Reviewerly/exHarmony huggingface dataset:
Research on Flight Type Recognition Technology Based on Information Fusion

It is one of the important tasks in air defense safety to identify the flight type attributes of the flight target and then determine the threat level of the flight target. Aiming at the problem of flight target type recognition, this paper combines pattern recognition based on knowledge graph and weighted D-S evidence theory method, and realizes the type recognition of flight target with the help of flight target knowledge graph. The proposal and application of this method are beneficial to improve the automation degree of model recognition.
Research on Artistic Guidance of Piano Accompaniment in Vocal Music Teaching under the Background of Big Data

With the rapid development of information technology, big data has touched all fields of society, and its influence on music has gradually appeared. Piano accompaniment of vocal music is not only conducive to enhancing the singer's understanding of the content and artistry of singing tracks, but also conducive to bringing singing atmosphere into the artistic scene, which can undoubtedly play a strong role in promoting the teaching effect of vocal music. While analyzing the relationship between vocal piano accompaniment and vocal music teaching, this paper briefly analyzes and discusses the artistic guidance of vocal piano accompaniment in all aspects of vocal music teaching. Through comparative observation, experience summary and research, this paper discusses the artistic guidance of vocal piano accompaniment in vocal music teaching.
Quantum Method of Moments for Characterization of Interconnects

Here we present two quantum algorithms: The Harrow/Hassidim/Lloyd (HHL) algorithm, and a Hamiltonian simulation procedure. Together, these algorithms provide a framework for the solution of general matrix equations using quantum computers. We also demonstrate the practical applicability of this method by means of a charge extraction problem using a quantum method of moments.
A Conceptual Model for Coeval Time Estimation of Virtual Reference Service

The objective of this paper is to propose a conceptual model of a virtual reference service (VRS) time estimation system that can estimate and display the time required to complete a transaction. The system will start estimating the duration before initiation of a transaction and update the estimated duration with the changing complexity of the transaction. Literature on industrial time estimation techniques and artificial-intelligence based VRS programs are reviewed to identify the gap. Four flowcharts are constructed with the aid of machine learning techniques like random forest regression and speech recognition, and natural language processing techniques like intent and entity recognition, and weighting. The pre-estimating, estimating, and post-estimating stages of the system are vividly explained with an example to enlighten the time-estimation process. The limitation of the paper is that the system is not practically developed and tested. However, developing such a system will help in transparent and unbiased transaction time estimation and assist the library professionals to manage queue time and maintain consistent timeliness. The estimated time may be used as a benchmark for evaluating different aspects of VRS. Providing quality service within the estimated time may increase the reliability and loyalty of the patrons toward the library professionals.
AirText: One-Handed Text Entry in the Air for COTS Smartwatches

Text entry for smartwatches is a useful service for many applications like sending text messages and replying emails. Traditional touchscreen-based approaches are two-handed text entry methods, that could be cumbersome when the user is performing other tasks with one hand. Therefore, we propose AirText, the first one-handed text entry method which achieves accurate and practical handwriting in the air for commercial smartwatches. By analyzing the inertial readings from the smartwatch worn on the wrist, AirText is able to accurately recognize the in-air handwritten characters. However, the wrist movements, which produce the inertial readings, are harmful to the user to focus on the screen. In order to address this challenge, AirText uses a novel cross-modal supervision design to achieve accurate character recognition from small wrist movements. AirText further includes a novel word recommendation method to speed up the text entry. We implement AirText on five smartwatches and evaluate its performance extensively with eight volunteers and more than 25,000 in-air handwritten characters. Results show that AirText outperforms two baseline methods and achieves comparable text entry speed as two-handed approaches.
Application of The Phonic Method to Improve The Early Reading Skills of Mentally Retarded Children

This study has the aim of conducting research, namely to determine the improvement of reading skills for mentally retarded children by applying a reading method, namely the phonic method. The phonic method for mentally retarded students starts from hearing the sounds of letters and letter shapes, combining letters by letter into syllables, to reading nouns. This study uses a descriptive quantitative approach with the type of experimental research. And this study uses an A-B-A research design with 4 subjects with mental retardation, carried out in the Baseline A phase carried out in 2 sessions, in the Intervention phase 6 sessions, and in the Baseline A phase 2 sessions. The results of this study can be seen from the progress in each session that has been carried out, the progress of the subject's reading ability, and the progress of the conditions raised by the research subject in each session. And the results of the 4 research subjects showed that there was an increase in early reading skills for mentally retarded children by applying the phonic method.
Elder Action Recognition Based on Convolutional Neural Network and Long Short-Term Memory

