PointGAT: Graph attention networks for 3D object detection

Zhou, Haoran; Wang, Wei; Liu, Gang; Zhou, Qing

doi:10.23919/icn.2022.0014

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…To create a 3D model of the gesture demonstration, we used the 3D Unity engine, which allows us to support 3D graphics, as well as visualize the architecture and model various types of interactive media [10].…”

Section: Software Implementation and Testing Results Of The Proposed ...mentioning

confidence: 99%

“…Then, a heuristic manta ray feature optimization (HMFO) technique is used to optimally select features by calculating the best fitness value. Paper [10] proposes a method for detecting 3D objects based on a point cloud and a graphical neural network (GNN) in combination with an attention mechanism. The paper [11] proposes a spatio-temporal GCN model for adaptive construction of spatio-temporal graphs that allow creating sign language recognition datasets based on a video skeleton.…”

Section: Comparative Analysis Of Existing Sign Language Interpretatio...mentioning

confidence: 99%

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Automatic sign language translation system using neural network technologies and 3D animation

Shovkovyi,

Grynyova,

Udovenko

et al. 2023

ITSSI

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Implementation of automatic sign language translation software in the process of social inclusion of people with hearing impairment is an important task. Social inclusion for people with hearing disabilities is an acute problem that must be solved in the context of the development of IT technologies and legislative initiatives that ensure the rights of people with disabilities and their equal opportunities. This substantiates the relevance of the research of assistive technologies, in the context of software tools, such as the process of social inclusion of people with severe hearing impairment in society. The subject of research is methods of automated sign language translation using intelligent technologies. The purpose of the work is the development and research of sign language automation methods to improve the quality of life of people with hearing impairments in accordance with the "Goals of Sustainable Development of Ukraine" (in the "Reduction of Inequality" part). The main tasks of the research are the development and testing of methods of converting sign language into text, converting text into sign language, as well as automating translation from one sign language to another sign language using modern intelligent technologies. Neural network modeling and 3D animation methods were used to solve these problems. The following results were obtained in the work: the main problems and tasks of social inclusion for people with hearing impairments were identified; a comparative analysis of modern methods and software platforms of automatic sign language translation was carried out; a system combining the SL-to-Text method is proposed and investigated; the Text-to-SL method using 3D animation to generate sign language concepts; the method of generating a 3D-animated gesture from video recordings; method of implementing the Sign Language1 to Sign Language2 technology. For gesture recognition, a convolutional neural network model is used, which is trained using imported and system-generated datasets of video gestures. The trained model has a high recognition accuracy (98.52%). The creation of a 3D model for displaying the gesture on the screen and its processing took place in the Unity 3D environment. The structure of the project, executive and auxiliary files used to build 3D animation for the generation of sign language concepts includes: event handler files; display results according to which they carry information about the position of the tracked points of the body; files that store the characteristics of materials that have been added to certain body mapping points. Conclusions: the proposed methods of automated translation have practical significance, which is confirmed by the demo versions of the software applications "Sign Language to Text" and "Text to Sign Language". A promising direction for continuing research on the topic of the work is the improvement of SL1-to-SL2 methods, the creation of open datasets of video gestures, the joining of scientists and developers to fill dictionaries with concepts of various sign languages.

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Section: Software Implementation and Testing Results Of The Proposed ...mentioning

confidence: 99%

Section: Comparative Analysis Of Existing Sign Language Interpretatio...mentioning

confidence: 99%

Automatic sign language translation system using neural network technologies and 3D animation

Shovkovyi,

Grynyova,

Udovenko

et al. 2023

ITSSI

View full text Add to dashboard Cite

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An Intrusion Detection Method Based on Attention Mechanism to Improve CNN-BiLSTM Model

Shou,

Li,

Wang

et al. 2023

The Computer Journal

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Security of computer information can be improved with the use of a network intrusion detection system. Since the network environment is becoming more complex, more and more new methods of attacking the network have emerged, making the original intrusion detection methods ineffective. Increased network activity also causes intrusion detection systems to identify errors more frequently. We suggest a new intrusion detection technique in this research that combines a Convolutional Neural Network (CNN) model with a Bi-directional Long Short-term Memory Network (BiLSTM) model for adding attention mechanisms. We distinguish our model from existing methods in three ways. First, we use the NCR-SMOTE algorithm to resample the dataset. Secondly, we use recursive feature elimination method based on extreme random tree to select features. Thirdly, we improve the profitability and accuracy of predictions by adding attention mechanism to CNN-BiLSTM. This experiment uses UNSW-UB15 dataset composed of real traffic, and the accuracy rate of multi-classification is 84.5$\%$; the accuracy rate of multi-classification in CSE-IC-IDS2018 dataset reached 98.3$\%$.

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An Enhanced Group Abnormity Detection Model in Social Networks Through Multi-Scale Knowledge Graph-Based Deep Learning

Zhang,

Zhang

2024

J CIRCUIT SYST COMP

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In area of group abnormity detection, deep learning-based methods have received more attention in recent years. But existing research works mostly failed to well-integrate complex graph-level characteristics of social network context into feature representation. To deal with this issue, this paper proposes an enhanced group abnormity detection model in social networks through multi-scale knowledge graph-based deep learning. First, multi-scale knowledge graph is formulated to extract and field knowledge as the fundamental support. Then, a recurrent neural network structure is utilized to express graph-level features, so as to aggregate the information of the surrounding nodes. For output of group abnormity detection results, the interaction information among users is further considered, and the correlation analysis of abnormal behaviors is conducted based on the probability expression. On this basis, probabilistic soft logic is used to express the relationship between abnormal association users and their behavior occurrence. Finally, a set of identification rules are developed to discover abnormal association users, and those who have the same association probability value are identified as abnormal user groups. Experiments are conducted on social network dataset to evaluate performance of the proposed model. The results reveal that it can make group abnormity identification with higher accuracy compared with baseline methods.

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PointGAT: Graph attention networks for 3D object detection

Cited by 3 publications

References 40 publications

Automatic sign language translation system using neural network technologies and 3D animation

Automatic sign language translation system using neural network technologies and 3D animation

An Intrusion Detection Method Based on Attention Mechanism to Improve CNN-BiLSTM Model

An Enhanced Group Abnormity Detection Model in Social Networks Through Multi-Scale Knowledge Graph-Based Deep Learning

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