970 research outputs found
Defining and Generating Axial Lines from Street Center Lines for better Understanding of Urban Morphologies
Axial lines are defined as the longest visibility lines for representing
individual linear spaces in urban environments. The least number of axial lines
that cover the free space of an urban environment or the space between
buildings constitute what is often called an axial map. This is a fundamental
tool in space syntax, a theory developed by Bill Hillier and his colleagues for
characterizing the underlying urban morphologies. For a long time, generating
axial lines with help of some graphic software has been a tedious manual
process that is criticized for being time consuming, subjective, or even
arbitrary. In this paper, we redefine axial lines as the least number of
individual straight line segments mutually intersected along natural streets
that are generated from street center lines using the Gestalt principle of good
continuity. Based on this new definition, we develop an automatic solution to
generating the newly defined axial lines from street center lines. We apply
this solution to six typical street networks (three from North America and
three from Europe), and generate a new set of axial lines for analyzing the
urban morphologies. Through a comparison study between the new axial lines and
the conventional or old axial lines, and between the new axial lines and
natural streets, we demonstrate with empirical evidence that the newly defined
axial lines are a better alternative in capturing the underlying urban
structure.
Keywords: Space syntax, street networks, topological analysis, traffic,
head/tail division ruleComment: 10 pages, 7 figures, and 2 tables, one figure added + minor revisio
Achieving non-discrimination in prediction
Discrimination-aware classification is receiving an increasing attention in
data science fields. The pre-process methods for constructing a
discrimination-free classifier first remove discrimination from the training
data, and then learn the classifier from the cleaned data. However, they lack a
theoretical guarantee for the potential discrimination when the classifier is
deployed for prediction. In this paper, we fill this gap by mathematically
bounding the probability of the discrimination in prediction being within a
given interval in terms of the training data and classifier. We adopt the
causal model for modeling the data generation mechanism, and formally defining
discrimination in population, in a dataset, and in prediction. We obtain two
important theoretical results: (1) the discrimination in prediction can still
exist even if the discrimination in the training data is completely removed;
and (2) not all pre-process methods can ensure non-discrimination in prediction
even though they can achieve non-discrimination in the modified training data.
Based on the results, we develop a two-phase framework for constructing a
discrimination-free classifier with a theoretical guarantee. The experiments
demonstrate the theoretical results and show the effectiveness of our two-phase
framework
SAFE: A Neural Survival Analysis Model for Fraud Early Detection
Many online platforms have deployed anti-fraud systems to detect and prevent
fraudulent activities. However, there is usually a gap between the time that a
user commits a fraudulent action and the time that the user is suspended by the
platform. How to detect fraudsters in time is a challenging problem. Most of
the existing approaches adopt classifiers to predict fraudsters given their
activity sequences along time. The main drawback of classification models is
that the prediction results between consecutive timestamps are often
inconsistent. In this paper, we propose a survival analysis based fraud early
detection model, SAFE, which maps dynamic user activities to survival
probabilities that are guaranteed to be monotonically decreasing along time.
SAFE adopts recurrent neural network (RNN) to handle user activity sequences
and directly outputs hazard values at each timestamp, and then, survival
probability derived from hazard values is deployed to achieve consistent
predictions. Because we only observe the user suspended time instead of the
fraudulent activity time in the training data, we revise the loss function of
the regular survival model to achieve fraud early detection. Experimental
results on two real world datasets demonstrate that SAFE outperforms both the
survival analysis model and recurrent neural network model alone as well as
state-of-the-art fraud early detection approaches.Comment: To appear in AAAI-201
Task-specific Word Identification from Short Texts Using a Convolutional Neural Network
Task-specific word identification aims to choose the task-related words that
best describe a short text. Existing approaches require well-defined seed words
or lexical dictionaries (e.g., WordNet), which are often unavailable for many
applications such as social discrimination detection and fake review detection.
However, we often have a set of labeled short texts where each short text has a
task-related class label, e.g., discriminatory or non-discriminatory, specified
by users or learned by classification algorithms. In this paper, we focus on
identifying task-specific words and phrases from short texts by exploiting
their class labels rather than using seed words or lexical dictionaries. We
consider the task-specific word and phrase identification as feature learning.
We train a convolutional neural network over a set of labeled texts and use
score vectors to localize the task-specific words and phrases. Experimental
results on sentiment word identification show that our approach significantly
outperforms existing methods. We further conduct two case studies to show the
effectiveness of our approach. One case study on a crawled tweets dataset
demonstrates that our approach can successfully capture the
discrimination-related words/phrases. The other case study on fake review
detection shows that our approach can identify the fake-review words/phrases.Comment: accepted by Intelligent Data Analysis, an International Journa
Adaptive Laplace Mechanism: Differential Privacy Preservation in Deep Learning
In this paper, we focus on developing a novel mechanism to preserve
differential privacy in deep neural networks, such that: (1) The privacy budget
consumption is totally independent of the number of training steps; (2) It has
the ability to adaptively inject noise into features based on the contribution
of each to the output; and (3) It could be applied in a variety of different
deep neural networks. To achieve this, we figure out a way to perturb affine
transformations of neurons, and loss functions used in deep neural networks. In
addition, our mechanism intentionally adds "more noise" into features which are
"less relevant" to the model output, and vice-versa. Our theoretical analysis
further derives the sensitivities and error bounds of our mechanism. Rigorous
experiments conducted on MNIST and CIFAR-10 datasets show that our mechanism is
highly effective and outperforms existing solutions.Comment: IEEE ICDM 2017 - regular pape
Spectrum-based deep neural networks for fraud detection
In this paper, we focus on fraud detection on a signed graph with only a
small set of labeled training data. We propose a novel framework that combines
deep neural networks and spectral graph analysis. In particular, we use the
node projection (called as spectral coordinate) in the low dimensional spectral
space of the graph's adjacency matrix as input of deep neural networks.
Spectral coordinates in the spectral space capture the most useful topology
information of the network. Due to the small dimension of spectral coordinates
(compared with the dimension of the adjacency matrix derived from a graph),
training deep neural networks becomes feasible. We develop and evaluate two
neural networks, deep autoencoder and convolutional neural network, in our
fraud detection framework. Experimental results on a real signed graph show
that our spectrum based deep neural networks are effective in fraud detection
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