Earlier Algorithms Research Articles

A new unsupervised method for boundary perception and word-like segmentation of sequence

In cognitive science research on natural language processing, motor learning and visual perception, perceiving boundary points and segmenting a continuous string or sequence is one of the fundamental problems. Boundary perception can also be viewed as a machine learning problem; supervised or unsupervised learning. In supervised learning approach for determining boundary points for segmentation of a sequence, it is necessary to have some pre-segmented training examples. In unsupervised mode, the learning is accomplished without any training data hence, the frequency of occurence of symbols within the sequence is normally used as the cue. Most of earlier algorithms use this cue while scanning the sequence in forward direction. In this paper we propose a novel approach of extracting the possible boundary points by using bi-directional scanning of the sequence. We show here that such an extension from unidirectional to bi-directional is not trivial and requires judicious consideration of datastructure and algorithm. We here propose a new algorithm which traverses the sequence unidirectionally but extracts the information bi-directionally. Our method yields better segmentation which is demonstrated by rigorous experimentation on several datasets.

A new unsupervised method for boundary perception and word-like segmentation of sequence

In cognitive science research on natural language processing, motor learning and visual perception, perceiving boundary points and segmenting a continuous string or sequence is one of the fundamental problems. Boundary perception can also be viewed as a machine learning problem; supervised or unsupervised learning. In supervised learning approach for determining boundary points for segmentation of a sequence, it is necessary to have some pre-segmented training examples. In unsupervised mode, the learning is accomplished without any training data hence, the frequency of occurence of symbols within the sequence is normally used as the cue. Most of earlier algorithms use this cue while scanning the sequence in forward direction. In this paper we propose a novel approach of extracting the possible boundary points by using bi-directional scanning of the sequence. We show here that such an extension from unidirectional to bi-directional is not trivial and requires judicious consideration of datastructure and algorithm. We here propose a new algorithm which traverses the sequence unidirectionally but extracts the information bi-directionally. Our method yields better segmentation which is demonstrated by rigorous experimentation on several datasets.

Effect of PCA Feature Reduction on Ventricular Ectopic Beat Classification

Due to the cardiac diseases called Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) causes sudden cardiac death, it is crucial to recognize Ventricular Ectopic Beats (VEB) in Electrocardio- gram for the early diagnosis. There are many algorithms proposed earlier to classify the VEBs. Even though those algorithms achieved good accu- racy, the size of the feature set is large and not precise. In addition, earlier algorithms used feature reduction methods for reducing the feature set. Therefore, in this paper, we extracted only five features namely, Pre RR interval, post RR interval, QRS duration, QR slope, and RS slope. Later, we applied the Principle Component Analysis (PCA) for reducing the size of the feature set to observe the effect of Feature Reduction (FR) on the accuracy of VEB Classification. We applied different classifiers for classifying the cardiac beats in to normal and VEBs. Finally, using K-means Nearest Neighborhood (KNN) classifier and cubic Support Vec- tor Machine (SVM) classifier, we achieved 97.4%classification accuracy, 98.38%, 88.89% & 98.37% sensitivity, specificity & positive predictivity respectively. In addition, it is observed that by applying PCA-FR, the classification accuracy was reduced by a maximum of 3.7%.

Effect of PCA Feature Reduction on Ventricular Ectopic Beat Classification

Due to the cardiac diseases called Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) causes sudden cardiac death, it is crucial to recognize Ventricular Ectopic Beats (VEB) in Electrocardio- gram for the early diagnosis. There are many algorithms proposed earlier to classify the VEBs. Even though those algorithms achieved good accu- racy, the size of the feature set is large and not precise. In addition, earlier algorithms used feature reduction methods for reducing the feature set. Therefore, in this paper, we extracted only five features namely, Pre RR interval, post RR interval, QRS duration, QR slope, and RS slope. Later, we applied the Principle Component Analysis (PCA) for reducing the size of the feature set to observe the effect of Feature Reduction (FR) on the accuracy of VEB Classification. We applied different classifiers for classifying the cardiac beats in to normal and VEBs. Finally, using K-means Nearest Neighborhood (KNN) classifier and cubic Support Vec- tor Machine (SVM) classifier, we achieved 97.4% classification accuracy, 98.38%, 88.89% & 98.37% sensitivity, specificity & positive predictivity respectively. In addition, it is observed that by applying PCA-FR, the classification accuracy was reduced by a maximum of 3.7%.

