Datasets:

debajyotidasgupta

/

dblp_2022_2023

like 0

Title: Small cardinals and small Efimov spaces Abstract: We introduce and analyze a new cardinal characteristic of the continuum, the splitting number of the reals, denoted s(R). This number is connected to Efimov's problem, which asks whether every infinite compact Hausdorff space must contain either a non-trivial convergent sequence, or else a copy of βN.

Title: Learning for Constrained Optimization: Identifying Optimal Active Constraint Sets Abstract: In many engineered systems, optimization is used for decision making at time scales ranging from real-time operation to long-term planning. This process often involves solving similar optimization problems over and over again with slightly modified input parameters, often under tight latency requirements. We consider the problem of using the information available through this repeated solution process to learn important characteristics of the optimal solution as a function of the input parameters. Our proposed method is based on learning relevant sets of active constraints, from which the optimal solution can be obtained efficiently. Using active sets as features preserves information about the physics of the system, enables interpretable results, accounts for relevant safety constraints, and is easy to represent and encode. However, the total number of active sets is also very large, as it grows exponentially with system size. The key contribution of this paper is a streaming algorithm that learns the relevant active sets from training samples consisting of the input parameters and the corresponding optimal solution, without any restrictions on the problem type, problem structure or probability distribution of the input parameters. The algorithm comes with theoretical performance guarantees and is shown to converge fast for problem instances with a small number of relevant active sets. It can thus be used to establish simultaneously learn the relevant active sets and the practicability of the learning method. Through case studies in optimal power flow, supply chain planning, and shortest path routing, we demonstrate that often only a few active sets are relevant in practice, suggesting that active sets provide an appropriate level of abstraction for a learning algorithm to target.

Title: On semilinear sets and asymptotic approximate groups Abstract: Let G be any group and A be a non-empty subset of G. The h-fold product set of A is defined asAh:={a1⋅a2⋯ah:a1,…,ah∈A}. Nathanson considered the concept of an asymptotic approximate group. Let r,l∈Z>0. The set A is said to be an (r,l)-approximate group in G if there exists a subset X in G such that |X|⩽l and Ar⊆XA. The set A is an asymptotic (r,l)-approximate group if the product set Ah is an (r,l)-approximate group for all sufficiently large h. Recently, Nathanson showed that every finite subset A of an abelian group is an asymptotic (r,l′)-approximate group (with the constant l′ explicitly depending on r and A). In this article, our motivations are three-fold:(1)We give an alternate proof of Nathanson's result.(2)From the alternate proof we deduce an improvement in the bound on the explicit constant l′.(3)We generalise the result and show that, in an arbitrary abelian group G, the union of k (unbounded) generalised arithmetic progressions is an asymptotic (r,(4rk)k)-approximate group.

Title: Convolutional neural networks based potholes detection using thermal imaging Abstract: The presence of potholes on the roads is one of the major causes of road accidents as well as wear and tear of vehicles. In order to solve this problem, various techniques have been implemented ranging from manual reporting to authorities to the use of vibration-based sensors to 3D reconstruction using laser imaging. But all these techniques have some drawbacks such as the high setup cost, risk while detection or no provision for night vision. Therefore, the objective of this work is to analyze the feasibility and accuracy of thermal imaging in the field of pothole detection. After collecting a suitable amount of data containing the images of potholes under various conditions and weather, and implementing augmentation techniques on the data, convolutional neural networks approach of deep learning has been adopted, that is a new approach in this problem domain using thermal imaging. Also, a comparison between the self built convolutional neural model and some of the pre-rained models has been done. The results show that images were correctly identified with the best accuracy of 97.08% using one of the pre-trained convolutional neural networks based residual network models. The results of this work will be helpful in guiding the future researches in this novel application of thermal imaging in pothole detection field.(c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: Realization of digraphs in Abelian groups and its consequences Abstract: Let G -> be a directed graph of order n with no component of order less than 3, and let Gamma be a finite Abelian group such that divide Gamma divide >= 2n+2n or if n is large enough with respect to an arbitrarily fixed epsilon>0 then divide Gamma divide >=(1+epsilon)n. We show that there exists an injective mapping phi from V(G ->) to the group Gamma such that n-ary sumation x is an element of V(C ->)phi(x)=0 for every connected component C -> of G ->, where 0 is the identity element of Gamma. Moreover we show some applications of this result to group distance magic labelings.

Title: Automatic detection of tree cutting in forests using acoustic properties Abstract: Deforestation is cutting trees of forests on a huge scale, often resulting in loss of habitat of millions of wild animals. About 30% of earths land is still covered with forests but due to deforestation we are losing them at the rate of about half the size of England per year. In forests, tree cutting activities are illegal but due to shortage of manpower and other resources, governments are not very successful in curbing this menace. One way to stop this is to detect the tree cutting process in an early stage so that timely measures can be taken to stop the same. The simplest method of early detection of tree cutting is to regularly monitor the forest area either manually or using some automatic techniques. As tree cutting generates lot of noise, it can be detected by regularly monitoring the acoustic signals inside the forest. An acoustic signature can provide valuable information about the activities of any intruder inside the forest. This paper proposes an algorithm for automatic detection of tree cutting in forest. The proposed algorithm is based on distance between parameters, along with K-means clustering, GMM and PCA for comparison. The efficiency of proposed algorithm is 92%. (C) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Title: A coupled Two-relaxation-time Lattice Boltzmann-Volume penalization method for flows past obstacles Abstract: In this article, a coupled Two-relaxation-time Lattice Boltzmann-Volume penalization (TRT-LBM-VP) method is presented to simulate flows past obstacles. Two relaxation times are used in the collision operator, of which one is related to the fluid viscosity and the other one is related to the numerical stability and accuracy. The volume penalization method is introduced into the TRT-LBM by an external forcing term. In the procedure of the TRT-LBM-VP, the processes of interpolating velocities on the boundaries points and distributing the force density to the Eulerian points are unneeded. Performing the TRT-LBM-VP on a certain point, only the variables of this point are needed. As a consequence, the TRT-LBM-VP can be conducted parallelly. From the comparison between the result of the cylindrical Couette flow solved by the TRT-LBM-VP and that solved by the Single-relaxation-time LBM-VP (SRT-LBM-VP), the accuracy of the TRT-LBM-VP is higher than that of the SRT-LBM-VP. Flows past a single circular cylinder, a pair of cylinders in tandem and side-by-side arrangements, two counter-rotating cylinders and a NACA-0012 airfoil are chosen as numerical experiments to verify the present method further. Good agreements between the present results and those in the previous literatures are achieved.

Title: CD-SPM: Cross-domain book recommendation using sequential pattern mining and rule mining Abstract: Recommender system suggests a personalized recommendation by filtering the information based on users interest. Nowadays, users like to purchase the best possible items and services to spend the shortest span of time. The cross-domain recommendation system is a method of recommendation wherein knowledge is gathered from multiple domains. With respect to the user's search term from the source domain, most similar items are recommended from the target domain. Semantic similarity between two different items can be achieved through Wpath method using Ontology. PrefixSpan is used for generating sequential patterns and Topseq rule mining algorithm is used for finding the frequent sequential rule. So, this work tries to extend cross domain recommendation by 1) finding the semantic similarity of items using Ontology; 2) applying Collaborative Filtering for finding similar items and users; 3) generating frequent item sequences using PrefixSpan sequential pattern mining algorithm and 4) recommending user preferred items using Topseq rule mining algorithm. The recommender system is evaluated considering precision, recall and F1 Score measures. It finds CD-SPM which yields better F1 Score. The proposed approach also alleviates the new user problem and sparsity problem to some extent. (C) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Title: Predicting Tactical Solutions to Operational Planning Problems Under Imperfect Information Abstract: This paper offers a methodological contribution at the intersection of machine learning and operations research. Namely, we propose a methodology to quickly predict expected tactical descriptions of operational solutions (TDOSs). The problem we address occurs in the context of two-stage stochastic programming, where the second stage is demanding computationally. We aim to predict at a high speed the expected TDOS associated with the second-stage problem, conditionally on the first-stage variables. This may be used in support of the solution to the overall two-stage problem by avoiding the online generation of multiple second-stage scenarios and solutions. We formulate the tactical prediction problem as a stochastic optimal prediction program, whose solution we approximate with supervised machine learning. The training data set consists of a large number of deterministic operational problems generated by controlled probabilistic sampling. The labels are computed based on solutions to these problems (solved independently and offline), employing appropriate aggregation and subselection methods to address uncertainty. Results on our motivating application on load planning for rail transportation show that deep learning models produce accurate predictions in very short computing time (milliseconds or less). The predictive accuracy is close to the lower bounds calculated based on sample average approximation of the stochastic prediction programs.

Title: Funnel control for a moving water tank Abstract: We study tracking control for a nonlinear moving water tank system modeled by the linearized Saint-Venant equations, where the output is given by the position of the tank and the control input is the force acting on it. For a given reference signal, the objective is that the tracking error evolves within a pre-specified performance funnel. Exploiting recent results in funnel control, this can be achieved by showing that inter alia the system’s internal dynamics are bounded-input, bounded-output stable.

Title: Robust scene text recognition: Using manifold regularized Twin-Support Vector Machine. Abstract: Abstract Text recognition in natural scene images endures as a challenging problem, attributable to a highly variable appearance in an unconstrained environment. The proposed work is novel of its kind, which improved the generalization of the Twin Support Vector Machine (T-SVM) by manifold regularization, that was further extended to validate and recognize the text in the natural scene images. The multi-class T-SVM is augmented with ambient regularizer term and intrinsic regularizer term, assisting to form a smooth function for the model. Text comprehension from natural scene includes the localization, recognition, and reconstruction of the text. The work includes an additional module, revalidation that discards the false positives from the text objects detected during the localization phase. Then in recognition phase, each letter from the pool of text objects is identified for the appropriate class and provided as output to the text construction phase, which builds the text using the pool of coordinates associated with the object. The model is evaluated against traditional methods like Support Vector Machine (SVM), T-SVM, LST-SVM(Least Square Twin Support Vector Machine) and also with other research of the same kind and shows the accuracy of 84.91% with ICDAR 2015, 84.21% with MSRA 500 and 86.21% with SVT. The analyses of the results show that the model was capable of recognizing most of the characters from the image along with consummate accuracy level.

Title: Multivariate Fast Iterative Filtering for the Decomposition of Nonstationary Signals Abstract: In this work, we present a new technique for the decomposition of multivariate data, which we call Multivariate Fast Iterative Filtering (MvFIF) algorithm. We study its properties, proving rigorously that it converges in finite time when applied to the decomposition of any kind of multivariate signal. We test MvFIF performance using a wide variety of artificial and real multivariate signals, showing its ability to: separate multivariate modulated oscillations; align frequencies along different channels; produce a quasi-dyadic filterbank when decomposing white Gaussian noise; decompose the signal in a quasi-orthogonal set of components; being robust to noise perturbation, even when the number of channels is increased considerably. Finally, we compare it and its performance with the main methods developed so far in the literature, proving that MvFIF produces, without any a priori assumption on the signal under investigation and in a fast and reliable manner, a uniquely defined decomposition of any multivariate signal.

