocks of regular and grouped convolutional layers, wherein various kernel sizes are configured to enrich representational features. Remarkably, the blocks in each stream are associated via skip-connection scheme to overcome the vanishing gradient problem, meanwhile, the blocks of two stream are jointly connected via a customized layer to partly share high-relevant knowledge gained during the model training process. In the experiments, the action recognition method is intensively evaluated on the NTU RGB+D dataset and its upgraded version with up to 120 action classes, where the proposed CNN achieves a competitive performance in terms of accuracy and complexity compared to several other deep models.
A Graph Convolution model considering label co-occurrence LC_GCN

Graph convolution network (GCN) is a Semi-supervised algorithm that applies the idea of convolution to graph structure data, and it is used for node classification tasks in graphs. Original algorithm only considers the characteristics and adjacency of the nodes in the graph, but fails to consider the association between label and simply represents the label as a one-hot vector. In this paper, we propose LC_GCN. This model contains a label convolution module based on the original GCN, and use it to get a better classifier. An open pre-trained word vector is used as the label feature, and we designed an algorithm to use the conditional probability of the association between label to generate adjacency matrix of labels to obtain the classifier by GCN. Then combine it with the original node GCN, and put the vector obtained by the node through the GCN into this classifier. Experimental results show that our proposed LC_GCN outperforms the existing algorithms.
Android based XRD data analysis software design for cube crystal structure with analytic and Cohen methods

An Android-based application has been made to analyze the lattice parameters of the cubic crystal structure. The application is built using processing application based C language. The system algorithm for finding lattice parameters uses analytical methods and Cohen for the crystal structure of the cube. Input data is diffraction angle 2θ and miller index hkl. Application testing uses cube crystal data obtained from the ICDD (International Center for Diffraction Data). The crystal molecules tested are molecules with a cube structure. Some samples taken include Cesium Chloride (CsCl), Alumunium Titanium Oxide (Al3Ti5O2), Barium Strontium Titanium Oxide (Ba0.5Sr0.5TiO3), Silver (Ag), Cadmium Sulfide (CdTe) and Indium Phosphide (InP). Application test results show that the lattice parameters obtained have good compatibility with ICDD data.
Ensemble Software Development Effort Estimation Using Data Envelopment Analysis

The field of Software Development Software Estimation in Software Engineering is critical due to its practical importance and very challenging due to the lack of a globally best model, able to predict accurately the effort (and therefore the cost) of any new software project. After a long research period on different models and their improvements, the research interest is directed towards ensemble methods, i.e. methods which combine the results of different single models. Furthermore, it is desirable to characterise the accuracy of models by different criteria. In this study, we develop a methodology based on the Data Envelopment Analysis technique, well-known in operation research, so as to rank different models based on multiple criteria and then to combine the best of them in order to achieve better prediction performance. The experimentation involves 93 models applied to 10 datasets and provides very promising results regarding the performance of the proposed ensemble approach.
NATIONAL INTEGRATION VALUES, LEADERSHIP TRAITS AND SOCIAL RESPONSIBILITIES AMONG THE PROSPECTIVE TEACHERS IN THANJAVUR DISTRICT

The main objective of the study is to nd the national integration values, leadership traits and social responsibilities among the prospective teachers in thanjavur district with their different dimensions of wishes for nation's better future, Values, sense of duty and citizenship, Equal treatment to all, and Respect for culture, language and traditions. To analyse national integration values among prospective teachers by collecting the data from thanjavur district B.Ed., college students. For this investigation, a questionnaire developed by the investigator and the research supervisor seek the information from the student teachers. we took 500 samples to investigate and the result shows there is no signicant difference in the national integration values of the prospective teachers and in all dimensions with respect to the variable 'participation in social activities'. The study also showed a signicance difference in married and unmarried student teacher related to the social responsibility. The leadership traits and social responsibilities are found to have signicant inuence on national integration values of the prospective teachers in thanjavur district.
Remote creation of quantum coherence via indefinite causal order

Quantum coherence is a prime resource in quantum computing and quantum communication. Quantum coherence of an arbitrary qubit state can be created at a remote location using maximally entangled state, local operation and classical communication. However, if there is a noisy channel acting on one side of the shared resource, then, it is not possible to create perfect quantum coherence remotely. Here, we present a method for the creation of quantum coherence at a remote location via the use of entangled state and indefinite causal order. We show this specifically for the superposition of two completely depolarizing channels, two partially depolarizing channels and one completely depolarizing channel along with a unitary operator. We find that when the indefinite causal order of channels act on one-half of the entangled pair, then the shared state looses entanglement, but can retain non-zero quantum discord. This finding may have some interesting applications on its own where discord can be consumed as a resource. Our results suggest that the indefinite causal order along with a tiny amount of quantum discord can act as a resource in creating non-zero quantum coherence in the absence of entanglement.
A self-exciting point process with cyclic component, trend component, triggering function, and response function

A point process is a collection of random points located in a particular area. A point process such that one event can trigger other events at a particular time is called a self-exciting point process. In a point process, an intensity function plays a crucial role in stating the probability of the number of events per time unit conditional on past events. The conditional intensity function can be estimated by considering the cyclic component, trend component, internal factor, and external factor impacting the occurrence of earthquakes. This research aims to derive the conditional intensity function of self-exciting point processes and to estimate the parameters of the conditional intensity function for earthquake data in Java Island and Sumatra Island. Conditional intensity function of the process can be presented as a function of time by considering cyclic component as a Fourier series and trend component as a sum of polynomials, while each of triggering function and response function as a sum of exponential functions. Data analysis showed that earthquakes that occurred in Sumatra have the same probability of triggering aftershocks as earthquakes that occurred in Java.
Design of Hololens-based Scene System for Spacecraft Simulation

In order to solve the problem of data “losing” when the existing comprehensive simulation platform displays the 3D image data on the 2D display screen, and it cannot provide immersive experience for researchers, the design of Hololens-based scene system for spacecraft simulation is proposed, which can better solve the mentioned problems and provide better technical support for researchers. This paper provides a basic introduction to Hololens, proposes a design of Hololens-based scene system for spacecraft simulation and describes the key technologies to achieve system functions. During the on-orbit operation simulation test or development process of the spacecraft, Hololens glass is used to display the status of the spacecraft in real time, and it can interact with the model to switch the perspective and zoom the model.