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Journal articles on the topic 'Computer algorithms. Data structures (Computer science)'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Manjula, V. "Graph Applications to Data Structures." Advanced Materials Research 433-440 (January 2012): 3297–301. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3297.

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This paper presents a topic on Graph theory and its application to data Structures which I consider basic and useful to students in APPLIED MATHEMATICS and ENGINEERING.This paper gives an elementary introduction of Graph theory and its application to data structures. Elements of Graph theory are indispensable in almost all computer Science areas .It can be used in Some areas such as syntactic analysis, fault detection, diagnosis in computers and minimal path problems. The computer representation and manipulation of graph are also discussed so that certain algorithms can be included .A major theme of this paper is to study Graph theory and its Application to data structures Furthermore I hope the students not only learn the course but also develop their analogy perceive, formulate and to solve mathematical programs Thus Graphs especially trees, binary trees are used widely in the representation of data structures this course one can develop mathematical maturity, ability to understand and create mathematical argumentsMethod of derivation is procedure given in the text books with necessary formulae and their application . Concepts and notations from discrete mathematics are useful in studying and describing objects and problems in branches of computer science, such as computer algorithms, programming languages.

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Taubenfeld, Gadi. "Contention-sensitive data structures and algorithms." Theoretical Computer Science 677 (May 2017): 41–55. http://dx.doi.org/10.1016/j.tcs.2017.03.017.

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Shand, Mark A. "Algorithms for corner stitched data-structures." Algorithmica 2, no. 1-4 (November 1987): 61–80. http://dx.doi.org/10.1007/bf01840349.

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Delgado-Friedrichs, Olaf. "Data structures and algorithms for tilings I." Theoretical Computer Science 303, no. 2-3 (July 2003): 431–45. http://dx.doi.org/10.1016/s0304-3975(02)00500-5.

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5

Laube, Ulrich, and Markus E. Nebel. "Maximum likelihood analysis of algorithms and data structures." Theoretical Computer Science 411, no. 1 (January 2010): 188–212. http://dx.doi.org/10.1016/j.tcs.2009.09.025.

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Vitter, Jeffrey Scott. "Algorithms and Data Structures for External Memory." Foundations and Trends® in Theoretical Computer Science 2, no. 4 (2006): 305–474. http://dx.doi.org/10.1561/0400000014.

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Franaszek, P. A., P. Heidelberger, D. E. Poff, and J. T. Robinson. "Algorithms and data structures for compressed-memory machines." IBM Journal of Research and Development 45, no. 2 (March 2001): 245–58. http://dx.doi.org/10.1147/rd.452.0245.

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Prokop, Yu V., O. H. Trofymenko, and O. V. Dykyi. "RESEARCH OF APPROACHES TO TEACHING THE COURSE “ALGORITHMS AND DATA STRUCTURES” FOR COMPUTER SCIENCE STUDENTS." Scientific notes of Taurida National V.I. Vernadsky University. Series: Technical Sciences 1, no. 2 (2021): 216–20. http://dx.doi.org/10.32838/2663-5941/2021.2-1/34.

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Knop, A., S. Lovett, S. McGuire, and W. Yuan. "Guest Column." ACM SIGACT News 52, no. 2 (June 14, 2021): 46–70. http://dx.doi.org/10.1145/3471469.3471479.

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Communication complexity studies the amount of communication necessary to compute a function whose value depends on information distributed among several entities. Yao [Yao79] initiated the study of communication complexity more than 40 years ago, and it has since become a central eld in theoretical computer science with many applications in diverse areas such as data structures, streaming algorithms, property testing, approximation algorithms, coding theory, and machine learning. The textbooks [KN06,RY20] provide excellent overviews of the theory and its applications.

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K, Bhargavi. "Data Dimensionality Reduction Techniques : Review." International Journal of Engineering Technology and Management Sciences 4, no. 4 (July 28, 2020): 62–65. http://dx.doi.org/10.46647/ijetms.2020.v04i04.010.

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Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured. Data science is related to computer science, but is a separate field. Computer science involves creating programs and algorithms to record and process data, while data science covers any type of data analysis, which may or may not use computers. Data science is more closely related to the mathematics field of Statistics, which includes the collection, organization, analysis, and presentation of data. Because of the large amounts of data modern companies and organizations maintain, data science has become an integral part of IT. For example, a company that has petabytes of user data may use data science to develop effective ways to store, manage, and analyze the data. The company may use the scientific method to run tests and extract results that can provide meaningful insights about their users.

