Algorithms doi: 10.3390/a10030085

Authors: Camilo Caraveo Fevrier Valdez Oscar Castillo

Fuzzy logic is a soft computing technique that has been very successful in recent years when it is used as a complement to improve meta-heuristic optimization. In this paper, we present a new variant of the bio-inspired optimization algorithm based on the self-defense mechanisms of plants in the nature. The optimization algorithm proposed in this work is based on the predator-prey model originally presented by Lotka and Volterra, where two populations interact with each other and the objective is to maintain a balance. The system of predator-prey equations use four variables (α, β, λ, δ) and the values of these variables are very important since they are in charge of maintaining a balance between the pair of equations. In this work, we propose the use of Type-2 fuzzy logic for the dynamic adaptation of the variables of the system. This time a fuzzy controller is in charge of finding the optimal values for the model variables, the use of this technique will allow the algorithm to have a higher performance and accuracy in the exploration of the values.

]]>Algorithms doi: 10.3390/a10030086

Authors: Wei Zheng Yanyan Tan Xiaonan Fang Shengtao Li

MOEA/D is a promising multi-objective evolutionary algorithm based on decomposition, and it has been used to solve many multi-objective optimization problems very well. However, there is a class of multi-objective problems, called many-objective optimization problems, but the original MOEA/D cannot solve them well. In this paper, an improved MOEA/D with optimal differential evolution (oDE) schemes is proposed, called MOEA/D-oDE, aiming to solve many-objective optimization problems. Compared with MOEA/D, MOEA/D-oDE has two distinguishing points. On the one hand, MOEA/D-oDE adopts a newly-introduced decomposition approach to decompose the many-objective optimization problems, which combines the advantages of the weighted sum approach and the Tchebycheff approach. On the other hand, a kind of combination mechanism for DE operators is designed for finding the best child solution so as to do the a posteriori computing. In our experimental study, six continuous test instances with 4–6 objectives comparing NSGA-II (nondominated sorting genetic algorithm II) and MOEA/D as accompanying experiments are applied. Additionally, the final results indicate that MOEA/D-oDE outperforms NSGA-II and MOEA/D in almost all cases, particularly in those problems that have complicated Pareto shapes and higher dimensional objectives, where its advantages are more obvious.

]]>Algorithms doi: 10.3390/a10030083

Authors: Juan Barraza Patricia Melin Fevrier Valdez Claudia Gonzalez

The main goal of this paper is to improve the performance of the Fireworks Algorithm (FWA). To improve the performance of the FWA we propose three modifications: the first modification is to change the stopping criteria, this is to say, previously, the number of function evaluations was utilized as a stopping criteria, and we decided to change this to specify a particular number of iterations; the second and third modifications consist on introducing a dispersion metric (dispersion percent), and both modifications were made with the goal of achieving dynamic adaptation of the two parameters in the algorithm. The parameters that were controlled are the explosion amplitude and the number of sparks, and it is worth mentioning that the control of these parameters is based on a fuzzy logic approach. To measure the impact of these modifications, we perform experiments with 14 benchmark functions and a comparative study shows the advantage of the proposed approach. We decided to call the proposed algorithms Iterative Fireworks Algorithm (IFWA) and two variants of the Dispersion Percent Iterative Fuzzy Fireworks Algorithm (DPIFWA-I and DPIFWA-II, respectively).

]]>Algorithms doi: 10.3390/a10030082

Authors: Cinthia Peraza Fevrier Valdez Patricia Melin

This article focuses on the dynamic parameter adaptation in the harmony search algorithm using Type-1 and interval Type-2 fuzzy logic. In particular, this work focuses on the adaptation of the parameters of the original harmony search algorithm. At present there are several types of algorithms that can solve complex real-world problems with uncertainty management. In this case the proposed method is in charge of optimizing the membership functions of three benchmark control problems (water tank, shower, and mobile robot). The main goal is to find the best parameters for the membership functions in the controller to follow a desired trajectory. Noise experiments are performed to test the efficacy of the method.

]]>Algorithms doi: 10.3390/a10030081

Authors: Xiaocong Wei Hongfei Lin Yuhai Yu Liang Yang

The literature [-5]contains several reports evaluating the abilities of deep neural networks in text transfer learning. To our knowledge, however, there have been few efforts to fully realize the potential of deep neural networks in cross-domain product review sentiment classification. In this paper, we propose a two-layer convolutional neural network (CNN) for cross-domain product review sentiment classification (LM-CNN-LB). Transfer learning research into product review sentiment classification based on deep neural networks has been limited by the lack of a large-scale corpus; we sought to remedy this problem using a large-scale auxiliary cross-domain dataset collected from Amazon product reviews. Our proposed framework exhibits the dramatic transferability of deep neural networks for cross-domain product review sentiment classification and achieves state-of-the-art performance. The framework also outperforms complex engineered features used with a non-deep neural network method. The experiments demonstrate that introducing large-scale data from similar domains is an effective way to resolve the lack of training data. The LM-CNN-LB trained on the multi-source related domain dataset outperformed the one trained on a single similar domain.