To assist in the identification of possible dangerous situations in the elderly care situation, such as a fall. This study utilizes action recognition to detect and record elder daily movement. If there is any abnormality, the detection system will send out a warning for help. The accuracy is 87.5% for the elder action recognition that developed with CNN and LSTM.
Dynamic analysis and simulation for under water engineering robot

In order to improve the safety and accuracy of underwater engineering robot, the dynamic modeling and simulation analysis of the robot body and driving device under the action of water flow are carried out. Firstly, the three-dimensional model of horizontal thruster, vertical propeller and ROV(Remotely Operated Vehicle) body is established, and then the hydrodynamics and static structure analysis are carried out. Then, the results of hydrodynamics and static structure are introduced into modal module, and the first ten modes of thruster and robot body under the condition of current are analyzed, and the parameters of vibration frequency, amplitude and deformation displacement are obtained under different order. The simulation results verify the safety of the robot under the action of water flow and provide guidance for the design of the robot structure parameters.
Symmetric binary Steinhaus triangles and parity-regular Steinhaus graphs

A binary Steinhaus triangle is a triangle of zeroes and ones that points down and with the same local rule as the Pascal triangle modulo 2. A binary Steinhaus triangle is said to be rotationally symmetric, horizontally symmetric or dihedrally symmetric if it is invariant under the 120 degrees rotation, the horizontal reflection or both, respectively. The first part of this paper is devoted to the study of linear subspaces of rotationally symmetric, horizontally symmetric and dihedrally symmetric binary Steinhaus triangles. We obtain simple explicit bases for each of them by using elementary properties of the binomial coefficients. A Steinhaus graph is a simple graph with an adjacency matrix whose upper-triangular part is a binary Steinhaus triangle. A Steinhaus graph is said to be even or odd if all its vertex degrees are even or odd, respectively. One of the main results of this paper is the existence of an isomorphism between the linear subspace of even Steinhaus graphs and a certain linear subspace of dihedrally symmetric binary Steinhaus triangles. This permits us to give, in the second part of this paper, an explicit basis for even Steinhaus graphs and for the vector space of parity-regular Steinhaus graphs; i.e., the linear subspace of Steinhaus graphs that are even or odd. Finally, in the last part of this paper, we consider the generalized Pascal triangles, that are triangles of zeroes and ones, that point up now, and always with the same local rule as the Pascal triangle modulo 2. New simple bases for each linear subspace of symmetric generalized Pascal triangles are deduced from the results of the first part.
Adaptive Polymorphic Fusion-Based Fast-Tracking Algorithm in Substations

Tracking multiple objects in a substation remains a challenging problem since pedestrians often overlap together and are occluded by infrastructures such as high-tension poles. In this paper, we propose an adaptive polymorphic fusion-based fast-tracking algorithm to address the problem. We first leverage the fast segmentation algorithm to obtain the fine masks of pedestrians and then combine the motion and performance information of pedestrians to realize the fast-tracking in substations. Our model is evaluated on the widely used MOT19 dataset and real-substation scenarios. Experimental results demonstrate that our model outperforms state-of-the-art models with a significant improvement in the MOT19 dataset and occlusion cases in substations.
Sensor Fusion in Machining System Monitoring

Multi-sensor information fusion utilizes multi-sensory sources and extract complementary information according to certain criteria, so that the information system obtained has a superior performance than its constituent components (Hall and Llinas in Handbook of multisensor data fusion: theory and practice. CRC Press, 2001; Khaleghi et al. in Multisensor data fusion: a review of the state-of-the-art, 2010). It was first proposed by the Joint Directors of Laboratories (JDL) of United States in 1994 (Hall and Llinas in Handbook of multisensor data fusion: theory and practice. CRC Press, 2001).
A Similarity Algorithm Based on Hash Feature and Random Mapping

Abstract Similar text algorithm is the main method for news topic extraction and online public opinion hot spot tracking. The traditional text similarity method has the problems of high data dimension and high computational complexity. This paper proposes a method of news text clustering based on hash feature and random mapping. The method based on hash feature reduces the dimensionality of the text. At the same time, the article clustering preprocessing based on random mapping can speed up retrieval. Experiments show that the algorithm proposed in this paper performs well on related data sets.
Error tuning and gap analysis of human psycho-emotional pattern recognition framework using time series forecasting model