Uniform Generation of Spanning Regular Subgraphs of a Dense Graph

Let $H_n$ be a graph on $n$ vertices and let $\overline{H_n}$ denote the complement of $H_n$. Suppose that $\Delta = \Delta(n)$ is the maximum degree of $\overline{H_n}$. We analyse three algorithms for sampling $d$-regular subgraphs ($d$-factors) of $H_n$. This is equivalent to uniformly sampling $d$-regular graphs which avoid a set $E(\overline{H_n})$ of forbidden edges. Here $d=d(n)$ is a positive integer which may depend on $n$.
 Two of these algorithms produce a uniformly random $d$-factor of $H_n$ in expected runtime which is linear in $n$ and low-degree polynomial in $d$ and $\Delta$. The first algorithm applies when $(d+\Delta)d\Delta = o(n)$. This improves on an earlier algorithm by the first author, which required constant $d$ and at most a linear number of edges in $\overline{H_n}$. The second algorithm applies when $H_n$ is regular and $d^2+\Delta^2 = o(n)$, adapting an approach developed by the first author together with Wormald. The third algorithm is a simplification of the second, and produces an approximately uniform $d$-factor of $H_n$ in time $O(dn)$. Here the output distribution differs from uniform by $o(1)$ in total variation distance, provided that $d^2+\Delta^2 = o(n)$.

Network Reconfiguration Algorithm (NRA) for scheduling communication-intensive graphs in heterogeneous computing environment

Distributed environments are widely used for computing complex applications modeled as task graphs. The computer network becomes more complex when the compute nodes are heterogeneous, however by choosing the appropriate network communication links for communication between a pair of compute tasks can enhance the computing efficiency(called network reconfiguration). One of the steps in heterogeneous network reconfiguration problem is mapping the application task graph edges on the network links. High Performance Computing (HPC) systems are usually heterogeneous, therefore mapping task graph edges on the communication links should consider the two factors: communication cost of task graph edges and the communication capability of network links. The system performance enhances if tasks are mapped on the compute nodes based on the computational costs of the tasks and the processing capability of compute nodes in addition to the edge scheduling on network links. In our earlier algorithm, Heterogeneous Edge and Task Scheduling (HETS) both edge and task mapping simultaneously improve the execution performance of task graphs. The proposed Network Reconfiguration Algorithm (NRA) minimizes the communication overhead and optimizes the schedule length with contention-aware model. NRA reduces an attribute Kirchhoff Index (KI) for optimal network reconfiguration providing minimum execution time. Both synthesized and task graphs of real applications are used for evaluation. The simulation results prove the efficiency of NRA in terms of average schedule length, schedule length ratio, speedup and system throughput. Comparisons with the baseline algorithms show that NRA provides 36% improved results specially for communication-intensive applications.

A Flexible Real-Time Algorithm for the RedStorm#2 Software Package

A new real-time algorithm for the C++ RedStorm#2 software package to radically suppress the background at the total fusion reactions on the new DC-280 cyclotron (Flerov Laboratory of Nuclear Reactions, Joint Institute for Nuclear Research) is reviewed. This algorithm assumes some flexibility with the execution of the data set program in contrast to earlier algorithms applied at the Dubna gas-filled recoil separator. Namely, the experimenter sets the allowable value of the relative real-time loss of target irradiation, but not a fixed time interval between the implantation of a nuclide into a silicon detector and its α-decay. The block diagram of the algorithm execution is presented and possible branches of the algorithm are considered. The edge effects and their role in collecting a nonequilibrium charge from the p–n junction of the DSSSD detector (double-sided silicon strip detector) of the focal plane of recoil separator are also considered.

