Reviewerly/exHarmony huggingface dataset:
Application of Computer Intelligent Control Technology in Physical Education Teaching

Research purposes: In the current society, information technology has penetrated into all aspects of people’s daily life and work; in the current process of college physical education, more and more schools began to use computer intelligent control technology; its importance is also highlighted, this paper focuses on the analysis of its specific application. Methods: The research was mainly carried out by searching literature and materials. The main results are as follows: Computer intelligent control technology is very important to improve the level of physical education teaching in colleges and universities. The role of. Main conclusions: On the basis of this development, the advantages of computer intelligent control technology itself are becoming more and more obvious, which can make the teaching resources and teaching methods of physical education more diversified, and also conducive to the emergence of new teaching ideas.
Google Scholar vs. Dblp vs. Microsoft Academic Search: An Indexing Comparison for Software Engineering Literature

Background: One of the necessary conditions for any substantial research work is to synthesis the depth and the breath of the existing published literature on that topic. It is, thus, of extreme importance for a researcher to understand and look for both credible and exhaustive information sources. This first (important) step can be made significantly easier if the researcher can employ a more systematic way to extract the maximum of the literature on the topic. Objective: Essentially, the objective of this preliminary study is to rank three freely available academic search engines (Google Scholar, DBLP, Microsoft Academic Search) on the basis of the indexed Software Engineering academic literature they contain. Method: We have used a systematic mapping to conduct the study. Results: After extracting and analyzing 1067 secondary studies (from 18 tertiary studies), we have concluded that Google Scholar has indexed 98.96%, DBLP has indexed 93.43%, and Microsoft Academic Search engine has indexed 97.46% of the secondary studies. Thus, this implies that Google Scholar and Microsoft Academic Search might be a better-suited option for searching for secondary studies.
Symmetric prior aerial object detection based on improved YOLOv3

Recently, the research of aerial images, images and videos taken by UAV(unmanned aerial vehicles) have developed rapidly. People use these images to get useful information. For example, investigate the density of vehicles, analyze the development of urban suburbs. Symmetry as one of the important features of objects, has long been a research hot area in computer vision. Symmetry detection aims to extract symmetric information from given objects. Aerial images and unmanned images have their special view angle. The symmetrical characteristics of industrial design leads to the special nature of the image from the top view angle. It shows more symmetry of the object more. The objects always like airplanes, ships, automobiles or industrial products like oil drums and chimneys. An aerial object detection method based on improved YOLOv3 algorithm is proposed in this paper. First, a symmetry prior is introduced into the proposed network based on the properties of symmetry patterns. For new convolutional layer, symmetric constrains are added during the process of kernel weights update. The symmetric kernels help the proposed network to find the corresponding symmetry objects. In addition, this paper uses k-means to calculate the size of anchors in order to make the size more suitable for the size of the aerial image object. Compared with the original YOLOv3 algorithm, the improved YOLOv3 algorithm can effectively solve the missing detection of symmetry objects and improve the confidence of detection.
Performance analysis and evaluation of Java-based InfiniBand Solutions

Low-latency network interconnects, such as InfiniBand, are widely used in HPC centers and are becoming available in public cloud offerings, too. For MPI applications accessing InfiniBand is transparent, but many big-data applications are written in Java, which does not provide direct access to InfiniBand networks, but relies on thid-party libraries. In this paper, we present Observatory, a benchmark for evaluating low-level libraries, providing InfiniBand access for Java applications. Observatory can be used for evaluating and comparing socket- and verbs-based libraries regarding throughput and latency. With transparency often traded for performance and vice versa, the benchmark helps developers with studying the pros and cons of each solution and supports them in their decision which solution is more suitable for their existing or new use-case. We also give an overview of existing and maintained InfiniBand libraries for Java and evaluate them with the proposed benchmark.
A Practical Application for Text-Based Sentiment Analysis Based on Bayes-LSTM Model

Abstract Text-based sentiment analysis algorithms have now become one of the active research areas in emotional analysis which has gained much attention nowadays. Text emotion classification can be widely used in social public opinion analysis, product use feedback, harmful information filtering, etc. In this paper, we first developed a robotic crawler to gather data about comment on Huawei cellphone from Sina weibo microblog sites (Chinese twitter). Then we generate the data text to be trained according to the input requirements of the Keras module, and perform formal training and learning on the model after data preprocessing. Subsequently, the classifier was constructed based on the Bayes-LSTM model in which TF-IDF model was used for feature selection. The LSTM model can be characterized by the ability to self-evaluate the usefulness of the information obtained, which makes up for the shortcoming of naive Bayes formula that only applies to two independent events. We finally have a practical application that generates a word cloud from text, showing frequently used words in larger font sizes, effectiveness of the algorithm was also verified by experiment.
Text Keyword Extraction Based on Multi-dimensional Features