]]>Algorithms doi: 10.3390/a10030080

Authors: Yasser El Khamlichi Abderrahim Tahiri Anouar Abtoy Inmaculada Medina-Bulo Francisco Palomo-Lozano

Wireless sensor network (WSN) applications are rapidly growing and are widely used in various disciplines. Deployment is one of the key issues to be solved in WSNs, since the sensor nodes’ positioning affects highly the system performance. An optimal WSN deployment should maximize the collection of the desired interest phenomena, guarantee the required coverage and connectivity, extend the network lifetime, and minimize the network cost in terms of energy consumption. Most of the research effort in this area aims to solve the deployment issue, without minimizing the network cost by reducing unnecessary working nodes in the network. In this paper, we propose a deployment approach based on the gradient method and the Simulated Annealing algorithm to solve the sensor deployment problem with the minimum number of sensor nodes. The proposed algorithm is able to heuristically optimize the number of sensors and their positions in order to achieve the desired application requirements.

]]>Algorithms doi: 10.3390/a10030079

Authors: Juan Carlos Guzman Patricia Melin German Prado-Arechiga

A neuro fuzzy hybrid model (NFHM) is proposed as a new artificial intelligence method to classify blood pressure (BP). The NFHM uses techniques such as neural networks, fuzzy logic and evolutionary computation, and in the last case genetic algorithms (GAs) are used. The main goal is to model the behavior of blood pressure based on monitoring data of 24 h per patient and based on this to obtain the trend, which is classified using a fuzzy system based on rules provided by an expert, and these rules are optimized by a genetic algorithm to obtain the best possible number of rules for the classifier with the lowest classification error. Simulation results are presented to show the advantage of the proposed model.

]]>Algorithms doi: 10.3390/a10030078

Authors: Yunqiu Shen Tjalling Ypma

The nonlinear least squares problem m i n y , z ∥ A ( y ) z + b ( y ) ∥ , where A ( y ) is a full-rank ( N + ℓ ) × N matrix, y ∈ R n , z ∈ R N and b ( y ) ∈ R N + ℓ with ℓ ≥ n , can be solved by first solving a reduced problem m i n y ∥ f ( y ) ∥ to find the optimal value y * of y, and then solving the resulting linear least squares problem m i n z ∥ A ( y * ) z + b ( y * ) ∥ to find the optimal value z * of z. We have previously justified the use of the reduced function f ( y ) = C T ( y ) b ( y ) , where C ( y ) is a matrix whose columns form an orthonormal basis for the nullspace of A T ( y ) , and presented a quadratically convergent Gauss–Newton type method for solving m i n y ∥ C T ( y ) b ( y ) ∥ based on the use of QR factorization. In this note, we show how LU factorization can replace the QR factorization in those computations, halving the associated computational cost while also providing opportunities to exploit sparsity and thus further enhance computational efficiency.

]]>Algorithms doi: 10.3390/a10030077

Authors: Emanuel Ontiveros-Robles Patricia Melin Oscar Castillo

Interval Type-2 fuzzy systems allow the possibility of considering uncertainty in models based on fuzzy systems, and enable an increase of robustness in solutions to applications, but also increase the complexity of the fuzzy system design. Several attempts have been previously proposed to reduce the computational cost of the type-reduction stage, as this process requires a lot of computing time because it is basically a numerical approximation based on sampling, and the computational cost is proportional to the number of samples, but also the error is inversely proportional to the number of samples. Several works have focused on reducing the computational cost of type-reduction by developing strategies to reduce the number of operations. The first type-reduction method was proposed by Karnik and Mendel (KM), and then was followed by its enhanced version called EKM. Then continuous versions were called CKM and CEKM, and there were variations of this and also other types of variations that eliminate the type-reduction process reducing the computational cost to a Type-1 defuzzification, such as the Nie-Tan versions and similar enhancements. In this work we analyzed and proposed a variant of CEKM by viewing this process as solving a root-finding problem, in this way taking advantage of existing numerical methods to solve the type-reduction problem, the main objective being eliminating the type-reduction process and also providing a continuous solution of the defuzzification.

