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Causal Fusion for Recommender System 4

Causal Fusion for Recommender System

引用

4th international conference on Computing and Data science, CONF-CDS 2022

作者： Sun, Liwen Zhang, Chi Su, Zilin Zhang, Feiran Zhou, Yuen Department of Computer Science University of Illinois at Urbana-Champaign Urbana-Champaign United States School of Electronic Engineering Xidian University Xi'an China Faculty of Science University of Melbourne Melbourne Australia Department of Art & Science University of Toronto Toronto Canada Faculty of Art and Science Queen's University Kingston Canada

Recommender system suffers from huge selection bias from users, which makes utilizing causal inference to solve this problem becomes a necessary problem. However, traditional rating model only utilizing biased dataset and unbiased estimator cannot remarkably be improved. thus, we fused the unbiased dataset into training model and combine it with jointly learning method to better improve the original baselines. Extensive experiments verify that the proposed doubly robust joint leaning method after fusing unbiased, missing rating data can significantly outperform the state-of-the-art methods and can dramatically reduce the error from rating model with the increasing number of unbiased data. © Published under licence by IOP Publishing Ltd.

关键词： Recommender systems

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R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks 4

R2V-PPI: Enhancing Prediction of Protein-Protein Interaction...

引用

4th international conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Oviya, I.R. Sravya N, Shanmukha Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350343670

Proteins play a crucial role in living organisms, and understanding protein-protein interactions is vital for comprehending their functions and aiding drug discovery. In recent years, advanced deep learning models have been developed to predict protein-protein interactions from sequences. However, these large-scale approaches often suffer from deficiencies, resulting in both false positive and false negative predictions. Traditional encoding methods struggle to effectively represent the semantic meaning of individual residues in protein sequences and their impact on neighbouring residues. To overcome these deficiencies, we propose a novel method named R2V-PPI (Residue-to-Vector embeddings based Protein-Protein Interaction Framework) that effectively represents protein sequences and predicts protein-protein interactions. Inspired by Word2Vec, R2V-PPI learns informative representations of individual amino acid residues, capturing the context and relationships between them in raw protein sequences. these representations provide valuable input features for a downstream deep learning model. Our methodology offers a robust and versatile approach for predicting protein-protein interactions, especially when structural information is limited or unavailable. Evaluation on the S. cerevisiae dataset demonstrates that R2V-PPI outperforms existing state-of-the-art machine learning models. © 2024 IEEE.

关键词： Deep Learning Machine Learning Protein Feature Encoding Protein Protein interaction Protein sequence

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Bridging the Resolution Gap in Remote Sensing: A Comparative Analysis of Deep Learning Models for Real-World Single Image Super-Resolution 4

Bridging the Resolution Gap in Remote Sensing: A Comparative...

引用

4th international conference on Digital Futures and Transformative Technologies, ICoDT2 2024

作者： Shami, U.A. Khan, B. Zafar, Z. Fraz, M.M. School of Electrical Engineering and Computer Science Islamabad Pakistan

ISBN: (纸本)9798350391862

In the field of remote sensing, the demand for improved spatial resolution has persisted across multiple domains despite the exponential rise in satellite images. Single Image Super-Resolution (SISR) aims to bridge this gap, and many state-of-the-art (SOTA) deep learning models have been utilized for this purpose. However, the generation of low-resolution (LR) image pairs from high-resolution (HR) images is questionable, as most datasets are synthetically generated either by bicubic down-sampling or by modeling degradation with blur kernels and imaging noises. therefore, SISR models trained on such datasets cannot perfectly model real-world scenarios. this study presents a comparative analysis of different CNN-based, GAN-based, Transformer-based SR models, and Image-to-Image translation models for the SISR task. these models have been trained on the WorldStrat dataset, a real-world dataset comprising LR and HR pairs from two different satellites, Sentinel-2 and SPOT 6/7, respectively, with a resolution ratio of approximately 6. Our experiments demonstrate that both Image-to-Image translation models and GAN-based SISR models yield better qualitative results compared to CNN-based and Transformer-based SISR models. © 2024 IEEE.

关键词： Satellite imagery

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Research on real-time denoising algorithm for hydrological data based on suspicious data caching mechanism 24

Research on real-time denoising algorithm for hydrological d...

