Reviewerly/exHarmony huggingface dataset:
Stream Data Event Acquisition Method Based on Multi-Granularity Top-k Query

The query and acquisition of events in streaming data is the basis of studying various operations of streaming data. The event query in the existing streaming data system only queries the abnormal data points in the stream. In reality, the events in the streaming data are mostly anomalies in a continuous period of time, including time, spatial location and other information. Therefore, the traditional threshold query method cannot comprehensively analyze the events from different time and space perspectives, and the query accuracy is extremely low. All information about the event cannot be obtained. To solve these problems, this paper proposes a method to obtain streaming data events based on multi-granularity Top-k queries. Firstly, a regional monitoring cluster based on projection partition is constructed in the monitoring region. On this basis, an improved multi-granularity spatial Top-k query method is proposed to query points and obtain the spatial location information of abnormal events. Secondly, based on the event peak and valley information, the time multi-granularity Top-k query is carried out on the streaming data to find out the trigger point and termination point of the abnormal event, so as to obtain the complete information of the event. Experimental results show that the proposed method has great advantages in system resource cost and query efficiency.
Rotated Top-View Fisheye Person Detection Based on Dynamic Attention

Addressing the issues of image distortion and difficulty in accurate target detection in overhead fisheye surveillance images, this paper proposes a real-time overhead fisheye image human target detection algorithm based on multi-attention fusion. Firstly, the dataset for object detection in aerial images (DOTA) class data processing method, used for remote sensing image target detection, is introduced into fisheye image processing. Then, based on the industrial-grade single-stage target detection algorithm RTMdet, the Dy-Head module is introduced to perform fusion attention processing on the output features of the original Path Aggregation Feature Pyramid Network (PAFPN). Finally, a shared detection head improves the detection accuracy while reducing the network parameters. Cross-comparison validation with different datasets show that the detection algorithm in this paper can achieve an accuracy of 90.6% on complex datasets, and the FPS can be stably maintained at 26.7, demonstrate superior human target detection capabilities on fisheye images compared to traditional target detection methods.
A Parameterless Clustering Algorithm Based on Strict Neighborhood Graph

As one of the important methods of data mining, clustering algorithm has a wide application prospect in artificial intelligence and other fields. In the clustering algorithm, most popular algorithms need to set a parameter K artificially to divide the data into different categories. This paper presents a robust nonparametric clustering algorithm based on Strict Neighborhood Graph. This algorithm uses natural neighborhood algorithm and optimized density formula for preprocessing noisy data sets, which can better process noisy data and outliers and improve the accuracy of clustering results. Compared with traditional clustering algorithms such as K-means and DBSCAN, the algorithm based on natural neighborhood can effectively eliminate the step of artificial K value setting, so as to ensure that the interference of human factors on experimental results is significantly reduced in experiments and applications. At the same time, the algorithm can obtain higher accuracy by combining the formula of noise point preprocessing. After the validity experiment and comparison experiment, the results show that the algorithm has better clustering effect without setting K value, and can deal with noisy data sets better. Compared with traditional algorithms, this algorithm does not need parameter adjustment, so it is simpler and more adaptable, and has better performance when dealing with different types of data sets. Therefore, in many clustering scenarios, this algorithm can efficiently complete the clustering task and its advantages will become more and more obvious with the increasing of data scale and complexity.
Experiment design of a key algorithm for feature extraction based on gesture recognition

Gestures are the most important means of human-computer interaction. With the human hand directly as one of the input devices of the computing machine, the communication between human and computer will no longer need to find an intermediate substitute, and users can simply use gestures to manipulate the surrounding machinery. Then before the algorithm experiment design, we should focus on how to design algorithms to realize this idea, so as to achieve a concise, natural and direct way. Therefore, the use of computers to recognize gestures provides a more natural human-computer interface mode. This paper focuses on the research of gesture algorithm based on image recognition. On this basis, the research of gesture trajectory monitoring is carried out. Through the implementation of the gesture trajectory system, the acceleration and displacement functions of the test system are debugged. The test shows that the system can complete gesture recognition and monitoring, which has certain application value.
Research on Unity Scene Optimization Based on Fast LoD Technique Performance Comparison on Android Mobile Platform

With the completion of Unity scene development, it is often necessary to publish the scene on all platform for different user communities to operate. However, due to the insufficient hardware performance of Android mobile devices, in order to provide players with a smoother operation experience, developers will optimize the scene before final release. This article elaborates on the process of optimizing models in a certain scene through fast LoD technique based on QEM algorithm. It demonstrates that simplifying mesh grids of models can effectively improve the smoothness of scene operation and reduce device resource consumption. Through comparative analysis, it is concluded that the fast LoD technique can achieve higher optimization efficiency and better optimization effects than common LoD technique, and it has brought better performance and experience improvement to scene operation.
Image Super-Resolution Using a Enhanced Swin Transformer Network

