Reviewerly/exHarmony huggingface dataset:
Monitoring system based on virtual reality technology and video processing technology

With the needs of the rapid development of society, video surveillance systems are more and more used in all aspects of life, but the current video surveillance systems mostly use PCs as platforms and cameras for long-term uninterrupted recording. This not only consumes high power, but also has poor portability and consumes a huge amount of storage devices. It will also be extremely tedious in the face of complex and massive redundant data when looking back at video to find key information afterwards. Based on virtual reality technology and video processing technology, this paper studies the design of monitoring system. This paper tests and analyzes the designed embedded system. Experiments show that all functional modules of the system work normally, and compared with ordinary video surveillance systems, the output frame rate is good and can reach 25FPS.
Construction of intelligent evaluation model for Topic Talk in the Mandarin proficiency test

In order to solve the problem that the Topic Talk in the Mandarin proficiency test is still unable to achieve machine evaluation, an intelligent evaluation model for Topic Talk is constructed in this paper. First, according to the requirements of the intelligent evaluation model, a large vocabulary continuous speech recognition module is designed in the front of the model. Then the recognition module is optimized from the two aspects of the acoustic model and the language model. Finally, the experiment is performed on the dataset which is collected from the Mandarin Proficiency Test Center. The experimental results show that the speech recognition module constructed with the improved DBLSTM-HMM acoustic model and n-gram+RNN interpolation language model can better complete the recognition task of front part in the intelligent evaluation model. The improved acoustic model can reduce the word error rate and sentence error rate to 14.08% and 18.22%. The improved language model can increase the word recognition rate by 5.87%, and the correlation between the posterior probability and the manual score by 3.82%.
Research on skeleton extraction algorithm based on distance transform

Aiming at the problem that the position of skeleton obtained by the method based on topology refinement is not accurate enough, and the connectivity of the skeleton obtained by distance transform cannot be guaranteed, this paper combines the advantages of the topology refinement algorithm and distance transformation algorithm to propose an improved skeleton extraction algorithm. This method first obtains the skeleton points through distance transform, and then obtains the initial skeleton through the refinement method; last uses the skeleton points obtained by distance transformation to screen each branch obtained through the refinement method to ensure the accuracy of the skeleton in the later applications. Experiments on the MPEG7 dataset demonstrate the effectiveness of the algorithm.
Automatic raw material screening device for wind turbine foundation anchor bolt

With excellent development prospects, wind power generation is one of the most important parts of new energy. Because it has a lot of advantages over traditional energy forms energy, such as availability, free pollution and renewability, etc, many researchers of different countries have concentrated on such field. As an important part of fan foundation, anchor bolt assembly realizes the transfer and dispersion of complex loads such as horizontal load, bending moment and torsion of wind turbine through anchoring, self-locking and synergistic action with concrete. In order to further promote the application of wind power generation, the cost reduction of equipment production involved in this field has attracted extensive attention; therefore, the automatic production of anchor bolt assembly has also become an important research direction in the production field. In the traditional production process of wind turbine foundation anchor bolt, manual screening method is adopted in this paper, and there is only one specification (diameter and length) of anchor bolt raw material on a production line at the same time. In this case, the efficiency of multi-task processing in the production line will be greatly reduced. Therefore, it is extremely important to develop control equipment that can realize automatic raw material screening and distribution.
Research on data kinetic energy measurement based on XGBoost algorithm

With the rapid development of Internet, data has become increasingly important in today’s society. With the widespread use of data, the issues in data use like low efficiency, high cost, lack of supervision might hinder the development of enterprises and companies. Therefore, the way to mine data resources as a new development power is extremely important. In this paper we applied the convolutional neural network in image for testing. We used LDA, XGBoost and other models, based on a large number of questionnaires questionnaire data. In the process of data processing, we fitted the 3D data after Emmett interpolation to make the data more intuitive. The analysis of the model shows that the fit of the regression curve, R², is 0.173 and Vif is less than 10, indicating that data has become an important driver of socioeconomic development, both from a macro and micro perspective.
Designing deep-network based novelty assessment model in Design education

