Reviewerly/exHarmony huggingface dataset:
Three-dimensional Route Planning of UAV Based on Improved Sparrow Search Algorithm

Aiming at solving the problem of UAV three-dimensional (3D) route planning, the cost function is established, and an improved sparrow search algorithm (ISSA) based on Logistic mapping and Cauchy mutation (LCSSA) is used to solve it. Firstly, this paper sets the terrain and threat space for UAV route planning. The three cost indicators of flight range, flight maneuver and threat level are transformed into the cost function of the route through normalization and weighted sum. Then, the basic SSA is improved. The sparrow population is initialized by using Logistic mapping, and the Cauchy mutation strategy is introduced to perturb the optimal position in the late convergence stage. Through simulation verification, it can be concluded that the 3D route of UAV planned by LCSSA is better than that planned by other algorithms. However, the planning in the real environment has yet to be verified.
An Improved Slap Swarm Algorithm Incorporating Tent Chaotic Mapping and Decay Factor

Aiming at the defects of local stagnation and slow convergence speed of Salp Swarm Algorithm in the optimization process, an improved Salp Swarm Algorithm (ISSA) incorporating Tent chaotic mapping and decay factor is proposed. To begin, the Tent chaotic mapping strategy is used to initialize the population, which allows the population to traverse the search space and improve the first stage of the algorithm's iteration speed; second, the decay factor is added to the leader position update, which expands the search range and improves the algorithm's search accuracy. The experimental results reveal that the improved algorithm's exploration ability and optimization accuracy have been greatly enhanced by a series of benchmark function simulations.
Detection Method of Casing Joint based on Computer Vision

Aiming at the current problem of low detection efficiency and high detection error when manually inspecting the casing joint for calibration depth in the visual inspection of oil and gas wells, a casing joint intelligent recognition based on YOLOv5 algorithm is proposed, which can realize the intelligent detection of casing joint. Firstly, a large number of pictures of oil and gas well casing joints were collected and the dataset was made by data enhancement method. Then, the enhanced dataset which was annotated with Labelimg tool was sent to YOLOv5 network for training. Finally, use the best trained weights for testing result. The test results show that the method has a high detection accuracy, short detection time, wide applicability, and great advancement and practicality in casing joint detection.
Context Multi-information Fusion Network Model Based on Human-machine Dialogue Context Multi-information Fusion Network

With the continuous deepening of human-computer interaction technology, the related technologies of human-computer dialogue have also been developed rapidly. At the same time, which dialogue matching based on retrieval has been used on a large scale. Although generative dialogue is also developing continuously, generative dialogue Dialogue is extremely unstable and it is easy to generate meaningless sentences. Therefore, retrieval dialogue is takes the lead in the research of human-computer dialogue practice, and the fusion of contextual semantic information is particularly important and challenging. So, in order to solve the difficulty of capturing context semantics in retrieval dialogue, we proposes a recurrent convolutional neural network model (MFRCNN) for context multi-information fusion, and we tested our model on the text dialogue question answer matching data, the experimental results show that the model based on our proposed has stronger adaptability than the previous model and accuracy. For contextual information fusion semantic representation performance, the MFRCNN model is stronger than the RCNN model, with 1.4% higher accuracy and 0.12 higher F1-score. From the comparison of the experimental results of all models, we can conclude that the MFRCNN network proposed in this paper has the best effect among all the compared models, with an accuracy rate of 80.2% and an F1-score of 0.816.
A Pulse Accumulation Method for LPI Radar Emitter Signal Recognition

Radar emitter signal recognition is a crucial means to distinguish unknown radars, and it depends on the high quality of received signals. However, signals of Low probability of intercept (LPI) radars are easily interfered with by noise, resulting in poor signal quality and low recognition accuracy. We propose a pulse accumulation method to improve the quality of pulse signals for LPI radar emitter signal recognition. Firstly, the pulse accumulation problem is described. Then we propose the time-domain alignment iterative method (TAIM) to improve the effect of pulse accumulation. Finally, time-frequency images of the accumulated signals are input to the deep residual network for recognition to verify the effect of the proposed method. This approach enhances the effect of pulse accumulation by employing the proposed TAIM, and ultimately improves the performance of signal recognition. Simulation results show that the proposed pulse accumulation method can improve the recognition accuracy by 7% more than that of single-pulse signals at −6dB.
The Application of NLP Technology In Customer Voice Analysis

Voice of the Customer (VoC) is an important part of providing value to customers and achieving customer satisfaction, and is usually stored in the form of unstructured text. The massive unstructured text is unacceptable in terms of time simply by relying on human processing. How to efficiently mine meaningful information from complex data is the key to customer voice analysis. This paper expounds two methods for processing customer voices using NLP methods.
Research of Shape Recognition Under Different Lights