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Burke, Adam. "Occluded algorithms." Big Data & Society 6, no. 2 (July 2019): 205395171985874. http://dx.doi.org/10.1177/2053951719858743.

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Two definitions of algorithm, their uses, and their implied models of computing in society, are reviewed. The first, termed the structural programming definition, aligns more with usage in computer science, and as the name suggests, the intellectual project of structured programming. The second, termed the systemic definition, is more informal and emerges from ethnographic observations of discussions of software in both professional and everyday settings. Specific examples of locating algorithms within modern codebases are shared, as well as code directly impacting social and ethical concerns. The structural distinction between algorithms and social concerns is explained as mirroring the engineering construct of algorithms and data structures. It is proposed that, rather than this separation being an attempt to enforce a professional boundary and evade social responsibility, it is a crucial technical distinction within code which makes it clearer and more transparent. The power structures reinforced by the broader, cultural interpretations of algorithm are reconsidered, along with what it would mean for software to have an inclusive design culture.

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Apostolico, A., and F. P. Preparata. "Data structures and algorithms for the string statistics problem." Algorithmica 15, no. 5 (May 1996): 481–94. http://dx.doi.org/10.1007/bf01955046.

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Friggstad, Zachary, Jörg-Rüdiger Sack, and Mohammad R. Salavatipour. "Special Issue on Algorithms and Data Structures (WADS 2019)." Algorithmica 83, no. 3 (February 3, 2021): 775. http://dx.doi.org/10.1007/s00453-021-00804-z.

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Chakrabarti, Soumen, and Katherine Yelick. "Distributed data structures and algorithms for Gr�bner basis computation." LISP and Symbolic Computation 7, no. 2-3 (1994): 147–72. http://dx.doi.org/10.1007/bf01018692.

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Van Der Wiede, T. "The design of data structures and algorithms." Information and Software Technology 32, no. 10 (December 1990): 698. http://dx.doi.org/10.1016/0950-5849(90)90104-y.

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GAVA, FRÉDÉRIC. "A MODULAR IMPLEMENTATION OF DATA STRUCTURES IN BULK-SYNCHRONOUS PARALLEL ML." Parallel Processing Letters 18, no. 01 (March 2008): 39–53. http://dx.doi.org/10.1142/s0129626408003211.

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A functional data-parallel language called BSML has been designed for programming Bulk-Synchronous Parallel algorithms. Many sequential algorithms do not have parallel counterparts and many non-computer science researchers do not want to deal with parallel programming. In sequential programming environments, common data structures are often provided through reusable libraries to simplify the development of applications. A parallel representation of such data structures is thus a solution for writing parallel programs without suffering from disadvantages of all the features of a parallel language. In this paper we describe a modular implementation in BSML of some data structures and show how those data types can address the needs of many potential users of parallel machines who have so far been deterred by the complexity of parallelizing code.

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De Stefani, Lorenzo, and Francesco Silvestri. "Exploiting non-constant safe memory in resilient algorithms and data structures." Theoretical Computer Science 583 (June 2015): 86–97. http://dx.doi.org/10.1016/j.tcs.2015.04.003.

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Bishop, J. "Introduction to Pascal and Structured Design; Pascal plus Data Structures, Algorithms and Advanced Programming." Computer Journal 35, no. 3 (June 1, 1992): 255. http://dx.doi.org/10.1093/comjnl/35.3.255.

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Ramaswamy, Vasu, and Vadim Shapiro. "Combinatorial Laws for Physically Meaningful Design." Journal of Computing and Information Science in Engineering 4, no. 1 (March 1, 2004): 3–10. http://dx.doi.org/10.1115/1.1645863.

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A typical computer representation of a design includes geometric and physical information organized in a suitable combinatorial data structure. Queries and transformations of these design representations are used to formulate most algorithms in computational design, including analysis, optimization, evolution, generation, and synthesis. Formal properties, and in particular existence and validity of the computed solutions, must be assured and preserved by all such algorithms. Using tools from algebraic topology, we show that a small set of the usual combinatorial operators: boundary (∂), coboundary (δ), and dualization *–are sufficient to represent a variety of physical laws and invariants. Specific examples include geometric integrity, balance and equilibrium, and surface smoothing. Our findings point a way toward systematic development of data structures and algorithms for design in a common formal computational framework.