Keyword extraction is a fundamental task of text mining, so extracting high-quality keywords is of great significance. Typical keyword extraction algorithms usually rely on the statistical features, but lack of the semantic information. At the same time, the supervised keyword extraction algorithms rely too much on sample labeling. Therefore, in this paper, an unsupervised keyword extraction algorithm based on multi-dimensional features called MDFKE is proposed, which combines statistical features, external knowledge-based features and semantic features. MDFKE mainly studies the semantic information of candidate keywords. LDA model is used to obtain text topic, and Word2vec word embedding is used to generate word vectors. Based on these, the similarity between candidate keyword and text topic is quantified as semantic feature. Nine specific features are extracted from five aspects: term frequency, length, position, external knowledge base, and semantics. Finally, this paper clusters on feature vectors to obtain the final keyword set. The experiment turns out that, compared with traditional keyword extraction algorithms based on statistical features, MDFKE can significantly improve extraction performance, and can also make up for the shortage of supervised learning overly relying on labels.
A Survey on Modularization of Chatbot Conversational Systems

Chatbots have attracted more and more attention and become one of the hottest technology topics. Deep learning has shown excellent performance in various fields such as image, speech, natural language processing and dialogue, it has greatly promoted the progress of chatbots, it can use large amounts of data to learn response generation and feature representations. Due to the rapid development of deep learning, hand-written rules and templates were quickly replaced by end-to-end neural networks. Neural networks is a powerful model that can solve generation problems in conversation response. People's requirements for chatbots have also increased with the continuous improvement of neural network models. In this article, we discuss three main technologies in chatbots to meet people's requirements, syntax analysis, text matching and sentiment analysis, and outline the latest progress and main models of three technologies in the field of chatbots in recent years.
A Multi-Modal Gaze Tracking Algorithm

A person’s emotional information, needs and cognitive processes can be described by eye movement states and concerns, so gaze tracking was first applied in the field of psychology. With the continuous development of information technology, the application range of gaze tracking has expanded from psychology to medical, military, commercial and many other fields. Aiming at the problem of high misjudgment rate and long time-consuming of traditional iris location methods, this paper proposes a gaze tracking method based on human eye geometric characteristics to improve the tracking accuracy in 2D environment. First, the human face is located by face location algorithm and the position of human eye is estimated roughly. Then the iris template is built by iris image, and the iris center location algorithm is used to locate the iris center position. Finally, the eyes corners and iris center points are extracted to locate the eye area accurately and obtain the binocular image. The binocular images are input into the feature extraction network as multi-modal information in parallel, and the convoluted feature channels are reconstructed using the weight redistribution module in the network. Then the reconstructed features are fused in the full connection layer. Finally, the output layer is used to classify the reconstructed features. Experiments were carried out on a self-built screen block dataset. For 12 classified data, the lowest recognition error rate is 5.34%.
An Experimental Study of Time Series Based Patient Similarity with Graphs

Finding similarities between patients has been used to effectively and reliably predict diagnoses and guide treatments. However, Electronic Health Records (EHRs) contain characteristics that make analysis and application difficult. Firstly, it is difficult to compare two patients' time series. Also, EHRs contain a vast amount of data, which proves to be a significant barrier to developing efficient systems for the widespread use of patient similarity. In this paper, we introduce a novel graph representation of time series EHRs. Our method compresses a patient's time series medical records to reduce the storage required by more than 50%. Our paper also presents similarity metrics that can be applied to vector and graph representations of patient's time series medical records and assesses the general performance for suggested metrics.
Influence of different components of data error on the result of solving identification and approximation problems