]]>Algorithms doi: 10.3390/a10030076

Authors: Wenke Zang Weining Zhang Wenqian Zhang Xiyu Liu

As one of the evolutionary heuristics methods, genetic algorithms (GAs) have shown a promising ability to solve complex optimization problems. However, existing GAs still have difficulties in finding the global optimum and avoiding premature convergence. To further improve the search efficiency and convergence rate of evolution algorithms, inspired by the mechanism of biological DNA genetic information and evolution, we present a new genetic algorithm, called GA-TNE+DRO, which uses a novel triplet nucleotide coding scheme to encode potential solutions and a set of new genetic operators to search for globally optimal solutions. The coding scheme represents potential solutions as a sequence of triplet nucleotides and the DNA reproduction operations mimic the DNA reproduction process more vividly than existing DNA-GAs. We compared our algorithm with several existing GA and DNA-based GA algorithms using a benchmark of eight unconstrained optimization functions. Our experimental results show that the proposed algorithm can converge to solutions much closer to the global optimal solutions in a much lower number of iterations than the existing algorithms. A complexity analysis also shows that our algorithm is computationally more efficient than the existing algorithms.

]]>Algorithms doi: 10.3390/a10030075

Authors: Stefan Maruster

We investigate the efficiency of multi-step Newton method (the classical Newton method in which the first derivative is re-evaluated periodically after m steps) for solving nonlinear equations, F ( x ) = 0 , F : D ⊆ R n → R n . We highlight the following property of multi-step Newton method with respect to some other Newton-type method: for a given n, there exist thresholds of m, that is an interval ( m i , m s ) , such that for m inside of this interval, the efficiency index of multi-step Newton method is better than that of other Newton-type method. We also search for optimal values of m.

]]>Algorithms doi: 10.3390/a10030074

Authors: Annabell Berger Berit Schreck

The discrepancy BR for an m × n 0, 1-matrix from Brualdi and Sanderson in 1998 is defined as the minimum number of 1 s that need to be shifted in each row to the left to achieve its Ferrers matrix, i.e., each row consists of consecutive 1 s followed by consecutive 0 s. For ecological bipartite networks, BR describes a nested set of relationships. Since two different labelled networks can be isomorphic, but possess different discrepancies due to different adjacency matrices, we define a metric determining the minimum discrepancy in an isomorphic class. We give a reduction to k ≤ n minimum weighted perfect matching problems. We show on 289 ecological matrices (given as a benchmark by Atmar and Patterson in 1995) that classical discrepancy can underestimate the nestedness by up to 30%.

]]>Algorithms doi: 10.3390/a10030073

Authors: Huanyu Chen Tong Chen Zhihao Zhang Guangyuan Liu

Variable selection is a key step for eliminating redundant information in spectroscopy. Among various variable selection methods, the physarum network (PN) is a newly-introduced and efficient one. However, the whole spectrum has to be equally divided into sub-spectral bands in PN. These division criteria limit the selecting ability and prediction performance. In this paper, we transform the spectrum division problem into a clustering problem and solve the problem by using an affinity propagation (AP) algorithm, an adaptive clustering method, to find the optimized number of sub-spectral bands and the number of wavelengths in each sub-spectral band. Experimental results show that combining AP and PN together can achieve similar prediction accuracy with much less wavelength than what PN alone can achieve.

]]>Algorithms doi: 10.3390/a10030072

Authors: Jie Yang Jian Xiong Guan Gui Rongfang Song Wang Luo Xianzhong Long

Video quality assessment (VQA) plays an important role in video applications for quality evaluation and resource allocation. It aims to evaluate video quality in a way that is consistent with human perception. In this letter, a hierarchical gradient similarity based VQA metric is proposed inspired by the structure of the primate visual cortex, in which visual information is processed through sequential visual areas. These areas are modeled with the corresponding measures to evaluate the overall perceptual quality. Experimental results on the LIVE database show that the proposed VQA metric significantly outperforms most of the state-of-the-art VQA metrics.

]]>Algorithms doi: 10.3390/a10020071

Authors: Qixuan Bi Wenhao Gui

In this paper, we consider the problem of estimating stress-strength reliability for inverse Weibull lifetime models having the same shape parameters but different scale parameters. We obtain the maximum likelihood estimator and its asymptotic distribution. Since the classical estimator doesn’t hold explicit forms, we propose an approximate maximum likelihood estimator. The asymptotic confidence interval and two bootstrap intervals are obtained. Using the Gibbs sampling technique, Bayesian estimator and the corresponding credible interval are obtained. The Metropolis-Hastings algorithm is used to generate random variates. Monte Carlo simulations are conducted to compare the proposed methods. Analysis of a real dataset is performed.

