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International Joint Conference on Neural networks (IJCNN)

作者： Qingao Wang Xingang Wang Jinxiao Yang Yurun Bi Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Multimodal sentiment analysis(MSA) aims to synthesize multiple pieces of information, including textual, visual, and auditory information, to more accurately infer sentiment polarity. Due to the existence of complex and diverse affective causal relationships between different modalities, they interact with each other and constitute a multidimensional expression of human emotions. In the face of unbalanced data, i.e., when there are significant differences in the number of samples between different categories, the attention mechanism of the model tends to focus on spurious correlations in the training data, seriously impairing the model’s generalization ability and leading to bias, unbalanced prediction, and false correlation identification problems. To address the above problems, this study proposes a new multimodal fusion method based on the "within-sample sampling" and "cross-sample sampling" front door adjustment methods in causal inference strategies: the multi-head causal attention fusion method (MCAF). This method aims to capture potential causal relationships between different modalities through different attention heads, helping the model focus more on key causal features and avoid paying too much attention to false correlations in the training data. This helps improve the generalization performance of the model, make the decision-making process of the model more interpretable, and enhance the accuracy and robustness of the model for multimodal sentiment analysis tasks, making it more suitable for practical application scenarios.

关键词： Analytical models Sentiment analysis Visualization Correlation Neural networks Training data Predictive models

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HGTA: News Recommendation Based on Hierarchical Granular Semantic Embeddings and Threshold Attention

HGTA: News Recommendation Based on Hierarchical Granular Sem...

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International Conference on computer Supported Cooperative Work in Design

作者： Yiwen Li Zhenyu Yang Zhijun Li Xiaoyang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Personalized news recommendation helps users find interesting content among much news information. Most existing news recommendation techniques densely interact a user’s historical clicked news with candidate news to capture their reading interest. However, during this process, even if certain historical clicked news is weakly correlated with the candidate news, the model still interacts with it, generating more noise information when constructing user interests. Simultaneously, the news representation learning process using neural networks is a fine-grained to coarse-grained learning process; i.e., as the network deepens, the fine-grained original news information is easily lost. Therefore, to solve the above problems, this paper proposes a news recommendation model based on hierarchical granular semantic embeddings and threshold attention (HGTA). In HGTA, we propose a threshold attention module to filter unimportant historical clicked news by setting an attention score threshold, i.e., interacting only with historical clicked news that is strongly correlated with the candidate news when capturing user interests. Thus, the model attention is focused, and the noisy interaction information is reduced. Furthermore, when training the neural network, to prevent the loss of fine-grained information, we design a hierarchical granular information extraction module to capture both coarse- and fine-grained news features to better enrich the news semantics. Experiments conducted on the large real-world Microsoft News Dataset (MIND) show that our model achieves highly competitive results in terms of popular metrics. Our code is published at the following URL: https://***/liyiweneven/HGTA.

关键词： Uniform resource locators Training Representation learning Federated learning Semantics Neural networks Noise

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FSSA: Efficient 3-Round Secure Aggregation for Privacy-Preserving Federated Learning

arXiv

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arXiv 2023年

作者： Luo, Fucai Al-Kuwari, Saif Wang, Haiyan Yan, Xingfu College of Computer and Information Engineering Zhejiang Gongshang University Hangzhou China College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of New Networks Peng Cheng Laboratory Shenzhen China School of Computer Science South China Normal University Guangzhou China

Federated learning (FL) allows a large number of clients to collaboratively train machine learning (ML) models by sending only their local gradients to a central server for aggregation in each training iteration, without sending their raw training data. Unfortunately, recent attacks on FL demonstrate that local gradients may leak information about local training data. In response to such attacks, Bonawitz et al. (CCS 2017) proposed a secure aggregation protocol that allows a server to compute the sum of clients’ local gradients in a secure manner. However, their secure aggregation protocol requires at least 4 rounds of communication between each client and the server in each training iteration. The number of communication rounds is closely related not only to the total communication cost but also the ML model accuracy, as the number of communication rounds affects client dropouts. In this paper, we propose FSSA, a 3-round secure aggregation protocol, that is efficient in terms of computation and communication, and resilient to client dropouts. We prove the security of FSSA in honest-but-curious setting and show that the security can be maintained even if an arbitrarily chosen subset of clients drop out at any time. We evaluate the performance of FSSA and show that its computation and communication overhead remains low even on large datasets. Furthermore, we conduct an experimental comparison between FSSA and Bonawitz et al.’s protocol. The comparison results show that, in addition to reducing the number of communication rounds, FSSA achieves a significant improvement in computational efficiency. © 2023, CC BY-NC-ND.