Image Super-Resolution is a technique in the field of image processing that involves enhancing low-resolution images to generate high-resolution images. This technique aims to improve the clarity and details of images, thereby enhancing their quality and usability. While state-of-the-art image restoration methods are based on convolutional neural networks, they still face challenges such as high demand for training data, computational resource requirements, and difficulty in handling fine details. In this paper, we propose ASTSR, a super-resolution reconstruction model based on data augmentation and Swin Transformer. ASTSR consists of four components: data augmentation, shallow feature extraction, deep feature extraction, and image reconstruction. The data augmentation layer generates new training samples by randomly cropping and blurring different regions of images, thereby expanding the training dataset and improving the model's generalization ability and robustness. The deep feature extraction module is composed of multiple Swin Transformer residual blocks (STRBs). We conduct experiments on different datasets, and the results demonstrate that ASTSR achieves superior performance compared to other state-of-the-art methods, with a performance gain ranging from 0.04 to 0.36 dB, while reducing the total number of parameters by 24%.
Swin Transformer-based Time-Frequency Image Classification of Frequency Hopping Modulated Signals

Frequency hopping technology is a kind of spread spectrum communication, the signal frequency will be randomly hopped in a relatively wide band during the communication process, so as to avoid interference and interception by the enemy, and due to its secrecy performance and anti-multipath fading performance and other characteristics make it is widely used in the field of military communications. The identification of the modulation type of the intercepted frequency hopping signals in electronic information warfare is very important for the subsequent study and processing of the signals. In this paper, the Transformer algorithm is introduced into the field of modulation recognition, and the average recognition rate is 1.82% higher than ResNet50 by experimental comparison. For the interference of pre-processing cropping and patch merging in Swin Transformer on the clarity of time-frequency images, this paper adds Fast-NLM denoising to improve the average recognition rate by 0.8%. In addition, for the problem of attention scattering in Swin Transformer, this paper adopts a variable attention model, and the key sampling points around the reference points on the feature map are moved closer to the feature area by the calculation of offset, so that the feature representation can be better obtained. The experimental results show that the recognition rate of the improved model is 2.35% higher than that of the original model, the perception ability of some modulated signals is enhanced, and the addition of denoising can improve another 0.840%.
Research on Knowledge Graph-Based Recommender Systems

A Knowledge Graph-based Recommendation System (KG-RS) employs a knowledge graph to represent data and generate precise recommendations for customers based on the given information. In this paper, we first investigate the various filtering techniques commonly utilized in recommendation systems and analyze the distinctions between Heterogeneous Information Networks (HINs) and Knowledge Graphs (KGs). Then, we classify models based on their embedding methods, loss functions, entity representations, and the integration of additional information. Also, we classified the extra models they used to facilitate research. Our research demonstrates that item information is consistently included in knowledge graphs, while user information is not. Additionally, KG-RSs are progressing by incorporating more advanced models into the recommendation process, rather than complicating the knowledge graph itself.
Study on the Adversarial Sample Generation Algorithm Based on Adversarial Quantum Generation Adversarial Network

In response to the inefficiency of the existing advGAN method, which requires calculating the differences between the adversarial samples and the normal samples one by one when generating adversarial perturbations, this paper proposes a quantum adversarial sample generation algorithm (QASGA) based on adversarial quantum generative adversarial networks. First, the real samples are encoded into quantum states, then the generator G of QGAN is used to generate adversarial perturbations, which are superimposed with normal samples to obtain adversarial samples. At the same time, the SWAP-TEST method is used to calculate the similarity between all real samples and adversarial samples at once, thereby accelerating adversarial attacks. Experimental results show that the QASGA algorithm proposed in this paper can generate high-fidelity adversarial samples with a one-time similarity calculation on the IRIS dataset, and can reduce the classification accuracy of the BP neural network from 96.67% to 26.67%, verifying the effectiveness of the proposed QASGA algorithm.
Water accidents severity classification based on Prompt-BERT

Abstarct—Accident report severity classification is a hot topic in the field of water transportation. Accident reports have the characteristics of strong professionalism and large amount of text data, which leads to the poor classification effect of traditional text classification models in this field. To address the above problems, Prompt-Bert is proposed in this paper.First, BERT is used to vectorize words, and then the probability of accident severity is predicted using the accident type as the prompt word, and the maximum probability of accident severity is selected as the output, and compared with three baseline models, Word2Vec-CNN, Word2Vec-LSTM, and BERT The accuracy is improved by 2.50%, 5.00%, and 11.00%, respectively, when compared on the same data set. This experiment demonstrates the feasibility of the model proposed in this paper.
Modulation Recognition Based on Instantaneous Feature Fusion

