Reviewerly/exHarmony huggingface dataset:
Image augmentation algorithm based on the detection of personnel' safety uniform in the workshop

The workshop is an important environment for the development of the manufacturing industry, and there are many hidden safety hazards for the workshop personnel. Considering the disadvantages of complex aviation electromechanical product workshop site, insufficient sample size of workshop personnel&#x0027;s safety uniform data set and lack of data feature information, this paper proposes two different image augmentation algorithms based on machine learning. The image generated by the data augmentation is compared in accordance with the parameter standards, and the comparison experiment is performed to select the optimal data augmentation method to solve the above problems.
Modeling Neural Networks Training Process with Markov Decision Process

With the development of computer technology, statistics-based machine learning method has made great break-throughs, and also improved the development of artificial intelligence. Nevertheless, as a very influential model, neural networks are still treated as &#x201C;black boxes&#x201D;. The results of neural networks are extremely sensitive to the training samples, which lead to great challenges to the controllability of the algorithm. With the wide application of machine learning, demand for interpretability and controllability of neural networks algorithms is increasing. As a result, various scholars have tried to explain and verify neural networks algorithms based on formal methods in recent years. In this paper, a method (called MNNTP) is presented to model the training process of neural networks by using a Markov decision process (MDP). Through MNNTP, the neural networks are abstracted into the form of MDP, which makes notable contributions for verifying some mathematical properties of the neural networks.
A Novel Optimal Clustering Model With Neutrosophic Clustering

In recent years, data clustering, as one of the main tasks of unsupervised learning, plays an important role in data mining. In order to consider both geometric shape and spatial structure of data points, a novel clustering model with neutrosophic clustering is proposed. Moreover, considering the impact of different attributes of data on clustering results, the given clustering model obtains attribute weight through automatic iteration. To describe the model, a suitable objection function is built, and the clustering problem is transformed into a constrained minimization problem and solved by the Lagrange multiplier method. Finally, the experimental results of artificial data and real data show that the algorithm is feasible.
PSOU-Net: A Neural Network Based on Improved Particle Swarm Optimization for Breast Ultrasound Image Segmentation

In order to accurately segment the lesions in breast ultrasound images with high noise, low contrast and poor uniformity, this paper proposes a neural network optimized by Particle Swarm Optimization algorithm. Based on the structure of U-Net network, the dynamic edge detection error function is designed, and then an improved PSO algorithm is proposed to optimize the error function to obtain the improved U-Net algorithm (PSOU-Net). By comparing the PSOU-Net network proposed in this paper with the traditional U-Net neural network on Dataset BUSI dataset, the experimental results show that the Precision value of PSOU-Net network is improved by 16.5&#x0025; and the <tex>$F_{1}$</tex> score is improved by 6.3&#x0025;, which effectively improves the segmentation performance of the network.
Blind Quality of a 3D Reconstructed MESH

This paper proposes blind mesh quality measures for a reconstructed 3D model. The assessment of 3D model quality is a fundamental step in the process of 3D reconstruction, to efficiently and iteratively improve the model quality. We first prove that metrics based on a reference model (extrinsic metrics) that have been shown to be correlated to subjective interpretation, are able to capture flaws that may occur while reconstructing. However, no reference is available during an iterative reconstruction process, so we study intrinsic measures on a 3D model to evaluate the quality of the 3D model being reconstructed. As expected, our results show that these intrinsic metrics give high responses in regions locally corrupted by noises.
Audio Replay Spoof Attack Detection Using A GMM-RFPNN Model as Back-end Classifier

Research on automatic speaker verification (ASV) techniques has received academic attention in recent years and has begun to be applied to authentication, but research on the security performance of ASV is just beginning. In this paper, we will focus on speech replay spoofing attack detection in speaker authentication techniques. Voice is a biological behavioral feature with high inter-class variability and susceptibility to environmental and temporal influences. In this paper, classical constant Q cepstral coefficient features (CQCC) and Gaussian super-vectors are used as front-end feature extractors and fuzzy polynomial neural network (FPNN) models with regularization processing are used as back-end classifiers for true and false speech detection. Compared with other traditional machine learning models and deep learning models, this model shows stronger robustness and generalization ability on acoustic environment and time variation, and good detection results can be obtained using a small number of samples for training. Tested on the ASV spoof 2017 version 2.0 database, the detection performance is improved by about 39&#x0025; compared to the original baseline system.
Deep Learning-based Multi-task Network for Intelligent Management of Garbage Deposit Points

