Reviewerly/exHarmony huggingface dataset:
Unmanned Ground Vehicle Autonomous Navigation in Unknown Indoor Environments

Unmanned ground vehicle (UGV) autonomous navigation in unknown indoor environments is a challenging task. The simultaneous use of the Gmapping algorithm, D*Lite algorithm and timed elastic bands (TEB) algorithm is proposed to accomplish the task as indicated above. Compared to traditional methods of separating mapping and navigation, the above method can reduce exploration and planning time while increasing the navigation efficiency of the UGV. Additionally, the planned paths are safer, more real-time, and more precise. In order to access better map building results and more suitable paths, this paper has made improvements to the traditional Gmapping algorithm and the D*Lite algorithm. By using simulation and real vehicle experiments, we demonstrate the validity and timeliness of the complete method.
Enhancement of Digital Printing Image Visual Experience Algorithm Based on HSV Color Space

Aiming at the defect that the color saturation of image acquisition is often distorted in the field of digital printing, an effective batch solution is proposed. Based on the existing image improvement algorithm, a new image improvement algorithm is proposed. The original image is de-sampled and converted into HSV color space. Through the new algorithm, improve the color saturation of the image; Then the processed image is sent back to RGB color space for processing, and finally the two fused images are resampled. The experimental results show that the improved image has good visual effect and reduces the complexity of the algorithm, which proves the effectiveness of the algorithm.
Research on Applying Dense Convolutional Neural Network in Chinese Character Font Recognition

The emergence of computer and artificial intelligence technology has subverted the traditional work model of pattern recognition and classification. Chinese character font recognition is one of the essential steps in developing and disseminating digitalized traditional calligraphy. In the traditional machine learning environment, dealing with the automatic recognition of a large number of fonts is often challenging. At the same time, font recognition under manual intervention also has substantial limitations. With the development of deep learning, the construction of font recognition algorithms using convolutional neural networks has become a mainstream research direction. This paper proposes a dense convolutional neural network, especially using fonts such as regular script, cursive script, and seal character as training targets to construct a mixed training set. By optimizing the model in three aspects: pooling rules, training strategies, and model pruning, the recognition performance of the model is further improved. It is hoped that the research in this paper will provide some help for the recognition of Chinese characters and the dissemination of digitalized traditional calligraphy.
UPerNet with ConvNeXt for Semantic Segmentation

In this article, we adopt UPerNet as the model and ConvNeXt as the backbone for semantic segmentation of cityscapes. UPerNet is a highly compatible architecture that can handle a variety of visual tasks. At the same time, as a convolutional network without adding attention mechanism, convnext has excellent feature extraction ability.In this article, we combine UPerNet and ConvNeXt to obtain a semantic segmentation model. Their excellent feature extraction ability and multi-scale object detection capability are utilized to enhance the performance of the network. In addition, we select the appropriate ConvNeXt structure, i.e. ConvNeXt-small, according to the size and characteristics of cityscapes dataset, and optimized the loss function to solve the problem of uneven class distribution. The experimental results show that ConvNeXt has excellent performance in feature extraction. Compared with the classic backbone ResNet50, it has improved by 1.96% on aAcc, 12.28% on mIoU and 11.39% on mAcc. After the loss function is optimized, all metrics also increase, which fully proves that our method is effective.
Study on Hyperparameter Adaptive Federated Learning

Federated Learning (FL) is a decentralized machine learning framework that distributes training data across mobile devices to learn a shared model by aggregating updates from local computations. FL protects the privacy of the user. In most federated learning algorithms, they usually experiment with different hyperparameter values to select a static hyperparameter value with better results. Therefore, one challenge associated with federated learning is the determination of the hyperparameter values in the algorithm, which limits the global model from providing good performance in each update. To address this issue, we propose a personalized FL algorithm that dynamically adjusts the hyperparameters (HAFed), using a log function to change the hyperparameter values in each round of updates, which helps the model to obtain different hyperparameter values in each update to address the limitation of choosing static hyperparameters. In theory, changing the value of the hyperparameters in each update by taking log for the parametric measure of the gap between client-side models allows the hyperparameters to be varied effectively over a range. In our experiments, we verified that HAFed outperforms other personalization algorithms.
News Text Classification Based on Deep Learning and TRBert Model

