Set Of New Algorithms Research Articles

Fast Algorithms for Denial Constraint Discovery

Denial constraints (DCs) are an integrity constraint formalism widely used to detect inconsistencies in data. Several algorithms have been devised to discover DCs from data, as manually specifying them is burdensome and, worse yet, error-prone. The existing algorithms follow two basic steps: building an intermediate data structure from records, then enumerating the DCs from that intermediate. However, current algorithms are often inefficient in computing these intermediates. Also, it is still unclear which enumeration algorithm performs best since some of the available algorithms have not yet been compared to each other. In response, we present a set of new algorithms with improved design choices. We introduce a parallel pipeline for rapidly computing the intermediate using custom data representations, algorithms, and indexes. For DC enumeration, we propose an inverted index, pruning, and parallel search strategies. We present hybrid approaches that integrate our techniques with previous enumeration algorithms, improving their performance in many scenarios. Our experimental study shows that the proposed DC discovery algorithms are consistently much faster (up to an order of magnitude) than the current state-of-the-art.

Improved spherical search with local distribution induced self-adaptation for hard non-convex optimization with and without constraints

Most metaheuristic optimizers rely heavily on precisely setting their control parameters and search operators to perform well. Considering the complexity of real-world problems, it is always preferable to adjust control parameter values automatically rather than clamping them to a fixed value. In recent years, Spherical Search (SS) has emerged as a population-based stochastic optimization method that exploits the concepts of random projection matrices in linear algebra. As a result of the success of SS in solving non-convex, real-parameter optimization problems of various complexity, we have significantly extended SS in this paper by introducing a set of new algorithms, collectively known as Self Adaptive Spherical Search (SASS). Our proposal aims to enhance the performance of SS by using different projection matrix schemes in conjunction with improved search-direction calculations and an adaptive modification of parameter values. In our proposed adaptation scheme, parameters are modified to relevant values by applying a self-adaptive process that does not rely upon prior knowledge of the correlation between the parameter values and characteristics of the problem space. Consequently, we may apply the algorithms to bound and nonlinearly constrained optimization problems. For the benchmark suites derived from the most recent IEEE Congress on Evolutionary Computation (CEC) competitions, simulation results indicate that the SASS family of algorithms performs better than or is comparable to state-of-the-art algorithms from the other paradigms concerning robustness and convergence.

Polygonal surface processing and mesh generation tools for the numerical simulation of the cardiac function.

In order to simulate the cardiac function for a patient‐specific geometry, the generation of the computational mesh is crucially important. In practice, the input is typically a set of unprocessed polygonal surfaces coming either from a template geometry or from medical images. These surfaces need ad‐hoc processing to be suitable for a volumetric mesh generation. In this work we propose a set of new algorithms and tools aiming to facilitate the mesh generation process. In particular, we focus on different aspects of a cardiac mesh generation pipeline: (1) specific polygonal surface processing for cardiac geometries, like connection of different heart chambers or segmentation outputs; (2) generation of accurate boundary tags; (3) definition of mesh‐size functions dependent on relevant geometric quantities; (4) processing and connecting together several volumetric meshes. The new algorithms—implemented in the open‐source software vmtk—can be combined with each other allowing the creation of personalized pipelines, that can be optimized for each cardiac geometry or for each aspect of the cardiac function to be modeled. Thanks to these features, the proposed tools can significantly speed‐up the mesh generation process for a large range of cardiac applications, from single‐chamber single‐physics simulations to multi‐chambers multi‐physics simulations. We detail all the proposed algorithms motivating them in the cardiac context and we highlight their flexibility by showing different examples of cardiac mesh generation pipelines.

