Reviewerly/exHarmony huggingface dataset:
Research on web text clustering based on DBSCAN optimization algorithm

As the amount of information on the Web continues to expand, the way people access information has also changed. Instead of searching for information in paper books or libraries, people are now searching for relevant information on the Web. However, the amount of data on the Web is so large that it is almost impossible to construct an effective data warehouse to replicate, store and integrate all the data on the Web, and there is neither a unified category code nor an effective organisation of indexing in such a large collection of data, which makes content management and information filtering on the Web increasingly difficult and important. In traditional Web applications there are a large number of redundant words, syntax and other problems that make it impossible to use computers to extract useful information accurately and efficiently, so how to effectively use the available resources is one of the urgent and pressing issues that need to be addressed. Therefore, this paper explores Web text clustering based on the DBSCAN optimization algorithm.
The construction and research of the research platform for the collaborative development of regional industries based on neural network

The coordinated development of regional economy is an important concept in the transitional period of my country's economic development. It is necessary to strengthen coordination and cooperation between regions to jointly promote regional economic growth and stability, mutual benefit and mutual benefit of various regions, and realize a development model of healthy and sustainable regional development. This article aims to study the construction of a research platform for the collaborative development of regional industries based on neural networks. Based on the analysis of the characteristics of the collaborative development of regional industries and the evaluation criteria of industrial synergy, a certain domestic economic circle is the research object, and the industrial synergy of this economic circle The degree evaluation index system was constructed, and the regional industrial synergy evaluation model was constructed through the neural network method, and finally the industrial synergy degree was measured. The calculation results show that the orderly development of the three sub-systems should be based on the self-coordinated development of the internal system, and the development gap between the order parameters should not be too large. It is necessary to seize the advantages of development and to develop in a stable and balanced manner.
Online process automatic monitoring technology of Internet of Things card life cycle based on time series optimization

Aiming at the problem of poor real-time monitoring performance of the Internet of Things (IoT) card, an online automatic monitoring method based on the optimized time series is designed. Based on the concept of time series, the automatic monitoring function of the life cycle of the Internet of Things card is deduced. On the basis of this function, the online process automatic monitoring model is constructed to shorten the time of data transmission in the monitoring process, to realize the online process real-time monitoring of the life cycle of the Internet of Things card. In this paper, the performance of the design method is analyzed by means of comparative experiments. The data transmission time of this automatic monitoring method is all below 0.40ms, and the transmission time of business statistical analysis is the highest, and reached 2.7ms. Therefore, the method effectively reduces the transmission time of monitoring process data and improves the monitoring efficiency.
Method for detecting the normalization of Word document title

To solve the problem that it is difficult and inaccurate to artificially detect the normalization of Word document titles, this paper proposes a normalization detection method for document titles based on XML logical structure description. This method converts the title content and style information of the template Word document and the test Word document into a document title tree with a logical structure. By comparing the consistency of the title trees of the two documents, the title normalization of the test Word document is realized. The experimental results show that this method can detect the logical structure, content, format, and other normalization of Word documents quickly and effectively, which is helpful to improve the document normalization level.
Research on spatial alignment method in detection collaborative of self-propelled anti-aircraft combat vehicles

The detection collaborative of self-propelled anti-aircraft combat vehicles effectively solves the problem that the combat vehicle cannot complete the combat function in the absence of key information, and improves the combat effectiveness of the combat vehicle. To ensure the availability of detection collaborative, spatio-temporal alignment of target information shared between combat vehicles is required. The transmission error of the target information obtained by the master vehicle during spatial alignment is affected by the relative position and vehicle attitude between the master and slave vehicles. In order to correct the transmission error and improve the accuracy of spatial alignment, this paper proposes an improved decorrelated unbiased converted measurement Kalman filtering algorithm based on BP neural network correction. The algorithm uses the untraced transform to calculate the statistics of one-step predicted values on the coordinate system that establishes the measurement equation, and uses singular value decomposition to solve the problem that the untraced transform is prone to crash in practical applications. After that, the parameters that affect the filtering accuracy are used as inputs, and the difference between the filtered state estimate and the true value of the state is used as the desired output to train the neural network. Use the trained neural network to correct the filtering results. The simulation results show the effectiveness of the method.
Prediction of employment and reemployment in China based on SARIMA-ARIMAX model