With the rapid development of 5G technology, many innovative technologies and various standards have emerged in the field of wireless communication. Modulation Recognition (MR) is one of the important technologies in communication system, whose main purpose is to recognize the modulation mode of the received signal to overcome the influence of noise interference. In order to solve the problem of modulation recognition accuracy under low signal-to-noise ratio, an instantaneous feature fusion algorithm based on deep learning framework is proposed. This method uses the different characteristics of the instantaneous characteristic values of each modulation signal and the complementary information between the different instantaneous characteristics to carry out modulation recognition. When SNR=0dB, the recognition accuracy can reach 88% when adding two eigenvalues, while the recognition accuracy is 86% when no eigenvalues are added. On the other hand with the increase of the number of added eigenvalues, the modulation recognition accuracy is improved.
High-Precision Robust Calibration of Circular Targets Based on Monocular Vision

Industrial cameras often use the method based on circular target for calibration, because of lens distortion and circular target center feature extraction error, this method has asymmetric projection and noise problems, which easily leads to calibration errors. Aiming at this calibration error, this paper independently designs a uniformly distributed circular target, uses the maximally stable extremal region algorithm (MSER) to extract the features of asymmetric projected posture images with a large amount of data, and obtains the center coordinates with strong robustness after adjustment. In the process of sorting the physical coordinates of the corresponding points of the target, the invariance of cross ratio is used to ensure the robustness of feature matching. The initial calibration result of the center of the adjustment is used as the initial calibration value, and the center coordinates are further iteratively corrected through back projection of the virtual physical target. Experimental results show that compared with the traditional elliptic center extraction method, the re-projection error of the proposed method is reduced from 0.89 pixel to 0.03 pixel, and the re-projection error of the proposed method is reduced from 0.06 pixel to 0.03 pixel compared with the calibration results of the checkerboard target under the same camera position, which realizes the high precision and robust calibration of the circular target.
Operator Fusion Scheduling Optimization for TVM Deep Learning Compilers

This paper addresses the problem that the fusion of memory-intensive operators in the automatic scheduling module of TVM deep learning compiler is not fully considered, which leads to the inefficient operation of computational tasks defined by tensor expressions. In this paper, we design a scheduling rule for fusing memory-intensive arithmetic structures by analyzing the arithmetic characteristics, combining the characteristics of different hardware architectures, and assigning corresponding scheduling policies to arithmetic using scheduling primitives. The differences of the scheduling tree before and after the new scheduling rule are also analyzed to verify the effectiveness of the scheduling rule. The experimental results show that the new scheduling rule shows significant optimization effects on several test sets of different sizes, and the average speedup ratios reach 1.02x, 1.21x and 1.23x on CPU, GPU and DCU platforms, respectively; the highest speedup ratio is 1.95x on DCU platform.
3D Reconstruction of Nonuniform Rational B-Spline Surface Based on Line-Structured Light

Structured light is considered one of the fundamental and reliable techniques for 3D surface reconstruction. In particular, the line-structured light reconstruction method has been widely adopted due to its high precision, robust stability, and simple design. However, using line-structured light to reconstruct complex surfaces, particularly those represented by nonuniform rational B-Spline (NURBS), presents significant challenges. In view of this, this paper focuses on the 3D reconstruction of NURBS surfaces and does the following work: First, the R-channel stripe center extraction algorithm is presented because the laser stripes used in this process often suffer from severe deformation, which can lead to difficulties in center extraction and discontinuity issues. Second, the 3D reconstruction of the NURBS surface is carried out. The average absolute error of the reconstructed surface in the x-direction is 0.1244mm, and the average relative error is 0.11%, reflecting the accuracy of the reconstruction algorithm in this paper. Finally, an improved Iterative Closest Point (ICP) algorithm is presented to overcome the challenge of point cloud registration caused by the similarity in shape between the peaks and valleys of NURBS surfaces. Experimental results show that it significantly improves the registration quality of NURBS surfaces.
Local Motion Blurred Image Restoration Based on the Reciprocal of DCT High-Frequency Mean Incremental Prior