Recognition technology for digital images has been widely used in all aspects of people's life and work. Therefore, image shape recognition has great research significance. Generally, the Canny algorithm uses blur to remove noise, which weakens edge information, and mainly relies on the recognition and detection of shapes under good lighting conditions. However, under different lighting conditions, the edge detection effect drops sharply, and it cannot achieve accurate and fast shape recognition. Therefore, this study proposes a Laplacian algorithm that can accurately detect the edge position under different lighting conditions and make the shape more complete.
Dynamic Modeling of P(VDF-TrFE-CTFE)-based Soft Actuators via Echo State Networks

This paper proposes to use a reservoir computation approach to model the non-linear dynamic behaviour of a novel electroactive soft actuator. The soft actuator is fabricated as a unimorph cantilever beam, in which the active layer is a mat of electrospun aligned nanofibers of the P(VDF-TrFE-CTFE) electrostrictive polymer integrated into a PDMS silicone matrix. The passive layer consists of kapton, while the soft electrodes are made of conductive carbon powder. The non-linear dynamic response of three specimens of the soft actuator, when stimulated by both DC and complex electric fields of varying frequency, is modeled by means of an echo state network. The proposed architecture is able to achieve a normalized root mean square error of 0.429 for the tip deflections and of 0.265 for the blocking forces, when compared to experimental data.
Research on frequency rise of photovoltaic data based on improved Enhanced Deep Residual Networks

Converting low-frequency photovoltaic (PV) data into high-frequency data is an effective means to improve the accuracy of PV output prediction. Since the traditional reconstruction algorithm is difficult to achieve high-frequency data reconstruction, an improved enhanced deep residual network (I-EDRN) is introduced to reconstruct low-frequency PV data into high-frequency data. The data redundancy is improved by converting the time series data into a two-dimensional matrix representation. I-EDRN builds a deep residual network based on the improved residual block structure to improve feature learning ability. At the same time, spatial attention (SA) mechanism is introduced to strengthen the local perception ability of the network. A PV power station is taken as an example to verify the algorithm. The verification results show that the proposed method can achieve better photovoltaic high-frequency reconstruction effect than the traditional method. The verification results show that compared with the traditional methods, the proposed method can achieve lower average absolute error and average absolute error percentage.
An Unmanned Aerial-Surface Vehicle Hybrid Outfitted with a Transformable Housing

Waterborne vessels have advanced in many ways over the last two decades. However, their usage and deployment are still labor and expertise-intensive. They need to be transported by a surface vessel to the deployment area, which causes a substantial increase in transportation and logistics costs. In this paper, a modular design approach to the universal robotic housing is presented for off-the-shelf UAVs that enable them to operate on the water surface using the thrust force transferred from the UAV rotors through a clutch mechanism. The transformable robotic housing is outfitted with a waterproof cover sliding back and forth on its frame by a servo motor integrated link mechanism. The waterproof cover opens and closes depending on whether the UAV unit is airborne or on the water surface. Compared to the custom-built UAV-USV hybrid vehicle or collaborative operations of two individual vehicles, the proposed housing, which is easily reproducible and disposable, can be straightforwardly scaled to accommodate off-the-shelf UAVs of any size. Initial experiments have been conducted in an in-lab testbed and demonstrated a promising validation toward its practical applications.
Learning Robot Inverse Dynamics Using Sparse Online Gaussian Process with Forgetting Mechanism

Online Gaussian processes (GPs), typically used for learning models from time-series data, are more flexible and robust than offline GPs. Both local and sparse approximations of GPs can efficiently learn complex models online. Yet, these approaches assume that all signals are relatively accurate and that all data are available for learning without misleading data. Besides, the online learning capacity of GPs is limited for high-dimension problems and long-term tasks in practice. This paper proposes a sparse online GP (SOGP) with a forgetting mechanism to forget distant model information at a specific rate. The proposed approach combines two general data deletion schemes for the basis vector set of SOGP: The position information-based scheme and the oldest points-based scheme. We apply our approach to learn the inverse dynamics of a collaborative robot with 7 degrees of freedom under a two-segment trajectory tracking problem with task switching. Both simulations and experiments have shown that the proposed approach achieves better tracking accuracy and predictive smoothness compared with the two general data deletion schemes.
Research on the method of locating and unwrapping the Center Point of Pipeline Inner Wall Image based on Linear projection

