Reviewerly/exHarmony huggingface dataset:
Book review

When writing a text, the choice between seemingly equivalent connectives, such as but and however, is not arbitrary: aside from preferences linked to the register or modality of the utterance, there are often pragmatic or syntactic constraints that limit the choice to only one particular connective. For French, theoretical approaches have extensively discussed the syntactic and pragmatic constraints that allow only either parce que (‘because’) or puisque (‘since’). However, experimental findings regarding the actual sensitivity of French speakers to these constraints remain inconclusive. In the current study, we examine the sensitivity of different speaker groups to the constraints associated with the use of parce que and puisque. In a controlled offline-task, we systematically evaluate the sensitivity of a crowd-sourced heterogeneous group of native speakers (Experiment 1) and a group of non-native speakers (Experiment 2). Results show that native speakers are more sensitive to syntactic constraints whereas pragmatic constraints are more equivocal. Non-native speakers, however, are not sensitive to either of these constraints. In addition, the pragmatically complex connective puisque is extremely problematic for non-native speakers, as even the mapping of a seemingly equivalent connective of their L1 did not warrant a good mastery of its pragmatic functions.
FOLMS-AMDCNet: an automatic recognition scheme for multiple-antenna OFDM systems

The existing recognition algorithms of space-time block code (STBC) for multi-antenna (MA) orthogonal frequency-division multiplexing (OFDM) systems use feature extraction and hypothesis testing to identify the signal types in a complex communication environment. However, owing to the restrictions on the prior information and channel conditions, these existing algorithms cannot perform well under strong interference and noncooperative communication conditions. To overcome these defects, this study introduces deep learning into the STBC-OFDM signal recognition field and proposes a recognition method based on the fourth-order lag moment spectrum (FOLMS) and attention-guided multi-scale dilated convolution network (AMDCNet). The fourth-order lag moment vectors of the received signals are calculated, and vectors are stitched to form two-dimensional FOLMS, which is used as the input of the deep learning-based model. Then, the multi-scale dilated convolution is used to extract the details of images at different scales, and a convolutional block attention module (CBAM) is introduced to construct the attention-guided multi-scale dilated convolution module (AMDCM) to make the network be more focused on the target area and obtian the multi-scale guided features. Finally, the concatenate fusion, residual block and fully-connected layers are applied to acquire the STBC-OFDM signal types. Simulation experiments show that the average recognition probability of the proposed method at −12 dB is higher than 98%. Compared with the existing algorithms, the recognition performance of the proposed method is significantly improved and has good adaptability to environments with strong disturbances. In addition, the proposed deep learning-based model can directly identify the pre-processed FOLMS samples without a priori information on channel and noise, which is more suitable for non-cooperative communication systems than the existing algorithms.
High Precision Target Detection Algorithm Based on Dual Channel Attention Perception Fusion

With the development of modern technology and the application of the times, the accuracy of high-resolution image target detection is gradually improved. In this paper, a multi feature extraction and multi feature fusion network is proposed for the loss function of feature extraction, feature fusion and uneven positive and negative samples. In order to solve the problem of multiple features in multi feature extraction, combined with the two-dimensional CA spatial attention mechanism and residual network, the global features are preserved and sensitive information extraction is strengthened; Aiming at the problem of feature redundancy in multi feature fusion, the improved bifpn structure is used to effectively solve the problem of feature redundancy after feature extraction; Aiming at the training model degradation caused by the imbalance of positive and negative samples, the focal loss function is improved to effectively solve the model degradation caused by samples. The improved multi feature extraction and multi feature fusion network effectively improves the accuracy of target detection. The improved algorithm has the highest accuracy in dota data set, and the test accuracy is 78.98%.
Multiple Extended Target Tracking Using PHD Filter and Convolutional Conditional Neural Process

In the extended target tracking using Gaussian process (GP) or other methods to estimate the extension state, all of them is necessary to obtain the measurement model, and the deviation from the true kinematic state affects the extension result easily. Meanwhile, with the growth of the shape complexity, the corresponding covariance matrix dimension increases, leading to a very high computational burden. To this end, we present an extended target tracking algorithm based on deep learning, which adopts Gaussian mixture (GM) probability hypothesis density (PHD) filter for extended target (ET) tracking, called the ET-GM-PHD filter, to estimate the kinematic state, and uses convolutional conditional neural process (ConvCNP) to estimate the extension state (i.e., the shape of the target) avoiding the complex computation and dependence on priors. Therefore, the proposed algorithm is named as the ConvCNP-GM-PHD filter. In our algorithm, ConvCNP lies on the neural network (NN) and GP, where the former can learn the rules from a large amount of data without any priors, while the latter uses the covariance function to express the distribution over possible functions according to a small number of context observations. Due the proposed algorithm can effectively combine the advantages of both, it is reliable to get a model via training which is able to realize function regression flexibly. Thus, ConvCNP is able to estimate the shape function by putting the angles and radial value between the kinematic state and associated measurements into the pre-trained model. The results show that the accuracy of the extension state estimation of the proposed ConvCNP-GM-PHD filter is improved and its computational complexity is also reduced. It implies that the proposed algorithm is more real-time and effective, and has a good application prospect.
Performance Evaluation Model of Human-Machine Interactions for UAV Cluster Attack

