9,900 research outputs found
Traffic Profiling for Mobile Video Streaming
This paper describes a novel system that provides key parameters of HTTP
Adaptive Streaming (HAS) sessions to the lower layers of the protocol stack. A
non-intrusive traffic profiling solution is proposed that observes packet flows
at the transmit queue of base stations, edge-routers, or gateways. By analyzing
IP flows in real time, the presented scheme identifies different phases of an
HAS session and estimates important application-layer parameters, such as
play-back buffer state and video encoding rate. The introduced estimators only
use IP-layer information, do not require standardization and work even with
traffic that is encrypted via Transport Layer Security (TLS). Experimental
results for a popular video streaming service clearly verify the high accuracy
of the proposed solution. Traffic profiling, thus, provides a valuable
alternative to cross-layer signaling and Deep Packet Inspection (DPI) in order
to perform efficient network optimization for video streaming.Comment: 7 pages, 11 figures. Accepted for publication in the proceedings of
IEEE ICC'1
Shuffling with a Croupier: Nat-Aware Peer-Sampling
Despite much recent research on peer-to-peer (P2P) protocols for the Internet, there have been relatively few practical protocols designed to explicitly account for Network Address Translation gateways (NATs). Those P2P protocols that do handle NATs circumvent them using relaying and hole-punching techniques to route packets to nodes residing behind NATs. In this paper, we present Croupier, a peer sampling service (PSS) that provides uniform random samples of nodes in the presence of NATs in the network. It is the first NAT-aware PSS that works without the use of relaying or hole-punching. By removing the need for relaying and hole-punching, we decrease the complexity and overhead of our protocol as well as increase
its robustness to churn and failure. We evaluated Croupier in simulation, and, in comparison with existing NAT-aware PSS’, our results show similar randomness properties, but improved robustness in the presence of both high percentages of nodes behind NATs and massive node failures. Croupier also has substantially lower protocol overhead
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
Person Re-Identification by Deep Joint Learning of Multi-Loss Classification
Existing person re-identification (re-id) methods rely mostly on either
localised or global feature representation alone. This ignores their joint
benefit and mutual complementary effects. In this work, we show the advantages
of jointly learning local and global features in a Convolutional Neural Network
(CNN) by aiming to discover correlated local and global features in different
context. Specifically, we formulate a method for joint learning of local and
global feature selection losses designed to optimise person re-id when using
only generic matching metrics such as the L2 distance. We design a novel CNN
architecture for Jointly Learning Multi-Loss (JLML) of local and global
discriminative feature optimisation subject concurrently to the same re-id
labelled information. Extensive comparative evaluations demonstrate the
advantages of this new JLML model for person re-id over a wide range of
state-of-the-art re-id methods on five benchmarks (VIPeR, GRID, CUHK01, CUHK03,
Market-1501).Comment: Accepted by IJCAI 201
Enhanced Feedback Iterative Decoding of Sparse Quantum Codes
Decoding sparse quantum codes can be accomplished by syndrome-based decoding
using a belief propagation (BP) algorithm.We significantly improve this
decoding scheme by developing a new feedback adjustment strategy for the
standard BP algorithm. In our feedback procedure, we exploit much of the
information from stabilizers, not just the syndrome but also the values of the
frustrated checks on individual qubits of the code and the channel model.
Furthermore we show that our decoding algorithm is superior to belief
propagation algorithms using only the syndrome in the feedback procedure for
all cases of the depolarizing channel. Our algorithm does not increase the
measurement overhead compared to the previous method, as the extra information
comes for free from the requisite stabilizer measurements.Comment: 10 pages, 11 figures, Second version, To be appeared in IEEE
Transactions on Information Theor
Recommended from our members
A survey on online monitoring approaches of computer-based systems
This report surveys forms of online data collection that are in current use (as well as being the subject of research to adapt them to changing technology and demands), and can be used as inputs to assessment of dependability and resilience, although they are not primarily meant for this use
- …