Title: <p>EMOTE - Multilayered encryption system for protecting medical images based on binary curve</p> Abstract: Digital images used in business, government and medical sectors insist the need for image security against various threats. An exclusive system that can make use of color spaces has been proposed to process sensitive images based on the binary curve. The proposed Encryption systeM fOr proTecting imagEs has been coined as EMOTE. This multilayered system works on binary curves as binary operations have no carries. Since binary squaring is lightweight, it is claimed to be smaller and faster in hardware than prime-field ones. Logistic mapping has been done to perform chaotic masking, which is then followed by DNA encoding in the next layer. ECC over GF(2(m)) that works on the spatial domain has been chosen as it is one of the proven technique for its mathematical strength. This proposed system can facilitate both symmetric and asymmetric combination and hence hybrid. Experimental results and the histogram analysis obtained comprehend this proposed system for its extensive deployment to process sensitive images, especially in the medical domain. Ideal measures attained for MSE, PSNR values for the standard benchmark images taken from "The whole brain atlas " database from Harvard University, substantiate the proposed EMOTE system as an expedient choice for processing sensitive images. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: A BUS-aided RSU access scheme based on SDN and evolutionary game in the Internet of Vehicle Abstract: The Internet of Vehicle (IoV) is known as a kind of highly dynamic network, in which the vehicle needs to exchange information with the fixed Roadside Unit (RSU) or other moving vehicles frequently. In order to improve the performance of existing RSU access schemes, in this paper, we proposed a BUS-aided RSU connection scheme based on the software-defined networking (SDN) and evolutionary game theory, in which the costs during multiple-user access and following RSU handoff are considered. At first, we constructed a SDN-IoV architecture and showed the benefits by introducing SDN into IoV. After that, we modified the original OpenFlow protocol stack in order to apply it to the wireless vehicular networks. Next, to explore more accessing opportunities in moving cases, the BUS was further introduced as a mobile RSU. With these fixed and mobile RSUs, an evolutionary game is then envisioned to model the multiple-user access process with the aim to maximize the rewards of all participants. To make our proposed protocol practical, we also illustrated the implementation procedure of our protocol on the OPNET platform and gave out the finite state machine (FSM) of major routines. Numerical results showed that our proposed scheme could outperform the schemes without BUS, SDN, and game theory enabled, in terms of RSU load ratio, throughput, and handoff times.

Title: Crop image classification using spherical contact distributions from remote sensing images Abstract: Land use and land cover classification from a remote sensing image is a long standing research problem. It ranges from simple classifications like mapping water bodies to complex classifications like crop and forest strands. Crop image classification is complex because of various stages of growth of the same crop, same spectral values for various crops, an other multitude of problems. Crop image classification is very essential for agriculture monitoring, crop yield production, global food security, etc. A new unsupervised algorithm, Spherical Contact Distribution Classification Algorithm (SCDCA) is proposed in this paper which uses mathematical morphology, spherical contact distributions, and first order statistics. Later SCDCA is compared with linear contact distribution classification algorithm (LCDCA). Quantitative analyses prove the efficiency of the algorithm and present that the complexity of SCDCA is very much less when compared to that of LCDCA.(c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: An improved bag of dense features for skin lesion recognition Abstract: Skin is the largest and fastest growing organ in the human body. There are various types of skin lesions in which malignancy are non-invasively detected and recognized based on their local and global attributes of the image using an image-guided system. In this work, Gradient Location and Orientation Histogram and color features are fused together to construct the Inherently Hybrid Image Descriptor for skin lesion classification. The features obtained from these descriptor are combined to form a bag of visual words. The improved bag is used to categorize the skin lesion classes as malignant or benign using Support Vector Machine. The performance of the proposed method has been found considerably better than the current state-of-art. It also simplifies the process of diagnosis for undeclared abnormalities in the skin region. (c) 2019 Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: Optimization driven cluster based indexing and matching for the document retrieval Abstract: Document retrieval methods concentrate on minimizing the time taken for the navigator to recall the entire document while analyzing the concepts, themes, and contents of the document based on their research goals. The exploitation of the repetitiveness in order to reduce the usage space is a hectic challenge. This paper proposes a document retrieval mechanism using an optimization, Monarch Butterfly optimization-based FireFly (MB-FF), developed with the integration of the Monarch Butterfly Optimization (MBO) and Firefly Algorithm (FA). The keywords from the documents are identified from the pre-processed document, which is pre-processed using stemming and stop word removal. The Term Frequency-Inverse Document Frequency (TF-IDF) is used in the extraction of the keywords and the concept of holoentropy is used in the selection of the significant keywords. The selected keywords assures the retrieval of the relevant documents, which initially is processed through cluster-based indexing using the Monarch Butterfly optimization-based firefly (MB-FF) that is followed with the two-level mod-Bhattacharya distance match. The performance of the MB-FF algorithm in document retrieval mechanism is evaluated using Precision, recall, and F-measure. (C) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Title: A feature adaptive image watermarking framework based on Phase Congruency and Symmetric Key Cryptography Abstract: In this paper, a Phase Congruency based digital color image watermarking algorithm is proposed which provides a higher degree of robustness against attacks and excellent imperceptibility. Here, Phase Congruency has been used to detect the local feature regions of the host image and then the watermark has been infused into it using a new technique called 'Adaptive alpha-beta Blending'. An accurate Human Visual System modeling has been incorporated via Lifting Wavelet Transform to take the full advantage of perceptual watermarking. The coefficients of the alpha-beta blending are selected adaptively based on the Phase Congruency feature map of the host image. Furthermore, the watermark is secured with a cryptographic algorithm called Arnold's Cat Map to prohibit further eavesdropping. From rigorous testing, results indicate that our approach is robust against various geometric, non-geometric and combined attacks while maintaining a sublime imperceptibility. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: UNDERSTANDING POPULAR MATCHINGS VIA STABLE MATCHINGS Abstract: An instance of the marriage problem is given by a graph G = (A boolean OR B, E), together with, for each vertex of G, a strict preference order over its neighbors. A matching M of G is popular in the marriage instance if M does not lose a head-to-head election against any matching where vertices are voters. Every stable matching is a min-size popular matching; another subclass of popular matchings that always exists and can be easily computed is the set of dominant matchings. A popular matching M is dominant if M wins the head-to-head election against any larger matching. Thus, every dominant matching is a max-size popular matching, and it is known that the set of dominant matchings is the linear image of the set of stable matchings in an auxiliary graph. Results from the literature seem to suggest that stable and dominant matchings behave, from a complexity theory point of view, in a very similar manner within the class of popular matchings. The goal of this paper is to show that there are instead differences in the tractability of stable and dominant matchings and to investigate further their importance for popular matchings. First, we show that it is easy to check if all popular matchings are also stable; however, it is co-NP hard to check if all popular matchings are also dominant. Second, we show how some new and recent hardness results on popular matching problems can be deduced from the NP-hardness of certain problems on stable matchings, also studied in this paper, thus showing that stable matchings can be employed to show not only positive results on popular matchings (as is known) but also most negative ones. Problems for which we show new hardness results include finding a min-size (resp., max-size) popular matching that is not stable (resp., dominant). A known result for which we give a new and simple proof is the NP-hardness of finding a popular matching when G is nonbipartite.

Title: On multisets, interpolated multiple zeta values and limit laws Abstract: In this work we discuss a parameter a on weighted k-element multisets of [n] = {1, ..., n}. The sums of weighted k-multisets are related to k-subsets, k-multisets, as well as special instances of truncated interpolated multiple zeta values. We study properties of this parameter using symbolic combinatorics. We (re)derive and extend certain identities for zeta(t)(n)({m}(k)). Moreover, we introduce random variables on the k-element multisets and derive their distributions, as well as limit laws for k or n tending to infinity.

Title: Fuzzy assessment of the risk factors causing cost overrun in construction industry. Abstract: Cost is considered as one of the most important parameters for the success of any construction project. Therefore the risk factors causing cost overrun in the construction industry should be assessed. In this study, 55 important risk factors causing cost overrun in Indian construction projects are identified through intensive literature review and expert opinion. A new fuzzy based model has been proposed to estimate the risk magnitude of these factors, as the theory has the potential to deal with the vagueness, uncertainty and subjective nature of any problems and It is capable of handling the almost same analogous which is found in the complex construction projects. In order to assess the risk factors causing cost overrun, probability index and severity index are considered. A new cost overrun factor index, namely fuzzy index for cost overrun is calculated which indicates the risk magnitude of a certain factor. The applicability of the model has been shown by an example. The risk magnitude for the factor “fluctuation in price material” is determined by collecting the data from the experts of Indian construction industry. On the basis of these risk magnitudes, the importance level the factors are assessed. Top ten factors for causing cost overrun in the Indian construction industry are recognised as fluctuation in price material, lowest bid procurement policy, inflation inappropriate govt. Policy, mistakes and discrepancies in the contract document, inaccurate time and cost estimate, additional work, frequent design change, unrealistic contract duration and financial difficulty faced by contractors.

Title: Supersaturation for Subgraph Counts Abstract: The classical extremal problem is that of computing the maximum number of edges in an F-free graph. In particular, Turán’s theorem entirely resolves the case where $$F=K_{r+1}$$ . Later results, known as supersaturation theorems, proved that in a graph containing more edges than the extremal number, there must also be many copies of $$K_{r+1}$$ . Alon and Shikhelman introduced a broader class of extremal problems, asking for the maximum number of copies of a graph T in an F-free graph (so that $$T=K_2$$ is the classical extremal number). In this paper we determine some of these generalized extremal numbers when T and F are stars or cliques and prove some supersaturation results for them.

Title: The Covering Radius and a Discrete Surface Area for Non-Hollow Simplices Abstract: We explore upper bounds on the covering radius of non-hollow lattice polytopes. In particular, we conjecture a general upper bound of d/2 in dimension d, achieved by the “standard terminal simplices” and direct sums of them. We prove this conjecture up to dimension three and show it to be equivalent to the conjecture of González-Merino and Schymura (Discrete Comput. Geom. 58(3), 663–685 (2017)) that the d-th covering minimum of the standard terminal n-simplex equals d/2, for every $$n\ge d$$ . We also show that these two conjectures would follow from a discrete analog for lattice simplices of Hadwiger’s formula bounding the covering radius of a convex body in terms of the ratio of surface area versus volume. To this end, we introduce a new notion of discrete surface area of non-hollow simplices. We prove our discrete analog in dimension two and give strong evidence for its validity in arbitrary dimension.

Title: A COSMIC function points based test effort estimation model for mobile applications Abstract: With the substantial proliferation in demand for applications running on mobile devices, developers and testers are foreseen to release applications with high caliber, on time and inside budget. Mobile application testing is imperative and perilous activity in the application development lifecycle, which confirms quality and unswervingly impacts the development effort and affluence of the application. The estimation of effort for testing of apps is a key figure that helps test managers in making approximate accurate decisions for coordinating testing resources. In this study, a regression model is presented to predict test effort for mobile applications considering COSMIC Function Size Measurement (FSM), mobile app characteristics/factors and test factors. The major benefit of this model is that it tends to be utilized at the beginning of mobile app testing life cycle, and thus can assist testers to effectively lead early effort estimation. The model presented is further validated and evaluated for their effectiveness by using a k-fold cros-svalidation method. MRE, MMRE, MdMRE, PRED (0.25) and PRED (0.50) indices are used for measuring the accuracy of the model and findings suggest that the proposed model gives a good prediction and can be exercised in the mobile software industry for predicting test effort. (C) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.H

Title: Bayesian Multinomial Logistic Normal Models through Marginally Latent Matrix-T Processes Abstract: Bayesian multinomial logistic-normal (MLN) models are popular for the analysis of sequence count data (e.g., microbiome or gene expression data) due to their ability to model multivariate count data with complex covariance structure. However, existing implementations of MLN models are limited to small datasets due to the non-conjugacy of the multinomial and logistic-normal distributions. Motivated by the need to develop efficient inference for Bayesian MLN models, we develop two key ideas. First, we develop the class of Marginally Latent Matrix-T Process (Marginally LTP) models. We demonstrate that many popular MLN models, including those with latent linear, non-linear, and dynamic linear structure are special cases of this class. Second, we develop an efficient inference scheme for Marginally LTP models with specific accelerations for the MLN subclass. Through application to MLN models, we demonstrate that our inference scheme are both highly accurate and often 4-5 orders of magnitude faster than MCMC.

