Reviewerly/exHarmony huggingface dataset:
Computer Digital Technology Combined with Dynamic Visual Communication Sensors in Target Tracking with Big Data

The detection algorithm is explored to improve the dynamic visual sensors (DVS) combined with computer digital technology, build a DVS network, and complete the monitoring and tracking of the target. Ultimately, the problem that needs to be solved is the poor quality of traditional communication sensor data transmission, which needs to be improved by DVS. Firstly, the structure and function of the network are described through dynamic visual perception requirements analysis. Secondly, by introducing a target tracking algorithm that combines event flow and grayscale images, two methods are proposed, namely, the event flow noise reduction method based on event density and the optical flow detection feature tracking algorithm. Finally, through experiments, the tracking and detection effect of the optical flow detection algorithm on the target object in the dark environment is verified in the high-speed motion scene and the reflection environment. The results show that the average error of target object detection and tracking is 3.2 pixels in a dark environment. The average error of target tracking in high-speed motion scenes and reflective environments is 4.86 pixels and 2.88 pixels, respectively. This research has practical reference value for the digital and intelligent development of digital video surveillance systems.
Multi-object Tracking for Autonomous Driving with a Monocular Camera Subject to Missing Data

In autonomous driving, visual surveillance may be blocked or damaged by external environment, leading to missing data for multi-object tracking (MOT) algorithms and tracking accuracy degradation. To overcome this problem, this work proposes a vehicular monocular camera MOT framework with missing data based on an expert evaluation error detection mechanism. In addition, our method avoids suffering from expensive hardware compared with those using LiDar and Radar and is more interpretative compared with those using end-to-end tracking algorithms relying on deep neural networks. The proposed method is tested on the KITTI dataset following several benchmark metrics (e.g., MOTA and ID-switch) as an evaluation criterion. Experimental results demonstrate that tracking with missing data based on our approach is still ideal compared with tracking without missing data.
An Interactive Virtual Home Navigation System Based on Home Ontology and Commonsense Reasoning

In recent years, researchers from the fields of computer vision, language, graphics, and robotics have tackled Embodied AI research. Embodied AI can learn through interaction with the real world and virtual environments and can perform various tasks in virtual environments using virtual robots. However, many of these are one-way tasks in which the interaction is interrupted only by answering questions or requests to the user. In this research, we aim to develop a two-way interactive navigation system by introducing knowledge-based reasoning to Embodied AI research. Specifically, the system obtains guidance candidates that are difficult to identify with existing common-sense reasoning alone by reasoning with the constructed home ontology. Then, we develop a two-way interactive navigation system in which the virtual robot can guide the user to the location in the virtual home environment that the user needs while repeating multiple conversations with the user. We evaluated whether the proposed system was able to present appropriate guidance locations as candidates based on users’ speech input about their home environment. For the evaluation, we extracted the speech data from the corpus of daily conversation, the speech data created by the subject, and the correct answer data for each data and calculated the precision, recall, and F-value. As a result, the F-value was 0.47 for the evaluation data extracted from the daily conversation corpus, and the F-value was 0.49 for the evaluation data created by the subject.
DeepCorrect: Building an Efficient Framework for Auto Correction for Subjective Questions Using GRU_LSTM Deep Learning

Our lives have become increasingly reliant on social media. It has evolved into one of the most vital information sources. In this work, we propose machine learning and deep learning algorithms. LSTM (one to three layers) and GRU are two deep learning approaches (one to three layers). Six machine learning techniques are used to compare the performance of the proposed methodologies. Decision tree (DT), logistic regression (LR), K nearest neighbor(KNN), random forest (RF), support vector machine (SVM), and NaiveBayes (NB) are the six machine learning approaches. Keras-tuner is used to optimise the parameters of deep learning techniques, whereas a grid search is used to optimise the parameters of machine learning techniques. Three Benchmark datasets were used to train and test models. For the baseline machine learning model and word embedding feature extraction method for deep neural network methods, two feature extraction methods (TF-ID with N-gram) were utilised to extract critical features from the three benchmark datasets. The proposed deep learning techniques always show the best performance because of their ability to learn the discriminatory features through the multiple hidden layers. LSTM(one layer) showed the best cross-validation accuracy (66.79%) on the dataset. In the case of the LSTM(two layers) showed the best cross-validation accuracy (66.07%). Finally, GRU (two layers) showed the best cross-validation accuracy (66.73%). The propsed framework has been categorized into some steps. Experimental results on bench challenging datasets demonstrate that our methods can achieve better performance than numerous state-of-the-art methods.
Information Hiding in Digital Textual Contents: Techniques and Current Challenges

