The edit distance between strings A and B is defined as the minimum number of edit operations needed in converting A into B or vice versa. The Levenshtein edit distance allows three types of operations: an insertion, a deletion or a substitution of a character. The Damerau edit distance allows the previous three plus in addition a transposition between two adjacent characters. To our best knowledge the best current practical algorithms for computing these edit distances run in time O(dm) and O(⌈m/w⌉(n + σ)), where d is the edit distance between the two strings, m and n are their lengths (m ≤ n), w is the computer word size and σ is the size of the alphabet. In this paper we present an algorithm that runs in time O(⌈d/w⌉m + ⌈n/w⌉σ) or O(⌈d/w⌉n + ⌈m/w⌉σ). The structure of the algorithm is such, that in practice it is mostly suitable for testing whether the edit distance between two strings is within some pre-determined error threshold. We also present some initial test results with thresholded edit distance computation. In them our algorithm works faster than the original algorithm of Myers.

A Bit-Vector Algorithm for Computing Levenshtein and Damerau Edit Distances.

Internet of Things (IoTs) are set to revolutionize our lives and are widely being adopted nowadays. The IoT devices have a range of applications including smart homes, smart industrial networks and healthcare. Since these devices are responsible for generating and handling large amounts of sensitive data, the security of the IoT devices always poses a challenge. It is observed that a security breach could effect individuals and eventually the world at large. Artificial intelligence (AI), on the other hand, has found many applications and is widely being explored in providing security specifically for IoT devices. Malicious insider attack is the biggest security challenge associated with the IoT devices. Although, most of the research in IoT security has pondered on the means of preventing illegal and unauthorized access to systems and information; unfortunately, the most destructive malicious insider attacks that are usually a consequence of internal exploitation within an IoT network remains unaddressed. Therefore, the focus of this research is to detect malicious insider attacks in the IoT environment using AI. This research presents a lightweight approach for detecting insider attacks and has the capability of detecting anomalies originating from incoming data sensors in resource constrained IoT environments. The results and comparison show that the proposed approach achieves better accuracy as compared to the state of the art in terms of: a) improved attack detection accuracy; b) minimizing false positives; and c) reducing the computational overhead.

/pdf/malicious-insider-attack-detection-in-iots-using-data-2sv94ym4j4.pdf

Malicious Insider Attack Detection in IoTs Using Data Analytics

In the string correction problem, we are to transform one string into another using a set of prescribed edit operations. In string correction using the Damerau-Levenshtein (DL) distance, the permissible edit operations are: substitution, insertion, deletion and transposition. Several algorithms for string correction using the DL distance have been proposed. The fastest and most space efficient of these algorithms is due to Lowrance and Wagner. It computes the DL distance between strings of length m and n, respectively, in O(mn) time and O(mn) space. In this paper, we focus on the development of algorithms whose asymptotic space complexity is less and whose actual runtime and energy consumption are less than those of the algorithm of Lowrance and Wagner. We develop space- and cache-efficient algorithms to compute the Damerau-Levenshtein (DL) distance between two strings as well as to find a sequence of edit operations of length equal to the DL distance. Our algorithms require O(s min{m,n}+m+n) space, where s is the size of the alphabet and m and n are, respectively, the lengths of the two strings. Previously known algorithms require O(mn) space. The space- and cache-efficient algorithms of this paper are demonstrated, experimentally, to be superior to earlier algorithms for the DL distance problem on time, space, and enery metrics using three different computational platforms. Our benchmarking shows that, our algorithms are able to handle much larger sequences than earlier algorithms due to the reduction in space requirements. On a single core, we are able to compute the DL distance and an optimal edit sequence faster than known algorithms by as much as 73.1% and 63.5%, respectively. Further, we reduce energy consumption by as much as 68.5%. Multicore versions of our algorithms achieve a speedup of 23.2 on 24 cores.

/pdf/string-correction-using-the-damerau-levenshtein-distance-44pte1vy8f.pdf

String correction using the Damerau-Levenshtein distance

Humans' decision making process often relies on utilizing visual information from different views or perspectives. However, in machine-learning-based image classification we typically infer an object's class from just a single image showing an object. Especially for challenging classification problems, the visual information conveyed by a single image may be insufficient for an accurate decision. We propose a classification scheme that relies on fusing visual information captured through images depicting the same object from multiple perspectives. Convolutional neural networks are used to extract and encode visual features from the multiple views and we propose strategies for fusing these information. More specifically, we investigate the following three strategies: (1) fusing convolutional feature maps at differing network depths; (2) fusion of bottleneck latent representations prior to classification; and (3) score fusion. We systematically evaluate these strategies on three datasets from different domains. Our findings emphasize the benefit of integrating information fusion into the network rather than performing it by post-processing of classification scores. Furthermore, we demonstrate through a case study that already trained networks can be easily extended by the best fusion strategy, outperforming other approaches by large margin.

/pdf/multi-view-classification-with-convolutional-neural-networks-4xgm1oobu1.pdf

Multi-view classification with convolutional neural networks.