The behavioral or cognitive approach of a person entirely depends upon his/her emotional state, which is very complicated to be tracked. Therefore, this technology shall be accepted by numerous sections of people, where one can easily detect that how the emotional sentiments of a person varies with respect to a particular scenario. With a varying stimulated situation, different confused emotional combination could be generated which does not have any effect in development of signature pattern of a particular user. As emotional sequences are time dependent multidimensional data so Time Series Forecasting model has been implemented to validate the result of proposed framework (GESPa Algorithm) with a learning based prediction model and also analyze the confused emotional combinations through Gap Analysis and Error Tuning.
Airworthiness Certification of IMA-based OMS on Transport Category Airplane

The Onboard Maintenance System (OMS) is key system to implement the Prognostics and Health Management (PHM) technology in the transport category airplane. Integrated Modular Avionics (IMA) and Avionics Full Duplex Switched Ethernet (AFDX) with the faster data exchange is utilized in the newly developed aircraft. This paper briefly introduces the system function and architecture of IMA-based OMS. The airworthiness certification requirement of Part 25 and Special Condition are systemically analyzed. The approaches and some key technology concerns of airworthiness certification for IMA-based OMS installed on transport category airplane are discussed in detail, which can be used as reference for aviation industry to achieve the airworthiness certification.
Tool-Supported Projects Evaluation for Decision Making of Institutional Planning

The objective of this work is to propose a tool to carry out the registration, control and monitoring of the Annual Operative Plan of the University, in order to support the management of Planning Department. The tool allows University officials to collect data on the projects to be implemented by each academic and administrative units. It allows you to make an adequate record of the planning where resources to be included in each project are presented. An efficient system administration and adequate control of the fulfillment of planned projects will allow the adequate execution of actions foreseen by the academic and administrative units, making an efficient use of institution resources. To support decision making, the evaluation of projects is proposed through a set of indicators that apply fuzzy logic and artificial neural networks. Through the tool use, results of the budget execution can be made transparent in each periods managed by the University. The obtained result provides a contribution to the improvement of tools to support the decision making in organizations to project-oriented production.
RDRLLJ: Integrating Deep Learning Approach with Latent Semantic Analysis for Document Retrieval

World Wide Web contains millions of documents which is useful for users in the fields of entertainment, politics, technology, health care, etc. Since, data is unorganized and dispersed globally. Getting relevant information for the user using search engines is challenging task. It can be obtained when the semantic gap is reduced between user query intention and the retrieved results. Concept-based querying over keyword-based query search would meet user requirements precisely. A RDRLLJ framework that discovers documents of relevance by amalgamation of LSTM, LSA, and Jaccard similarity has been proposed. Cosine similarity is used for the computation of cluster similarity, and similarity between user query and document is computed using Jaccard. A hierarchical clustering is applied for documents in the offline phase using LSTM and LSA. The RDRLLJ framework has been experimented on rectus corpus volume-1 (RCV1) dataset. Experiments suggest that the proposed RDRLLJ framework has improved results, when compared to other baseline strategies.
Design of English Translation Computer Intelligent Proofreading System Based on Fuzzy Decision Algorithm

research-article Design of English Translation Computer Intelligent Proofreading System Based on Fuzzy Decision Algorithm Share on Author: Jinlian Chai Xinjiang University, China Xinjiang University, ChinaView Profile Authors Info & Claims ICISCAE 2021: 2021 4th International Conference on Information Systems and Computer Aided EducationSeptember 2021 Pages 568–571https://doi.org/10.1145/3482632.3482752Online:22 November 2021Publication History 0citation6DownloadsMetricsTotal Citations0Total Downloads6Last 12 Months6Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Comprehensive Comparative Study on Several Image Captioning Techniques Based on Deep Learning Algorithm

Image captioning is evolving as an interesting area of research that involves generating a caption or describing the content in the image automatically. The idea behind image captioning is to make the computer perceive a given image like a human mind leading to automatic description. Image captioning is a challenging task that involves capturing semantically correct information and expressing in a simple sentence. A large number of methods have been proposed in the recent past, and we aim to do a comprehensive survey in the different deep learning algorithms used in image captioning based on the method framework.
Extraction of soil mineral information based on hyperspectral image