Novelty is a significant factor in evaluating creative aptitude of mass examination in Design education. Novelty is a measure of uniqueness which is computed based on relative comparison among solutions. Presently, novelty assessment of solutions illustrating creative aptitude in mass examination is conducted by domain-specific experts. During the process of evaluation, examiners drill their thought processes and assesses based on their frame of reference. Moreover due to the ever-increasing number of students in these examinations where students compete for admission to Design schools, examiners are confronted with multiple challenges viz., evaluation in stipulated time, evaluation to be conducted in large scale, etc. These difficulties might frustrate examiners and might lead to errors in evaluation. The investigation in this paper is exactly geared towards this issue and we explored whether technology can support examiners in situations like this. Features are extracted for evaluating novelty by human-centred design approach. A model is proposed to evaluate novelty in academic settings specifically in mass examinations. This model is validated by utilizing it in different case studies based on the type of Design solutions in mass examinations. This model is implemented using Deep Learning (DL)-based architectures. Findings emphasize that there is negligible difference in outcome of these architectures and human expert-based evaluation, which confirms the capacity of the devised model. This study would support pedagogues in the evaluation process that are conducted on a large scale. It would also help reducing logistics, time, and man-power in evaluation process. Consistency in subjective assessment would ensure selection of right candidate, thereby increases trust in the assessment system.
A text classification method of power grid assets based on improved FastText

In today’s era, information technology is constantly updated, and the degree of social information is getting higher and higher. Power grid enterprises have a large amount of asset data, and There are many types of assets, such as tools, information system and vehicles. These assets need to be managed. Although there are unified codes for the types of assets at present, manual input is still inaccurate, so there will be some inaccurate descriptions, which makes it very difficult to promote the management of power grid assets. In this paper, an improved FastText text classification method is proposed to identify the information of various power grid assets. The purpose is to ensure that the technical object type coding of power grid assets matches the type of power grid assets, and to realize the efficient quality management of power grid asset object type coding data Concrete by using n-gram model information, read between the word and the word order in hidden layer of network by traditional method and the average improvement for the average sum of squares, and the network output layer for the softmax improved hierarchical softmax and negative sampling method, improved rapid extraction and classification of information, and can better meet the automatic identification data grid assets. The experimental study in this paper proves that the classification accuracy of the improved FastText model on the grid asset data set is significantly improved, and the intelligent classification of grid assets can be realized.
Multi-input fusion underwater image enhancement technology

Images are one of the important carriers for acquiring underwater information. This paper proposes an underwater image enhancement algorithm with multi-input fusion to address the problems of low image contrast and colour degradation caused by the underwater environment in the imaging process of underwater optical images. Firstly, the dark channel a priori defogging is used to remove the inhomogeneous turbidity in the image and equalise the image color; then, the restricted contrast adaptive histogram equalisation is used to uniformly clip the image brightness components to further improve the image contrast; at the same time, the homomorphic filtering is combined to solve the problems such as uneven illumination of the image. Finally, the multiple outputs obtained are weighted using the Euclidean rule to obtain the final fused image. Experiments show that the images enhanced by this method are clearer to the naked eye and the target information is more prominent.
Reciprocal collaboration network for 3D skeleton-based human motion prediction

Noticing that the human motion in both the positive and inverted time sequence are predictable, we design a reciprocal network which contains a forward and a backward prediction network to learn this information respectively, and then train the two networks in a collaborative and iterative way with the guidance of consistency constraint. Based on the reciprocal network, we establish an error compensation network to approximate the error of forward prediction network for making the forecast closer to the real pose. Extensive experiments show that our approach outperforms most recent methods in both short and long term motion predictions on Human 3.6M and CMU Mocap.
Optimization of nonlinear functions based on improved genetic algorithm

Premature convergence and falling into local optimum easily are important problems for genetic algorithm. Therefore a new algorithm is constructed in this paper, which can be performed among multiple populations by introducing different optimization parameters. The final formation is aggregated into an elite population, where genetic operations such as selection, crossover, mutation, etc. do not exist, for the purpose of ensuring that the optimal individuals selected between different populations are not destroyed and lost. The simulation results show that the algorithm used in this paper has better optimization ability and higher calculation accuracy for solving local optimal problems of nonlinear functions.
GDBID: fusion gradient distinction binary image descriptor