Title: An enhanced time efficient technique for image watermarking using ant colony optimization and light gradient boosting algorithm Abstract: For a secure multimedia data exchange, watermarking is used. Generally, meta-heuristic optimization is required to optimize scheme parameters in the watermarking embedding. Even though meta-heuristic methods have been widely used because of their enhancing capability, due to their large time consump-tion nature they are not applicable for time-sensitive applications. This paper proposes a time efficient optimization method based on machine learning algorithms to detect the best embedding parameter for image watermarking with both robustness and imperceptibility. Initially, for providing robustness against watermarking attacks, a method for watermarking embedding is designed in the domain of Discrete Cosine Transform. After that, Ant Colony optimization is used for finding the optimization parameters. Then an observation data is obtained, which includes the feature vector and optimum strength values of the training images. The image features are extracted at different embedded strength values by the calculation of optimum fitness function. Finally Light Gradient Boosting algorithm (LGBA) is applied to predict the optimum embedding parameters of the set of new images which are to be water -marked. When compared with the existing optimization methods, it has been found that the proposed method consumes very less time for the evaluation of optimum solutions. From the results, it has been identified that the proposed algorithm satisfies the image watermarking with the improvement in time enhancement. The effectiveness of the proposed method is analyzed using MATLAB 2018b.(c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: Generalizing the Balance Heuristic Estimator in Multiple Importance Sampling Abstract: In this paper, we propose a novel and generic family of multiple importance sampling estimators. We first revisit the celebrated balance heuristic estimator, a widely used Monte Carlo technique for the approximation of intractable integrals. Then, we establish a generalized framework for the combination of samples simulated from multiple proposals. Our approach is based on considering as free parameters both the sampling rates and the combination coefficients, which are the same in the balance heuristics estimator. Thus our novel framework contains the balance heuristic as a particular case. We study the optimal choice of the free parameters in such a way that the variance of the resulting estimator is minimized. A theoretical variance study shows the optimal solution is always better than the balance heuristic estimator (except in degenerate cases where both are the same). We also give sufficient conditions on the parameter values for the new generalized estimator to be better than the balance heuristic estimator, and one necessary and sufficient condition related to <mml:semantics>chi 2</mml:semantics> divergence. Using five numerical examples, we first show the gap in the efficiency of both new and classical balance heuristic estimators, for equal sampling and for several state of the art sampling rates. Then, for these five examples, we find the variances for some notable selection of parameters showing that, for the important case of equal count of samples, our new estimator with an optimal selection of parameters outperforms the classical balance heuristic. Finally, new heuristics are introduced that exploit the theoretical findings.

Title: Identifying intentions in forum posts with cross-domain data Abstract: In this paper, we present a method to identify forum posts expressing user intentions in online discussion forums. The results of this task, for example buying intentions, can be exploited for targeted advertising or other marketing tasks. Our method utilizes labeled data from other domains to help the learning task in the target domain by using a Naive Bayes (NB) framework to combine the data statistics . Because the distributions of data vary from domain to domain, it is important to adjust the contributions of different data sources when constructing the learning model, to achieve accurate results. Here, we propose to adjust the parameters of the NB classifier by optimizing an objective, which is equivalent to maximizing the between-class separation, using stochastic gradient descent. Experimental results show that our method outperforms several competitive baselines on a benchmark dataset consisting of forum posts from four domains: Cellphone, Electronics, Camera, and TV. In addition, we explore the possibility of combining NB posteriors computed during the optimization process with another classifier, namely Support Vector Machines. Experimental results show the usefulness of optimized NB class posteriors when using as features for SVMs in the cross-domain settings.

Title: Web accessibility investigation and identification of major issues of higher education websites with statistical measures: A case study of college websites Abstract: The World Wide Web Consortium (W3C) has provided the most important set of guidelines for web accessibility which is popularly known as Web Content Accessibility Guidelines (WCAG). The accessibility analysis of higher education websites becomes paramount important to make them inclusive considering the growing number of enrollments of persons with disabilities (PwDs) in higher education, in countries such as India. This paper presents the accessibility analysis of higher education websites with the case study of college websites (N = 44) affiliated with the University of Kashmir and Cluster University Srinagar. The study has been carried out with two major accessibility evaluation tools called web accessibility test, denoted as TAW and the accessibility engine powering browser extensions called the accessibility engine, denoted as aXe. This paper lists the major accessibility barriers exposed by these sites in terms of metrics such as a number of problems, warnings and a status of success criteria violations. With respect to TAW tool, a number of problems observed were 2646, a large number of warnings to the scale of 15995 and the not reviewed items were 1356. With aXe tool, the total violations observed were 1951 and items needing review were 1733. Findings of the statistical analysis are also presented in this paper. This paper presents a roadmap of steps for making these websites inclusive and barrier-free for PwDs. (C) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Title: English–Vietnamese cross-language paraphrase identification using hybrid feature classes Abstract: Paraphrase identification plays an important role with various applications in natural language processing tasks such as machine translation, bilingual information retrieval, plagiarism detection, etc. With the development of information technology and the Internet, the requirement of textual comparing is not only in the same language but also in many different language pairs. Especially in Vietnamese, detecting paraphrase in the English–Vietnamese pair of sentences is a high demand because English is one of the most popular foreign languages in Vietnam. However, the in-depth studies on cross- language paraphrase identification tasks between English and Vietnamese are still limited. Therefore, in this paper, we propose a method to identify the English–Vietnamese cross-language paraphrase cases, using hybrid feature classes. These classes are calculated by using the fuzzy-based method as well as the siamese recurrent model, and then combined to get the final result with a mathematical formula. The experimental results show that our model achieves 87.4% F-measure accuracy.

Title: New bounds for the b-chromatic number of vertex deleted graphs Abstract: A b-coloring of a graph is a proper coloring of its vertices such that each color class contains a vertex adjacent to at least one vertex of every other color class. The b-chromatic number of a graph is the largest integer k such that the graph has a b-coloring with k colors. In this work we present lower bounds for the b-chromatic number of a vertex-deleted subgraph of a graph, particularly regarding two important classes, claw-free graphs and chordal graphs. We also get bounds for the b-chromatic number of G - {x}, when G is a graph with large girth. (C) 2021 Published by Elsevier B.V.

Title: Hybrid based cluster head selection for maximizing network lifetime and energy efficiency in WSN Abstract: A wireless sensor network (WSN) includes more low-cost and less-power sensor nodes. All the sensor nodes are positioned in a particular area and form a wireless network by way of self-organizing. They has the ability to work normally at any of the special or wicked environ that people cannot close. However, the data transmission among nodes in an effective way is almost not possible due to various complex factors. Clustering is a renowned technique to make the transmission of data more effective. The clustering model divides the sensor nodes into various clusters. Every cluster in network has unique cluster head node, which send the information to other sensor nodes in cluster. In such circumstances, it is the key role of any clustering algorithm to choose the optimal cluster head under various constraints like less energy consumption, delay and so on. This paper develops a new cluster head selection model to maximize the lifetime of network as well as energy efficiency. Further, this paper proposes a new Fitness based Glowworm swarm with Fruitfly Algorithm (FGF), which is the hybridization of Glowworm Swarm Optimization (GSO) and Fruitfly Optimization algorithm (FFOA) to choose the best CH in WSN. The performance of developed FGF is compared to other existing methods like Particle swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), GSO, Ant Lion Optimization (ALO) and Cuckoo Search (CS), Group Search Ant Lion with Levy Flight (GAL-LF), Fruitfly Optimization algorithm (FFOA) and grasshopper Optimization algorithm (GOA) in terms of alive node analysis, energy analysis and cost function and the betterments of proposed work is also proven. (C) 2019 Production and hosting by Elsevier B.V. on behalf of King Saud University.

Title: A novel approach for drones positioning in mission critical applications Abstract: Recently, the flying sensor systems have been given a careful interest to provide critical administrations and services in a fiasco territory. Since severe debacles require the trading of geo-referenced area-based data among the salvage group and additionally the people in question, an independent exact situating framework is required. A Worldwide situating framework, or a global positioning system (GPS), gives close precise area data when there is an immediate viewable pathway with something like four satellites. Nonetheless, GPS beneficiaries may experience blackouts inside regions where satellite signs are hindered by impediments, for example, the normal mists in the sky and jammed structures in urban areas. This would prompt significant corruption in crisply developing remote detecting/flying applications, for example, those utilizing Drones. Therefore, to conquer this issue, GPS is coordinated with inertial navigation system (INS) to get a dependable route arrangement, particularly amid GPS blackouts. This study presents test and reproduction aftereffects of incorporation strategy dependent on machine learning that coordinates GPS with INS progressively. A field information was utilized to execute the combination and acquire promising outcomes. In addition, a discourse about benefits and impediments of the proposed strategy is given.

Title: Insight into the computation of Steiner minimal trees in Euclidean space of general dimension Abstract: We present well known properties related to the topology of Steiner minimal trees and to the geometric position of Steiner points, and investigate their application in the exact algorithms that have been proposed for the Euclidean Steiner problem. We discuss the difficulty in the application of properties that were very successfully applied to solve the problem in the plane, when the dimension of the space increases, and point out that the application of some geometric conditions for Steiner points is hindered when the well known implicit enumeration scheme proposed by Smith in 1992 is considered. Finally, we mention mathematical-optimization methods as a direction to explore in the search for good formulations of inequalities that would allow the application of these restrictive geometric conditions. (c) 2019 Elsevier B.V. All rights reserved. <comment>Superscript/Subscript Available</comment

Title: Multiobjective memetic algorithm based on adaptive local search chains for vehicle routing problem with time windows. Abstract: This paper presents a multiobjective memetic algorithm based on adaptive local search chains (MMA-ALSC) for vehicle routing problem with time window (VRPTW) which is an important research area in logistics. As shown in most previous studies, VRPTW is essentially a multiobjective optimization problem and can be solved effectively by the multiobjective algorithms with various local search operators. We have observed, however, that the promising solutions obtained during the process of evolution are not fully utilized to guide the search together with different local search operators. This will lead to the discontinuous and insufficient search in the regions showing promise. To alleviate this drawback, MMA-ALSC is proposed and characterized by combining a multi-directional local search strategy (MD-LS) with an enhanced local search chain technique (eLS-Chain). In MMA-ALSC, on the one hand, with MD-LS, different local search operators are designed to perform the search towards multiple directions with distinct problem-specific knowledge of multiobjective VRPTW (MOVRPTW). On the other hand, with eLS-Chain, the promising solutions obtained during the process of evolution are adaptively selected for the subsequent local search operators. In this way, MMA-ALSC can not only effectively explore the search space in multiple directions, but also fully exploit the promising solutions in a chain-based way. Experimental results on two suites of benchmark instances have demonstrated the competitive performance of MMA-ALSC when compared with other representative algorithms.