In the attack task stage of UAV cluster, the frequency of human-machine interaction and amount of information are growing rapidly, which puts forward higher requirements for the response speed and accuracy of ground control station operators. The comprehensive use of a variety of new human-machine interaction modes is an effective way to improve the efficiency of human-machine interaction of operators. This paper first analyzes the problems of human-machine interaction in UAV cluster control; then, based on the total time of attack request processing, the number of attack requests processing, the number of UAV attack requests, and the number of attack requests not processed in time, the performance evaluation model of human-machine interaction for UAV cluster attack task is established. Finally, the task performance of different interactive modes is compared through simulation experiments, and the advantages and disadvantages of touch screen, voice and gesture are analyzed. The combination strategy of human-machine interaction mode under UAV cluster attack task is proposed.
Voxel R-CNN-MCA: Coordinate Attention for 3D Object Detection

With the development of autonomous driving technology, autonomous vehicles have gradually moved from ideal highways to more realistic urban environments. Compared to highways, urban roads have various traffic participants. 3D object detection is of great significance to the safe driving and path planning of autonomous vehicles while LiDAR becoming an important primary 3D object detection sensor. But long-distance and small objects pose challenges for LiDAR’s data processing. Our proposed solution, Voxel R-CNN-MCA, is trained on One millioN sCenEs (ONCE) dataset in order to be closer to the urban traffic situation in China. Its baseline is Voxel R-CNN, which is an efficient voxel-based 3D detector. We add Multi-scale Coordinate Attention (MCA) to the baseline, which embeds multi-scale positional information into channel attention. Besides, residual structure is added to 3D sparse convolution backbone. We use the ONCE dataset to evaluate the proposed model. The results show that the overall mAP value of this model is 59.7%, 3.36% higher than the baseline. Our innovation considerably enhances the detection performance for long-range and small objects as compared to the baseline. Experimental results indicate that the model achieves state-of-art on the ONCE dataset.
Multi-sensor Data Fusion for UAV Landing Based on Federal Variational Bayesian Filtering

This paper proposes a multi-sensor positioning technology for unmanned aerial vehicle (UAV) landing based on inertial navigation system (INS)/Global navigation satellite system (GNSS)/Radar integrated guidance system. In the harsh environment where sensor prior information is unreliable, measurement noise is non-stationary and measurement outliers are frequently generated, an adaptive federated filter based on variational Bayesian is used to achieve high accuracy and robustness of navigation system. Simulation results demonstrate that this guidance technology has a strong ability to adapt to non-stationary noise and frequent outliers, and the fusion accuracy is satisfactory.
Multi-path Following for Underactuated USV Based on Deep Reinforcement Learning

Recently, deep reinforcement learning (DRL) has attracted much attention in learning control policies for unmanned surface vehicles (USV) path following. However, existing DRL methods can only learn to follow one target path, and generalize poorly to other paths. This paper proposes a novel DRL based multi-path following method. Based on the generalized hindsight mechanism, our method can effectively reuse data generated in one path for training on other paths. Experiments show that the control policy trained by our method generalizes well to a wide range of paths, with significantly higher accuracy and success rate.
BC-PC-Share: Blockchain-Based Patient-Centric Data Sharing Scheme for PHRs in Cloud Computing