]]>Algorithms doi: 10.3390/a10020070

Authors: Ying Mei Guanzheng Tan Zhentao Liu

Classification is an important task of machine intelligence in the field of information. The artificial neural network (ANN) is widely used for classification. However, the traditional ANN shows slow training speed, and it is hard to meet the real-time requirement for large-scale applications. In this paper, an improved brain-inspired emotional learning (BEL) algorithm is proposed for fast classification. The BEL algorithm was put forward to mimic the high speed of the emotional learning mechanism in mammalian brain, which has the superior features of fast learning and low computational complexity. To improve the accuracy of BEL in classification, the genetic algorithm (GA) is adopted for optimally tuning the weights and biases of amygdala and orbitofrontal cortex in the BEL neural network. The combinational algorithm named as GA-BEL has been tested on eight University of California at Irvine (UCI) datasets and two well-known databases (Japanese Female Facial Expression, Cohn–Kanade). The comparisons of experiments indicate that the proposed GA-BEL is more accurate than the original BEL algorithm, and it is much faster than the traditional algorithm.

]]>Algorithms doi: 10.3390/a10020069

Authors: Anak Agung Putri Ratna Prima Dewi Purnamasari Boma Anantasatya Adhi F. Astha Ekadiyanto Muhammad Salman Mardiyah Mardiyah Darien Jonathan Winata

Computerized cross-language plagiarism detection has recently become essential. With the scarcity of scientific publications in Bahasa Indonesia, many Indonesian authors frequently consult publications in English in order to boost the quantity of scientific publications in Bahasa Indonesia (which is currently rising). Due to the syntax disparity between Bahasa Indonesia and English, most of the existing methods for automated cross-language plagiarism detection do not provide satisfactory results. This paper analyses the probability of developing Latent Semantic Analysis (LSA) for a computerized cross-language plagiarism detector for two languages with different syntax. To improve performance, various alterations in LSA are suggested. By using a linear vector quantization (LVQ) classifier in the LSA and taking into account the Frobenius norm, output has reached up to 65.98% in accuracy. The results of the experiments showed that the best accuracy achieved is 87% with a document size of 6 words, and the document definition size must be kept below 10 words in order to maintain high accuracy. Additionally, based on experimental results, this paper suggests utilizing the frequency occurrence method as opposed to the binary method for the term–document matrix construction.

]]>Algorithms doi: 10.3390/a10020068

Authors: Radu-Emil Precup Radu-Codrut David Alexandra-Iulia Szedlak-Stinean Emil M. Petriu Florin Dragan

This paper proposes an easily understandable Grey Wolf Optimizer (GWO) applied to the optimal tuning of the parameters of Takagi-Sugeno proportional-integral fuzzy controllers (T-S PI-FCs). GWO is employed for solving optimization problems focused on the minimization of discrete-time objective functions defined as the weighted sum of the absolute value of the control error and of the squared output sensitivity function, and the vector variable consists of the tuning parameters of the T-S PI-FCs. Since the sensitivity functions are introduced with respect to the parametric variations of the process, solving these optimization problems is important as it leads to fuzzy control systems with a reduced process parametric sensitivity obtained by a GWO-based fuzzy controller tuning approach. GWO algorithms applied with this regard are formulated in easily understandable terms for both vector and scalar operations, and discussions on stability, convergence, and parameter settings are offered. The controlled processes referred to in the course of this paper belong to a family of nonlinear servo systems, which are modeled by second order dynamics plus a saturation and dead zone static nonlinearity. Experimental results concerning the angular position control of a laboratory servo system are included for validating the proposed method.

]]>Algorithms doi: 10.3390/a10020067

Authors: Yancai Xiao Yujia Wang Huan Mu Na Kang

The misalignment of the drive system of the DFIG (Doubly Fed Induction Generator) wind turbine is one of the important factors that cause damage to the gears, bearings of the high-speed gearbox and the generator bearings. How to use the limited information to accurately determine the type of failure has become a difficult study for the scholars. In this paper, the time-domain indexes and frequency-domain indexes are extracted by using the vibration signals of various misaligned simulation conditions of the wind turbine drive system, and the time-frequency domain features—energy entropy are also extracted by the IEMD (Improved Empirical Mode Decomposition). A mixed-domain feature set is constructed by them. Then, SVM (Support Vector Machine) is used as the classifier, the mixed-domain features are used as the inputs of SVM, and PSO (Particle Swarm Optimization) is used to optimize the parameters of SVM. The fault types of misalignment are classified successfully. Compared with other methods, the accuracy of the given fault isolation model is improved.