Abstract The dependences of approximation and identification errors for solving problems of approximation of functions associated with the presence of various components of data errors in the numerical solution are investigated. Using the study of problems that have an exact solution, we found patterns of increasing errors associated with rounding numbers and conditionality of the matrix of the equations system. It is shown that the error of identification (finding the coefficients of representation of the desired function as the linear combination of the basic ones) can be significantly greater than the error of approximation, despite the fact that the second one was estimated using crude values of the coefficients. The found dependencies limit the accuracy of the solution for approximation of other functions. Artificial distortion of the original data by random error is applied. It is shown that the identification error increases rapidly with increasing degree of approximation polynomial and significantly exceeds the amount of distortion. It is also shown that an increase in the number of specified points leads to a decrease of identification and approximation errors caused by random perturbation, inversely proportional to the number of points. Using examples of approximation of various functions, it is shown that the result of approximation, and especially identification, significantly depends on the convergence of the Taylor series (or its absence) on the segment under consideration.
An Extensible Framework of Leveraging Syntactic Skeleton for Semantic Relation Classification

Relation classification is one of the most fundamental upstream tasks in natural language processing and information extraction. State-of-the-art approaches make use of various deep neural networks (DNNs) to extract higher-level features directly. They can easily access to accurate classification results by taking advantage of both local entity features and global sentential features. Recent works on relation classification devote efforts to modify these neural networks, but less attention has been paid to the feature design concerning syntax. However, from a linguistic perspective, syntactic features are essential for relation classification. In this article, we present a novel linguistically motivated approach that enhances relation classification by imposing additional syntactic constraints. We investigate to leverage syntactic skeletons along with the sentential contexts to identify hidden relation types. The syntactic skeletons are extracted under the guidance of prior syntax knowledge. During extraction, the input sentences are recursively decomposed into syntactically shorter and simpler chunks. Experimental results on the SemEval-2010 Task 8 benchmark show that incorporating syntactic skeletons into current DNN models enhances the task of relation classification. Our systems significantly surpass two strong baseline systems. One of the substantial advantages of our proposal is that this framework is extensible for most current DNN models.
Discrimination of Text and Non-text Images

“The configuration of the problem is frequently more necessary than its solution, which may be merely a matter of analytical or experimental skill.” In this paper, we solve the problem of the discrimination of the text and the non-text images in natural images. In the paper, we introduce a novice system, which can take the colour images, and extracts the feature values, i.e., the values extracted from the distance transform values of the image. The values extracted are plotted as a bar graph and the curve obtained by joining the top points of the bar graph is then studied. The experiment was further conducted by studying the accuracy and performance of the classifier in distinction between text and non-text images. The experiment has been done on the image containing pure text and the image that is a pure non-text image.
Writing between languages: the case of Arabizi

ABSTRACT The purpose of this paper is to explore the properties of Arabizi as a system with a ‘bottom-up’ orthography that emerged in the specific context of Computer-Mediated Communication (CMC). The study focuses on the dialects spoken in the city of Nazareth and the villages surrounding it (N = 75). We examined the consistency of choice of Latin letters and Arabic numerals for Spoken Arabic (SA) phonemes from two complimentary perspectives: subjective reports and the corpus-based analysis of a unique writing sample in Arabizi, elicited texts. Our results show a consensus on conventions in the transcription process, with some expected variation reflecting differences between regional dialects. This indicated a high degree of normativization or standardisation in Arabizi orthography.
GDPC: A GPU-Accelerated Density Peaks Clustering Algorithm

Density Peaks Clustering (DPC) is a recently proposed clustering algorithm that has distinctive advantages over existing clustering algorithms. However, DPC requires computing the distance between every pair of input points, therefore incurring quadratic computation overhead, which is prohibitive for large data sets. To address the efficiency problem of DPC, we propose to use GPU to accelerate DPC. We exploit a spatial index structure VP-Tree to help efficiently maintain the data points. We first propose a vectorized GPU-friendly VP-Tree structure, based on which we propose GDPC algorithm, where the density \(\rho \) and the dependent distance \(\delta \) can be efficiently computed by using GPU. Our results show that GDPC can achieve over 5.3–78.8\(\times \) acceleration compared to the state-of-the-art DPC implementations.
Convolutional Elman Jordan Neural Network for Reconstruction and Classification Using Attention Window

In deep learning-based visual pattern recognition systems, typically the entire image is presented to the system for recognition. However, the human visual system often scans a large visual object by sequential shifts of attention, which is integrated for visual classification. Even in artificial domains, such sequential integration is particularly useful when the input image is too large. Some previous studies based on Elman and Jordan networks have explored only with fully connected layers using full image as input but not with convolutional layers using attention window as input. To this end, we present a novel recurrent neural network architecture which possesses spatiotemporal memory called Convolutional Elman Jordan Neural Network (CEJNN) to integrate the information by looking at a series of small attentional windows applied over the full image. Two variations of CEJNN with some modifications have been developed for two tasks: reconstruction and classification. The network is trained on 48 K images and tested on 10 K images of MNIST handwritten digit database for both tasks. Our experiment shows that the network captures better correlation of the spatiotemporal information by providing the result with a mean square error (MSE) of 0.012 for reconstruction task and also claiming the classification with 97.62% accuracy on the testing set.
Tools with Histories: Exploring NFC-Tagging to Support Hybrid Documentation Practices and Knowledge Discovery in Makerspaces