Qualitative Analysis of Commercial Services in MEC as Phased-Mission Systems

Currently, mobile edge computing (MEC) is one of the most popular techniques used to respond to real-time services from a wide range of mobile terminals. Compared with single-phase systems, commercial services in MEC can be modeled as phased-mission systems (PMS) and are much more complex, because of the dependencies across the phases. Over the past decade, researchers have proposed a set of new algorithms based on BDD for fault tree analysis of a wide range of PMS with various mission requirements and failure behaviors. The analysis to be performed on a fault tree can be either qualitative or quantitative. For the quantitative fault tree analysis of PMS by means of BDD, much work has been conducted. However, for the qualitative fault tree analysis of PMS by means of BDD, no much related work can be found. In this paper, we have presented some efficient methods to calculate the MCS encoding by a PMS BDD. Firstly, three kinds of redundancy relations-inclusive relation, internal-implication relation, and external-implication relation-within the cut set are identified, which prevent the cut set from being minimal cut set. Then, three BDD operations, IncRed, InImpRed, and ExImpRed, are developed, respectively, for the elimination of these redundancy relations. Using some proper combinations of these operations, MCS can be calculated correctly. As an illustration, some experimental results on a benchmark MEC system are given.

Dynamic service provisioning in space-division multiplexing elastic optical networks

In space-division multiplexing elastic optical networks (SDM-EONs) based on multicore fibers, the problem of finding a lightpath for a new connection request involves routing, modulation, spectrum, and core assignment (RMSCA). A lightpath can be constructed using contiguous free frequency slot (FS) blocks with different frequency slot allocation patterns (FSAPs), where each FSAP can incur different amounts of wasted FSs. Aiming at using the FSAP with the least amount of wasted FSs and exploiting path diversity, a set of new algorithms are proposed to solve the RMSCA problem. Before any connection requests arrive, all candidate paths between each pair of nodes and all FSAPs for each possible bandwidth requirement are identified and sorted. When a connection request arrives, two approaches can be used to establish a lightpath: the path-first or spectrum-first approach. We show that by packing lightpaths toward FSs with the smallest FS indexes, the spectrum-first approach leaves bigger contiguous FS blocks for future requests. As a result, the request blocking probability is minimized.

RCTL: New Temporal Logic for Improved Formal Verification of Reconfigurable Discrete-Event Systems

This article deals with improved formal verification of reconfigurable discrete-event systems (DESs) modeled by reconfigurable timed net condition event systems (R-TNCESs). An R-TNCES consists of a set of timed net condition event systems, each of which represents a particular behavior of a DES, and a reconfiguration scenario is a switching mode from a timed net condition event system to another. However, the verification with the classical computation tree logic (CTL) as well as the related extensions increases the number of properties for complete verification of a complex R-TNCES. We propose reconfigurable CTL as a new extension of CTL to reduce such a number. New connectors of reconfigurable CTL are proposed, with their formal syntax and semantics, and a set of new algorithms is proposed to control the complexity of model checking. We use a benchmark production system for the performance evaluation of the proposed approach. Reduction in the number of properties to be checked is shown, and consequently, the related validation time is reduced. Note to Practitioners —This research represents a new orientation for guiding efficiently the model checking of reconfigurable discrete-event systems. A classification of properties described in computation tree logic (CTL), according to their dominance and equivalence relations, allows one to conduct an efficient verification by avoiding inefficient calculation due to redundant properties. In this case, giving a verification order for these properties allows one to shorten their verification time. An extension named reconfigurable CTL describes the new syntax of the proposed classification. This approach can be applied in modeling and verification of advanced reconfigurable systems arising from smart grids, adaptive sensor networks, intelligent transportation, reconfigurable manufacturing, and embedded systems.

Stochastic Localization Methods for Discrete Convex Simulation Optimization

We propose a set of new algorithms based on stochastic localization methods for large-scale discrete simulation optimization problems with convexity structure. All proposed algorithms, with the general idea of localizing potential good solutions to an adaptively shrinking subset, are guaranteed with high probability to identify a solution that is close enough to the optimal given any precision level. Specifically, for one-dimensional large-scale problems, we propose an enhanced adaptive algorithm with an expected simulation cost asymptotically independent of the problem scale, which is proved to attain the best achievable performance. For multi-dimensional large-scale problems, we propose statistically guaranteed stochastic cutting-plane algorithms, the simulation costs of which have no dependence on model parameters such as the Lipschitz parameter, as well as low polynomial order of dependence on the problem scale and dimension. Numerical experiments are implemented to support our theoretical findings. The theory results, joint the numerical experiments, provide insights and recommendations on which algorithm to use in different real application settings.

