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In this paper, a methodology for design of fuzzy Kalman filter, using interval type-2 fuzzy models, in discrete time domain, via spectral decomposition of experimental data, is proposed. The adopted methodology consists of recursive parametric estimation of local state space linear submodels of interval type-2 fuzzy Kalman filter for tracking and forecasting of the dynamics inherited to experimental data, using an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm. The partitioning of the experimental data is performed by interval type-2 fuzzy Gustafson–Kessel clustering algorithm. The interval Kalman gains in the consequent proposition of interval type-2 fuzzy Kalman filter are updated according to unobservable components computed by recursive spectral decomposition of experimental data. Computational results illustrate the efficiency of proposed methodology for filtering and tracking the time delayed state variables of Chen’s chaotic attractor in a noisy environment, and experimental results illustrate its applicability for adaptive and real time forecasting the dynamic spread behavior of novel Coronavirus 2019 (COVID-19) outbreak in Brazil.

Finite mixtures of generalized linear models are commonly fitted by maximum likelihood and the EM algorithm. The estimation process and subsequent inferential and classification procedures can be badly affected by the occurrence of outliers. Actually, contamination in the sample at hand may lead to severely biased fitted components and poor classification accuracy. In order to take into account the potential presence of outliers, a robust fitting strategy is proposed that is based on the weighted likelihood methodology. The technique exhibits a satisfactory behavior in terms of both fitting and classification accuracy, as confirmed by some numerical studies and real data examples.

Many people enjoy origami, an art of paper folding, since childhood. Origami is a more powerful geometry construction tool than straight and compass. But there are some inconvenience when you practice traditional origami on geometry. In this study, a computational origami environment has been developed. Huzita axioms are implemented with a computer algebra system (CAS). CAS not only deals with fundamental computation of axioms but also can prove some geometric consequences of folding steps. Furthermore, the process of paper folding is visualized. Users can observe the 3D animation of folding steps from different viewpoints.

Sensoria is an IST project funded by the EU as Integrated Project (IP) in the 6th Framework Programme (FP6) as part of the Global Computing Initiative (GC). It started on September 2005 and ended on February 2010.

The measurement of stacked-sheet quantity is an essential step in packaging and printing production, and its counting accuracy has a direct impact on economic efficiency of related companies. With its noncontact, non-destructivity and real-time measurement merits, the machine vision method has been widely applied to quality control for high-end printing products. In this paper, we aim to circumvent the fringe detection problem in stacked-sheet images by introducing a level line guided line-segment growing algorithm. Then, a high-accuracy measurement of stack quantity can be realized with the improvement of precision and completeness on fringe identification.Our work mainly consists of three parts: 1) A unidirectional gradient operator is adopted to eliminate multiple responses on a single fringe. 2) The gradient magnitude and level-line direction are combined to improve the growth of line support regions in noisy environment. 3) To completely identify each sheet fringe, a connected component analysis algorithm is integrated to remedy the local gap in line detection. The performance of our algorithm has been verified in experiments using various kinds of printed-papers with a large number. It is shown that the long-term measurement error is less than 0.75‰ and is sufficient to meet the requirement of factory applications.

A new informed image watermarking technique is proposed incorporating perceptual factors into dirty paper coding. Due to the equi-energetic nature of the adopted codewords and to the use of a correlation-based decoder, invariance to constant value-metric scaling (gain attack) is automatically achieved. By exploiting the simple structure of orthogonal and Gold codes, an optimal informed embedding technique is developed, permitting to maximize the watermark robustness while keeping the embedding distortion constant. The maximum admissible distortion level is computed on a block by block basis, by using Watson’s model of the Human Visual System (HVS). The performance of the watermarking algorithm are improved by concatenating dirty paper coding with a turbo coding (decoding) step. The validity of the assumptions underlying the theoretical analysis is evaluated by means of numerical simulations. Experimental results confirm the effectiveness of the proposed approach.

The design of bone scaffold involves the analysis of stress shielding, which can occur when the Young’s modulus of the implant is higher than the Young’s modulus of the bone it is replacing, leading to bone decay in the surrounding tissue. It is therefore very important that the material is adequately designed to match the properties of the surrounding tissue, allowing an appropriate load transfer. While some approaches exist in the literature exploring functional gradients of material density, there are much less solutions based on biological laws. A homogenized model of gyroid infill obtained with PLA (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E = 3145$$\end{document}E=3145 MPa) was obtained through mechanical tests of 3D printed specimens, namely tensile and compression, and the obtained model was implemented in a bone remodelling algorithm. The homogenized law was compared to the results obtained with a bone tissue law to assess the equivalence of density distribution and mechanical properties. Through a radial point interpolation method, it was found that similar density fields were obtained for the gyroid infill and for bone tissue when subject to the same boundary conditions. The finite element method was also used for comparison and validation. With the density field results, the gyroid mechanical behaviour was extrapolated to other materials, and similar stiffness values were obtained for bone tissue and titanium alloy (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$E = 110$$\end{document}E=110 GPa) scaffold, which justify this proposal of gyroid scaffolds for mimicking bone properties.

