64 research outputs found
Exploring the Role of AI Assistants in Computer Science Education: Methods, Implications, and Instructor Perspectives
The use of AI assistants, along with the challenges they present, has sparked
significant debate within the community of computer science education. While
these tools demonstrate the potential to support students' learning and
instructors' teaching, they also raise concerns about enabling unethical uses
by students. Previous research has suggested various strategies aimed at
addressing these issues. However, they concentrate on the introductory
programming courses and focus on one specific type of problem. The present
research evaluated the performance of ChatGPT, a state-of-the-art AI assistant,
at solving 187 problems spanning three distinct types that were collected from
six undergraduate computer science. The selected courses covered different
topics and targeted different program levels. We then explored methods to
modify these problems to adapt them to ChatGPT's capabilities to reduce
potential misuse by students. Finally, we conducted semi-structured interviews
with 11 computer science instructors. The aim was to gather their opinions on
our problem modification methods, understand their perspectives on the impact
of AI assistants on computer science education, and learn their strategies for
adapting their courses to leverage these AI capabilities for educational
improvement. The results revealed issues ranging from academic fairness to
long-term impact on students' mental models. From our results, we derived
design implications and recommended tools to help instructors design and create
future course material that could more effectively adapt to AI assistants'
capabilities
BASAR:Black-box Attack on Skeletal Action Recognition
Skeletal motion plays a vital role in human activity recognition as either an
independent data source or a complement. The robustness of skeleton-based
activity recognizers has been questioned recently, which shows that they are
vulnerable to adversarial attacks when the full-knowledge of the recognizer is
accessible to the attacker. However, this white-box requirement is overly
restrictive in most scenarios and the attack is not truly threatening. In this
paper, we show that such threats do exist under black-box settings too. To this
end, we propose the first black-box adversarial attack method BASAR. Through
BASAR, we show that adversarial attack is not only truly a threat but also can
be extremely deceitful, because on-manifold adversarial samples are rather
common in skeletal motions, in contrast to the common belief that adversarial
samples only exist off-manifold. Through exhaustive evaluation and comparison,
we show that BASAR can deliver successful attacks across models, data, and
attack modes. Through harsh perceptual studies, we show that it achieves
effective yet imperceptible attacks. By analyzing the attack on different
activity recognizers, BASAR helps identify the potential causes of their
vulnerability and provides insights on what classifiers are likely to be more
robust against attack. Code is available at
https://github.com/realcrane/BASAR-Black-box-Attack-on-Skeletal-Action-Recognition.Comment: Accepted in CVPR 202
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box Attack
Human Activity Recognition (HAR) has been employed in a wide range of
applications, e.g. self-driving cars, where safety and lives are at stake.
Recently, the robustness of existing skeleton-based HAR methods has been
questioned due to their vulnerability to adversarial attacks, which causes
concerns considering the scale of the implication. However, the proposed
attacks require the full-knowledge of the attacked classifier, which is overly
restrictive. In this paper, we show such threats indeed exist, even when the
attacker only has access to the input/output of the model. To this end, we
propose the very first black-box adversarial attack approach in skeleton-based
HAR called BASAR. BASAR explores the interplay between the classification
boundary and the natural motion manifold. To our best knowledge, this is the
first time data manifold is introduced in adversarial attacks on time series.
Via BASAR, we find on-manifold adversarial samples are extremely deceitful and
rather common in skeletal motions, in contrast to the common belief that
adversarial samples only exist off-manifold. Through exhaustive evaluation, we
show that BASAR can deliver successful attacks across classifiers, datasets,
and attack modes. By attack, BASAR helps identify the potential causes of the
model vulnerability and provides insights on possible improvements. Finally, to
mitigate the newly identified threat, we propose a new adversarial training
approach by leveraging the sophisticated distributions of on/off-manifold
adversarial samples, called mixed manifold-based adversarial training (MMAT).
MMAT can successfully help defend against adversarial attacks without
compromising classification accuracy.Comment: arXiv admin note: substantial text overlap with arXiv:2103.0526
Refactoring for Reuse: An Empirical Study
Refactoring is the de-facto practice to optimize software health. While several studies propose refactoring strategies to optimize software design through applying design patterns and removing design defects, little is known about how developers actually refactor their code to improve its reuse. Therefore, we extract, from 1,828 open source projects, a set of refactorings that were intended to improve the software reusability. We analyze the impact of reusability refactorings on the state-of-the-art reusability metrics, and we compare the distribution of reusability refactoring types, with the distribution of the remaining mainstream refactorings. Overall, we found that the distribution of refactoring types, applied in the context of reusability, is different from the distribution of refactoring types in mainstream development. In the refactorings performed to improve reusability, source files are subject to more design level types of refactorings. Reusability refactorings significantly impact, high-level code elements, such as packages, classes, and methods, while typical refactorings, impact all code elements, including identifiers, and parameters. These findings provide practical insights into the current practice of refactoring in the context of code reuse involving the act of refactoring
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack
Action recognition has been heavily employed in many applications such as
autonomous vehicles, surveillance, etc, where its robustness is a primary
concern. In this paper, we examine the robustness of state-of-the-art action
recognizers against adversarial attack, which has been rarely investigated so
far. To this end, we propose a new method to attack action recognizers that
rely on 3D skeletal motion. Our method involves an innovative perceptual loss
that ensures the imperceptibility of the attack. Empirical studies demonstrate
that our method is effective in both white-box and black-box scenarios. Its
generalizability is evidenced on a variety of action recognizers and datasets.
Its versatility is shown in different attacking strategies. Its deceitfulness
is proven in extensive perceptual studies. Our method shows that adversarial
attack on 3D skeletal motions, one type of time-series data, is significantly
different from traditional adversarial attack problems. Its success raises
serious concern on the robustness of action recognizers and provides insights
on potential improvements.Comment: Accepted in CVPR 2021. arXiv admin note: substantial text overlap
with arXiv:1911.0710
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