Mai Alzamel

Using brain signals patterns for biometric identity verification systems

In the natural human computer interaction filed, researchers started to consider the other interaction modalities for diversity of applications. Among these modalities are the speech interaction systems, eye gaze interaction systems and recently Brain Computer Interfacing (BCI) systems. In BCI systems, the tools are deployed to manipulate the brain activity to produce signals that can be used to control computers or communication devices. Implementing this technology in real life varies from: entertainment systems to control layers through the user thoughts, to disability assistive devices to reduce care given. Currently the BCI technologies are developed for the purposes of boosting the disability assistive devices especially in the command controlled systems. In addition, the currently demanding research emphasis is to use brain signals for personal identifications and verification; known as biometric verification. Biometric verification was first used in as an authentication technique for systems operating devices in real environment. At that time, authentication was based on unimodal biometric identity verification systems, which compare only one trait or biometrical feature (such as voice, iris, or fingerprint) to a previous sample. However, the performance of such modals varies depending on the presence of outside factors such as background noises in a speech recognition system, or the illumination problems for a face recognition system. Another cause of pitfalls in these models is their dependency on the health of the authenticated user. In order to overcome the weaknesses of the unimodal biometric system, a multimodal biometric system was introduced.

Faster Algorithms for 1-Mappability of a Sequence

In the k-mappability problem, we are given a string x of length n and integers m and k, and we are asked to count, for each length-m factor y of x, the number of other factors of length m of x that are at Hamming distance at most k from y. We focus here on the version of the problem where \(k=1\). The fastest known algorithm for \(k=1\) requires time \(\mathcal {O}(mn \log n/\log \log n)\) and space \(\mathcal {O}(n)\). We present two new algorithms that require worst-case time \(\mathcal {O}(mn)\) and \(\mathcal {O}(n \log n \log \log n)\), respectively, and space \(\mathcal {O}(n)\), thus greatly improving the state of the art. Moreover, we present another algorithm that requires average-case time and space \(\mathcal {O}(n)\) for integer alphabets of size \(\sigma \) if \(m=\varOmega (\log _\sigma n)\). Notably, we show that this algorithm is generalizable for arbitrary k, requiring average-case time \(\mathcal {O}(kn)\) and space \(\mathcal {O}(n)\) if \(m=\varOmega (k\log _\sigma n)\).

Efficient Computation of Sequence Mappability

Sequence mappability is an important task in genome re-sequencing. In the (k, m)-mappability problem, for a given sequence T of length n, our goal is to compute a table whose ith entry is the number of indices \(j \ne i\) such that length-m substrings of T starting at positions i and j have at most k mismatches. Previous works on this problem focused on heuristic approaches to compute a rough approximation of the result or on the case of \(k=1\). We present several efficient algorithms for the general case of the problem. Our main result is an algorithm that works in \(\mathcal {O}(n \min \{m^k,\log ^{k+1} n\})\) time and \(\mathcal {O}(n)\) space for \(k=\mathcal {O}(1)\). It requires a careful adaptation of the technique of Cole et al. [STOC 2004] to avoid multiple counting of pairs of substrings. We also show \(\mathcal {O}(n^2)\)-time algorithms to compute all results for a fixed m and all \(k=0,\ldots ,m\) or a fixed k and all \(m=k,\ldots ,n-1\). Finally we show that the (k, m)-mappability problem cannot be solved in strongly subquadratic time for \(k,m = \varTheta (\log n)\) unless the Strong Exponential Time Hypothesis fails.

Efficient Computation of Palindromes in Sequences with Uncertainties

In this work, we consider a special type of uncertain sequence called weighted string. In a weighted string every position contains a subset of the alphabet and every letter of the alphabet is associated with a probability of occurrence such that the sum of probabilities at each position equals 1. Usually a cumulative weight threshold Open image in new window is specified, and one considers only strings that match the weighted string with probability at least Open image in new window . We provide an \(\mathcal {O}(nz)\)-time and \(\mathcal {O}(nz)\)-space off-line algorithm, where n is the length of the weighted string and Open image in new window is the given threshold, to compute a smallest maximal palindromic factorization of a weighted string. This factorization has applications in hairpin structure prediction in a set of closely-related DNA or RNA sequences. Along the way, we provide an \(\mathcal {O}(nz)\)-time and \(\mathcal {O}(nz)\)-space off-line algorithm to compute maximal palindromes in weighted strings.