New algorithms for estimating chlorophyll-a in the Spanish waters of the Western Mediterranean Sea from multiplatform imagery

ABSTRACTThe utility of global and regional algorithms for retrieving surface chlorophyll-a values from satellite images of MODIS (Moderate-Resolution Imaging Spectroradiometer) onboard Aqua and Terra Satellites, SeaWiFS (Sea-Viewing Wide Field-of-View Sensor), MERIS (MEdium Resolution Imaging Spectrometer) and VIIRS (Visible Infrared Imaging Radiometer Suite) is tested in a wide region of the Western Mediterranean Sea, the Spanish Mediterranean. The comparison between chlorophyll-a concentration obtained from samples collected and satellite concentrations calculated with global and regional algorithms for this area demonstrates that satellites overestimate the surface chlorophyll-a. In this work, a set of new algorithms, namely SMED (Spanish MEDiterranean algorithms), are proposed to improve the estimations of surface chlorophyll-a in our study area. The SMED set of algorithms employs the linear function to fit the standard Maximum Band Ratios (MBR) to the in situ surface chlorophyll-a concentration (in logarithmic scale). The implementation of the SMED algorithms is simple, and the accuracy is as good as more complex algorithms like OC5 (5 band algorithms tuned for European Atlantic and tested successfully in the Mediterranean Sea). The improvement of SMED algorithms is important respect to MedOC (built based in data of the Mediterranean Sea) for all sensors, ranging 17%-37% in terms of the coefficient of determination (R2). In the case of OC algorithms (standard global algorithms currently operative for most ocean colour sensors), SMED also improves the estimates in 11%-22%, except in 4% for MERIS. Additionally, when comparing satellite estimates with in situ chlorophyll-a in log-log scale, SMED achieved the best correspondence, even with respect to OC5. During the calibration procedure MERIS remote sensing reflectances (RRS) showed a higher sensitivity to the presence of mineral aerosols, and therefore it is strongly recommended to include the value of Aerosol Optical Thickness (AOT) in the algorithms for chlorophyll in the region under study. A regional algorithm for VIIRS ocean colour sensor is also proposed based on a matchup database built from spatially and temporary distributed SMED chlorophyll-a values of MODIS-Aqua and VIIRS RRS. Our analysis suggests that SeaWiFS, MODIS-Aqua, and VIIRS based SMED algorithms can support generating a multiplatform time series in the region.

A new depression‐dominated delineation (D‐cubed) method for improved watershed modelling

AbstractPrior to hydrologic modelling, topographic features of a surface are derived, and the surface is divided into sub‐basins. Surface delineation can be described as a procedure, which leads to the quantitative rendition of surface topography. Different approaches have been developed for surface delineation, but most of them may not be applicable to depression‐dominated surfaces. The main objective of this study is to introduce a new depression‐dominated delineation (D‐cubed) method and highlight its unique features by applying it to different topographic surfaces. The D‐cubed method accounts for the hierarchical relationships of depressions and channels by introducing the concept of channel‐based unit (CBU) and its connection with the concept of puddle‐based unit (PBU). This new delineation method implements a set of new algorithms to determine flow directions and accumulations for puddle‐related flats. The D‐cubed method creates a unique cascaded channel‐puddle drainage system based on the channel segmentation algorithm. To demonstrate the capabilities of the D‐cubed method, a small laboratory‐scale surface and 2 natural surfaces in North Dakota were delineated. The results indicated that the new method delineated different surfaces with and without the presence of depressional areas. Stepwise changes in depression storage and ponding area were observed for the 3 selected surfaces. These stepwise changes highlighted the dynamic filling, spilling, and merging processes of depressions, which need to be considered in hydrologic modelling for depression‐dominated areas. Comparisons between the D‐cubed method and other methods emphasized the potential consequences of use of artificial channels through the flats created by the depression‐filling process in the traditional approaches. In contrast, in the D‐cubed method, sub‐basins were further divided into a number of smaller CBUs and PBUs, creating a channel‐puddle drainage network. The testing of the D‐cubed method also demonstrated its applicability to a wide range of digital elevation model resolutions. Consideration of CBUs, PBUs, and their connection provides the opportunity to incorporate the D‐cubed method into different hydrologic models and improve their simulation of topography‐controlled runoff processes, especially for depression‐dominated areas.

