816 research outputs found
DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation
Driving trajectory representation learning is of great significance for
various location-based services, such as driving pattern mining and route
recommendation. However, previous representation generation approaches tend to
rarely address three challenges: 1) how to represent the intricate semantic
intentions of mobility inexpensively; 2) complex and weak spatial-temporal
dependencies due to the sparsity and heterogeneity of the trajectory data; 3)
route selection preferences and their correlation to driving behavior. In this
paper, we propose a novel multimodal fusion model, DouFu, for trajectory
representation joint learning, which applies multimodal learning and attention
fusion module to capture the internal characteristics of trajectories. We first
design movement, route, and global features generated from the trajectory data
and urban functional zones and then analyze them respectively with the
attention encoder or feed forward network. The attention fusion module
incorporates route features with movement features to create a better
spatial-temporal embedding. With the global semantic feature, DouFu produces a
comprehensive embedding for each trajectory. We evaluate representations
generated by our method and other baseline models on classification and
clustering tasks. Empirical results show that DouFu outperforms other models in
most of the learning algorithms like the linear regression and the support
vector machine by more than 10%.Comment: 11 pages, 7 figure
Modeling Spatial Trajectories using Coarse-Grained Smartphone Logs
Current approaches for points-of-interest (POI) recommendation learn the
preferences of a user via the standard spatial features such as the POI
coordinates, the social network, etc. These models ignore a crucial aspect of
spatial mobility -- every user carries their smartphones wherever they go. In
addition, with growing privacy concerns, users refrain from sharing their exact
geographical coordinates and their social media activity. In this paper, we
present REVAMP, a sequential POI recommendation approach that utilizes the user
activity on smartphone applications (or apps) to identify their mobility
preferences. This work aligns with the recent psychological studies of online
urban users, which show that their spatial mobility behavior is largely
influenced by the activity of their smartphone apps. In addition, our proposal
of coarse-grained smartphone data refers to data logs collected in a
privacy-conscious manner, i.e., consisting only of (a) category of the
smartphone app and (b) category of check-in location. Thus, REVAMP is not privy
to precise geo-coordinates, social networks, or the specific application being
accessed. Buoyed by the efficacy of self-attention models, we learn the POI
preferences of a user using two forms of positional encodings -- absolute and
relative -- with each extracted from the inter-check-in dynamics in the
check-in sequence of a user. Extensive experiments across two large-scale
datasets from China show the predictive prowess of REVAMP and its ability to
predict app- and POI categories.Comment: IEEE Transactions on Big Dat
Modeling Time-Series and Spatial Data for Recommendations and Other Applications
With the research directions described in this thesis, we seek to address the
critical challenges in designing recommender systems that can understand the
dynamics of continuous-time event sequences. We follow a ground-up approach,
i.e., first, we address the problems that may arise due to the poor quality of
CTES data being fed into a recommender system. Later, we handle the task of
designing accurate recommender systems. To improve the quality of the CTES
data, we address a fundamental problem of overcoming missing events in temporal
sequences. Moreover, to provide accurate sequence modeling frameworks, we
design solutions for points-of-interest recommendation, i.e., models that can
handle spatial mobility data of users to various POI check-ins and recommend
candidate locations for the next check-in. Lastly, we highlight that the
capabilities of the proposed models can have applications beyond recommender
systems, and we extend their abilities to design solutions for large-scale CTES
retrieval and human activity prediction. A significant part of this thesis uses
the idea of modeling the underlying distribution of CTES via neural marked
temporal point processes (MTPP). Traditional MTPP models are stochastic
processes that utilize a fixed formulation to capture the generative mechanism
of a sequence of discrete events localized in continuous time. In contrast,
neural MTPP combine the underlying ideas from the point process literature with
modern deep learning architectures. The ability of deep-learning models as
accurate function approximators has led to a significant gain in the predictive
prowess of neural MTPP models. In this thesis, we utilize and present several
neural network-based enhancements for the current MTPP frameworks for the
aforementioned real-world applications.Comment: Ph.D. Thesis (2022
Privacy-Preserving Individual-Level COVID-19 Infection Prediction via Federated Graph Learning
Accurately predicting individual-level infection state is of great value
since its essential role in reducing the damage of the epidemic. However, there
exists an inescapable risk of privacy leakage in the fine-grained user mobility
trajectories required by individual-level infection prediction. In this paper,
we focus on developing a framework of privacy-preserving individual-level
infection prediction based on federated learning (FL) and graph neural networks
(GNN). We propose Falcon, a Federated grAph Learning method for
privacy-preserving individual-level infeCtion predictiON. It utilizes a novel
hypergraph structure with spatio-temporal hyperedges to describe the complex
interactions between individuals and locations in the contagion process. By
organically combining the FL framework with hypergraph neural networks, the
information propagation process of the graph machine learning is able to be
divided into two stages distributed on the server and the clients,
respectively, so as to effectively protect user privacy while transmitting
high-level information. Furthermore, it elaborately designs a differential
privacy perturbation mechanism as well as a plausible pseudo location
generation approach to preserve user privacy in the graph structure. Besides,
it introduces a cooperative coupling mechanism between the individual-level
prediction model and an additional region-level model to mitigate the
detrimental impacts caused by the injected obfuscation mechanisms. Extensive
experimental results show that our methodology outperforms state-of-the-art
algorithms and is able to protect user privacy against actual privacy attacks.
