Reviewerly/exHarmony huggingface dataset:
Road Traffic Update and Control Traffic Light using Dynamic Patterns from Video Streaming Data

Controlling traffic congestion is one of the most challenging issues in the real world, and an automated traffic monitoring system established on the computer vision and internet of things is an emerging scientific field in research. Traffic Congestion becomes severe in the most densely populated area to move timely from one place to another, passing through massive traffic. Although several research papers have presented their solutions for monitoring traffic congestion, we do not receive a permanent solution. In this paper, We try to find a different but unique way to overcome it. Our proposed system works with real-time video sequential data and detects different vehicle shapes using contour-based learning and a convex hull algorithm. We execute the procedure in a way where we work with per-second frames (fps) of the video, and then we determine the total, running, and motionless stagnant vehicles in each frame. After observing vehicle differentiation, an automated decision will be generated, i.e., JAM or NOT JAM which will be transmitted to the Map for users’ assistance. After that, the decision about the traffic situation of all nodes makes a combination, and then we find two patterns, one will identify the traffic situation of all connected roads, and another will control the traffic signal automatically.
Continuous Sign Language Interpretation to Text Using Deep Learning Models

The COVID-19 pandemic has obligated people to adopt the virtual lifestyle. Currently, the use of videoconferencing to conduct business meetings is prevalent owing to the numerous benefits it presents. However, a large number of people with speech impediment find themselves handicapped to the new normal as they cannot communicate their ideas effectively, especially in fast paced meetings. Therefore, this paper aims to introduce an enriched dataset using an action recognition method with the most common phrases translated into American Sign Language (ASL) that are routinely used in professional meetings. It further proposes a sign language detecting and classifying model employing deep learning architectures, namely, CNN and LSTM. The performances of these models are analysed by employing different performance metrics like accuracy, recall, F1- Score and Precision. CNN and LSTM models yield an accuracy of 93.75% and 96.54% respectively, after being trained with the dataset introduced in this study. Therefore, the incorporation of the LSTM model into different cloud services, virtual private networks and softwares will allow people with speech impairment to use sign language, which will automatically be translated into captions using moving camera circumstances in real time. This will in turn equip other people with the tool to understand and grasp the message that is being conveyed and easily discuss and effectuate the ideas.
What Intraclass Covariance Structures Can Symmetric Bernoulli Random Variables Have?

The covariance matrix of random variables $$X_{1},\dots,X_{n}$$ is said to have an intraclass covariance structure if the variances of all the $$X_{i}$$ ’s are the same and all the pairwise covariances of the $$X_{i}$$ ’s are the same. We provide a possibly surprising characterization of such covariance matrices in the case when the $$X_{i}$$ ’s are symmetric Bernoulli random variables.
An Empirical Study on Bengali News Headline Categorization Leveraging Different Machine Learning Techniques

Bengali News Headline Categorization Using Machine Learning aims to categorize Bengali online news headlines into six distinct categories using Natural Language Processing. Researchers in different application fields have recently paid great attention to the fantastic accomplishments of Machine Learning Models in Natural Language Processing. Several machine learning algorithms categorize Bengali news headlines, including Logistic Regression, Random Forest Classifier, Multinomial Naive Bayes, and RBF Support Vector Machine. Also, deep learning models like LSTM, Bi-LSTM, GRU, Bi-GRU, and CNN, and the Bangla-BERT and XLM-RoBERTa transformer learning models are presented in this research. This paper’s primary purpose is to provide a comparative observation of several machine learning models, deep learning models, and transformer learning methods in Bengali news headline classification. We used 1,36,811 text data of Bengali news headlines for evaluation, and our dataset had an accuracy of 86.50% with XLM-RoBERTa.
Modulation Recognition Method of Communication Signal Based on CNN-LSTM Dual Channel

Considering the problem of low recognition rate and a large number of parameters in end-to-end modulation recognition methods based on deep learning, a CNN-LSTM dual channel(DCNN-LSTM) modulation recognition method based on the combination of Convolutional Neural Network (CNN) and Long Short-Term Memory(LSTM) network is proposed in this paper. Firstly, the proposed method extracts the frequency domain features of the IQ signal using one-dimensional convolution in the CNN channel and the time-domain features of the transformed AP data of the IQ signal in the LSTM channel. Then, fusing the features of the two channels by the Concatenate operation and convolution layer. Finally, the method uses Global Average Pooling (GAP) instead of Flatten, which integrates the feature information of the vectors without additional parameters. Experimentally, the number of parameters of the proposed method is respectively reduced by 98.4%, 77.8%, and 88.9% compared with CNN, LSTM, and SCRNN, while the recognition rate is respectively improved by 9.7%, 3.3%, and 0.4%. The method proposed in this paper has theoretical reference value for the research field of communication modulation signal recognition, and engineering reference significance for the study of the intelligent classification of spatial signals in complex environments.
A Unified Perspective of Multi-level Cross-Modal Similarity for Cross-Modal Retrieval