Title: Automatic Brain Tumor Segmentation from MRI using Greedy Snake Model and Fuzzy C-Means Optimization Abstract: The automatic brain tumor segmentation in MRI (Magnetic Resonance Images) is becoming a challenging task in the field of medicine, since the brain tumor occurs in different shapes, intensities and sizes. This paper proposes an efficient automatic brain tumor segmentation using Greedy Snake Model and Fuzzy C-Means optimization. This method initially identifies the approximate Region Of Interest (ROI), by removing the non-tumor part by two level morphological reconstruction such as dilation and erosion. A mask is formed by thresholding the reconstructed image and is eroded to improve the accuracy of segmentation in Greedy Snake algorithm. Using the mask boundary as initial contour of the snake, the greedy snake model estimates the new boundaries of tumor. These boundaries are accurate in regions where there is sharp edge and are less accurate where there are ramp edges. The inaccurate boundaries are further optimized by using Fuzzy C-Means algorithm to obtain the accurate segmentation output. The region that has large perimeter is finally chosen, to eliminate the in-accurate segmented regions. The experimental verification were done on T-1-weighted contrast-enhanced image data set, using the metrics such as dice score, specificity, sensitivity and Hausdorff distance. The proposed method outperforms when compared with the traditional brain tumor segmentation methods in MRI images. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: Reliability analysis of continuous emission monitoring system with common cause failure based on fuzzy FMECA and Bayesian networks Abstract: Continuous emission monitoring system (CEMS) has been widely used in many engineering fields. Common cause failures (CCFs) have remarkable effects on the system reliability of CEMS, because of shared work conditions and dependent failures for different components. A method for reliability evaluation of CEMS with CCFs is proposed based on fuzzy Failure Mode Effects and Criticality Analysis (FMECA) as well as Bayesian network (BN). By introducing the system composition and function principles of CEMS, the CEMS failure mode is clearly defined and the weak components of the system are identified. According to the hazard ranking of the CEMS failure modes, the places where reliability improvement or preventive maintenance should be implemented are found out. Then, BN-based reliability model of the sampling system, which is the weakest subsystem of CEMS, is constructed according to the results of a fault tree analysis. The behavior of CCF is further incorporated, and the α-factor model is used to evaluate the probability of CCF. Lastly, a numerical example is used to illustrate the proposed method. A comparison between the proposed method and the one without considering CCF is carried out. The result demonstrates that the proposed method has better reliability assessment accuracy for the CEMS with CCF than the one without considering CCF.

Title: Efficient computation of permanents, with applications to Boson sampling and random matrices Abstract: •Implementations of efficient algorithms for computing the permanent.•Algorithms for matrices of limited bandwidth and sparse matrices.•Distribution of permanents for Gaussian matrices gives support for Aaronson & Arkhipov's anti-concentration conjecture.•Variational distance between permanents of two families of random matrices suggests another conjecture by A&A is too strong.

Title: On the sizes of large subgraphs of the binomial random graph Abstract: We consider the binomial random graph G(n, p), where p is a constant, and answer the following two questions. First, given e(k) = p((k)(2))+ O(k), what is the maximum k such that a.a.s. the binomial random 2 graph G(n, p) has an induced subgraph with k vertices and e(k) edges? We prove that this maximum is not concentrated in any finite set (in contrast to the case of a small e(k)). Moreover, for every constant C > 0, with probability bounded away from 0, the size of the concentration set is bigger than C root n/ lnn, and, for every omega(n) -> infinity, a.a.s. it is smaller than omega root nn/ lnn. Second, given k > epsilon n, what is the maximum mu such that a.a.s. the set of sizes of k vertex subgraphs of G(n, p) contains a full interval of length mu? The answer is mu = Theta(root(n - k)n ln ((n)(k)) (c) 2021 Published by Elsevier B.V.

Title: Algorithm for studying polynomial maps and reductions modulo prime number Abstract: We explore further properties of the algorithm and the class of Pascal finite maps described in Adamus et al. (2017), when using Segre homotopy and reductions modulo prime number. We consider polynomial maps over Q. Those can be transformed into maps with coefficients in Z by denominators clearing procedure. We give a method of retrieving an inverse of a given polynomial automorphism F with integer coefficients from a finite set of inverses of its reductions modulo prime numbers. We estimate the computational complexity of the proposed algorithm. Some examples illustrate effective aspects of our approach.

Title: Track deformable objects from point clouds with structure preserved registration Abstract: Manipulating deformable objects is a challenging task for robots. The major difficulty lies in how to track these objects accurately, robustly, and efficiently, considering they have infinite-dimensional configuration space. To deal with these problems, this paper proposes a novel state estimator to track deformable objects from point clouds. A non-rigid registration method, named structure preserved registration (SPR), is developed to update the estimation by registering the object model towards the current point cloud measurement. Both the local structure and the global topology of the deformable object are considered during registration, which improves the estimation robustness under noise, outliers, and occlusions. The tracking result is further refined by running a dynamic simulation in parallel, which enforces the estimates to satisfy the physical constraints of the object. A series of real-time tracking experiments on 1D objects (ropes) and 2D objects (clothes) are performed to evaluate the proposed state estimator. A wire harness manipulation platform is also introduced where robots can manipulate soft wires to desired shapes and autonomously evaluate the manipulation quality through visual feedback.

Title: An evolutionary framework based microarray gene selection and classification approach using binary shuffled frog leaping algorithm Abstract: Since last few years, microarray technology has got tremendous application in many bio-medical researches. However, in order to efficiently recognize and apply this technology into the bio-medical areas is still very difficult and expensive. There are many metaheuristic approaches has been developed with different biological interpretation. Despite the existence of several approaches, there is always a requirement of development of more robust and efficient approach. In this work a new metaheuristic approach is proposed implementing binary shuffled frog leaping algorithm (BSFLA) for gene selection. To obtain an optimal gene subset, 20 different combination of gene subset is extracted from the original dataset. Out of which the optimal gene subset is identified implementing KNN classifier. Superiority of these gene set is shown using few other classifiers such as ANN and SVM. The model performance is also compared with few other metaheuristic approaches such as particle swarm optimization, differential evolution and genetic algorithm. (C) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Title: Semantic-geometric visual place recognition: a new perspective for reconciling opposing views Abstract: Human drivers are capable of recognizing places from a previous journey even when viewing them from the opposite direction during the return trip under radically different environmental conditions, without needing to look back or employ a 360 degrees camera or LIDAR sensor. Such navigation capabilities are attributed in large part to the robust semantic scene understanding capabilities of humans. However, for an autonomous robot or vehicle, achieving such human-like visual place recognition capability presents three major challenges: (1) dealing with a limited amount of commonly observable visual content when viewing the same place from the opposite direction; (2) dealing with significant lateral viewpoint changes caused by opposing directions of travel taking place on opposite sides of the road; and (3) dealing with a radically changed scene appearance due to environmental conditions such as time of day, season, and weather Current state-of-the-art place recognition systems have only addressed these three challenges in isolation or in pairs, typically relying on appearance-based, deep-learnt place representations. In this paper, we present a novel, semantics-based system that for the first time solves all three challenges simultaneously. We propose a hybrid image descriptor that semantically aggregates salient visual information, complemented by appearance-based description, and augment a conventional coarse-to-fine recognition pipeline with keypoint correspondences extracted from within the convolutional feature maps of a pre-trained network. Finally, we introduce descriptor normalization and local score enhancement strategies for improving the robustness of the system. Using both existing benchmark datasets and extensive new datasets that for the first time combine the three challenges of opposing viewpoints, lateral viewpoint shifts, and extreme appearance change, we show that our system can achieve practical place recognition performance where existing state-of-the-art methods fail.

Title: Computing the Lie algebra of the differential Galois group: The reducible case Abstract: In this paper, we explain how to compute the Lie algebra of the differential Galois group of a reducible linear differential system. We achieve this by showing how to transform a block-triangular linear differential system into a Kolchin-Kovacic reduced form. We combine this with other reduction results to propose a general algorithm for computing a reduced form of a general linear differential system. In particular, this provides directly the Lie algebra of the differential Galois group without an a priori computation of this Galois group.

Title: A new representation method for probability distributions of multimodal and irregular data based on uniform mixture model Abstract: Randomness is a major characteristic of observed data from practical engineering. Sometimes the probability distributions of observed data may exhibit bimodal, multimodal or even irregular features. Under these circumstances, the adequacy of typical unimodal distributions may be questioned. To solve this issue, a new representation method for probability distributions of multimodal and irregular data is presented in this study. Firstly, an uniform mixture model (UMM) is developed by a weighted combination of multiple uniform distribution components. Then, the UMM is applied to approximate probability distributions of multimodal and irregular data, and the weighting coefficients of UMM can be easily derived from the frequency histogram of observed data. Finally, the fatigue crack growth data of 2024-T351 aluminum alloy are used to verify the validity of the proposed method. The results indicate that the proposed method is accurate and flexible enough to characterize the probability distributions of various kinds of multimodal and irregular data. The average relative errors of the proposed method are very small, and the approximation accuracy can be improved by reducing the interval width.

Title: Fuzzy testing of operating performance index based on confidence intervals Abstract: The operating performance index (OPI) was developed by Chen and Yang (J Comput Appl Math 343:737–747, 2018) from the Six Sigma process quality index. The fact that OPIs include unknown parameters means that they must be formulated using estimates based on sample data. Unfortunately, cost and effectiveness considerations in practice have led to sample size limitation and measurement uncertainty. In this study, we sought to enhance testing accuracy and overcome the uncertainties in measurement by applying confidence intervals of OPI to derive a fuzzy number and membership function for OPI. We developed a one-tailed fuzzy test method to determine whether performance reaches the required level. We also developed a two-tailed fuzzy testing method based on two OPIs to serve as a verification model for the effectiveness of improvement measures. Both fuzzy testing methods are proposed based on confidence intervals of the indices to reduce the risk of misjudgment caused by sampling errors and enhance testing accuracy.

Title: Importance analysis of underframe connection system for the pantograph lower arm rod Abstract: This paper is aiming at the high severity and failure rates of the parts connected by the lower arm rod and the underframe of the electric multiple unit pantograph, and a variety of state problems presented in the process of performance degradation and repair. A method of importance analysis by using multi-valued decision diagrams and logical partial derivative is proposed. Firstly, the multi-valued decision diagrams are introduced into the importance analysis of the underframe connection system for the pantograph lower arm rod. Secondly, the mathematical model for every importance is established by utilizing the principle of logic differential theory, and component importance, as well as the corresponding state, is analyzed. The importance of Birnbaum, structure, fussell–vesely, reliability results and reliability decrease value are calculated for each component of underframe connection system for the pantograph lower arm rod. The influence of component state on system state is determined in the third part. This method serves as a reference for the reliability analysis, safety assessment and maintenance optimization of underframe connection system for the pantograph lower arm rod.

Title: An optimal algorithm for stopping on the element closest to the center of an interval Abstract: •Version of the secretary problem with the goal to stop on the central element.•Recursive construction of an optimal stopping rule.•Optimal stopping algorithm has very irregular stopping region.•Class of algorithms with rectangular stopping region and the same asymptotic behavior.•Asymptotic performance of the optimal stopping algorithm is of order 1n2π.

Title: Power allocation for multisource, multidestination cooperative vehicular networks under an outage probability constraint Abstract: In multisource, multidestination vehicular networks, vehicles equipped with wireless communication devices are capable of sharing data with each other as well as relaying data using roadside infrastructures. Therefore, low-power wireless communications play a critical role in improving the performance of transportation systems. To substantially reduce the power consumption of the transportation systems, a new power allocation strategy operating with an amplify-and-forward and hybrid decode-amplify-forward (AF-HDAF) protocol is proposed and analyzed. The cooperative manners of the employed relay nodes in the AF-HDAF strategy are manipulated, relying on the smartly designed forward strategies of the cooperative nodes. Furthermore, based on an outage probability constraint, the optimal power allocation that minimizes the sum power consumption is investigated. Numerical results confirm the validity of the proposed theoretical analysis, showing that the performance of the proposed power allocation scheme can be substantially improved by implementing the proposed AF-HDAF protocol.

Title: Infinite Horizon Stochastic Impulse Control with Delay and Random Coefficients Abstract: We study a class of infinite horizon impulse control problems with execution delay when the dynamics of the system is described by a general stochastic process adapted to the Brownian filtration. The problem is solved by means of probabilistic tools relying on the notion of Snell envelope and infinite horizon reflected backward stochastic differential equations. This allows us to establish the existence of an optimal strategy over all admissible strategies.

