Checking sequence construction using adaptive and preset distinguishing sequences

Methods for testing from finite state machine-based specifications often require the existence of a preset distinguishing sequence for constructing checking sequences. It has been shown that an adaptive distinguishing sequence is sufficient for these methods. This result is significant because adaptive distinguishing sequences are strictly more common and up to exponentially shorter than preset ones. However, there has been no study on the actual effect of using adaptive distinguishing sequences on the length of checking sequences. This paper describes experiments that show that checking sequences constructed using adaptive distinguishing sequences are almost consistently shorter than those based on preset distinguishing sequences. This is investigated for three different checking sequence generation methods and the results obtained from an extensive experimental study are given

Using a SAT solver to generate checking sequences

Methods for software testing based on Finite State Machines (FSMs) have been researched since the early 60’s. Many of these methods are about generating a checking sequence from a given FSM which is an input sequence that determines whether an implementation of the FSM is faulty or correct. In this paper, we consider one of these methods, which constructs a checking sequence by reducing the problem of generating a checking sequence to finding a Chinese rural postman tour on a graph induced by the FSM; we re-formulate the constraints used in this method as a set of Boolean formulas; and use a SAT solver to generate a checking sequence of minimal length

Bloom Filters in Adversarial Environments

Many efficient data structures use randomness, allowing them to improve upon deterministic ones. Usually, their efficiency and correctness are analyzed using probabilistic tools under the assumption that the inputs and queries are independent of the internal randomness of the data structure. In this work, we consider data structures in a more robust model, which we call the adversarial model. Roughly speaking, this model allows an adversary to choose inputs and queries adaptively according to previous responses. Specifically, we consider a data structure known as "Bloom filter" and prove a tight connection between Bloom filters in this model and cryptography. A Bloom filter represents a set $S$ of elements approximately, by using fewer bits than a precise representation. The price for succinctness is allowing some errors: for any $x \in S$ it should always answer `Yes', and for any $x \notin S$ it should answer `Yes' only with small probability. In the adversarial model, we consider both efficient adversaries (that run in polynomial time) and computationally unbounded adversaries that are only bounded in the number of queries they can make. For computationally bounded adversaries, we show that non-trivial (memory-wise) Bloom filters exist if and only if one-way functions exist. For unbounded adversaries we show that there exists a Bloom filter for sets of size $n$ and error $\varepsilon$, that is secure against $t$ queries and uses only $O(n \log{\frac{1}{\varepsilon}}+t)$ bits of memory. In comparison, $n\log{\frac{1}{\varepsilon}}$ is the best possible under a non-adaptive adversary

Theory of reliable systems

The analysis and design of reliable systems are discussed. The attributes of system reliability studied are fault tolerance, diagnosability, and reconfigurability. Objectives of the study include: to determine properties of system structure that are conducive to a particular attribute; to determine methods for obtaining reliable realizations of a given system; and to determine how properties of system behavior relate to the complexity of fault tolerant realizations. A list of 34 references is included

Lower Bounds for Oblivious Near-Neighbor Search

We prove an $\Omega(d \lg n/ (\lg\lg n)^2)$ lower bound on the dynamic cell-probe complexity of statistically $\mathit{oblivious}$ approximate-near-neighbor search ($\mathsf{ANN}$) over the $d$-dimensional Hamming cube. For the natural setting of $d = \Theta(\log n)$, our result implies an $\tilde{\Omega}(\lg^2 n)$ lower bound, which is a quadratic improvement over the highest (non-oblivious) cell-probe lower bound for $\mathsf{ANN}$. This is the first super-logarithmic $\mathit{unconditional}$ lower bound for $\mathsf{ANN}$ against general (non black-box) data structures. We also show that any oblivious $\mathit{static}$ data structure for decomposable search problems (like $\mathsf{ANN}$) can be obliviously dynamized with $O(\log n)$ overhead in update and query time, strengthening a classic result of Bentley and Saxe (Algorithmica, 1980).Comment: 28 page

Simulating the cross-linguistic pattern of optional infinitive errors in children’s declaratives and wh- questions [forthcoming]

One of the most striking features of children’s early multi-word speech is their tendency to produce non-finite verb forms in contexts in which a finite verb form is required (Optional Infinitive [OI] errors, Wexler, 1994). MOSAIC is a computational model of language learning that simulates developmental changes in the rate of OI errors across several different languages by learning compound finite constructions from the right edge of the utterance (Freudenthal, Pine & Gobet, 2006a; 2009; Freudenthal, Pine, Aguado-Orea & Gobet, 2007). However, MOSAIC currently only simulates the pattern of OI errors in declaratives, and there are important differences in the cross-linguistic patterning of OI errors in declaratives and Wh- questions. In the present study, we describe a new version of MOSAIC that learns from both the right and left edges of the utterance. Our simulations demonstrate that this new version of the model is able to capture the cross-linguistic patterning of OI errors in declaratives in English, Dutch, German and Spanish by learning from declarative input, and the cross-linguistic patterning of OI errors in Wh- questions in English, German and Spanish by learning from interrogative input. These results show that MOSAIC is able to provide an integrated account of the cross-linguistic patterning of OI errors in declaratives and Wh- questions, and provide further support for the view, instantiated in MOSAIC, that OI errors are compound-finite utterances with missing modals or auxiliaries