Employment has an important impact on the social development needs and economic development needs of the country. It is also the fundamental demand for people’s increasingly better life and the basic premise and important way to improve people 's lives. In order to understand the current employment form in China, the time series analysis method is used to predict the number of new employment and re-employment in China. According to the number of new employment and re-employment in cities and towns from the first quarter of 2013 to the fourth quarter of 2021, the number of new employment and re-employment in cities and towns in the first quarter of 2022 is predicted, and the combined prediction model of seasonal time series model and dynamic regression model is constructed. From the prediction results, most of the combined prediction accuracy is concentrated in less than 3%. Compared with the single prediction model, the prediction accuracy has been significantly improved. The number of predicted people is 237.7535 million, and the prediction results are credible. It can provide valuable reference for the problems existing in the employment situation.
Research on prediction model of platform turning speed based on ASSA-ELM

In the automatic unpacking control system, the control accuracy of the flipping speed of the flipping platform is of great significance to the dumping effect and the service life of the equipment. This paper conducts research based on this. Aiming at the fact that the standard extreme learning machine (ELM) is prone to fall into local optimum, this paper proposes a model for extreme learning machine (ASSA-ELM) optimization based on improved salp swarm optimization algorithm is proposed and applied to an example of flipping speed prediction of flipping platform. Based on the salp group optimization algorithm (SSA), a position update strategy combining the adaptive weight method and the proportional weight of the improved step size Euclidean distance is introduced. The weights and hidden layer biases are optimized, which greatly improves the generalization ability of the ELM model and the accuracy of the predicted value. The algorithm models before and after the improvement are compared and analyzed. The results show that the predicted value of the ASSA-ELM model has the highest fitting degree with the actual value collected in the industrial field, and has a high prediction accuracy, which verifies the feasibility and effectiveness of the ASSA-ELM model in the prediction of the turning speed of the turning platform.
Negative feedback scheduling algorithm based on similarity and historical performance

The existing scheduling strategies do not have enough granularity in the division of execution units, and can not flexibly use the attack feedback information. Lack of security to some extent. Therefore, this paper proposes a scheduling algorithm considering the similarity between components and the historical performance of executors. Firstly, a specific component similarity quantification method is designed to improve the heterogeneous differentiation of executors. Then, the historical work performance is incorporated into the executor scheduling strategy to improve the utilization efficiency and system security of the executor. Finally, two attributes are integrated to calculate the comprehensive score. Experiments show that compared with existing algorithms, this method can better distinguish similar executors. At the same time, it dynamically adjusts the selection of executors through the attack feedback information, taking into account the system dynamics and security, and has good practicality.
A Quantifiable Framework for Describing Immersion

Abstract Current definitions of immersion describe its relationship to presence and allow for relative comparisons between the immersive qualities of Virtual Reality (VR) systems, but lack the ability to describe the immersion supported by a system as an absolute quantity. In this article, we present an abstract model of perception, defining sensory units as the smallest biological registers of sensation within the body. Two metrics of immersion are introduced: the immersed sensory range, and the immersed sensory field, which can be defined for both individual sensory units and entire sensory categories. We define an isolated sensory unit as one that is shielded from non-VR stimuli, and derive the terms isolated field and isolated range from this definition. These metrics are further described as ratios, resulting in a set of theoretical and practical attributes which can be used to quantify the immersive potential of a VR experience.
Design of Quantitative Investment Strategy Based on Domestic Chip Industry

Select 59 concept stocks in the chip industry for quantitative analysis, use principal component analysis method and k-means clustering to process our financial indicators; use historical simulation method, variance-covariance method and Monte Carlo simulation method to calculate the VaR value of each stock for effective risk management. Based on Markowitz theory, the effective frontier of five stocks is constructed, combined with the utility of investors in different risk aversion levels, the best portfolio is selected to obtain the best portfolio of concept stocks in the chip field, using the Sharp ratio and CV ratio for performance evaluation, and the corresponding investment suggestions are given.
Modeling and analysis of two Normal populations based on an unlabeled paired sample

