Reviewerly/exHarmony huggingface dataset:
Highly Adaptive Ship Detection Based on Arbitrary Quadrilateral Bounding Box

With the rapid development of space remote sensing technology, accurate ship detection based on high-resolution optical remote sensing images has steadily attracted considerable research interest. However, most of the current methods adopt a fixed horizontal detection frame to predict the target. Although these methods have good detection accuracy, because the ship's orientation is arbitrary in reality, a large error occurs in the matching degree of the detection effective area, resulting in inaccurate target detection. Therefore, this paper proposes a ship detection algorithm based on an arbitrary quadrilateral prediction frame. We redefine the loss function and directly predict the detection frame's four vertices through the designed eight-parameter regression process. In addition, the convolutional block attention module (CBAM) is introduced to optimize the original network structure, and the clustering method is used to optimize the calculation of the anchor point. To replace the intersection over union (IoU), which cannot distinguish different alignments of objects, we adopt a generalized intersection over union (GIoU). Finally, we conduct experiments based on the DOTA ship dataset and the HRSC2016 dataset. The results show that our method is better than YOLOv3 and other commonly used target detection algorithms in terms of accuracy and visualization. Meanwhile, we compared with SOTA algorithm in real-time and dense ship detection. Experimental results prove that its speed and performance on mobile platform are in the lead, and it has a great effect on dense ship detection.
Heuristic Reward Function for Reinforcement Learning Based Manipulator Motion Planning

In recent years, the motion planning algorithm based on reinforcement learning has shown its potential to meet the requirements of high-dimensional planning space, complex task environments, and fast online planning simultaneously. However, the performance of the reinforcement learning-based motion planning algorithm for manipulators is closely related to the quality of the reward function, which is typically designed manually. In this paper, inspired by heuristic functions, a new learning-based reward function design framework is proposed. By relaxing the constraint of the original motion planning problem, a simple task is established. The cost of a solution to the simple task is taken as a heuristic for the original problem, and the heuristic is then incorporated into the reward function. We tested the performance of heuristic-guided reward functions based on the soft-actor-critic algorithm in various difficult scenarios. The experimental results showed that our reward function design method improves the convergence speed of the reinforcement learning training process and the planning success rate of the policy obtained after the training. In addition, the trained motion planning policy could be directly transferred to a real manipulator for online execution without modification, thus verifying the flexibility of the strategy between simulated and real environments.
GORITE: A BDI Realisation of Behavior Trees

Behavior trees are becoming increasingly used in both the computer games and robotics industries as a mechanism for non-programmers to specify individual entity (non-playing character or robot) behaviors. The tree representation that is employed is amenable to graphical representation and the availability of graphical editors has contributed significantly to the uptake of the approach. Working with graphical representations of behavior makes development by non-programmers easier. However, existing behavior tree representations are limited in their ability to represent team behavior and complex entity reasoning. Agent frameworks exist that address these issues and while they have been successfully employed in military war gaming applications, the underlying behavior representation that they use are fundamentally different to behavior trees. In this paper, we introduce GORITE, a BDI agent framework in which agent behavior is specified using goal-based process models which are representationally similar to behavior trees. Unlike behavior trees, process models can be used to represent both individual and team behaviors. Furthermore, GORITE provides support to enable an agent (or team of agents) to reason about the goals that it intends to pursue as well as the goals that it is currently pursuing. A simple example is used to demonstrate how team behavior can be modelled using GORITE process models.
Consensus-Based Bundle Algorithm-Based Task Allocation for Unmanned Vehicles in Dangerous Environment

This paper solves the distributed task allocation problem based on the danger-broadcast algorithm for unmanned vehicles with the Consensus-Based Bundle Algorithm (CBBA). By defining the dangerous region that is found by the flying unmanned vehicle, the traditional Consensus-Based Bundle Algorithm is extended with the danger broadcast, which is named as Danger-broadcast Consensus-Based Bundle Algorithm (DBCBBA). In the unknown task areas, the time discount and distance are used to describe the task's revenue function. Compared with the traditional CBBA, the proposed algorithm can be adopted to a real and complex environment, and more unmanned vehicles are saved by distributed danger-broadcast architecture. In the end, simulation experiments demonstrate the performance of the proposed algorithm for the unknown danger zone.
Simulation Analysis of Tracking and Control Link Level in Launch Vehicle Based on STK

