- Publication Date:
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- Journal Name:
- ACM Transactions on Software Engineering and Methodology
- Page Range or eLocation-ID:
- 1 to 31
- Sponsoring Org:
- National Science Foundation
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Obeid, I. ; Selesnick, I. (Ed.)The Neural Engineering Data Consortium at Temple University has been providing key data resources to support the development of deep learning technology for electroencephalography (EEG) applications [1-4] since 2012. We currently have over 1,700 subscribers to our resources and have been providing data, software and documentation from our web site  since 2012. In this poster, we introduce additions to our resources that have been developed within the past year to facilitate software development and big data machine learning research. Major resources released in 2019 include: ● Data: The most current release of our open source EEG data is v1.2.0 of TUH EEG and includes the addition of 3,874 sessions and 1,960 patients from mid-2015 through 2016. ● Software: We have recently released a package, PyStream, that demonstrates how to correctly read an EDF file and access samples of the signal. This software demonstrates how to properly decode channels based on their labels and how to implement montages. Most existing open source packages to read EDF files do not directly address the problem of channel labels . ● Documentation: We have released two documents that describe our file formats and data representations: (1) electrodes and channels : describes how tomore »
Too Afraid to Drive: Systematic Discovery of Semantic DoS Vulnerability in Autonomous Driving Planning under Physical-World AttacksIn high-level Autonomous Driving (AD) systems, behavioral planning is in charge of making high-level driving decisions such as cruising and stopping, and thus highly securitycritical. In this work, we perform the first systematic study of semantic security vulnerabilities specific to overly-conservative AD behavioral planning behaviors, i.e., those that can cause failed or significantly-degraded mission performance, which can be critical for AD services such as robo-taxi/delivery. We call them semantic Denial-of-Service (DoS) vulnerabilities, which we envision to be most generally exposed in practical AD systems due to the tendency for conservativeness to avoid safety incidents. To achieve high practicality and realism, we assume that the attacker can only introduce seemingly-benign external physical objects to the driving environment, e.g., off-road dumped cardboard boxes. To systematically discover such vulnerabilities, we design PlanFuzz, a novel dynamic testing approach that addresses various problem-specific design challenges. Specifically, we propose and identify planning invariants as novel testing oracles, and design new input generation to systematically enforce problemspecific constraints for attacker-introduced physical objects. We also design a novel behavioral planning vulnerability distance metric to effectively guide the discovery. We evaluate PlanFuzz on 3 planning implementations from practical open-source AD systems, and find that it can effectively discover 9more »
Numerous cloud-based services are provided to help customers develop and deploy deep learning applications. When a customer deploys a deep learning model in the cloud and serves it to end-users, it is important to be able to verify that the deployed model has not been tampered with. In this paper, we propose a novel and practical methodology to verify the integrity of remote deep learning models, with only black-box access to the target models. Specifically, we define Sensitive-Sample fingerprints, which are a small set of human unnoticeable transformed inputs that make the model outputs sensitive to the model's parameters. Even small model changes can be clearly reflected in the model outputs. Experimental results on different types of model integrity attacks show that we proposed approach is both effective and efficient. It can detect model integrity breaches with high accuracy (>99.95%) and guaranteed zero false positives on all evaluated attacks. Meanwhile, it only requires up to 103× fewer model inferences, compared with non-sensitive samples.
Metagenomics is the study of genetic materials directly sampled from natural habitats. It has the potential to reveal previously hidden diversity of microscopic life largely due to the existence of highly parallel and low-cost next-generation sequencing technology. Conventional approaches align metagenomic reads onto known reference genomes to identify microbes in the sample. Since such a collection of reference genomes is very large, the approach often needs high-end computing machines with large memory which is not often available to researchers. Alternative approaches follow an alignment-free methodology where the presence of a microbe is predicted using the information about the unique k-mers present in the microbial genomes. However, such approaches suffer from high false positives due to trading off the value of k with the computational resources. In this article, we propose a highly efficient metagenomic sequence classification (MSC) algorithm that is a hybrid of both approaches. Instead of aligning reads to the full genomes, MSC aligns reads onto a set of carefully chosen, shorter and highly discriminating model sequences built from the unique k-mers of each of the reference sequences.
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Availability and implementation
The implementations are freely available for non-commercial purposes. They can be downloaded from https://drive.google.com/open?id=1XirkAamkQ3ltWvI1W1igYQFusp9DHtVl.