Energy-Aware Mapping and Live Migration of Virtual Networks

Network virtualization facilitates the deployment of new protocols and applications without the need to change the core of the network. One key step in instantiating virtual networks (VNs) is the allocation of physical resources to virtual elements (routers and links), which can be then targeted for the minimization of energy consumption. However, such mappings need to support the quality-of-service requirements of applications. Indeed, the search for an optimal solution for the VN mapping problem is NP-hard, and approximated algorithms must be developed for its solution. The dynamic allocation and deallocation of VNs on a network substrate can compromise the optimality of a mapping designed to minimize energy consumption, since such allocation and deallocation can lead to the underutilization of the network substrate. To mitigate such negative effects, techniques such as live migration can be employed to rearrange already mapped VNs in order to improve network utilization, thus minimizing energy consumption. This paper introduces a set of new algorithms for the mapping of VNs on network substrates designed to reduce network energy consumption. Moreover, two new algorithms for the migration of virtual routers and links are proposed with simulation showing the efficacy of the algorithms.

Patch-Based Forest Change Detection from Landsat Time Series

In the species-rich and structurally complex forests of the Eastern United States, disturbance events are often partial and therefore difficult to detect using remote sensing methods. Here we present a set of new algorithms, collectively called Vegetation Regeneration and Disturbance Estimates through Time (VeRDET), which employ a novel patch-based approach to detect periods of vegetation disturbance, stability, and growth from the historical Landsat image records. VeRDET generates a yearly clear-sky composite from satellite imagery, calculates a spectral vegetation index for each pixel in that composite, spatially segments the vegetation index image into patches, temporally divides the time series into differently sloped segments, and then labels those segments as disturbed, stable, or regenerating. Segmentation at both the spatial and temporal steps are performed using total variation regularization, an algorithm originally designed for signal denoising. This study explores VeRDET’s effectiveness in detecting forest change using four vegetation indices and two parameters controlling the spatial and temporal scales of segmentation within a calibration region. We then evaluate algorithm effectiveness within a 386,000 km2 area in the Eastern United States where VeRDET has overall error of 23% and omission error across disturbances ranging from 22% to 78% depending on agent.

Scalable Algorithms for NFA Multi-Striding and NFA-Based Deep Packet Inspection on GPUs

Finite state automata (FSA) are used by many network processing applications to match complex sets of regular expressions in network packets. In order to make FSA-based matching possible even at the ever-increasing speed of modern networks, multi-striding has been introduced. This technique increases input parallelism by transforming the classical FSA that consumes input byte by byte into an equivalent one that consumes input in larger units. However, the algorithms used today for this transformation are so complex that they often result unfeasible for large and complex rule sets. This paper presents a set of new algorithms that extend the applicability of multi-striding to complex rule sets. These algorithms can transform nondeterministic finite automata (NFA) into their multi-stride form with reduced memory and time requirements. Moreover, they exploit the massive parallelism of graphical processing units for NFA-based matching. The final result is a boost of the overall processing speed on typical regex-based packet processing applications, with a speedup of almost one order of magnitude compared to the current state-of-the-art algorithms.