关键词： Machine learning

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Overcoming Data Limitations and Cross-Modal Interaction Challenges in Medical Visual Question Answering

Overcoming Data Limitations and Cross-Modal Interaction Chal...

引用

International Joint Conference on Neural networks (IJCNN)

作者： Yurun Bi Xingang Wang Qingao Wang Jinxiao Yang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Medical Visual Question Answering (Med-VQA) is a sophisticated multimodal technology within the medical domain that utilizes natural language processing and artificial intelligence techniques. It serves as a tool in the medical field to provide information and answers, aiming to correctly respond to medical questions related to specific professional medical images. The primary goal is to assist patients in understanding their physical condition and provide decision support for healthcare professionals. While Generalized Visual Question Answering has seen significant advancements, challenges persist in the medical domain. Firstly, medical data has inherent limitations, such as the scarcity of annotated medical datasets and the complexity of medical images (including factors like noise, artifacts, and low contrast). Secondly, in terms of cross-modal fusion, there exists an intricate relationship between medical images and textual information. The complexity of the relationship between medical images and medical text, as well as the incorporation of global contextual details, pose challenges. To address these issues, this paper proposes the Feature-Enhanced Network with Conditional Mixing. This innovative approach aims to overcome the scarcity and complexity of medical images without the need for additional datasets. Simultaneously, to tackle the challenge of cross-modal fusion and information interaction and to address the complex relationship between medical images and text, we propose the Cross-Adaptive Fusion Network. Experimental results conducted on the VQA-RAD dataset demonstrate noticeable improvements in accuracy compared to mainstream Med-VQA models.

关键词： Visualization Accuracy Noise Neural networks Medical services Question answering (information retrieval) Data models

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JTEA: Implementing Semi-Supervised Entity Alignment Using Joint Teaching Strategies

JTEA: Implementing Semi-Supervised Entity Alignment Using Jo...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Kai Lu Jing Zhao Lichao Ding Zenghao Hao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Computer Science and Technology Shandong Engineering Research Center of Big Data Applied Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Entity alignment(EA) aims to identify equivalent entities in different knowledge graphs or data sources and establish correspondences between them. In recent years, embedding-based methods have made significant progress in the field of entity alignment, yet the shortage of adequate training data (i.e., seed entity pairs) continues to be a primary challenge. Traditional semi-supervised methods typically address this issue by generating potential mappings (i.e., pseudo-mappings) for unlabeled entities. However, these methods often suffer from the influence of erroneous potential mappings or overlook the uncertainty of potential mappings. Therefore, we propose a novel semi-supervised entity alignment method called JTEA. This method combines seed entity pairs with potential mappings through end-to-end joint training, providing strong guidance for model training. In constructing potential mappings, we propose a bidirectional matching strategy (BMS), which generates more detailed potential mappings by jointly adjusting decisions in different directions and uses a joint matching confidence score to characterize its uncertainty. Furthermore, we also introduce a method of matching diversity alignment selection, aiming to screen out more reliable entity alignments to further improve the performance and stability of the model. We conducted a large number of experiments on the benchmark dataset to fully validate the effectiveness of the JTEA method.

关键词： Training Uncertainty Soft sensors Noise Training data Knowledge graphs Data models Stability analysis Reliability Thermal stability

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Secure Precoding Design for RSMA Transmission in HAP-Assisted Non-Terrestrial networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Huang, Mengyan Gong, Fengkui Zhao, Jiahui Yin, Zhisheng Li, Xingwang Pham, Quoc-Viet Xidian University State Key Laboratory of Integrated Services Networks Xi'an710071 China Trinity College Dublin School of Computer Science and Statistics D02 PN40 Ireland Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo China