Sharing of personal health records (PHR) in cloud computing is an essential functionality in the healthcare system.However, how to securely, efficiently and flexibly share PHRs data of the patient in a multi-receiver setting has not been well addressed.For instance, since the trust domain of the cloud server is not identical to the data owner or data user, the semi-trust cloud service provider may intentionally destroy or tamper shared PHRs data of user or only transform partial ciphertext of the shared PHRs or even return wrong computation results to save its storage and computation resource, to pursue maximum economic interest or other malicious purposes.Thus, the PHRs data storing or sharing via the cloud server should be performed with consistency and integrity verification.Fortunately, the emergence of blockchain technology provides new ideas and prospects for ensuring the consistency and integrity of shared PHRs data.To this end, in this work, we leverage the consortium blockchain technology to enhance the trustworthiness of each participant and propose a blockchain-based patient-centric data sharing scheme for PHRs in cloud computing (BC-PC-Share).Different from the state-of-art schemes, our proposal can achieve the following desired properties: (1) Realizing patient-centric PHRs sharing with a public verification function, i.e., which can ensure that the returned shared data is consistent with the requested shared data and the integrity of the shared data is not compromised.(2) Supporting scalable and fine-grained access control and sharing of PHRs data with multiple domain users, such as hospitals, medical research institutes, and medical insurance companies.(3) Achieving efficient user decryption by leveraging the transformation key technique and efficient user revocation by introducing time-controlled access.The security analysis and simulation experiment demonstrate that the proposed BC-PC-Share scheme is a feasible and promising solution for PHRs data sharing via consortium blockchain.
Multi-Stage Improvement of Marine Predators Algorithm and Its Application

The metaheuristic algorithms are widely used in solving the parameters of the optimization problem.The marine predators algorithm (MPA) is a novel population-based intelligent algorithm.Although MPA has shown a talented foraging strategy, it still needs a balance of exploration and exploitation.Therefore, a multi-stage improvement of marine predators algorithm (MSMPA) is proposed in this paper.The algorithm retains the advantage of multistage search and introduces a linear flight strategy in the middle stage to enhance the interaction between predators.Predators further away from the historical optimum are required to move, increasing the exploration capability of the algorithm.In the middle and late stages, the search mechanism of particle swarm optimization (PSO) is inserted, which enhances the exploitation capability of the algorithm.This means that the stochasticity is decreased, that is the optimal region where predators jumping out is effectively stifled.At the same time, self-adjusting weight is used to regulate the convergence speed of the algorithm, which can balance the exploration and exploitation capability of the algorithm.The algorithm is applied to different types of CEC2017 benchmark test functions and three multidimensional nonlinear structure design optimization problems, compared with other recent algorithms.The results show that the convergence speed and accuracy of MSMPA are significantly better than that of the comparison algorithms.
Research on Autonomy Level Based on ODAL and Comprehensive Evaluation of Unmanned Systems

This paper proposes an autonomy-level model of unmanned systems based on ODAL. Based on the degree of human-computer interaction, with the technical dimensions of “Observe”, “Decide”, “Act” and “Learn”, the unmanned systems’ autonomy is divided into 6 levels. Meanwhile, this paper puts forward the basic process of the comprehensive evaluation of the autonomy level of unmanned systems and takes the autonomy evaluation of a certain UAV as a case to put forward the evaluation index system of the autonomy level of the unmanned system by using analytic hierarchy process, which provides technical support for the quantitative evaluation of the autonomy of unmanned systems.
Application of Improved Antlion Algorithm in Carrier-Based UAVs Recovery Scheduling

Orderly and efficient recovery of carrier-based UAVs is of great significance to enhance the operational capability of aircraft carriers. Based on the analysis of carrier-based UAV recovery route, this paper establishes a carrier-based UAV recovery scheduling model, taking the minimum weighted delay time and the maximum landing completion time as the optimization objectives, and comprehensively considering the influence of residual fuel quantity, battle damage program, follow-up task priority and other factors on carrier-based UAV recovery scheduling. Furthermore, an improved antlion optimization algorithm is proposed to solve the model. Based on the standard antlion optimization algorithm, dynamic weighting and micro disturbance mechanisms are introduced to improve the global convergence and local search ability of the antlion optimization algorithm. Finally, through recovery scheduling cases and the algorithm comparison, it shows that the improved antlion optimization algorithm has stronger optimization performance and better robustness in solving large-scale recovery scheduling problems, and has practical application values in engineering.
Lightweight Target Detection in High Resolution Remote Sensing Images

Deep learning network becomes more and more complex and demands higher computing resources. However, embedded devices not only demands for high performance, but also for computing efficiency such as real-time computation. Therefore, it is important to compress the classification model. Based on YOLOv3 network, this paper uses lightweight networks to identify aircraft, oiltank, overpasses and other targets in high resolution remote sensing images of RSOD-Dataset. MobileNet was taking as the backbone network of YOLOv3. In the experimental results, the mAP of MobileNet-YOLOv3 are 58.34. The experiment shows that MobileNet-YOLOv3 model has good performance and fast running speed, which improves the efficiency of target detection, and it is suitable for embedded devices.
An Enhanced Adaptive Differential Evolution Approach for Constrained Optimization Problems