Quality of a scanned ink-on-paper fingerprint (or offline fingerprint) is generally poorer than that of online fingerprint and therefore, it requires an efficient enhancement method. This paper presents an algorithm to enhance the offline fingerprints using adaptive techniques. The algorithm handles certain problems that are specific to this type of fingerprints. It involves fingerprint extraction, intra-image classification followed by adaptive region enhancement of classified regions. It has been tested on IIT Kanpur database. The results show that the proposed algorithm performs well in enhancing ink-on-paper fingerprints.

In this paper, we focus on computing the prices of securities represented by logical formulas in combinatorial prediction markets when the price function is represented by a Bayesian network. This problem turns out to be a natural extension of the weighted model counting (WMC) problem [1], which we call generalized weighted model counting (GWMC) problem. In GWMC, we are given a logical formula F and a polynomial-time computable weight function. We are asked to compute the total weight of the valuations that satisfy F. Based on importance sampling, we propose a Monte-Carlo meta-algorithm that has a good theoretical guarantee for formulas in disjunctive normal form (DNF). The meta-algorithm queries an oracle algorithm that computes marginal probabilities in Bayesian networks, and has the following theoretical guarantee. When the weight function can be approximately represented by a Bayesian network for which the oracle algorithm runs in polynomial time, our meta-algorithm becomes a fully polynomial-time randomized approximation scheme (FPRAS).

The development of SNS provides a new platform and application prospect for the realization of Personalized Recommendation System. It is becoming a fire new research hot spot in social science and e-commence about how to apply community discovery algorithm (CDA) to find community structures in large network and to effectively conduct personalized recommendation. In terms of our recent research, we applied PSO based CDA as principle to divide social communities and based on this to conduct personalized recommendation in a scientific paper management system. The system’s running results proved that by applying PSO based CDA, the accuracy of PR and the popularity of the platform had been both improved greatly.

We describe the structure and the usage of a new software tool, called r-TuBound, for deriving symbolic loop iteration bounds in the worst-case execution time (WCET) analysis of programs. r-TuBound implements algorithms for pattern-based recurrence solving and program flow refinement, and it was successfully tested on a wide range of examples. The purpose of this article is to illustrate what r-TuBound can do and how it can be used to derive the WCET of programs.

This paper proposes an approach to support a wrapping process from an object and a piece of wrapping papers by the computer animation method. The combinational information about an object and a piece of wrapping papers generates possibly multiple feasible wrapping processes. Therefore, we first argue the state of paper wrapping which represents each stage in the wrapping process, and then assume that sequences of ordered states, called state space, construct the wrapping process successively. Second, the folding operations called wrap-foldingare defined. These operations generate multiple wrapping processes on a state space which are physically feasible. Third, an algorithm of searching effective wrapping processes which satisfy demand of user is described. Finally, we give some experimental results to make the practical efficacy of proposed method clear. With the processing flow, we describe a computer assisted paper wrapping proto-type system.

We discuss the following variant of incremental edit distance computation: Given strings A and B with lengths m and n , respectively, the task is to compute, in n successive iterations j = n ...1, an encoding of the edit distances between A and all prefixes of B j ..n . Here B j ..n is the suffix of B that begins at its j th character. This type of consecutive suffix alignment [3] is powerful e.g. in solving the cyclic string comparison problem [3]. There are two previous efficient algorithms that are capable of consecutive suffix alignment under edit distance: the algorithm of Landau et al. [2] that runs in O (kn ) time and uses O (m + n + k 2) space, and the algorithm of Kim and Park [1] that runs in O ((m + n )n ) time and uses O (mn ) space. Here k is a user-defined upper limit for the computed distances (0 ≤ k ≤ max {m ,n }). In this paper we propose the first efficient linear space algorithm for consecutive suffix alignment under edit distance. Our algorithm uses O ((m + n )n ) time and O (m + n ) space.