Information hiding is the science of concealing a secret message or watermark inside a cover media (a host file/message) for providing various security purposes such as content authentication, integrity verification, covert communication, and so on.
Prediction of Labor Unemployment Based on Time Series Model and Neural Network Model

With the advent of big data, statistical accounting based on artificial intelligence can realistically reflect the dynamics of labor force and market segmentation. Therefore, based on the combination of machine learning algorithm and traditional statistical data under big data, a prediction model of unemployment in labor force based on the combination of time series model and neural network model is built. According to the theoretical parameters, the algorithm of the two-weight neural network is proposed, and the unemployment rate in labor force is predicted according to the weight combination of the two. The outcomes show that the fitting effect based on the combined model is superior to that of the single model and the traditional BP neural network model; at the same time, the prediction results with total unemployment and unemployment rate as evaluation indexes are excellent. The model can offer new ideas for assisting to solve the unemployment of the labor force in China.
Time-of-flight measurement-based three-dimensional profiler system employing a lightweight Fresnel-type Risley prism scanner

A time-of-flight measurement-based three-dimensional (3D) profiler system employing a lightweight scanning system is demonstrated. To reduce the weight of the scanning system, and thereby achieve faster scanning speeds, two Fresnel prism sheets were employed as the scanning optics and installed to work as a pair of Risley prisms. Each Fresnel prism sheet has a diameter of 102 mm and mass of 15 g, which is about 12 times lighter than ordinary bulky prism. By scanning the laser beam with the developed scanning system, a 3D point cloud image of a target object located 8 m away could be successfully obtained. The image distortion was removable by correcting six geometrical parameters of the scanner using a simple optimization algorithm. It was confirmed by the experiment that once the distortion has been corrected, it is valid for other scanning speeds (and trajectories), enabling 3D profile measurements that do not require postprocessing of measured data. Measurement results for a standard target composed of square extrusions were in good agreement with the reference values, with deviations of <1 mm.
<scp>MLPNN‐RF</scp>: Software fault prediction based on robust weight based optimization and Jacobian adaptive neural network

Summary Software fault prediction (SFP) is a vital objective in software engineering. This might permit effective resource allocation and also enhance informed decisions about the release quality. SFP is being a critical issue for software professionals as well as the tech industry. Thus, SFP is necessary. The study intends to perform efficient error rate estimation using the proposed hybrid robust weight based optimization and Jacobian adaptive neural network (RWO‐JANN). It also aims to classify the software faults in an efficient way using multi‐layer perceptron neural network‐random forest (MLPNN‐RF). Various processes are involved to accomplish SFP. At first, the dataset is taken as input. After this, data preprocessing is performed. Subsequently, weights are initialized using the proposed RWO‐JANN. Weight initialization is performed through a series of steps. Then, the position and the weight parameter are updated to perform weight initialization. After this, the error rate is estimated and the weight is updated on the basis of the learning rate and Jacobian matrix calculation. Lastly, the decay rate is analyzed. If the error rate extends beyond the threshold value, the process repeats from weight initialization. If not, the testing process is performed and lastly the classified output for SFP is obtained by the proposed MLPNN‐RF. The proposed system is comparatively analyzed with the existing methods in terms of accuracy, precision, recall, F1 score, sensitivity, specificity, and error rate. The analytical results revealed effective outcomes of proposed system than the existing techniques with accuracy of 99.01%.
Spanish Emotion Recognition Method Based on Cross-Cultural Perspective

Linguistic communication is an important part of the cross-cultural perspective, and linguistic textual emotion recognition is a key massage in interpersonal communication. Spanish is the second largest language system in the world. The purpose of this paper is to identify the emotional features in Spanish texts. The improved BiLSTM framework is proposed. We select three widely used Spanish dictionaries as the datasets for our experiments, and then we finally obtain text sentiment classification results through text preprocessing, text emotion feature extraction, text topic detection, and emotion classification. We inserted the attention mechanism in the improved BiLSTM framework. It enables the shared feature encoder to obtain weighted representation results in the extraction of emotion features, which enhances the generalization ability of the model for text emotion feature recognition. Experimental results demonstrate that our approach performs better for specialized Spanish dictionary datasets. In terms of emotion recognition accuracy, the average value is as high as 76.21%. The overall performance outperforms current comparable machine learning methods and convolutional neural network methods.
A Loss Function Base on Softmax for Expression Recognition

