Trade relations between the Mycenaean Greeks and the civilizations of the Eastern Mediterranean in the Late Bronze Age

The Mycenaean Greeks are often assumed to have been in contact with the civilizations of the Mediterranean throughout the Late Bronze Age. The extent of this contact however is not as clearly understood, and the archaeological evidence that has survived provides a sample of what must have exchanged hands. This thesis will examine the archaeological, textual and iconographic evidence from a number of sites and sources, from the Anatolian plains to the Kingdom of Egypt and major settlements in-between during the Late Bronze Age to examine what trade may have looked like for the Mycenaeans. Due to the extensive finds in some regions and a lack of evidence in others, this paper will also try to understand the relationship between the Mycenaeans and other cultures to determine whether a trade embargo was enacted on the Mycenaeans by the Central Anatolian Hittites during this period, or whether other factors contributed to the paucity of objects in Central Anatolia.

Classifying And Predicting Software Security Vulnerabilities based on Reproducibility

Security defects are common in large software systems because of their size and complexity. Although efficient development processes, testing, and maintenance policies are applied to software systems, there are still a large number of vulnerabilities that can remain, despite these measures. Some vulnerabilities stay in a system from one release to the next one because they cannot be easily reproduced through testing. These vulnerabilities endanger the security of the systems. We propose vulnerability classification and prediction frameworks based on vulnerability reproducibility. The frameworks are effective to identify the types and locations of vulnerabilities in the earlier stage, and improve the security of software in the next versions (referred to as releases). We expand an existing concept of software bug classification to vulnerability classification (easily reproducible and hard to reproduce) to develop a classification framework for differentiating between these vulnerabilities based on code fixes and textual reports. We then investigate the potential correlations between the vulnerability categories and the classical software metrics and some other runtime environmental factors of reproducibility to develop a vulnerability prediction framework. The classification and prediction frameworks help developers adopt corresponding mitigation or elimination actions and develop appropriate test cases. Also, the vulnerability prediction framework is of great help for security experts focus their effort on the top-ranked vulnerability-prone files. As a result, the frameworks decrease the number of attacks that exploit security vulnerabilities in the next versions of the software. To build the classification and prediction frameworks, different machine learning techniques (C4.5 Decision Tree, Random Forest, Logistic Regression, and Naive Bayes) are employed. The effectiveness of the proposed frameworks is assessed based on collected software security defects of Mozilla Firefox.