Conducting hypothesis tests on two populations is essential in various applications. However, the data quality cannot always be guaranteed in practice. It is common for a data analyst to handle unlabeled data due to missing information or for the protection of privacy. This paper derives the sampling distribution of the ratio of the minimum to the maximum of two random numbers drawn from two normally distributed populations. The Likelihood Ratio Test conducted based on the Min-Max ratio distribution is able to detect the differences or shifts of means or variances based on the poor quality or confidential data consisting of unlabeled paired samples where the conventional hypothesis tests are malfunctioning. Numerical examples are provided to illustrate how to conduct the related hypothesis tests based on the derived distribution and to demonstrate the effectiveness of the proposed test by comparing it with the conventional tests on the population means or variances. In addition, to further demonstrate the practical value of the proposed methods, the potential applications related to rapid quality inspection in the mass production of symmetric mechanical pieces and applications requiring privacy protection are also discussed.
The impact of LEGO and analog objects in co-creating and prototyping ideas

The main purpose of this study is to examine the separate and combined efficacy of LEGO and analogue objects as prototyping tools in idea generation activities. The study took place in an environment where ‘Design Jams’ were conducted, involving 58 participants with diverse backgrounds. Results showed that LEGO and analogue objects encouraged designers and stakeholders to collaborate and think more critically through prototyping activities emphasizing experimentation, reflection, and decision making. Using co-creation methods, larger quantities of design solutions and longer-term design directions were generated through the establishment of a common visual language, the sharing of legitimate experiences, and improved decision-making.
Biomedical Image Classification via Dynamically Early Stopped Artificial Neural Network

It is well known that biomedical imaging analysis plays a crucial role in the healthcare sector and produces a huge quantity of data. These data can be exploited to study diseases and their evolution in a deeper way or to predict their onsets. In particular, image classification represents one of the main problems in the biomedical imaging context. Due to the data complexity, biomedical image classification can be carried out by trainable mathematical models, such as artificial neural networks. When employing a neural network, one of the main challenges is to determine the optimal duration of the training phase to achieve the best performance. This paper introduces a new adaptive early stopping technique to set the optimal training time based on dynamic selection strategies to fix the learning rate and the mini-batch size of the stochastic gradient method exploited as the optimizer. The numerical experiments, carried out on different artificial neural networks for image classification, show that the developed adaptive early stopping procedure leads to the same literature performance while finalizing the training in fewer epochs. The numerical examples have been performed on the CIFAR100 dataset and on two distinct MedMNIST2D datasets which are the large-scale lightweight benchmark for biomedical image classification.
Research on Lazy Theta* Route Planning Algorithm Based on Grid Point Optimization

In recent years, the problem of route planning in complex battlefield environments has attracted significant attention. With the increasingly worrying international situation, safety and flyability in a continuously changing threat environment are critical factors in route planning research. Thus, this paper proposes an improved Lazy Theta* algorithm that adapts to a complex battlefield environment and finds the optimal route. Specifically, given the low computational efficiency and data redundancy of the existing environmental threat modeling, the developed scheme first employs an octree grid to divide the environment into a grid. Furthermore, based on a real environmental threat model and flight constraints, we design a Lazy Theta* algorithm based on octree grid points, which shortens the planning path and reduces the path cost. Finally, this paper proposes an equally spaced B-spline to smooth the route and improve its smoothness and flyability. Several simulated experiments verify that the smoothed route improves safety and flight ability while reducing the route’s distance. Overall, the simulation results prove that the proposed method significantly improves the planning efficiency and flyability compared with traditional methods.
Text similarity of autonomous marking system based on deep learning

With the practical application of the self-marking system, it can give an objective and fair score, but it can not meet the needs of classroom teaching. It also needs to give students rapid feedback on the vocabulary, sentence, text structure, content relevance, and other dimensions presented in the test paper. The scoring method based on artificial features is the earliest self-marking scoring method, which uses experts to design some scoring features from the language quality, content quality, and text structure of the test paper. It takes the scoring task as a regression or classification task to score or rate the test paper. In this paper, based on the deep learning theory, a method for text similarity detection using a twin network is proposed. Considering the interaction between text pairs, we integrate expressivity pooling based on bidirectional GRU and measure the similarity by distance calculation formula. The experimental data show that the two-way GRU network integrated with expression pooling can obtain the interaction between text pairs so that the extracted features of the text are more comprehensive. The model has a better effect in the study of the similarity of text pairs. This method can reduce the difference in scoring results caused by different subjective consciousness of raters, make the scoring results more objective and persuasive, and improve the accuracy and efficiency of scoring.
End-to-end image captioning based on reduced feature maps of deep learners pre-trained for object detection

