Streaming Property Testing of Visibly Pushdown Languages

In the context of formal language recognition, we demonstrate the superiority of streaming property testers against streaming algorithms and property testers, when they are not combined. Initiated by Feigenbaum et al., a streaming property tester is a streaming algorithm recognizing a language under the property testing approximation: it must distinguish inputs of the language from those that are eps-far from it, while using the smallest possible memory (rather than limiting its number of input queries). Our main result is a streaming eps-property tester for visibly pushdown languages (V_{PL}) with memory space poly(log n /epsilon). Our construction is done in three steps. First, we simulate a visibly pushdown automaton in one pass using a stack of small height but whose items can be of linear size. In a second step, those items are replaced by small sketches. Those sketches rely on a notion of suffix-sampling we introduce. This sampling is the key idea for taking benefit of both streaming algorithms and property testers in the third step. Indeed, the last step relies on a (non-streaming) property tester for weighted regular languages based on a previous tester by Alon et al. This tester can directly be used for streaming testing special cases of instances of V_{PL} that are already hard for both streaming algorithms and property testers. We then use it to decide the correctness of completed items, given their sketches, before removing them from the stack

Collaborative Verification-Driven Engineering of Hybrid Systems

Hybrid systems with both discrete and continuous dynamics are an important model for real-world cyber-physical systems. The key challenge is to ensure their correct functioning w.r.t. safety requirements. Promising techniques to ensure safety seem to be model-driven engineering to develop hybrid systems in a well-defined and traceable manner, and formal verification to prove their correctness. Their combination forms the vision of verification-driven engineering. Often, hybrid systems are rather complex in that they require expertise from many domains (e.g., robotics, control systems, computer science, software engineering, and mechanical engineering). Moreover, despite the remarkable progress in automating formal verification of hybrid systems, the construction of proofs of complex systems often requires nontrivial human guidance, since hybrid systems verification tools solve undecidable problems. It is, thus, not uncommon for development and verification teams to consist of many players with diverse expertise. This paper introduces a verification-driven engineering toolset that extends our previous work on hybrid and arithmetic verification with tools for (i) graphical (UML) and textual modeling of hybrid systems, (ii) exchanging and comparing models and proofs, and (iii) managing verification tasks. This toolset makes it easier to tackle large-scale verification tasks

Extraction and integration of data from semi-structured documents into business applications

Cover title.Includes bibliographical references (p. 8).Ph. Bonnet & S. Bressan

Streaming Tree Transducers

Theory of tree transducers provides a foundation for understanding expressiveness and complexity of analysis problems for specification languages for transforming hierarchically structured data such as XML documents. We introduce streaming tree transducers as an analyzable, executable, and expressive model for transforming unranked ordered trees in a single pass. Given a linear encoding of the input tree, the transducer makes a single left-to-right pass through the input, and computes the output in linear time using a finite-state control, a visibly pushdown stack, and a finite number of variables that store output chunks that can be combined using the operations of string-concatenation and tree-insertion. We prove that the expressiveness of the model coincides with transductions definable using monadic second-order logic (MSO). Existing models of tree transducers either cannot implement all MSO-definable transformations, or require regular look ahead that prohibits single-pass implementation. We show a variety of analysis problems such as type-checking and checking functional equivalence are solvable for our model.Comment: 40 page

Anomaly detection in computer networks using hierarchically organized teams of learning automata

With the increasing number of computer systems connected to the Internet, security becomes a critical issue. To combat this problem, several attack detection methods have emerged in the past years, such as the rule based Intrusion Detection System (IDS) Snort - or anomaly based alternatives that are able to detect novel attacks without any prior knowledge about them. Most current anomaly based IDS require labeled attacks or extensively filtered training data, such that certain attack types, which generate large amounts of noise in terms of false positives, are effectively removed. This thesis describes a novel anomaly based scheme for detecting attacks, using frequent itemset mining, without performing extensive filtering of the input data. In brief, the scheme, which is named the Grimstad Data Classifier (GRIDAC), uses teams of hierarchically organized Learning Automata to generate a rule tree with a set of linked nodes – where the granularity of each node increases along with the current level in the tree. In turn, GRIDAC was implemented as an anomaly based IDS called Inspectobot, and evaluated using the 1999 DARPA IDS Evaluation Sets. At best, the prototype was able to detect 51 out of 62 attacks in the 1999 DARPA IDS Evaluation Sets with 56 false alarms, giving a detection rate of 82 %, after training on one week of attack-free traffic, and classifying another full week of data containing attacks. The empirical results are quite conclusive, demonstrating that the prototype shows an excellent ability to mine frequent itemsets from network packets, such that normal behavior can be modeled. With an average detection rate of 73 % of all attacks in the DARPA set, and a fairly low amount of false positives, it is also shown that Inspectobot can be used for IDS purposes. In its current state, Inspectobot requires a high processing capacity to perform the rule matching. When compared to the popular IDS Snort, it is currently not as useful outside of a testbed environment. Nonetheless, the scheme has the potential of serving as a complementary anomaly based IDS alongside Snort for detecting novel attacks, given a more optimized implementation

An O(n) time discrete relaxation architecture for real-time processing of the consistent labeling problem

technical reportDiscrete relaxation techniques have proven useful in solving a wide range of problems in digital signal and digital image processing, artificial intelligence, operations research, and machine vision. Much work has been devoted to finding efficient hardware architectures. This paper shows that a conventional hardware design for a Discrete Relaxation Algorithm (DRA) suffers from 0(n2m3 ) time complexity and Oinhn2) space complexity. By reformulating DRA into a parallel computational tree and using a multiple tree-root pipelining scheme, time complexity is reduced to O(nm), while the space complexity is reduced by a factor of 2. For certain relaxation processing, the space complexity can even be decreased to O(nm). Furthermore, a technique for dynamic configuring an architectural wavefront is used which leads to an O(n) time highly configurable DRA3 architecture