Stabilizing 3D in vivo intravital microscopy images with an iteratively refined soft-tissue model for immunology experiments

We describe a set of new algorithms and a software tool, StabiTissue, for stabilizing in vivo intravital microscopy images that suffer from soft-tissue background movement. Because these images lack predetermined anchors and are dominated by noise, we use a pixel weighted image alignment together with a correction for nonlinear tissue deformations. We call this correction a poor man׳s diffeomorphic map since it ascertains the nonlinear regions of the image without resorting to a full integral equation method. To determine the quality of the image stabilization, we developed an ensemble sampling method that quantifies the coincidence between image pairs from randomly distributed image regions. We obtain global stabilization alignment through an iterative constrained simulated annealing optimization procedure. To show the accuracy of our algorithm with existing software, we measured the misalignment error rate in datasets taken from two different organs and compared the results to a similar and popular open-source solution. Present open-source stabilization software tools perform poorly because they do not treat the specific needs of the IV-2pM datasets with soft-tissue deformation, speckle noise, full 5D inter- and intra-stack motion error correction, and undefined anchors. In contrast, the results of our tests demonstrate that our method is more immune to noise and provides better performance for datasets’ possessing nonlinear tissue deformations. As a practical application of our software, we show how our stabilization improves cell tracking, where the presence of background movement would degrade track information. We also provide a qualitative comparison of our software with other open-source libraries/applications. Our software is freely available at the open source repository http://sourceforge.net/projects/stabitissue/.

Influence Spreading Path and Its Application to the Time Constrained Social Influence Maximization Problem and Beyond

Influence maximization is a fundamental research problem in social networks. Viral marketing, one of its applications, is to get a small number of users to adopt a product, which subsequently triggers a large cascade of further adoptions by utilizing “Word-of-Mouth” effect in social networks. Time plays an important role in the influence spread from one user to another and the time needed for a user to influence another varies. In this paper, we propose the time constrained influence maximization problem. We show that the problem is NP-hard, and prove the monotonicity and submodularity of the time constrained influence spread function. Based on this, we develop a greedy algorithm. To improve the algorithm scalability, we propose the concept of Influence Spreading Path in social networks and develop a set of new algorithms for the time constrained influence maximization problem. We further parallelize the algorithms for achieving more time savings. Additionally, we generalize the proposed algorithms for the conventional influence maximization problem without time constraints. All of the algorithms are evaluated over four public available datasets. The experimental results demonstrate the efficiency and effectiveness of the algorithms for both conventional influence maximization problem and its time constrained version.

Detection and Tracking of Dynamic Objects by Using a Multirobot System: Application to Critical Infrastructures Surveillance

The detection and tracking of mobile objects (DATMO) is progressively gaining importance for security and surveillance applications. This article proposes a set of new algorithms and procedures for detecting and tracking mobile objects by robots that work collaboratively as part of a multirobot system. These surveillance algorithms are conceived of to work with data provided by long distance range sensors and are intended for highly reliable object detection in wide outdoor environments. Contrary to most common approaches, in which detection and tracking are done by an integrated procedure, the approach proposed here relies on a modular structure, in which detection and tracking are carried out independently, and the latter might accept input data from different detection algorithms. Two movement detection algorithms have been developed for the detection of dynamic objects by using both static and/or mobile robots. The solution to the overall problem is based on the use of a Kalman filter to predict the next state of each tracked object. Additionally, new tracking algorithms capable of combining dynamic objects lists coming from either one or various sources complete the solution. The complementary performance of the separated modular structure for detection and identification is evaluated and, finally, a selection of test examples discussed.

A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients

In this study, we propose a set of new algorithms to enhance the effectiveness of classification for 5-year survivability of breast cancer patients from a massive data set with imbalanced property. The proposed classifier algorithms are a combination of synthetic minority oversampling technique (SMOTE) and particle swarm optimization (PSO), while integrating some well known classifiers, such as logistic regression, C5 decision tree (C5) model, and 1-nearest neighbor search. To justify the effectiveness for this new set of classifiers, the g-mean and accuracy indices are used as performance indexes; moreover, the proposed classifiers are compared with previous literatures. Experimental results show that the hybrid algorithm of SMOTE+PSO+C5 is the best one for 5-year survivability of breast cancer patient classification among all algorithm combinations. We conclude that, implementing SMOTE in appropriate searching algorithms such as PSO and classifiers such as C5 can significantly improve the effectiveness of classification for massive imbalanced data sets.