In order to obtain a clear panoramic, unfold image of the inner wall in the video pipeline inspection system, the problems of camera center point offset and panoramic image distortion must be solved. In view of the above situation, this paper proposes an optimized unwrapping method for the undistorted estimation of string image in the case of camera eccentricity. In this method, the central coordinates of the pipe string image are located automatically by the Otsu algorithm and region marking method, and the three-dimensional pipe linear projection model is established by the proportion relationship before and after the pinhole camera imaging. Combined with the image region coordinate mapping, the panoramic image of the circular pipe is expanded without distortion. The experimental results show that the unfolded image has high definition and each pixel represents the same physical size, which provides a basis for the subjective evaluation of pipeline inner wall detection and lays a foundation for subsequent quantitative analysis.
A Framework for Supporting Privacy Preservation Functions in a Mobile Cloud Environment

The problem of privacy protection of trajectory data has received increasing attention in recent years with the significant grow in the volume of users that contribute trajectory data with rich user information. This creates serious privacy concerns as exposing an individual's privacy information may result in attacks threatening the user's safety. In this demonstration we present <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$TP^{3}$</tex> a novel practical framework for supporting trajectory privacy preservation in Mobile Cloud Environments (MCEs). In <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$TP^{3}$</tex> , non-expert users submit their trajectories and the system is responsible to determine their privacy exposure before sharing them to data analysts in return for various benefits, e.g. better recommendations. <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$TP^{3}$</tex> makes a number of contributions: (a) It evaluates the privacy exposure of the users utilizing various privacy operations, (b) it is latency-efficient as it implements the privacy operations as serverless functions which can scale automatically to serve an increasing number of users with low latency, and (c) it is practical and cost-efficient as it exploits the serverless model to adapt to the demands of the users with low operational costs for the service provider. Finally, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$TP^{3}$</tex> 's Web-UI provides insights to the service provider regarding the performance and the respective revenue from the service usage, while enabling the user to submit the trajectories with recommended preferences of privacy.
GoMap Verification: A Labelled Traffic Sign Source

Images are an important data source for many applications such as autonomous driving and map annotations. However, it is expensive to collect large imagery sets and annotate objects such as traffic signs. In the paper, we present a web solution where a user efficiently can label traffic signs by verifying or correcting the labels provided by an object-detection model. All verified/corrected objects can immediately be downloaded by the user for usage in for example semi-supervised or supervised learning. The tool is available via the URL https://gomap.cs.aau.dk
SNR-aware Automatic Modulation Recognition based on Modified Deep Residual Networks

Recently, automatic modulation recognition (AMR), i.e., identifying the modulation modes of signals using deep learning (DL) has received much attention. This paper proposes an AMR method based on DL (i.e., SG-NET), including a novel DL architecture (i.e., GuResNet) based on the deep residual network (ResNet) and a SNR-aware mechanism, which can effectively extract signal characteristics to achieve a better recognition accuracy. Specifically, we design a deep residual network model that mainly consists of six novel Residual Units to abstract effective signal features and prevent over-fitting. Then, to further improve the recognition performance in low SNR scenarios (i.e., the SNR is lower than 0dB), we train different parameters in the GuResNets based on the aware SNR. That is, we firstly use original signals with high and low SNRs to train a general GusResNet. When SNR is larger than 0dB, we directly adopt the general GuResNet for AMR. While, in low SNR scenarios, we exploit the Legendre method to extract signal features and then re-train the parameters in the GuResNets under different SNR conditions to improve the recognition accuracy further. The simulations demonstrate that our proposed SG-NET can obtain nearly 30% accuracy gain when the SNR is lower than 0dB, and 10% improvement when SNR is larger than 0dB compared with existing schemes.
Intelligent Surveillance System with Mask Detection and Temperature Monitoring

An efficient and adroit surveillance system is im-perative in the fast-paced digital world, with a monumental rise in video-based surveillance systems for security and monitoring. The pandemic has created the need for effective surveillance and made it more relevant than ever before. The AI-based surveillance system proposed in this paper is capable of performing the traditional functions of a surveillance system and checking if a person is wearing a mask and if his temperature is below a certain threshold. The proposed surveillance system is a video-based surveillance system capable of logging people who are not wearing a mask or whose temperature is not below a specified value. This system is implemented with Raspberry Pi as the central hub for processing, extracting, and analyzing the video stream from a camera. The proposed system aims to identify the mask on people by using a cascade classifier generated by Machine learning techniques, thus mulling down the effects of external factors (lighting condition, position, etc.) that affect the performance of a traditional video surveillance system.
Quality Control on Interpolation-based Reversible Audio Data Hiding using Bit Threshold

