In the modern digital age, users are exposed to a vast amount of content and information, and the importance of recommendation systems is increasing accordingly. Traditional recommendation systems mainly use matrix factorization and collaborative filtering methods, but problems with scalability due to an increase in the amount of data and slow learning and inference speeds occur due to an increase in the amount of computation. To overcome these problems, this study focused on optimizing LightGCN, the basic structure of the graph-convolution-network-based recommendation system. To improve this, techniques and structures were proposed. We propose an embedding enhancement method to strengthen the robustness of embedding and a non-combination structure to overcome LightGCN’s weight sum structure through this method. To verify the proposed method, we have demonstrated its effectiveness through experiments using the SELFRec library on various datasets, such as Yelp2018, MovieLens-1M, FilmTrust, and Douban-book. Mainly, significant performance improvements were observed in key indicators, such as Precision, Recall, NDCG, and Hit Ratio in Yelp2018 and Douban-book datasets. These results suggest that the proposed methods effectively improved the recommendation performance and learning efficiency of the LightGCN model, and the improvement of LightGCN, which is most widely used as a backbone network, makes an important contribution to the entire field of GCN-based recommendation systems. Therefore, in this study, we improved the learning method of the existing LightGCN and changed the weight sum structure to surpass the existing accuracy.

With the growth of e-commerce platforms, the number of fraud reviews has rapidly increased, making fraud detection for platform data critically important. To detect fraud reviews in platform data, recent approaches have leveraged graph neural networks that model users as nodes in a heterogeneous multi-relational graph structure. This approach represents platform users as nodes in a graph, and the relationships among users who share commonalities in their reviews or products as edges, in order to identify fraudulent reviewers. However, existing graph-based fraud detection methods may suffer from the unstable training of the classifier networks that process embedding vectors. In this paper, we identify that this issue arises from the excessive deviation and scale expansion caused by some of the aggregated values from adjacent nodes in conventional node embeddings, and we propose a scale regularization method to mitigate this. To verify the effectiveness of the proposed method, we conduct validation on the Amazon-Fraud dataset, which is a multi-relational graph dataset constructed from review data of Amazon E-Commerce. The experimental results show that the proposed scale regularization achieves superior performance compared to previous verified graph fraud detection models.

This paper proposes a zero-shot based spatial recognition AI algorithm by fusing and developing multi-dimensional vision identification technology adapted to the situation in large indoor and underground spaces. With the expa... | Find, read and cite all the research you need on Tech Science Press

This research aims to propose a practical framework designed for the automatic analysis of a product’s comprehensive functionality and security vulnerabilities, generating applicable guidelines based on real-world software. The existing analysis of software security vulnerabilities often focuses on specific features or modules. This partial and arbitrary analysis of the security vulnerabilities makes it challenging to comprehend the overall security vulnerabilities of the software. The key novelty lies in overcoming the constraints of partial approaches. The proposed framework utilizes data from various sources to create a comprehensive functionality profile, facilitating the derivation of real-world security guidelines. Security guidelines are dynamically generated by associating functional security vulnerabilities with the latest Common Vulnerabilities and Exposure (CVE) and Common Vulnerability Scoring System (CVSS) scores, resulting in automated guidelines tailored to each product. These guidelines are not only practical but also applicable in real-world software, allowing for prioritized security responses. The proposed framework is applied to virtual private network (VPN) software, wherein a validated Level 2 data flow diagram is generated using the Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of privilege (STRIDE) technique with references to various papers and examples from related software. The analysis resulted in the identification of a total of 121 vulnerabilities. The successful implementation and validation demonstrate the framework’s efficacy in generating customized guidelines for entire systems, subsystems, and selected modules.

As large, room-scale environments become increasingly common, their spatial complexity increases due to variable, unstructured elements. Consequently, demand for room-scale service robots is surging, yet most technologies remain corridor-centric, and autonomous navigation in expansive rooms becomes unstable even around static obstacles. Existing approaches face several structural limitations. These include the labor-intensive requirement for large-scale object annotation and continual retraining, as well as the vulnerability of vanishing point or line-based methods when geometric cues are insufficient. In addition, the high cost of LiDAR and 3D perception errors caused by limited wall cues and dense interior clutter further limit their effectiveness. To address these challenges, we propose a zero-shot vision-based algorithm for robust 3D map reconstruction in geometry-deficient room-scale environments. The algorithm operates in three layers: Layer 1 performs dimension-wise boundary detection; Layer 2 estimates vanishing points, refines the precise perspective space, and extracts a floor mask; and Layer 3 conducts 3D spatial mapping and obstacle recognition. The proposed method was experimentally validated across various geometric-deficient room-scale environments, including lobbies, seminar rooms, conference rooms, cafeterias, and museums—demonstrating its ability to reliably reconstruct 3D maps and accurately recognize obstacles. Experimental results show that the proposed algorithm achieved an F1 score of 0.959 in precision perspective space detection and 0.965 in floor mask extraction. For obstacle recognition and classification, it obtained F1 scores of 0.980 in obstacle absent areas, 0.913 in solid obstacle environments, and 0.939 in skeleton-type sparse obstacle environments, confirming its high precision and reliability in geometric-deficient room-scale environments.

Demand for OIS (Optical Image Stabilization) actuator modules, developed for shake correction technologies in industries such as smartphones, drones, IoT, and AR/VR, is increasing. To enable real-time and precise inspection of these modules, an AI algorithm that maximizes defect detection accuracy is required. This study proposes an unsupervised learning-based algorithm that is robust to noise and capable of real-time processing for accurate defect classification of OIS actuators in a smart factory environment. The proposed algorithm performs noise-reduction preprocessing, considering the sensitivity of small components and lighting imbalances, and defines three dynamic Regions of Interest (ROIs) to address positional deviations. A customized AutoEncoder (AE) is trained for each ROI, and defect classification is conducted based on reconstruction errors, followed by a final comprehensive decision. Experimental results show that the algorithm achieves an accuracy of 0.9944 and an F1 score of 0.9971 using only a camera without the need for expensive sensors. Furthermore, it demonstrates an average processing time of 2.79 ms per module, ensuring real-time capability. This study contributes to precise quality inspection in smart factories by proposing a robust and scalable unsupervised inspection algorithm.