Finding best algorithmic components for clustering microarray data

The analysis of microarray data is fundamental to microbiology. Although clustering has long been realized as central to the discovery of gene functions and disease diagnostic, researchers have found the construction of good algorithms a surprisingly difficult task. In this paper, we address this problem by using a component-based approach for clustering algorithm design, for class retrieval from microarray data. The idea is to break up existing algorithms into independent building blocks for typical sub-problems, which are in turn reassembled in new ways to generate yet unexplored methods. As a test, 432 algorithms were generated and evaluated on published microarray data sets. We found their top performers to be better than the original, component-providing ancestors and also competitive with a set of new algorithms recently proposed. Finally, we identified components that showed consistently good performance for clustering microarray data and that should be considered in further development of clustering algorithms.

Optimal design of Service Overlay Networks with economic and performance constraints

AbstractIn the last years, Service Overlay Networks (SONs) have emerged as a promising means to address some of the issues (e.g. end‐to‐end QoS) affecting the current Internet and to favor the development and deployment of new value‐added Internet services. The deployment of an SON is a capital‐intensive investment, since bandwidth with certain QoS guarantees must be purchased from the individual network domains through bilateral Service Level Agreements. Thus, minimizing the economic cost of the logical end‐to‐end service delivery infrastructure is one of the key objectives for the SON provider. When a SON is aimed at end‐to‐end QoS provisioning, its topology must be designed so as to also satisfy the specific requirements of QoS‐sensitive applications. This paper deals with the problem of planning the SON topology in order to take into account both cost and QoS constraints. More specifically, the paper proposes a set of new algorithms for the design of an optimized SON topology, which minimizes the economic cost while simultaneously meeting bandwidth and delay constraints. A performance comparison among such algorithms is finally carried out. Copyright © 2009 John Wiley & Sons, Ltd.

Towards automatic personalization of device controls

People are increasingly using customizable remotes to interact with devices in new and interesting ways that are influenced by the idiosyncrasies of their behaviors and environments. With the growing use of advanced processors in small consumer electronics, it is becoming more practical to have such remotes execute machine-learning algorithms that can automatically specify the idiosyncrasies. This paper addresses two especially useful and common types of features of personalizable remotes: based button grouping and macros. Task-based button grouping addresses clutter and frequent screen switching by only presenting the commands (or buttons) a user needs to accomplish a given active task. Macros allow users to efficiently invoke a sequence of commands across multiple devices that are used in the task. The contributions of this work include: (a) an identification of several usage patterns that show limitations of previous work in task and macro based commands, (b) a set of new algorithms that apply these patterns to address these limitations, and (c) an evaluation of each algorithm using real-world interaction data. We show that our algorithms, which uniquely apply fuzzy techniques and time-based heuristics, can offer a significant improvement from the state-of-the-art in automation and accuracy.

Line-art illustration of dynamic and specular surfaces

Line-art illustrations are effective tools for conveying shapes and shading of complex objects. We present a set of new algorithms to render line-art illustrations of dynamic and specular (reflective and refractive) surfaces. We first introduce a real-time principal direction estimation algorithm to determine the line stroke directions on dynamic opaque objects using neighboring normal ray triplets. To render reflections or refractions in a line-art style, we develop a stroke direction propagation algorithm by using multi-perspective projections to propagate the stroke directions from the nearby opaque objects onto specular surfaces. Finally, we present an image-space stroke mapping method to draw line strokes using the computed or propagated stroke directions. We implement these algorithms using a GPU and demonstrate real-time illustrations of scenes with dynamic and specular 3D models in line-art styles.