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Novelty Detection Under the Multi-Instance Multi-Label Framework
Novelty detection plays an important role in machine learning and signal processing. This
project studies novelty detection in a new setting where the data object is represented as
a bag of instances and associated with multiple class labels, referred to as multi-instance
multi-label (MIML) learning. Contrary to the common assumption in MIML that each
instance in a bag belongs to one of the known classes, in novelty detection, we focus on
the scenario where bags may contain novel-class instances. The goal is to determine,
for any given instance in a new bag, whether it belongs to a known class or a novel
class. Detecting novelty in the MIML setting captures many real-world phenomena and
has many potential applications. For example, in a collection of tagged images, the tag
may only cover a subset of objects existing in the images. Discovering an object whose
class has not been previously tagged can be useful for the purpose of soliciting a label
for the new object class. To address this novel problem, we present a discriminative
framework for detecting new class instances. Experiments demonstrate the effectiveness
of our proposed method, and reveal that the presence of unlabeled novel instances in
training bags is helpful to the detection of such instances in testing stage.
To the best of our knowledge, novelty detection in the MIML setting has not been
investigated. Our main contributions are: (i) We propose a new problem – novelty
detection in the MIML setting. (ii) We offer a framework based on score functions to
solve the problem. (iii) We illustrate the efficacy of our method on a real-world MIML
bioacoustics data
Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers
Although various techniques have been proposed to generate adversarial
samples for white-box attacks on text, little attention has been paid to
black-box attacks, which are more realistic scenarios. In this paper, we
present a novel algorithm, DeepWordBug, to effectively generate small text
perturbations in a black-box setting that forces a deep-learning classifier to
misclassify a text input. We employ novel scoring strategies to identify the
critical tokens that, if modified, cause the classifier to make an incorrect
prediction. Simple character-level transformations are applied to the
highest-ranked tokens in order to minimize the edit distance of the
perturbation, yet change the original classification. We evaluated DeepWordBug
on eight real-world text datasets, including text classification, sentiment
analysis, and spam detection. We compare the result of DeepWordBug with two
baselines: Random (Black-box) and Gradient (White-box). Our experimental
results indicate that DeepWordBug reduces the prediction accuracy of current
state-of-the-art deep-learning models, including a decrease of 68\% on average
for a Word-LSTM model and 48\% on average for a Char-CNN model.Comment: This is an extended version of the 6page Workshop version appearing
in 1st Deep Learning and Security Workshop colocated with IEEE S&
Dynamic Object Tracking for Quadruped Manipulator with Spherical Image-Based Approach
Exactly estimating and tracking the motion of surrounding dynamic objects is
one of important tasks for the autonomy of a quadruped manipulator. However,
with only an onboard RGB camera, it is still a challenging work for a quadruped
manipulator to track the motion of a dynamic object moving with unknown and
changing velocities. To address this problem, this manuscript proposes a novel
image-based visual servoing (IBVS) approach consisting of three elements: a
spherical projection model, a robust super-twisting observer, and a model
predictive controller (MPC). The spherical projection model decouples the
visual error of the dynamic target into linear and angular ones. Then, with the
presence of the visual error, the robustness of the observer is exploited to
estimate the unknown and changing velocities of the dynamic target without
depth estimation. Finally, the estimated velocity is fed into the model
predictive controller (MPC) to generate joint torques for the quadruped
manipulator to track the motion of the dynamical target. The proposed approach
is validated through hardware experiments and the experimental results
illustrate the approach's effectiveness in improving the autonomy of the
quadruped manipulator
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