We propose the direct production of 3D CSG models from sketches as a way of relieving the user from having to input detailed 3D CAD models. This shortens the CAD/CAM process and simplifies it, allowing non-expert end-...
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This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vecto...
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ISBN:
(纸本)9781479913282
This paper addresses the problem of transferring useful knowledge from a source network to predict node labels in a newly formed target network. While existing transfer learning research has primarily focused on vector-based data, in which the instances are assumed to be independent and identically distributed, how to effectively transfer knowledge across different information networks has not been well studied, mainly because networks may have their distinct node features and link relationships between nodes. In this paper, we propose a new transfer learning algorithm that attempts to transfer common latent structure features across the source and target networks. The proposed algorithm discovers these latent features by constructing label propagation matrices in the source and target networks, and mapping them into a shared latent feature space. The latent features capture common structure patterns shared by two networks, and serve as domain-independent features to be transferred between networks. Together with domain-dependent node features, we thereafter propose an iterative classification algorithm that leverages label correlations to predict node labels in the target network. Experiments on real-world networks demonstrate that our proposed algorithm can successfully achieve knowledge transfer between networks to help improve the accuracy of classifying nodes in the target network.
We introduce a Horn description logic called Horn-DL, which is strictly and essentially richer than Horn-scriptSscriptRscriptOscriptIscriptQ, while still has PTime data complexity. In comparison with Horn- scriptSscri...
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In this paper we present a solution to the problem of positioning a team of Micro Aerial Vehicles for a surveillance task in an environment of arbitrary and unknown morphology. The problem is addressed taking into acc...
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ISBN:
(纸本)9781479909964
In this paper we present a solution to the problem of positioning a team of Micro Aerial Vehicles for a surveillance task in an environment of arbitrary and unknown morphology. The problem is addressed taking into account physical and environmental constraints like limited sensor capabilities and obstacle avoidance. The goal is to maximize the area monitored by the team, by identifying the best configuration of the team members. The proposed method is a distributed extension of our previous work based on the Cognitive Adaptive Optimization (CAO) algorithm. This distributed and scalable approach allows us to obtain coordinated and safe trajectories to accomplish the task in 3D environments. The different formulation of the problem considered in this paper allows also dealing with communication constraints. We provide extensive experimental results using data collected by a team of aerial robots and compare the efficiency of the distributed and centralized approach.
The International Workshop on Analytics on Video-based Learning (WAVe2013) aims to connect research efforts on Video-based Learning with Learning Analytics to create visionary ideas and foster synergies between the tw...
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Automated Guided Vehicles (AGVs) fleet scheduling is one of the big problems in Flexible Manufacturing System (FMS) control. The problem is more complicated when concurrent multi-product manufacturing and resource dea...
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Privacy risks have been addressed through technical solutions such as Privacy-Enhancing Technologies (PETs) as well as regulatory measures including Do Not Track. These approaches are inherently limited as they are gr...
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ISBN:
(纸本)9781479909308
Privacy risks have been addressed through technical solutions such as Privacy-Enhancing Technologies (PETs) as well as regulatory measures including Do Not Track. These approaches are inherently limited as they are grounded in the paradigm of a rational end user who can determine, articulate, and manage consistent privacy preferences. This assumes that self-serving efforts to enact privacy preferences lead to socially optimal outcomes with regard to information sharing. We argue that this assumption typically does not hold true. Consequently, solutions to specific risks are developed -- even mandated -- without effective reduction in the overall harm of privacy breaches. We present a systematic framework to examine these limitations of current technical and policy solutions. To address the shortcomings of existing privacy solutions, we argue for considering information sharing to be transactions within a community. Outcomes of privacy management can be improved at a lower overall cost if peers, as a community, are empowered by appropriate technical and policy mechanisms. Designing for a community requires encouraging dialogue, enabling transparency, and supporting enforcement of community norms. We describe how peer production of privacy is possible through PETs that are grounded in the notion of information as a common-pool resource subject to community governance.
A vague spatial data warehouse allows multidimensional queries with spatial predicates to support the analysis of business scores related to vague spatial data, addressing real world phenomena characterized by inexact...
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A vague spatial data warehouse allows multidimensional queries with spatial predicates to support the analysis of business scores related to vague spatial data, addressing real world phenomena characterized by inexact locations or indeterminate boundaries. However, vague spatial data are usually represented and stored as multiple geometries and impair the query processing performance. In this paper, we introduce an index called VSB-index to improve the query processing performance in vague spatial data warehouses, focusing on range queries and vague regions. We also conduct an experimental evaluation demonstrating that our VSB-index provided remarkable performance gains up to 97% over existing solutions.
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