We present a framework for generating paper-cutting effects on mesh models. Our system involves the construction of model defined paper-cutting patterns, extraction of the medial axis (MA) from the mesh models and position location for patterns on the mesh surface based on MA. Patch sets for placing patterns on the mesh surface are determined by the region growing algorithm. After patch sets parameterization, patterns are drawn on the parameterized domain and mapped onto the mesh surface by inverse sampling.

This paper investigates an aggressive back off strategy as part of the congestion management algorithms developed for Direct Broadcast Satellites with Return Channel Systems (DBS-RCS). The satellite architecture considered in this work is based on an asymmetric architecture with the high capacity forward link provided by the DBS and low speed return channel is provided by a constellation of Low Earth Orbiting (LEO) satellites. The network carries both Moving Pictures Expert Group (MPEG) coded video traffic and other data traf-fic having available bit rates (ABR) which is based on Reliable DBS Multicast Protocol (RDMP). Due to overwhelming complexity of real DBS-RCS systems, unless unduly simplifications are made, an exact analysis of this system be-comes impossible. Therefore, we rely on the testbed results for investigation of the proposed aggressive back off strategy. In this paper, we first present a brief background on adaptive resource allocation and management (ARAM) system developed in our earlier work [1]. Then, we provide the proof of concept ex-periments for the newly introduced aggressive back off strategy. We show that the distributed control provided by the watermarks provides performance com-parable to the baseline ARAM model. We also show that an aggressive back off strategy should be used for increased QoS.

At PODC'05, Subramanian, Katz, Roth, Shenker and Stoica (SKRSS) introduced and formulated a new theoretical problem called reliable broadcast problems in unknown fixed-identity networks [3] and further proposed a feasible result to this problem. Since the size of signatures of a message traversing a path grows linearly with the number of hops in their implementations, this leaves an interesting research problem (an open problem advertised by Subramanian et al in [3]) – how to reduce the communication complexity of their reliable broadcast protocol? In this paper, we provide a novel implementation of reliable broadcast problems in unknown fixed-identity networks with lower communication complexity. The idea behind of our improvement is that we first transfer the notion of path-vector signatures to that of sequential aggregate path-vector signatures and show that the notion of sequential aggregate path-vector is a special case of the notion of sequential aggregate signatures. As a result, the currently known results regarding sequential aggregate signatures can be used to solve the open problem. We then describe the work of [3] in light of sequential aggregate signatures working over independent RSA, and show that if the size of an node vi,j's public key |g(vi,j)| is ti,j and the number of hops in a path pi is di in the unknown fixed-identity graph G (with k adversaries), the reduced communication complexity is approximate to while the computation (time) complexity of our protocol is the same as that presented in [3].

Dirty-paper trellis-code watermarking with random-valued arcs is slow since the embedding into the cover work is performed on path-level which entails many Viterbi decodings through random convergence. We present a fast deterministic embedding in a trellis-code with orthogonal arcs. The proposed algorithm has a speedup factor of the message size since it is based on arc-level modification of the cover work in a bit-by-bit manner. Experimental results show that the proposed embedding provides higher fidelity and lower BER against various types of attacks, compared with conventional informed methods.

The possibilities of exploiting the special structure of d.c. programs, which consist of optimizing the difference of convex functions, are currently more or less limited to variants of the DCA proposed by Pham Dinh Tao and Le Thi Hoai An in 1997. These assume that either the convex or the concave part, or both, are evaluated by one of their subgradients. In this paper we propose an algorithm which allows the evaluation of both the concave and the convex part by their proximal points. Additionally, we allow a smooth part, which is evaluated via its gradient. In the spirit of primal-dual splitting algorithms, the concave part might be the composition of a concave function with a linear operator, which are, however, evaluated separately. For this algorithm we show that every cluster point is a solution of the optimization problem. Furthermore, we show the connection to the Toland dual problem and prove a descent property for the objective function values of a primal-dual formulation of the problem. Convergence of the iterates is shown if this objective function satisfies the Kurdyka--\L ojasiewicz property. In the last part, we apply the algorithm to an image processing model.

Achieving peak performance in important numerical kernels such as dense matrix multiply or sparse-matrix vector multiplication usually requires extensive, machine-dependent tuning by hand. In response, a number automatic tuning systems have been developed which typically operate by (1) generating multiple implementations of a kernel, and (2) empirically selecting an optimal implementation. One such system is FFTW (Fastest Fourier Transform in the West) for the discrete Fourier transform. In this paper, we review FFTW’s inner workings with an emphasis on its code generator, and report on our empirical evaluation of the system on two different hardware and compiler platforms. We then describe a number of our own extensions to the FFTW code generator that compute effcient discrete cosine transforms and show promising speed-ups over a vendor-tuned library. We also comment on current opportunities to develop tuning systems in the spirit of FFTW for other widely-used kernels.