引用

4th international conference on artificial Intelligence, Big Data and Algorithms, CAIBDA 2024

作者： Song, Xueying Wang, Xiaofeng Wei, Na Xie, Jiancang School of Civil Engineering and Architecture Xi'an University of Technology Shaanxi Xi'an China Apparel and Art Design College Xi'an Polytechnic University Shaanxi Xi'an China School of Water Resources and Hydropower Xi'an University of Technology Shaanxi Xi'an China

ISBN: (纸本)9798400710247

Real time remote data collection and precise processing are the foundation for smart water conservancy to carry out business applications. this paper studies real-time denoising algorithms for hydrological monitoring data in the context of higher requirements for real-time and accurate data processing in smart water conservancy. Based on the analysis of current data noise types and characteristics, as well as the main denoising algorithms and their advantages and disadvantages, a real-time denoising algorithm based on suspicious data caching mechanism is proposed. through theoretical analysis and practical application verification, this algorithm is progressiveness in terms of time consumption, space consumption, accuracy and adaptability. © 2024 Copyright held by the owner/author(s).

关键词： Data accuracy

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A Comparative Review of AI-Generated vs Real Images and Classification Techniques 4

A Comparative Review of AI-Generated vs Real Images and Clas...

引用

4th international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Muthaiah, U. Divya, A. Swarnalaxmi, T.N. Vidhyasagar, B.S. Amrita School of Computing Amrita Vishwa Vidhyapeetham Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529635

Images often play a crucial role in art, data analysis, and communication. Images visually represent the physical world. With the development of AI, differentiating between AI-generated and real images has become very difficult over time. So in this review, we have focused on the techniques for forming images, which focus on real images and the images created by Stable diffusion and other such techniques. this paper aims to identify the differences between AI-generated and real images by understanding how they are generated, processed, and represented. A study on CNN techniques that can be utilized to classify AI and real images has been mentioned. the specific architecture of CNN such as VGG, EfficientNet, and ResNet has been explored to find their effectiveness in the classification. In addition, an overview of available datasets has also been provided. this study provides a review of studies that classify AI or real images. this employs approaches such as StyleGAN, DALL-E, and Stable Diffusion. the difficulties that these generation techniques present to the conventional models which include CNN, DenseNet, ResNet, and VGG are also interrogated. While ResNet-50 and Swin-T models achieve good merit in practical applications, classifying images produced by advanced techniques such as ProGAN models is still a challenge. Our analysis incorporates an evaluation and comparison of classification models and their performance as well as directions for future work including the application of EfficientNet and hybrid models aimed at improving propositions on differentiating real and fake images by their accurate classification. © 2024 IEEE.

关键词： Gallium nitride

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GTT: Visual Tracking with Gaussion Transformer 24

GTT: Visual Tracking with Gaussion Transformer

引用

4th international conference on artificial Intelligence, Automation and High Performance Computing, AIAHPC 2024

作者： Ding, Shuyan Song, Chunlin Yao, Yu Guo, Jianhui Li, Lunbo School of Electronic and Optical Engineering Nanjing University of Science and Technology China Hangzhou Zhiyuan Research Institute Co. Ltd China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798400710049

Recently, Transformer has been successfully deployed in VOT tasks due to its remarkable capability of capturing dependencies between each pixel, thus solving the long-range dependency problem caused by CNNs. However, Transformer considers each pixel equally due to the global attention mechanism, which means that the target and background pixels are treated with equal weight, resulting in the vulnerability of the model to interference from the background. therefore, we propose a novel Transformer architecture integrated with Gaussian prior, named Gaussian Transformer, to solve the above issue. Besides, we incorporate template features into the search image features through the Deformable Fusion Attention module, called DFA for short, to enhance the feature representation and further improve the accuracy of the model. Finally, by combining Gaussian Transformer and the DFA module, we present a simple and robust tracker, called GTT. Extensive experiments and comparisons with many competing trackers on challenging benchmarks, OTB100, UAV123, VOT2018, and VOT2019, while running at over 50 FPS, show that our tracker is better than state-of-the-art trackers in terms of tracking performance. the source code will be available soon. © 2024 Copyright held by the owner/author(s).

关键词： Gaussian distribution

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Point cloud data repair based on Linear Maximum Entropy algorithm 4

Point cloud data repair based on Linear Maximum Entropy algo...