Title: Graded Persistence Diagrams and Persistence Landscapes Abstract: We introduce a refinement of the persistence diagram, the graded persistence diagram. It is the Möbius inversion of the graded rank function, which is obtained from the rank function using the unary numeral system. Both persistence diagrams and graded persistence diagrams are integer-valued functions on the Cartesian plane. Whereas the persistence diagram takes non-negative values, the graded persistence diagram takes values of 0, 1, or  $$-1$$ . The sum of the graded persistence diagrams is the persistence diagram. We show that the positive and negative points in the kth graded persistence diagram correspond to the local maxima and minima, respectively, of the kth persistence landscape. We prove a stability theorem for graded persistence diagrams: the 1-Wasserstein distance between kth graded persistence diagrams is bounded by twice the 1-Wasserstein distance between the corresponding persistence diagrams, and this bound is attained. In the other direction, the 1-Wasserstein distance is a lower bound for the sum of the 1-Wasserstein distances between the kth graded persistence diagrams. In fact, the 1-Wasserstein distance for graded persistence diagrams is more discriminative than the 1-Wasserstein distance for the corresponding persistence diagrams.

Title: A deep learning based intelligent framework to mitigate DDoS attack in fog environment Abstract: Fog computing (FC) is a contemporary computing paradigm that gives additional support to cloud environment by carrying out some local data analysis in edge of the devices, facilitating networking, computing, infrastructure and storage support as backbone for end user computing. Still enterprises are not convinced to use this as security and privacy are most of the open and challenging issues. Availability among the security requirements is the one which is about rendering on demand service to different client applications without any disruptions. It can often be demolished by Denial of service (DoS) and distributed denial of service (DDoS) attacks in fog and cloud computing environment. In this paper we propose a novel Source based DDoS defence mechanism which can be used in fog environment as well as the cloud environment to mitigate DDoS attacks. It makes use of Software Defined Network (SDN) to deploy the DDoS defender module at SDN controller to detect the anomalous behavior of DDoS attacks in Network/Transport level. The proposed work provides deep learning (DL) based detection method which makes use of the network traffic analysis mechanisms to filter and forward the legitimate packets to the server and can block the infected packets to cause further attacks. (C) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Title: An enhanced whale optimization algorithm for vehicular communication networks Abstract: In the field of vehicle communication networks, vehicular ad hoc network (VANET) is established with the help of road side unit (RSU). Each vehicle in the traffic system is managed by RSU with certain mobility factor, but still, there are some issues in the mobility management. In order to avoid such expensive RSUs and to enhance the mobility management of VANET system, the paper proposes a novel algorithm for the process of organizing a cluster structure and cluster head (CH) election suitable for VANETs. The proposed adaptive weighted clustering protocol (AWCP) groups the random nodes, and then the optimal CH is attained by the optimization of network parameters. For the purpose of optimization, an innovative algorithm called the enhanced whale optimization algorithm (EWOA) is introduced. For each vehicle in a trusted clustering model, its movement is analyzed by vehicle network mobility routing protocol with identified speed and position. The distance between the trusted vehicle node and RSU is analyzed by the proposed AWCP-EWOA model. The results demonstrate the proposed AWCP-EWOA model outperforms compared with AWCP and AWCP-Whale Algorithm (WA) protocols in terms of clustering efficiency and its mobility enhancement.

Title: Cognitive Internet of Vehicles and disaster management: A proposed architecture and future direction Abstract: Historically, the vehicle has just been a component of the human ambulatory system and slave to the commands of driving force. However, recent advancement in technologies such as 5G wireless systems, cloud/edge computing, machine learning, artificial intelligence, and deep learning have opened the new paradigm of the Cognitive Internet of Vehicles (CIoV). The network of heterogeneous intelligent vehicles, not only having the social interaction capabilities but also have the ability to visualize, capture, and disseminate information to dynamic cognitive engines for identifying and analyzing different patterns and prediction of optimized outcomes. From secure navigation to traffic control, transportation security for pollution control has advocated plausible applications in real-time operations of Internet of Vehicles in humanitarian operations. Proceeding this research, this paper aimed to propose a state-of-the-art architecture based on CIoV for identifying emerging capabilities of available technologies to observe, detect, and mitigate natural disasters, and to develop the CIoV ecosystem for processing the global scale data for intelligent decision making and better service provisioning in natural disaster management.

Title: A novel dynamic framework to detect DDoS in SDN using metaheuristic clustering Abstract: Security is a crucial factor in the continuously evolving programmable networks. With the emergence of programmable networking terminals, the need to protect the networks has become mandatory. Software-defined networks (SDNs) provide programmable switches, thereby isolating the data plane from the control plane. Many security algorithms have been proposed to protect the network; however, they have failed to protect SDNs from attacks such as distributed denial of service (DDoS), jamming, and man-in-the-middle attacks. In this article, we only address the DDoS attack that prevails in SDNs. Isolation of the control plane from the data plane increases the probability of an attack on the data plane. Therefore, a framework that can handle the dynamic traffic and can protect the network from DDoS attacks is required. Our proposed whale optimization algorithm-based clustering for DDoS detection (WOA-DD) avoids the DDoS attacks using a metaheuristic approach by clustering the attack requests. We evaluated this algorithm for robustness in comparison with several existing solutions and found it to be safe under several conditions. The proposed attack request clustering is explored to check its feasibility with various machine learning approaches and found to be stable with the prevailing mechanisms. Analysis of the algorithm under varied conditions reveals that WOA-DD is robust, stable, and efficient against DDoS attacks.

Title: An elliptic curve cryptography based mutual authentication scheme for smart grid communications using biometric approach Abstract: Smart grid (SG) provides a suitable adjustment in the amount of power generation by providing the ability to supervise consumer behavior. SG uses in the smart system to encourage cultural heritage because it is accountable for providing power without any interruption. SG is one of the vital components to authorize smart systems with a lot of smart features to attract visitors to come and visit heritage. In SG, environment security and privacy are the major concern for communications. An authentication protocol provides secure communication between users and service provider for security and privacy purpose. Several authentication protocols are available in the literature. However, they are enabled to known security attacks easily or they are not computationally efficient for SG communication. In the present paper, we design an ECC-based mutual authentication protocol for smart grid communication using biometric approach. The present framework satisfy various security features such as replay attack, user anonymity, man in the middle attack, key freshness, message authentication, session key agreement, impersonation attack, non-traceability and non-transferability. Further, the proposed protocol takes much less communication and computation costs compared with other existing protocols in SG environment. Therefore, our scheme is convenient for practical application in SG communication. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: Fault-tolerant data transport backbone for 3D wireless sensor networks Abstract: Existing approaches for clustering wireless sensor networks by using relay nodes (RNs) suffer from two major problems: impracticability for realistic scenarios and lack of fault tolerance. The first problem is due to the assumption of a two-dimensional Region of Interest (Rol). The second problem stems from the fact that these approaches do not address the construction of disjoint routing paths, where multiple routing paths have common intermediate RNs, which create a bottleneck for the data transport and points of failure. In this paper, we tackle these two problems by proposing a new RNs deployment approach based on more realistic assumptions. Indeed, a more realistic communication model is considered, which takes into account the impact of the Rol topography on wireless communications. Additionally, a practical position constraint is adopted, where the RNs are placed only on the Rol crest points, to minimize the topography impact on the wireless communications. By relying on an efficient algorithm that constructs from a connected graph many Steiner trees, covering the same subset of vertices, the proposed RNs deployment approach selects the appropriate positions of the RNs and ensures a reliable data transport from the sensor nodes to the collector node, according to a two-tier topology while satisfying other secondary objectives related to the cost and the quality of the communication links. The simulation results demonstrate the feasibility of the proposed approach and its ability to minimize cost and improve communication links quality.

Title: Resource allocation in PD-NOMA-based mobile edge computing system: Multiuser and multitask priority Abstract: Mobile edge computing (MEC) is a new network architecture concept that enables cloud computing capabilities at the edge of the cellular network, and hence, MEC has the ability to process the computation tasks in close proximity to the network edge. In this paper, we consider a multiuser MEC system with multiple computation tasks such that each computation task is independent with different priorities. For this purpose, we devise a novel multiuser, multitask, and nonpreemptive priority M/G/1 queuing model in the MEC server environment. In doing so, we propose a priority-based task scheduling policy that aims to maximize the profit of mobile network operator by jointly optimizing service rate, transmit power, and subcarrier allocation with satisfying power and delay constraints. This is necessary to handle the physical channel opportunities to obtain higher spectral efficiency in the proposed task offloading scheme. In this line, we also consider power-domain nonorthogonal multiple access (PD-NOMA) technique. Numerical results show that, by using the Priority queuing in PD-NOMA-SCA and Priority queuing in PD-NOMA-matching schemes, the performance can be improved considerably compared to No-priority queuing in OMA-SCA and No-priority queuing in OMA-matching, eg, the Priority queuing in PD-NOMA-SCA scheme improves nearly 54% compared to the No-priority queuing in OMA-SCA scheme. It is noteworthy that the performance gap between exhaustive search and the Priority queuing in PD-NOMA-SCA scheme is nearly 7%.

Title: FAST APPROXIMATION OF THE p-RADIUS, MATRIX PRESSURE, OR GENERALIZED LYAPUNOV EXPONENT FOR POSITIVE AND DOMINATED MATRICES Abstract: If A(1), ... , A(N) are real d x d matrices, then the p-radius, generalized Lyapunov exponent, or matrix pressure is defined to be the asymptotic exponential growth rate of the sum Sigma(N)(i1),...,i(n) parallel to A(in) ... A(i1)parallel to(p) where p is a real parameter. Under its various names this quantity has been investigated for its applications to topics including wavelet regularity and refinement equations, fractal geometry, and the large deviations theory of random matrix products. In this article we present a new algorithm for computing the p-radius under the hypothesis that the matrices are all positive (or more generally under the hypothesis that they satisfy a weaker condition called domination) and of very low dimension. This algorithm is based on interpreting the p-radius as the leading eigenvalue of a trace-class operator on a Hilbert space and estimating that eigenvalue via approximations to the Fredholm determinant of the operator. In this respect our method is closely related to the work of Z.-Q. Bai and M. Pollicott on computing the top Lyapunov exponent of a random matrix product. For pairs of positive matrices of dimension two our method yields substantial improvements over existing methods.

Title: Principal component analysis for process monitoring in distributed system environment Abstract: In modern industrial production processes, process monitoring plays a major role in improving process efficiency and quality of product, and it is an important task in industrial process. The best way to implement process monitoring is to develop models that describe different phenomena in physics, chemistry, and processes. However, because the modeling of industrial processes is often very complex and has significant intrinsic nonlinearities, a reasonable theoretical modeling method are often impractical. With the adoption of distributed control systems, the application of multivariate statistical monitoring is becoming more and more popular in the distributed system environment. This paper uses the technology for multivariate monitoring of continuous production processes. Based on principal component analysis (PCA) and making full use of system statistics information, the method can well deal with the nonlinear and multimode distribution of industrial data, which is difficult to be dealt with by traditional methods. This method can be used not only in process detection but also in fault detection. Finally, the method is applied to the actual example and the Tennessee-Eastman model; the simulation results prove the feasibility of the proposed method and achieve better results.