To solve daily problems, sometimes information needs to be accessible digitally or online by authorized parties. However, accessing some information may need secure data transmission. This reliable process can be achieved by using data security techniques to protect the transmitted information, such as cryptography or steganography. As part of data hiding methods, steganography works by hiding private information inside other public data, mainly multimedia data, in plain sight. Nevertheless, there are some main concerns regarding previous research, such as the similarity level and hiding capacity of the respective audio file. In this research, private information is embedded into an audio file as cover data so it can be retrieved later by the receiver without any suspicion of other parties. Therefore, this research focuses on providing flexibility in the embedding capacity of cover data by using an interpolation-based approach as the core sampling technique and then enhancing the similarity level by adding bit threshold values to evenly distribute hiding capacity on each sample as even as possible. Experimental results show that this proposed method can achieve an average similarity level of 103.56 dB on 100 Kb private data to an average of 93.95 dB on 300 Kb private data. It is better than some existing studies.
Polarity and Subjectivity Analysis for Social Media Sentiments Amidst COVID-19

Textual content in the form of messages, texts, tweets, comments, etc. is generated on a daily basis on various social media platforms. The foremost objective of this paper is to work on social media comments to evaluate polarity and subjectivity of the created corpus during COVID-19. A novel CPA: Context Based Polarity Analysis and an ADDT: Annotated Dependency based Decision Tree are used to achieve classification and sentiment score for the corpus. The findings of the work include disclosure integration and pragmatic results that in turn provide decision for sentiment behind the corpus. The results can be used to gain insights to researchers working on Sentiment Analysis and authorities concerning to current pandemic. The proposed work is also compared with some state of art findings by other researchers and is believed to have produced better results in the field of Polarity and Subjectivity of a corpus based text analysis on the basis of three parameters i.e. Document Level accuracy, Sentence Level accuracy and Aspect Level accuracy. To achieve this, NLP is practiced to extract sentiments and comparatively evaluate them on three major online social media platforms.
Selection of Optimal Frequencies in Multiple-Frequency Fringe Projection Profilometry

The selection of frequencies (or wavelengths) which are optimal in some sense is still an open research problem in phase shifting profilometry. We propose a novel method for selecting multiple frequencies for phase shifting profilometry which provide the best possible resistance to unwrapping errors when temporal phase unwrapping is used. The proposed frequency selection method is based on the maximization of the minimal distance between fixed straight lines in the wrapped phase space. It is realized as an exhaustive search over fringe counts, which is more efficient than searching over frequencies or wavelengths. We demonstrate the applicability of the proposed frequency selection method through a theoretical construction and experimental validation of several frequency designs which contain high frequencies only and which are insensitive to large errors in wrapped phase.
Strain Fields Measurement Using Frequency Domain Savitzky-Golay Filters in Digital Image Correlation

Savitzky-Golay (SG) filters are an effective and easy-to-use gradient calculation method, so it is intensively used in full-field strain measurement in digital image correlation (DIC). At present, the strain field is commonly obtained by moving smoothing filtering with SG filters. This is a way of spatial filtering, which has the advantages of easy implementation and high accuracy. In this paper, a strain calculation method based on frequency domain SG (FD-SG) filtering is proposed. Before FD-SG filtering is performed, data extension operations involving outer-boundary padding and zero-padding are performed on the displacement field data. Similarly, the SG filters template are extended with zero-padding and circularly shift operations. Then, FD-SG filters are generated by applying the fast Fourier transform (FFT) to the expanded SG template. Subsequently, FD-SG filtering is implemented by multiplying the displacement in the Fourier-domain by a FD-SG filter. Ultimately, the strain field can be obtained by fast inverse Fourier transform (IFFT) and valid data interception operation. Both simulation and practical experiments demonstrated that FD-SG filtering and spatial domain SG (SD-SG) filtering have equivalent accuracy, and the proposed FD-SG filtering method have great potential for real-time strain measurement in DIC.
A Multiple-Image Encryption Algorithm Based On Orthogonal Arrays with Strength 3

In recent years, remote working and studying lead to numerous images being transmitted on the Internet. Many of these images need protection from illegal obtaining or malicious peeping. Under the circumstances, the demand for image encryption algorithms with sufficient capacity and high efficiency is increasing. Multiple-image encryption algorithms can suit the demand well. This paper proposes a multiple-image encryption algorithm based on orthogonal arrays with strength 3. Orthogonal array is a typical configuration in combinatorial design theory, but the application research of orthogonal arrays with strength 3 in image encryption is still a blank. The main contributions of the paper are reflected in three aspects: the orthogonal arrays with strength 3 are applied to image encryption for the first time; orthogonal arrays and chaotic maps are combined perfectly to generate multiple 3D permutation maps and substitution maps, then multiple images can be encrypted efficiently; the good statistical performances and strong diffusibility can be realized by only one round of encryption.