The utilization of the Internet and various social media platforms has led to an increase in the amount of text information available to people. Natural language processing technology has become increasingly practical for dealing with large amounts of text data, with news text classification being a particularly important task in terms of formulating news retrieval strategies, news recommendations, and social public opinion monitoring. While news texts on the Internet are typically divided into titles and texts, most existing deep learning-based research does not take into account the processing of titles and texts. The single processing method of merging titles into texts often results in unsatisfactory classification results. This chapter will focus on the importance of headlines in news texts, which are summaries of the main content of the news, contain a great deal of information, and express the central idea of the news. The TRBert model is proposed to complete text classification tasks, which incorporates the attention mechanism and hierarchical BERT weighted title word vectors.
Integration of Quantum-Safe Algorithms into X.509v3 Certificates

Public key cryptography has become an essential tool for protecting today’s network traffic. Algorithms such as RSA and ECC, based on the mathematical problems of integer factorization or discrete logarithm, are commonly used However, these problems can be solved in polynomial time on a large-scale quantum computer with Shor’s algorithm. To counter this threat, the cryptography community is actively developing post-quantum cryptography (PQC). To enable public key cryptography algorithms, including PQC algorithms, it is necessary to solve a problem of associating the cryptographic key with the entity it belongs to, usually done through public key infrastructure (PKI) and digital certifica In this paper, we explore how to integrate quantum-safe algorithms into X.509v3 certificates, analyze the schemes for migrating to quantum-safe certificates, compare them, and provide migration recommendations.
Handwritten Note Recognition in Digital Music Classroom Teaching Based on Trusted Neural Network

Optical music score recognition mainly studies how to form music score images into computer recognizable semantic symbols. Especially for handwritten music score images, it is particularly difficult to recognize and delete spectral lines due to the complexity and variety of handwritten notes and the addition of various deformations and noises. Therefore, the study of spectral line deletion in handwritten music score images has important theoretical significance and practical value. In this paper, a handwritten music score line deletion algorithm based on multi-scale and multi-directional LBP and XGBoost is proposed. LBP features are designed according to the characteristics of music score images. LBP features can be automatically learned through supervised learning. Spectral line deletion is always performed pixel by pixel, and the test takes a long time. Therefore, the parallel design can be used to improve the efficiency of the operation. The results of this paper have certain reference value for handwritten note recognition in digital music classroom teaching based on trusted neural network.
SkeFormer: Sign Language Segmentation Based on Skeleton Input Transformer

The work aimed to segment each gesture in the sign language video dataset collected by our school. To effectively capture the fine-grained features of gesture changes, a segmentation model based on skeleton input is adopted. We propose a sign language segmentation model combining ST-GCN with ASFormer. Our framework was compared to two strong baselines(ST-GCN, MS-GCN) for three tasks, and the experimental results were all the better than the baseline. At the same time, the F1 score in the experimental results has been significantly improved, which means that the number of segmentation errors can be effectively reduced.
Image Scene Transformation Based on Dynamic Mutual Information Generative Adversarial Networks

Generative Adversarial Network (GAN) has outstanding performance in the field of image scene conversion. It has been applied to visual tasks such as the conversion of satellite remote sensing images to floor plans, and has important application value in industrial production and other fields. However, the long training time of GAN model has become a common phenomenon in the field of image generation. Increasing the iteration speed can effectively speed up the project development progress, so speeding up the model training speed has gradually become a research hotspot. This work uses feature matching and maximizing mutual information, assigns dynamic weights to data such as features and information, maximizes the loss of distribution difference, speeds up the training cycle, dynamically adjusts the image data input according to the model learning progress, and makes the generated image retain more details. A dynamic mutual information generative adversarial network model is proposed. The experimental results show that compared with other models, the amplitude and duration of this model are reduced by 7.1% and 4.1%, respectively, which can reduce the training cost.
An improved YOLOv5 algorithm for construction solid waste detection

Construction solid waste contains many small objects, which can limit the model's recognition performance and lead to suboptimal small object detection. This may cause misidentification, missed detection, and repeated detection of construction solid waste, posing challenges for sorting operations. To address this issue, we propose an improved YOLOv5 algorithm for construction solid waste detection. This algorithm incorporates the Vision Transformer (ViT) Transformer Encoder structure into the backbone network and designs a Fusion-Concat module to replace the original Concat module in the Neck network, thus enhancing the model's detection capability. Moreover, we created an annotated construction solid waste detection dataset to validate the model's detection performance. Experimental results show that, while reducing the model's parameter size and computational complexity, the improved YOLOv5 algorithm outperforms the original YOLOv5 algorithm in small target detection, with a Mean Average Precision increase of 1.1%.
A Study of Multi-Task Recommendation Models Incorporating Preference Propagation