With the economic and social development and the substantial improvement of material conditions, the generation of domestic waste has grown rapidly and has become a constraint factor for the development of new urbanization. In the past few years, research on the domestic waste industry has been limited to intelligent waste sorting, neglecting the role of intelligent management of waste storage sites. To relieve it, We propose a deep learning-based multi-task network for intelligent management of garbage deposit points, which combines algorithms such as YoloV5,Deepsort, Insightface, and Openpose to achieve waste bin detection, waste bin status recognition and analysis, face recognition, action recognition, and multiple object tracking based on real-time surveillance video. Besides, we propose a new dataset named Waste Bin Status, which provides a meaningful addition to the existing field of waste bin identification. Experiments on WBS dataset validate that our method is superior to other methods for garbage point status identification. Moreover, our network is trained to work with different scenarios of garbage deposits, demonstrating state-of-the-art performance in real-world tests.
A Quantum Method for Subchannel allocation in Device-to-Device Communication

Spectrum is an expensive commodity in any wireless communication problem and proper utilization of spectrum is always the goal. In Device-to-Device (D2D) communications, the users are allocated a spectrum that is already pre-allocated to them. However, the challenge of the scheme is that it causes mutual interference between D2D and nearby cellular communication. Thus, there is a need to reduce the mutual interference. To do so, resource allocation, such as sub channel and power allocation, is effective. To manage the resource and achieve the maximum total system capacity, one needs to solve a non-deterministic polynomial (NP) hard optimization problem. This is typically done using heuristics. The choice of any heuristic should look for faster optimization of multiple variables that not only consider uplink and downlink interference but also inter-channel interference between n-devices. To efficiently solve the problem, we have used capacitated Max k-Cut formulation and run in a metaheuristic system like Quantum annealer. We believe and proved that QUBO is a better-suited approach to the D2D resource subchannel allocation problem in this paper.
Research on Classroom Teaching Reform of Engineering Mechanics in Application-Oriented Universities

Engineering mechanics is a fundamental major course for engineering majors in application-oriented universities. Based on reviewing current situation of classroom teaching of engineering mechanics, the paper puts forward the countermeasures for reforming classroom teaching in the student-centered principle, including making the innovation of teaching pattern, integrating theoretical knowledge with engineering practice and introducing curriculum ideological and political education, in order to achieve the cultivation of comprehensive and all-round applied-type talents. The research can provide reference for the education of fundamental mechanics courses.
Speech Recognition Based Blind Chess Education System Chinese Chess as an Example

This paper makes a brief introduction of the design and application of Blind Chess education system based on speech recognition. We put forward a new online system for Blind Chess education on the basis of speech recognition. Our system enables users to move pieces by virtue of voice messages when they are not looking at the chessboard so as to make Blind Chess training more effective. Meanwhile, it also removes site restrictions and save much manpower. The paper proposes a Chinese Chess terminology analysis algorithm based on the rule base. The algorithm can use existing, well-established Chinese Chess terminologies as voice input, into texts through speech recognition techniques, and extract the initial and end positions of pieces after querying the rule base. The experiments on the application of our Blind Chess education system have been carried out using the computer to improve the recognition accuracy of some common words and make the system more humanized. Practical operations have proved that the Blind Chess education system can provide better experiences for users, effectively promote traditional cultures and help develop user's intelligence and thinking ability.
To What Extent Virtual Simulation Technology Can Aid Education?

Virtual simulation technology has recently been used in a variety of fields including architecture, medicine and more. Nevertheless, there is not much application of this technology in education, particularly high school education. Through the COVID-19 pandemic, people have begun to recognize the power of online learning. One limitation of current online education (aka 1.0 version of online education) is that it continues to adopt the conventional classroom education method and only resolves the difficulty of long-distance communication, rather than fully leveraging computer technology, which leads to a lack of interaction and practical experience. Virtual simulation technology (aka 2.0 version of online education) can assist to plug this breach by creating virtual classrooms and erecting an education system based on virtual scenes that is more advantageous to students. Additionally, Virtual Classroom is open-source and intended to run on the majority of devices; thus it can benefit more students, including those who have no access to high-quality resources, at an extremely low rate.
Ontology Construction and Application Based on Internet Financial Risk Management

