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