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Incremental and Flexible Extraction of Parallel Corpus from the Web 3

Incremental and Flexible Extraction of Parallel Corpus from ...

引用

3rd International Conference on Artificial Intelligence and Computer Information technology, AICIT 2024

作者： Liu, Xiaofeng Zhen, Yucheng Li, Dongyang School of Software Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350365443

Extracting parallel corpus from the web at scale is important for machine translation and other multilingual processing tasks. The paper proposes an incremental and flexible web parallel corpus extraction approach, which incrementally updates language text length statistics for domains by continuously downloading, scanning and analyzing Common Crawl's web crawling archives, and extracting parallel corpus. For any given interested language pairs, the web sites to be crawled are determined based on language text length statistics for domains and crawled according to the target language pairs, and non-target domains and links are discarded. The paper also proposes a new intermediate method for sentences alignment, which globally aligns sentences based on semantic similarity within a multilingual domain. Experiments have shown that: (1) our extraction method can continuously and flexibly extract interested parallel corpus via specifying target language pairs;(2) the proposed intermediate method is significantly better than the global method in terms of alignment efficiency, and can complete some alignments that cannot be done by the local method;(3) out of 6 language directions, the extracted parallel corpora are superior to existing web open source parallel corpora in 4 low-medium resource directions and close to the best available web open source parallel corpus in 2 high-resource directions. © 2024 IEEE.

关键词： Semantics

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Research on Weather Process Similarity Based on 3D CNN Autoencoder 3

Research on Weather Process Similarity Based on 3D CNN Autoe...

引用

3rd IEEE International Conference on Electrical engineering, Big Data and Algorithms, EEBDA 2024

作者： Cheng, Xingguo Zhou, Yajing Li, Zeping School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798350380989

This article proposes a weather process similarity and retrieval algorithm based on a 3D CNN Autoencoder. It uses 3D CNN to extract features of weather elements in multi-dimensional space. The encoder outputs a one-dimensional vector of size 512. The cosine similarity between two weather processes is then calculated based on the two one-dimensional vectors, representing their similarity. Experimental data demonstrates the effectiveness of this algorithm. By searching historical weather processes based on the forecast date and referring to the evolution of historical weather processes, it provides positive guidance for current weather forecasts. © 2024 IEEE.

关键词： Autoencoder cosine similarity decoder encoder weather process similarity

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Lossless compression algorithm of medical image based on weighted template matching prediction 4

Lossless compression algorithm of medical image based on wei...

引用

4th International Conference on Biomedicine and Bioinformatics engineering, ICBBE 2024

作者： Zhao, Zhiwei Wan, Lin School of Software Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781510682443

In order to enhance the transmission and storage efficiency of medical images, this paper proposes a lossless compression algorithm designed for medical imaging. The algorithm processes input images using an improved weighted template matching predictor, yielding error calculation results and symbol matrices. These outcomes are then entropy-encoded using PNG and Deflate encoding for the predictive results, achieving compression. Experimental verification was conducted using a test dataset comprised of cross-sectional images extracted from multiple scan sequences. The results indicate that the proposed algorithm achieves an average compression ratio of 1:3.00926, outperforming other lossless compression algorithms based on linear prediction, such as PNG and TIFF. © 2024 SPIE.

关键词： Template matching

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Design Based on Industrial Material Inspection and Defect Analysis System 5

Design Based on Industrial Material Inspection and Defect An...

引用

5th International Conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2024

作者： Zhang, Sijie Chengdu University of Information Technology School of Software Engineering Chengdu China

ISBN: (纸本)9798331506612

The industrial material inspection and defect analysis system is designed for material quality control and fault detection in industrial production. Physical properties of the system through the acquisition of steel as the experimental data set, in combination with particle swarm algorithm to optimize ParticleSwarmOptimization, (PSO) algorithm of BP neural network (BackpropagationNeuralNetwork) artificial intelligence, the analysis of testing data and forecast, Ability to quickly identify potential defects or failure hazards in materials. At the same time, this paper also studies the state transition of steel in the process of industrial production, and proposes the state monitoring based on FiniteStateMachine (FSM) to provide timely warning and solutions for the fault types predicted by the algorithm. This research is mainly to solve the problem of automatic defect monitoring and intelligent early warning of key manufacturing links in the industry, and proposes the combination of PSO-BP neural network algorithm and finite state machine, so as to improve the detection accuracy and problem-solving efficiency, reduce the maintenance cost and production interruption risk, and ensure the safety and reliability of industrial materials. © 2024 IEEE.