To address the problem that knowledge graph can effectively reduce the triadic relationships of entities from multi-source heterogeneous data, but is not conducive to recommendation tasks and it is difficult to explore the potential association relationships of data using single-task learning, a multi-task recommendation model with fused preference propagation (MAPKR) is proposed. Firstly, the user’s preference feature set is extracted from the knowledge graph using ripple propagation; secondly, the potential features are shared based on the similar nearest neighbor structure, and the higher-order feature representations of items and entities are extracted by cross-compression units; finally, the recommendation module and the knowledge graph embedding module are trained alternately with multi-task learning, and the extracted feature vectors are predicted and recommended after normalized inner product operation. Experiments are conducted on three publicly available datasets and compared with five baseline models. Compared with MKR and RippleNet, the AUC improved by 2.53% and 2.66% on average on the three datasets; ACC improved by 3.07% and 3.46% on average. The results show that the model presented in this paper has good recommended performance.
Research on Mobile APP Online Short-Term Rental System Based on Computer Artificial Intelligence Technology

This paper designs a short-term rental APP based on Android. Then this paper designs and realizes the house rental management system through the function analysis and demand analysis of some house renters. The system architecture adopts B/S structure, with the help of MySQL relational database, and uses the Bootstrap framework to build the front-end interface. Responsive web design enables users to browse the website using devices of different sizes to obtain excellent visual effects. At the same time, this paper explores the C2C online short-term rental cross-platform landlord matching algorithm based on traditional user matching. This paper introduces housing information to design a two-stage landlord matching algorithm (TSHM) based on housing information. Finally, the simulation of this paper found that the accuracy rates of common data sets and difficult data sets reached 99.12% and 89.54%, respectively. This method verifies the effectiveness of the matching model and matching features.
A Method for Automatically Constructing Event Logic Graph from Online News Texts

This paper proposes a framework to address the challenge of extracting critical information from the substantial growth of online news in the Internet age by automatically constructing event logic graph from unstructured news reports. The proposed framework consists of three layers. The data support layer employs crawler technology to gather news from different media platforms and store them in a MySQL database. The data mining layer employs natural language processing technology to analyze the news data and identify events and event relationships, which is utilized to create an event-centered news event logic graph by extracting information. In the visual interaction layer, the Neo4j graph database is utilized to showcase the examples for the news event logic graph. The event logic graph created through online news can enhance users' efficiency in obtaining news event information and aid them in making more comprehensive and scientifically-informed analytical decisions.
Research on Anti-Leakage and Tampering Guarantee Strategy for Experimental Data Sharing Based on Blockchain

Aiming at the security issues such as data leakage and tampering faced by experimental data sharing, research is conducted on data security sharing under multiple security mechanisms such as mixed encryption and secure storage on the blockchain against leakage, as well as experimental data tampering identification and recovery strategies based on an improved practical Byzantine fault-tolerant (PBFT) consensus algorithm. An integrated scheme for secure storage, sharing, and tamper resistant recovery of test data is proposed to address the contradiction between the security and sharing of sensitive data. Provide support for the security application of blockchain in experimental data management.
Defect recognition of small samples in substations based on the domain adaptive YOLOv7

The computer vision technology based on deep learning provides a new perspective on equipment inspection in power system. The intelligent technologies are now widely used in substations and definitely achieve positive effects, but they also face the problem of poor accuracy due to small samples. To improve the defect recognition in substations, this paper presents an improved YOLOv7 model that additionally includes the domain adaptation module, the SimAM attention module and the auxiliary detection head. The domain adaptation module strengthens the domain invariant features to improve the adaptability to unknown data. The SimAM attention module focuses on the key area and the objects. The auxiliary detection head can reduce the influence of large differences in object scale. The experiments on real-world data demonstrate that the improved model can better deal with the small samples and achieve better performance in the detection rate and the error rate.
Positive Weighted Feature Attack: Toward Transferable Adversarial Attack to SAR Target Recognition