Towards the proposal of Mobile securityEncryptionAlgorithm:“RHINO256”

Background/objectives: Encryption is one of the most important factors of any security structure. Data cannot be protected without an efficient encryption technique. Different encryption algorithms are available nowadays that apply techniques such as encrypting using symmetric or asymmetric keys, using stream or block ciphers, and others such as Feistel networks, substitution, and permutation, etc. Methods/statistical analysis: We have analysed the existing algorithms so as to get the problem. Findings: The proposed algorithm will solve the problem of maintaining the authenticity of the sender as another third party cannot send a message in the former’s name, as was the problem with earlier algorithms such as DES. Improvements/applications: The main idea of performing this research was to propose an algorithm that can encrypt a message with the sender’s private key and the receiver’s public key together and the same can be decrypted using the receiver’s private key and sender’s public key.Keywords: S-box, P-box, Feistel Networks, DES

A machine learning method for green energy saving in space division multiplexing elastic optical networks

Abstract Elastic optical networks (EONs) and space division multiplexing (SDM) are promising technologies for future core optical networks with high transmission capacity. However, energy consumption and crosstalk are two inevitable problems of practical application of space division multiplexing elastic optical networks (SDM-EONs). In this paper, we leverage machine learning techniques to decrease the energy consumption and crosstalk of SDM-EONs. Specifically, we use an online sequence extreme learning machine to forecast traffic load on the lightpath, and reconstruct an energy model containing energy-consumption components in the whole network so as to reduce energy consumption. We also present a suitable scheme to decrease crosstalk. Numerical results show that the proposed algorithm is effective in achieving substantial savings in energy consumption while maintaining blocking probability and crosstalk at levels comparable to those of earlier algorithms.

Derivative grammars: a symbolic approach to parsing with derivatives

We present a novel approach to context-free grammar parsing that is based on generating a sequence of grammars called derivative grammars from a given context-free grammar and input string. The generation of the derivative grammars is described by a few simple inference rules. We present an O ( n 2 ) space and O ( n 3 ) time recognition algorithm, which can be extended to generate parse trees in O ( n 3 ) time and O ( n 2 log n ) space. Derivative grammars can be viewed as a symbolic approach to implementing the notion of derivative languages , which was introduced by Brzozowski. Might and others have explored an operational approach to implementing derivative languages in which the context-free grammar is encoded as a collection of recursive algebraic data types in a functional language like Haskell. Functional language implementation features like knot-tying and lazy evaluation are exploited to ensure that parsing is done correctly and efficiently in spite of complications like left-recursion. In contrast, our symbolic approach using inference rules can be implemented easily in any programming language and we obtain better space bounds for parsing. Reifying derivative languages by encoding them symbolically as grammars also enables formal connections to be made for the first time between the derivatives approach and classical parsing methods like the Earley and LL/LR parsers. In particular, we show that the sets of Earley items maintained by the Earley parser implicitly encode derivative grammars and we give a procedure for producing derivative grammars from these sets. Conversely, we show that our derivative grammar recognizer can be transformed into the Earley recognizer by optimizing some of its bookkeeping. These results suggest that derivative grammars may provide a new foundation for context-free grammar recognition and parsing.

Simple Fixpoint Iteration To Solve Parity Games

A naive way to solve the model-checking problem of the mu-calculus uses fixpoint iteration. Traditionally however mu-calculus model-checking is solved by a reduction in linear time to a parity game, which is then solved using one of the many algorithms for parity games. We now consider a method of solving parity games by means of a naive fixpoint iteration. Several fixpoint algorithms for parity games have been proposed in the literature. In this work, we introduce an algorithm that relies on the notion of a distraction. The idea is that this offers a novel perspective for understanding parity games. We then show that this algorithm is in fact identical to two earlier published fixpoint algorithms for parity games and thus that these earlier algorithms are the same. Furthermore, we modify our algorithm to only partially recompute deeper fixpoints after updating a higher set and show that this modification enables a simple method to obtain winning strategies. We show that the resulting algorithm is simple to implement and offers good performance on practical parity games. We empirically demonstrate this using games derived from model-checking, equivalence checking and reactive synthesis and show that our fixpoint algorithm is the fastest solution for model-checking games.

Mining Changes in Temporal Patterns in Latest Time Window for Knowledge Discovery

Temporal Association Rule mining uncovers time integrated associations in a transactional database. However, in an environment where database is regularly updated, maintenance of rules is a challenging process. Earlier algorithms suggested for maintaining frequent patterns either suffered from the problem of repeated scanning or the problem of larger storage space. Therefore, this paper proposes an algorithm “Probabilistic Incremental Temporal Association Rule Mining (PITARM)” that uncovers the changed behaviour in an updated database to maintain the rules efficiently. The proposed algorithm defines two support measures to identify itemsets expected to be frequent in the successive segment in advance. It reduces unnecessary scanning of itemsets in the entire database through three-fold verification and avoids generating redundant supersets and power sets from infrequent itemsets. Implementation of pruning technique in incremental mining is a novel approach that makes it better than earlier incremental mining algorithms and consequently reduces search space to a great extent. It scans the entire database only once, thus reducing execution time. Experimental results confirm that it is an enhancement over earlier algorithms.