In recent years, binary descriptors have attracted more and more attention due to their low memory consumption and high speed. It is well known that these representations are worse than higher-dimensional and histogram-based descriptors such as SIFT. Therefore, this paper proposes a fusion gradient distinction binary image descriptor (GDBID). Gradient comparison is added on the basis of the original gray comparison to enrich the information contained in the descriptor. At the same time, the comparison patches of different sizes are obtained by constructing concentric circles to achieve anti-noise. In addition, a threshold is set to filter patches to reduce the dimension of descriptors. Experimental results show that the GDBID has a precision is close to the best algorithm (SIFT), and the time consumption is lower than the fastest ORB in the literature.
Tibetan medical named entity recognition study for Tibetan clinical electronic medical records

In current years, there are many open corpus and research results for Chinese clinical natural language processing (for short NLP) of Biomedical and Traditional Chinese Medicine. However, the research in this area for Traditional Tibetan Medicine lags behind. We know that Traditional Tibetan Medicine has its own unique set of theory system in treating and saving people. Therefore, it is imperative to speed up the research on the Tibetan Clinical Natural Language Processing. Medical named entity recognition is an important subtask of Clinical Natural Language Processing. So Tibetan medical named entity recognition is an urgent and basic research work for Traditional Tibetan Medicine. Due to the scarcity of labeled datasets, the Medical Named Entity Recognition task of Traditional Tibetan Medicine clinical text is still an unvisited researching area. In this work, we firstly manually construct a labeled dataset for this task and then explore this area with deep learning approaches by designing a Tibetan Lattice-LSTM-CRF neural network architecture. To further improve the model performance, we also incorporate both syllable and word level pre-trained representation. The final empirical results show that the proposed models could produce accuracy rate, recall rate and F1 values of 91.89%, 93.15% and 92.52%, respectively on our test set, which shows the validity of the model in the paper.
An objective method of measuring students’ achievement

This paper introduces an objective method to measure students’ achievement. Among many popular methods for evaluating and testing students’ scores, different participants often obtain different results on subjective questions, which obviously lose the objective and fair standard for students’ evaluation. In order to overcome these defects, a new scoring method is recommended in this paper, so that different raters can obtain more objective and fair results in the test of subjective questions as much as possible. In this paper, this scoring method is divided into some main essential steps, which can be summarized as: find out the key points of subjective questions, and divide the questions into several different scoring grades according to different scoring steps.
Pixel-level semantic segmentation based on gradient features

Due to the mechanism of pooling and convolutional layers, many important features and the correlation between the features are lost in the forward propagation process in the pixel-level semantic segmentation tasks. Therefore, here we analyze the edge features of the image by means of second-order difference, propose gradient features and design the corresponding gradient convolution layer. Based on the gradient convolution layer, we use the residual structure to achieve the fusion of high-resolution gradient features and low-resolution gradient features. Finally, we designed the GraDNet. In the tests on the Cityscapes and ADE20K datasets, GraDNet achieves the best results in both accuracy and speed compared to some SOTA algorithms.
Visualization analysis of university information literacy education research data

Based on objective data and visual knowledge graphs, this paper summarizes the main research content, theme evolution, research trajectory and development trend of international university information literacy education. It draws on highquality and novel teaching concepts, methods and contents, and proposes constructive suggestions for the development plan of information quality education in China, and provides references for the curriculum construction of “Document Retrieval”, as well as the future development and subsequent research of information literacy education.
Research on transportation optimal path planning based on improved ant colony algorithm

In this paper, an improved ant colony algorithm is proposed to solve the transportation optimal path problem. The algorithm can quickly find the optimal path by improving heuristic function, pheromone and neighbourhood-search. Experimental simulation shows that the improved ant colony algorithm proposed in this paper has certain advantages over genetic algorithm and ant colony algorithm. It not only obtains the minimum value of the optimal path length, but also has the fastest convergence speed, which effectively solves the problem of optimal transportation path planning. The improved algorithm effectively accelerates the transportation speed, shortens the transportation time and saves the transportation cost.
Improved ant colony optimization based robot path planning

