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Matching user accounts with spatio-temporal awareness across social networks
تطبیق حساب های کاربری با آگاهی مکانی-زمانی در سراسر شبکه های اجتماعی-2021 User identification aims at matching user accounts across social sites, which benefits many
real-world applications. Existing works based on user trajectories usually address spatial
and temporal data separately while not fully utilizing the coupling relation between them.
Differently, in this work, we jointly consider spatialtemporal information in users’ acitvities to improve the user identification method. In particular, we observe that check-in
records of different users tend to create inconsistent spatialtemporal information. These
inconsistencies are useful for eliminating false user matching. Inspired by this observation,
we propose a novel user identification method that captures the correlation of spatial and
temporal information and the inconsistency in check-in records. It contains three main
steps. 1) We measure the similarity of users’ trajectories based on a kernel density estimation, which considers spatial and temporal information simultaneously. 2) We assign a
weight to each check-in record to favor discriminative ones. 3) We utilize the inconsistency
among check-in records to compute penalties for trajectory similarity. The pair of accounts
with higher similarity (than a predefined threshold) is then considered to be from the same
user. We evaluate our approach on three ground-truth datasets. The results show that the
proposed method offers competitive performance, with F1 values reaching 86.12%, 85.08%
and 78.34%, which demonstrates the superiority of the proposed method over state-of-theart methods.
keywords: شناسه کاربر | آگاهی مکانی-زمانی | مطابقت با حساب های کاربری | داده های ورود | مسیر کاربر | User identification | Spatio-temporal awareness | Match user accounts | Check-in data | User trajectory |
مقاله انگلیسی |
2 |
Deep-learning-based reading eye-movement analysis for aiding biometric recognition
خواندن تجزیه و تحلیل حرکت چشم مبتنی بر یادگیری عمیق برای کمک به تشخیص بیومتریک-2021 Eye-movement recognition is a new type of biometric recognition technology. Without considering the characteristics of the stimuli, the existing eye-movement recognition technology is based on eye- movement trajectory similarity measurements and uses more eye-movement features. Related studies on reading psychology have shown that when reading text, human eye-movements are different between individuals yet stable for a given individual. This paper proposes a type of technology for aiding biometric recognition based on reading eye-movement. By introducing a deep-learning framework, a computational model for reading eye-movement recognition (REMR) was constructed. The model takes the text, fixation, and text-based linguistic feature sequences as inputs and identifies a human subject by measuring the similarity distance between the predicted fixation sequence and the actual one (to be identified). The experimental results show that the fixation sequence similarity recognition algorithm obtained an equal error rate of 19.4% on the test set, and the model obtained an 86.5% Rank-1 recognition rate on the test set.© 2020 Elsevier B.V. All rights reserved. Keywords: Eye tracking | Eye-movement model | Deep-learning | Biometrics | Identity authentication | Reading eye-movement |
مقاله انگلیسی |
3 |
High-performance spatiotemporal trajectory matching across heterogeneous data sources
کارایی بالا تطبیق مسیر مکانی و مکانی در منابع داده ناهمگن - سایپرز ، باشگاه دانش-2020 In the era of big data, the movement of the same object or person can be recorded by different
devices with different measurement accuracies and sampling rates. Matching and conflating these
heterogeneous trajectories help to enhance trajectory semantics, describe user portraits, and discover
specified groups from human mobility. In this paper, we proposed a high-performance approach
for matching spatiotemporal trajectories across heterogeneous massive datasets. Two indicators, i.e.,
Time Weighted Similarity (TWS) and Space Weighted Similarity (SWS), are proposed to measure
the similarity of spatiotemporal trajectories. The core idea is that trajectories are more similar if
they stay close in a longer time and distance. A distributed computing framework based on Spark
is built for efficient trajectory matching among massive datasets. In the framework, the trajectory
segments are partitioned into 3-dimensional space–time cells for parallel processing, and a novel
method of segment reference point is designed to avoid duplicated computation. We conducted
extensive matching experiments on real-world and synthetic trajectory datasets. The experimental
results illustrate that the proposed approach outperforms other similarity metrics in accuracy, and the
Spark-based framework greatly improves the efficiency in spatiotemporal trajectory matching. Keywords: Distributed computing | Spatiotemporal big data | Trajectory similarity | Trajectory matching |
مقاله انگلیسی |
4 |
A generic trajectory similarity operator in moving object databases
یک اپراتور شباهت مسیری عمومی در پایگاه داده های حرکتی شی-2017 Evaluating similarity between trajectories of moving objects is important for wide range
of applications. The existing similarity measures typically define some meaning of similarity and
propose algorithms for computing it. We think that the meaning of similarity is application dependant, and should only be determined by the user. Therefore, there is a need for a generic approach
where users can define the meaning of similarity. In this paper, we propose a parametrized similarity
operator, based on the time warped edit distance, where the meaning of similarity is generic and left
for user to define. Our proposed operator is implemented in SECONDO and evaluated using both synthetic and real datasets. The results were promising and as expected.
KEYWORDS : Trajectory similarity | Moving objects databases | SECONDO | TWED |
مقاله انگلیسی |
5 |
Towards an Efficient Top-K Trajectory Similarity Query Processing Algorithm for Big Trajectory Data on GPGPUs
به سوی یک الگوریتم پردازش پرس و جوی مسیریابی کارا برای داده های بزرگ بر روی GPGPU مشابه-2016 Through the use of location-sensing devices, it
has been possible to collect very large datasets of trajectories. These datasets make it possible to issue spatio-temporal
queries with which users can gather information about the
characteristics of the movements of objects, derive patterns
from that information, and understand the objects themselves.
Among such spatio-temporal queries that can be issued is
the top-K trajectory similarity query. This query finds many
applications, such as bird migration analysis in ecology and
trajectory sharing in social networks. However, the large size
of the trajectory query sets and databases poses significant
computational challenges. In this work, we propose a parallel
GPGPU algorithm Top-KaBT that is specifically designed to
reduce the size of the candidate set generated while processing
these queries, and in doing so strives to address these computational challenges. The experiments show that the state of
the art top-K trajectory similarity query processing algorithm
on GPGPUs, TKSimGPU, achieves a 6.44X speedup in query
processing time when combined with our algorithm and a 13X
speedup over a GPGPU algorithm that uses exhaustive search.
Keywords: Trajectory | Trajectory similarity | GPGPU | High performance |
مقاله انگلیسی |