With the frequent outbreak of Internet financial risk events, how to use information technology to effectively manage Internet financial risks has become an urgent problem to be solved. The vigorous development of ontology construction and other technologies provides a more scientific and reasonable solution to this problem. First of all, extracts and analyzes conceptual terms in the field of Internet financial risk by obtaining text resources in the field of Internet financial risk management. then, uses a seven-step method to construct the Internet financial risk management ontology model and verify the validity and accuracy of the model through the OntoQA method. Finally, the Internet financial risk management ontology is applied to the P2P platform collapse event. The ontology model of Internet financial risk management not only enriches and expands and innovates the theoretical and methodological system of the construction of the Internet financial risk management ontology in the field of Internet financial risk, but also provides a powerful reference for the prevention and management decision-making of Internet financial risk events through ontology application.
An Evaluation Approach of English Language Proficiency with Eye Tracking

People spent a lot of time in English learning. Despite their plethora work in English learning, traditional English learners have yet to evaluate their English language proficiency quickly and accurately. This paper tackles this problem by proposing a novel approach for assessing one's English proficiency which utilizes his/her eye tracking data while watching the English videos with English subtitles. Using random forest, our approach provides standalone English proficiency scores based on eye tracking, which even can be demonstrated to predict one's CET-4 score. Our approach provides a new idea for accurate and efficient second language proficiency assessment.
Efficiency for Face Mask Detection in Neural Network

Because of COVID-19, wearing a face mask has become the most efficient and convenient way to spread this virus. Face mask detection can fulfill the function of warning those people who do not wear a face mask. Using the Convolutional neural network, the Feedforward Neural Network and the MobileNet V2, a high recognition rate for the face mask detecting system can be achieved. This study compares the accuracy, the loss and the training time for these models and concludes that CNN is the best model based on its high accuracy of 100&#x0025;. The result that comes out from our study can improve the efficiency of the face mask detecting system. In general, the identification model in our study can be changed easily to apply in other areas, such as medical image classification and geographic image classification.
Reinspecting Classification and Regression in the Sibling Head for Visual Tracking

The sibling head has been widely used in Siamese-based trackers, however, the structure of the sibling head is the same between classification and regression tasks, which limits the ability of the tracker to obtain more robust and accurate prediction. To solve this issue, we reinspect the network structure of tracking-head for classification and regression tasks, since recognizing the target category needs translation invariant feature while the position-sensitive information facilitates target bounding-box regression task. Further, we propose a differenti-ated tracking-head network, named SiamDTH, by exploiting the feature response module (FRM) and the differentiated sibling head (DSH) to alleviate misalignments between classification and regression task domains. Extensive experiments on visual tracking benchmarks including VOT2019 and OTB100 demon-strate that SiamDTH achieves state-of-the-art performance with a considerable real-time speed. Our source code is available at: https://github.com/x10312/SiamDTH.
Features Correlation Analysis and Classification based on EdNet User Online Learning Behavior

Online learning has become an increasingly popular way of learning. In order to improve users' online learning experience and learning effectiveness, analysis on users' online learning behavior has become a hot issue in the field of education big data. Based on the EdNet data set, this research randomly selects some users, counts these user's answer scores, elapsed time and other learning behavior data, and then extracts feature. Meanwhile, features of questions difficulty are calculated on the basis of the EdNet raw data, and construct user completion difficulty features. By extracting and constructing the features of users' learning behavior, a random forest model is used to classify and predict the user's level. The experimental result shows that, on the issue of classifying user's level, the user completion difficulty features are conducive to model performance. It also confirms that the features of questions difficulty has a great relationship with user's learning effectiveness. Moreover, this research gives some suggestions for users to improve their learning performance.
Research on Badminton Motion Recognition Based on Hidden Markov Model

In order to improve problems such as uncoordinated movements of exercisers and sports injuries, a badminton motion recognition method based on hidden Markov model is proposed, which uses non-visual sensing motion recognition based on multiple sensors to find out the problems existing in the technical movement during the movement and obtain the best motion effect. Firstly, four wearable inertial sensors were placed in several important parts of the experimental object, and each sensor collected signals of triaxial acceleration and angular velocity signals in three-dimensional space. Secondly, the HMM model is established through data acquisition, preprocessing, window segmentation, feature extraction and selection, classification and recognition. Finally, through three comparative experiments, the recognition rate was raised from 91.99% and 91.60% to 98.1%, effectively improving the recognition rate of badminton movements. The results of this study can provide scientific solutions to the technical movement problems existing in exercitation and promote the health management of the whole life cycle.
Image Translation based on Attention Residual GAN