Image caption generation is a task to generate a descriptive sentence automatically for a given image which combines image process and natural language process together. Most existing approaches divided the whole process into two parts, encoding and decoding. Our research proposed an image caption generation method based on the most popular architecture with the YOLO as encoder and the LSTM as decoder. The encoder is used to encode images into feature vectors while the decoder is used to decode the feature maps for natural language sentences generation to describe the images. The proposed method is compared with other existing methods.
Knowledge distillation-based privacy-preserving data analysis

Over the past decade, there has been an increasing focus on data privacy, which is one of the most popular areas of research today. Due to legal restrictions, it is difficult to centralize data, so multi-party collaborative learning becomes difficult to achieve. In this paper, we propose a data collaborative analysis method for protecting data privacy: Knowledge Distillation-based Analysis (KDDA). The proposed method can effectively solve the above problems. The framework of the proposed method is based on "teacher-student" network. Specifically, each institution (e.g., hospital) used sensitive data to train local models, which we call teacher models. Then we introduce unlabeled public datasets. We design an aggregator that aggregates all teacher models to generate pseudo-labels for the public dataset. It is worth noting that to minimize the risk of data privacy exposure, we limit the number of generated pseudo-labels. Finally, we train the student model using semi-supervised learning on the public dataset with pseudo-labels. Since the student model is not directly trained on sensitive data, it will not lead to the leakage of data privacy, so it can be publicized. To evaluate the effectiveness of the proposed method, we conduct experiments on three popular image benchmark datasets: MNIST, SVHN, and CIFAR-10. Experimental results show that the proposed method is effective.
Sentence embedding based emotion recognition from text data

Automatic emotion recognition from text is an important task in the field of natural language processing (NLP) with applications in data mining, e-learning, information filtering systems, human-computer interaction, and internet services. Recent advances in machine learning and deep neural networks have boosted the NLP systems' performance tremendously. The availability of large pre-trained language models has simplified the feature extraction and facilitated the building of systems from small amounts of data by transfer learning. In this study, we investigate and compare various methods of sentence embedding including simple embedding matrix as well as sophisticated models such as BERT. The recognition back-end consists of standard SVR or Feed-Forward DNN regressors. In our experiments, we used the EmoBank corpus where each sentence is labeled with Valence-Arousal scores. The results clearly show the benefits of the transfer learning and fine-tuning of pre-trained models with respect to classical model training from scratch.
Computationally efficient physics approximating neural networks for highly nonlinear maps

This paper puts forth a method that is easily transposable to a realtime environment by utilising a "Physics Approximating Neural Network" to predict 1D output signal. The technique described in this paper is inspired by Physics Informed Neural Networks put forth by Raissi et al. The model demonstrated in this paper makes use of a recurrent input. This is passed to a "preprocessing" stage of 2 layers by 8 neurons wide. The output of the preprocessing stage is passed to the "approximation layer". Lastly, the output of the "approximation layer" is passed to a "postprocessing" layer of 5 layers by 8 neurons wide. The architecture of the model is explained and tested on a specially developed dataset. The results show closer similarity to the ground truth compared to a linear model or a dense-layer neural network.
SInC: Semantic approach and enhancement for relational data compression

Data compression has been widely adopted in the industry to reduce storage or bandwidth consumption by removing redundant data or encoding information. Redundancy in semantics implies that some facts in a knowledge base can be inferred from the others. For relational databases, it is possible to remove records due to semantic equivalence. In this paper, we present a purely semantic approach, which losslessly compresses relational data in the first place and also enhances data file compression to further reduce the storage. Our Semantic Inductive Compressor ( SInC ) works not only for intra-relation patterns but also inter-relation cases. SInC achieves around 1/3 to 2/3 of semantic compression ratios, and the original data can be entirely retrieved with the informative patterns induced by SInC . We apply industrial data compression tools on semantically compressed databases, and the experiment results indicate an enhanced compression ratio up to 35%. Almost all efforts in our technique turn to the enhancement.
Analysis of distance matrices

The relationships between eigenvalues of the distance matrices and outliers are largely unexplored. We show a discrepancy in the sizes of eigenvalues of the distance matrix that is contaminated with outliers. We present test statistics to determine when a distance matrix has a constant structure. We relate the eigenvalues of the two-sample distance matrix to the tests of equality of distributions, study dissimilarity matrices and their eigenvalues in high dimensional and low sample size setting.