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Nielsen, Soren S., and Stavros A. Zenios. "Data structures for network algorithms on massively parallel architectures." Parallel Computing 18, no. 9 (September 1992): 1033–52. http://dx.doi.org/10.1016/0167-8191(92)90016-z.

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Ahn, Kook Jin, Graham Cormode, Sudipto Guha, Andrew McGregor, and Anthony Wirth. "Correlation Clustering in Data Streams." Algorithmica 83, no. 7 (March 13, 2021): 1980–2017. http://dx.doi.org/10.1007/s00453-021-00816-9.

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AbstractClustering is a fundamental tool for analyzing large data sets. A rich body of work has been devoted to designing data-stream algorithms for the relevant optimization problems such as k-center, k-median, and k-means. Such algorithms need to be both time and and space efficient. In this paper, we address the problem of correlation clustering in the dynamic data stream model. The stream consists of updates to the edge weights of a graph on n nodes and the goal is to find a node-partition such that the end-points of negative-weight edges are typically in different clusters whereas the end-points of positive-weight edges are typically in the same cluster. We present polynomial-time, $$O(n\cdot {{\,\mathrm{polylog}\,}}n)$$ O ( n · polylog n ) -space approximation algorithms for natural problems that arise. We first develop data structures based on linear sketches that allow the “quality” of a given node-partition to be measured. We then combine these data structures with convex programming and sampling techniques to solve the relevant approximation problem. Unfortunately, the standard LP and SDP formulations are not obviously solvable in $$O(n\cdot {{\,\mathrm{polylog}\,}}n)$$ O ( n · polylog n ) -space. Our work presents space-efficient algorithms for the convex programming required, as well as approaches to reduce the adaptivity of the sampling.

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Amir, A., A. Efrat, P. Indyk, and H. Samet. "Efficient Regular Data Structures and Algorithms for Dilation, Location, and Proximity Problems." Algorithmica 30, no. 2 (June 2001): 164–87. http://dx.doi.org/10.1007/s00453-001-0013-y.

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Corney, J., C. Hayes, V. Sundararajan, and P. Wright. "The CAD/CAM Interface: A 25-Year Retrospective." Journal of Computing and Information Science in Engineering 5, no. 3 (July 19, 2005): 188–97. http://dx.doi.org/10.1115/1.2033009.

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The vision of fully automated manufacturing processes was conceived when computers were first used to control industrial equipment. But realizing this goal has not been easy; the difficulties of generating manufacturing information directly from computer aided design (CAD) data continued to challenge researchers for over 25 years. Although the extraction of coordinate geometry has always been straightforward, identifying the semantic structures (i.e., features) needed for reasoning about a component’s function and manufacturability has proved much more difficult. Consequently the programming of computer controlled manufacturing processes such as milling, cutting, turning and even the various lamination systems (e.g., SLA, SLS) has remained largely computer aided rather than entirely automated. This paper summarizes generic difficulties inherent in the development of feature based CAD/CAM (computer aided manufacturing) interfaces and presents two alternative perspectives on developments in manufacturing integration research that have occurred over the last 25 years. The first perspective presents developments in terms of technology drivers including progress in computational algorithms, enhanced design environments and faster computers. The second perspective describes challenges that arise in specific manufacturing applications including multiaxis machining, laminates, and sheet metal parts. The paper concludes by identifying possible directions for future research in this area.

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Mansoor, Asif, Muhammad Waleed Usman, Noreen Jamil, and M. Asif Naeem. "Deep Learning Algorithm for Brain-Computer Interface." Scientific Programming 2020 (August 25, 2020): 1–12. http://dx.doi.org/10.1155/2020/5762149.

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Electroencephalography-(EEG-) based control is a noninvasive technique which employs brain signals to control electrical devices/circuits. Currently, the brain-computer interface (BCI) systems provide two types of signals, raw signals and logic state signals. The latter signals are used to turn on/off the devices. In this paper, the capabilities of BCI systems are explored, and a survey is conducted how to extend and enhance the reliability and accuracy of the BCI systems. A structured overview was provided which consists of the data acquisition, feature extraction, and classification algorithm methods used by different researchers in the past few years. Some classification algorithms for EEG-based BCI systems are adaptive classifiers, tensor classifiers, transfer learning approach, and deep learning, as well as some miscellaneous techniques. Based on our assessment, we generally concluded that, through adaptive classifiers, accurate results are acquired as compared to the static classification techniques. Deep learning techniques were developed to achieve the desired objectives and their real-time implementation as compared to other algorithms.

