5,838 research outputs found
Large Area Crop Inventory Experiment (LACIE). CAMS detailed analysis procedures
There are no author-identified significant results in this report
Repairing Deep Neural Networks: Fix Patterns and Challenges
Significant interest in applying Deep Neural Network (DNN) has fueled the
need to support engineering of software that uses DNNs. Repairing software that
uses DNNs is one such unmistakable SE need where automated tools could be
beneficial; however, we do not fully understand challenges to repairing and
patterns that are utilized when manually repairing DNNs. What challenges should
automated repair tools address? What are the repair patterns whose automation
could help developers? Which repair patterns should be assigned a higher
priority for building automated bug repair tools? This work presents a
comprehensive study of bug fix patterns to address these questions. We have
studied 415 repairs from Stack overflow and 555 repairs from Github for five
popular deep learning libraries Caffe, Keras, Tensorflow, Theano, and Torch to
understand challenges in repairs and bug repair patterns. Our key findings
reveal that DNN bug fix patterns are distinctive compared to traditional bug
fix patterns; the most common bug fix patterns are fixing data dimension and
neural network connectivity; DNN bug fixes have the potential to introduce
adversarial vulnerabilities; DNN bug fixes frequently introduce new bugs; and
DNN bug localization, reuse of trained model, and coping with frequent releases
are major challenges faced by developers when fixing bugs. We also contribute a
benchmark of 667 DNN (bug, repair) instances
OSPAR-OIC Intercalibration Study on metals in produced water samples: a QUASIMEME Laboratory performance Study
The Offshore Industry Committee (OIC) of OSPAR discussed in its 2008 meeting the reporting of inputs of metals from offshore installations. INPUT is currently compiling data and information on discharges and emissions to the OSPAR maritime area to be used in the Quality Status Report (QSR). This includes an assessment of the inputs of cadmium, lead, and mercury in produced water. Initial estimates were considered by OIC to be unrepresentative, since many of the analyses recorded values below the analytical detection limits of the techniques used. Given the urgency of producing reliable information that could be used to prepare estimates of inputs of cadmium, lead, and mercury for the QSR, OIC agreed to conduct a further study, using the assistance of Quasimeme, to ensure quality assurance from sampling to measurement and reporting. This report focuses on the intercalibration exercise
Security and Privacy Issues in Wireless Mesh Networks: A Survey
This book chapter identifies various security threats in wireless mesh
network (WMN). Keeping in mind the critical requirement of security and user
privacy in WMNs, this chapter provides a comprehensive overview of various
possible attacks on different layers of the communication protocol stack for
WMNs and their corresponding defense mechanisms. First, it identifies the
security vulnerabilities in the physical, link, network, transport, application
layers. Furthermore, various possible attacks on the key management protocols,
user authentication and access control protocols, and user privacy preservation
protocols are presented. After enumerating various possible attacks, the
chapter provides a detailed discussion on various existing security mechanisms
and protocols to defend against and wherever possible prevent the possible
attacks. Comparative analyses are also presented on the security schemes with
regards to the cryptographic schemes used, key management strategies deployed,
use of any trusted third party, computation and communication overhead involved
etc. The chapter then presents a brief discussion on various trust management
approaches for WMNs since trust and reputation-based schemes are increasingly
becoming popular for enforcing security in wireless networks. A number of open
problems in security and privacy issues for WMNs are subsequently discussed
before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the
author's previous submission in arXiv submission: arXiv:1102.1226. There are
some text overlaps with the previous submissio
Multi-level analysis of Malware using Machine Learning
Multi-level analysis of Malware using Machine Learnin
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