检索结果-内蒙古大学图书馆

16th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discover, CyberC 2024

作者： Deng, Qinghua Chen, Lanxiang Zhu, Yizhao Mu, Yi City University of Macau Faculty of Data Science 999078 China Hanshan Normal University School of Computer and Information Engineering Chaozhou521041 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou China

ISBN: (纸本)9798331506896

Symmetric searchable encryption (SSE) is crucial for promoting cloud storage. It enables users to encrypt private data and outsource it to untrusted servers while retaining query capabilities. To prevent the cloud from obtaining users' private data, researchers have proposed numerous schemes to protect users' privacy. However, the issue of volume hiding has received less attention. Recent studies have demonstrated that volume leakage can be exploited to infer query *** mitigate the aforementioned leakage problem, we introduce an efficient volume-hiding SSE scheme supporting conjunctive queries, denoted as VHXT. The primary strategy for implementing volume hiding comprises two aspects: first, employing a bitmap-based inverted index to obscure the query response length of the least frequent keyword, and second, introducing laconic PSI for efficient membership testing. The laconic PSI enables our scheme to achieve approximately optimal overhead. Moreover, security analysis and performance evaluation demonstrate that VHXT effectively prevents volume leakage with minimal overhead. © 2024 IEEE.

关键词： Information leakage

来源：评论

学校读者我要写书评

暂无评论

Improving Machine Learning based ASD Diagnosis with Effective Feature Selection

Improving Machine Learning based ASD Diagnosis with Effectiv...

引用

International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Zhino Safahi Ehsan Azimipour Shima Saedi Sadegh Sulaimany Department of Computer Engineering Social & Biological Network Laboratory (SBNA) University of Kurdistan Sanandaj Iran

Autism spectrum disorder (ASD) poses major public health challenges due to its increasing prevalence and the critical need for early intervention to improve outcomes. This study investigates machine learning approaches for automated screening and diagnosis of ASD, leveraging questionnaire and demographic data from large cohorts of toddlers (n=1,054) and adults (n=704). After data cleaning and preprocessing, an efficient sequential feature selection technique identifies subsets of the most predictive features. Several classification algorithms, including neural networks, random forests, support vector machines, and logistic regression, are developed using these refined datasets and rigorously evaluated through 10-fold cross-validation. The models demonstrate excellent predictive performance, achieving 100% accuracy in identifying ASD in both toddlers and adults. This signifies that the selected feature sets are highly relevant for capturing autistic traits across age groups, although manifestations vary developmentally. Compared to existing methods, the models show substantially improved accuracy, highlighting machine learning’s potential for accessible and scalable assessment of ASD. Additional validation on bigger real-world datasets would further establish generalizability. Overall, this study demonstrates that machine learning can assist evidence-based clinical decision-making for ASD diagnosis. Promising future work should focus on enhancing model interpretability, translating these technologies responsibly to bolster early intervention, and evaluating impact on patient outcomes through rigorous trials. Machine learning shows immense promise in aiding the identification and timely treatment of this import yet often underdiagnosed public health issue.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Resource allocation in UAV assisted air ground intelligent inspection system

引用

Cognitive Robotics 2022年 2卷 1-12页

作者： Zhang, Zhuoya Xu, Fei Qin, Zengshi Xie, Yue School of ordnance science and technology Xi'an Technological University Xi'An China Engineering Laboratory The State and Provincial Joint Engineering Lab. of Advanced Network Monitoring and Control Xi'An China School of Computer Science and Engineering Xi'an Technological University Xi'An China

With the progress of power grid technology and intelligent technology, intelligent inspection robot (IR) came into being and are expected to become the main force of substation inspection in the future. Among them, mobile edge computing provides a promising architecture to meet the explosive growth of communication and computing needs of inspection robot. Inspection robot can transmit the collected High Definition (HD) video to adjacent edge servers for data processing and state research and judgment. However, the communication constraints of long-distance transmission, high reliability and low delay pose challenges to task offloading optimization. Therefore, this paper introduced Unmanned Aerial Vehicle (UAV) and established UAV assisted mobile edge computing system. UAV assisted and mobile edge computing are combined to form edge computing nodes. In this way, it provided communication and computing services to the IR for fast data processing. Specifically, in order to optimize the system energy consumption, a resource allocation strategy based on genetic algorithm is proposed. By optimizing the offloading decision and computing resource allocation of the IRs, the computing task of the IRs are offloaded to an energy-efficient UAV. The experimental results show that the resource allocation strategy based on genetic algorithm can effectively reduce the energy consumption and cost of UAVs and IRs, and effectively realize the reasonable allocation of resources. The results verify the effectiveness and reliability of the algorithm in the real scene. © 2021

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Multilingual Knowledge Graph Completion via Multimodal Decouple and Relation-based Ensemble Inference 29

Multilingual Knowledge Graph Completion via Multimodal Decou...

