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See detailA Lightweight Network Proximity Service Based On Neighborhood Models
Liao, Yongjun; Du, Wei; Leduc, Guy ULiege

in 22nd IEEE Symposium on Communications and Vehicular Technology in the Benelux (SCVT) (2015, November 24)

This paper proposes a network proximity service based on the neighborhood models used in recommender systems. Unlike previous approaches, our service infers network proximity without trying to recover the ... [more ▼]

This paper proposes a network proximity service based on the neighborhood models used in recommender systems. Unlike previous approaches, our service infers network proximity without trying to recover the latency between network nodes. By asking each node to probe a number of landmark nodes which can be servers at Google, Yahoo and Facebook, etc., a simple proximity measure is computed and allows the direct ranking and rating of network nodes by their proximity to a target node. The service is thus lightweight and can be easily deployed in e.g. P2P and CDN applications. Simulations on existing datasets and experiments with a deployment over PlanetLab showed that our service achieves an accurate proximity inference that is comparable to state-of-the-art latency prediction approaches, while being much simpler. [less ▲]

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See detailRating Network Paths for Locality-Aware Overlay Construction and Routing
Du, Wei; Liao, Yongjun ULiege; Tao, Narisu et al

in IEEE/ACM Transactions on Networking (2015), 23(5), 1661-1673

This paper investigates the rating of network paths, i.e. acquiring quantized measures of path properties such as round-trip time and available bandwidth. Comparing to finegrained measurements, coarse ... [more ▼]

This paper investigates the rating of network paths, i.e. acquiring quantized measures of path properties such as round-trip time and available bandwidth. Comparing to finegrained measurements, coarse-grained ratings are appealing in that they are not only informative but also cheap to obtain. Motivated by this insight, we firstly address the scalable acquisition of path ratings by statistical inference. By observing similarities to recommender systems, we examine the applicability of solutions to recommender system and show that our inference problem can be solved by a class of matrix factorization techniques. A technical contribution is an active and progressive inference framework that not only improves the accuracy by selectively measuring more informative paths but also speeds up the convergence for available bandwidth by incorporating its measurement methodology. Then, we investigate the usability of rating-based network measurement and inference in applications. A case study is performed on whether locality awareness can be achieved for overlay networks of Pastry and BitTorrent using inferred ratings. We show that such coarse-grained knowledge can improve the performance of peer selection and that finer granularities do not always lead to larger improvements. [less ▲]

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See detailCross-check of Analysis Modules and Reasoner Interactions
Manferdini, U.; Traverso, S.; Mellia, Marco et al

Report (2015)

This deliverable presents an extended set of Analysis Modules, including both the improvements done to those presented in deliverable D4.1 as well as the new analysis algorithms designed and developed to ... [more ▼]

This deliverable presents an extended set of Analysis Modules, including both the improvements done to those presented in deliverable D4.1 as well as the new analysis algorithms designed and developed to address use-cases. The deliverable also describes a complete workflow description for the different use-cases, including both stream processing for real-time monitoring applications as well as batch processing for “off-line” analysis. This workflow description specifies the iterative interaction loop between WP2, WP3, T4.1, and T4.2, thereby allowing for a cross-checking of the analysis modules and the reasoner interactions. [less ▲]

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See detailEnergy-Efficient Sensor Selection for Data Quality and Load Balancing in Wireless Sensor Networks
Bijarbooneh, Farshid; Du, Wei ULiege; Ngai, Edith et al

in IEEE 22nd International Symposium of Quality of Service (IWQoS), Hong Kong, 2014 (2014)

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See detailDMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction
Liao, Yongjun ULiege; Du, Wei; Geurts, Pierre ULiege et al

in IEEE/ACM Transactions on Networking (2013), 21(5), 1511-1524

The knowledge of end-to-end network distances is essential to many Internet applications. As active probing of all pairwise distances is infeasible in large-scale networks, a natural idea is to measure a ... [more ▼]

The knowledge of end-to-end network distances is essential to many Internet applications. As active probing of all pairwise distances is infeasible in large-scale networks, a natural idea is to measure a few pairs and to predict the other ones without actually measuring them. This paper formulates the prediction problem as matrix completion where the unknown entries in a pairwise distance matrix constructed from a network are to be predicted. By assuming that the distance matrix has a low-rank characteristics, the problem is solvable by lowrank approximation based on matrix factorization. The new formulation circumvents the well-known drawbacks of existing approaches based on Euclidean embedding. A new algorithm, so-called Decentralized Matrix Factorization by Stochastic Gradient Descent (DMFSGD), is proposed. By letting network nodes exchange messages with each other, the algorithm is fully decentralized and only requires each node to collect and to process local measurements, with neither explicit matrix constructions nor special nodes such as landmarks and central servers. In addition, we compared comprehensively matrix factorization and Euclidean embedding to demonstrate the suitability of the former on network distance prediction. We further studied the incorporation of a robust loss function and of non-negativity constraints. Extensive experiments on various publicly-available datasets of network delays show not only the scalability and the accuracy of our approach, but also its usability in real Internet applications. [less ▲]