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Ramle, Rosni, D'oria Islamiah Rosli, Shelena Soosay Nathan, and Mazniha Berahim. "Digital Game Based Learning of Stack Data Structure Using Question Prompts." International Journal of Interactive Mobile Technologies (iJIM) 13, no. 07 (July 11, 2019): 90. http://dx.doi.org/10.3991/ijim.v13i07.10778.

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<p class="0abstract">Data structure and algorithm is an important course in computer science and information technology programs, applied in almost all courses. Failure to master it will affect student's academic performance during study, getting job interviews, passing job interviews, and create an inefficient information technology worker. However, learning data structure is a worldwide problem because of its complex nature. Gameful visualization of data structures’ algorithms has been gaining momentum as it resulted in increased motivation, engagement and learning outcome. But effectiveness of game-based learning could be hindered if improper learning strategies used. Instructional scaffold in game-based learning in the form of question prompts have been found to be the most effective way to scaffold self-learn in computer-based learning. Thus, a game-based learning of stack data structure using question prompts was designed, developed and tested based on an adopted model to help students understand the algorithms of stack’s insert and delete operations for array implementation with gameplay that could create meaningful learning. A pre-game and post-game test was conducted to compare students’ performance on the topic. Results indicated a generally positive outcome.</p>

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Black, Paul E., David Flater, and Irena Bojanova. "Algorithms and Data Structures for New Models of Computation." IT Professional 23, no. 1 (January 1, 2021): 9–15. http://dx.doi.org/10.1109/mitp.2020.3042858.

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Hwang, Hsien-Kuei, Ralph Neininger, and Marek Zaionc. "Preface." Combinatorics, Probability and Computing 28, no. 4 (July 2019): 483–84. http://dx.doi.org/10.1017/s0963548319000166.

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This special issue is devoted to the Mathematical Analysis of Algorithms, which aims to predict the performance of fundamental algorithms and data structures in general use in Computer Science. The simplest measure of performance is the expected value of a cost function under natural models of randomness for the data, and finer properties of the cost distribution provide a deeper understanding of the complexity. Research in this area, which is intimately connected to combinatorics and random discrete structures, uses a rich variety of combinatorial, analytic and probabilistic methods.

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Esponda-Argüero, Margarita. "Techniques for Visualizing Data Structures in Algorithmic Animations." Information Visualization 9, no. 1 (January 29, 2009): 31–46. http://dx.doi.org/10.1057/ivs.2008.26.

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This paper deals with techniques for the design and production of appealing algorithmic animations and their use in computer science education. A good visual animation is both a technical artifact and a work of art that can greatly enhance the understanding of an algorithm's workings. In the first part of the paper, I show that awareness of the composition principles used by other animators and visual artists can help programmers to create better algorithmic animations. The second part shows how to incorporate those ideas in novel animation systems, which represent data structures in a visually intuitive manner. The animations described in this paper have been implemented and used in the classroom for courses at university level.

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Chikhi, Rayan, Jan Holub, and Paul Medvedev. "Data Structures to Represent a Set of k -long DNA Sequences." ACM Computing Surveys 54, no. 1 (April 2021): 1–22. http://dx.doi.org/10.1145/3445967.

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The analysis of biological sequencing data has been one of the biggest applications of string algorithms. The approaches used in many such applications are based on the analysis of k -mers, which are short fixed-length strings present in a dataset. While these approaches are rather diverse, storing and querying a k -mer set has emerged as a shared underlying component. A set of k -mers has unique features and applications that, over the past 10 years, have resulted in many specialized approaches for its representation. In this survey, we give a unified presentation and comparison of the data structures that have been proposed to store and query a k -mer set. We hope this survey will serve as a resource for researchers in the field as well as make the area more accessible to researchers outside the field.

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Brandon, M. C., D. C. Wallace, and P. Baldi. "Data structures and compression algorithms for genomic sequence data." Bioinformatics 25, no. 14 (May 15, 2009): 1731–38. http://dx.doi.org/10.1093/bioinformatics/btp319.