Inference in mechanistic models of non-linear differential equations is a challenging problem in current computational statistics. Due to the high computational costs of numerically solving the differential equations in every step of an iterative parameter adaptation scheme, approximate methods based on gradient matching have become popular. However, these methods critically depend on the smoothing scheme for function interpolation. The present article adapts an idea from manifold learning and demonstrates that a time warping approach aiming to homogenize intrinsic length scales can lead to a significant improvement in parameter estimation accuracy. We demonstrate the effectiveness of this scheme on noisy data from two dynamical systems with periodic limit cycle, a biopathway, and an application from soft-tissue mechanics. Our study also provides a comparative evaluation on a wide range of signal-to-noise ratios.

Background Lipofilling or autologous fat transfer is an established technique in plastic surgery. Herein, we describe the lipofilling effects after implant-based breast reconstruction in post-radiation patients and propose an algorithm for indication of lipofilling. Methods Forty patients with a history of breast cancer were included in this retrospective analysis. Patients had undergone either breast conserving therapy or mastectomy. Twenty-six patients underwent additional radiation therapy. Patients were assessed using a post-radiation skin scoring classification. Results In total, 68 lipofilling procedures were analyzed. Scar release, skin softening, improved quality of life, and improvement of post-radiation findings are results of lipofilling with a closed filtration system. In all patients with post-surgical radiation, an improvement of tissue quality was observed. Staging revealed that lipofilling improved mean post-radiation skin scores of 2.40 ± 0.89 to 1.21 ± 0.76 (p ≤ 0.000). There was no recurrence of breast cancer in our study patients. Conclusions This study introduces an algorithm using lipofilling in reconstructive breast surgery and especially in post-radiation patients with low risks as well as very high acceptance in patients with various indications for this procedure. A regenerative aspect was also detectable in patients following radiation therapy and reconstruction. Lipofilling is a safe and effective procedure with a low incidence of minor complications. It is therefore a feasible method to resolve volume deficiencies and asymmetric results after oncologic breast surgery. Nevertheless, a prospective study has now been initiated focusing on the oncologic safety of lipofilling including ultrasound and radiological examinations to validate the findings of this initial study. Level of Evidence: Level IV, therapeutic study.

A linear chain on a simple cubic lattice was simulated by the Metropolis Monte Carlo method using a combination of local and non-local chain modifications. Kink-jump, crankshaft, reptation and end-segment moves were used for local changes of the chain conformation, while for non-local chain rearrangements the "cut-and-paste" algorithm was employed. The statistics of local micromodifications was examined. An approximate method for estimating the conformational entropy of a polymer chain, based on the efficiency of the kink-jump motion respecting chain continuity and excluded volume constraints, was proposed. The method was tested by calculating the conformational entropy of the undisturbed chain, the chain under tension and in different solvent conditions (athermal, theta and poor) and also of the chain confined in a slit. The results of these test calculations are qualitatively consistent with expectations. Moreover, the obtained values of the conformational entropy of self avoiding chain with ends fixed over different separations, agree very well with the available literature data. Figure Visualization of the neighborhood of two local chain microconformations containing a bead (indicated by the arrow) which a) can and b) cannot be moved by the kink-jump. In red there are marked the possible trajectories of chain fragment which can block the adjacent site

Pavement distress, the various defects such as holes and cracks, represent a significant engineering and economic concern. This paper based on Beamlet algorithm using MATLAB software to process the pavement crack images and classify the different cracks into four types: horizontal, vertical, alligator, and block types. Experiment results show that the proposed method can effectively detect and classify of the pavement cracks with a high success rate, in which transverse crack and longitudinal crack detection rate reach to 100%, and alligator crack and block crack reach more than 85%.

The detailed analyses of the weighted sum rate maximization problem in the previous two chapters have revealed that the optimum transmit covariance matrices and the accompanied maximum weighted sum rate are usually not available in closed form for a multi-user MIMO broadcast channel system setup. In fact, the global optimum can be found by means of iterative algorithms in case of dirty paper coding (see Chap. 6) but only few qualitative conclusions can be deduced therefrom. For linear transceivers, the situation is even worse. Up to now, a globally optimum algorithm does not seem to exist although we conjecture that the combinatorial stream allocation algorithm presented in Chap. 7 achieves this global optimum in most of the cases.

