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3rd International Symposium on computer technology and Information science, ISCTIS 2023

作者： Wang, Tian Jiao Wu, Jie School of Computer and Software Engineering University of Science and Technology Liaoning Liaoning Anshan China

ISBN: (纸本)9798350325386

A social recommendation system based on graph neural networks is a system that uses social relationships between users to generate personalized recommendations. To improve recommendation accuracy, it is usually necessary to collect a large amount of user behavior data, which may lead to user privacy breaches and data misuse. Existing privacy protection methods often sacrifice recommendation effectiveness or increase computational costs. Therefore, this article proposes a federated social recommendation system (FRSRec) based on Rényi differential privacy. By combining hash encryption technology with Rényi differential privacy, user data privacy can be protected while ensuring recommendation effectiveness and computational efficiency. This article conducted experimental evaluations on three real datasets. The experimental results show that the FRSRec model achieves optimal results in both MAE and RMSE, demonstrating that the FRSRec model can effectively achieve privacy protection while improving recommendation quality. © 2023 IEEE.

关键词： Recommender systems

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ST-ConvLSGRU: A Radar Echo Extrapolation Method Based on LSTM and GRU 25

ST-ConvLSGRU: A Radar Echo Extrapolation Method Based on LST...

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Cheng, Hailang Cui, Mengmeng Xu, Xiaolong School of Computer Science and Software Nanjing University of Information Science and Technology Nanjing China Nanjing China

ISBN: (纸本)9798350330014

Severe convective weather poses a great threat to human production activities, and high-precision prediction results are of great value for accurate prevention and control of natural disasters. For example, the traditional short-term and imminent precipitation prediction methods mainly focus on the task of radar echo extrapolation. At present, the deep learning method to achieve radar echo extrapolation has become a research hotspot. These methods effectively improve the quality of prediction results from the perspective of data-driven and statistical. Although deep learning-based methods show excellent performance, they often fail to consider their high spatio-temporal resolution, resulting in high computing resource consumption and fuzzy extrapolation results. Radar echo extrapolation can be regarded as a spatio-temporal prediction problem. In this paper, this problem is modelled by a recurrent neural network (ST-ConvLSGRU), which uses LSTM to improve the GRU model by making gradient connections from the temporal and spatial dimensions to establish long-term dependencies. Also configure Convolutional Block Attention Modules (CBAM) and Depthwise Separable Convolution (DSC) to achieve lower model complexity when building multi-layer models. Experiment shows that ST-ConvLSGRU has better extrapolation prediction ability than other baseline methods. © 2023 IEEE.

关键词： Extrapolation

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MedNetV3: A Deep Learning Model for MRI Brain Tumor Detection with MobileNetV3 and Advanced Data Augmentation 21

MedNetV3: A Deep Learning Model for MRI Brain Tumor Detectio...

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21st International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2024

作者： Tariq, Hussain Ahmed Fengli, Zhang Salim Shakib, Muhamud Md Javed, Iram School of Information and Software Engineering University of Electronic Science and Technology of China China University of Agriculture Department of Computer Science Faisalabad Pakistan

ISBN: (纸本)9798331519254

In this paper, we present a novel deep learning model medical network (MedNetV3) developed for brain tumor detection. It incorporates advanced data augmentation techniques based on the MobileNetV3 architecture. MedNetV3 is developed for higher classification accuracy at the cost of slightly lower computational efficiency and is therefore suitable for clinical use. The proposed model overcomes the class imbalance problem and can make the model robust to the variations of MRI scans included in the extensive data augmentation methods, such as random rotations, flips. The proposed model trained and evaluated using a publicly available MRI dataset. It achieved a high Area Under the Curve (AUC) value of 0.99 for both the Precision-Recall and Receiver Operating Characteristic (ROC) curves. The results show that MedNetV3 is effective in correctly distinguishing brain tumors and therefore has the potential to be used in medical image classification. © 2024 IEEE.