引用

29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Liu, Xiaojing Jiang, Xuesong Yi, Fengge Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9798350330717

The objective of Multimodal Knowledge Graph Completion (MKGC) is to forecast absent entities within a knowledge graph by leveraging additional textual and visual modalities. Existing studies commonly utilize a singular relationship embedding to depict all modalities within an entity pair, thus connecting several relationships derived from diverse modalities. However, this coupling may introduce interference from conflicting information between modalities, as the relationships between modalities for a given entity pair can be contradictory. Moreover, existing Ensemble Inference methods fail to dynamically adjust modal weights based on their differences and importance, despite the varying contributions of different modalities. In this paper, we propose the Multimodal Decouple and Relation-based Ensemble inference (MDRE) model. For each modality, we construct corresponding relationship embeddings and build separate triple representations to avoid interferences among modalities. During the training phase, we employ confidence-constrained training with temperature scaling to alleviate conflicting information in textual and visual modalities. For inference, we utilize the Relation-based Ensemble Inference method to adjust modal weights at the relationship level, thus achieving improved prediction results. Experimental results on two datasets demonstrate that MDRE outperforms existing single-modal and multimodal knowledge graph completion methods in terms of performance. © 2023 IEEE.

关键词： knowledge graph knowledge graph completion multimodal fusion

来源：评论

学校读者我要写书评

暂无评论

Community Detection to Improve Machine Learning Based Heart Disease Prediction

Community Detection to Improve Machine Learning Based Heart ...

引用

International Symposium on Artificial Intelligence and Signal Processing (AISP)

作者： Abolfazl Dibaji Sadegh Sulaimany Department of Computer Engineering Social & Biological Network Laboratory (SBNA) University of Kurdistan Sanandaj Iran

This research article introduces a novel approach to enhance the accuracy of heart disease classification through the integration of graph-based techniques and community detection with machine learning. The paper explores the potential of capturing complex relationships within heart disease datasets using community detection and encode the membership of a node as a feature set, using on hot encoding, in order to enrich the current available features as the input of machine learning classifiers. The study utilizes a comprehensive dataset amalgamated from multiple sources and employs graph conversion, community detection, and machine learning algorithms to analyze the data. Results indicate that the proposed approach surpasses traditional machine learning models, achieving an accuracy of 94%. This study highlights the potential of graph-based methods to improve heart disease classification. It provides insights into opportunities, limitations, and areas for future research in this field. While there are certain limitations around sample size and challenges in real-world deployment, the research presents strong evidence supporting the effectiveness of community detection techniques for heart disease classification. The findings have implications for diagnosis and treatment.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Wishart Deeplab network for Polarimetric SAR Image Classification 3rd

Wishart Deeplab Network for Polarimetric SAR Image Classific...

引用

3rd China Intelligent Robotics Annual Conference, CCF CIRAC 2022

作者： Shi, Junfei He, Tiansheng Jin, Haiyan Wang, Hua Xu, Weinan State Key Laboratory of Geo-Information Engineering Shaanxi Xi’an710048 China School of Computer Science and Engineering Xi’an University of Technology Shaanxi Xi’an710048 China Shaanxi Key Laboratory for Network Computing and Security Technology Shaanxi Xi’an710048 China

ISBN: (纸本)9789819903009

Polarimetric Synthetic Aperture Radar (PolSAR) images have attracted much attention with abundant polarimetric scattering information. In recent years, many deep learning models have been proposed and highly expanded to PolSAR image classification. However, how to learn the complex matrix information is an indispensable problem. To address this problem, a Wishart Deeplab network is developed, which can not only learn the polarimetric matrix structure by designing Wishart network level but also learn multi-scale polarimetric scattering features by incorporating dilated convolution. Specifically, the Wishart-Deeplab based PolSAR classification model is constructed by designing the Wishart convolution operation to learn the statistical information of PolSAR data. Then, a deeplabV3 + network is followed to obtain the multi-scale semantic features by dilated convolution. By this way, statistical distrubtion-based and high-level semantic features are adaptively learned to improve the performance of the network. The experiments are conducted on two sets of real PolSAR data and results show that this method can obtain better classification results. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

来源：评论

学校读者我要写书评

暂无评论

HAPG-SAQAM: Human Auditory Perception Guided Spatial Audio Quality Assessment Metric

HAPG-SAQAM: Human Auditory Perception Guided Spatial Audio Q...