引用

4th international conference on Geoscience and Remote Sensing Mapping, GRSM 2022

作者： Jia, Xiangping Chen, Tianwei Liu, Peng Liu, Jin Surveying and Mapping Science and Technology Guilin University of Technology Guilin541006 China

ISBN: (数字)9781510662179

ISBN: (纸本)9781510662162

Aiming at the problem of missing data after point cloud data filtering, this paper proposes a point cloud repair method based on the principle of Linear Maximum Entropy, which converts the Maximum Entropy Model into a linear model, and then uses linear programming to solve the model. the mathematical expectation and variance in the elevation value is ued to establish linear constraints, and the weight coefficient of the sampling point is solved by the maximum entropy value to determine the elevation value of the fixed point, so as to complete the point cloud repair. By comparing with Maximum Entropy Method and Inverse Distance Weight method, the feasibility of Linear Maximum Entropy Model in point cloud data repairs is discussed. the results show that the point cloud data repaired by the Linear Maximum Entropy Model is more accurate, and a high-quality model can be established. © 2023 SPIE.

关键词： Linear programming

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2nd international Workshop on the art, science, and engineering of Quantum programming (QP 2024)

Programming Companion 2024 - Proceedings of the 8th Internat...

引用

programming Companion 2024 - Proceedings of the 8th international conference on on the art, science, and engineering of programming 2024年 XV-XVI页

作者： Arcaini, Paolo Yue, Tao Zhao, Jianjun National Institute of Informatics Japan Beihang University China Kyushu University Japan

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GDformer: A lightweight decoder for efficient semantic segmentation of remote sensing Urban Scene Imagery 4

GDformer: A lightweight decoder for efficient semantic segme...

引用

4th international conference on Computer Vision and Data Mining, ICCVDM 2023

作者： Liu, Bing Zhong, Zhaohao School of Computer Science and Engineering Changchun University of Technology Changchun130012 China

ISBN: (纸本)9781510674448

Semantic segmentation of remote sensing urban scene imagery is a dense prediction task, which has been applied to the land-cover or land-use category. However, the dimension of remote sensing image is huge, which will result in the huge computation cost. In order to reduce the computation cost, a common method is to design a lightweight decoder to achieve a good trade-off between accuracy and computation cost. For this purpose, we design a lightweight transformer-based decoder GDformer. the GDformer consists of our proposed Global Value Transformer and Dynamic feature fusion module. the Global Value Transformer can extract the global semantic feature and the Dynamic feature fusion module can fuse the local feature and global semantic feature dynamic to capture the local-global context with a good trade-off, and the local-global context has been proved is necessary for the semantic segmentation of remote sensing. Extensive experiments prove that our proposed method can achieve a good trade-off between and state-of-the-art performance. © 2024 SPIE.

关键词： Remote sensing

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EAST Experimental Data Gateway Based on RESTful and Microservice Architecture 4

EAST Experimental Data Gateway Based on RESTful and Microser...

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4th international conference on Computer engineering and Application, ICCEA 2023

作者： Fan, Dixin Wang, Feng He, Jiaxin Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China

ISBN: (纸本)9798350347548

this paper designs and implements the EAST ex-perimental data gateway based on RESTful and microservice architecture as a centralized control point for traffic and services. Different from the single-architecture UDA method with high complexity and limited scalability, our method aims to realize the decoupling of data services at the business logic and technology stack levels, thereby improving the usability and maintainability of data services, and Unified provision of protocol conversion and adaptation, service routing and governance, traffic management and fault tolerance, log recording and monitoring and other functions. To achieve this goal, this paper designs and implements the EAST experimental data gateway based on the technical solution of the combination of Nginx and Flask backend frame-work. On the one hand, the gateway provides a unified RESTful experimental data query service for various external terminal devices, enabling cross-platform data access without maintaining various programming language clients. On the other hand, the gateway internally accesses microservices implemented by different technology stacks to realize the decoupling of data access services. In order to further improve the maintainability of the system, this paper also implements registration discovery and health detection of microservices through Consul, and implements distributed link tracing based on Elastic APM to record relevant logs and display global topology relationships. the test results show that the EAST experimental data gateway meets the established functional requirements and performance requirem-ents. © 2023 IEEE.

关键词： Fault tolerance

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