Cross-modal retrieval is an intelligent understanding task between cross-modal data, and it comes with challenges to measure the similarity between cross-modal data. Existing methods mainly learned a common space by feature-wise or label-based supervised learning. Still, feature-wise methods only focused on the interactions between pairs of cross-modal data and label-based supervised learning relied excessively on classification accuracy. In the same space, these methods cannot capture more comprehensive interaction between cross-mode data, that is, given a query, this query and the retrieved data exist one-to-many correspondence, and the similarity between the pair-wise data is the largest. Therefore, a unified perspective of multi-level cross-modal similarity (MCMS) is proposed for cross-modal retrieval. Core ideas of MCMS are as follows: 1) The local similarity between cross-modal data is integrated to enrich the fine-grained cross-modal information. 2) The similarity between common feature vector and label is designed to obtain one-to-many correspondences between cross-modal data. In addition, Normalize Discounted Cumulative Gain (NDCG) as the evaluation metric is first used to comprehensively evaluate the results of cross-modal retrieval. Extensive experiments demonstrate that MCMS has better performance in cross-modal retrieval tasks.
Automatic Phonemic Transcription Using Feature Fusing of Articulatory Attribute and Syllable Type

In this paper, we aim to improve the accuracy of automatic phonemic transcription which takes as inputs frame-level feature sequences and outputs phoneme sequences. The more stable and abstract linguistic knowledge, including the syllable type and the articulatory attribute, are explored and developed as inputting feature. We formally employ the designated bottleneck layer of the DNN model to obtain the feature representation from the two information. Then, the two types of features are connected to form the final feature representation. Based on the learned feature representation, the automatic phonemic transcription is processed as the end-to-end sequence labelling, and the encoder-decoder architecture is chosen to dealt with different lengths in both input and output. These experimental results on two languages shown that the learned feature outperforms the traditional acoustic feature, and the proposed method achieves the phoneme accuracy improvements.
A Correlation-Based Local Differential Privacy Scheme Design for Trajectory Data Collection

In recent years, new industrial forms has continuously been generated with the rapid development of big data technology. However, the problem of data privacy and security has become increasingly prominent. In numerous geographically related applications, location trajectory data which involve sen- sitive personal privacy need more stringent protection measures. Thus a method has been proposed before that combining hidden Markov model with local differential privacy mechanism, for training and generating a biased trajectory dataset. Nevertheless, this method did not take into account the spatial correlation between track coordinates, which causes a large deviation be- tween the disturbed trajectory and the real trajectory. Aiming at this problem, this paper brings forward a <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\varepsilon,\delta)$</tex> -local differential privacy mechanism based on trajectory coordinate correlation. On the basis of existing schemes, it emphasizes the spatial correlation of trajectory coordinates. Furthermore, it may ensure the availability of track data by reasonably setting the dynamic parameter of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\delta$</tex> . Finally, we conduct experiments on data sets of Taxi trajectory prediction. The results show that our Correlation Multivariate Random Response (CMRR) algorithm presents significantly better performance than Generalized Randomized Response (GRR) in data availability. In the meanwhile, it strictly meets the privacy protection requirements.
Dynamic Reinforcement and Alignment Graph Convolution Networks for Few-Shot Learning

The dependence of deep learning models on large-scale labeled training data limits their application in real-world scenarios. To address this problem, researchers have proposed few-shot learning. However, most existing few-shot learning methods tend to ignore the contribution of local detailed information with class characteristics to classification. In this paper, we propose the dynamic reinforcement and alignment graph convolution networks (DRAGCN). Our proposed model can learn to generate the reinforcement basis that contains valuable information of local details with class characters based on experiential knowledge and obtain the reinforced feature maps by solving the neural ordinary differential equations (Neural ODE). These reinforced feature maps of the input images are constructed as graph-structured data, and the node features and edge features of the graph are optimized with the semantic alignment graph convolution networks, which introduces the semantic alignment operation to prevent the over-smoothing phenomenon. Experimental results on two popular datasets show that the proposed DRAGCN outperforms existing methods on few-shot learning tasks.
Blockchain-Based Cross-domain Access Control Mechanism