To solve the problems that ant colony algorithm often gets sub optimal solution, the turning angle is too large and the convergence effect is poor when planning the global path of the robot, the traditional ant colony algorithm is improved. The grid map is applied to construct the working surrounding, the path heuristic function is designed to enhance the searching ability and convergence effect of the algorithm. Considering the influence of turning Angle on travel time, the heuristic function of turning Angle is introduced to ensure the ant colony to have more guidance in the planning and make the path smoother. Pheromone updating rules and path evaluation mechanism are established to guide ants to approach the path with the best comprehensive index. The algorithm is simulated on the MATLAB platform, the simulation indicates that the optimized algorithm has good convergence effect and strong optimization ability.
An aerial image data cleaning of power lines with multi-information fusion perception

Recently, unmanned aerial vehicles (UAVs) have been used to conduct the task of power line inspection. Unfortunately, most of the images acquired by UAVs are invalid for visual inspection. The main reason is that the collected images are low quality and repetitive. To obtain a set of valid images with high quality, a novel multi-information fusion perception (MIFP) model is proposed to automatically clean the large-scale aerial image data. Firstly, the image quality features and image content features are extracted, in which the weights of different features are used to evaluate the effect on the final image quality. Secondly, the image spatial features are exploited to aggregate spatial information in weight maps of different sizes. Then, the image quality and content features are merged into the multi-information features that characterize the quality of the final image. As a result, the image is picked out according to the quality score. Finally, experimental results show that the proposed multi information fusion perception model has excellent performance on real databases.
Defects analysis of blockchain PoW consensus protocol

Blockchain technology represented by cryptocurrencies has increasingly become the focus of social attention.The consensus protocol is the foundation for how the blockchain works.PoW, as the most widely used protocol, received more attention from researchers.This paper analyzes the defects of PoW, the most popular public chain consensus protocol in Blockchain, from five perspectives and points out the natural defects of PoW in high energy consumption, electronic waste, carbon footprint, expensive transaction fees, and centralization.This paper encourages the use of PoS and DPoS protocols instead of PoW protocols as they reduce the intensity of competition and may address the root cause of the aforementioned issues.
Three-dimensional reconstruction using phase-shifted square-wave patterns

Among structured light strategies, phase measuring profilometry (PMP) is considered to be an accurate and robust 3D reconstruction method. However, in practical scenes, gamma distortion is an inevitable factor that affects the accuracy and resolution of the final measurement result. In this paper, we propose a square-wave encoding scheme, which is gammafree, and we also demonstrate why the scheme is immune to gamma distortion. Then, based on the epipolar geometry, the invalid phase is removed by the epipolar constraint. Experiments demonstrate that the method can effectively reconstruct the surface of the scanned object and remove the invalid phase. With the proposed method, the accuracy for reconstructing a vertical plane is effectively improved. The method can be an effective alternative in the gamma distorted systems.
Is it possible to predict speaker’s body size and oral cavity characteristics from speech signals: a preliminary study on Mandarin Chinese

This paper proposes a study on whether the speaker’s body size (height, weight) and oral cavity (lip protrusion LP, lip opening LO, front cavity FC) characteristics can be predicted based on the acoustic features of speech. Firstly, Pearson’s correlation analysis was first conducted to examine the relationships between acoustic features and body size and oral cavity characteristics. Further, the effects of acoustic features in predicting body size and oral cavity characteristics were examined using random forest and decision tree models. The results showed that fundamental frequency statistics (i.e., mean, max, min) exhibited significant negative correlations with height, weight, and FC. Besides, good accuracies of classification in height, LP range, LO range, and FC range could be achieved based on the acoustic features. The findings in the current paper imply that acoustic features could be the potential features for identification of the speaker’s body size and oral cavity characteristics. This paper will not only contribute to the research and practices in forensic speaker profiling and but also provides foundations for the technology of automatic speaker recognition.
Research on text classification of telecom user query

Text classification plays a more and more important role in many practical applications. However, there are a large number of unevenly distributed but important data about the complaints of Chinese telecom users. How to efficiently and accurately implement these telephone consultation contents with only a small number of samples to specific departments and improve the user experience has become an urgent problem to be solved. In this paper, we propose a multi modelbased machine learning method—telecom textCNN predictor which can effectively improve the accuracy and recall of the model on the long tail data, and automatically mark the text, so as to accurately locate the class of the problem. The results show that our model is helpful for business personnel to quickly identify the type of complaint business, conduct targeted business acceptance and user services, and improve user perception and produces better accuracy than the SVM model.