Accurate identifications of ligand binding sites (LBS) on protein structure is critical for understanding protein function and designing structure-based drug. As the previous pocket-centric methods are usually based on the investigation of pseudo surface points (PSPs) outside the protein structure, thus inherently cannot incorporate the local connectivity and global 3D geometrical information of the protein structure. In this paper, we propose a novel point clouds segmentation method, PointSite, for accurate identification of protein ligand binding atoms, which performs protein LBS identification at the atom-level in a protein-centric manner. Specifically, we first transfer the original 3D protein structure to point clouds and then conduct segmentation through Submanifold Sparse Convolution (SSC) based U-Net. With the fine-grained atom-level binding atoms representation and enhanced feature learning, PointSite can outperform previous methods in atom-IoU by a large margin. Furthermore, our segmented binding atoms can work as a filter on predictions achieved by previous pocket-centric approaches, which significantly decreases the false-positive of LBS candidates. Through cascaded filter and re-ranking aided by the segmented atoms, state-of-the-art performance can be achieved over various canonical benchmarks and CAMEO hard targets in terms of the commonly used DCA criteria. Our code is publicly available through https://github.com/PointSite.

https://www.biorxiv.org/content/biorxiv/early/2019/11/05/831131.full.pdf

PointSite: a point cloud segmentation tool for identification of protein ligand binding atoms

An RNA folding/RNA secondary structure prediction algorithm determines the non-nested/pseudoknot-free structure by maximizing the number of complementary base pairs and minimizing the energy. Several implementations of Nussinov’s classical RNA folding algorithm have been proposed. Our focus is to obtain run time and energy efficiency by reducing the number of cache misses. Three cache-efficient algorithms, ByRow, ByRowSegment and ByBox, for Nussinov’s RNA folding are developed. Using a simple LRU cache model, we show that the Classical algorithm of Nussinov has the highest number of cache misses followed by the algorithms Transpose (Li et al.), ByRow, ByRowSegment, and ByBox (in this order). Extensive experiments conducted on four computational platforms–Xeon E5, AMD Athlon 64 X2, Intel I7 and PowerPC A2–using two programming languages–C and Java–show that our cache efficient algorithms are also efficient in terms of run time and energy. Our benchmarking shows that, depending on the computational platform and programming language, either ByRow or ByBox give best run time and energy performance. The C version of these algorithms reduce run time by as much as 97.2% and energy consumption by as much as 88.8% relative to Classical and by as much as 56.3% and 57.8% relative to Transpose. The Java versions reduce run time by as much as 98.3% relative to Classical and by as much as 75.2% relative to Transpose. Transpose achieves run time and energy efficiency at the expense of memory as it takes twice the memory required by Classical. The memory required by ByRow, ByRowSegment, and ByBox is the same as that of Classical. As a result, using the same amount of memory, the algorithms proposed by us can solve problems up to 40% larger than those solvable by Transpose.

/pdf/cache-and-energy-efficient-algorithms-for-nussinov-s-rna-ctf55u0h8a.pdf

Cache and energy efficient algorithms for Nussinov’s RNA Folding

We develop cache and energy efficient algorithms to align very long sequences. These algorithms were evaluated experimentally on a single node of the IBM Blue Gene/Q. We were able to reduce the run time of the classical Myers and Miller linear space alignment algorithm by up to 43%; energy consumption was reduced by up to 45% on our test data.

Cache and energy efficient alignment of very long sequences

The Damerau-Levenshtein (DL) distance metric has been widely used in the biological science. It tries to identify the similar region of DNA,RNA and protein sequences by transforming one sequence to the another using the substitution, insertion, deletion and transposition operations. Lowrance and Wagner have developed an O(mn) time O(mn) space algorithm to find the minimum cost edit sequence between strings of length m and n, respectively. In our previous research, we have developed algorithms that run in O(mn) time using only O(s∗min{m,n}+m+n) space, where s is the size of the alphabet comprising the strings, to compute the DL distance as well as the corresponding edit sequence. These are so far the fastest and most space efficient algorithms. In this paper, we focus on the development of algorithms whose asymptotic space complexity is linear. We develop linear space algorithms to compute the Damerau-Levenshtein (DL) distance between two strings and determine the optimal trace (corresponding edit operations.)Extensive experiments conducted on three computational platforms–Xeon E5 2603, I7-x980 and Xeon E5 2695–show that, our algorithms, in addition to using less space, are much faster than earlier algorithms. Besides using less space than the previously known algorithms,significant run-time improvement was seen for our new algorithms on all three of our experimental platforms. On all platforms, our linear-space cache-efficient algorithms reduced run time by as much as 56.4% and 57.4% in respect to compute the DL distance and an optimal edit sequences compared to previous algorithms. Our multi-core algorithms reduced the run time by up to 59.3% compared to the best previously known multi-core algorithms.

/pdf/linear-space-string-correction-algorithm-using-the-damerau-43gfyd3v16.pdf

Chunchun Zhao

Papers

String correction using the Damerau-Levenshtein distance

Cache and energy efficient algorithms for Nussinov’s RNA Folding

Cache and energy efficient alignment of very long sequences

Linear space string correction algorithm using the Damerau-Levenshtein distance

Linear Space String Correction Algorithm Using The Damerau-Levenshtein Distance