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Dragoni, Nicola, Olga Gadyatskaya, Fabio Massacci, and Anton Philippov. "High-level algorithms and data structures requirements for security-by-contract on Java cards." International Journal of Critical Computer-Based Systems 3, no. 4 (2012): 284. http://dx.doi.org/10.1504/ijccbs.2012.053207.

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Austin, M. A., and J. L. Preston. "Solid Modeling of RC Beams: I. Data Structures and Algorithms." Journal of Computing in Civil Engineering 6, no. 4 (October 1992): 389–403. http://dx.doi.org/10.1061/(asce)0887-3801(1992)6:4(389).

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Roman, Raul-Cristian, Radu-Emil Precup, Emil M. Petriu, and Florin Dragan. "Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems." Energies 12, no. 8 (April 24, 2019): 1548. http://dx.doi.org/10.3390/en12081548.

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In this paper a second-order data-driven Active Disturbance Rejection Control (ADRC) is merged with a proportional-derivative Takagi-Sugeno Fuzzy (PDTSF) logic controller, resulting in two new control structures referred to as second-order data-driven Active Disturbance Rejection Control combined with Proportional-Derivative Takagi-Sugeno Fuzzy Control (ADRC–PDTSFC). The data-driven ADRC–PDTSFC structure was compared with a data-driven ADRC structure and the control system structures were validated by real-time experiments on a nonlinear Multi Input-Multi Output tower crane system (TCS) laboratory equipment, where the cart position and the arm angular position of TCS were controlled using two Single Input-Single Output control system structures running in parallel. The parameters of the data-driven algorithms were tuned in a model-based way using a metaheuristic algorithm in order to improve the efficiency of energy consumption.

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Kumar, Ravindra. "Study of Impact of Computer Vision in Detecting Human Emotions." International Journal of Innovative Technology and Exploring Engineering 10, no. 11 (September 30, 2021): 82–83. http://dx.doi.org/10.35940/ijitee.j9394.09101121.

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Emotions play a powerful role in people's thinking and behaviors. Emotions act as a compulsion to take any action and can influence daily life decisions. Human facial expressions show humans share the same set of emotions. From the setting, the concept of emotion-sensing facial recognition was brought up. Humans have been working actively on computer vision algorithms, the algorithm will help determine the emotions of an individual and can determine the set of intentions accompanied by the emotions. The emotion-sensing facial expression computers are designed using data-centric skills in machine learning and can achieve their desired work by emotion identification and a set of intentions related to the emotion obtained.

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Keyes, D. E., H. Ltaief, and G. Turkiyyah. "Hierarchical algorithms on hierarchical architectures." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2166 (January 20, 2020): 20190055. http://dx.doi.org/10.1098/rsta.2019.0055.

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A traditional goal of algorithmic optimality, squeezing out flops, has been superseded by evolution in architecture. Flops no longer serve as a reasonable proxy for all aspects of complexity. Instead, algorithms must now squeeze memory, data transfers, and synchronizations, while extra flops on locally cached data represent only small costs in time and energy. Hierarchically low-rank matrices realize a rarely achieved combination of optimal storage complexity and high-computational intensity for a wide class of formally dense linear operators that arise in applications for which exascale computers are being constructed. They may be regarded as algebraic generalizations of the fast multipole method. Methods based on these hierarchical data structures and their simpler cousins, tile low-rank matrices, are well proportioned for early exascale computer architectures, which are provisioned for high processing power relative to memory capacity and memory bandwidth. They are ushering in a renaissance of computational linear algebra. A challenge is that emerging hardware architecture possesses hierarchies of its own that do not generally align with those of the algorithm. We describe modules of a software toolkit, hierarchical computations on manycore architectures, that illustrate these features and are intended as building blocks of applications, such as matrix-free higher-order methods in optimization and large-scale spatial statistics. Some modules of this open-source project have been adopted in the software libraries of major vendors. This article is part of a discussion meeting issue ‘Numerical algorithms for high-performance computational science’.

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Fujimori, Tomoyuki, Hiromasa Suzuki, Yohei Kobayashi, and Kiwamu Kase. "Contouring Medial Surface of Thin-Plate Structures Using Local Marching Cubes." Journal of Computing and Information Science in Engineering 5, no. 2 (February 22, 2005): 111–15. http://dx.doi.org/10.1115/1.1891823.

