168 research outputs found
Query Processing In Location-based Services
With the advances in wireless communication technology and advanced positioning systems, a variety of Location-Based Services (LBS) become available to the public. Mobile users can issue location-based queries to probe their surrounding environments. One important type of query in LBS is moving monitoring queries over mobile objects. Due to the high frequency in location updates and the expensive cost of continuous query processing, server computation capacity and wireless communication bandwidth are the two limiting factors for large-scale deployment of moving object database systems. To address both of the scalability factors, distributed computing has been considered. These schemes enable moving objects to participate as a peer in query processing to substantially reduce the demand on server computation, and wireless communications associated with location updates. In the first part of this dissertation, we propose a distributed framework to process moving monitoring queries over moving objects in a spatial network environment. In the second part of this dissertation, in order to reduce the communication cost, we leverage both on-demand data access and periodic broadcast to design a new hybrid distributed solution for moving monitoring queries in an open space environment. Location-based services make our daily life more convenient. However, to receive the services, one has to reveal his/her location and query information when issuing locationbased queries. This could lead to privacy breach if these personal information are possessed by some untrusted parties. In the third part of this dissertation, we introduce a new privacy protection measure called query l-diversity, and provide two cloaking algorithms to achieve both location kanonymity and query l-diversity to better protect user privacy. In the fourth part of this dissertation, we design a hybrid three-tier architecture to help reduce privacy exposure. In the fifth part of this dissertation, we propose to use Road Network Embedding technique to process privacy protected queries
Attitude-Tracking Control with Path Planning for Agile Satellite Using Double-Gimbal Control Moment Gyros
In view of the issue of rapid attitude maneuver control of agile satellite, this paper presents an attitude-tracking control algorithm with path planning based on the improved genetic algorithm, adaptive backstepping control as well as sliding mode control. The satellite applies double gimbal control moment gyro as actuator and is subjected to the external disturbance and uncertain inertia properties. Firstly, considering the comprehensive mathematical model of the agile satellite and the double gimbal control moment gyro, an improved genetic algorithm is proposed to solve the attitude path-planning problem. The goal is to find an energy optimal path which satisfies certain maneuverability under the constraints of the input saturation, actuator saturation, slew rate limit and singularity measurement limit. Then, the adaptive backstepping control and sliding mode control are adopted in the design of the attitude-tracking controller to track accurately the desired path comprised of the satellite attitude quaternion and velocity. Finally, simulation results indicate the robustness and good tracking performance of the derived controller as well as its ability to avert the singularity of double gimbal control moment gyro
Exponentially weighted particle filter for simultaneous localization and mapping based on magnetic field measurements
This paper presents a simultaneous localization and mapping (SLAM) method that utilizes the measurement of ambient magnetic fields present in all indoor environments. In this paper, an improved exponentially weighted particle filter was proposed to estimate the pose distribution of the object and a Kriging interpolation method was introduced to update the map of the magnetic fields. The performance and effectiveness of the proposed algorithms were evaluated by simulations on MATLAB based on a map with magnetic fields measured manually in an indoor environment and also by tests on the mobile devices in the same area. From the tests, two interesting phenomena have been discovered; one is the shift of location estimation after sharp turning and the other is the accumulated errors. While the latter has been confirmed and investigated by a few researchers, the reason for the first one still remains unknown. The tests also confirm that the interpolated map by using the proposed method improves the localization accuracy
Delving into E-Commerce Product Retrieval with Vision-Language Pre-training
E-commerce search engines comprise a retrieval phase and a ranking phase,
where the first one returns a candidate product set given user queries.
Recently, vision-language pre-training, combining textual information with
visual clues, has been popular in the application of retrieval tasks. In this
paper, we propose a novel V+L pre-training method to solve the retrieval
problem in Taobao Search. We design a visual pre-training task based on
contrastive learning, outperforming common regression-based visual pre-training
tasks. In addition, we adopt two negative sampling schemes, tailored for the
large-scale retrieval task. Besides, we introduce the details of the online
deployment of our proposed method in real-world situations. Extensive
offline/online experiments demonstrate the superior performance of our method
on the retrieval task. Our proposed method is employed as one retrieval channel
of Taobao Search and serves hundreds of millions of users in real time.Comment: 5 pages, 4 figures, accepted to SIRIP 202
ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
Recommender system (RS) devotes to predicting user preference to a given item
and has been widely deployed in most web-scale applications. Recently,
knowledge graph (KG) attracts much attention in RS due to its abundant
connective information. Existing methods either explore independent meta-paths
for user-item pairs over KG, or employ graph neural network (GNN) on whole KG
to produce representations for users and items separately. Despite
effectiveness, the former type of methods fails to fully capture structural
information implied in KG, while the latter ignores the mutual effect between
target user and item during the embedding propagation. In this work, we propose
a new framework named Adaptive Target-Behavior Relational Graph network (ATBRG
for short) to effectively capture structural relations of target user-item
pairs over KG. Specifically, to associate the given target item with user
behaviors over KG, we propose the graph connect and graph prune techniques to
construct adaptive target-behavior relational graph. To fully distill
structural information from the sub-graph connected by rich relations in an
end-to-end fashion, we elaborate on the model design of ATBRG, equipped with
relation-aware extractor layer and representation activation layer. We perform
extensive experiments on both industrial and benchmark datasets. Empirical
results show that ATBRG consistently and significantly outperforms
state-of-the-art methods. Moreover, ATBRG has also achieved a performance
improvement of 5.1% on CTR metric after successful deployment in one popular
recommendation scenario of Taobao APP.Comment: Accepted by SIGIR 2020, full paper with 10 pages and 5 figure
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
Click-through rate (CTR) prediction is a critical task for many industrial
systems, such as display advertising and recommender systems. Recently,
modeling user behavior sequences attracts much attention and shows great
improvements in the CTR field. Existing works mainly exploit attention
mechanism based on embedding product when considering relations between user
behaviors and target item. However, this methodology lacks of concrete
semantics and overlooks the underlying reasons driving a user to click on a
target item. In this paper, we propose a new framework named Multiplex
Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex
relations between user behaviors and target item to enhance CTR prediction.
Multiplex relations consist of meaningful semantics, which can bring a better
understanding on users' interests from different perspectives. To explore and
model multiplex relations, we propose to incorporate various graphs (e.g.,
knowledge graph and item-item similarity graph) to construct multiple
relational paths between user behaviors and target item. Then Bi-LSTM is
applied to encode each path in the path extractor layer. A path fusion network
and a path activation network are devised to adaptively aggregate and finally
learn the representation of all paths for CTR prediction. Extensive offline and
online experiments clearly verify the effectiveness of our framework.Comment: Accepted by CIKM202
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