In this work, classification of cellular structures in the high resolutional histopathological images and the discrimination of cellular and non-cellular structures have been investigated. The cell classification is a...
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In this work, classification of cellular structures in the high resolutional histopathological images and the discrimination of cellular and non-cellular structures have been investigated. The cell classification is a very exhaustive and time-consuming process for pathologists in medicine. The development of digital imaging in histopathology has enabled the generation of reasonable and effective solutions to this problem. Morever, the classification of digital data provides easier analysis of cell structures in histopathological data. Convolutional neural network (CNN), constituting the main theme of this study, has been proposed with different spatial window sizes in RGB color spaces. Hence, to improve the accuracies of classification results obtained by supervised learning methods, spatial information must also be considered. So, spatial dependencies of cell and non-cell pixels can be evaluated within different pixel neighborhoods in this study. In the experiments, the CNN performs superior than other pixel classification methods including SVM and k-Nearest Neighbour (k-NN). At the end of this paper, several possible directions for future research are also proposed.
This article focuses on an imperfect production inventory model considering product reliability and reworking of imperfect items in three-layer supply chain under fuzzy rough environment. In the model, the supplier re...
This article focuses on an imperfect production inventory model considering product reliability and reworking of imperfect items in three-layer supply chain under fuzzy rough environment. In the model, the supplier receives the raw materials, all are not of perfect quality, in a lot and delivers the items of superior quality to the manufacturer and the inferior quality items are sold at a reduced price in a single batch by the end of the cent percent screening process. The manufacturer produces a mixture of perfect and imperfect quality items. A portion of the imperfect items is transformed into perfect quality items after rework. Another portion of imperfect items, termed as `less perfect quality items', is sold at a reduced price to the retailer, and the portion which cannot be either transformed to the perfect quality items or sold at a reduce price is being rejected. Here, retailer purchases both the perfect and imperfect quality items from the manufacturer to sell the items to the customers through his/her respective showrooms of finite capacities. A secondary warehouse of infinite capacity is hired by the retailer on rental basis to store the excess quantity of perfect quality items. This model considers the impact of business strategies such as optimal order size of raw materials, production rate, and unit production cost in different sectors in a collaborating marketing system that can be used in the industry, like textile, footwear, and electronics goods. An analytical method has been used to optimize the production rate and raw material order size for maximization of the average profit of the integrated model. Finally, a numerical example is given to illustrate the model.
In this paper we study strong and weak bisimulation equivalences for continuous-time Markov decision processes (CTMDPs) and the logical characterizations of these relations with respect to the continuous-time stochast...
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Sum of squares (SOS) optimization has been a powerful and influential addition to the theory of optimization in the past decade. Its reliance on relatively large-scale semidefinite programming, however, has seriously ...
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Sum of squares (SOS) optimization has been a powerful and influential addition to the theory of optimization in the past decade. Its reliance on relatively large-scale semidefinite programming, however, has seriously challenged its ability to scale in many practical applications. In this paper, we introduce DSOS and SDSOS optimization as more tractable alternatives to sum of squares optimization that rely instead on linear programming and second order cone programming. These are optimization problems over certain subsets of sum of squares polynomials and positive semidefinite matrices and can be of potential interest in general applications of semidefinite programming where scalability is a limitation.
Optical character recognition(OCR) can be used in some management mechanisms of state and business world to organize documents scanned or captured by camera. Therefore, OCR is one of the subjects rapidly evolving in t...
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Optical character recognition(OCR) can be used in some management mechanisms of state and business world to organize documents scanned or captured by camera. Therefore, OCR is one of the subjects rapidly evolving in the recent times. This study investigates the procedure steps until character recognition. That's because, the more correctly preprocessings are applied;the better results can be obtained in character recognition. In this study, the algorithms providing the best results are determined by following the procedure stages of gray scale transformation, noise removal and image thresholding. Binarization plays an important role in character recognition. For this reason, algorithms dynamically determine thresholds are preferred for character recognition in this study. Accordingly, Otsu thresholding method was determined to give the best results in terms of both picture quality and speed. Therefore, this method was used for character recognition. Primarily, lines were determined for character separation and then letters were individually obtained.