The target feature analysis is essential for sea based launch vehicle tracking and control. The analysis method is performed with recognizing gain changes of telemetry antenna pattern in task usually and computing the automatic gain control (AGC) signal received by TT &C station manually. Aiming at the problems of low efficiency, complicated process and human errors, the method is discussed for a simulation analysis of tracking and control link level in launch vehicle based on STK, which elaborated emphatically for generating the electromagnetic field radiation pattern accord with STK format by launch vehicle antenna pattern. Compare the results of simulation and actual tracking by the launch vehicle in a flying, it indicates that the proposed method can satisfy the requirement in task and the result of simulation can be the reference by target feature analysis on launch vehicle.
An Adversarial Defense Algorithm Based on Triplet Network and Voting Decision

In the field of artificial intelligence, neural network is one of the key technologies used for image classification and recognition. However, recent work has demonstrated that deep neural networks are easily attacked by adversarial examples to make misjudgments. Adversarial examples are almost indistin-guishable from normal examples and yet cannot be classified correctly by neural networks. The existence of adversarial examples is a major obstacle to the practical application and deployment of neural networks, so the research on adversarial defense algorithms is an important topic in the field of AI security. This paper proposes an adversarial example defense algorithm based on a triplet network and voting decision mechanism. Firstly, two neural networks with different structures are trained based on normal dataset. Secondly, the first network is fine-tuned using the adversarial examples generated by these two networks, resulting in a third neural network. Then, these three neural networks are used as sub-networks in parallel to construct a triplet network. Through adversarial training and differences in structures, the transferability of adversarial examples among the three sub-networks is weakened. Finally, the final classification result is obtained by majority voting, based on the parallel output results of the three sub-networks. Through the complementarity between these three sub-networks, the defense against adversarial examples is realized. The experimental results demonstrate the effectiveness of this algorithm.
HGGAN: Visible to Thermal Translation Generative Adversarial Network Guided by Heatmap

The realization of multi-modal image fusion requires sufficient cross domain data. Translating the visible images is an effective method to obtain thermal-visible paired images from the visible image domain to the thermal image domain. The current translation methods have some disadvantages, such as unreasonable distribution of thermal radiation intensity, blurred edges, spatial distortion and feature loss. So they are not friendly to downstream tasks. Based on the generation and reconstruction strategy of CycleGAN, we propose an image to image translation network guided by heatmap which is called HGGAN. We use the heatmap of object that detected by network to encode the heatmap code, and combine the image edge code to improve the image generation performance. We test the image standard of the generated image, and use the object detection network to verify.
Attention-based Deep Pyramidal Network for Low-light Image Enhancement

Various images taken under complex lighting conditions often suffer from degraded image quality. Such poor quality not only fails to meet user expectations but also leads to significant performance degradation in many applications. In this paper, we propose a new low-light image enhancement method that exploits the feature validity of the attention mechanism and the spatial validity of the pyramidal structure. Specifically, the proposed method is able to recover image details from the original input and enhance the lighting and combine them at the end of the network. Moreover, the pyramid structure defined in the feature space based on the rich connection of higher-order residuals in the multi-scale structure makes the recovery process more efficient. This decomposition-based scheme is quite ideal for learning the highly nonlinear relationship between degraded images and their enhancement results. Experimental results on different datasets show that the proposed ADPN outperforms existing methods. The code and model are publicly available at: https://github.com/zhangxueshi0717/Attention-based-Deep-Pyramid-Network-for-Low-Light-Image-Enhancement.
Offline Speech Recognition Technology of Mobile Terminal Based on Artificial Intelligence

Traditional speech recognition methods need to convert speech into text content before text recognition, which requires high computational power. Therefore, speech data is usually transmitted to back-end application recognition through the network. When sensitive information is involved, it also brings the risk of data leakage. To solve the above problems, intelligent terminal offline voice analysis is realized. Using acoustic model implementation, to avoid the risk of leakage in the process of data transmission, at the same time, the design fully combined with mobile terminal co., LTD is implementation, eliminating the process of the text to speech, directly in the aspect of phonetic pronunciation of words in speech characteristics, which can identify calculate power demand is low, faster recognition and the rarely used word and vague pronunciation with higher compatibility, Greatly improve the efficiency of speech recognition.
Target Intention Recognition Model Based on SMOTE-AdaBoost under Unbalanced Samples