Benefiting from deep learning, the accuracy of face expression recognition tasks based on convolutional neural networks has been greatly improved. However, the traditional SoftMax activation function lacks the ability to discriminate between classes. To solve this problem, the industry has proposed several activation functions based on softmax, such as A-softmax, LMCL, etc. We investigate the geometric significance of the weights from a fully connected layer and consider the weights as the class centers. By extracting the feature vector of several samples and extending the corresponding means to the weights, the model can develop the ability to recognize custom classes without training, while maintaining the accuracy of the original classification. On the expression task, the original seven-category classification is validated to obtain 97.10% accuracy on the CK+ dataset and 88% accuracy on the custom dataset.
Secure and Traceable QR Code Using Blockchain-enabled Certificates

The blockchain has been considered to be a breakthrough technology solution for problems based on security aspects. It is a Distributed Ledger Technology (DLT) which can be used to established trust among different trustless entities or nodes present in the network. Furthermore, Quick Response (QR) Codes are likewise one of the most promising innovations going on in the markets. In QR code, small dots are present which get converted to binary numbers and get validated with error correcting algorithms. Hence, QR codes are beneficial for both storage and authentication. In this article, we discuss the prospects of combination of these two technologies for better results. In this paper, we aim to develop permission-less Blockchain, which is relatively easier to compute and have shown to increase its data capacity. It also endeavored to add the QR Code incorporation to a block and make it more scalable with considering the limited space in a such block.
Statistical Optimization of FPN Hyperparameters for improved Pedestrian Detection

Feature Pyramid Network (FPN) has recently been extensively applied in the area of object detection for its multi-scale framework. A majority of tasks utilizing FPN use the pre-trained models or the popular architectural variations as-is. We argue that the architecture to be used depends upon the problem being tackled and that the performance is tightly correlated with the key architecture parameters. We demonstrate our hypothesis on the task of visible pedestrian detection where the bounding boxes are of varied shapes and sizes. To study the impact of various network parameters and to identify suitable values for them, we propose a statistical method based on the property of the dataset being used. We demonstrate the effectiveness of the statistical selection of the parameters on the task of Caltech-USA pedestrian detection and show that with RetinaNet, the modified network performs significantly better on miss rate and mean Average Precision than the baseline network.
Application of Three-Layer Stacked LSTM Model Assisted by Educational Robots in Children’s Learning

To enable educational robots to better accompany children’s learning and growth, first, the encoder part of the image description model is established based on the convolutional neural network (CNN), and the decoder part of the image description model is built by the long short-term memory (LSTM). The attention mechanism is combined with the input stage, LSTM, and the output stage of the model. Second, three-layer stacked LSTM is used to improve and optimize the model. Artificial intelligence (AI) challenger data set is used to test the constructed model, and bilingual evaluation understudy (BLEU) and metric for evaluation of translation with explicit ordering (METEOR) are used to evaluate the model. Finally, 48 children are used as the research objects to study the application effect of the model. The results show that the BLEU-1, BLEU-2, BLEU-3, and BLEU-4 values of the constructed model are higher than those of the neural image caption (NIC) model during the entire iteration process. This model begins to converge when the number of iterations is 20,000. However, the NIC model starts to converge when the number of iterations is 45,000. The constructed model has a faster convergence speed. The scores of BLEU-1, BLEU-2, BLEU-3, and BLEU-4 of the constructed model are 0.687, 0.54, 0.432, and 0.348, respectively, and the score of METEOR is 0.187, which is higher than other models. In the actual application, the learning efficiency and effect of children who use this model for learning are better than the traditional teaching effect. The research results can help to improve the auxiliary role of educational robots in children’s learning, which has very important practical significance.
Verification of colorable hypergraph states with stabilizer test

Abstract Many-body quantum states, as a matter of fact, are extremely essential to solve certain mathematical problems or simulate quantum systems in measurement-based quantum computation. However, how to verify large-scale quantum states, such as hypergraph states, is an exceedingly hard task for many-body quantum systems. Here, we propose a novel fault-tolerant solution for the verification of colorable hypergraph states by using the stabilizer test. Furthermore, our protocol is dramatically facilitated by making only Pauli-X and Pauli-Z measurements. For geometric structure hypergraph states, the computational complexity of our protocol is polynomial. As to appliance, it will be also applied to blind quantum computing based on the no-signaling principle.
NEW OPTIMAL LINEAR CODES OVER

Abstract We present novel approaches for constructing linear codes over $\mathbb {Z}_{4}$ from the known ones. We obtain new linear codes, many of which are optimal. In particular, we find all optimal codes of type $4^{k_{1}}2^{k_{2}}$ for $k_{1}=2,~k_{2}=0$ and many optimal codes for $k_{1}=3,~k_{2}=0.$
Quantum correlation propagation in a waveguide-QED system with long-range interaction

