A Triple-GCN: Enhanced Multi-Feature Graph Convolutional Network for Aspect-Based Sentiment Analysis

Aspect-Based Sentiment Analysis (ABSA) aims to predict the sentiment polarity of the given aspect word within the sentence. Recent studies frequently treat syntactic and semantic features as independent representations, thereby overlooking their intrinsic correlation. Concurrently, most of the existing methods largely neglect the significance of dependency types, which eventually impacts the accuracy of sentiment analysis. Research based on cognitive theory indicates a mutual influence between syntax and semantics. Based on this, we propose an ABSA model based on enhancd multi-feature graph convolutional network(Triple-GCN). Firstly, a shared enhanced graph convolutional module is proposed to integrate syntactic and semantic information. Following this, a thorough fusion of this syntactic and semantic information is carried out. Besides, relation and adjacency matrices are utilized for the innovative reconstruction of hidden state vectors. Syntactic graph convolution module dynamically fuses hidden state vectors and dependency features. Additionally, a position weight encoding function is designed to comprehend sentiment dependencies by drawing attention to aspect-near words. On the semantic side, dynamic semantic graphs are constructed, enabling the capture of semantic features. The model has been evaluated on three public datasets: Twitter, Laptop14, and Restaurant14. Compared to existing baseline models, the effectiveness of this model has noticeably improved

New Family of Linear 3-Erasure Correcting Block Codes with Possible Application in Storage Systems

A construction of a new family of three erasure correcting linear block codes over GF(q) with characteristic two together with their syndrome decoding procedures are presented in this paper. The designed code distance of four was confirmed by demonstrating a decoding algorithm capable of correcting three erasures. The second confirmation was obtained from the weight spectra of selected codes, which were calculated using Krawtchouck polynomials derived from the weight spectra of their dual codes

Research on Dense Detection Algorithm for Brown Mushroom Based on Improved YOLOv7

In the complex environment of industrialized brown mushroom cultivation, a dense brown mushroom detection algorithm based on improved YOLOv7 is proposed to address the issues of low real-time detection accuracy and speed, and the high false detection rate of picking robots in densely grown brown mushroom clusters. To prevent network degradation, improve the detection accuracy and speed of the network, and reduce the network's computational cost, the ELAN_PS module is introduced to replace the original ELAN module. The AFPN network is used to replace the original network's Neck part for multi-scale fusion, allocating different spatial weights to feature maps to enhance the model's ability to separate dense targets. The MDIoU loss function is introduced as the algorithm's bounding box loss function to optimize the convergence speed of network training and improve the detection accuracy of dense occluded brown mushroom individuals. The improved algorithm is trained and tested on a self-built industrialized brown mushroom dataset. Compared to the original YOLOv7, the model's detection speed has increased by 15.5 %, detection accuracy has increased by 6.4 %, and average precision [email protected] has increased by 6.9 %

Machine Learning Approach for Ecological Public Transport Systems

Using Convolutional neural networks and Genetic programming, this study presents a new composite technique for modeling bicycle traffic in the town of Novo mesto, Slovenia. Every town needs public passenger transportation because the current transportation system has well-known issues like congestion, environmental effect, a lack of parking spaces, increased safety hazards, and excessive energy consumption. Urban transport is crucial for the functionality of any city. High-quality and usable urban transport not only affects the functionality of the city as an economic and social center, but it also reduces the number of passenger cars on the streets. The Novo mesto region, which has a population of around 30 000 people, is a major industrial center that is strongly reliant on metropolitan transportation. Unfortunately, the urban traffic of Novo mesto still has a relatively weak influence on the transport connectivity of the wider area. The study's goal is to examine and simulate bicycle rentals. For 35 weeks, convolutional neural networks and genetic programming were utilized to anticipate bicycle traffic. Three types of models were applied to study the impact of weather conditions on bicycle traffic: linear regression, genetic programming, and feed-forward neural networks. The proposed approach will be useful for cities with similar needs around the world

Optimizing Security and Performance in Blockchain-Enhanced Federated Learning Through Participant Selection with Role Determination

Federated learning (FL) allows distributed devices to jointly train a global model while safeguarding the privacy of their local data. However, selecting and securing clients, especially in environments with potentially malicious participants, remains a critical challenge. This study proposes an innovative participant selection method to enhance both security and efficiency in centralized and decentralized FL frameworks. In the centralized framework, this method effectively excludes clients with weak privacy protections and optimization capabilities, thus increasing overall system security. For decentralized FL, a blockchain-supported approach is introduced, which further strengthens the robustness of the system. Using a dynamic role assignment algorithm, roles such as worker, validator, and miner are allocated based on security and performance metrics for each training round. The findings show that this method performs on a par with the scenarios free of malicious clients, demonstrating the value of blockchain technology in improving FL protocols. By addressing security vulnerabilities and improving training efficiency, this research contributes to the development of more secure and efficient FL systems, underscoring the importance of advanced participant selection and role assignment strategies

