Reviewerly/exHarmony huggingface dataset:
Use the Direction of the Combined Vector of Accelerometers to Classify Daily Activities

Human Activity Recognition (HAR) is important for human physical fitness. Wearable sensor-based HAR uses accelerometers (A), gyroscopes (G), and other sensors to collect human motion data. To obtain a longer battery life for the wearable device and conduct long-term monitoring of human daily activities, it is necessary to adopt a method that can extract features from signals of sensors efficiently. In this paper, we study the extraction method of accelerometer data and combine the three axes of accelerometers into a direction ax. Then encode the extracted directions into numbers 1–62. In different activities, the distribution of directions is significantly different. Using Artificial Neural Network (ANN), K-nearest neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM) as classifiers, and the direction distributions as features input to classifiers, the classification accuracy up to 99.3%, in the best case.
A Trajectory Planning Method Based on Quad-tree Convex Feasible Set in Unstructured Environments

Trajectory planning in unstructured environments is a basic task for mobile robots. When trajectory planning is considered an optimization problem, the nonlinear kinematic constraints and the non-convex collision avoidance constraints often make it challenging. A trajectory planning algorithm that consists of two steps is proposed to address these problems in this paper. The first step is to combine the quad-tree map with the improved convex feasible set(CFS) method to construct a local convex feasible space. The second step is to solve the optimization problem iteratively, and an optimal margin judgment condition is proposed to reuse the local feasible spaces. The main contribution of this paper is that we combine the quad-tree map with the improved CFS to construct local convex feasible spaces efficiently. Numerical simulation results show that this algorithm can obtain collision-free and feasible vehicle trajectory in a short time. And the completeness of the trajectory can be guaranteed under a certain number of iterations.
SSIM-Based Sparse Image Super-Resolution with Rotation Strategy and Nonlocal Regularization

The methods based on sparse representation have been proved to be effective in solving the problem of image super-resolution. In this paper, we propose a structural similarity (SSIM) based sparse image super-resolution method. We incorporate SSIM index, a perceptual image quality assessment measure, into the sparse representation model. The modified orthogonal matching pursuit (IOMP) algorithm is used to calculate the sparse coefficients. At the same time, we use rotation strategy for sparse reconstruction from multiple angles. Moreover, we further regularize the sparse reconstructed image using the global reconstruction constraint combined with nonlocal similarity. Our experimental results demonstrate the effectiveness of the proposed method in both objective and perceptual quality.
Improved Echo State Network With Multiple Activation Functions

Echo state network (ESN) is a computational model based on reservoir computing framework, but regulation of activation function within the reservoir cannot be done, which affects its nonlinear mapping capability. In this paper, in view of design problem of activation function, we put forward a new activation function design method combining the original function and its derivative functions linearly. In reservoirs, different nodes have different activation functions, which improves the Nonlinear mapping capability of the ESN. Then the outputs of reservoirs are determined by the combined activation functions and the weight of the output layer is obtained by the least square estimation (LSE) method. Finally, simulation results in NARMA and Lorenz systems demonstrate the good effect of the new design method.
Optimal Combination Scheduling of Drone Based on Forest Fire Ignition Point Data

Forest fires have occurred from time to time, each time accompanied by huge loss of public property and even threatening human life. In order to solve this problem, drones can be used to replace part of the manual monitoring work. To make drone monitoring more scientific, the importance of different areas is divided by analyzing the forest fire point data over the years. Through the meshing of the target area, the entire data is coordinated and planned to bring the model closer to reality. Then drones group deployment model based on the imperialist competition algorithm is established, and the model can effectively reduce costs and improve efficiency through simulation verification. Finally, considering the influence of altitude on UAV communication, a new positioning model is proposed, and the Imperialist Competition Algorithm is used to optimize the coordinates of the relay station. Simulation results show that the model can effectively reduce costs and improve efficiency.
Enhancing Relation Classification Using Focal Loss

Relation classification (RC) is becoming an increasingly significant subject in natural language processing. However, the public datasets suffer from the problem of class imbalance, which limits the performance and generalizability of the model. In this paper, we propose an approach that integrates the bidirectional encoder representation from transformers (BERT) model with focal loss to alleviate the influence of the class imbalance problem in RC datasets. By this way, the model can decrease the contribution of high-frequency instances and easy instances during training, allowing the model to rapidly concentrate on low-frequency instances and hard instances. As a consequence, experiments on the SemEval-2010 Task 8 benchmark dataset indicate that our method achieves a considerable advance above the previous methods, with a Macro-F1 score of 90.62%.
Discriminative Feature Extraction Network for Generalized Zero Shot Learning

