Findings of the 2016 Conference on Machine Translation (WMT16)

This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments)

Modelling polycrystalline materials and interfaces

Polycrystalline materials are ubiquitous and dominate the synthetic and natural worlds. They are characterised by the presence of defects such as grain boundaries in the crystal structure. Grain boundaries can significantly influence underlying electrical, magnetic and mechanical properties of materials. In this thesis interatomic potentials have been used to model grain boundaries in Fe, Cu and Ni. A high throughput computational approach is employed to determine the atomic structure, formation energy and excess volume of a large number of tilt grain boundaries in Fe, Cu and Ni. There is a systematic difference of ~0.2 Å between the excess volumes in Cu and Ni which is in agreement with experiment. It is predicted that the differences in the elastic moduli may give rise to larger differences in excess volume than expected. Novel plan-view high-resolution transmission electron microscopy and first principles calculations have been employed to provide atomic level understanding of the structure and properties of grain boundaries in the MgO barrier layer of a magnetic tunnel junction. Transmission electron microscopy images reveal grain boundaries in the MgO film including (210)[001] symmetric tilt grain boundaries and (100)/(110)[001] asymmetric tilt grain boundaries amongst others. First principles calculations show how these grain boundaries are associated with locally reduced band gaps (by up to 3 eV). The knowledge from the modelling of Fe, Cu, Ni and MgO is used to study interfaces of Fe and MgO to further understand magnetic tunnel junctions. The orientational relationship between the Fe and MgO is not known explicitly. Density functional theory is used to predict the energetic stability of Fe/MgO interfaces in different orientational configurations. It is found that the most energetically favourable interface between Fe and MgO is when the atomic columns are in registry

Investigating the detection of stored scripting attacks using machine learning

Web applications now play an essential role in our daily lives; through them we can make bank transfers, purchase products and/or make bookings on the Internet. This makes them a target for attackers who will attempt to exploit security vulnerabilities in web applications in order to obtain access to sensitive user information or gain unauthorized privileges. One of the most common attacks aimed at stealing user information is Cross-Site Scripting; this is ranked among the top 10 security vulnerabilities in web applications. Traditional defense systems rely on a signature database describing known attacks; however, XSS attacks written in JavaScript are very variable; they do not exist only in a single form. The most common cause of XSS security vulnerabilities is weakness of verification of the user’s input. This provides the motivation for finding a method for identifying malicious code, written in JavaScript, that an attacker attempts to have executed on the server. Machine learning has contributed to the security of web applications. Several studies have been conducted in relation to Intrusion Detecting Systems (IDS) which detect and prevent attacks against web applications. Cross-Site Scripting is one of the attacks that has been studied employing a number of methods: for example, using features to identify obfuscated scripts or using JavaScript keywords, evaluating machine learning algorithms in term of detecting attacks against web applications such as random forest, and SVM. These studies have achieved highly accurate results by using machine learning to detect XSS attacks. They often attained better results than dynamic and static analysis in terms of acting as a protection layer for web applications. This present study will demonstrate the use of machine learning methods, incorporated into a web application at the user input validation stage - prior to the request being passed to the application server. Classifiers will be used to prevent persistent or stored XSS attacks, which are caused by malicious code injections via an input point in the web application. This study relies on supervised machine learning and the application of Boolean feature sets, in order to achieve ease and speed of classification. Furthermore, this study examined the use of such methods on two other types of injection attacks: SQL-i and LDAP. Cascading classifiers and ensemble techniques were used to reduce complexity while maintaining accuracy and speed. To understand how a decision is made in the classifier, an approximate Boolean function is extracted; this is done based on the techniques which have been employed to extract rules from black box classifiers