YOLO-DTO: Automotive Door Panel Fastener Detection Algorithm Based on Deep Learning

The common detection of fasteners of automobile door panels is based on the method of template matching, which has the problems of low detection accuracy and poor real-time performance under the influence of different lighting and different placement positions. To improve the detection speed and accuracy of fasteners in complex scenes, a small object detection algorithm, YOLO-DTO (Detect Tiny Object), was proposed based on the YOLOv8 algorithm. Firstly, considering that the algorithm uses strided convolution to compress the input image prematurely, resulting in the loss of fine-grained information in the early stage of the image, which makes it difficult to recover the complete detail information in the subsequent feature fusion process, this paper modifies the convolution module in the early stage of the algorithm and introduces the SPD (SPace-to-Depth) module to reconstruct the early stage of the original algorithm. Secondly, a selective attention module is embedded in the Neck output position of the algorithm to enhance the algorithm's ability to pay attention to the context information of fasteners. Finally, to optimize the regression efficiency of the bounding box, the MPDIoU loss function replaced the CIoU loss function. Experimental results show that the average detection accuracy of the YOLO-DTO algorithm is 98.8 %, which is 9.1 % and 1.7 % higher than that of the template matching method and YOLOv8 algorithm, respectively, which meets the detection standards of factory production lines and has the practical value

Resource-Efficient Model for Deep Kernel Learning

According to Hughes phenomenon, the major challenges encountered in computations with learning models come from the scale of complexity, e.g. the so-called curse of dimensionality. Approaches for accelerated learning computations range from model- to implementation-level. The first type is rarely used in its basic form. Perhaps, this is due to the theoretical understanding of mathematical insights. We describe a model-level decomposition approach that combines both the decomposition of the objective function and of data. We perform a feasibility analysis of the resulting algorithm, both in terms of accuracy and scalability

Local Matrix Factorization with Network Embedding for Recommender Systems

In recommender systems, the rating matrix is usually not a global low-rank but local low-rank. Constructing low-rank submatrices for matrix factorization can improve the accuracy of rating prediction. This paper proposes a novel network embedding-based local matrix factorization model, which can built more meaningful sub-matrices. To alleviate the sparsity of the rating matrix, the social data and the rating data are integrated into a heterogeneous information network, which contains multiple types of objects and relations. The network embedding algorithm extracts the node representations of users and items from the heterogeneous information network. According to the correlation of the node representations, the rating matrix is divided into different sub-matrices. Finally, the matrix factorization is performed on the sub-matrices for rating prediction. We test our network embedding-based method on two real-world public data sets (Yelp and Douban). Experimental results show that our method can obtain more accurate prediction ratings

Max Planck Theory for Digital Image Processing: A New Algorithm for Mammogram Image Segmentation to Identify Masses in Regions of the Breast

Breast cancer, per WHO, ranks top in diagnoses and cancer fatalities. Early detection via mammography reduces mortality significantly, yet mammogram images often have indistinct features. Hence, precise tumor edge identification requires both image enhancement and segmentation. In response, we introduce the Max Planck Algorithm, a novel segmentation method rooted in Planck's quantum theory, specifically his thermal radiation principles. We innovatively converted this theory to create a unique segmentation tool applicable to digital image processing and medical imaging. The algorithm works by relating mammogram pixel values to X-ray wavelengths, adapting Planck's Law to use 'temperature' as an arbitrary variable (originally tied to actual temperature in Planck's work). Gradually adjusting 'temperature' optimizes the mammogram image's meaningfulness. The Max Planck Algorithm boasts advantageous properties, delivering higher efficiency and superior segmentation results. This innovative model introduces new methods for enhancing and segmenting mammograms, establishing itself as a unique technique without comparison to existing methods

Adaptive Non-Overlapping Community Detection Based on Gravitational Field Stability in Social Networks

Community structure is a common feature of social networks and many community discovery algorithms have emerged through the study of this feature. The gravitational field model is an effective method to realize community division. However, the current gravitational field model lacks a comprehensive consideration of field properties such as the internal stability of the gravitational field. Therefore, in this paper, we define and quantify the attributes of the gravitational field by taking advantage of the field's strength in describing the joint action of groups. Then, we propose a social network gravitational field community detection model (GF-CDM). GF-CDM selects the field kernel node based on a random walk and then presents an adaptive expansion function of fusion field stability to divide the observable network into overlapping and non-overlapping clusters. The model was evaluated on four real network datasets and five artificial network datasets of different sizes. Experimental results show that our proposed model outperforms the other four benchmark algorithms in modularity, ARI index, and field average stability, which can improve the quality of cluster division