引用

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zheng, Yuanming Yao, Jiaxuan Deng, Xiangyu Yang, Yuhong Liao, Ruiqi Tu, Weiping Lin, Cedar National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan China Guangdong OPPO Mobile Telecommunications Corp. Guangdong China

ISBN: (纸本)9798350368741

Spatial audio quality evaluation is essential for applications like virtual and augmented reality, where accurate sound reproduction enhances user immersion. While subjective listening tests are the gold standard, they are costly and time-consuming. To address this, we propose HAPG-SAQAM, an objective metric for assessing timbre quality, spatial quality, and overall quality of binaural audio, guided by human auditory perception. Our contributions include: (1) the Multi-scale Auditory Guided Feature Extraction (MAGFE) module, incorporating gammatone frequency cepstral coefficients for better alignment with human perception;(2) Perceptual Weighted Loss (PWL), optimizing the weighting of timbre quality (TQ) and spatial quality (SQ) loss based on subjective test data;and (3) data augmentation techniques to enhance robustness by amplifying perceptual distortions. Experimental results show HAPG-SAQAM improves correlation with subjective scores by 10%, with ablation studies confirming the contributions of its components to enhanced spatial and overall audio quality. © 2025 IEEE.

关键词： auditory perception spatial audio Speech and audio quality assessment subjective experiment timbre quality

来源：评论

学校读者我要写书评

暂无评论

Joint Optimization of Latency and Energy Consumption for Mobile Edge Computing Based Proximity Detection in Road networks

引用

China Communications 2022年第4期19卷 274-290页

作者： Tongyu Zhao Yaqiong Liu Guochu Shou Xinwei Yao School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Beijing Laboratory of Advanced Information Networks Beijing Key Laboratory of Network System Architecture and ConvergenceBeijing 100876China School of Computer Science and Technology Zhejiang University of TechnologyChina

In recent years, artificial intelligence and automotive industry have developed rapidly, and autonomous driving has gradually become the focus of the industry. In road networks, the problem of proximity detection refers to detecting whether two moving objects are close to each other or not in real time. However, the battery life and computing capability of mobile devices are limited in the actual scene,which results in high latency and energy consumption. Therefore, it is a tough problem to determine the proximity relationship between mobile users with low latency and energy consumption. In this article, we aim at finding a tradeoff between latency and energy consumption. We formalize the computation offloading problem base on mobile edge computing(MEC)into a constrained multiobjective optimization problem(CMOP) and utilize NSGA-II to solve it. The simulation results demonstrate that NSGA-II can find the Pareto set, which reduces the latency and energy consumption effectively. In addition, a large number of solutions provided by the Pareto set give us more choices of the offloading decision according to the actual situation.

关键词： proximity detection mobile edge computing road networks constrained multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

A Theory of Transfer-Based Black-Box Attacks: Explanation and Implications 37

A Theory of Transfer-Based Black-Box Attacks: Explanation an...

引用

37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Chen, Yanbo Liu, Weiwei School of Computer Science Wuhan University National Engineering Research Center for Multimedia Software Wuhan University Institute of Artificial Intelligence Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ISBN: (纸本)9781713899921

Transfer-based attacks [1] are a practical method of black-box adversarial attacks in which the attacker aims to craft adversarial examples from a source model that is transferable to the target model. Many empirical works [2-6] have tried to explain the transferability of adversarial examples from different angles. However, these works only provide ad hoc explanations without quantitative analyses. The theory behind transfer-based attacks remains a mystery. This paper studies transfer-based attacks under a unified theoretical framework. We propose an explanatory model, called the manifold attack model, that formalizes popular beliefs and explains the existing empirical results. Our model explains why adversarial examples are transferable even when the source model is inaccurate as observed in Papernot et al. [7]. Moreover, our model implies that the existence of transferable adversarial examples depends on the "curvature" of the data manifold, which further explains why the success rates of transfer-based attacks are hard to improve. We also discuss our model's expressive power and applicability. © 2023 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Adversarial Self-Training Improves Robustness and Generalization for Gradual Domain Adaptation 37

Adversarial Self-Training Improves Robustness and Generaliza...

引用

37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Shi, Lianghe Liu, Weiwei School of Computer Science Wuhan University National Engineering Research Center for Multimedia Software Wuhan University Institute of Artificial Intelligence Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University

ISBN: (纸本)9781713899921

Gradual Domain Adaptation (GDA), in which the learner is provided with additional intermediate domains, has been theoretically and empirically studied in many contexts. Despite its vital role in security-critical scenarios, the adversarial robustness of the GDA model remains unexplored. In this paper, we adopt the effective gradual self-training method and replace vanilla self-training with adversarial self-training (AST). AST first predicts labels on the unlabeled data and then adversarially trains the model on the pseudo-labeled distribution. Intriguingly, we find that gradual AST improves not only adversarial accuracy but also clean accuracy on the target domain. We reveal that this is because adversarial training (AT) performs better than standard training when the pseudo-labels contain a portion of incorrect labels. Accordingly, we first present the generalization error bounds for gradual AST in a multiclass classification setting. We then use the optimal value of the Subset Sum Problem to bridge the standard error on a real distribution and the adversarial error on a pseudo-labeled distribution. The result indicates that AT may obtain a tighter bound than standard training on data with incorrect pseudo-labels. We further present an example of a conditional Gaussian distribution to provide more insights into why gradual AST can improve the clean accuracy for GDA. © 2023 Neural information processing systems foundation. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：