Computational origami is the computer assisted study of origami as a branch of science of shapes. The origami construction is a countably finite sequence of fold steps, each consisting in folding along a line. In this paper, we formalize origami construction. We model origami paper by a set of faces over which we specify relations of overlay and adjacency. A fold line is determined by a specific fold method. After folding along the fold line, the structure of origami is transformed; some faces are divided and moved, new faces are created and therefore the relations over the faces change. We give a formal method to construct the model origami. The model furthermore induces a graph of layers of faces. We give two origami examples as the application of our model. They exhibit non-trivial aspects of origami which are revealed only by formal modeling. The model is the abstraction of the implemented core of the system of computational origami called Eos (E-origami system).

What forms of origami can be designed automatically by algorithms? What shapes can result by folding a piece of paper flat and making one complete straight cut? What polyhedra can be cut along their surface and unfolded into a flat piece of paper without overlap? When can a linkage of rigid bars be untangled or folded into a desired configuration? Folding and unfolding is a branch of discrete and computational geometry that addresses these and many other intriguing questions. I will give a taste of the many results that have been proved in the past few years, as well as the several exciting open problems that remain open. Many folding problems have applications in areas including manufacturing, robotics, graphics, and protein folding.

In this paper, we employed two machine learning algorithms – namely, a clustering and a neural network algorithm – to analyze the network traffic recorded from three sources. Of the three sources, two of the traffic sources were synthetic, which means the traffic was generated in a controlled environment for intrusion detection benchmarking. The main objective of the analysis is to determine the differences between synthetic and real-world traffic, however the analysis methodology detailed in this paper can be employed for general network analysis purposes. Moreover the framework, which we employed to generate one of the two synthetic traffic sources, is briefly discussed.

Recently, informed watermarking schemes based on Costa's dirty paper coding are drawing more attention than spread spectrum based techniques because these kinds of watermarking algorithms do not need an original host signal for watermark detection and the host signal does not affect the performance of watermark detection. For practical implementation, they mostly use uniform scalar quantizers, which are very vulnerable against amplitude modification. Hence, it is necessary to estimate the amplitude modification, i.e., a modified quantization step size, before watermark detection. In this paper, we propose a robust algorithm to estimate the modified quantization step size with an optimal search interval. It searches the quantization step size to minimize the quantization error of the received audio signal. It does not encroach the space for embedding watermark message because it just uses the received signal itself for estimation of the quantization step size. The optimal searching interval is determined to satisfy both detection performance and computational complexity. Experimental results show that the proposed algorithm can estimate the modified quantization step size accurately under amplitude modification attacks.

Origami (paper folding) has a long tradition in Japan’s culture and education. We are developing a computational origami system, based on symbolic computation system Mathematica, for performing and reasoning about origami on the computer. This system is based on the implementation of the six fundamental origami folding steps (origami axioms) formulated by Huzita. In this paper, we show how our system performs origami folds by constraint solving, visualizes each step of origami construction, and automatically proves general theorems on the result of origami construction using algebraic methods. We illustrate this by a simple example of trisecting an angle by origami. The trisection of an angle is known to be impossible by means of a ruler and a compass. The entire process of computational origami shows nontrivial combination of symbolic constraint solving, theorem proving and graphical processing.

In this short paper we show how the convergence of the iterative aggregation-disaggregation methods for computing the Perron eigenvector of a large sparse irreducible stochastic matrix can be improved by an appropriate ordering of the data and by the choice of a basic iteration matrix. Some theoretical estimates are introduced and a fast algorithm is proposed for obtaining the desired ordering. Numerical examples are presented.

The firefighter problem is defined as follows. Initially, a fire breaks out at a vertex r of a graph G. In each subsequent time unit, a firefighter chooses a vertex not yet on fire and protects it, and the fire spreads to all unprotected neighbors of the vertices on fire. The objective is to choose a sequence of vertices for the firefighter to protect so as to save the maximum number of vertices. The firefighter problem can be used to model the spread of fire, diseases, computer viruses and suchlike in a macro-control level. In this paper, we study algorithmic aspects of the firefighter problem on trees, which is NP-hard even for trees of maximum degree 3. We present a (1 - 1/e)-approximation algorithm based on LP relaxation and randomized rounding, and give several FPT algorithms using a random separation technique of Cai, Chan and Chan. Furthermore, we obtain an $2^{O(\sqrt{n}\log n)}$-time subexponential algorithm.