The non-terrestrial networks (NTNs) are recognized as a key component to provide cost-effective and high-capacity ubiquitous connectivity in the future wireless communications. To improve the spectral efficiency in NTNs, rate-splitting multiple access (RSMA) has attracted considerable attention. Nevertheless, the major issue of the considered system is to overcome security hurdles. In this paper, a robust secure precoding transmission scheme based on RSMA is proposed, which is the first time being used for high-altitude platform (HAP)- assisted satellite NTNs. The system features a low-earth orbit satellite that serves multiple ground user equipments (GUEs) using a rate-splitting strategy, while a decode-and-forward HAP relay assists the satellite in establishing communication links with the GUEs. Assuming imperfect channel state information, we formulate an optimization problem to maximize the sum secrecy rate (SSR) of GUEs. The problem jointly optimizes the precoding vectors for both the satellite and the HAP relay, subject to constraints on their maximum transmit powers and the achievable secrecy rates for each GUE. Subsequently, leveraging successive convex approximation and semidefinite relaxation, we present an approach to transform the non-convex constraints into convex ones, enabling the precoding vectors to be obtained through an iterative procedure. Simulation results show that the RSMA can achieve SSR of up to 3.25 bps/Hz and 3.75 bps/Hz compared to non-orthogonal multiple access and time division multiple access, respectively © 1967-2012 IEEE.

关键词： Satellite links

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Understanding the Bad Development Practices of Android Custom Permissions in the Wild

引用

IEEE Transactions on Dependable and Secure Computing 2025年

作者： Zhang, Xiaohan Yu, Zhiyuan Li, Xinghua Zhang, Cen Sun, Cong Zhang, Ning Deng, Robert H. Xidian University State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xi'an710071 China Ministry of Education Engineering Research Center of Big data Security China Washington University in St. Louis Department of Computer Science and Engineering St. LouisMO63130 United States Nanyang Technological University 639798 Singapore Singapore Management University 188065 Singapore

Android system provides application developers with the ability to define custom permissions, which serve to regulate the sharing of resources and functionalities with other applications. However, developers' improper development practices can render the permission mechanism ineffective, facilitating easy exploitation by attackers. This paper presents a comprehensive examination of the problematic practices surrounding custom permissions employed by developers, referred to as Bad Practices of Custom Permissions (BPCP issues). To accomplish this, we conducted an empirical study and identified nine common BPCP issue patterns that can lead to various adverse consequences, such as installation failures, crashes, and even component hijacking. To automatically identify these patterns of bad practices, we devised PERMEAGRE, a static analysis tool. Employing PERMEAGRE, we performed a large-scale analysis of 83,085 applications obtained from seven major app markets, aiming to detect instances of BPCP issues. The results revealed that more than 26% of the analyzed apps contained at least one issue, and a significant number of apps had garnered millions of downloads. Our analysis delved into the underlying causes of these issues. Consequently, this analysis sheds light on the potential threat landscape associated with bad practices in custom permissions, emphasizing the urgent requirement for effective mitigation strategies. © 2004-2012 IEEE.

关键词： Android custom permission empirical study static analysis

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A Survey of Graph-Based Resource Management in Wireless networks -Part I: Optimization Approaches

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IEEE Transactions on Cognitive Communications and Networking 2024年

作者： Dai, Yanpeng Lyu, Ling Cheng, Nan Sheng, Min Liu, Junyu Wang, Xiucheng Cui, Shuguang Cai, Lin Shen, Xuemin Dalian Maritime University School of Information Science and Technology Dalian116026 China Xidian University State Key Laboratory of ISN School of Telecommunications Engineering Xian710071 China Guangdong Provincial Key Laboratory of Future Networks of Intelligence Shenzhen518066 China University of Victoria Department of Electrical and Computer Engineering VictoriaBCV8P 5C2 Canada University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

The evolution of wireless communications and networking technologies has led significantly expansion of the dimensionality of network resources, which compels innovations in resource management. Graphs, a classic discrete mathematical tool, have long been widely used for resource management thanks to their capabilities to model complex relationships and interactions among elements in wireless networks. Recently, resource management over graphs embraces various advanced approaches of graph optimization and graph learning, aligned with evolving demands in future wireless networks. To better learn recent research landscape and explore important trends, this twopart survey provides a comprehensive overview for resource management via graph optimization and learning. Part I presents the fundamentals of graph optimization and provides a recent literature review of graph optimization for resource management in various wireless communication scenarios, including cellular networks, device-to-device communications, multi-hop networks, multi-antenna systems, edge caching and computing, and nonterrestrial networks. Part II gives the basics of graph learning and provides a state-of-the-art literature review of graph learning in wireless networks for addressing various resource management issues, covering power control, spectrum management, beamforming design, task scheduling, and aerial coverage planning. A discussion of technical challenges and future research directions is covered in Part II. © 2015 IEEE.