Using Generative Adversarial Networks (GAN) to translate images is a significant field in computer vision. There are partial distortion, artifacts and detail loss in the images generated by current image translation algorithms. In order to solve this problem, this paper adds attention-based residual neural network to the generator of GAN. Attention-based residual neural network can improve the representation ability of the generator by weighting the channels of the feature map. Experiment results on the Facades dataset show that Attention Residual GAN can translate images with excellent quality.
Keyword extraction method of text combining random walk and variational inference

Keyword extraction is an important way to explore text semantic information. It is mainly composed of topic model and parameter estimation algorithm. Firstly, the mapping relationship between topic space and text space is constructed by topic model, and then the model parameters are estimated by expectation maximization (EM) algorithm. The traditional EM algorithm constructs the logarithmic likelihood function of topic variables as the target expectation, and maximizes it by Lagrange multiplier method. In terms of expectation construction, this method does not consider setting an approximate distribution for the posterior distribution of topic variables, and in the aspect of programming solution, a large number of partial derivative operations are required, which makes it difficult to analyze the convergence of the algorithm. In this paper, a keyword extraction method combining random walk and variational inference is proposed. The variational parameter distribution is set to estimate the posterior distribution of topic variables, and the random walk state transition matrix is used to iteratively calculate the parameters instead of Lagrange function. Not only can it be proved theoretically that this method has a stable distribution, but also through comparative experiments, it is proved that this method is superior to the ordinary EM algorithm in three evaluation metrics:precision, recall and F1 Measure, and its performance is not easily affected by the change of the number of extracted words.
Research on the construction of teenagers' virtual self-efficacy under computer games

In the era of high-tech development such as artificial intelligence and computing engineering, computer games also develop. It not only brings diversity to people's way of life and entertainment, but also brings some negative effects. Teenagers are in school age, their self-control ability is not mature, and they are easy to indulge in computer games. Using the methods of literature mate analysis and literature collation, this paper studies the construction of teenagers' virtual self-efficacy under computer games, and believes that the main reason for their addiction to computer games comes from their satisfaction of virtual self-efficacy. By participating in computer games, teenagers can obtain “self-realization”, “sense of value”, “sense of belonging” and “sense of dependence” that cannot be obtained in the real world to a certain extent, so as to meet the construction of their virtual self-efficacy. Therefore, we should increase the participation of teenagers in real social activities according to the psychological factors behind their virtual self-efficacy in computer games, so as to weaken their virtual self-efficacy in computer games.
Temporal Feature Enhancement Module for Action Recognition

Although convolutional neural network has achieved great success in the field of image classification, there are still problems to be solved for video action recognition. Compared with image classification, temporal modeling remains key and challenging for action recognition in videos. To mitigate this issue, this paper presents an action recognition network module, termed as Temporal Feature Enhancement module, with a focus on enhancing the use of temporal feature to achieve efficient action recognition. The module is composed of a Short-term temporal feature enhancement module and a Long-term temporal feature enhancement module. These two modules work together to alleviate the problem of temporal feature modeling in action recognition networks. Through experiments on UCF-101 and HMDB-51 datasets, the proposed algorithm is superior to the comparison algorithms, which proves its effectiveness.
Representation and Extraction of Diesel Engine Maintenance Knowledge Graph with Bidirectional Relations Based on BERT and the Bi-LSTM-CRF Model

The effective and efficient maintenance of diesel engines in a power plant highly relies upon the knowledge accumulated in previous maintenance and overhaul practices. A large part of this knowledge exists in maintenance reports in the form of engineering know-what, know-how and know-why with complex elements and relationships. As such, the need of supporting automatic extractions of such knowledge using state-of-the-art machine learning and NLP techniques is raised. This paper proposes a framework for the presentation and extraction of the knowledge graph from unstructured text in maintenance reports. Different from previous work, this framework supports extractions of bidirectional relations through a novel combination of reports prepossessing, the BERT model and the Bi-LSTM-CRF model. Specifically, the BERT model is applied to get character vectors and the Bi-LSTM-CRF model is applied to realize automatic extractions of entities and relations. We use an application named Neo4j to construct and storage knowledge graphs, and an application named Protégé to construct bidirectional relations. All the results from users' search is ranked by a scoring formula developed by Yahya et al. Conducted experiments over both public and private data set show that the proposed framework achieves good performance and hence provide a promising solution.