Aiming at the blur caused by fast-moving objects in surveillance video, we propose a new method to restore blurred images. First, we use the PiCANet saliency detection algorithm to obtain the saliency map of the blurred area. Then we use the saliency map to guide the soft segmentation algorithm to divide the blurred image into foreground and background layers. Second, we propose a reciprocal of DCT high-frequency mean incremental prior to constraining the solution space of clear latent images under the framework of maximum a posteriori probability. Finally, we also propose an improved bilateral filtering algorithm to enhance the details of the restored image. The experimental results show that our algorithm's deblurring visual effect and objective evaluation index are superior to other algorithms.
Power Equipment 3D Reconstruction Method for Distribution Network

With the rapid development of oblique photography modeling technology, automatic production of oblique photography 3D models has become increasingly mature. The singulation of oblique photography 3D models has focused on urban buildings at home and abroad, but there are few studies on power distribution network equipment. The fine modeling method of true 3D singulation model based on tilted photographic images requires manual interactive editing. The research and realization of singulation of 3D model of tilted photography based on Cesium also did not solve it. Automatic and regular three-dimensional model contours is the most advanced model to aim at distribution network equipment. We analyze the eb3d data specifications and the principle of unitization supported by the power grid GIS platform, summarizes the existing research results of building unitization, and studies the key technologies of 3D reconstruction of the distribution network. We realized the three-dimensional reconstruction of the distribution network, service release, and three-dimensional display of the distribution network in real urban applications.
Iterative Refinement via Adaptive Subproblem Generation Algorithm (ASGA) for Anytime Multi-Agent Path Finding

Multi-Agent Path Finding (MAPF) is the problem of finding a set of conflict-free paths from a starting location to a goal for multiple agents. Since MAPF is NP-hard, the optimal algorithm takes a long time to solve and is severely limited by the scale of the problem. The suboptimal algorithm is faster, but usually finds a low-quality solution. It is a feasible solution to use the suboptimal algorithm to quickly obtain the initial solution, and then iteratively refine the sub-problem. The key is how to select the agent subset. There are still many limitations in the current research on MAPF iterative refinement and subset selection rules. In view of the deficiencies of the existing MAPF iterative refinement scheme, this paper proposes corresponding improvement schemes:1) An adaptive sub-problem generation algorithm (ASGA) is proposed, which improves the defects of each member rule and eliminates the disadvantages of contingency and insufficiency in the combination rules of fixed order. 2) A global supervision mechanism (GSM) is proposed to solve the problem of repeated inspection of the same agent, which leads to the failure of sub-problem generation and misjudgment of the performance of the rules. 3) Using the ASGA algorithm, an anytime MAPF iterative refinement framework is proposed. Experimental results show that our algorithm outperforms other similar algorithms in refinement efficiency, solution quality improvement and scalability.
A Coupling Analysis Method for Airborne Software Based on Requirement Model

In order to measure the data coupling and control coupling between system components and to clarify the dependency relationships among variables in the requirements, this study offers an airborne software requirements coupling analysis approach based on the requirement model. A data coupling and control coupling analysis method based on the VRM model (Variable Requirement Model) is presented in the paper, which uses a coupling measurement method based on hierarchical dependency relationships among variables. Variable relationships are defined as an n-ary dependency tree structure, and weights are assigned using algorithms. This study helps with complex system design and raises the standard and dependability of creating airborne software.
Design of a Flexible Analysis System for Coal Mine Monitoring

With the continuous development of the coal mining industry and the improvement of automation, monitoring software plays a crucial role in ensuring the safety and efficiency of mining operations. However, the reliability and accuracy of monitoring software are crucial for the safety and production of the coal mining industry. Traditional manual testing can no longer ensure accurate measurement of monitoring system stability and performance in complex scenarios. This paper will explore the background and challenges of monitoring software testing in the coal mining industry, and introduce a flexible analysis system for coal mining monitoring.
Hardware Design of SM2 Coprocessor Based on RISC-V

The algorithm SM2 is used as the Elliptic Curve Cryptography (ECC) standard in China, due to the complexity of its computational process, it faces the challenge of low implementation efficiency. In this paper, we present a hardware design of SM2 coprocessor based on RISC-V architecture. The coprocessor is designed with hardware/software co-design and utilizes an improved traditional binary expansion method to achieve parallel execution of point addition and point doubling operations, and hardware acceleration through custom instruction extensions to improve the efficiency of point multiplication computation in the prime and binary domains. After experimental verification on Xilinx ARTIX-7 FPGA, during the encryption and decryption processes, compared to the traditional software algorithm, the cycles and instructions are only 5.47% and 2.96% respectively. In terms of resource utilization, the coprocessor occupies a total of 6754 slice. The experimental results show that the remarkable advantages of our coprocessor in terms of processing speed and resource efficiency, providing a feasible solution for the software/hardware co-design of the SM2 algorithm.