关键词： graph resource management Wireless networks

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FedSTDN: A Federated Learning-Enabled Spatial-Temporal Prediction Model for Wireless Traffic Prediction

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IEEE Transactions on Mobile Computing 2025年

作者： Li, Zhenyu Fu, Yuchuan Tian, Mengqiu Li, Changle Yu, F. Richard Cheng, Nan Xidian University State Key Laboratory of Integrated Services Networks Shaanxi 710071 China Xidian University Research Institute of Smart Transportation Shaanxi Xi'an710071 China Carleton University Department of Systems and Computer Engineering OttawaONK1S 5B6 Canada

Wireless Traffic Prediction (WTP) plays a significant role in achieving intelligent resource management for communication systems. However, WTP still faces challenges such as inaccurate prediction resulting from the complex spatial-temporal characteristics due to user mobility, high communication overhead caused by the complexity of the prediction model, and user privacy issues stemming from Centralized Learning (CL). To address the aforementioned issues, this paper proposes a WTP framework under the Federated Learning (FL) strategy called Federated Spatial-Temporal Dual-attention based Network (FedSTDN). Aiming at improving communication efficiency and simultaneously representing various wireless traffic patterns, a data augmentation-based clustering algorithm is adopted, which groups cells into different regions using a small augmented dataset, facilitating subsequent processing. To improve prediction performance, a local prediction model based on Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is proposed to capture the short- and long-term dependencies of traffic. Additionally, a novel Kolmogorov-Arnold Network (KAN) layer is introduced to replace the traditional Multi-Layer Perceptron (MLP) layer, further enhancing prediction performance. Simulations on two different real-world datasets verify the effectiveness and efficiency of FedSTDN. Compared to the well-performing baseline, the proposed FedSTDN achieves up to 32.83% and 24.30% improvements in Mean Square Error (MSE) and Mean Absolute Error (MAE) on the Milan dataset, respectively. For the Trentino dataset, FedSTDN achieves up to 17.25% and 5.86% improvements in MSE and MAE, respectively. © 2002-2012 IEEE.

关键词： Prediction models

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Handoff-Aware Distributed Computing in High Altitude Platform Station (HAPS)–Assisted Vehicular networks

arXiv

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arXiv 2023年

作者： Ren, Qiqi Abbasi, Omid Kurt, Gunes Karabulut Yanikomeroglu, Halim Chen, Jian State Key Laboratory of Integrated Service Networks Xidian University Shaanxi Xi’an710071 China Lab Department of Systems and Computer Engineering Carleton University OttawaON Canada Lab Department of of Systems and Computer Engineering Carleton University OttawaON Canada Department of Electrical Engineering Polytechnique Montréal Montréal Canada

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform station (HAPS) computing can be a promising addition to existing distributed computing frameworks because of its wide coverage and strong computational capabilities. In this regard, this paper proposes an adaptive scheme in a new distributed computing framework that involves HAPS computing to deal with the two problems of the IoV. Based on the diverse demands of vehicles, network dynamics, and the time-sensitivity of handoffs, the proposed scheme flexibly divides each task into three parts and assigns them to the vehicle, roadside units (RSUs), and a HAPS to perform synchronous computing. The scheme also constrains the computing of tasks at RSUs such that they are completed before handoffs to avoid the risk of computing interruptions. On this basis, we formulate a delay minimization problem that considers task-splitting ratio, transmit power, bandwidth allocation, and computing resource allocation. To solve the problem, variable replacement and successive convex approximation–based method are proposed. The simulation results show that this scheme not only avoids the negative effects caused by handoffs in a flexible manner, it also takes delay performance into account and maintains the delay stability. Copyright © 2023, The Authors. All rights reserved.

关键词： Vehicles

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