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See detailOrdinal Rating of Network Performance and Inference by Matrix Completion
Du, Wei; Liao, Yongjun ULiege; Geurts, Pierre ULiege et al

Report (2012)

This paper addresses the large-scale acquisition of end-to-end network performance. We made two distinct contributions: ordinal rating of network performance and inference by matrix completion. The former ... [more ▼]

This paper addresses the large-scale acquisition of end-to-end network performance. We made two distinct contributions: ordinal rating of network performance and inference by matrix completion. The former reduces measurement costs and unifies various metrics which eases their processing in applications. The latter enables scalable and accurate inference with no requirement of structural information of the network nor geometric constraints. By combining both, the acquisition problem bears strong similarities to recommender systems. This paper investigates the applicability of various matrix factorization models used in recommender systems. We found that the simple regularized matrix factorization is not only practical but also produces accurate results that are beneficial for peer selection. [less ▲]

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See detailDMFSGD: A Decentralized Matrix Factorization Algorithm for Network Distance Prediction
Liao, Yongjun ULiege; Du, Wei; Geurts, Pierre ULiege et al

Report (2012)

The knowledge of end-to-end network distances is essential to many Internet applications. As active probing of all pairwise distances is infeasible in large-scale networks, a natural idea is to measure a ... [more ▼]

The knowledge of end-to-end network distances is essential to many Internet applications. As active probing of all pairwise distances is infeasible in large-scale networks, a natural idea is to measure a few pairs and to predict the other ones without actually measuring them. This paper formulates the distance prediction problem as matrix completion where unknown entries of an incomplete matrix of pairwise distances are to be predicted. The problem is solvable because strong correlations among network distances exist and cause the constructed distance matrix to be low rank. The new formulation circumvents the well-known drawbacks of existing approaches based on Euclidean embedding. A new algorithm, so-called Decentralized Matrix Factorization by Stochastic Gradient Descent (DMFSGD), is proposed to solve the network distance prediction problem. By letting network nodes exchange messages with each other, the algorithm is fully decentralized and only requires each node to collect and to process local measurements, with neither explicit matrix constructions nor special nodes such as landmarks and central servers. In addition, we compared comprehensively matrix factorization and Euclidean embedding to demonstrate the suitability of the former on network distance prediction. We further studied the incorporation of a robust loss function and of non-negativity constraints. Extensive experiments on various publicly-available datasets of network delays show not only the scalability and the accuracy of our approach but also its usability in real Internet applications. [less ▲]

Detailed reference viewed: 33 (3 ULiège)
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See detailDecentralized Prediction of End-to-End Network Performance Classes
Liao, Yongjun ULiege; Du, Wei; Geurts, Pierre ULiege et al

in Proc. of the 7th International Conference on emerging Networking EXperiments and Technologies (CoNEXT) (2011, December 08)

In large-scale networks, full-mesh active probing of end-to-end performance metrics is infeasible. Measuring a small set of pairs and predicting the others is more scalable. Under this framework, we ... [more ▼]

In large-scale networks, full-mesh active probing of end-to-end performance metrics is infeasible. Measuring a small set of pairs and predicting the others is more scalable. Under this framework, we formulate the prediction problem as matrix completion, whereby unknown entries of an incomplete matrix of pairwise measurements are to be predicted. This problem can be solved by matrix factorization because performance matrices have a low rank, thanks to the correlations among measurements. Moreover, its resolution can be fully decentralized without actually building matrices nor relying on special landmarks or central servers. In this paper we demonstrate that this approach is also applicable when the performance values are not measured exactly, but are only known to belong to one among some predefined performance classes, such as "good" and "bad". Such classification-based formulation not only fulfills the requirements of many Internet applications but also reduces the measurement cost and enables a unified treatment of various performance metrics. We propose a decentralized approach based on Stochastic Gradient Descent to solve this class-based matrix completion problem. Experiments on various datasets, relative to two kinds of metrics, show the accuracy of the approach, its robustness against erroneous measurements and its usability on peer selection. [less ▲]

Detailed reference viewed: 182 (17 ULiège)
Peer Reviewed
See detailVideo analysis for continuous sign language recognition
Piater, Justus ULiege; Hoyoux, Thomas ULiege; Du, Wei ULiege

in 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies (2010)

