To match the signatures of malicious traffic across packet boundaries, network-intrusion detection (and prevention) systems (NIDS) typically perform pattern matching after flow reassembly or packet reordering. However, this may lead to the need for large packet buffers, making detection vulnerable to denial-of-service (DoS) attacks, whereby attackers exhaust the buffer capacity by sending long ...

The function of a router is to switch arriving packets to their correct output destination. A router is built to achieve a specified capacity (the sum of the rates of its interfaces), and users expect a router to consistently achieve this capacity. However, no commercial high-speed router can guarantee today that it will achieve its full capacity for all arrival traffic patterns. This is becaus...

This paper presents novel approaches to reordering in phrase-based statistical machine translation. We perform consistent reordering of source sentences in training and estimate a statistical translation model. Using this model, we follow a phrase-based monotonic machine translation approach, for which we develop an efficient and flexible reordering framework that allows to easily introduce dif...

Reordering models are one of essential components of statistical machine translation. In this paper, we propose a topic-based reordering model to predict orders for neighboring blocks by capturing topic-sensitive reordering patterns. We automatically learn reordering examples from bilingual training data, which are associated with document-level and word-level topic information induced by LDA t...

Resequencing problem is a crucial issue in communication systems, databases, production and information networks because correct processing of information by them may often be performed only if original order of packets, queries, jobs is preserved. In this paper consideration is given to one of the queueing systems that may model processes of discrete nature where resequencing phenomenon may ar...

This paper surveys several state-of-the-art reordering techniques employed in Statistical Machine Translation systems. Reordering is understood as the word-order redistribution of the translated words. In original SMT systems, this different order is only modeled within the limits of translation units. Relying only in the reordering provided by translation units may not be good enough in most l...

In this survey, we briefly study various reordering models that are used with statistical translation models. Reordering model is one of the important component of any statistical machine translation system. Problem of reordering is NP-Hard itself. In this survey, we study various reordering approaches that can be used to solve this problem. We first study simple distortion-based reordering whi...

Phrase reordering is of great importance for statistical machine translation. According to the movement of phrase translation, the pattern of phrase reordering can be divided into three classes: monotone, BTG (Bracket Transduction Grammar) and hierarchy. It is a good way to use different styles of reordering models to reorder different phrases according to the characteristics of both the reorde...

Word reordering is one of the most difficult aspects of Statistical Machine Translation (SMT), and an important factor of its quality and efficiency. While short and mediumrange reordering is reasonably handled by the phrase-based approach (PSMT), long-range reordering still represents a challenge for state-of-the-art PSMT systems. As a major cause of this problem, we point out the inadequacy o...