Generalized zero shot learning (GZSL), which aims to recognize both the seen and unseen classes conditioned on shared semantic information, has achieved remarkable progress. Generative based methods have proved an effective way in addressing the data-imbalanced problem in GZSL via generating the unseen visual samples. Nevertheless, non- discriminative information caused by cross dataset bias issue still severely deteriorates the model performance. To address this problem, a novel method named discriminative feature extraction network (DFEN) for GZSL was proposed. DFEN incorporated a discriminative feature extraction (DFE) module in a unified generative model to extract the discriminative information of visual samples by visual to semantic mapping. Meanwhile, semantic consistency constraint was used to guide the DFE module to learn the semantically relevant representation. To further boost the discriminative feature, adversarial learning manner was adopted to encourage the mapped semantic representations are consistent with the actual semantic embeddings. Numerous experiments have implemented on four benchmark datasets, which demonstrates that proposed method performs better than the recent advanced methods significantly.
Accelerated Spark Streaming based on FPGA

Most of the big data computing frameworks run on the CPU. For compute-intensive tasks, CPU has high latency and cannot meet the real-time demand. Therefore, adopting hardware accelerators to improve the performance of big data computing framework has attracted a lot of attention. This paper proposes to accelerate Spark Streaming system based on FPGA (ASSF), uses PCIe technology and the OpenCL standard of heterogeneous systems to achieve lightweight integration of the system, optimizes the data transmission process through pointer mapping and batch transmission, and optimizes OpenCL programming framework and kernel functions to enhance the parallelism of the FPGA. Finally, the experimental results demonstrate that in the case of matrix multiplication, the delay is greatly reduced, the throughput is improved, and the CPU occupancy rate is reduced. Therefore, the method proposed in this paper can effectively improve the overall performance of Spark Streaming system, and has strong generality.
Knowledge Graph Civil Aviation Question Answering Based on Deep Learning

With the continuous expansion of the scale of civil aviation, the passenger traffic volume of civil aviation has increased year by year, and the demand of passengers for civil aviation travel information has also increased sharply. The traditional manual customer service has problems such as heavy customer service pressure and untimely message response, which can no longer meet the development of modern civil aviation service industry. Therefore, this paper proposes a knowledge graph question answering method for civil aviation based on deep learning, which is used to quickly and accurately obtain the information of civil aviation question and match the question answers. Firstly based on the collected civil aviation data, the method extracts triples based on rules to complete the civil aviation knowledge graph, then the Aho-Corasick(AC)automata is constructed for entity recognition, and Convolutional Neural Network(CNN)is used for classifying user intention. Finally, according to the recognized entities and the results of user intention classification, the question is converted into a query sentence of the knowledge graph, and the answer is returned after querying in the knowledge graph. The experimental results show that the proposed method in this paper can better understand user’s intention and accurately answer the relevant questions compared with the comparative methods, which proves the validity of the method.
Vehicle Extreme Control based on Offline Reinforcement Leaning

This paper studies the extreme driving control of vehicles based on offline reinforcement learning (RL). Our work is mainly about the racing car, which has complex dynamic characteristics and is more difficult to control than steady-state drift. In order to ensure safety and fast convergence, we use the framework that takes a traditional control method as a basic policy and takes offline RL to fine-tune the imperfect traditional controller. For the basic policy, we use the principle of minimum curvature to generate a reference trajectory and a non-linear feedback controller to track the trajectory. For the fine-tune item, we use a simple yet state-of-the-art offline RL algorithm called TD3+BC to add some adjustive to the base policy. For the simulation platform, we use Matlab Simulink combined with the Silverstone track data as the environment, and use Carsim to provide vehicle dynamic models. The simulation results show that the proposed control policy can effectively shorten the time for the car to complete the track.
A Novel End-Edge-Cloud collaborative Architecture for Industrial Microservice

The distributed microservice architecture has been becoming the mainstream industrial software architecture in the era of Industrial Internet. This article proposes a novel end-edge-cloud collaborative architecture for industrial microservice to improve the real time, reliability and security performance of industrial microservice architecture. The two key technologies including an industrial persistent connection and fountain code technology are introduced to enable the industrial microservice architecture. The industrial persistent connection increases reliability and security through the continuous connection. Fountain code technology accomplishes deterministic bounded latency over imperfect communication networks and improve the real time, reliability and security of the microservice architecture. The experimental results show that the proposed architecture improve the comprehensive performance of industrial microservice.
An Repairing Algorithm for Infrared Video Brightness Mutation Based on Information Entropy