关键词： Deep learning

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A Comparative Analysis of Query Generation Methods for IoT Middleware Evaluation 17

A Comparative Analysis of Query Generation Methods for IoT M...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： De Silva, Ravindi Zaslavsky, Arkady Loke, Seng W. Jayaraman, Prem Prakash Deakin University School of Information Technology Melbourne Australia Swinburne University of Technology Department of Computer Science and Software Engineering Melbourne Australia

ISBN: (纸本)9798350351637

The rapid expansion of Internet of Things (IoT) applications necessitates the demand for efficient IoT middleware platforms, especially Context Management Platforms (CMPs) for accessing real-time contextual information. Current IoT middleware evaluation methods lack accessibility, posing a significant challenge for standardized performance analysis of CMPs. This paper introduces a novel scenario-based context query generation approach for assessing the data retrieval performance of a CMP. The proposed approach enables scene creation using either IoT data or real-world images, employing both data-driven and image-driven scene generation methods. This paper presents the challenges, limitations, and applicability of each approach in scene generation through a qualitative evaluation. The scenes are transformed into scene graphs, enabling inference to identify situations. Both approaches generate queries reflecting dynamic real-world situations, allowing for random query loads to evaluate CMPs fairly. This paper validates the proposed approach using real-world IoT datasets and camera images with a focus on bicycle riding safety use cases. The evaluation provides insights into the contextual completeness of scene graph representations crucial for accurately capturing real-world situations. The proposed approach generates queries of varying complexity, with the only difference being the underlying mechanism of scenario generation, providing a streamlined and user-friendly method for evaluating the data retrieval performance of CMPs. © 2024 IEEE.

关键词： Middleware

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AI LipReader-Transcribing Speech from Lip Movements 6

AI LipReader-Transcribing Speech from Lip Movements

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ajitha, D. Dutta, Disha Saha, Falguni Giri, Parus Kant, Rohan School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Software Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Database Systems Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore Department of Information Security Vellore India

ISBN: (纸本)9798350306613

Lip Reading AI is a discipline that is rapidly changing and has numerous applications in security, accessibility and human-computer interaction. This paper proposes a model which combines Convolutional Neural Networks (CNNs) to capture spatial capabilities, Long Short-Term Memory (LSTM) networks to examine temporal dependencies, and an adaptive interest mechanism. Meticulous preprocessing of the MIRACL VC-l dataset addressing challenges including one of a kind lip moves and occlusions accompanied with the aid of transitioning this study effortlessly to LRS2 dataset to complement lexemic versatility is one of its key function. The effects verify its robustness throughout unique datasets with superior overall performance towards cutting-edge techniques. Ablation checks suggest the crucial significance of every element in phrases of improving lip analyzing accuracy. Our proposed model version additionally suggests flexibility in restricted and naturalistic language situations. © 2024 IEEE.

关键词： Attention Mechanism Convolutional Neural Networks (CNNs) Deep Learning Lip Reading Long Short-Term Memory (LSTM) LRS2 Dataset MIRACLVC-J Dataset

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Functional and Structural Fusion based Web API Recommendations in Heterogeneous Networks

Functional and Structural Fusion based Web API Recommendatio...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Wang, Xuanye Xi, Meng Yin, Jianwei Zhejiang University School of Software Technology Hangzhou China Zhejiang University School of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350304855