Many different shape from shading (SFS) algorithms have emerged during the last three decades. Recently, we proposed [1] a unified framework that is capable of solving the SFS problem under various settings of imaging conditions representing the image irradiance equation of each setting as an explicit Partial Differential Equation (PDE). However, the result of any SFS algorithm is mainly affected by errors in the given image brightness, either due to image noise or modeling errors. In this paper, we are concerned with quantitatively assessing the degree of robustness of our unified approach with respect to these errors. Experimental results have revealed promising performance against noisy images but has also lacked in reconstructing the correct shape due to error in the modeling process. This result emphasizes the need for robust algorithms for surface reflectance estimation to aid SFS algorithms producing more realistic shapes.

The “Peaceably Coexisting Armies of Queens” problem [1] is a difficult optimisation problem on a chess-board, requiring equal numbers of black and white papers to be placed on the board so that the white queens cannot attack the black queens (and necessarily vice versa).

After producing a paper reel from the paper machine it must be converted to a form that is suitable for the use by customers. This operation called converting is carried out in Caledonian Paper by the winders located in the finishing area. Occasionally a problem may arise during the operation and various defects may appear on customer reels affecting the quality of the final product. The company suspected that the causes of deviations were profiles related and as the database of the mill contains a vast amount of profiles for both good and defective reels, specialists started investigations to find out a technology which would be able to extract a relevant and hidden information from the historical data. This information would then be used to predict a fault arising at the winders so that corrective actions could be applied before the defect becomes too significant. A CBR solution was chosen.

We give an algorithm to minimize the total completion time on-line on a single machine, using restarts, with a competitive ratio of 3/2. The optimal competitive ratio without using restarts is 2 for deterministic algorithms and e/(e - 1) ? 1.582 for randomized algorithms. This is the first restarting algorithm to minimize the total completion time that is proved to be better than an algorithm that does not restart.

Recent research on security and trustworthiness of micro-fluidic biochips has exposed several backdoors in their established design flows that can lead to compromises in assay results. This is a serious concern, considering the fact that these biochips are now extensively used for clinical diagnostics in healthcare. In this paper, we propose a novel scheme for root-causing assay manipulation attacks for actuations on digital microfluidic biochips that manifest as errors after execution. In particular, we show how the presence of a functionally correct reaction sequence graph has a significant advantage in the micro-fluidic context for debugging errors resulting out of such attacks. Such a sequence graph is the basis from which the actuation sequence to be implemented on a target Lab-on-chip is synthesized. In this paper, we investigate the possibility of using this sequence graph as a reference model for debugging erroneous reaction executions with respect to the desired output concentration. Our debugging method consists of program slicing with respect to the observable error in the golden implementation. During slicing, we also perform a step-by-step comparison between the slices of the erroneous output with other erroneous and error-free outputs. The reaction steps are then compared to accurately locate the root cause of a given error. In this paper, we consider two different types of assay descriptions, namely (a) unconditional assays, which have a fixed execution path, and (b) conditional assays that alter the execution at runtime depending on the outputs of sensor observations. Experimental results on the Polymerase Chain Reaction (PCR) and Linear Dilution Tree (LDT) and its conditional variant show that our method is able to pinpoint the errors.

Nature has been a very effective source to develop various Nature Inspired Optimisation algorithms and this has developed into an active area of research. The focus of this paper is to develop a Hybrid Nature-inspired Optimisation Technique and study its application in Face Recognition Problem. Two different hybrid algorithms are proposed in this paper. First proposed algorithm is a hybrid of Gravitational Search Algorithm (GSA) and Big Bang-Big Crunch (BBBC). The other algorithm is an improvement of the first algorithm, which incorporates Stochastic Diffusion Search (SDS) algorithm along with Gravitational Search Algorithm (GSA) and Big Bang-Big Crunch (BB-BC). The hybrid is an enhancement of a single algorithm which when incorporated with similar other algorithms performs better in situations where single algorithms fail to perform well. The algorithm is used to optimize the Eigen vectors generated from Principal Component Analysis. The optimized Eigen faces supplied to SVM classifier provides better face recognition capabilities compared to the traditional PCA vectors. Testing on the face recognition problem, the algorithm showed 95% accuracy in the ORL dataset and better optimization capability on functions like Griewank-rosenbrock, Schaffer F7 in comparison to standard algorithms like Rosenbrock, GA and DASA during the Benchmark Testing.