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This paper describes a new algorithm for contouring a medial surface from CT (computed tomography) data of a thin-plate structure. Thin-plate structures are common in mechanical structures, such as car body shells. When designing thin-plate structures in CAD (computer-aided design) and CAE (computer-aided engineering) systems, their shapes are usually represented as surface models associated with their thickness values. In this research, we are aiming at extracting medial surface models of thin-plate structures from their CT data for use in CAD and CAE systems. Commonly used isosurfacing methods, such as marching cubes, are not applicable to contour the medial surface. Therefore, we first extract medial cells (cubes comprising eight neighboring voxels) from the CT data using a skeletonization method to apply the marching cubes algorithm for extracting the medial surface. It is not, however, guaranteed that the marching cubes algorithm can contour those medial cells (in short, not “marching cubeable”). In this study, therefore we developed cell operations that correct topological connectivity to guarantee such marching cubeability. We then use this method to assign virtual signs to the voxels to apply the marching cubes algorithm to generate triangular meshes of a medial surface and map the thicknesses of thin-plate structures to the triangle meshes as textures. A prototype system was developed to verify some experimental results.

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Gancarczyk, Joanna, and Joanna Sobczyk. "Data Mining Approach to Image Feature Extraction in Old Painting Restoration." Foundations of Computing and Decision Sciences 38, no. 3 (September 1, 2013): 159–74. http://dx.doi.org/10.2478/fcds-2013-0007.

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Abstract In this paper a new approach to image segmentation was discussed. A model based on a data mining algorithm set on a pixel level of an image was introduced and implemented to solve the task of identification of craquelure and retouch traces in digital images of artworks. Both craquelure and retouch identification are important steps in art restoration process. Since the main goal is to classify and understand the cause of damage, as well as to forecast its further enlargement, a proper tool for a precise detection of the damaged area is needed. However, the complex nature of the pattern is a reason why a simple, universal detection algorithm is not always possible to be implemented. Algorithms presented in this work apply mining structures which depend of expandable set of attributes forming a feature vector, and thus offer an elastic structure for analysis. The result obtained by our method in craquelure segmentation was improved comparing to the results achieved by mathematical morphology methods, which was confirmed by a qualitative analysis.

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DEZANI, MARIANGIOLA, SABRINA MANTACI, and MARINELLA SCIORTINO. "Preface." Mathematical Structures in Computer Science 27, no. 2 (August 5, 2015): 92–93. http://dx.doi.org/10.1017/s0960129515000420.

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This special issue of Mathematical Structures in Computer Science is devoted to the fourteenth Italian Conference on Theoretical Computer Science (ICTCS) held at University of Palermo, Italy, from 9th to 11th September 2013. ICTCS is the conference of the Italian Chapter of the European Association for Theoretical Computer Science and covers a wide spectrum of topics in Theoretical Computer Science, ranging from computational complexity to logic, from algorithms and data structure to programming languages, from combinatorics on words to distributed computing. For this reason, the contributions here included come from very different areas of Theoretical Computer Science. In fact this special issue is motivated by the desire to give people who have presented their ideas at the 14th ICTCS the opportunity to publish papers on their work. Submitted papers have been subject to a careful and severe reviewing process and 11 of them were selected for this special issue.

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Isiaka, Fatima, Kassim S. Mwitondi, and Adamu M. Ibrahim. "Detection of natural structures and classification of HCI-HPR data using robust forward search algorithm." International Journal of Intelligent Computing and Cybernetics 9, no. 1 (March 14, 2016): 23–41. http://dx.doi.org/10.1108/ijicc-08-2015-0029.