As an important part of battlefield situation estimation, target intention recognition plays a key role in helping command decision makers quickly perceive the battlefield situation. For the intention data of combat targets, there is often a characteristic of sample imbalance. Aiming at the problem of target intention recognition under unbalanced samples, this paper proposes a model based on SMOTE-AdaBoost, which first uses the SMOTE algorithm to balance the sample data, so that the number of samples with different intention is roughly balanced, and then balanced data are fed into AdaBoost based on decision tree for intention recognition. The experimental results show that the model proposed in this paper has a better recognition effect on the combat target intention under the unbalanced data.
Intelligent Metrological Verification Strategy under the Guidance of Internet of Things Technology

It is intended to improve residents' awareness of energy saving and emission reduction, and promote the behavioral energy saving fundamentally, so as to achieve the goal of conducting energy saving. In this work, automation control technology, Internet of Things (IoT) technology, artificial intelligence and big data analysis technology are combined and applied to the improvement and optimization of heating metering. It is attempted to build an intelligent metering management platform under the IoT. The entire heating system is optimized by optimizing the energy-saving plan and implementation approach. As a result, an intelligent metering management system based on the IoT is successfully built. The system can analyze the heating habits of users and other related key information as the heating data of users are monitored and understood in real time. Finally, the targeted regulation can be conducted to achieve the flexible heating.
Visual Performance of DCGAN Model for Analyzing Emoji Generation

As social media is rapidly developing among the young generations, emojis are becoming increasingly popular in recent years, establishing the need to expand emojis. However, continuously creating new emojis is hard to accomplish due to potential excessive costs and time-consuming. This paper introduces a DCGAN model to tackle this issue, which can generate new emojis by simply learning from the existing emojis input into the model. This application is built up mainly using the Pytorch library in Python. The implementation uses the generator to create plausible emojis to cheat the discriminator by training the model with input real emojis. Then with the results of their operation, these two models are trained repeatedly to improve their abilities. Thus, with this adversarial operation, the generator can produce emojis that have the reasonably authentic quality to pass the test from the discriminator, while the discriminator's accuracy is also continually improving during the training. Eventually, a GUI is created as an image gallery to display the implemented emojis using Tkinter in Python. Overall, this DCGAN model is entirely satisfactory to create new emojis randomly with good quality. However, those emojis generated by this implementation have some quality defects that still need to be refined manually, such as disordered or unclear face features. Therefore, this model can be used as a basic emoji generation program simply for entertainment or commercial use, while emojis inevitably require editing by designers.
A Grouping Method of Sea Battlefield Targets Based on The Improved Nearest Neighbor Algorithm

In the situation analysis and assessment aspect, the group identification of battlefield targets is very important. It is one of the key issues that commanders are concerned about in commanding operations, and it is also a necessary part of identifying the enemy's combat intentions. Considering inaccurate grouping phenomena of special patterns (non-“near-circle” patterns) that appeared on the battlefield, this paper adopts the improved nearest neighbor method to group sea battlefield targets. Firstly, the spatial features and attribute features of fusion targets are obtained, and then both the Euclidean distance (used to measure spatial similarity) and Mahalanobis distance (used to measure attribute similarity) between targets are calculated. Secondly, based on these two distance parameters, an improved nearest neighbor algorithm is used to finish the target grouping. Finally, update group parameters and maintain the addition, splitting, and missing changes of groups. The simulation results show that this improved algorithm can effectively accomplish the target grouping tasks of different patterns rising on the sea battlefield, and provide an effective basis for further battlefield situation understanding.
LCGPose: Local-Cross-Global Transformer for 2D Human Pose Estimation

2D Human pose estimation aims to locate the human keypoints from input data, and most CNN-based methods have achieved good performance for human pose estimation, however, the CNN-based methods lack the ability of capturing the relationship between different human keypoints, which limits the model performance to some extent. In this paper, we combine the beneficial qualities of CNN in processing the low-level vision and the advantages of Transformer in handling the relationship between various vision elements or objects, and then propose a new model framework-LCGPose for 2D human pose estimation. In detail, LCGPose mainly consist of Local Transformer Module, Cross Transformer Module and Global Transformer Module, which are respectively adopted to capture local dependencies, cross dependencies and global dependencies by self-attention mechanism of Transformer. A large number of experiment results show that our proposed LCGPose achieves competitive performance compared with the state-of-the-art methods via fewer model parameters and GFLOPs.
An Object Detection Algorithm for Military Vehicles Based on Image Style Transfer and Domain Adversarial Learning

