The study of operating systems is a fundamental component of all undergraduate computer science degree programmes. Making operating system concepts concrete typically entails large programming projects. Such projects traditionally involve enhancing an existing module in a real-world operating system or extending a pedagogical operating system. The latter programming projects represent the gold standard in the teaching of operating systems and their value is undoubted. However, there is room in introductory operating systems courses for supplementary approaches and tools that support the demonstration of operating system concepts in the context of a live, real-world operating system. This paper describes an approach where the Linux monitoring tool SystemTap is used to capture kernel-level events in order to illustrate, with concrete examples, operating system concepts in the areas of scheduling, file system implementation and memory management. For instructors and students (where often for the latter seeing is believing) this approach offers an additional simple and valuable resource for solidifying understanding of concepts that might otherwise remain purely theoretical.

Distributed denial of service attacks pose a serious threat to many businesses which rely on constant availability of their network services. Companies like Google, Yahoo and Amazon are completely reliant on the Internet for their business. It is very hard to defend against these attacks because of the many different ways in which hackers may strike. Distinguishing between legitimate and malicious traffic is a complex task. Setting up filtering by hand is often impossible due to the large number of hosts involved in the attack. The goal of this paper is to explore the effectiveness of machine learning techniques in developing automatic defences against DDoS attacks. As a first step, a data collection and traffic filtering framework is developed. This foundation is then used to explore the potential of artificial neural networks in the defence against DDoS attacks.

One of the most successful approaches to synthesizing speech, concatenative synthesis, combines recorded speech units to build full utterances. However, the prosody of the stored units is often not consistent with that of the target utterance and must be altered. Furthermore, several types of mismatch can occur at unit boundaries and must be smoothed. Thus, both pitch and time-scale modification techniques as well as smoothing algorithms play a crucial role in such concatenation based systems. In this paper, we describe novel approaches to each of these issues. First, we present a conceptually simple technique for pitch and time-scale modification of speech. Our method is based upon a harmonic coding of each speech frame, and operates entirely within the original sinusoidal model. Crucially, it makes no use of "pitch pulse onset times." Instead, phase coherence, and thus shape invariance, is ensured by exploiting the harmonic relation existing between the sine waves used to code each analysis frame so that their phases at each synthesis frame boundary are consistent with those derived during analysis. Secondly, a smoothing algorithm, aimed specifically at correcting phase mismatches at unit boundaries, is described. Results are presented showing our prosodic modification techniques to be highly suitable for use within a concatenative speech synthesizer.

A new and simple approach to shape invariant time-scale modification of speech is presented. The method based upon a harmonic coding of each speech frame operates entirely within the original sinusoidal model and makes no use of "pitch-pulse onset times" used by conventional algorithms. Instead, phase coherence, and thus shape invariance, are ensured by exploiting the harmonic relation existing between the sine waves to cause them to be in phase at each adjusted frame boundary. Results suggest this approach to be an excellent candidate for use within a concatenative text-to-speech synthesiser where scaling factors typically lie within a range well handled by this algorithm.

The growing industrial take-up of spoken language dialogue systems (SLDSs), their constantly increasing sophistication, and the scarcity of teams which master the full system complexity as well as all the necessary steps in the SLDSs lifecycle, has created a felt need for a best practice model for development and evaluation of SLDSs. An obvious first step towards establishing a best practice model is to build a solid overview of current practice. This paper presents a model for the description of SLDS current practice with particular focus on dialogue management and human factors.