Title: Inner-Imaging Networks: Put Lenses Into Convolutional Structure Abstract: Despite the tremendous success in computer vision, deep convolutional networks suffer from serious computation costs and redundancies. Although previous works address that by enhancing the diversities of filters, they have not considered the complementarity and the completeness of the internal convolutional structure. To respond to this problem, we propose a novel inner-imaging (InI) architecture, which allows relationships between channels to meet the above requirement. Specifically, we organize the channel signal points in groups using convolutional kernels to model both the intragroup and intergroup relationships simultaneously. A convolutional filter is a powerful tool for modeling spatial relations and organizing grouped signals, so the proposed methods map the channel signals onto a pseudoimage, like putting a lens into the internal convolution structure. Consequently, not only is the diversity of channels increased but also the complementarity and completeness can be explicitly enhanced. The proposed architecture is lightweight and easy to be implement. It provides an efficient self-organization strategy for convolutional networks to improve their performance. Extensive experiments are conducted on multiple benchmark datasets, including CIFAR, SVHN, and ImageNet. Experimental results verify the effectiveness of the InI mechanism with the most popular convolutional networks as the backbones.

Title: Group Chat Ecology in Enterprise Instant Messaging: How Employees Collaborate Through Multi-User Chat Channels on Slack Abstract: AbstractDespite the long history of studying instant messaging usage, we know very little about how today's people participate in group chat channels and interact with others inside a real-world organization. In this short paper, we aim to update the existing knowledge on how group chat is used in the context of today's organizations. The knowledge is particularly important for the new norm of remote works under the COVID-19 pandemic. We have the privilege of collecting two valuable datasets: a total of 4,300 group chat channels in Slack from an R&D department in a multinational IT company; and a total of 117 groups' performance data. Through qualitative coding of 100 randomly sampled group channels from the 4,300 channels dataset, we identified and reported 9 categories such as Project channels, IT-Support channels, and Event channels. We further defined a feature metric with 21 meta-features (and their derived features) without looking at the message content to depict the group communication style for these group chat channels, with which we successfully trained a machine learning model that can automatically classify a given group channel into one of the 9 categories. In addition to the descriptive data analysis, we illustrated how these communication metrics can be used to analyze team performance. We cross-referenced 117 project teams and their team-based Slack channels and identified 57 teams that appeared in both datasets, then we built a regression model to reveal the relationship between these group communication styles and the project team performance. This work contributes an updated empirical understanding of human-human communication practices within the enterprise setting, and suggests design opportunities for the future of human-AI communication experience.

Title: hp-VERSION DISCONTINUOUS GALERKIN METHODS ON ESSENTIALLY ARBITRARILY-SHAPED ELEMENTS Abstract: We extend the applicability of the popular interior penalty discontinuous Galerkin method discretizing advection-diffusion-reaction problems to meshes comprising extremely general, essentially arbitrarily-shaped element shapes. In particular, our analysis allows for curved element shapes, without the use of non-linear elemental maps. The feasibility of the method relies on the definition of a suitable choice of the discontinuity penalization, which turns out to be explicitly dependent on the particular element shape, but essentially independent on small shape variations. This is achieved upon proving extensions of classical trace and Markov-type inverse estimates to arbitrary element shapes. A further new H-1 - L-2-type inverse estimate on essentially arbitrary element shapes enables the proof of inf-sup stability of the method in a streamline-diffusion-like norm. These inverse estimates may be of independent interest. A priori error bounds for the resulting method are given under very mild structural assumptions restricting the magnitude of the local curvature of element boundaries. Numerical experiments are also presented, indicating the practicality of the proposed approach.

Title: Bayesian active learning with abstention feedbacks Abstract: We study pool-based active learning with abstention feedbacks where a labeler can abstain from labeling a queried example with some unknown abstention rate. This is an important problem with many useful applications. We take a Bayesian approach to the problem and develop two new greedy algorithms that learn both the classification problem and the unknown abstention rate at the same time. These are achieved by simply incorporating the estimated average abstention rate into the greedy criteria. We prove that both algorithms have near-optimality guarantees: they respectively achieve a (1-1e) constant factor approximation of the optimal expected or worst-case value of a useful utility function. Our experiments show the algorithms perform well in various practical scenarios.

Title: Nonatomic aggregative games with infinitely many types Abstract: •Nonatomic aggregative games with an infinity of player types and coupling constraints.•Adapted notion of equilibrium, referred to as variational Wardrop equilibrium.•Equilibrium arbitrary close to the one of a game with a finite number of player types.•Practical estimation method relying on finite-dimensional variational inequalities.•Smart grid application: interaction of energy consumers described as a nonatomic game.

Title: Surface fluctuating hydrodynamics methods for the drift-diffusion dynamics of particles and microstructures within curved fluid interfaces Abstract: •Formulation of surface fluctuating hydrodynamics for curved fluid interfaces.•Methods for handling fluid-structure interactions of particles and microstructures.•Development of Stochastic Immersed Boundary (SIB) methods for curved surfaces.•Analysis of velocity correlations of interfacial surface fluctuating hydrodynamics.•Investigations of drift-diffusion phenomena within curved fluid interfaces.

Title: A New Face Iterator for Polyhedra and for More General Finite Locally Branched Lattices Abstract: We discuss a new memory-efficient depth-first algorithm and its implementation that iterates over all elements of a finite locally branched lattice. This algorithm can be applied to face lattices of polyhedra and to various generalizations such as finite polyhedral complexes and subdivisions of manifolds, extended tight spans and closed sets of matroids. Its practical implementation is very fast compared to state-of-the-art implementations of previously considered algorithms. Based on recent work of Bruns, García-Sánchez, O’Neill, and Wilburne, we apply this algorithm to prove Wilf’s conjecture for all numerical semigroups of multiplicity 19 by iterating through the faces of the Kunz cone and identifying the possible bad faces and then checking that these do not yield counterexamples to Wilf’s conjecture.

Title: Monochromatic connected matchings in 2-edge-colored multipartite graphs Abstract: A matching M $M$ in a graph G $G$ is connected if all the edges of M $M$ are in the same component of G $G$ . Following Luczak, there have been many results using the existence of large connected matchings in cluster graphs with respect to regular partitions of large graphs to show the existence of long paths and other structures in these graphs. We prove exact Ramsey-type bounds on the sizes of monochromatic connected matchings in 2-edge-colored multipartite graphs. In addition, we prove a stability theorem for such matchings.

Title: A new big data approach for topic classification and sentiment analysis of Twitter data Abstract: Twitter is a major micro-blogging service, with millions of active users. These users use Twitter to post status messages called tweets and share their opinions using hash tags on various events. Hence, Twitter is considered a major real time streaming source and one of an effective and accurate indicator of opinions. The amount of data generated by Twitter is huge and it is difficult to scan entire data manually. This paper proposes a Hybrid Lexicon-Naive Bayesian Classifier (HL-NBC) method for sentimental analysis. In addition to that, Sentiment analysis engine is preceded by topic classification, which classifies tweets into different categories and filters irrelevant tweets. The proposed method is compared with Lexicon, Naïve Bayesian classifier for uni-gram and bi-gram features. Out of the different approaches, the proposed HL-NBC method does sentiment classification in an improved way and gives accuracy of 82%, which is comparatively better than other methods. Also, the sentiment analysis is performed in a shorter time compared to traditional methods and achieves 93% improvement in processing time for larger datasets.

Title: Reliability analysis of multi-state systems with common cause failures based on Bayesian network and fuzzy probability Abstract: Multi-state components, common cause failures (CCFs) and data uncertainty are the general problems for reliability analysis of complex engineering systems. In this paper, a method incorporating fuzzy probability and Bayesian network (BN) into multi-state systems (MSSs) with CCFs is proposed. In particular, basic theories of multi-state BN and fuzzy probability are developed. Moreover, a model integrating CCFs with BN has also been illustrated. In order to incorporate fuzzy probability into MSSs reliability evaluation considering common parent node generated by CCFs, fuzzy probability has to be translated into accurate probability through defuzzification and normalization methods which are both elaborated. In addition, quantitative analysis based on BN is carried out. In this paper, feed system of boring spindle in computer numerical control machine is analyzed as an example to validate the feasibility of the proposed method. It can improve the ability of BN on reliability evaluation of complex system with uncertainty issues.

Title: On rates of convergence for sample average approximations in the almost sure sense and in mean Abstract: We study the rates at which optimal estimators in the sample average approximation approach converge to their deterministic counterparts in the almost sure sense and in mean. To be able to quantify these rates, we consider the law of the iterated logarithm in a Banach space setting and first establish under relatively mild assumptions almost sure convergence rates for the approximating objective functions, which can then be transferred to the estimators for optimal values and solutions of the approximated problem. By exploiting a characterisation of the law of the iterated logarithm in Banach spaces, we are further able to derive under the same assumptions that the estimators also converge in mean, at a rate which essentially coincides with the one in the almost sure sense. This, in turn, allows to quantify the asymptotic bias of optimal estimators as well as to draw conclusive insights on their mean squared error and on the estimators for the optimality gap. Finally, we address the notion of convergence in probability to derive rates in probability for the deviation of optimal estimators and (weak) rates of error probabilities without imposing strong conditions on exponential moments. We discuss the possibility to construct confidence sets for the optimal values and solutions from our obtained results and provide a numerical illustration of the most relevant findings.

Title: Impact of sisal fiber reinforced concrete and its performance analysis: a review Abstract: Sisal fiber cement is comprehensively deployed in the construction works owing to their flexibility as cladding panels and ridged equipment, and water containers that are accessible in a massive number of cultivation and construction applications. The most important reason for integrating the sisal fibers into the cement matrix is to increase the toughness; tensile strength and the bend features of the resulting composite. In recent times, the sisal fibers have been employed as reinforcement in concretes. These cementitious composites are presently deliberated to be one of the most accomplished structural equipment in the modern industrial technology. Accordingly, in the presented survey, several papers are taken for analyzing the performance of sisal fibers. In addition, the papers taken for review are studied depending on the composition of concrete and sisal constituents in the work of art of building. Moreover, the sisal fiber with a composition other than concrete is also illustrated. The contributions regarding the tensile strength and compression strength in the adopted papers are analyzed together with their percentage of composition. The evolutions of the adopted papers along with their various applications are moreover analyzed in detail.

Title: A Signer Independent Sign Language Recognition with Co-articulation Elimination from Live Videos: An Indian Scenario Abstract: Due to the high population of hearing impaired and vocal disabled people in India, a sign language interpretation system is becoming highly important for minimizing their isolation in society. This paper proposes a signer independent novel vision-based gesture recognition system which is capable of recognizing single handed static and dynamic gestures, double-handed static gestures and finger spelling words of Indian Sign Language (ISL) from live video. The use of Zernike moments for key frame extraction reduces the computation speed to a large extent. It also proposes an improved method for co-articulation elimination in fingerspelling alphabets. The gesture recognition module comprises mainly three steps Preprocessing, Feature Extraction, and Classification. In the preprocessing phase, the signs are extracted from a real-time video using skin color segmentation. An appropriate feature vector is extracted from the gesture sequence after co-articulation elimination phase. The obtained features are then used for classification using Support Vector Machine(SVM). The system successfully recognized finger spelling alphabets with 91% accuracy and single-handed dynamic words with 89% accuracy. The experimental results show that the system has a better recognition rate compared to some of the existing methods. (C) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Title: Semiconductor chip’s quality analysis based on its high dimensional test data Abstract: A semiconductor chip usually has thousands test parameters in order to guaranteed its quality. Hence, a batch of chips’ test data set include thousands of float data. The primary goal of dealing with this test data is to obtain the fault parameter distribution and judge the chip’s quality. It is a challenge due to the large scale and complex relationship of the test data set. This paper presents a novel method to analyze the test data set by meshing the quality theory and scientific data visualization. First, transfer the test data set to a quality classifier matrix Q: a series of quality region is defined based on quality theory, which is the baseline to classify the test data set into different group and mark them with various number. Second, form a quality-spectrum: define a color rule based on the RGB color model and color the quality classifier matrix Q. Hence chip’s quality distribution could be observed through the quality-spectrum. Furthermore, by analyzing the quality-spectrum, the chip’s quality could be quantitative and fault diagnose has a data basic. One case is included to illustrate appropriateness of the proposed method.

