Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning-Based Path Planning Approach for Unmanned Arial Vehicles

Path planning plays an important role in autonomous flight control design for Unmanned Arial Vehicles (UAVs). In this paper, a path planning method based on the concepts of Reinforcement Learning (RL) is proposed. A Q-learning algorithm with a dynamic reward function is proposed and successfully implemented to ensure flyable paths with efficient collision avoidance. The dynamic reward function is introduced allowing the UAV to use the real-time distance between its current position and the destination as instructional data during training. Such an improved training mechanism leads to minimize the path length, reduce the learning cost, avoid the inefficient exploration of the search space, and ameliorate the convergence of the Q-planning algorithm. Many performance indicators are considered to evaluate the proposed Q-learning-based UAV's planner. Demonstrative results are implemented to illustrate the efficiency and practicability of the proposed artificial intelligence-based path planning approach.
Dynamic Scalability Mechanisms for Microservices in Federated Cloud Platform

In recent years, cloud platforms tailored for the development of microservices applications have garnered significant attention. Traditional cloud computing platforms suffer from remoteness from end-users and unreliable network connectivity, making them ill-suited for meeting real-time task requirements. Moreover, the complex and dynamic nature of cloud computing environments, coupled with the collaborative nature of microservices applications, introduces a substantial risk of service failures in cloud services. Existing cloud platforms primarily rely on service load elasticity to mitigate service failures but fail to consider the dynamic nature of federated cloud clusters, resulting in service oscillations and delayed fault handling, thereby compromising the reliability of microservices applications in federated clouds. To address these challenges, we propose a dynamic microservices adjustment framework based on the MAPE control model. The framework regularly collects runtime service data and proactively identifies and adjusts abnormal services. It employs a dynamic window-based elasticity approach, enhanced by fast scaling and slow shrinking adjustment strategies, to enhance service stability in the face of fluctuating workloads. In real-world operational scenarios, experimental results demonstrate the effectiveness of the proposed approach compared to traditional methods.
Power System Speech Recognition Method Based on Natural Language Processing

Aiming at the problem that the recognition accuracy of the system will decline when the target speaker voice tested in the speech recognition system and the speaker voice of the training data are significantly different, a power system speech recognition method based on natural language processing is proposed. It is speaker adaptation in the feature space. By adding speaker identity vector I-Vector auxiliary information to the DNN acoustic model, the speaker difference information in the feature is removed, the impact of speaker difference is reduced, and semantic information is retained. The experimental results on TEDLIUM open source data set show that the word error rate WER of the system is 7.7% and 6.7% higher than that of the baseline DNN acoustic model when the feature of this method is fbank and fMLLR respectively.
Improved Grey Wolf Optimization Algorithm Based on Inertia Weights of Inverse Triangular Exponents

In this paper, an improved grey Wolf optimization algorithm based on inverse triangular power index inertia weight(ITGWO) is proposed to solve the problem of slow convergence and low accuracy of gray Wolf optimization algorithm(GWO).By constructing the inverse trigonometric power index function, the inertial weight is adjusted nonlinearly, and the solving precision and convergence speed of the algorithm are improved. Seven classical test functions are used to compare ITGWO with other 5 swarm intelligence algorithms. The experimental results show that ITGWO is superior to the other 5 algorithms in convergence speed and solving accuracy, which shows that ITGWO has stronger optimization ability for solving complex optimization problems.
Research on UAV Passive Localization Based on Greedy Strategy and Two-degree Error Analysis Model

Positioning UAVs with each other and maintaining formation is the key factor to achieve UAV cluster positioning. In this paper, it is proposed to adopt the method of pure azimuth passive positioning, through part of the UAV transmitting the signal, part of the UAV receiving the signal, adjusting the UAV position, and realizing the UAV cluster positioning. The pure azimuth target positioning and tracking problem can be divided into two problems: the target positioning problem and the error control problem. Firstly, aiming at the positioning problem of UAVs, this paper uses the filtering method and MGEKF tracking filtering algorithm to establish the basic positioning model of UAVs to carry out preliminary positioning of UAVs. Secondly, because of the positioning error problem of UAV, to make up for the shortcomings of the multi-base array pure azimuth EKF filter model method commonly used in existing research, this paper creates a greedy ferrule dynamic adjustment model based on the two-degree error analysis model and greedy strategy, which can control the error in the formation adjustment position of UAV. This study has reference value for the rapid and accurate positioning of UAVs.
Adaptive step size-based wolf pack optimization algorithm for UAV path planning

