This book constitutes the refereed proceedings of six workshops of the 14th International Conference on Web-Age Information Management, WAIM 2013, held in Beidaihe, China, June 2013. The 37 revised full papers are org...
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ISBN:
(数字)9783642395277
ISBN:
(纸本)9783642395260
This book constitutes the refereed proceedings of six workshops of the 14th International Conference on Web-Age Information Management, WAIM 2013, held in Beidaihe, China, June 2013. The 37 revised full papers are organized in topical sections on the six following workshops: The International Workshop on Big Data Management on Emerging Hardware (HardBD 2013), the Second International Workshop on Massive Data Storage and Processing (MDSP 2013), the First International Workshop on Emergency Management in Big Data Age (BigEM 2013), the International Workshop on Trajectory Mining in Social Networks (TMSN 2013), the First International Workshop on Location-based Query Processing in Mobile Environments (LQPM 2013), and the First International Workshop on Big Data Management and Service (BDMS 2013).
As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile,...
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As an advanced carrier of on-board sensors, connected autonomous vehicle (CAV) can be viewed as an aggregation of self-adaptive systems with monitor-analyze-plan-execute (MAPE) for vehicle-related services. Meanwhile, machine learning (ML) has been applied to enhance analysis and plan functions of MAPE so that self-adaptive systems have optimal adaption to changing conditions. However, most of ML-based approaches don’t utilize CAVs’ connectivity to collaboratively generate an optimal learner for MAPE, because of sensor data threatened by gradient leakage attack (GLA). In this article, we first design an intelligent architecture for MAPE-based self-adaptive systems on Web 3.0-based CAVs, in which a collaborative machine learner supports the capabilities of managing systems. Then, we observe by practical experiments that importance sampling of sparse vector technique (SVT) approaches cannot defend GLA well. Next, we propose a fine-grained SVT approach to secure the learner in MAPE-based self-adaptive systems, that uses layer and gradient sampling to select uniform and important gradients. At last, extensive experiments show that our private learner spends a slight utility cost for MAPE (e.g., \(0.77\%\) decrease in accuracy) defending GLA and outperforms the typical SVT approaches in terms of defense (increased by \(10\%\sim 14\%\) attack success rate) and utility (decreased by \(1.29\%\) accuracy loss).
Multimodal sarcasm analysis is one of the most challenging research branch of the sentiment analysis area, due to the presence of cross-modality incongruity. However, existing works mainly attend to the coarse-grained...
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Multimodal sarcasm analysis is one of the most challenging research branch of the sentiment analysis area, due to the presence of cross-modality incongruity. However, existing works mainly attend to the coarse-grained incongruity analysis, and totally ignore the sentiment semantic coupling issue. This indeed limits the discriminate capability and robustness of the sarcasm analysis model. In order to address the above issue, we propose a novel fine-grained semantic disentanglement network (FSDN). Specifically, the intra-modality semantic disentanglement is performed to investigate the more intrinsic semantic cues of the same modality. Additionally, the inter-modality semantic disentanglement is leveraged to simultaneously facilitate the common and intrinsic semantic cues across modalities. Furthermore, the dual-spatial semantic interaction block is presented to explore the long-range cross-spatial semantic context between the obtained verbal and non-verbal semantic space with the global view. The above semantic disentanglement processes with both local and global views significantly unleash much more robustness even for the sarcasm case consists of multiple semantic message. Various experiments indicate that FSDN can receive state-of-the-art or competitive performance.
The Anchor-based Multi-view Subspace Clustering (AMSC) has turned into a favourable tool for large-scale multi-view clustering. However, there still exist some limitations to the current AMSC approaches. First, they t...
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The Anchor-based Multi-view Subspace Clustering (AMSC) has turned into a favourable tool for large-scale multi-view clustering. However, there still exist some limitations to the current AMSC approaches. First, they typically recover anchor graph structure in the original linear space, restricting their feasibility for nonlinear scenarios. Second, they usually overlook the potential benefits of jointly capturing the inter-view and intra-view information for enhancing the anchor representation learning. Third, these approaches mostly perform anchor-based subspace learning by a specific matrix norm, neglecting the latent high-order correlation across different views. To overcome these limitations, this paper presents an efficient and effective approach termed Large-scale Tensorized Multi-view Kernel Subspace Clustering (LTKMSC). Different from the existing AMSC approaches, our LTKMSC approach exploits both inter-view and intra-view awareness for anchor-based representation building. Concretely, the low-rank tensor learning is leveraged to capture the high-order correlation (i.e., the inter-view complementary information) among distinct views, upon which the \(l_{1,2}\) norm is imposed to explore the intra-view anchor graph structure in each view. Moreover, the kernel learning technique is leveraged to explore the nonlinear anchor-sample relationships embedded in multiple views. With the unified objective function formulated, an efficient optimization algorithm that enjoys low computational complexity is further designed. Extensive experiments on a variety of multi-view datasets have confirmed the efficiency and effectiveness of our approach when compared with the other competitive approaches.
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