We present the design research process towards a novel learning technology to improve instructional documentation in makerspaces. Our focus is on the ways in which smart tools can better support learning practices, with a particular emphasis on role of documentation plays. We first describe a co-design process with educational stakeholder that generated concepts for NFC-enabled forms of hybrid documentation. This solution developed offers a process for coupling physical tools, parts and materials to online resources in order to help make documentation ready-at-hand for learners. Findings from a subsequent focus group led to a refined implementation. Our preliminary evaluation in a high school settings highlighted the value of integrating online documentation formats to support youth navigating a broad array of fabrication tools and parts. Educators valued the solutions ability to support self-directed learning and to increase student access and agency with instructional resources.
Research on Convolutional Neural Network in the Field of Object Detection

This paper summarizes the research progress of convolution neural networks in the field of target detection in recent years. The development of these convolutional neural networks makes the development of deep learning to a new level. On the basis of consulting data, this paper summarizes the main network structure and working principle of convolutional neural network application in the field of target detection, and focuses on several commonly used convolution neural networks, including AlexNet, VGGNet, GoogLeNet and ResNet, and analyzes their technologies, analyzes their network structure characteristics, and summarizes their respective advantages and disadvantages. And in the last part, it points out the current research situation and the future development direction.
Exploring the MSER-based hyperspectral remote sensing image registration

Image registration is an essential preprocessing task in many applications of hyperspectral images capturing the Earth surface. Maximally Stable Extremal Regions (MSER) is a feature–based method for image registration which extracts regions by thresholding the image at different grey levels. Its invariance to affine transformations makes it ideal for image registration. This method is usually employed in text detection and recognition as well as in the medical domain. Hyperspectral images contain spectral information that can be used for improving the image alignment. This article presents a first approach to a hyperspectral remote sensing image registration method based on MSER that efficiently exploits the information contained in the different spectral bands. The experimental results over four hyperspectral images show that the proposed method is promising as it achieves a higher number of correct registration cases than other feature–based methods.
Temporal Sentiment Analysis of Learners: Public Versus Private Social Media Communication Channels in a Women-in-Tech Conversion Course

Social media is ubiquitous, a continuous part of our daily lives; it offers new ways of communication. This is especially crucial in education, where various online systems make use of (perceived) public or private communication, as a means to support the learning process, often in real-time. However, not much research has been carried out in analysing and comparing such channels and the way participants use them. Thus, this paper analyses a course offering both public and private types of communication to its participants. Participants communicate via two social media channels (beyond traditional email etc.): Twitter (open to the public) and Microsoft Teams (for internal communication). In this paper, we specifically analyse the communication patterns of learners, focusing on the temporal analysis of their sentiments on the public versus the private platform. The comparison shows that, as possibly expected, there exist similarities between expressed sentiment in public and private channels. Interestingly however, the private platform is more likely to be used for negative utterances. It also shows that sentiment can be clearly connected to events in the course (e.g., the residentials increase both volume and positivity of comments). Finally, we propose new measures for sentiment analysis to better express the nature of change and speed of change of the sentiment in the two channels used by our learners during their learning process.
Pair-Wise Convolution Network with Transformers for Sequential Recommendation

Sequential recommendations seek to employ the sequence of interactions between users and commodities to predict their next behavior based on the behavior they have recently made. Previously, some recommendation systems have been built on Markov chains and recurrent neural networks (among others). However, these methods have many limitations that they emphasize too much sequence change to fully emphasize the correlation between adjacent items; Besides, they generally ignore the influence of contextual information. To solve the shortcomings of the existing sequential recommendations, we try to model the relationship between items, get an effective representation of sequential features, and capture complex sequence correlations. Specifically, we propose a pair-wise convolution network with transformers for the sequential recommendation. The two-dimensional convolution networks encodes the sequence into a three-dimensional tensor and learns the relationships of features between the sequences. We adopt a residual connection to prevent the gradient from disappearing and solve the loss of feature information. The experimental results show that our method is superior to various advanced sequential models on sparse and dense data sets and different evaluation indicators.