66 research outputs found
QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites
In this paper, we present a framework for Question Difficulty and Expertise
Estimation (QDEE) in Community Question Answering sites (CQAs) such as Yahoo!
Answers and Stack Overflow, which tackles a fundamental challenge in
crowdsourcing: how to appropriately route and assign questions to users with
the suitable expertise. This problem domain has been the subject of much
research and includes both language-agnostic as well as language conscious
solutions. We bring to bear a key language-agnostic insight: that users gain
expertise and therefore tend to ask as well as answer more difficult questions
over time. We use this insight within the popular competition (directed) graph
model to estimate question difficulty and user expertise by identifying key
hierarchical structure within said model. An important and novel contribution
here is the application of "social agony" to this problem domain. Difficulty
levels of newly posted questions (the cold-start problem) are estimated by
using our QDEE framework and additional textual features. We also propose a
model to route newly posted questions to appropriate users based on the
difficulty level of the question and the expertise of the user. Extensive
experiments on real world CQAs such as Yahoo! Answers and Stack Overflow data
demonstrate the improved efficacy of our approach over contemporary
state-of-the-art models. The QDEE framework also allows us to characterize user
expertise in novel ways by identifying interesting patterns and roles played by
different users in such CQAs.Comment: Accepted in the Proceedings of the 12th International AAAI Conference
on Web and Social Media (ICWSM 2018). June 2018. Stanford, CA, US
Semi-supervised Embedding in Attributed Networks with Outliers
In this paper, we propose a novel framework, called Semi-supervised Embedding
in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector
representation that systematically captures the topological proximity,
attribute affinity and label similarity of vertices in a partially labeled
attributed network (PLAN). Our method is designed to work in both transductive
and inductive settings while explicitly alleviating noise effects from
outliers. Experimental results on various datasets drawn from the web, text and
image domains demonstrate the advantages of SEANO over state-of-the-art methods
in semi-supervised classification under transductive as well as inductive
settings. We also show that a subset of parameters in SEANO is interpretable as
outlier score and can significantly outperform baseline methods when applied
for detecting network outliers. Finally, we present the use of SEANO in a
challenging real-world setting -- flood mapping of satellite images and show
that it is able to outperform modern remote sensing algorithms for this task.Comment: in Proceedings of SIAM International Conference on Data Mining
(SDM'18
Symmetrization for Embedding Directed Graphs
Recently, one has seen a surge of interest in developing such methods
including ones for learning such representations for (undirected) graphs (while
preserving important properties). However, most of the work to date on
embedding graphs has targeted undirected networks and very little has focused
on the thorny issue of embedding directed networks. In this paper, we instead
propose to solve the directed graph embedding problem via a two-stage approach:
in the first stage, the graph is symmetrized in one of several possible ways,
and in the second stage, the so-obtained symmetrized graph is embedded using
any state-of-the-art (undirected) graph embedding algorithm. Note that it is
not the objective of this paper to propose a new (undirected) graph embedding
algorithm or discuss the strengths and weaknesses of existing ones; all we are
saying is that whichever be the suitable graph embedding algorithm, it will fit
in the above proposed symmetrization framework.Comment: has been accepted to The Thirty-Third AAAI Conference on Artificial
Intelligence (AAAI 2019) Student Abstract and Poster Progra
Connected Autonomous Vehicle Motion Planning with Video Predictions from Smart, Self-Supervised Infrastructure
Connected autonomous vehicles (CAVs) promise to enhance safety, efficiency,
and sustainability in urban transportation. However, this is contingent upon a
CAV correctly predicting the motion of surrounding agents and planning its own
motion safely. Doing so is challenging in complex urban environments due to
frequent occlusions and interactions among many agents. One solution is to
leverage smart infrastructure to augment a CAV's situational awareness; the
present work leverages a recently proposed "Self-Supervised Traffic Advisor"
(SSTA) framework of smart sensors that teach themselves to generate and
broadcast useful video predictions of road users. In this work, SSTA
predictions are modified to predict future occupancy instead of raw video,
which reduces the data footprint of broadcast predictions. The resulting
predictions are used within a planning framework, demonstrating that this
design can effectively aid CAV motion planning. A variety of numerical
experiments study the key factors that make SSTA outputs useful for practical
CAV planning in crowded urban environments.Comment: 2023 IEEE 26th International Conference on Intelligent Transportation
Systems (ITSC
Self-Supervised Traffic Advisors: Distributed, Multi-view Traffic Prediction for Smart Cities
Connected and Autonomous Vehicles (CAVs) are becoming more widely deployed,
but it is unclear how to best deploy smart infrastructure to maximize their
capabilities. One key challenge is to ensure CAVs can reliably perceive other
agents, especially occluded ones. A further challenge is the desire for smart
infrastructure to be autonomous and readily scalable to wide-area deployments,
similar to modern traffic lights. The present work proposes the Self-Supervised
Traffic Advisor (SSTA), an infrastructure edge device concept that leverages
self-supervised video prediction in concert with a communication and
co-training framework to enable autonomously predicting traffic throughout a
smart city. An SSTA is a statically-mounted camera that overlooks an
intersection or area of complex traffic flow that predicts traffic flow as
future video frames and learns to communicate with neighboring SSTAs to enable
predicting traffic before it appears in the Field of View (FOV). The proposed
framework aims at three goals: (1) inter-device communication to enable
high-quality predictions, (2) scalability to an arbitrary number of devices,
and (3) lifelong online learning to ensure adaptability to changing
circumstances. Finally, an SSTA can broadcast its future predicted video frames
directly as information for CAVs to run their own post-processing for the
purpose of control.Comment: 2022 IEEE 25th International Conference on Intelligent Transportation
Systems (ITSC
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