Title: A privacy-preserving framework for smart context-aware healthcare applications Abstract: Smart connected devices are widely used in healthcare to achieve improved well-being, quality of life, and security of citizens. While improving quality of healthcare, such devices generate data containing sensitive patient information where unauthorized access constitutes breach of privacy leading to catastrophic outcomes for an individual as well as financial loss to the governing body via regulations such as the General Data Protection Regulation. Furthermore, while mobility afforded by smart devices enables ease of monitoring, portability, and pervasive processing, it introduces challenges with respect to scalability, reliability, and context awareness. This paper is focused on privacy preservation within smart context-aware healthcare emphasizing privacy assurance challenges within Electronic Transfer of Prescription. We present a case for a comprehensive, coherent, and dynamic privacy-preserving system for smart healthcare to protect sensitive user data. Based on a thorough analysis of existing privacy preservation models, we propose an enhancement to the widely used Salford model to achieve privacy preservation against masquerading and impersonation threats. The proposed model therefore improves privacy assurance for smart healthcare while addressing unique challenges with respect to context-aware mobility of such applications.

Title: Transition coverage based test case generation from state chart diagram Abstract: State-based testing is a challenging area in the software testing field. This paper proposes a set of algorithms to generate test cases from a state chart diagram based on various coverage criteria. The objective is to find various types of state-based faults by covering states and transitions of an object. First, the state chart diagram is transformed into an intermediate graph, State Chart Intermediate Graph (SCIG). Then, for a given coverage criteria, traversing method is applied on SCIG to generate test cases. Different trees from SCIG based on various coverage criteria are extracted for test case generation. Various coverage criteria such as All Transition (AT), Round Trip Path (RTP) and All Transition Pair (ATP) are considered. We introduce algorithms for two most efficient state-based criteria, RTP and ATP. Two case studies, Stack Operation and Vending Machine Automation system, are discussed throughout the paper. We experimentally observed that (i) AT consumes the most test resources (ii) ATP can't achieve 100% transition coverage (iii) test cases generated based on RTP is efficient, and it overcomes the transition explosion problem of AT. This analysis is beneficial in the area of semi-automatic test case generation in model-based testing.(c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: Variational Analysis of Composite Models with Applications to Continuous Optimization Abstract: The paper is devoted to a comprehensive study of composite models in variational analysis and optimization the importance of which for numerous theoretical, algorithmic, and applied issues of operations research is difficult to overstate. The underlying theme of our study is a systematical replacement of conventional metric regularity and related requirements by much weaker metric subregulatity ones that lead us to significantly stronger and completely new results of first-order and second-order variational analysis and optimization. In this way, we develop extended calculus rules for first-order and second-order generalized differential constructions while paying the main attention in second-order variational theory to the new and rather large class of fully subamenable compositions. Applications to optimization include deriving enhanced no-gap second-order optimality conditions in constrained composite models, complete characterizations of the uniqueness of Lagrange multipliers, strong metric subregularity of Karush-Kuhn-Tucker systems in parametric optimization, and so on.

Title: Machine learning for wearable IoT-based applications: A survey Abstract: This paper gives an overview about applying machine learning (ML) in wearable Wireless Body Area Network (WBAN). It highlights the main challenges and open issues for deploying ML models in such sensitive networks. The WBAN is an emerging technology in the last few years, which attracts lots of interest from the academic and industrial communities. It enables a wide range of IoT-based applications in medical, lifestyle, sport, and entertainment. WBAN are constrained in many aspects such as those related to power resources, communication capabilities, and computation power. Moreover, these networks generate ample amount of sensory data. To overcome the limitations of these networks and make use of the big data generated, artificial intelligence is the best way to deal with gigantic data and help in automating several aspects of the network and its applications. This survey paper aims at reporting numerous ways ML is used to benefit these networks, the design factors that are considered when implementing the ML algorithms, and the communication technologies used in connecting wearable WBAN in the IoT era. The reported studies are based on real overviewed experiment and extensive simulation results.

Title: Target coverage in random wireless sensor networks using cover sets Abstract: There are numerous coverage algorithms which efficiently monitor targets in sensor networks by dividing the sensor network into cover sets where each cover monitors all the targets. Creating maximum number of cover sets is a NP Complete problem and therefore problems providing subprime solutions have been proposed. In this paper we propose a novel and energy efficient target coverage algorithm that produces disjoint as well as non-disjoint cover sets. Our proposed solution along with generating cover sets for monitoring targets also gives the energy optimized minimum path from sink to the sensor node and from cover set to the sink. Our algorithm keeps a track of the number of targets a sensor is monitoring along with the energy remaining to find a path which is energy optimized to increase the lifetime of the sensor network. Through simulations, we have shown that our proposed algorithm outperforms similar algorithms found in literature. The increased network lifetime provided by energy optimized path and energy efficient cover sets makes our algorithm desirable for a wider range of applications. (C) 2019 The Author. Production and hosting by Elsevier B.V. on behalf of King Saud University.

Title: An Algorithm for the Complete Solution of the Quartic Eigenvalue Problem Abstract: AbstractThe quartic eigenvalue problem (λ4A+λ3B+λ2C+λD+E)x = 0 naturally arises in a plethora of applications, such as when solving the Orr–Sommerfeld equation in the stability analysis of the Poiseuille flow, in theoretical analysis and experimental design of locally resonant phononic plates, modeling a robot with electric motors in the joints, calibration of catadioptric vision system, or, for example, computation of the guided and leaky modes of a planar waveguide. This article proposes a new numerical method for the full solution (all eigenvalues and all left and right eigenvectors) that, starting with a suitable linearization, uses an initial, structure-preserving reduction designed to reveal and deflate a certain number of zero and infinite eigenvalues before the final linearization is forwarded to the QZ algorithm. The backward error in the reduction phase is bounded column wise in each coefficient matrix, which is advantageous if the coefficient matrices are graded. Numerical examples show that the proposed algorithm is capable of computing the eigenpairs with small residuals, and that it is competitive with the available state-of-the-art methods.

Title: Cutoff on graphs and the Sarnak-Xue density of eigenvalues Abstract: It was recently shown in Lubetzky and Peres (2016) and Sardari (2019) that Ramanujan graphs, i.e., graphs with the optimal spectrum, exhibit cutoff of the simple random walk in an optimal time and have an optimal almost-diameter. We show that this spectral condition can be replaced by a weaker condition, the Sarnak-Xue density property, to deduce similar results. This allows us to prove that some natural families of Schreier graphs of the SL2 (F-t)-action on the projective line exhibit cutoff, thus proving a special case of a conjecture of Rivin and Sardari (2019).(C) 2022 Elsevier Ltd. All rights reserved.

Title: Enhanced time-slotted channel hopping Abstract: With the development of technology for the Internet of Things (IoT), a large number of mobile, intelligent sensors are being connected to the Internet. The IoT is a heterogeneous environment. In industrial environments, wireless technologies coexist on the same frequency and generate different kinds of traffic. The use of different wireless technologies that are not designed to be compatible with each other causes an external interference problem, which degrades the system performance. The medium access control (MAC) sublayer protocols must be carefully designed to overcome such a problem. The time-slotted channel-hopping (TSCH) mode, which is dedicated for industrial applications, can mitigate such a problem because it uses the channel-hopping technique. Because these channels are subjected to different levels of interference, this may greatly degrade the system performance when wireless sensors with mobility devices hop blindly from one channel to another. This paper improves the reliability of communications of the TSCH MAC sublayer protocol, proposed by the IEEE 802.15.4e standard, in wireless sensor networks, by applying a new, dynamic blacklisting technique. The technique selects and uses only high-quality channels and blacklists those with low quality. The proposed solution has been simulated using Network Simulator 3. The results show significant improvements in terms of throughput, energy, and reliability.

Title: Coupled recursive estimation for online interactive perception of articulated objects Abstract: We present online multi-modal perception systems for extracting kinematic and dynamic models of articulated objects from physical interactions with the environment. The systems rely on a RGB-D stream, contact wrenches, and proprioception. The proposed systems share an algorithmic foundation: they are based on an architecture of coupled recursive estimation processes. We present and advocate this architecture as a general, versatile, and robust solution for online interactive perception problems. We validate the architecture in extensive experiments to extract kinematic models interactively, varying the appearance, size, structure, and dynamic properties of objects for different tasks and under different environmental conditions. In addition, we experimentally show that the information acquired by the online perception systems enables robot manipulation of articulated objects. Furthermore, we discuss the relationship between the proposed architecture for robot perception and insights about biological perception systems.

Title: Phase transition in random intersection graphs with communities Abstract: The "random intersection graph with communities" (RIGC) models networks with communities, assuming an underlying bipartite structure of groups and individuals. Each group has its own internal structure described by a (small) graph, while groups may overlap. The group memberships are generated by a bipartite configuration model. The model generalizes the classical random intersection graph model, a special case where each community is a complete graph. The RIGC model is analytically tractable. We prove a phase transition in the size of the largest connected component in terms of the model parameters. We prove that percolation on RIGC produces a graph within the RIGC family, also undergoing a phase transition with respect to size of the largest component. Our proofs rely on the connection to the bipartite configuration model. Our related results on the bipartite configuration model are of independent interest, since they shed light on interesting differences from the unipartite case.

Title: Multipath routing identification for network measurement built on end-to-end packet order Abstract: Multipath routing, which provides multiple paths for ubiquitous communications, has been considered as promising routing mechanism to optimize network performance for Internet. However, it will incur adverse effects on the existing and emerging network measurement schemes, for example incomplete and inaccurate measurement results, to understand network characteristics, since many of them commonly do the work under single-path routing rather than multipath-routing. In order to eliminate this emerging issue on single-path-based network measurement in Internet, it requires to identify whether there is multipath routing between two reachable hosts in the network. Notice that no out-of-order delivery among a strip of packets along multiple paths seldom occurs, in this paper, an efficient multipath routing identification approach has been proposed to achieve this goal, by introducing a composite probe built on out-of-order delivery. We have elaborated our theoretical observation on the current probe composed of a strip of packets, and then presented our composite probe design in detail. Our proposed approach not only can efficiently identify the existing multipath routing, but also accurately recognize its type, referring to flow-based or packet-based routing. Corroborated by experiments and simulations, conducted on Planetlab and NS2, respectively, our approach outperforms other schemes in terms of effectiveness and accuracy.

Title: Homotopy Theory in Digital Topology Abstract: Digital topology is part of the ongoing endeavor to understand and analyze digitized images. With a view to supporting this endeavor, many notions from algebraic topology have been introduced into the setting of digital topology. But some of the most basic notions from homotopy theory remain largely absent from the digital topology literature. We embark on a development of homotopy theory in digital topology, and define such fundamental notions as function spaces, path spaces, and cofibrations in this setting. We establish digital analogues of basic homotopy-theoretic properties such as the homotopy extension property for cofibrations, and the homotopy lifting property for certain evaluation maps that correspond to path fibrations in the topological setting. We indicate that some depth may be achieved by using these homotopy-theoretic notions to give a preliminary treatment of Lusternik-Schnirelmann category in the digital topology setting. This topic provides a connection between digital topology and critical points of functions on manifolds, as well as other topics from topological dynamics.

Title: Optimizing an AltaRica simulator Abstract: Since the AltaRica language has, in fact, become the European industry standard, the AltaRica-based simulation method is one of the important research contents in the field of safety analysis methods. At the same time, in practical applications, simulations are mostly executed tens of thousands of times. Therefore, it is practical to optimize the execution time of the simulation method. We first analyze the hotspots in the execution of simulation methods and then propose two optimization strategies for two hot issues. The optimization method is designed and implemented by the method of space for time. Finally, the effectiveness and application scenarios of the optimization method are illustrated by experiments with different scale models. Moreover, the experiment data shows that the optimization method is effective in most cases.

