Data Mining - Concepts and Techniques.

IEEE International Conference on Robotics and Automation (ICRA) におけるフルードパワー技術の研究動向

Online social networks can often be represented as heterogeneous information networks containing abundant information about: who, where, when and what. Nowadays, people are usually involved in multiple social networks simultaneously. The multiple accounts of the same user in different networks are mostly isolated from each other without any connection between them. Discovering the correspondence of these accounts across multiple social networks is a crucial prerequisite for many interesting inter-network applications, such as link recommendation and community analysis using information from multiple networks. In this paper, we study the problem of anchor link prediction across multiple heterogeneous social networks, i.e., discovering the correspondence among different accounts of the same user. Unlike most prior work on link prediction and network alignment, we assume that the anchor links are one-to-one relationships (i.e., no two edges share a common endpoint) between the accounts in two social networks, and a small number of anchor links are known beforehand. We propose to extract heterogeneous features from multiple heterogeneous networks for anchor link prediction, including user's social, spatial, temporal and text information. Then we formulate the inference problem for anchor links as a stable matching problem between the two sets of user accounts in two different networks. An effective solution, MNA (Multi-Network Anchoring), is derived to infer anchor links w.r.t. the one-to-one constraint. Extensive experiments on two real-world heterogeneous social networks show that our MNA model consistently outperform other commonly-used baselines on anchor link prediction.

https://web.cs.wpi.edu/~xkong/publications/papers/cikm13.pdf

Inferring anchor links across multiple heterogeneous social networks

A method and apparatus for obtaining an image to determine a three dimensional shape of a stationary or moving object using a bi dimensional coded light pattern having a plurality of distinct identifiable feature types. The coded light pattern is projected on the object such that each of the identifiable feature types appears at most once on predefined sections of distinguishable epipolar lines. An image of the object is captured and the reflected feature types are extracted along with their location on known epipolar lines in the captured image. Displacements of the reflected feature types along their epipolar lines from reference coordinates thereupon determine corresponding three dimensional coordinates in space and thus a 3D mapping or model of the shape of the object at any point in time.

3d geometric modeling and motion capture using both single and dual imaging

A method and apparatus for obtaining an image to determine a three dimensional shape of a stationary or moving object using a bi dimensional coded light pattern having a plurality of distinct identifiable feature types. The coded light pattern is projected on the object such that each of the identifiable feature types appears at most once on predefined sections of distinguishable epipolar lines. An image of the object is captured and the reflected feature types are extracted along with their location on known epipolar lines in the captured image. The locations of identified feature types in the 2D image are corresponded to 3D coordinates in space in accordance to triangulation mappings. Thus a 3D mapping or model of imaged objects at an point in time is obtained.

3d geometric modeling and 3d video content creation

Inter-social networks operations and functionalities are required in several scenarios (data integration, data enrichment, information retrieval, etc.). To achieve this, matching user profiles is required. Current methods are so restrictive and do not consider all the related problems. Particularly, they assume that two profiles describe the same physical person only if the values of their Inverse Functional Property or IFP (e.g. the email address, homepage, etc.) are the same. However, the observed trend in social networks is not fully compatible with this assumption since users tend to create more than one social network account (for personal use, for work, etc.) while using same or different email addresses. In this work, we address the problem of matching user profiles in its globality by providing a suitable matching framework able to consider all the profile’s attributes. Our framework allows users to give more importance to some attributes and assign each attribute a different similarity measure. The set of experiments conducted with our default/recommended attribute/similarity measures shows the superiority of our proposal in comparison with current ones.

/pdf/user-profile-matching-in-social-networks-36uqkbq6so.pdf

User Profile Matching in Social Networks

Large applications of sensor networks, such as environmental risk monitoring, require the deployment of hundreds or even thousands of nodes. This study proposes and implements a novel stochastic physics-based optimisation algorithm that is both efficient (guarantees full target coverage with a reduced number of sensors) and scalable (meaning that it can be executed for very large-scale problems in a reasonable computation time). The algorithm employs ‘virtual sensors’ which move, merge, recombine, and ‘explode’ during the course of the algorithm, where the process of merging and recombining virtual sensors reduces the number of actual sensors while maintaining full coverage. The parameters which control sensor merging and explosion are varied during the algorithm to perform the same function as an annealing schedule in simulated annealing. Simulation results illustrate the rapidity and the effectiveness of the proposed method.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-wss.2016.0076

Efficient scalable sensor node placement algorithm for fixed target coverage applications of wireless sensor networks

Normal and pathological gait classification LSTM model

Towards a real-time 3D shape reconstruction using a structured light system

The release of the Microsoft Kinect has attracted the attention of researchers in a variety of computer science domains. Even though this device is still relatively new, its recent applications have shown some promising results in terms of replacing current conventional methods like the stereo-camera for robotics navigation, multi-camera system for motion detection and laser scanner for 3D reconstruction. While most work around the Kinect is on how to take full advantage of its capabilities, so far only a few studies have been carried out on the limitations of this device and fewer that provide solutions to enhance the precision of its measurements. In this paper, we review and analyse current work in this area, and present and evaluate a temporal denoising algorithm to reduce the instability of the depth measurements provided by the Kinect over different distances.

Albert Dipanda

Papers

User Profile Matching in Social Networks

Efficient scalable sensor node placement algorithm for fixed target coverage applications of wireless sensor networks

Normal and pathological gait classification LSTM model

Towards a real-time 3D shape reconstruction using a structured light system

Temporal Denoising of Kinect Depth Data