Effective constrained optimization algorithms have been proposed for engineering problems recently.It is common to consider constraint violation and optimization algorithm as two separate parts.In this study, a pbest selection mechanism is proposed to integrate the current mutation strategy in constrained optimization problems.Based on the improved pbest selection method, an adaptive differential evolution approach is proposed, which helps the population jump out of the infeasible region.If all the individuals are infeasible, the top 5% of infeasible individuals are selected.In addition, a modified truncated ε-level method is proposed to avoid trapping in infeasible regions.The proposed adaptive differential evolution approach with an improved ε constraint process mechanism (IεJADE) is examined on CEC 2006 and CEC 2010 constrained benchmark function series.Besides, a standard IEEE-30 bus test system is studied on the efficiency of the IεJADE.The numerical analysis verifies the IεJADE algorithm is effective in comparison with other effective algorithms.
Three-Dimensional Collaborative Path Planning for Multi-UAVs Based on Improved GWO

In recent years, UAVs have been widely used on the battlefield, showing their great advantages over traditional aircraft. In response to the current hotly researched UAV clustering problem, this paper specifically investigates UAV swarm trajectory planning. Aiming at the problem of multi-UAVs cooperative trajectory planning in complex environments, the trajectory planning of each UAV and the mutual coordination of cluster members are comprehensively considered, and it is transformed into a multi-constrained mathematical problem that can be solved. By adopting a new track point update strategy, the actual practicability of the track is improved. An improved gray wolf optimizer-variable convergence dynamic weight adaptive position adjustment gray wolf optimizer (VCDWAP-GWO) is proposed. The algorithm combines three improved strategies: new convergence factor, Adaptive adjustment of position and dynamic weights. Through the improvement of the algorithm, the convergence speed and accuracy of the algorithm for solving the trajectory planning problem are greatly improved.
Infrared Face Part Tracking Based on Correlation Filtering

Compared with RGB images, infrared images have simple scene structure and little background interference, to achieve more accurate target detection. Therefore, target tracking for infrared thermal imaging has also become the focus of research. In order to achieve stable tracking of infrared face under complex background and multi-interference conditions, we propose a tracking algorithm using correlation filtering based on adaptive scales. In order to extract effective features from infrared face, this paper uses a tracking framework based on correlation filters and an adaptive scale mechanism to mark 12 groups of infrared video sequences under different conditions, and track infrared face parts. Compared with the traditional algorithm, the average tracking success rate of this method is significantly improved in complex background. It can effectively track infrared targets under such factors as partial occlusion, motion blur, fast motion, illumination changes, background clutter, and scale changes.
UOUU: User-Object Distance and User-User Distance Combined Method for Collaboration Task

Augmented reality superimposes digital information onto objects in the physical world and enables multi-user collaboration.Despite that previous proxemic interaction research has explored many applications of user-object distance and user-user distance in an augmented reality context, respectively, and combining both types of distance can improve the efficiency of users' perception and interaction with task objects and collaborators by providing users with insight into spatial relations of user-task object and user-user, less is concerned about how the two types of distances can be simultaneously adopted to assist collaboration tasks across multi-users.To fulfill the gap, we present UOUU, the user-object distance and user-user distance combined method for dynamically assigning tasks across users.We conducted empirical studies to investigate how the method affected user collaboration tasks in terms of collaboration occurrence and overall task efficiency.The results show that the method significantly improves the speed and accuracy of the collaboration tasks as well as the frequencies of collaboration occurrences.The study also confirms the method's effects on stimulating collaboration activities, as the UOUU method has effectively reduced the participants' perceived workload and the overall moving distances during the tasks.Implications for generalising the use of the method are discussed.
Research on Modeling and Simulation of Autonomous Maneuver of Unmanned Combat Aircraft

In order to provide a maneuvering model for the battlefield simulation and training of UAVs, and to provide some help for the development of multi-type UAVs joint combat experimental system, this paper carries out modeling and simulation of UAVs, and explores the typical maneuvering trajectory control methods. At the same time, in order to enhance the visibility of simulation results and realize the distributed connection with the laboratory battlefield situation display system, this paper designs a distributed interactive flight simulation program based on OpenGL under the DIS framework. In this paper, the three degrees of freedom mass center motion equations, coupled with the aircraft mechanical model and atmospheric environment model constitute the basic model of a certain unmanned combat aircraft. Then the PID algorithm is used to control the course, speed and altitude of the aircraft model. MuDIS software framework is used to realize the distributed simulation and connect with the laboratory battlefield situation display system. Finally, on the basis of DIS framework, OpenGL graphics library is used to construct 3D visual environment, and the simulation results are displayed in the 3D model to make the simulation results more vivid.