The boom in deep learning has gained amazing progress in the synthetic aperture radar automatic target recognition (SAR ATR) in recent years. There are strong expectations on the reliability and safety of SAR ATR to avoid security accidents due to applications with high security risks. Unfortunately, it has been demonstrated that deep neural networks are inclined to be highly susceptible to adversarial examples finely crafted by the adversary, which can cause models to deliver an inaccurate prediction with high confidence. Since the adversary's access to and ability to learn about the target SAR systems and classifiers is severely constrained in real-world scenarios. For the SAR ATR task, this necessitates that the adversarial examples produced be highly transferable across the key deep neural network (DNN) classifiers. Additionally, SAR images' redundant speckle noise displays non-robustness and is challenging to generalize across various models. To this end, we in this paper propose an effective untargeted black-box attack algorithm called Positive Weighed Feature Attack (PFWA) for SAR target recognition and introduce novel positive weighted features associated with the region of interest of the model's decision. The primary goal of PWFA is to carry out adversarial attacks by altering the primary motivator for the victim model's choices. The proposed PWFA could produce adversarial examples that are more transferable between various models in this way. Furthermore, we present a random masking approach for reducing the influence of speckle noises. Experiments conducted on the MSTAR dataset prove that the proposed PWFA method is indeed capable of creating adversarial samples that achieve superior performance in terms of transferability.
Irregular Shape Image Retargeting Based on Seam Carving

Image redirection refers to the size adjustment of digital images to match the aspect ratio of different display terminals. In view of the problem of regular scaling and low computational efficiency of the existing image reorientation methods, the algorithm of irregular-shape image reorientation (Irregular Shape Image Retargeting based on Seam Carving, SC-ISIR) based on joint cropping is proposed. The SC-ISIR algorithm emphasizes image color information through gradient map to avoid image distortion; uses the gradient map as the image importance map for joint cutting, redirect the original image to the minimum rectangular domain that includes the target shape. Finally, select the seam segment at the edge (the Seam Segment Carving, SC) more effectively from the image edge, reconstruct the image into an irregular shape, and improve the effectiveness and calculation efficiency of the algorithm. The experiment verified that SC-ISIR algorithm can transform images into various kinds of irregular shape images.
The establishment and adaptive iteration of service community based on context association mining

The scale and types of service resources on the internet are becoming increasingly complex, and providing services for users' personalized needs is a research hotspot. This article proposes a user-centered service community concept to create a service community that can meet their needs. Existing methods for constructing service communities typically cannot meet the requirements of a user-centered service community as proposed in this article. The main method for creating a user-centered service community is to analyze the QoS data of users' historical service calls and incorporate temporal and spatial contexts into a collaborative filtering algorithm to obtain resources that are relevant to the user. The obtained resources are then added to the service community. Finally, the community mean square error is used to verify the effectiveness and feasibility of the algorithm. In order to ensure the dynamic nature of the service community and consider changes in user needs, this article defines two rules to trigger adaptive iteration and updates the resources of the service community based on the triggering rules.
Application of Auxiliary Models in Neural Network Knowledge Distillation and Gap Reduction Strategies

With the development of deep learning and advances in the hardware industry, deep neural network with large amounts of parameters has ushered in better and more accurate prediction results, but also increases GPU resource costs. In this way, the deployment of models in terminals become a new problem, deduction time also costs a lot. To address these issues, we propose a model-assisted approach on feature-based knowledge distillation by introducing an auxiliary model together with the teacher model to guide the learning of the student model. We use auxiliary model to reducing the gap caused by structural and scale differences between the teacher model and the student model. The result shows that the prediction accuracy improved significantly also maintain a good generation ability. The choice of student models still remains problem in the field of knowledge distillation still, and the differences in the structure of the teacher model student models are still the important factors affecting the results of knowledge distillation. Therefore, this paper focuses on the accuracy and generation ability of student model on the distillation results, based on which a model-assisted boosting method is proposed, in which the knowledge are migrated to the student model by the teacher model together with the auxiliary model.
Research on the Motion Model of Chaotic Phenomena Based on Computer Artificial Intelligence Simulation

A generalized synchronous chaotic system is constructed by using discrete generalized chaotic synchronization theory and 2D-CS. The improved chaotic system consists of three continuous chaotic systems. The control sequence is used to switch according to certain rules. Through the analysis of phase diagram, bifurcation diagram and maximum Lyapunov exponent, it is found that compared with the subsystem, the system parameter interval, traversal range and maximum Lyapunov exponent increase. Convert the sampled results to IEEE Binary floating-point Arithmetic Standard (IEEE754) format. The final random number is then generated by sequentially truncation, S-box transformation and XOR operation. The result of testing the generated random number sequence shows that the generated random number can pass all the tests.