We investigate the excitation and correlation propagations among a one-dimensional atom chain with exponentially decaying, ideal long-range, and power-law decaying interactions. We show that although a clear light-cone-like structure can appear in both the excitation and correlation propagation patterns under the exponentially decaying interaction, only an obscure light-cone-like structure appears with multi-power-law decaying interaction and surprisingly an inverse light-cone-like structure appears in the ideal long-range interaction case. The extracted excitation and correlation propagation velocities in the ideal long-range interaction case are about one order of magnitude larger than those in the multi-power-law interaction case and about two orders of magnitude larger than those in the short-range interaction case. These results indicate that the waveguide-quantum electrodynamics system with long-range interaction can boost the quantum information transfer speed and is beneficial for building fast quantum network and scalable quantum computer.
Vital node searcher: find out critical node measure with deep reinforcement learning

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Adaptive measuring trajectory for scanning lidars: proof of concept

Abstract Application of nacelle-mounted long-range scanning lidars is facing the challenge that the nacelle motion causes deviations in the measuring trajectories. Such lidars are very sensitive to even the slightest trajectory deviation due to its long measuring range. Motivated by this need, we propose the concept of adaptive measuring trajectory and explore its use for eliminating the effect of the rotational motion of the lidar on the target measuring trajectory. This work first aims to experimentally test the performance of adaptive measuring trajectory under ideal conditions by installing a scanning lidar on a 6DOF motion platform to model the lidar motion on the turbine nacelle. The real trajectory with and without correction is measured by a camera. The comparison confirms that the adaptive measuring trajectory could stablize the target measuring trajectory given the lidar motion. Then, the possibility of using Kalman filter to estimate lidar motion given noisy motion measurements is investigated. The results show great potential of Kalman filter for lidar motion estimation, which could be very useful for its future application.
Configuration Transformation Planning Method of Unmanned Aerial Vehicle Cluster Based on Improved Hungarian Algorithm

Abstract For the planning problem of Unmanned Aerial Vehicle (UAV) formation configuration transformation, a formation configuration transformation planning method based on the improved Hungarian algorithm is proposed. By selecting a straight-line path as the movement path of UAVs in configuration transformation, the configuration transformation problem is transformed into a multi-UAV position assignment problem with constraint handling in a dynamic environment. For the Hungarian algorithm efficiency matrix power function treatment, the improved algorithm can be simultaneously bivariate integrated optimization; Introduce the time axis in the configuration space to establish the configuration-time space, and analyze the collision constraints during the dynamic transformation process in this space, and finally, the feasibility of the method is verified by simulation.
Insider threat prevention in distributed database as a service cloud environment

This paper addresses the problem of increasing chances of insider threats in cloud computing. In Database as a Service (DBaaS), where multiple dependencies exist among data. Insiders tries to infer knowledge on the bases of dependencies. There is a requirement of Insider threat prevention mechanism for DBaaS due to its dynamic nature. Various researchers have proposed a Single PDP and Multiple PEPs to solve this problem with the limitations on authentication, scalability, tracking of insiders. This research aims to fill the gap by providing an effective framework on access control mechanism designed to prevent insider threats using a Blockchain-based authentication mechanism, a distributed architecture-based, and insider activity tracking mechanism. The framework enables us to handle authentication mechanism, distributed DBaaS insider activity tracking. Experimental results show that this framework is effective, dynamic and scalable. The validation of this framework has been done with the help of synthetic data due to lack of standardized dataset and its applicability has been demonstrated with the help of a case study involving the use of real cloud data.
Label distribution learning through exploring nonnegative components

Label distribution learning (LDL) is a new machine learning paradigm to solve label ambiguity and has drawn increasing attention in recent years. The importance of all labels needs to be considered under the LDL settings. A series of approaches have been proposed to deal with the LDL problem by considering the correlation of labels or instances. However, none of them focuses on finding interpretable bases to reduce the dimensions of the feature space. Inspired by the semi-nonnegative matrix factorization (semi-NMF) method, we propose a new LDL learning framework to deal with the problem through learning nonnegative components. The key insight is to explore the bases, each of which represents a class, through the label distribution and to transform the input matrix into a coefficient matrix of the space constructed by the bases. Consequently, a maximum entropy model can be adopted to learn the label distribution from the coefficient matrix. Experimental results on real-world datasets comparing our method with several state-of-the-art methods validate the performance of our approach.