78 research outputs found
Generalized residual vector quantization for large scale data
Vector quantization is an essential tool for tasks involving large scale
data, for example, large scale similarity search, which is crucial for
content-based information retrieval and analysis. In this paper, we propose a
novel vector quantization framework that iteratively minimizes quantization
error. First, we provide a detailed review on a relevant vector quantization
method named \textit{residual vector quantization} (RVQ). Next, we propose
\textit{generalized residual vector quantization} (GRVQ) to further improve
over RVQ. Many vector quantization methods can be viewed as the special cases
of our proposed framework. We evaluate GRVQ on several large scale benchmark
datasets for large scale search, classification and object retrieval. We
compared GRVQ with existing methods in detail. Extensive experiments
demonstrate our GRVQ framework substantially outperforms existing methods in
term of quantization accuracy and computation efficiency.Comment: published on International Conference on Multimedia and Expo 201
Transformer-based Joint Source Channel Coding for Textual Semantic Communication
The Space-Air-Ground-Sea integrated network calls for more robust and secure
transmission techniques against jamming. In this paper, we propose a textual
semantic transmission framework for robust transmission, which utilizes the
advanced natural language processing techniques to model and encode sentences.
Specifically, the textual sentences are firstly split into tokens using
wordpiece algorithm, and are embedded to token vectors for semantic extraction
by Transformer-based encoder. The encoded data are quantized to a fixed length
binary sequence for transmission, where binary erasure, symmetric, and deletion
channels are considered for transmission. The received binary sequences are
further decoded by the transformer decoders into tokens used for sentence
reconstruction. Our proposed approach leverages the power of neural networks
and attention mechanism to provide reliable and efficient communication of
textual data in challenging wireless environments, and simulation results on
semantic similarity and bilingual evaluation understudy prove the superiority
of the proposed model in semantic transmission.Comment: 6 pages, 5 figures. Accepted by IEEE/CIC ICCC 202
DPSS-based Codebook Design for Near-Field XL-MIMO Channel Estimation
Future sixth-generation (6G) systems are expected to leverage extremely
large-scale multiple-input multiple-output (XL-MIMO) technology, which
significantly expands the range of the near-field region. While accurate
channel estimation is essential for beamforming and data detection, the unique
characteristics of near-field channels pose additional challenges to the
effective acquisition of channel state information. In this paper, we propose a
novel codebook design, which allows efficient near-field channel estimation
with significantly reduced codebook size. Specifically, we consider the
eigen-problem based on the near-field electromagnetic wave transmission model.
Moreover, we derive the general form of the eigenvectors associated with the
near-field channel matrix, revealing their noteworthy connection to the
discrete prolate spheroidal sequence (DPSS). Based on the proposed near-field
codebook design, we further introduce a two-step channel estimation scheme.
Simulation results demonstrate that the proposed codebook design not only
achieves superior sparsification performance of near-field channels with a
lower leakage effect, but also significantly improves the accuracy in
compressive sensing channel estimation.Comment: 6 pages, 5 figure
Anonymizing continuous queries with delay-tolerant mix-zones over road networks
This paper presents a delay-tolerant mix-zone framework for protecting the location privacy of mobile users against continuous query correlation attacks. First, we describe and analyze the continuous query correlation attacks (CQ-attacks) that perform query correlation based inference to break the anonymity of road network-aware mix-zones. We formally study the privacy strengths of the mix-zone anonymization under the CQ-attack model and argue that spatial cloaking or temporal cloaking over road network mix-zones is ineffective and susceptible to attacks that carry out inference by combining query correlation with timing correlation (CQ-timing attack) and transition correlation (CQ-transition attack) information. Next, we introduce three types of delay-tolerant road network mix-zones (i.e.; temporal, spatial and spatio-temporal) that are free from CQ-timing and CQ-transition attacks and in contrast to conventional mix-zones, perform a combination of both location mixing and identity mixing of spatially and temporally perturbed user locations to achieve stronger anonymity under the CQ-attack model. We show that by combining temporal and spatial delay-tolerant mix-zones, we can obtain the strongest anonymity for continuous queries while making acceptable tradeoff between anonymous query processing cost and temporal delay incurred in anonymous query processing. We evaluate the proposed techniques through extensive experiments conducted on realistic traces produced by GTMobiSim on different scales of geographic maps. Our experiments show that the proposed techniques offer high level of anonymity and attack resilience to continuous queries. © 2013 Springer Science+Business Media New York
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