There has been an unexpected increase in the amount of healthcare waste during the COVID-19 pandemic. Managing healthcare waste is vital, as improper practices in the waste system can lead to the further spread of the virus. To develop effective and sustainable waste management systems, decisions in all processes from the source of the waste to its disposal should be evaluated together. Strategic decisions involve locating waste processing centers, while operational decisions deal with waste collection. Although the periodic collection of waste is used in practice, it has not been studied in the relevant literature. This paper integrates the periodic inventory routing problem with location decisions for designing healthcare waste management systems and presents a bi-objective mixed-integer nonlinear programming model that minimizes operating costs and risk simultaneously. Due to the complexity of the problem, a two-step approach is proposed. The first stage provides a mixed-integer linear model that generates visiting schedules to source nodes. The second stage offers a Bi-Objective Adaptive Large Neighborhood Search Algorithm (BOALNS) that processes the remaining decisions considered in the problem. The performance of the algorithm is tested on several hypothetical problem instances. Computational analyses are conducted by comparing BOALNS with its other two versions, Adaptive Large Neighborhood Search Algorithm and Bi-Objective Large Neighborhood Search Algorithm (BOLNS). The computational experiments demonstrate that our proposed algorithm is superior to these algorithms in several performance evaluation metrics. Also, it is observed that the adaptive search engine increases the capability of BOALNS to achieve high-quality Pareto-optimal solutions.

Hierarchical linear models are widely used in many research disciplines and estimation issues for such models are generally well addressed. Design issues are relatively much less discussed for hierarchical linear models but there is an increasing interest as these models grow in popularity. This paper discusses the G-optimality for predicting individual parameters in such models and establishes an equivalence theorem for confirming the G-optimality of an approximate design. Because the criterion is non-differentiable and requires solving multiple nested optimization problems, it is much harder to find and study G-optimal designs analytically. We propose a nature-inspired meta-heuristic algorithm called competitive swarm optimizer (CSO) to generate G-optimal designs for linear mixed models with different means and covariance structures. We further demonstrate that CSO is flexible and generally effective for finding the widely used locally D-optimal designs for nonlinear models with multiple interacting factors and some of the random effects are correlated. Our numerical results for a few examples suggest that G and D-optimal designs may be equivalent and we establish that D and G-optimal designs for hierarchical linear models are equivalent when the models have only a random intercept only. The challenging mathematical question of whether their equivalence applies more generally to other hierarchical models remains elusive.

Macular edema (ME) is an essential sort of macular issue caused due to the storing of fluid underneath the macula. Age-related Macular Degeneration (AMD) and diabetic macular edema (DME) are the two customary visual contaminations that can lead to fragmentary or complete vision loss. This paper proposes a deep learning-based predictive algorithm that can be used to detect the presence of a Subretinal hemorrhage. Region Convolutional Neural Network (R-CNN) and faster R-CNN are used to develop the predictive algorithm that can improve the classification accuracy. This method initially detects the presence of Subretinal hemorrhage, and it then segments the Region of Interest (ROI) by a semantic segmentation process. The segmented ROI is applied to a predictive algorithm which is derived from the Fast Region Convolutional Neural Network algorithm, that can categorize the Subretinal hemorrhage as responsive or non-responsive. The dataset, provided by a medical institution, comprised of optical coherence tomography (OCT) images of both pre- and post-treatment images, was used for training the proposed Faster Region Convolutional Neural Network (Faster R-CNN). We also used the Kaggle dataset for performance comparison with the traditional methods that are derived from the convolutional neural network (CNN) algorithm. The evaluation results using the Kaggle dataset and the hospital images provide an average sensitivity, selectivity, and accuracy of 85.3%, 89.64%, and 93.48% respectively. Further, the proposed method provides a time complexity in testing as 2.64s, which is less than the traditional schemes like CNN, R-CNN, and Fast R-CNN.

In this paper, the binary tomographic reconstruction problem for very limited projection data availability is considered. Being this inverse problem highly ill-posed, we propose a new reconstruction model that uses a shape centroid-based regularization term, i.e., we assume that the center of gravity of the object of interest is known, at least approximately, in advance. Motivation for this regularization is found in the close connection between the projection data and the object centroid, as we will show. Experimental evaluation underpins that reasonable results can be obtained from practically minimal amount of projection data, gathered from just one projection direction.

Several lattice-based cryptosystems require to sample from a discrete Gaussian distribution over the integers. Existing methods to sample from such a distribution either need large amounts of memory or they are very slow. In this paper we explore a different method that allows for a flexible time-memory trade-off, offering developers freedom in choosing how much space they can spare to store precomputed values. We prove that the generated distribution is close enough to a discrete Gaussian to be used in lattice-based cryptography. Moreover, we report on an implementation of the method and compare its performance to existing methods from the literature. We show that for large standard deviations, the Ziggurat algorithm outperforms all existing methods.