With the increasing development of cloud computing, a large number of innovative Mashup applications and Web APIs have emerged on the Internet. The expansion of technology and information presents a significant challenge to the discovery of Web APIs from multiple service ecosystems. Various Web API recommendation methods have been proposed in mashup creation, but most either assign equal weight to model factorization interactions or solely rely on requirements information for API recommendation. Unfortunately, these methods face several challenges, such as explicit and implicit dependencies among APIs, ambiguous API semantics, and the undervaluation of tail APIs. In this work, we propose a Functional and Structural Fusion Model (FSFM) based on Web API recommendation for Mashup creation. To address the above deficiencies, we first design the structural interaction component to encode the latent structural relationships in the heterogeneous network of Mashups and APIs and capture the topological structure signals between different Mashups and APIs. Then, we introduce the functional semantic component to generate text embedding vectors for Mashups and APIs, enhancing their requirement semantics at multiple levels of abstraction. Finally, we fuse the output vectors to obtain the list of candidate Web APIs. Experiences are performed on real datasets, and statistical results show that FSFM outperforms other state-of-the-art models in both overall and long-tail Web API recommendations. © 2023 IEEE.

关键词： Heterogeneous networks

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MESH-POLYPDET: SURFACE MESHES BASED POLYP DETECTION IN VIRTUAL COLONOSCOPY 18

MESH-POLYPDET: SURFACE MESHES BASED POLYP DETECTION IN VIRTU...

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18th International Conference on Interfaces and Human computer Interaction 2024, IHCI 2024, the 17th International Conference on Game and Entertainment Technologies 2024, GET 2024 and the 18th International Conference on computer Graphics, Visualization, computer Vision and Image Processing 2024, CGVCVIP 2024, Part of the 18th Multi Conference on computer science and Information Systems 2024, MCCSIS 2024

作者： Ma, Ming Sun, Chenyang Lei, Na Department of Graduate Computer Science and Engineering Katz School of Science and Health Yeshiva University United States School of Software Technology Dalian University of Technology China International School of Information Science & Engineering Dalian University of Technology China

ISBN: (纸本)9789898704580

Colorectal polyps can evolve into colon cancer over time. Early screening or detection of colon polyps using computer-aided detection (CAD) techniques along with removal of the polyps can lower the risk of colon cancer. As an alternative to optimal colonoscopy (OC), virtual colonoscopy (VC) has the advantage of non-invasiveness in the detection of colon polyps. Recent decades have witnessed rapid advancement in VC based polyp detection and false positive (FP) reduction techniques using machine learning or deep learning methods. However, the majority of existing methods are based on the computation using computed tomography (CT) images of patient data, which are in some cases very computationally expensive, particularly in the scenario of necessity of large dataset for model training. In this paper, we propose a novel automatic polyp detection method termed Mesh-PolypDet using the surface meshes of colorectal data. The initial detection of polyp candidates is performed on 3D colon surface meshes, and the subsequent FP reduction is carried out using the visual transformer and conformal mapping. Experiments are conducted to evaluate the performance of the proposed method in comparison with other existing methods. © 2024 Interfaces and Human computer Interaction. All Rights Reserved.

关键词： Conformal mapping

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FairCoRe: Fairness-aware Recommendation through Counterfactual Representation Learning

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IEEE Transactions on Knowledge and Data Engineering 2025年第07期37卷 4049-4062页

作者： Bin, Chenzhong Liu, Wenqiang Zhang, Feng Chang, Liang Gu, Tianlong Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Guilin University of Electronic Technology School of Business Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Guangzhou510632 China

Eliminating bias from data representations is crucial to ensure fairness in recommendation. Existing studies primarily focus on weakening the correlation between data representations and sensitive attributes, yet may inadvertently steer the user representations toward another potential bias direction of the target attribute. Furthermore, they often overlook the impact of user preferences on capturing sensitive information, incurring inadequate bias elimination. In this paper, we propose a Fair Counterfactual Representations (FairCoRe) learning framework, which aims to ensure the neutrality of representations among all bias directions. Firstly, we intervene on sensitive attributes to construct a counterfactual scenario. Then, two opposing attribute prediction tasks are respectively performed in ground-truth and counterfactual scenarios to encode sensitive information along different bias directions. Secondly, we design a bias-aware enhancement learning method that quantifies the respective correlation of user preferences and sensitive attributes to enhance sensitive information encoding. Finally, we introduce two mutual information optimization methods that optimize the representations to capture users' interests and disentangle sensitive factors. Moreover, we propose an attribute neutralization strategy that refines the learned representations, ensuring sensitive attribute neutrality. Extensive experiments demonstrate that our method achieves the optimal fairness and competitive accuracy compared to state-of-the-art methods. The source code is available at: https://***/FairCoRe2024/FairCoRe. © 1989-2012 IEEE.