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Purpose – The purpose of this paper is to proposes a forward search algorithm for detecting and identifying natural structures arising in human-computer interaction (HCI) and human physiological response (HPR) data. Design/methodology/approach – The paper portrays aspects that are essential to modelling and precision in detection. The methods involves developed algorithm for detecting outliers in data to recognise natural patterns in incessant data such as HCI-HPR data. The detected categorical data are simultaneously labelled based on the data reliance on parametric rules to predictive models used in classification algorithms. Data were also simulated based on multivariate normal distribution method and used to compare and validate the original data. Findings – Results shows that the forward search method provides robust features that are capable of repelling over-fitting in physiological and eye movement data. Research limitations/implications – One of the limitations of the robust forward search algorithm is that when the number of digits for residuals value is more than the expected size for stack flow, it normally yields an error caution; to counter this, the data sets are normally standardized by taking the logarithmic function of the model before running the algorithm. Practical implications – The authors conducted some of the experiments at individual residence which may affect environmental constraints. Originality/value – The novel approach to this method is the detection of outliers for data sets based on the Mahalanobis distances on HCI and HPR. And can also involve a large size of data with p possible parameters. The improvement made to the algorithm is application of more graphical display and rendering of the residual plot.

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Frederickson, Greg N., and Mandayam A. Srinivas. "Algorithms and Data Structures for an Expanded Family of Matroid Intersection Problems." SIAM Journal on Computing 18, no. 1 (February 1989): 112–38. http://dx.doi.org/10.1137/0218008.

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Akram Abdulrazzaq, Atheer, Nur’Aini Abdul Rashid, and Ahmed Majid Taha. "The Enhanced Hybrid Algorithm for the AbdulRazzaq and Berry-Ravindran Algorithms." International Journal of Engineering & Technology 7, no. 3 (August 10, 2018): 1709. http://dx.doi.org/10.14419/ijet.v7i3.12436.

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Exact string matching is one of the critical issues in the field of computer science. This study proposed a hybrid string matching algorithm called E- AbdulRazzaq. This algorithm used the best properties of two original algorithms; AbdulRazzaq and Berry-Ravindran Algorithms. The proposed algorithm showed an efficient performance in the number of attempts and number of character comparison when compared the original and recent to the standard algorithms. The proposed algorithm was applied in several types of databases, which are DNA sequences, Protein sequences, XML structures, Pitch characters, English texts, and Source codes. The Pitch database was the best match for E-AbdulRazzaq with the number of attempts involving long and short patterns, while the DNA database was the worst match. No data is specified as the best or worst with the E-AbdulRazzaq algorithm in terms of the character comparisons. The E-AbdulRazzaq algorithms ranked first in most databases when using short and long patterns, in terms of number of attempts and character comparisons.

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Jansson, Jesper. "Editorial: Special Issue on Efficient Data Structures." Algorithms 12, no. 7 (July 5, 2019): 136. http://dx.doi.org/10.3390/a12070136.

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Lesev, Hristo, and Alexander Penev. "A Framework for Visual Dynamic Analysis of Ray Tracing Algorithms." Cybernetics and Information Technologies 14, no. 2 (July 15, 2014): 38–49. http://dx.doi.org/10.2478/cait-2014-0018.

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Abstract A novel approach is presented for recording high volume data about ray tracing rendering systems' runtime state and its subsequent dynamic analysis and interactive visualization in the algorithm computational domain. Our framework extracts light paths traced by the system and leverages on a powerful filtering subsystem, helping interactive visualization and exploration of the desired subset of recorded data. We introduce a versatile data logging format and acceleration structures for easy access and filtering. We have implemented a plugin based framework and a tool set that realize all ideas presented in this paper. The framework provides data logging API for instrumenting production-ready, multithreaded, distributed renderers. The framework visualization tool enables deeper understanding of the ray tracing algorithms for novices, as well as for experts.

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Kozhevnikova, G. P. "Formalized complexity analysis based on a priori investigation of the properties of algorithms and data structures." Cybernetics 21, no. 6 (1986): 743–47. http://dx.doi.org/10.1007/bf01069415.

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Roussel, Jean-Romain, Alexis Achim, and David Auty. "Classification of high-voltage power line structures in low density ALS data acquired over broad non-urban areas." PeerJ Computer Science 7 (August 31, 2021): e672. http://dx.doi.org/10.7717/peerj-cs.672.