An image segmentation process can be considered as a process of solving a pixel clustering problem. This paper represents and combines a new clustering algorithm that we call as a Diffusion Tracking (DT) algorithm and a new clustering based image segmentation algorithm. The DT algorithm is related to classical spectral clustering techniques but overcomes some of their problems which guarantees a better starting point for the image segmentation process. The image segmentation process introduced in this paper joins seamlessly to the DT algorithm but can also be used together with other clustering methods like k-means. The segmentation algorithm is based on oversampling pixels from classified patches and using simple statistical methods for joining the information collected. The experimental results at the end of this paper show clearly that the algorithms proposed suit well also for very demanding segmentation tasks.

In this paper we will describe two new optimisations implemented in MadGraph5_aMC@NLO, both of which are designed to speed-up the computation of leading-order processes (for any model). First we implement a new method to evaluate the squared matrix element, dubbed helicity recycling, which results in factor of two speed-up. Second, we have modified the multi-channel handling of the phase-space integrator providing tremendous speed-up for VBF-like processes (up to thousands times faster)., Comment: Submitted to European Physical Journal C

Application development by integration of existing, atomic services reduces development cost and time by extensive reuse of service components. In Service Oriented Architecture, there exist alternative versions of services supplying the same functionality but differing in Quality of Service (QoS) attributes, which enables developers to select services with optimal QoS. Existing algorithms of service selection focus on the formal model of the composite service refraining from interoperability issues that may affect the integration process. In this paper, the author proposes a methodology that extends existing service selection algorithms by introducing additional constraints and processing rules representing interoperability. Two computational models are considered: the graph-based model and the combinatorial model. The extensions enable a straightforward application of a wide range of existing algorithms. The paper also describes a system that implements chosen service selection algorithms together with appropriate extensions for interoperability analysis.

Graph generation serves many useful purposes: cataloguing, testing conjectures, to which we would like to add that of producing test instances for graph algorithms. Strongly chordal graphs are a subclass of chordal graphs for which polynomial-time algorithms could be designed for problems which are NP-complete for the parent class of chordal graphs. In this paper, we propose three different algorithms for generating strongly chordal graphs, each based on a different characterization of strongly chordal graphs. Each one of them is interesting in its own right, but the third one has turned out to be the most versatile in the sense that it can generate strongly chordal graphs with multiple components and does not require a chordal graph as input.

MQTT is a publish-subscribe communication protocol being increasingly used for implementing internet-of-things (IoT) applications. In earlier work we have developed a formal and executable model of the MQTT protocol using Coloured Petri Nets (CPNs) and performed an initial verification of behavioural properties. The contribution of this paper is to investigate the use of the sweep-line method for verification of the MQTT CPN model in order to alleviate the effect of the state explosion problem. We formulate the behavioural properties using Computation Tree Logic (CTL) and show how to formulate a progress measure for the sweep-line method based on the main phases of the MQTT protocol. To perform the verification of properties, we provide some property-specific CTL model checking algorithms compatible with the sweep-line method.

If the procedures discussed above fail to give a satisfactory separation or if a special problem is set, other techniques often effect progress.

Lung auscultation is an important part of a physical examination. However, its biggest drawback is its subjectivity. The results depend on the experience and ability of the doctor to perceive and distinguish pathologies in sounds heard via a stethoscope. This paper investigates a new method of automatic sound analysis based on neural networks (NNs), which has been implemented in a system that uses an electronic stethoscope for capturing respiratory sounds. It allows the detection of auscultatory sounds in four classes: wheezes, rhonchi, and fine and coarse crackles. In the blind test, a group of 522 auscultatory sounds from 50 pediatric patients were presented, and the results provided by a group of doctors and an artificial intelligence (AI) algorithm developed by the authors were compared. The gathered data show that machine learning (ML)–based analysis is more efficient in detecting all four types of phenomena, which is reflected in high values of recall (also called as sensitivity) and F1-score. Conclusions: The obtained results suggest that the implementation of automatic sound analysis based on NNs can significantly improve the efficiency of this form of examination, leading to a minimization of the number of errors made in the interpretation of auscultation sounds. What is Known: • Auscultation performance of average physician is very low. AI solutions presented in scientific literature are based on small data bases with isolated pathological sounds (which are far from real recordings) and mainly on leave-one-out validation method thus they are not reliable. What is New: • AI learning process was based on thousands of signals from real patients and a reliable description of recordings was based on multiple validation by physicians and acoustician resulting in practical and statistical prove of AI high performance.