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Automated defect prioritization based on defects resolved at various project periods
(Elsevier, 2021-09)
Defect prioritization is mainly a manual and error-prone task in the current state-of-the-practice. We evaluated the effectiveness of an automated approach that employs supervised machine learning. We used two alternative ...
Content-aware playback speed control for low-latency live streaming of sports
(The ACM Digital Library, 2021)
There are two main factors that determine the viewer experience during the live streaming of sports content: latency and stalls. Latency should be low and stalls should not occur. Yet, these two factors work against each ...
COSMOS on steroids: a Cheap detector for cheapfakes
(The ACM Digital Library, 2021)
The growing prevalence of visual disinformation has become an important problem to solve nowadays. Cheapfake is a new term used for the altered media generated by non-AI techniques. In their recent COSMOS work, the authors ...
Time-correlated sparsification for communication-efficient federated learning
(IEEE, 2021)
Federated learning (FL) enables multiple clients to collaboratively train a shared model, with the help of a parameter server (PS), without disclosing their local datasets. However, due to the increasing size of the trained ...
Dynamic filtering and prioritization of static code analysis alerts
(IEEE, 2021)
We propose an approach for filtering and prioritizing static code analysis alerts while these alerts are being reviewed by the developer. We construct a Prolog knowledge base that captures the data flow information in the ...
Data model extension impact analysis
(IEEE, 2021)
Relational database schemas are subject to change. For instance, columns of a table can be modified, deleted or extended. These changes have an impact on the source code that utilizes the corresponding table. They also ...
Road to salvation: Streaming clients and content delivery networks working together
(IEEE, 2021-11-01)
Streaming media has truly become one of the most popular applications on the Internet. Viewers are spoiled for choice, and content providers compete to provide the best viewer experience. Traditionally, it has been challenging ...
Hierarchical mixtures of generators for adversarial learning
Generative adversarial networks (GANs) are deep neural networks that allow us to sample from an arbitrary probability distribution without explicitly estimating the distribution. There is a generator that takes a latent ...
Weight update skipping: Reducing training time for artificial neural networks
(IEEE, 2021-12)
Artificial Neural Networks (ANNs) are known as state-of-the-art techniques in Machine Learning (ML) and have achieved outstanding results in data-intensive applications, such as recognition, classification, and segmentation. ...
Misclassification risk and uncertainty quantification in deep classifiers
(IEEE, 2021)
In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a ...
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