Enhancing the Performance of Large-scale Profitable Itemset Mining using Efficient Data Structures

The process of extracting the most frequently bought items from a transactional database is termed as frequent itemset mining. Although it provides us with an idea of the best-selling itemsets, the method fails to identify the most profitable items from the database. It is not uncommon to have minimal intersection between frequent itemsets and profitable itemsets, and the process of extracting the most profitable itemsets is termed as Greater Profitable Itemset (GPI) mining. There have been various approaches to mine GPI in which [7] proposed a two-phased algorithm to optimize regeneration of GPI when the profit value of any item changes. This constituted of keeping track of the pruned items in the first phase and using it to efficiently regenerate GPI in the second phase. This paper proposes an enhancement to the way these changes are tracked by storing the pruned itemsets according to their constituent items, unlike the earlier algorithm that stored records iteration wise. By storing the itemsets according to their constituent items, we make sure that only the required items are being retrieved. In contrast, the earlier algorithm would fetch all the items pruned in any iteration, regardless of its relevance. By fetching only relevant itemset, the proposed method would significantly bring down the computational requirements.

A SLAM-Based Mobile Augmented Reality Tracking Registration Algorithm

This paper proposes a simultaneous localization and mapping (SLAM)-based markerless mobile-end tracking registration algorithm to address the problem of virtual image drift caused by fast camera motion in mobile-augmented reality (AR). The proposed algorithm combines the AGAST-FREAK-SLAM algorithm with inertial measurement unit (IMU) data to construct a scene map and localize the camera’s pose. The extracted feature points are matched with feature points in a map library for real-time camera localization and precise registration of virtual objects. Experimental results show that the proposed method can track feature points in real time, accurately construct scene maps, and locate cameras; moreover, it improves upon the tracking and registration robustness of earlier algorithms.

String correction using the Damerau-Levenshtein distance

BackgroundIn 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.ResultsWe 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.ConclusionOur 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.

Derivation representation using binary subtree sets

This paper introduces sets of binary subtree representations as an alternative to shared packed parse forests as the output of a generalised parser, and shows how these may be generated by Earley's algorithm, by a new GLL-style parser and by Johnson's continuation passing combinator style parsers. The set based output removes the clerical overhead associated with graph constructions, making the parsers simpler. • Introduces BSR sets, an alternative to the SPPF representation of all derivations of a string. • General BSR combinator parsers and Earley-style parsers are presented. • A new BSR GLL-style parser (CNP) is presented which has a more efficient combined call stack. • Data structure size comparisons for GLL and CNP are given for C, C++ and Java grammars.

A novel algorithm for better distinction of primary mucinous ovarian carcinomas and mucinous carcinomas metastatic to the ovary

Primary mucinous ovarian carcinomas (MOC) are notoriously difficult to distinguish from mucinous carcinomas metastatic to the ovary (mMC). Studies performed on small cohorts reported algorithms based on tumor size and laterality to aid in distinguishing MOC from mMC. We evaluated and improved these by performing a large-scale, nationwide search in the Dutch Pathology Registry. All registered pathology reports fulfilling our search criteria concerning MOC in the Netherlands from 2000 to 2011 were collected. Age, histology, laterality, and size were extracted. An existing database covering the same timeline containing tumors metastatic to the ovary was used, extracting all mMC, age, size, laterality, and primary tumor location. Existing algorithms were applied to our cohort. Subsequently, an algorithm based on tumor histology, laterality, and a nomogram based on age and size was created for differentiating MOC and mMC. We identified 735 MOC and 1018 mMC. Patients with MOC were significantly younger and MOC were significantly larger and more often unilateral than mMC. Signet ring cell carcinomas were rarely primary. Our algorithm used signet ring cell histology, bilaterality, and a nomogram integrating patient age and tumor size to diagnose mMC. Sensitivity and specificity for mMC was 90.1% and 59.0%, respectively. Applying existing algorithms on our cohort yielded a far lower sensitivity. The algorithm described here using tumor histology, laterality, size, and patient age has higher sensitivity but lower specificity compared to earlier algorithms and aids in indicating tumor origin, but for conclusive diagnosis, careful integration of morphology, immunohistochemistry, and clinical and imaging data is recommended.