In order to improve the accuracy of object detection based on deep learning for military vehicles., an object detection algorithm based on image style transfer and domain adversarial learning is proposed. Aiming at the problem of the small number of military vehicle images., a military vehicle image dataset is constructed by using games., miniature models and spider technology. However., due to the visual difference between these images., the accuracy of the trained detection model is poor when it is directly used to detect the actual collected images. CycleGAN is used for image style transfer to obtain images with a similar style to military vehicle images at the data level. Domain adversarial learning is used to optimize the one-stage object detection algorithm to make the network learn domain invariant features and reduce the domain discrepancy at the feature level. The proposed algorithm is implemented with YOLOv5s as an example. The test results show that the proposed algorithm improves the average precision (AP) by 5.7 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> without increasing the amount of inference computation., compared with the YOLOv5s algorithm.
Research on Enterprise Information Security Risk Assessment System Based on Bayesian Neural Network

Information security construction is a social issue, and the most urgent task is to do an excellent job in information risk assessment. The bayesian neural network currently plays a vital role in enterprise information security risk assessment, which overcomes the subjective defects of traditional assessment results and operates efficiently. The risk quantification method based on fuzzy theory and Bayesian regularization BP neural network mainly uses fuzzy theory to process the original data and uses the processed data as the input value of the neural network, which can effectively reduce the ambiguity of language description. At the same time, special neural network training is carried out for the confusion that the neural network is easy to fall into the optimal local problem. Finally, the risk is verified and quantified through experimental simulation. This paper mainly discusses the problem of enterprise information security risk assessment based on a Bayesian neural network, hoping to provide strong technical support for enterprises and organizations to carry out risk rectification plans. Therefore, the above method provides a new information security risk assessment idea.
A Unified Framework for Super-Resolution Based on Segmentation-Prior and Self-Attention

Convolutional Neural Network (CNN) is intensively applied to super-resolution (SR) task because of its superior performance. However, the problem of SR task is still challenging due to the lack of prior knowledge and small receptive field of CNN. We propose a unified framework for single image SR based on segmentation-prior and self-attention, named Segmentation-Prior Self-Attention Generative Adversarial Network (SPSAGAN). This combination is led by a carefully designed weighted addition to balance the influence of feature and segmentation attentions. Thus, the SPSAGAN can emphasize textures in the same segmentation category and meanwhile focus on the long-distance feature relationship. Extensive experiments show that SPSAGAN can generate more realistic and visually pleasing textures compared to state-of-the-art SFTGAN [1] and ESRGAN [2] on OST and BSD100 datasets
Machine vision-based information collection system for collector coin grading boxes

With the goal of automating the information system acquisition of collector coin grading boxes, this paper designs a vision-based grading box target acquisition robot platform. For the problem of information extraction of grading box images, a character recognition algorithm based on watershed area segmentation is proposed to improve the recognition accuracy of sticky characters, and an information acquisition robot application software is developed. The experimental and application results show that the image acquisition of the collector coin grading box information acquisition system is clear and the character information recognition rate reaches 1000%, which realizes the efficient and fully automated information acquisition of the collector coin grading box information acquisition system.
Design of a chaotic sequence cipher algorithm

To protect the security of video information use encryption technology to be effective means. In practical applications, the structural complexity and real-time characteristics of video information make the encryption effect of some commonly used algorithms have some shortcomings. According to the characteristics of video, to design practical encryption algorithm is necessary. This paper proposed a novel scheme of chaotic image encryption, which is based on scrambling and diffusion structure. Firstly, the breadth first search method is used to scramble the pixel position in the original image, and then the pseudo-random sequence generated by the time-varying bilateral chaotic symbol system is used to transform each pixel of the scrambled image ratio by ratio or encryption. In the simulation experiment and analysis, the performance of the encrypted image message entropy displays that the new chaotic image encryption scheme is effective.
UBFH-BOC navigation signal encryption authentication technology based on MMA

Due to the existing global navigation satellite system (GNSS) is an open, without certification system, satellite receiver is vulnerable to the potential for fraud. Therefore, it is urgent to solve the security certification problem of GNSS civil signals. Aiming at the above problems, this paper proposes a navigation encryption authentication technology based on modulation Method authentication (MMA) based on UBFH-BOC signal system. The results show that the authentication scheme can effectively resist the threat of spoofing, ensure the security of navigation signal transmission, and provide a reference for the subsequent application of high security navigation signal structure.