With the increase in technological development and social needs, the diversity of missions that can be performed by UAVs has received widespread attention, so UAV path planning in variable environments has become an important prerequisite for completing UAV flight missions. This study focuses on the UAV path planning problem in complex environments and proposes a heuristic-based algorithm for wolf pack optimisation. Firstly, this paper uses the original terrain obstacle area and other factors to model the environment, customises a 3D equivalent terrain model and constructs a UAV trajectory planning scenario; then the wolf pack optimisation algorithm is used as the basis for optimising and modelling the wolf pack's wandering call and siege behaviour; finally, multiple sets of simulation experiments are conducted using the optimal flight distance between the starting point and the endpoint as the fitness function. The simulation results show that the Wolf Pack Optimisation algorithm has good global convergence and computational robustness, and has the advantages of good optimisation and fast convergence, shorter trajectory and more stable effect in UAV path planning.
IDE Debugging in the Cloud

In the previous chapter, I introduced debugging implementation patterns in the context of the Python command-line debugger. This chapter and the next one will look at the same patterns in the context of some Python IDEs.
Fundamental Vocabulary

Debugging complex software issues in machine learning and cloud computing environments requires not only the knowledge of the Python language and its interpreter (or compiler), plus standard and external libraries, but also necessary and relevant execution environment and operating system internals. In this chapter, you will review some necessary fundamentals from software diagnostics and debugging languages to have the same base level of understanding for the following chapters. In this book, I assume that you are familiar with the Python language and its runtime environment.
Debugging Implementation Patterns

In this chapter, you will look at specific debugging implementation patterns in the context of the Python debugger, pdb.
Research on Interactive Platform System of Power Load Interactive Management in Distribution Network under Smart Grid

On this basis, a method of using wireless communication technology to achieve load control is proposed. The entire system includes three major parts: the main station computer system, data communication network, and on-site terminals. Analyzed the key functional modules in the power grid load regulation system. Then the power load forecasting method of improved extreme learning machine is proposed. The key characteristics were extracted by means of data mining and Pearson correlation analysis, and input into the extreme speed learning machine based on Spike algorithm to achieve load prediction of power grid. The experiment has proven that the established prediction model has a high prediction accuracy, with a prediction coefficient of 0.95. This method has good prediction results.
C source code clustering using bisecting K-means

Due to the COVID-19 pandemic, education institutions have to rely on e-learning tools, including in programming courses. Automatic graders can be used to speed up the process of evaluating the correctness. Unfortunately, answers for coding exercises can be easily plagiarized. Manual grading of all student submissions may notbe feasible. Therefore, a system that can help detecting similar codes is needed. The detection can be done by grouping similar source codes based on their structure. This method is used in previous research by using automatic K-means iterations algorithm. That algorithm, although produced decent clusters, had a long execution time. The purpose of this research is to improve the time efficiency and clusters result quality by using bisecting K-means algorithm. The results showed a significant improvement in execution time from 11.68 seconds to 6.64 seconds. Bisecting K-means also produced fewer clusters with slightly better Rand Index than K-means iterations. We also conduct experiments using 2-grams to 6-grams and confirm that 4-grams result in the best performance.
Flight Training Quality Assessment of ARJ21 Simulator Based on Multi-source Data Fusion

Flight training assessment is a complex multi-indicator decision-making process. In this paper, flight phases, subjects and items are taken as research objects, and an ARJ21 simulator flight training quality assessment model based on multi-source data fusion is constructed. According to the simulator flight training subjects, items and its standards, the training subjects and items can be effectively identified and the flight training quality can be quantitatively evaluated. This method significantly improves the pilot's flying level, saves 60% of class C flight instructor's human resources, effectively guarantees the safety operation of ARJ21 aircraft, and has been verified in practical application.
Sign Language Recognition Based on CNN and Data Augmentation

Sign language recognition is an important assistive communication technology, which is one of the main communication methods for hearing and language impaired individuals. However, the development of sign language recognition technology has been facing issues such as insufficient data and low recognition rates. This paper proposed a sign language recognition method based on convolutional neural networks and data augmentation, which effectively improves the accuracy and robustness of sign language recognition. This paper evaluated the system's performance on sign language MNIST dataset. The experimental results show that the system can achieve average accuracy of 99.32% for the dataset after data augmentation for training dataset and significantly outperformed the models in the literature using the similar dataset. The research findings presented in this study provide valuable technical assistance for the implementation of sign language recognition technology in the fields of assistive communication and human-machine interaction.
Practicing requirements elicitation in a serious game: comparison between non-immersive and immersive virtual reality