Detailed reference viewed: 48 (11 ULiège)
Peer Reviewed
See detailSignspeak--understanding, recognition, and translation of sign languages
Dreuw, Philippe; Forster, Jens; Gweth, Yannick et al

in 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies (2010)

Detailed reference viewed: 15 (10 ULiège)
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See detailSignSpeak -- Scientific Understanding and Vision-Based Technological Development for Continuous Sign Language Recognition and Translation
Dreuw, Philippe; Forster, Jens; Gweth, Yannick et al

in 4th Workshop on the Representation and Processing of Sign Languages: Corpora and Sign Language Technologies (2010)

Detailed reference viewed: 70 (6 ULiège)
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See detailGround-Target Tracking in Multiple Cameras Using Collaborative Particle Filters and Principal Axis-Based Integration
Du, Wei ULiege; Hayet, Jean-Bernard; Verly, Jacques ULiege et al

in IPSJ Transactions on Computer Vision and Applications (2009), 1

Detailed reference viewed: 99 (4 ULiège)
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See detailA Probabilistic Approach to Integrating Multiple Cues in Visual Tracking
Du, Wei ULiege; Piater, Justus ULiege

in 10th European Conference on Computer Vision (2008)

Detailed reference viewed: 26 (0 ULiège)
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See detailMulti-Camera People Tracking by Collaborative Particle Filters and Principal Axis-Based Integration
Du, Wei ULiege; Piater, Justus ULiege

in Asian Conference on Computer Vision (2007)

Detailed reference viewed: 25 (0 ULiège)
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See detailSequential Variational Inference for Distributed Multi-Sensor Tracking and Fusion
Du, Wei ULiege; Piater, Justus ULiege

in The 10th International Conference on Information Fusion (2007)

Detailed reference viewed: 16 (1 ULiège)
Peer Reviewed
See detailData Fusion by Belief Propagation for Multi-Camera Tracking
Du, Wei ULiege; Piater, Justus ULiege

in The 9th International Conference on Information Fusion (2006)

Detailed reference viewed: 28 (0 ULiège)
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See detailCollaborative Multi-Camera Tracking of Athletes in Team Sports
Du, Wei ULiege; Hayet, Jean-Bernard; Piater, Justus ULiege et al

in Workshop on Computer Vision Based Analysis in Sport Environments (CVBASE) (2006)

Detailed reference viewed: 48 (7 ULiège)
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See detailMulti-view object tracking using sequential belief propagation
Du, Wei ULiege; Piater, Justus ULiege

in Computer Vision – ACCV 2006 (2006)

Multiple cameras and collaboration between them make possible the integration of information available from multiple views and reduce the uncertainty due to occlusions. This paper presents a novel method ... [more ▼]

Multiple cameras and collaboration between them make possible the integration of information available from multiple views and reduce the uncertainty due to occlusions. This paper presents a novel method for integrating and tracking multi-view observations using bidirectional belief propagation. The method is based on a fully connected graphical model where target states at different views are represented as different but correlated random variables, and image observations at a given view are only associated with the target states at the same view. The tracking processes at different views collaborate with each other by exchanging information using a message passing scheme, which largely avoids propagating wrong information. An efficient sequential belief propagation algorithm is adopted to perform the collaboration and to infer the multi-view target states. We demonstrate the effectiveness of our method on video-surveillance sequences. [less ▲]

Detailed reference viewed: 149 (4 ULiège)
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See detailTracking by cluster analysis of feature points and multiple particle filters
Du, Wei ULiege; Piater, Justus ULiege

in 3rd International Conference on Advances in Pattern Recognition (2005)

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See detailTracking by cluster analysis of feature points and multiple particle filters
Du, Wei ULiege; Piater, Justus ULiege

in Pattern Recognition and Image Analysis (2005)

A moving target produces a coherent cluster of feature points in the image plane. This motivates our novel method of tracking multiple targets by cluster analysis of feature points and Multiple particle ... [more ▼]

A moving target produces a coherent cluster of feature points in the image plane. This motivates our novel method of tracking multiple targets by cluster analysis of feature points and Multiple particle filters. First, feature points are detected by a Harris corner detector and tracked by a Lucas-Kanade tracker. Clusters of moving targets are then initialized by grouping spatially co-located points with similar motion using the EM algorithm. Due to the non-Gaussian distribution of the points in a cluster and the multi-modality resulting from multiple targets, multiple particle filters are applied to track all the clusters simultaneously: one particle filter is started for one cluster. The proposed method is well Suited for the typical video surveillance configuration where the cameras are still and targets of interest appear relatively small in the image. We demonstrate the effectiveness of our method on different PETS datasets. [less ▲]

Detailed reference viewed: 90 (3 ULiège)