Soil is an important link in the process of maintaining ecological balance, which is in the central position of connecting the organic and inorganic world in nature. As an important part of soil, mineral composition not only affects its physical and chemical properties, but also plays an important role in the adsorption and migration of heavy metals and organic pollutants in the ecological environment.In this paper, using the ZY-1-02D satellite hyperspectral data, combined with the USGS standard spectral library, analyze the overall spectral shape and local feature positions of soil mineral spectra.The method of combining spectral feature enhancement matching degree and feature parameter information extraction is used to extract typical minerals such as montmorillonite, kaolinite, calcite, dolomite, mica, hematite, and limonite in bare soil and invert its relative content,obtained the spatial distribution status and distribution characteristics of different soil minerals,it can provide theoretical basis for subsequent research on soil classification, soil physical and chemical parameter prediction, and provide technical support for soil fine mapping, utilization and protection of soil and mineral resources.
Cinematic Virtual Reality as a Rehabilitative Tool in Subjects Affected by Schizophrenia

Although Virtual Reality Social Skills Training has proven its effectiveness in treating psychiatric disorders, this VR application field is still under-researched for two main reasons. The first one is the unavailability of low-cost VR technologies with sufficient computational capacity needed to render realistic Virtual Environments. The second one consists of the need for specialized VR application developers, usually far from the mental health research field. The recent diffusion of low-cost stereoscopic viewers and the introduction of easy and fast VR content authoring systems, such as Cinematic Virtual Reality (CVR), allow overcoming these limitations. CVR makes it possible to capture real scenes through 360 cameras, augment them with additional virtual objects, and finally immerse the user in these synthetic but highly immersive environments. We present the design and the features of the Entellect360 prototype -an innovative tool supporting the rehabilitation process of subjects affected by schizophrenia. It exploits CVR technology to create Virtual Environments aimed at the rehabilitation of psychiatric patients. The Entellect360 features allow for rehabilitation sessions and patient-performance data-collection even under conditions of social distancing. We also explain the experimental protocol and the validation procedure the prototype will undergo to assess its effectiveness.
Investigation and Analysis of Preschool Education Information Data Based on Information Technology Analysis

With the vigorous development of educational information technology, the effective application of information technology in pre-school integrated education is a general trend. This article is based on the investigation and research on the application of information technology in 6 kindergartens in Xi'an that implement pre-school integrated education, and conducts an in-depth analysis of the reasons, and provides some reference research materials for the future application of information technology in pre-school integrated education.
Improvement of Detection Rate for Small Objects Using Pre-processing Network

Artificial intelligence (AI) has been developing in a variety of methods over the past decade. However most AI experts worried to build a deep or wide network because the accuracy of AI models depends heavily on the depth of the network. In general deep and wide networks are better at learning than those that are less deep and wide and wide. On the other hand deeper networks are more complex and have many disadvantages such as computational cost and system specification dependency. We propose a novel method to improve the average recall rate for small objects in the deep convolutional network in the paper. The proposed method added pre-processing layer before the network rather than stacking the networks deeper or wide. The presented pre-processing layer consists of two major parts: up-sampling and down-sampling of the data. The overall objective of up-sampling and down-sampling is to enhance the resolution of small objects in the input image. The pre-processing network improves the average recall rate of the base network to 3.56%. This experiment result depicts that the proposed method outperforms the small object detection performance.
A New Direction of Students Education Based on Network Information Technology

Now, as China enters a new era, the overall coverage of new media system derived from network information technology is increasing. Network technology has been integrated with the learning and living system of teachers and students. At the same time, the traditional education system has been affected and impacted, especially under the background of increasing network group and increasing application level of new media technology. In addition, the new media system shows a rapid development momentum, as far as the education system of colleges and universities is concerned, it not only creates opportunities, but also brings a series of challenges. In order to make the education work of colleges and universities keep pace with the times and conform to the development trend under the new era and new situation, it is urgent to innovate the new direction of information technology of students education base network.
An inertial neuro-dynamic system for solving zero-one integer programming

This paper presents an inertial neuro-dynamic system to solve zero-one integer programming. We transform the problem of zero-one integer programming into a relatively easy problem only subject to linear equality constraints by using a nonlinear complementarity problem function. Subsequently, a inertial neuro-dynamic system is proposed to solve above transformed problem. It is shown that the proposed inertial neuro-dynamic system is stable and the equilibrium point converges to optimal solution set. It is worth emphasizing that our developed inertial neuro-dynamic system achieves faster convergence speed and explores more optimal solutions in a fixed initial value by adjusting the inertial factor. Finally, according to the experimental results on two illustrative examples, the proposed method is highly effective.