One of the biggest problems in teaching Requirements Engineering at universities is the lack of access to real projects, which could allow students to interact with stakeholders and practice communication skills while conducting requirements elicitation. This article presents a serious game for practicing requirements elicitation through guided interviews with stakeholders. Two versions of the game were developed. The Immersive VR game was designed to run on mobile phones with Google Cardboard. A Non-Immersive version was also developed to run on Windows. Systematic literature reviews point out a lack of immersive VR games for learning Software Engineering. Moreover, the game proposed here is a pioneer in the addressed subject, including the use of low-cost technologies, such as Google Cardboard. The game was tested for 20 minutes by 46 students in two classes of the Bachelor's Degree in Software Engineering. After the test in the first class, the game was improved, and another test was conducted. The purpose of the validation was to verify the sense of presence among the students through a presence questionnaire (PQ). The highest averages were identified in the non-immersive version. For the immersive version, the statistically significant lowest evaluated values pertained to movement within the environment and the feeling of disorientation at the end of the experience. This indicates the need for improvements regarding movement. The literature suggests two directions: the first is to reduce the duration of experimentation with immersive environments, and the second suggests exploring alternative mechanisms for movement within the environment. It is important to remember that the students experimented with their own mobile phones, that is, they did not use dedicated VR equipment. Therefore, it can be concluded that developing VR solutions for these devices requires more restrictive decisions.
DA-UNet: Deformable Attention U-Net for Nucleus Segmentation

Abstract: Cell nucleus segmentation plays a significant role in Computer-Aided systems for cancer diagnosis. However, complex visual features, such as blurring and irregular shapes, increase the difficulty of segmentation. This paper proposes a deformable attention U-Net (DA-UNet) to enhance the learning of nucleus complex visual features. Based on the traditional U-Net, we introduce a deformable attention (DA) module, which aims to learn the more suitable shape features by the attention mechanism and deformable convolution. Experiments on the 2018 Data Science Bowl and MoNuSeg datasets show that the proposed DA-UNet can achieve good results.
Study on Multi-Pedestrian Trajectory Tracking based on improved YOLOv5+DeepSort

A multi-object tracking algorithm based on improved YOLOv5+Deepsort is proposed to improve the tracking effect in crowded and fuzzy scenes. The algorithm is improved as follows: the SKNet visual attention mechanism is integrated into the Backbone of YOLOv5 to strengthen the ability of recognizing fuzzy crowded groups; the FPN+PAN structure of the feature fusion module of YOLOv5 is replaced with the BiFPN structure to achieve efficient bidirectional cross-scale connectivity and weighted feature fusion;finally,constantly velocity model in the Kalman Filter is replaced with the constantly acceleration model to optimize the pedestrian motion model,and use DIOU quadratic matching to match detection frames that are not matched successfully,to improve the DeepSort tracking performance. The experimental results show that the accuracy on MOT17 is improved by 5.20% and the precision is improved by 1.85%; the accuracy on MOT20 is improved by 4.09% and the precision is improved by 1.33%.
An Empirical Study on Genetic Algorithm for 2D Path Planning of Unmanned Aerial Vehicles with Obstacle Avoidance

Unmanned aerial vehicle (UAV), with high efficiency, low manpower costs, and flexible convenience, has attracted widespread attention in logistics. UAV trajectory planning is an essential component in this context. Genetic algorithm(GA) is an effective tool for solving complex route optimization problems. However, selecting the optimal combination of parameters for UAV path planning remains a challenging problem, especially in complex urban environments with numerous obstacles. This paper proposes a genetic algorithm-based UAV path planning approach(UAV_GA) that considers obstacle avoidance on a two-dimensional grid-based map. Furthermore, to investigate the general patterns in selecting parameters for GA in such problems, a comprehensive empirical study is conducted. The study analyzes the effects of population size, number of iterations, crossover, and mutation probabilities on algorithm performance. It aims to identify universal patterns for optimal parameter combinations and provide guidance for parameter selection in GA for UAV trajectory planning with obstacle avoidance.
Abnormal monitoring Method of Radar Operation and Maintenance Information Based On Risk Extraction