In view of the problems of high application cost, opaque permission judgment process, mutual trust between domains, and difficulty in coordinating access control information of data resources in distributed environment, this paper proposes a blockchain-based cross-domain access control mechanism that supports inter-domain policy privacy protection, so as to achieve cross-domain secure sharing of data in the centerless mode. First, based on attribute-based access control, access control information such as policies and attributes is stored on the blockchain to achieve efficient distributed permission management; At the same time, an inter-domain policy privacy protection method based on SM4 and CP-ABE is designed to protect the privacy of access control policies between domains. Subsequently, the smart contract is used to realize the distributed trusted judgment of access control based on the on-chain permission information; Finally, compared with the existing cross-domain access control mechanism, it is verified that the proposed mechanism in this paper has certain advantages and good scalability, and can provide strong support for cross-domain data security access in a distributed environment.
Optimized Algebraic Decoding Algorithm for (47, 24, 11) QR Code

A fast decoding algorithm scheme is proposed for the quadratic residue code with code length of 47 and large error-correcting capacity of 5 errors in this paper, called optimized algebraic decoding algorithm (OADA). The main contributions of this article are to deduce a method to quickly compute the related unknown syndrome of (47,24,11) QR code and a simpler discriminant condition to detect whether four errors occurred in the received word. The decoding algorithm scheme needs lower memory requirement compared with the algorithm based on the full hard decision algebraic decoding algorithm. For exhaustive simulation test of all the error patterns shows that the proposed algorithm of (47, 24, 11) QR code improves the decoding performance and decoding efficiency.
Decision Fusion Based Multi-type Shot Boundary Detection in Real Time

With the development of mobile Internet and cloud computing, especially the popularization of lightweight handheld cameras, online videos are growing rapidly. For efficient re-editing of the existing videos, intelligent content analysis and automatic video processing become focused. Shot boundary detection (SBD) is a basic algorithm of intelligent video processing. From threshold-based boundary detection to deep learning-based boundary detection, it is difficult for one algorithm to work well on the multi-type shot boundary (a straight transition and various types of gradual transitions). What's more, the detection accuracy is limited by the abrupt changes in videos caused by fast motion and illumination variation. This paper proposes a multi-type shot boundary detection algorithm based on decision fusion (MSBD-DF). The distance clustering based on LAB histogram, instead of shot clustering, directly tells if it is a frame in a shot or in the transiting. Decision fusion further decides whether it is a boundary of a cut or a gradual transition. Also, a key-point matching based on ORB improves the detection robustness against the fast motion and illumination variation. The experimental results show that the F1 value of cut boundary detection reaches 96.45%, and concurrently the F1 value of gradual transition boundary detection reaches 92.31%. The average detection speed is 9.03ms/frame, being applicable to real-time video processing.
Moving Target Tracking Based on Multiple Hypothesis Tracking in Video SAR

The moving target is always defocused and shifted out of the scene in image sequences in video synthetic aperture radar (video SAR), resulting in a shadow in the real position. The tracking-by-detection framework is often adopted in solving tracking problems. However, when multiple targets are tracked simultaneously, the association between targets and observations can be in conflict. In this paper, a multiple target tracking method based on shadow with multiple hypothesis tracking is proposed. We first use the improved Faster-RCNN to detect the shadows of the moving targets. Then we apply MHT to track the moving targets. The processing results of the real data are provided to verify the effectiveness of the proposed method.
Localization Method of Underwater Robot Based on Sonar Image

In this paper, we propose a positioning algorithm based on sonar images, which is mainly used for the positioning of underwater robots, so that the robots can obtain the position information in real time when operating underwater and avoid colliding with the underwater walls. First, the underwater space was detected with multibeam sonar, and the sonar images of the underwater space wall were found to have line segment characteristics; Composite denoising, threshold segmentation and Canny edge detection are applied to the sonar image to extract the contours of the underwater spatial wall. Then the characteristic line segments of the underwater spatial wall are detected based on the LSD (Line Segment Detector) line segment detection algorithm. In terms of line segment classification, a method is proposed to effectively classify line segments using the origin of the sonar image and the slope of the detected line segment. To further demonstrate the effectiveness of the localization algorithm in this paper, the specific steps of the algorithm are illustrated with the example of underwater rectangular space.
Sound Speed Profiles Inversion by Pressure Inverted Echo Sounder and Reconstruction on Product Graph