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Airborne laser scanning (ALS) has gained importance over recent decades for multiple uses related to the cartography of landscapes. Processing ALS data over large areas for forest resource estimation and ecological assessments requires efficient algorithms to filter out some points from the raw data and remove human-made structures that would otherwise be mistaken for natural objects. In this paper, we describe an algorithm developed for the segmentation and cleaning of electrical network facilities in low density (2.5 to 13 points/m2) ALS point clouds. The algorithm was designed to identify transmission towers, conductor wires and earth wires from high-voltage power lines in natural landscapes. The method is based on two priors i.e. (1) the availability of a map of the high-voltage power lines across the area of interest and (2) knowledge of the type of transmission towers that hold the conductors along a given power line. It was tested on a network totalling 200 km of wires supported by 415 transmission towers with diverse topographies and topologies with an accuracy of 98.6%. This work will help further the automated detection capacity of power line structures, which had previously been limited to high density point clouds in small, urbanised areas. The method is open-source and available online.

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Daily, Kenny, Paul Rigor, Scott Christley, Xiaohui Xie, and Pierre Baldi. "Data structures and compression algorithms for high-throughput sequencing technologies." BMC Bioinformatics 11, no. 1 (2010): 514. http://dx.doi.org/10.1186/1471-2105-11-514.

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NING, QI, VINCENT VAN DONGEN, and GUANG R. GAO. "AUTOMATIC DATA AND COMPUTATION DECOMPOSITION FOR DISTRIBUTED-MEMORY MACHINES." Parallel Processing Letters 05, no. 04 (December 1995): 539–50. http://dx.doi.org/10.1142/s0129626495000485.

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In this paper, we develop an automatic compile-time computation and data decomposition technique for distributed-memory machines. Our method handles complex programs containing perfect and non-perfect loop nests with or without loop-carried dependences. Applying our algorithms, a program will be divided into collections (called clusters) of loop nests, such that data redistributions are allowed only between the clusters. Within each cluster of loop nests, decomposition and data locality constraints are formulated as a system of homogeneous linear equations which is solved by polynomial time algorithms. Our algorithm can selectively relax data locality constraints within a cluster to achieve a balance between parallelism and data locality. Such relaxations are guided by exploiting the hierarchical program nesting structures from outer to inner nesting levels to keep the communications at a outer-most level possible.

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WATSON, BRUCE W., DERRICK G. KOURIE, TINUS STRAUSS, ERNEST KETCHA, and LOEK CLEOPHAS. "EFFICIENT AUTOMATA CONSTRUCTIONS AND APPROXIMATE AUTOMATA." International Journal of Foundations of Computer Science 19, no. 01 (February 2008): 185–93. http://dx.doi.org/10.1142/s0129054108005619.

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In this paper, we present data structures and algorithms for efficiently constructing approximate automata. An approximate automaton for a regular language L is one which accepts at leastL. Such automata can be used in a variety of practical applications, including network security pattern matching, in which false-matches are only a performance nuisance. The construction algorithm is particularly efficient, and is tunable to yield more or less exact automata.

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Elmroth, Erik, Fred Gustavson, Isak Jonsson, and Bo Kågström. "Recursive Blocked Algorithms and Hybrid Data Structures for Dense Matrix Library Software." SIAM Review 46, no. 1 (January 2004): 3–45. http://dx.doi.org/10.1137/s0036144503428693.

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Ushizima, Daniela, Ke Xu, and Paulo J. M. Monteiro. "Materials Data Science for Microstructural Characterization of Archaeological Concrete." MRS Advances 5, no. 7 (2020): 305–18. http://dx.doi.org/10.1557/adv.2020.131.

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ABSTRACTAncient Roman concrete presents exceptional durability, low-carbon footprint, and interlocking minerals that add cohesion to the final composition. Understanding of the structural characteristics of these materials using X-ray tomography (XRT) is of paramount importance in the process of designing future materials with similar complex heterogeneous structures. We introduce Materials Data Science algorithms centered on image analysis of XRT that support inspection and quantification of microstructure from ancient Roman concrete samples. By using XRT imaging, we access properties of two concrete samples in terms of three different material phases as well as estimation of materials fraction, visualization of the porous network and density gradients. These samples present remarkable durability in comparison with the concrete using Portland cement and nonreactive aggregates. Internal structures and respective organization might be the key to construction durability as these samples come from ocean-submersed archeological findings dated from about two thousand years ago. These are preliminary results that highlight the advantages of using non-destructive 3D XRT combined with computer vision and machine learning methods for systematic characterization of complex and irreproducible materials such as archeological samples. One significant impact of this work is the ability to reduce the amount of data for several computations to be held at minimalistic computational infrastructure, near real-time, and potentially during beamtime while materials scientists are still at the imaging facilities.

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