Images with abnormal brightness changes in videos pose a serious obstacle to video analysis tasks. In this paper, an information entropy-based brightness variation repair algorithm for infrared video images is proposed. In this paper, image average pixel value and image information entropy are used to find image frames with abnormal brightness changes. The adaptive Gamma transform determined by the information entropy value is used to repair the video image with brightness variation, and the "dead zone" pixels are replaced with the image pixels at the moment before the brightness change. The experimental results show that the algorithm can effectively repair the sudden change of video brightness caused by moving objects approaching the camera, and provides more reliable data for subsequent video analysis tasks.
Online Multi-Object Tracking with Generalized IoU Gating Mechanism

Multi-object tracking (MOT) aims at associating bounding boxes and assigning identities of objects in videos. Data association is the core problem of MOT, which is essentially the solution to a bipartite graph problem, and real-time processing places higher demands on the performance and complexity of the algorithm. Generally, a spatio-temporal gating mechanism is employed for the accurate and fast association. However, the traditional distance gating mechanism could suffer from long- range ID switch in some situations. In this paper, we propose a cascade matching algorithm with Generalized IoU Gating Mechanism (GIGM) to prevent confused association, and leverage CIoU matching as a second association strategy after the cascade matching to obtain more reasonable tracking results. Experiments on the MOT16 dataset verify the capability of the proposed tracking method, the key metrics including IDF1, MOTA, and HOTA, have been boosted compared to the popular online tracking methods.
A method for constructing domain knowledge graph based on literature review

Domain knowledge graph, as a branch of knowledge graph, focuses both the coverage and usage of knowledge in a specific field, so it has high requirements for the depth and accuracy of knowledge in this field. At present, there are still problems in the rapid construction and application of domain knowledge graphs. Especially before construction, complex domain scenarios involve a wide range of knowledge dimensions, and it is difficult for business experts to comprehensively construct a conceptual data model in this domain in a short period of time. In response to this problem, this paper proposes a domain knowledge graph construction method based on review documents, extracts the concepts and definitions of related fields in the review documents, constructs a domain knowledge graph, and uses NEO4J to visualize the knowledge graph from the extracted knowledge data set. In this paper, the field of spatiotemporal prediction is used as the experimental object to construct a knowledge graph in this field. The results show that this method can construct a knowledge graph framework with high accuracy in a short time. The domain knowledge graph is small and has yet to be expanded.
Kernel Adaptive Dynamic Filtering for Hypersonic Maneuvering Target Tracking

Without dependency on a prior model, kernel method provides a novel way of state estimation for nonlinear dynamic system with uncertainty. To perform maneuver target tracking without system model, a sliding-windowed adaptive kernel dynamic filtering method is proposed in this paper. The method performs Kalman filtering on a Reproducible Kernel Hilbert Space (RKHS) by estimating conditional embedding operator (CEO) as system state transfer function with training data. For system stochastic uncertainty, maximum correntropy criterion (MCC) is introduced for realizing kernel parameter optimization and the balance of estimation performance. The novel design of a sliding window is necessary for updating the estimates of the state transfer function online and to generate adaptability to unknown system dynamics. Such construction of the kernel Kalman filtering (KKF) is helpful to extend its application range from time-series signal processing to state estimation of uncertain dynamic systems. A typical scenario of hypersonic maneuvering target tracking is used for simulation. The results have demonstrated that the proposed method can realize effective model-free tracking to a target with nonlinear dynamical motion, showing the adaptability to non-cooperative target maneuver and better precision than typical model-based algorithms.
Particle Swarm Optimization based Trajectory Propagation for RLV Approach and Landing Phase

The trajectory propagation is used to estimate the flight states of the RLV when landing with a designed trajectory, so as to verify that whether the trajectory is suitable or not. Most of existing results are developed based on Euler numerical integration technique, and due to the implementation of the method, the efficiency of propagation may be low if a small integration step is used for high accuracy. To handle such issue, the paper propose a particle swarm optimization (PSO) based trajectory propagation method. With the proposed method, the efficiency of propagation can be significantly improved without losing accuracy. Simulations are done to show the advantages of our method.
Knowledge distillation and attention mechanism analysis of traffic sign detection