Speckle noise removal in SAR images using Multi-Objective PSO (MOPSO) algorithm

SAR images are inherently affected by speckle noise, and although attempts made earlier to remove speckle succeeded, there is still the challenge of preserving the edges of images. This is due to the smoothing effect of most of the earlier algorithms that work on thresholding coefficients in the transform domain. There exists a trade-off between denoising and the ability to preserve edges in selecting a suitable threshold. Estimation of an optimal threshold is a major concern and is compounded by the requirement for concurrent smoothing of noise and preservation of structural/edge information in an image. Considering the search for an optimal threshold as exhaustive and the requirements as contradictory, we model this as a Multi-Objective Particle Swarm Optimization (MOPSO) task and propose a MOPSO framework for despeckling an SAR image using a Dual-Tree Complex Wavelet Transform (DTCWT) in the frequency domain. Two counteractive reference metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Mean Structural Similarity Index Metric (MSSIM), and non-reference metrics such as the alpha-beta (αβ) and Despeckling Evaluation Index (DEI) have been used as the objective functions of MOPSO. An optimal threshold derived from this multi-objective optimization is chosen for despeckling the SAR images. The proposed solution has been found to outperform state-of-the-art filters such as Lee, Kaun, Frost and SAR-BM3D filters. Also, the proposed MOPSO framework superior than the competing optimization technique Multi-Objective Evolutionary Algorithm (MOEA) based on Differential Evolution (DE) framework for despeckling.

An Optimal Algorithm for an Outerplanar Facility Location Problem with Improved Time Complexity

We consider a network facility location problem with unbounded production levels. This problem is NP-hard in the general case and is known to have an optimal solution with quadratic complexity on a tree network. We study the case of a network representable by an outerplanar graph, i.e., by a graph whose vertices belong to one (outer) face. This problem is known to have an optimal algorithm with time complexity O(nm3), where n is the number of vertices and m is the number of possible facility locations. Using some properties of outerplanar graphs (binary 2-trees) and the existence of an optimal solution with a family of centrally connected service areas, we obtain recurrence relations for the construction of an optimal algorithm with time complexity that is smaller by a factor of \(\sqrt m \) than the time complexity of the earlier algorithm.

Generalized Kakeya sets for polynomial evaluation and faster computation of fermionants

We present two new data structures for computing values of an n-variate polynomial P of degree at most d over a finite field of q elements. Assuming that d divides q-1, our first data structure relies on (d+1)^{n+2} tabulated values of P to produce the value of P at any of the q^n points using O(nqd^2) arithmetic operations in the finite field. Assuming that s divides d and d / s divides q-1, our second data structure assumes that P satisfies a degree-separability condition and relies on (d/s+1)^{n+s} tabulated values to produce the value of P at any point using Oleft( nq^ssqright) arithmetic operations. Our data structures are based on generalizing upper-bound constructions due to Mockenhaupt and Tao (Duke Math J 121(1):35–74, 2004), Saraf and Sudan (Anal PDE 1(3):375–379, 2008) and Dvir (Incidence theorems and their applications, 2012. arXiv:1208.5073) for Kakeya sets in finite vector spaces from linear to higher-degree polynomial curves. As an application we show that the new data structures enable a faster algorithm for computing integer-valued fermionants, a family of self-reducible polynomial functions introduced by Chandrasekharan and Wiese (Partition functions of strongly correlated electron systems as fermionants, 2011. arXiv:1108.2461v1) that captures numerous fundamental algebraic and combinatorial functions such as the determinant, the permanent, the number of Hamiltonian cycles in a directed multigraph, as well as certain partition functions of strongly correlated electron systems in statistical physics. In particular, a corollary of our main theorem for fermionants is that the permanent of an mtimes m integer matrix with entries bounded in absolute value by a constant can be computed in time 2^{m-Omega left( sqrt{m/log log m}right) }, improving an earlier algorithm of Björklund (in: Proceedings of the 15th SWAT, vol 17, pp 1–11, 2016) that runs in time 2^{m-Omega left( sqrt{m/log m}right) }.