Accelerating the integrated development of mechanization, informatization and intelligence in weapons and equipment is the only way for the armed forces to realize the modernization of weapons and equipment. However, due to reasons such as network security, data security, technical reliability and so on, the intelligent progress of military units themselves is very slow. In the field of integrated radar system support, the operation and maintenance information of the radar system is usually sent by each component device to the operation and maintenance software in the integrated electronic chassis, and the operation and maintenance data is displayed by the display and control software. The effective method of automatic monitoring of operation and maintenance information is lacking through the observation of abnormal situations by the on-duty personnel, and professional operation and maintenance personnel are lacking when anomalies occur. It is difficult to locate the fault and requires remote technical personnel to locate the problem, which is time-consuming and labor-intensive. In this paper, aiming at this problem, we developed an anomaly monitoring method for radar system operation and maintenance information that can perform anomaly prediction and anomaly factor analysis, describes the event extraction and anomaly prediction methods in detail, and verified the feasibility of the anomaly prediction method through comparative experiments.
DO-UNet:Improved UNet Model for CT Image Segmentation using DBB and Octave Convolution

Abstract Artificial intelligence techniques such as machine learning and deep learning are prevalent in the field of medical imaging, especially image segmentation. In the field of medicine, image segmentation helps to diagnose diseases accurately and hence has sparked interest. Neural-driven image segmentation methods, especially U-Net, are prominent.U-Net, although effective, suffers from severe information loss during downsampling and insufficiently detailed texture of the segmentation results, which limits its application. To solve this problem, we introduce DO-UNet, which strengthens the structure of U-Net. DO-UNet employs jump links at the downsampling layer, preserves high-resolution features, and suppresses data loss. Dense Block with Bottleneck (DBB) replaces the 3*3 convolutional block to efficiently extract spinal features and improve performance. Octave convolution extends the model during upsampling, improving accuracy by expanding the feature space and minimizing parameters. Experiments on the Verse2019 and Verse2020 datasets show that DO-UNet outperforms other models in terms of dice coefficients and accuracy. Visualization results confirm DO-UNet's superiority in accurately segmenting spinal structures. The model excels in capturing details and shows robustness and interpretability. These results highlight the practical significance and promising future of DO-UNet in spine segmentation.
A Research of Local-based Line Segment Detection Algorithms

Line segment detection is an important part of image feature engineering, with the local-based line segment detection algorithms being the most widely used. Despite the availability of several line segment detection algorithms, their performance evaluation remains inadequate. Moreover, there are few studies on improvement schemes for line segment detection algorithms. In this paper, six mainstream local-based line segment detection algorithms, including the LSD, FLD, EDLines, CannyLines, AG3line and ELSED, are investigated and a series of experiments are conducted to evaluate these algorithms. The performance in terms of precision, recall, F-score, and time consumption is discussed qualitatively and quantitatively. In short, the AG3line has the best performance in terms of detection quality, rendering it appropriate for scenes that demand high-quality results, such as 3D reconstruction. Meanwhile, the ELSED has the best performance in terms of detection speed, making it well-suited for scenes that require raid detection, such as visual SLAM. Moreover, three improvement methods, including bilateral filtering, line segment post-processing, and adaptive gamma correction, are introduced to achieve better line segment detection results. Experiments demonstrate that these three methods have a significant promoting effect on improving the precision and robustness of line segment detection algorithms.
Web Emotion Recognition for College Students Based on Microblog Text Mining

The new crown pneumonia epidemic is characterized by its suddenness, urgency, and danger, and the prolonged closed management and prevention and control measures are very likely to trigger strong emotional reactions from college students. In this paper, the microblogging texts of college students during the closed management period from October 28 to November 11, 2022 were used as the research object. After using text sentiment analysis and semantic network analysis techniques, we found that the comment contents of Sina Weibo student users were basically dominated by positive emotion expressions, and a few comments had negative emotions such as fear, worry and anxiety. Based on this, universities need to pay more attention to the physical and mental health of college students, respond to reasonable demands, pacify bad emotions, dominate the network space with mainstream public opinion, mainstream culture and mainstream values, and abate the influence of negative network opinions.
Research on xss Detection based on ALSTM Model and Traditional Feature Engineering

This paper proposes an XSS attack detection method based on hybrid LSTM-Attention model combined with traditional feature engineering. In this method, Long Short-Term Memory (LSTM) model was used to capture sequence features, and Attention mechanism was introduced to automatically learn the weights of key features. By combining LSTM and Attention, the model is better able to handle the sequential nature of XSS attacks and is able to focus attention on the features that are most important for detection. At the same time, combined with the traditional XSS feature selection, special symbols and special words are added to the detection standard.The experimental results show that compared with traditional methods, this method can more accurately detect cross site attacks.The innovation of this paper is to combine the LSTM-Attention model combined with traditional feature engineering to XSS attack detection, thereby improving the accuracy and performance of XSS attack detection.Key words: XSS attack; LSTM-Attention; Feature engineering