Safe driving has been the core of the traditional traffic, it is also a top priority for the future of autonomous driving. In recent years, with the development of target detection, a large number of proven technique have entered the realm of driverless cars. Traffic sign detection has always been an important task of pattern recognition in the traffic field. After the rise of deep learning, it quickly replaced the traditional methods and became the mainstream technology path. Object detection algorithms represented by RCNN family and YOLO series have gained extensive attention and applications. These algorithms are difficult to strike a good balance between speed and quality, especially when deployed on mobile platforms with less ability for calculate. In this paper, YOLOv5 algorithm is selected as the basis for knowledge distillation and multiple attention modes are used to improve the accuracy of the algorithm, and sparse training is used to further reduce the size of the model to achieve ultra-lightweight. The TT100K data set was used for training and verification results. However, the 45 types of traffic signs in this data set do not have the most important signal light data in traffic indication,ours made a large amount of such data to expand TT100K and named it as ETT100K data set.Experimental results show that the addition of attention to YOLOv5 can not effectively improve the model accuracy compared with knowledge distillation, but knowledge distillation can significantly improve the results.
Infrared Small-Sample Target Recognition With Content-focused Domain Adaption Network

The domain adaptation learning paradigm provides an approach for the small-sample infrared image recognition task by introducing a source domain with sufficient visible optical samples. It aims to learn domain-shared features from the source samples and transfer them to the infrared image domain. However, the performance will suffer from the domain shift problem which is in part caused by the inductive preference of the learning model on image style instead of its content. To address the above issues, this paper develops a novel content-focused domain adaption network. We propose two novel inductive biases from both intra-domain and cross-domain perspectives to make the learning model more focused on the content. First, an infrared intra-domain content-focused sub-network based on retinex theory is designed so that the style accounting for the image noise will not influence the intra-domain induction. Next, we construct a cross-domain content-focused sub-network to alleviate the attention on the image style. Thanks to these two networks, the overall domain adaptation model will pay more attention to the robust discriminative feature of image content rather than domain styles. Extensive experiments demonstrate the effectiveness of our algorithm. In particular, the recognition accuracy of our method can exceed the baseline method by up to 8%.
Multi-metric Depth Pyramid and Bilateral Segmentation Based Depth Inference for Multi-View Stereo

An efficient and efficient Pyramid Multiview Stereo (MVS) network, which is a 3D Bilateral Network (3D Bisenet), is presented to reconstruct the dense point cloud accurately and completely. First, a multimetric depth map pyramid is introduced, which can be used to reconstruct the shallow missing features by efficiently aggregating the depth estimates at the different layers. Then, a 3-D bipartite segmentation network is presented to keep matching pairs in all views and to enhance the detail properties of the model, so as to recover the lost scene. Furthermore, the high resolution and precision of the scene model can be obtained by combining the rapid down-sampling and the semiu-Net-type context paths. Experiments show that the proposed method achieves an excellent performance on DTU data sets, and significantly improves performance.
Target Recognition Based on Improved Fourier Descriptors

To address the problems of low recognition accuracy, slow computing speed, large memory consumption, high requirements for hardware facilities of traditional image target recognition algorithms and the deep learning method data set collection difficulties. In this paper, we propose an improved fast Fourier descriptors that can recognize target patterns quickly and accurately. The method first performs image enhancement and other pre-processing on the acquired image. Then uses the butterfly operation algorithm to accelerate the Fourier transform, calculates the Fourier descriptors of the target shape and improves the operation speed by reducing the number of key points of the target shape. Finally, the Fourier descriptors contours of the pattern to be detected and the template pattern are matched to determine the authenticity of the target recognition, and the OpenCV library is used for experimental simulation. The experimental results show that the method can accurately identify the target pattern and improve the recognition speed.
Video Anomaly Detection based on Optical Flow Information

Although feature descriptors based on optical flow information, are widely utilized in the field of abnormal video anomaly detection at crowd scenes, the performance of descriptors to detection result, with getting rid of the influence of recognition method, is worth to pay more attention. In respect of the accuracy not assured during calculation process, we present a novel feature descriptor in this paper, and name it to be ‘Probability Descriptor based on Optical Flow Orientation and Magnitude’. We firstly do respectively build 1D statistics histogram based on magnitude and orientation of optical flow with the Fuzzy-Partition Mechanism for each pixel; furthermore, build Combinatorial Histogram of Orientation and Magnitude with joint probability distribution for each pixel; finally, build one feature vector for video region through summation operation. For evaluation our proposed descriptor’s performance, we do a series of experiments on publicly available UCSD anomaly detection datasets. The experimental results demonstrate that it outperforms other state-of-the-art descriptors based on optical flow information field.