关键词： Accuracy Mutual Information Correlation Data Visualization Medical Services Training Measurement Disentangled Representation Learning Semantics Recommender Systems Recommender System Recommendation Fairness Representation Learning Counterfactual Representations Potential Bias Mutual Information User Preferences Sensitive Attributes Counterfactual Scenario User Representation Time Complexity Generative Adversarial Networks Learning Objectives Demographic Groups Baseline Methods Recommender Systems Minimum Method Prediction Loss Preference Information Graph Convolution User Side Recommendation Model Counterfactual Thinking Maximum Mutual Information Representation Learning Methods Maximization Method Recommendation Accuracy Recommendation Method Recommendation Task Notions Of Fairness Negative Samples Mutual Information Estimation

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An attention-based channel estimation network with residual mechanism for 5G OFDM systems 4

An attention-based channel estimation network with residual ...

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4th International Conference on Electronic Communication, computer science and technology, ECCST 2024

作者： Jiang, Jinjing Huang, Weipeng Zhang, Xiao Yang, Kun Peng, Tong School of Information Engineering Zhejiang Ocean University China School of Computer Science and Software Engineering Shenzhen Institute of Information Technology China

Due to the significant increase in data transmission speed and gradual increase in Doppler frequency shift, channel estimation accuracy has become one of the most prioritized considerations in many cases. Specifically, we propose an attention-based channel estimation network with a residual mechanism, named TRCE-Net. In our design, the use of the multi-head attention mechanism at the input of the encoder is to obtain richer feature details. A linear series decoder module is implemented by leveraging multiple residual networks with skip connections. This novel design is capable of improving the channel estimation accuracy after deepening the network while effectively preventing degradation. To lessen the number of parameters that the network needs to learn, we introduce the bi-linear interpolation mechanism to replace the traditional up-sampling layer to mitigate both computational overhead and model complexity. According to the simulation results, our proposed TRCE-Net is superior to other state-of-the-art methods with regard to estimation accuracy and robustness to different maximum Doppler shifts, such as ResNet and InterpolateNet. © Published under licence by IOP Publishing Ltd.

关键词： Orthogonal frequency division multiplexing

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Semi-supervised Learning to Defer Algorithm for Lung Disease Diagnosis

Semi-supervised Learning to Defer Algorithm for Lung Disease...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Chen, Haoqing Jin, Bo Wang, Xiangfeng East China Normal University School of Computer Science and Technology Shanghai China Tongji University School of Software Engineering Shanghai China

ISBN: (纸本)9798350362480

Recent research highlights the advantages of leveraging complementary strengths of both human expert and model in decision-making processes. Learning to Defer(L2D) is proposed to build a system consisting of both and improve the performance of expert or model alone. For each instance, L2D algorithms search for the optimal decision-maker between model and human expert to improve the human-ai system accuracy. However, most previous work is based on the assumption that human predictions are available for every instance, which is unrealistic in practical scenarios due to the high expense of manual annotation. To address this, we consider L2D problem where human predictions are available only for a subset of image. We propose a multistep framework for this scenario and integrate a consistency regularization loss to learn expert capability from limited human predictions. The consistency regularization loss is designed to encourage the expert to learn intrinsic information and make similar predictions for similar instances. Empirical validation on real-world data from airspace opacity diagnosis shows that the proposed framework not only outperforms several competitive baselines but also enhances the performance of various L2D algorithms under constraints of minimal human predictions. © 2024 IEEE.

关键词： Self-supervised learning

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