关键词： Ability testing

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Text-to-image Retrieval Based on Zero-shot Transfer Learning with CLIP Model and Vector Database 11

Text-to-image Retrieval Based on Zero-shot Transfer Learning...

引用

11th IEEE International Conference on Behavioural and Social Computing, BESC 2024

作者： Xie, Junfeng Chen, Junying School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798331531904

With the continuous increase in the number of internet users, the explosion of multimodal data on the web has led to a growing demand for image retrieval. Current text-to-image retrieval systems often rely on keyword matching, which frequently fails to accurately capture users' retrieval needs. This work proposes a text-to-image retrieval method based on zero-shot transfer learning with the CLIP model and vector database, designed to be applied in text-to-image retrieval systems. By leveraging the semantic alignment capability of the CLIP model for text and images, this study effectively addresses the semantic gap problem in text-to-image retrieval. Additionally, the method utilizes the Milvus database for efficient storage and retrieval of image feature vectors, thereby enhancing system stability. Experimental results on the Flickr30k dataset demonstrate that this method improves the accuracy of text-to-image retrieval, verifying its effectiveness. Furthermore, this work extends the proposed method by incorporating the iFLYTEK Spark Cognitive Model to support image retrieval using Chinese text descriptions, further broadening the system's applicability and reliability. © 2024 IEEE.

关键词： Zero-shot learning

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Application of Video Resource Caching Method based on Graph Neural Network Recommendation Algorithm

Application of Video Resource Caching Method based on Graph ...

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Li, Ziheng School of Software Liaoning Engineering Technology University Liaoning Huludao China

ISBN: (纸本)9798350383348

The application of caching method plays an important role in intelligent video resources, but there are problems of inefficient and inaccurate caching process. The traditional normal caching method cannot solve the problem of caching video resources in intelligent video resources, and the caching effect is not ideal. Therefore, this paper proposes the application of video resource caching method based on graph neural network recommendation algorithm and analyzes the video resource caching method. Firstly, the graph structure data theory is used to locate the influencing factors, and the indicators is divided according to the requirements of the application of the cache method to reduce the interference factors in the application of the cache method. Then, the graph structure data theory is used to form the application scheme of the video resource caching method of the graph neural network recommendation algorithm, and the application results of the caching method is comprehensively analyzed. The MATLAB simulation results show that under certain evaluation criteria, the graph neural network recommendation algorithm is better than the traditional ordinary caching method in terms of the application accuracy of the video resource caching method and the time of the influencing factors of the caching method. © 2024 IEEE.

关键词： caching method application graph neural network recommendation algorithm graph structure data theory Intelligent caching Video positioning

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An interactive procedural modeling method for buildings based on template matching algorithm 4

An interactive procedural modeling method for buildings base...

引用

4th International Symposium on Artificial Intelligence and Intelligent Manufacturing, AIIM 2024

作者： Wang, Min He, Xiaoxi Guo, Yunhui Hu, Liang Chen, Jinwei Ren, He School of Software Engineering Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331541729

With the rapid development of computer graphics technology, procedural modeling is a method to efficiently generate virtual 3D models, and complex geometric models are generated through certain algorithm rules. Traditional procedural modeling techniques have a high degree of automation, but the rule design is cumbersome, and it is difficult to adapt to different styles or highly customized architectural needs, and the flexibility and editability are limited. In this paper, a procedural generation method of urban building single model based on template matching algorithm is proposed, which introduces CGA (Computer Generated Architecture) shape grammar to guide the layout generation of building facade structure. The creation process of the model is to create a free-form white mold as the building basic model, then create the building facade with the custom constraint layout of the subsidiary structure, and finally combine the white mold and the facade structure through the template matching algorithm proposed in this paper. The entire creation process is parametrically controlled, mapping from the building layer to the building parameters, and by adjusting the parameters, it is possible to generate a 3D building model that meets the needs and has a variety of appearances and is realistic. Experimental results show that this method can realize rapid building modeling and significantly improve the modeling efficiency. © 2024 IEEE.

关键词： Building Information Model

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Build a Dataset of Query-Code Pairs Based on CodeSearchNet for Code Search 36

Build a Dataset of Query-Code Pairs Based on CodeSearchNet f...