We present a programming methodology and runtime performance case study comparing the declarative data flow coordination language S-Net with Intel's Concurrent Collections (CnC). As a coordination language S-Net a...
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ISBN:
(纸本)9781479941155
We present a programming methodology and runtime performance case study comparing the declarative data flow coordination language S-Net with Intel's Concurrent Collections (CnC). As a coordination language S-Net achieves a near-complete separation of concerns between sequential software components implemented in a separate algorithmic language and their parallel orchestration in an asynchronous data flow streaming network. We investigate the merits of S-Net and CnC with the help of a relevant and non-trivial linear algebra problem: tiled Cholesky decomposition. We describe two alternative S-Net implementations of tiled Cholesky factorization and compare them with two CnC implementations, one with explicit performance tuning and one without, that have previously been used to illustrate Intel CnC. Our experiments on a 48-core machine demonstrate that S-Net manages to outperform CnC on this problem.
Visual Cryptography (VC) is a technique to encrypt a secret image into transparent shares such that stacking a sufficient number of shares reveals the secret image without any computation. Cheating is possible in the ...
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Visual Cryptography (VC) is a technique to encrypt a secret image into transparent shares such that stacking a sufficient number of shares reveals the secret image without any computation. Cheating is possible in the Visual Cryptographic Schemes (VCS) by dishonest or malicious participant called a cheater, may provide a Fake Share (FS) to cheat the other participants. To achieve cheating prevention in VC we have proposed a steganographic scheme to embed a secret message in each of the shares in random location during share generation phase called stego share. Before stacking receiver can extract hidden message from stego share for checking authentication of shares. In this method no verification share is required to prevent cheating in VC.
Cardiotocography is one of the most widely used technique for recording changes in fetal heart rate(FHR) and uterine contractions. Assessing cardiotocography is crucial in that it leads to identifying fetuses which su...
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Cardiotocography is one of the most widely used technique for recording changes in fetal heart rate(FHR) and uterine contractions. Assessing cardiotocography is crucial in that it leads to identifying fetuses which suffer from lack of oxygen, i.e. hypoxia. This situation is defined as fetal distress and requires fetal intervention in order to prevent fetus death or other neurological disease caused by hypoxia. In this study a computer-based approach for analyzing cardiotocogram including diagnostic features for discriminating a pathologic fetus. In order to achieve this aim adaptive boosting ensemble of decision trees and various other machine learning algorithms are employed.
Uninitialized variables can cause system crashes when used and security vulnerabilities when exploited. With source rather than binary instrumentation, dynamic analysis tools such as MSan can detect uninitialized memo...
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ISBN:
(纸本)9781450326704
Uninitialized variables can cause system crashes when used and security vulnerabilities when exploited. With source rather than binary instrumentation, dynamic analysis tools such as MSan can detect uninitialized memory uses at significantly reduced overhead but are still *** this paper, we introduce a static value-flow analysis, called Usher, to guide and accelerate the dynamic analysis performed by such tools. Usher reasons about the definedness of values using a value-flow graph (VFG) that captures def-use chains for both top-level and address-taken variables interprocedurally and removes unnecessary instrumentation by solving a graph reachability problem. Usher works well with any pointer analysis (done a priori) and facilitates advanced instrumentation-reducing optimizations (with two demonstrated here). Implemented in LLVM and evaluated using all the 15 SPEC2000 C programs, Usher can reduce the slowdown of MSan from 212% -- 302% to 123% -- 140% for a number of configurations tested.
A tree transducer is a set of mutually recursive functions transforming an input tree into an output tree. Macro tree transducers extend this recursion scheme by allowing each function to be defined in terms of an arb...
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ISBN:
(纸本)9781450323895
A tree transducer is a set of mutually recursive functions transforming an input tree into an output tree. Macro tree transducers extend this recursion scheme by allowing each function to be defined in terms of an arbitrary number of accumulation parameters. In this paper, we show how macro tree transducers can be concisely represented in Haskell, and demonstrate the benefits of utilising such an approach with a number of examples. In particular, tree transducers afford a modular programming style as they can be easily composed and manipulated. Our Haskell representation generalises the original definition of (macro) tree transducers, abolishing a restriction on finite state spaces. However, as we demonstrate, this generalisation does not affect compositionality.
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