Pressure inverted echo sounder (PIES) is an ocean observation instrument which is anchored to the seabed and measures the vertical acoustic travel time (VATT). Gravest Empirical Mode (GEM) and Optimal Interpolation are used to inverse sound speed profiles (SSPs) and reconstruct sound speed field (SSF) with the data of PIES array. A vertical SSP can be regarded as a known observation in three-dimension SSF but in fact the number of these observations is very small that leads to a challenge for reconstruction of SSF. To circumvent such an issue, graph signal processing (GSP) is introduced in this paper. The SSF is modeled as a product graph that can be decomposed into two smaller factor graphs. In this way, the original search space on product graph is reduced into the union of two much smaller spaces so that more accurate reconstruction results of SSF can be obtained by observing only a small subset of vertices on product graph. Besides the underlying structure of product graph, the bandlimited characteristic in graph spectrum analysis is leveraged to reconstruct SSF. The numerical results show that our proposed method performs better and achieves lower root mean square error (RMSE) in reconstruction of SSF.
Discussion on the Effect of Classroom Concept Learning Based on BERT Text Classification

In the process of concept learning, students will gradually construct concepts and eventually form a profound and complete concept system. Analyzing what students discuss in class can help teachers effectively understand students' level of conceptual learning and contribute to the development of teaching evaluation level. In this paper, we analyze students' conceptual learning levels by introducing the BERT combination model in deep learning. The research steps mainly include the introduction and formulation of concept learning classification metrics, the collection and preprocessing of datasets, and the construction of combinatorial optimization based on BERT models.Finally, the BERT-RCNN model achieved the best results, with an precision of 83.33%, a recall of 83.34%, and an F1-score of 83.34%.
System Error Real-Time Estimation of Radar Network based on Iterative Generalized Least Squares

Because system error may cause the same target measured by different radar to be misjudged as two targets, the system error estimation is an important process for data fusion and target tracking in radar networking system. In order to eliminate system error in real time, a new algorithm based on iterative generalized least squares (GLS) algorithm in earth-centered earth-fixed (ECEF) coordinate systems(CS) is studied in this paper. In this method the system error is modeled by state vector, and the corresponding state equation is obtained. The measurement equation is modeled based on GLS algorithm, and the iterative estimation filtering model is used to improve the real-time and convergent performance of the system error estimation in ECEF. Simulation results reveal the feasibility and validity of the algorithm presented in this paper.
Podsen’s Writing Attitude Survey: How Reliable is the Scale in Bahasa Indonesia?

It's crucial to approach writing with positivity.Students who approach writing in a positive way will complete the writing task well.However, little study has been done to examine the validity of the writing attitude scale.This study aims to examine the validity of the writing attitude scale that Podsen established in 1997.The design of this study is a correlational study because its purpose is to find out the writing scale internal consistency.The instrument used is the writing attitude scale (WAS).The population of the study was the Indonesian Language Education Department students in the Faculty of Cultural Studies, Universitas Brawijaya.The data are in the form of numbers in ordinal scale.To analyze the data, the researchers used Cronbach alpha in SPSS 25.The result of the study shows that the internal consistency of Podsen's writing attitude scale is high.This implies that Podsen's WAS is applicable and highly reliable in terms of its internal consistency to measure the students' writing attitude.
Cascaded Convolution Neural Network for Color Image Recovery

Reconstructing the high-quality image from its degraded version has attracted more interest in recent years. This data recovery problem can be first defined as an ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> norm minimization problem and then solved by deep learning techniques. In the paper, the task of color image recovery from partly observed gray scale data is tackle. It is assumed that some blocks or rectangular area of the gray scale is not observed, making the problem more complicated. The baseline convolutional auto-encoder network is first described. By dividing it into tasks of completion of missing values and image coloring, two sub-networks are proposed with similar architectures to solve the two sub-problems, and they are combined to get the final satisfying results. Experimental results shows that the proposed cascaded network can recover the image with higher PSNR and SSIM performance comparing to the baseline model.
A Case Study of Formative Course Evaluation by Diary Approach

To collect off-line out-of-class data on student engagement in learning has always been a challenge in formative course evaluation.This paper attempts to use the diary approach to collecting off-line data for formative evaluation of an EFL course and, by doing so, to understanding student engagement in the course out of class.Twenty-five sophomores took part in this evaluative case study.The results showed diaries were capable of capturing students' recurrent and routinized learning practices out of class together with their learning-related affective reaction.Data collected from student learning diaries were valuable for formative course evaluation for improvement purposes.
The Cultivation of Innovation Ability of English Majors for Comprehensive English Course in Colleges

Contemporary college students are living in a rapidly changing society.They have to get ready for adapting to this rapidly changing world.Cultivating contemporary college students' innovation for professional knowledge and social problems is helpful to deepen their understanding and rational use of professional knowledge and help themselves to adapt to the society faster and better.