引用

36th International Conference on software engineering and Knowledge engineering, SEKE 2024

作者： Xu, Yang Chen, Chaowei School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)1891706594

CodeSearchNet is a widely used dataset of comment-code pairs for training code search models. However, code search models trained on the datasets of comment-code pairs usually have lower performance in real-world applications because of the semantic gap between comments and real queries. The existing datasets of query-code pairs are smaller in size and lower in quality than CodeSearchNet for code search research. In this paper, we build a Python dataset of query-code pairs, named QCSN, from CodeSearchNet using a text generator. We train the text generator by fine-tuning the pre-trained model PLBART on StaQC’s query-code pairs. To address the issue of domain shift between StaQC and CodeSearchNet, we exploit a deep adversarial neural network that trains a feature extractor to extract domain-invariant features. The experimental results show that the MRR of the typical code search model trained on QCSN improves by over 18.5%. The ablation study indicates that the deep adversarial neural network module improves the quality of the generated dataset. Further evaluations reveal that the generated queries closely match the real queries in terms of lexical overlap and semantic similarity, accurately expressing the functionality of the source code. © 2024 Knowledge Systems Institute Graduate school. All rights reserved.

关键词： Structured Query Language

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Research on 3D cloud modeling based on the combination of meteorological data and multiple noises 5

Research on 3D cloud modeling based on the combination of me...

引用

5th International Conference on Computers, Information Processing and Advanced Education, CIPAE 2024

作者： Ren, He He, Xiaoxi Hu, Liang Guo, Yunhui Chen, Jinwei Wang, Min The School of Software Engineering Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798331527662

In order to create a more realistic cloud simulation environment, volumetric cloud modeling combined with real meteorological data is usually considered. At present, cloud simulation based on real meteorological data basically adopts physical modeling methods, such as solving fluid dynamics equations and particle system modeling, etc. However, these methods involve a large amount of calculation for solving calculus equations, so they are often limited in the scale of drawing, and it is difficult to perform real-time simulation in large-scale scenes. The authenticity and real-time performance of volumetric cloud simulation still need to be further improved. Therefore, through the analysis of existing research, a three-dimensional volumetric cloud construction method based on meteorological data is proposed. The specific method is to generate a two-dimensional cloud map based on humidity data, and then combine the cloud map with an improved height density function to make the shape and density of the cloud change with height and meteorological data, and combine it with multiple noises to construct the shape and details of the cloud. Finally, the Raymarching algorithm is used for rendering, and finally a large-scale volumetric cloud that matches the meteorological data is constructed. This paper combines meteorological data with non-physical modeling methods effectively. Experimental results show that the volumetric cloud modeled by this method matches the cloud map generated by meteorological data, and is more realistic in shape and appearance. The cloud body modeling results have more realistic details without distortion. © 2024 IEEE.

关键词： Cloud platforms

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MolCLW: Molecular Contrastive Learning With Learnable Weighted Substructures

MolCLW: Molecular Contrastive Learning With Learnable Weight...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Jiahe Du, Wenjie Wang, Yang University of Science and Technology of China School of Software Engineering Suzhou China

ISBN: (纸本)9798350386226

Predicting molecular properties is crucial across scientific and industrial domains such as drug discovery and material science. Contrastive learning has gained prominence as an effective method. However, routine strategies often overlook substructure information and run the risk of guiding the model to learn incorrect knowledge due to molecular property changes during augmentation. To address these issues, we propose a novel molecular contrastive learning model named Molecular Contrastive Learning with Learnable Weighted Substructures (MolCLW). Our model aims to enhance the learning of molecular substructures by comparing molecular representations generated at the molecular-level and substructure-level. Meanwhile, a transformer encoder module is dedicated to learning the weight scores of substructures, enabling the model to adjust the similarity level between positive pairs based on the varying masked substructures. In comparison to baselines, MolCLW demonstrates an averaged 2.2% improvement in ROC-AUC on 6 classification benchmarks and an averaged 4.3% decrease in error on 5 regression benchmarks. Furthermore, during the finetuning process, the model can attend to the substructure relevant to the downstream task by adjusting the weights of different substructures. The visualization results establish a link between downstream targets and substructure function, providing valuable insights and strong support for drug discovery, chemical reactions, and research. © 2024 IEEE.

关键词： Learnable weighted substructures Molecular contrastive learning Molecular property prediction

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