836 research outputs found
The Impact of Live Polling Quizzes on Student Engagement and Performance in Computer Science Lectures
Prior to the COVID-19 pandemic, the adoption of live polling and real-time
feedback tools gained traction in higher education to enhance student
engagement and learning outcomes. Integrating live polling activities has been
shown to boost attention, participation, and understanding of course materials.
However, recent changes in learning behaviours due to the pandemic necessitate
a reevaluation of these active learning technologies. In this context, our
study focuses on the Computer Science (CS) domain, investigating the impact of
Live Polling Quizzes (LPQs) in undergraduate CS lectures. These quizzes
comprise fact-based, formally defined questions with clear answers, aiming to
enhance engagement, learning outcomes, and overall perceptions of the course
module. A survey was conducted among 70 undergraduate CS students, attending CS
modules with and without LPQs. The results revealed that while LPQs contributed
to higher attendance, other factors likely influenced attendance rates more
significantly. LPQs were generally viewed positively, aiding comprehension and
maintaining student attention and motivation. However, careful management of
LPQ frequency is crucial to prevent overuse for some students and potential
reduced motivation. Clear instructions for using the polling software were also
highlighted as essential.Comment: Submitte
Frequentist Model Averaging for Global Fr\'{e}chet Regression
To consider model uncertainty in global Fr\'{e}chet regression and improve
density response prediction, we propose a frequentist model averaging method.
The weights are chosen by minimizing a cross-validation criterion based on
Wasserstein distance. In the cases where all candidate models are misspecified,
we prove that the corresponding model averaging estimator has asymptotic
optimality, achieving the lowest possible Wasserstein distance. When there are
correctly specified candidate models, we prove that our method asymptotically
assigns all weights to the correctly specified models. Numerical results of
extensive simulations and a real data analysis on intracerebral hemorrhage data
strongly favour our method
Multipath Time-delay Estimation with Impulsive Noise via Bayesian Compressive Sensing
Multipath time-delay estimation is commonly encountered in radar and sonar
signal processing. In some real-life environments, impulse noise is ubiquitous
and significantly degrades estimation performance. Here, we propose a Bayesian
approach to tailor the Bayesian Compressive Sensing (BCS) to mitigate impulsive
noises. In particular, a heavy-tail Laplacian distribution is used as a
statistical model for impulse noise, while Laplacian prior is used for sparse
multipath modeling. The Bayesian learning problem contains hyperparameters
learning and parameter estimation, solved under the BCS inference framework.
The performance of our proposed method is compared with benchmark methods,
including compressive sensing (CS), BCS, and Laplacian-prior BCS (L-BCS). The
simulation results show that our proposed method can estimate the multipath
parameters more accurately and have a lower root mean squared estimation error
(RMSE) in intensely impulsive noise
Explore the Power of Dropout on Few-shot Learning
The generalization power of the pre-trained model is the key for few-shot
deep learning. Dropout is a regularization technique used in traditional deep
learning methods. In this paper, we explore the power of dropout on few-shot
learning and provide some insights about how to use it. Extensive experiments
on the few-shot object detection and few-shot image classification datasets,
i.e., Pascal VOC, MS COCO, CUB, and mini-ImageNet, validate the effectiveness
of our method.Comment: arXiv admin note: substantial text overlap with arXiv:2210.0640
SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability
Interpretability of Deep Learning (DL) is a barrier to trustworthy AI.
Despite great efforts made by the Explainable AI (XAI) community, explanations
lack robustness -- indistinguishable input perturbations may lead to different
XAI results. Thus, it is vital to assess how robust DL interpretability is,
given an XAI method. In this paper, we identify several challenges that the
state-of-the-art is unable to cope with collectively: i) existing metrics are
not comprehensive; ii) XAI techniques are highly heterogeneous; iii)
misinterpretations are normally rare events. To tackle these challenges, we
introduce two black-box evaluation methods, concerning the worst-case
interpretation discrepancy and a probabilistic notion of how robust in general,
respectively. Genetic Algorithm (GA) with bespoke fitness function is used to
solve constrained optimisation for efficient worst-case evaluation. Subset
Simulation (SS), dedicated to estimate rare event probabilities, is used for
evaluating overall robustness. Experiments show that the accuracy, sensitivity,
and efficiency of our methods outperform the state-of-the-arts. Finally, we
demonstrate two applications of our methods: ranking robust XAI methods and
selecting training schemes to improve both classification and interpretation
robustness.Comment: Accepted by the IEEE/CVF International Conference on Computer Vision
2023 (ICCV'23
Reachability Verification Based Reliability Assessment for Deep Reinforcement Learning Controlled Robotics and Autonomous Systems
Deep Reinforcement Learning (DRL) has achieved impressive performance in
robotics and autonomous systems (RASs). A key impediment to its deployment in
real-life operations is the spuriously unsafe DRL policies--unexplored states
may lead the agent to make wrong decisions that may cause hazards, especially
in applications where end-to-end controllers of the RAS were trained by DRL. In
this paper, we propose a novel quantitative reliability assessment framework
for DRL-controlled RASs, leveraging verification evidence generated from formal
reliability analysis of neural networks. A two-level verification framework is
introduced to check the safety property with respect to inaccurate observations
that are due to, e.g., environmental noises and state changes. Reachability
verification tools are leveraged at the local level to generate safety evidence
of trajectories, while at the global level, we quantify the overall reliability
as an aggregated metric of local safety evidence, according to an operational
profile. The effectiveness of the proposed verification framework is
demonstrated and validated via experiments on real RASs.Comment: Submitted, under revie
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