Our code and datasets are available at the link:
https://github.com/wjfu99/FL-epidemic.Comment: accepted by TOI
Autoregressive Attention Neural Networks for Non-Line-of-Sight User Tracking with Dynamic Metasurface Antennas
User localization and tracking in the upcoming generation of wireless
networks have the potential to be revolutionized by technologies such as the
Dynamic Metasurface Antennas (DMAs). Commonly proposed algorithmic approaches
rely on assumptions about relatively dominant Line-of-Sight (LoS) paths, or
require pilot transmission sequences whose length is comparable to the number
of DMA elements, thus, leading to limited effectiveness and considerable
measurement overheads in blocked LoS and dynamic multipath environments. In
this paper, we present a two-stage machine-learning-based approach for user
tracking, specifically designed for non-LoS multipath settings. A newly
proposed attention-based Neural Network (NN) is first trained to map noisy
channel responses to potential user positions, regardless of user mobility
patterns. This architecture constitutes a modification of the prominent vision
transformer, specifically modified for extracting information from
high-dimensional frequency response signals. As a second stage, the NN's
predictions for the past user positions are passed through a learnable
autoregressive model to exploit the time-correlated channel information and
obtain the final position predictions. The channel estimation procedure
leverages a DMA receive architecture with partially-connected radio frequency
chains, which results to reduced numbers of pilots. The numerical evaluation
over an outdoor ray-tracing scenario illustrates that despite LoS blockage,
this methodology is capable of achieving high position accuracy across various
multipath settings.Comment: 5 pages, 3 figures, accepted for presentation by 2023 IEEE
International Workshop on Computational Advances in Multi-Sensor Adaptive
Processing (CAMSAP 2023
System Optimisation for Multi-access Edge Computing Based on Deep Reinforcement Learning
Multi-access edge computing (MEC) is an emerging and important distributed computing paradigm that aims to extend cloud service to the network edge to reduce network traffic and service latency. Proper system optimisation and maintenance are crucial to maintaining high Quality-of-service (QoS) for end-users. However, with the increasing complexity of the architecture of MEC and mobile applications, effectively optimising MEC systems is non-trivial. Traditional optimisation methods are generally based on simplified mathematical models and fixed heuristics, which rely heavily on expert knowledge. As a consequence, when facing dynamic MEC scenarios, considerable human efforts and expertise are required to redesign the model and tune the heuristics, which is time-consuming.
This thesis aims to develop deep reinforcement learning (DRL) methods to handle system optimisation problems in MEC. Instead of developing fixed heuristic algorithms for the problems, this thesis aims to design DRL-based methods that enable systems to learn optimal solutions on their own. This research demonstrates the effectiveness of DRL-based methods on two crucial system optimisation problems: task offloading and service migration. Specifically, this thesis first investigate the dependent task offloading problem that considers the inner dependencies of tasks. This research builds a DRL-based method combining sequence-to-sequence (seq2seq) neural network to address the problem. Experiment results demonstrate that our method outperforms the existing heuristic algorithms and achieves near-optimal performance. To further enhance the learning efficiency of the DRL-based task offloading method for unseen learning tasks, this thesis then integrates meta reinforcement learning to handle the task offloading problem. Our method can adapt fast to new environments with a small number of gradient updates and samples. Finally, this thesis exploits the DRL-based solution for the service migration problem in MEC considering user mobility. This research models the service migration problem as a Partially Observable Markov Decision Process (POMDP) and propose a tailored actor-critic algorithm combining Long-short Term Memory (LSTM) to solve the POMDP. Results from extensive experiments based on real-world mobility traces demonstrate that our method consistently outperforms both the heuristic and state-of-the-art learning-driven algorithms on various MEC scenarios
- …