2,933 research outputs found
A Study of Concurrency Bugs and Advanced Development Support for Actor-based Programs
The actor model is an attractive foundation for developing concurrent
applications because actors are isolated concurrent entities that communicate
through asynchronous messages and do not share state. Thereby, they avoid
concurrency bugs such as data races, but are not immune to concurrency bugs in
general. This study taxonomizes concurrency bugs in actor-based programs
reported in literature. Furthermore, it analyzes the bugs to identify the
patterns causing them as well as their observable behavior. Based on this
taxonomy, we further analyze the literature and find that current approaches to
static analysis and testing focus on communication deadlocks and message
protocol violations. However, they do not provide solutions to identify
livelocks and behavioral deadlocks. The insights obtained in this study can be
used to improve debugging support for actor-based programs with new debugging
techniques to identify the root cause of complex concurrency bugs.Comment: - Submitted for review - Removed section 6 "Research Roadmap for
Debuggers", its content was summarized in the Future Work section - Added
references for section 1, section 3, section 4.3 and section 5.1 - Updated
citation
Testing Feedforward Neural Networks Training Programs
Nowadays, we are witnessing an increasing effort to improve the performance
and trustworthiness of Deep Neural Networks (DNNs), with the aim to enable
their adoption in safety critical systems such as self-driving cars. Multiple
testing techniques are proposed to generate test cases that can expose
inconsistencies in the behavior of DNN models. These techniques assume
implicitly that the training program is bug-free and appropriately configured.
However, satisfying this assumption for a novel problem requires significant
engineering work to prepare the data, design the DNN, implement the training
program, and tune the hyperparameters in order to produce the model for which
current automated test data generators search for corner-case behaviors. All
these model training steps can be error-prone. Therefore, it is crucial to
detect and correct errors throughout all the engineering steps of DNN-based
software systems and not only on the resulting DNN model. In this paper, we
gather a catalog of training issues and based on their symptoms and their
effects on the behavior of the training program, we propose practical
verification routines to detect the aforementioned issues, automatically, by
continuously validating that some important properties of the learning dynamics
hold during the training. Then, we design, TheDeepChecker, an end-to-end
property-based debugging approach for DNN training programs. We assess the
effectiveness of TheDeepChecker on synthetic and real-world buggy DL programs
and compare it with Amazon SageMaker Debugger (SMD). Results show that
TheDeepChecker's on-execution validation of DNN-based program's properties
succeeds in revealing several coding bugs and system misconfigurations, early
on and at a low cost. Moreover, TheDeepChecker outperforms the SMD's offline
rules verification on training logs in terms of detection accuracy and DL bugs
coverage
Towards exploring adversarial learning for anomaly detection in complex driving scenes
One of the many Autonomous Systems (ASs), such as autonomous driving cars, performs various safety-critical functions. Manyof these autonomous systems take advantage of Artificial Intelligence (AI) techniques to perceive their environment. But these perceiving components could not be formally verified, since, the accuracy of such AI-based components has a high dependency on the quality of training data. So Machine learning (ML) based anomaly detection, a technique to identify data that does not belong to the training data could be used as a safety measuring indicator during the development and operational time of such AI-based components. Adversarial learning, a sub-field of machine learning has proven its ability to detect anomalies in images and videos with impressive results on simple data sets. Therefore, in this work, we investigate and provide insight into the performance of such techniques on a highly complex driving scenes dataset called Berkeley DeepDrive
Assessing the Impact of Game Day Schedule and Opponents on Travel Patterns and Route Choice using Big Data Analytics
The transportation system is crucial for transferring people and goods from point A to point B. However, its reliability can be decreased by unanticipated congestion resulting from planned special events. For example, sporting events collect large crowds of people at specific venues on game days and disrupt normal traffic patterns.
The goal of this study was to understand issues related to road traffic management during major sporting events by using widely available INRIX data to compare travel patterns and behaviors on game days against those on normal days. A comprehensive analysis was conducted on the impact of all Nebraska Cornhuskers football games over five years on traffic congestion on five major routes in Nebraska. We attempted to identify hotspots, the unusually high-risk zones in a spatiotemporal space containing traffic congestion that occur on almost all game days. For hotspot detection, we utilized a method called Multi-EigenSpot, which is able to detect multiple hotspots in a spatiotemporal space. With this algorithm, we were able to detect traffic hotspot clusters on the five chosen routes in Nebraska. After detecting the hotspots, we identified the factors affecting the sizes of hotspots and other parameters. The start time of the game and the Cornhuskers’ opponent for a given game are two important factors affecting the number of people coming to Lincoln, Nebraska, on game days. Finally, the Dynamic Bayesian Networks (DBN) approach was applied to forecast the start times and locations of hotspot clusters in 2018 with a weighted mean absolute percentage error (WMAPE) of 13.8%
Towards exploring adversarial learning for anomaly detection in complex driving scenes
One of the many Autonomous Systems (ASs), such as autonomous driving cars,
performs various safety-critical functions. Many of these autonomous systems
take advantage of Artificial Intelligence (AI) techniques to perceive their
environment. But these perceiving components could not be formally verified,
since, the accuracy of such AI-based components has a high dependency on the
quality of training data. So Machine learning (ML) based anomaly detection, a
technique to identify data that does not belong to the training data could be
used as a safety measuring indicator during the development and operational
time of such AI-based components. Adversarial learning, a sub-field of machine
learning has proven its ability to detect anomalies in images and videos with
impressive results on simple data sets. Therefore, in this work, we investigate
and provide insight into the performance of such techniques on a highly complex
driving scenes dataset called Berkeley DeepDrive.Comment: 2
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