Title: Analysis and design of an efficient handoff management strategy via velocity estimation in HetNets Abstract: In this paper, we present an efficient handoff decision strategy and a mobility state detection (MSD) scheme for heterogeneous networks (HetNets). Due to base station (BS) densification, HetNets occasionally face unnecessary handovers and service failures. Since velocity plays a crucial role in handover process, its knowledge is imperative for effective mobility management. Furthermore, due to limited power in mobile devices, emphasis is in investigation of velocity estimators that are at service provider end. Thus, we propose two maximum likelihood-based velocity estimators exploiting handover count and sojourn time measurements, respectively. Our analysis illustrates that both the estimators are asymptotically unbiased and efficient. In addition, we also notice that variance of sojourn time-based estimator is smaller than that of handover count-based estimator. Considering superiority of sojourn time-based estimator, we propose a handover decision strategy to minimize unnecessary handovers. Here, we first predict the sojourn time of upcoming BSs on the basis of velocity estimated via sojourn time samples. Next, BSs whose predicted sojourn time are greater than a prespecified threshold are then considered to identify the next serving BS. Finally, we select next serving BS on the basis of minimum cost for satisfying quality-of-service parameters such as bandwidth, security, monetary cost, and available power at user device. This work also exploits estimated velocity to investigate MSD specified in LTE standards. The numerical and simulation results validate our approach by illustrating accuracy in velocity estimation, reduction in frequent handovers and service failures, and improvement in MSD.

Title: Application of OPTICS and ensemble learning for Database Intrusion Detection Abstract: In this paper, we have proposed a novel approach for detecting intrusive activities in databases by the use of clustering and information fusion through ensemble learning. We have applied OPTICS cluster-ing on the transaction attributes for building user behavioral profiles. A transaction is initially passed through the clustering module for computing its cluster belongingness and an outlier factor that sig-nifies its degree of outlierness. Depending on the outlier factor value, the transaction is classified as genuine or an outlier. Each outlier transaction is further analyzed by passing it onto an Ensemble Learner that applies three different aggregation methods, bagging, boosting and stacking. We have conducted experiments using stochastic models to demonstrate the effectiveness of the proposed sys-tem. The performance of the three different ensembles are evaluated and compared based on various metrics. Moreover, our system is found to exhibit better performance as compared to other approaches taken from the literature.(c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Title: Tiling Enumeration of Hexagons with Off-Central Holes Abstract: This paper is the sequel of the author's previous paper about tiling enumerations of the cored versions of a doubly-intruded hexagon (Electron. J. Combin. 2020), in which we generalized Ciucu's work about F-cored hexagons (Adv. Math. 2017). This paper provides an extensive list of thirty tiling enumerations of hexagons with three collinear chains of triangular holes. Besides two chains of holes attaching to the boundary of the hexagon, we remove one more chain of triangles that is slightly off the center of the hexagon. Two of our enumerations imply two conjectures posed by Ciucu, Eisenko spacing diaeresis lbl, Krattenthaler, and Zare (J. Combin. Theory Ser. A 2001). Mathematics Subject Classifications: 05A15, 05B45, 05C30

Title: A simple and efficient numerical method for pricing discretely monitored early-exercise options Abstract: We present a simple, fast, and accurate method for pricing a variety of discretely monitored options in the Black-Scholes framework, including autocallable structured products, single and double barrier options, and Bermudan options. The method is based on a quadrature technique, and it employs only elementary calculations and a fixed one-dimensional uniform grid. The convergence rate is O (1 /N-4) and the complexity is O(MN log N ) , where N is the number of grid points and M is the number of observation dates. Besides Black-Scholes, our method is also applicable to more general frameworks such as Merton's jump diffusion model. (C)& nbsp;2022 Elsevier Inc. All rights reserved.

Title: Context-aware cognitive disaster management using fog-based Internet of Things Abstract: The natural and man-made disasters are inevitable in many circumstances. Thus, an effective disaster management system (DMS) is vital for any community that can use state-of-the-art technologies to deal with such cataclysmic events. There is plethora of latest technologies such as Internet of Things (IoT) and cloud and fog Computing that have provided the required infrastructure and connectivity to collect and analyze data from users and physical environment for cognitive decision-making. This article analyzes different stages of DMSs, and existing IoT solutions are also discussed that are focused on prevention, preparation, response, and recovery from disasters. It also proposes a context-aware fog-based IoT architecture to realize a cognitive DMS that can learn from the collected and synthesized data to reduce the impact of catastrophic events by taking immediate actions. Furthermore, the significance of the architecture is validated in different scenarios and inherent open research challenges are emphasized.

Title: A novel joint histogram equalization based image contrast enhancement Abstract: The limitation to the most commonly used histogram equalization (HE) technique is the inconsideration of the neighborhood info near each pixel for contrast enhancement. This gives rise to noise in the output image. To overcome this effect, a novel joint histogram equalization (JHE) based technique is suggested. The focus is to utilize the information among each pixel and its neighbors, which improves the contrast of an image. The suggested method is developed in a truly two-dimensional domain. The joint histogram is constructed using the original image and its average image. Further, it does not require a target uniform distribution for generating the output. The two-dimensional cumulative distribution function (CDF) is utilized as a mapping function to get the output pixel intensity. Extensive experiments are performed using 300 test images from BSD database. The experimental analysis indicates that the procedure produces better results than the state-of-the-art HE based contrast enhancement algorithms. More significantly, it produces the best results even for images having a narrow dynamic range. The implementation simplicity of the proposed algorithm may attract researchers to explore the idea for new applications in image processing.

Title: DBFS: Dragonfly Bayes Fusion System to detect the tampered JPEG image for forensic analysis Abstract: Due to the advancement of a variety of photo editing and image processing software, image forensics analysis has become an important research topic in recent years. Numbers of research works are presented for image forensic analysis. Accordingly, this paper proposes a method named as Dragonfly Bayes Fusion System (DBFS) by integrating the Naive Bayes (NB) classifier and the Dragonfly optimization for detecting the tampered Joint Photographic Experts Group image. Initially, the input image is applied to the existing six forensic tools separately, and the classified binary map is generated. Then, the proposed DBFS fuses these decisions for generating the optimal decision. Here, the NB classifier creates the model by finding the mean and variance of every feature and this model is given as input to the Dragonfly optimization for optimally generating the probabilistic measures. Finally, the posterior probability of each feature is determined with respect to the tampered class, and the original class and the tampered image block is determined. The performance of the proposed system is evaluated with the existing methods, such as fuzzy theory based classification, rule-based classification, average method, and weighted average method for the evaluation metrics accuracy, False Positive Rate (FPR), and True Positive Rate (TPR). The experimental results show that the proposed system outperforms the existing methods by obtaining the maximum accuracy of 0.9519, minimum FPR of 0.0490, and maximum TPR of 0.8720 when compared to the existing methods.

Title: Koszul algebras and flow lattices Abstract: We provide a homological algebraic realization of the lattices of integer cuts and integer flows of graphs. To a finite 2-edge-connected graph Γ with a spanning tree T, we associate a finite dimensional Koszul algebra AΓ,T. Under the construction, planar dual graphs with dual spanning trees are associated Koszul dual algebras. The Grothendieck group of the category of finitely-generated AΓ,T modules is isomorphic to the Euclidean lattice ZE(Γ), and we describe the sublattices of integer cuts and integer flows on Γ in terms of the representation theory of AΓ,T. The grading on AΓ,T gives rise to q-analogs of the lattices of integer cuts and flows; these q-lattices depend non-trivially on the choice of spanning tree. We give a q-analog of the matrix-tree theorem, and prove that the q-flow lattice of (Γ1,T1) is isomorphic to the q-flow lattice of (Γ2,T2) if and only if there is a cycle preserving bijection from the edges of Γ1 to the edges of Γ2 taking the spanning tree T1 to the spanning tree T2. This gives a q-analog of a classical theorem of Caporaso-Viviani and Su-Wagner.

Title: Ramsey and Gallai-Ramsey Number for Wheels Abstract: Given a graph G and a positive integer k, define the Gallai-Ramsey number to be the minimum number of vertices n such that any k-edge coloring of K-n contains either a rainbow (all different colored) triangle or a monochromatic copy of G. Much like graph Ramsey numbers, Gallai-Ramsey numbers have gained a reputation as being very difficult to compute in general. As yet, still only precious few sharp results are known. In this paper, we obtain bounds on the Gallai-Ramsey number for wheels and the exact value for the wheel on 5 vertices.

Title: Skew key polynomials and a generalized Littlewood-Richardson rule Abstract: Young's lattice is a partial order on integer partitions whose saturated chains correspond to standard Young tableaux, one type of combinatorial object that generates the Schur basis for symmetric functions. Generalizing Young's lattice, we introduce a new partial order on weak compositions that we call the key poset. Saturated chains in this poset correspond to standard key tableaux, the combinatorial objects that generate the key polynomials, a nonsymmetric polynomial generalization of the Schur basis. Generalizing skew Schur functions, we define skew key polynomials in terms of this new poset. Using weak dual equivalence, we give a nonnegative weak composition Littlewood-Richardson rule for the key expansion of skew key polynomials, generalizing the flagged Littlewood-Richardson rule of Reiner and Shimozono.& nbsp;(c) 2022 Elsevier Ltd. All rights reserved.

Title: Memetic EDA-Based Approaches to QoS-Aware Fully Automated Semantic Web Service Composition Abstract: Quality-of-service (QoS)-aware automated semantic Web service composition aims to find a composite service with optimized or near-optimized QoS and quality of semantic matchmaking within polynomial time. To cope with this NP-hard problem with high complexity, a variety of evolutionary computation (EC) techniques has been developed. To improve the effectiveness and efficiency of these techniques, in this article, we proposed a novel memetic estimation of the distribution algorithm-based approach, namely, MEEDA, to tackle this problem. In particular, MEEDA explores four different domain-dependent local search methods that search for effective composite services by utilizing several neighborhood structures. Apart from that, to significantly reduce the computational time of MEEDA, an efficient local search strategy is introduced by combining a uniform fitness distribution scheme for selecting suitable solutions and stochastic local search operators for effectively and efficiently exploiting neighbors. To better demonstrate MEEDA’s effectiveness and scalability, we create a more challenging, augmented version of the service composition benchmark dataset. Experimental results on this benchmark show that MEEDA with newly developed domain-dependent local search operator, i.e., layer-based constrained one-point swaps, significantly outperforms existing state-of-the-art algorithms in finding high-quality composite services.

Title: Free Ride on LDPC Coded Transmission Abstract: In this paper, we formulate the problem to cope with the transmission of extra bits over an existing coded transmission link (referred to as coded payload link) without any cost of extra transmission energy or extra bandwidth. This is possible since a gap to the channel capacity typically exists for a practical code. A new concept, termed as accessible capacity, is introduced to s...

Title: Quantum-Inspired Support Vector Machine Abstract: Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyzes data for both classification and regression, whose usual algorithm complexity scales polynomially with the dimension of data space and the number of data points. To tackle the big data challenge, a quantum SVM algorithm was proposed, which is claimed to achieve exponential speedup for least squares SVM (LS-SVM). Here, inspired by the quantum SVM algorithm, we present a quantum-inspired classical algorithm for LS-SVM. In our approach, an improved fast sampling technique, namely indirect sampling, is proposed for sampling the kernel matrix and classifying. We first consider the LS-SVM with a linear kernel, and then discuss the generalization of our method to nonlinear kernels. Theoretical analysis shows our algorithm can make classification with arbitrary success probability in logarithmic runtime of both the dimension of data space and the number of data points for low rank, low condition number, and high dimensional data matrix, matching the runtime of the quantum SVM.