Palindromic Decompositions with Gaps and Errors

Identifying palindromes in sequences has been an interesting line of research in combinatorics on words and also in computational biology, after the discovery of the relation of palindromes in the DNA sequence with the HIV virus. Efficient algorithms for the factorization of sequences into palindromes and maximal palindromes have been devised in recent years. We extend these studies by allowing gaps in decompositions and errors in palindromes, and also imposing a lower bound to the length of acceptable palindromes.

Degenerate String Comparison and Applications

A generalised degenerate string (GD string) S^ is a sequence of n sets of strings of total size N, where the ith set contains strings of the same length k_i but this length can vary between different sets. We denote the sum of these lengths k_0, k_1,...,k_{n-1} by W. This type of uncertain sequence can represent, for example, a gapless multiple sequence alignment of width W in a compact form. Our first result in this paper is an O(N+M)-time algorithm for deciding whether the intersection of two GD strings of total sizes N and M, respectively, over an integer alphabet, is non-empty. This result is based on a combinatorial result of independent interest: although the intersection of two GD strings can be exponential in the total size of the two strings, it can be represented in only linear space. A similar result can be obtained by employing an automata-based approach but its cost is alphabet-dependent. We then apply our string comparison algorithm to compute palindromes in GD strings. We present an O(min{W,n^2}N)-time algorithm for computing all palindromes in S^. Furthermore, we show a similar conditional lower bound for computing maximal palindromes in S^. Finally, proof-of-concept experimental results are presented using real protein datasets.

Recent Advances of Palindromic Factorization.

This paper provides an overview of six particular problems of palindromic factorization and recent algorithmic improvements in solving them.

How to Answer a Small Batch of RMQs or LCA Queries in Practice

In the Range Minimum Query (RMQ) problem, we are given an array A of n numbers and we are asked to answer queries of the following type: for indices i and j between 0 and \(n-1\), query \(\text {RMQ}_A(i,j)\) returns the index of a minimum element in the subarray \(A[i\mathinner {.\,.}j]\). Answering a small batch of RMQs is a core computational task in many real-world applications, in particular due to the connection with the Lowest Common Ancestor (LCA) problem. With small batch, we mean that the number q of queries is o(n) and we have them all at hand. It is therefore not relevant to build an \(\varOmega (n)\)-sized data structure or spend \(\varOmega (n)\) time to build a more succinct one. It is well-known, among practitioners and elsewhere, that these data structures for online querying carry high constants in their pre-processing and querying time. We would thus like to answer this batch efficiently in practice. With efficiently in practice, we mean that we (ultimately) want to spend \(n + \mathcal {O}(q)\) time and \(\mathcal {O}(q)\) space. We write n to stress that the number of operations per entry of A should be a very small constant. Here we show how existing algorithms can be easily modified to satisfy these conditions. The presented experimental results highlight the practicality of this new scheme. The most significant improvement obtained is for answering a small batch of LCA queries. A library implementation of the presented algorithms is made available.

Efficient Identification of k-Closed Strings

A closed string contains a proper factor occurring as both a prefix and a suffix but not elsewhere in the string. Closed strings were introduced by Fici (WORDS 2011) as objects of combinatorial interest. In this paper, we extend this definition to k-closed strings, for which a level of approximation is permitted up to a number of Hamming distance errors, set by the parameter k. We then address the problem of identifying whether or not a given string of length n over an integer alphabet is k-closed and additionally specifying the border resulting in the string being k-closed. Specifically, we present an \(\mathcal {O}(kn)\)-time and \(\mathcal {O}(n)\)-space algorithm to achieve this along with the pseudocode of an implementation.

Fast Fingerprint Rotation Recognition Technique Using Circular Strings in Lexicographical Order

Out of the commonly used techniques, fingerprint authentication till date, remains the most reliable. Previously, a plethora of schemes for identification has been employed, however they failed to address a notable challenge- rotational issues, associated with the fingerprint scheme. This leads to incorrect orientation identification, ultimately leading to error in results. Our paper attempts to solve this issue, by proposing a fast pattern matching technique that caters for differences in orientation by firstly, implementing a pre-matching level called the orientation identification stage, and then match the correctly identified oriented fingerprint image to the stored image. To this end, the derived fingerprint image is intercepted with several scan circles to obtain the minutiae information. This information then, is translated into a string, having its staring point as the least lexicographical rotation value. Using approximate string matching techniques, this string information is matched against a database of stored images. The experiment was conducted on solving the rotation stage to prove the efficiency of this method, where the extracting and re-rotation is done in less than a second, with a linear time algorithm, yet practically sub linear in respect to the short extracted binary strings.