]]>Algorithms doi: 10.3390/a10020066

Authors: Hamid Hassannejad Guido Matrella Paolo Ciampolini Ilaria Munari Monica Mordonini Stefano Cagnoni

A balanced diet is the key to a healthy lifestyle and is crucial for preventing or dealing with many chronic diseases such as diabetes and obesity. Therefore, monitoring diet can be an effective way of improving people’s health. However, manual reporting of food intake has been shown to be inaccurate and often impractical. This paper presents a new approach to food intake quantity estimation using image-based modeling. The modeling method consists of three steps: firstly, a short video of the food is taken by the user’s smartphone. From such a video, six frames are selected based on the pictures’ viewpoints as determined by the smartphone’s orientation sensors. Secondly, the user marks one of the frames to seed an interactive segmentation algorithm. Segmentation is based on a Gaussian Mixture Model alongside the graph-cut algorithm. Finally, a customized image-based modeling algorithm generates a point-cloud to model the food. At the same time, a stochastic object-detection method locates a checkerboard used as size/ground reference. The modeling algorithm is optimized such that the use of six input images still results in an acceptable computation cost. In our evaluation procedure, we achieved an average accuracy of 92 % on a test set that includes images of different kinds of pasta and bread, with an average processing time of about 23 s.

]]>Algorithms doi: 10.3390/a10020065

Authors: Xin Tian Song Li

We introduce a seismic signal compression method based on nonparametric Bayesian dictionary learning method via clustering. The seismic data is compressed patch by patch, and the dictionary is learned online. Clustering is introduced for dictionary learning. A set of dictionaries could be generated, and each dictionary is used for one cluster’s sparse coding. In this way, the signals in one cluster could be well represented by their corresponding dictionaries. A nonparametric Bayesian dictionary learning method is used to learn the dictionaries, which naturally infers an appropriate dictionary size for each cluster. A uniform quantizer and an adaptive arithmetic coding algorithm are adopted to code the sparse coefficients. With comparisons to other state-of-the art approaches, the effectiveness of the proposed method could be validated in the experiments.

]]>Algorithms doi: 10.3390/a10020064

Authors: Sergio Amat Ioannis Argyros Miguel Hernández-Verón Natalia Romero

This paper is devoted to the semilocal convergence of a Househölder-like method for nonlinear equations. The method includes many of the studied third order iterative methods. In the present study, we use our new idea of restricted convergence domains leading to smaller γ -parameters, which in turn lead to the following advantages over earlier works (and under the same computational cost): larger convergence domain; tighter error bounds on the distances involved, and at least as precise information on the location of the solution.

]]>Algorithms doi: 10.3390/a10020063

Authors: Prima Purnamasari Anak Ratna Benyamin Kusumoputro

The development of automatic emotion detection systems has recently gained significant attention due to the growing possibility of their implementation in several applications, including affective computing and various fields within biomedical engineering. Use of the electroencephalograph (EEG) signal is preferred over facial expression, as people cannot control the EEG signal generated by their brain; the EEG ensures a stronger reliability in the psychological signal. However, because of its uniqueness between individuals and its vulnerability to noise, use of EEG signals can be rather complicated. In this paper, we propose a methodology to conduct EEG-based emotion recognition by using a filtered bispectrum as the feature extraction subsystem and an artificial neural network (ANN) as the classifier. The bispectrum is theoretically superior to the power spectrum because it can identify phase coupling between the nonlinear process components of the EEG signal. In the feature extraction process, to extract the information contained in the bispectrum matrices, a 3D pyramid filter is used for sampling and quantifying the bispectrum value. Experiment results show that the mean percentage of the bispectrum value from 5 × 5 non-overlapped 3D pyramid filters produces the highest recognition rate. We found that reducing the number of EEG channels down to only eight in the frontal area of the brain does not significantly affect the recognition rate, and the number of data samples used in the training process is then increased to improve the recognition rate of the system. We have also utilized a probabilistic neural network (PNN) as another classifier and compared its recognition rate with that of the back-propagation neural network (BPNN), and the results show that the PNN produces a comparable recognition rate and lower computational costs. Our research shows that the extracted bispectrum values of an EEG signal using 3D filtering as a feature extraction method is suitable for use in an EEG-based emotion recognition system.

]]>Algorithms doi: 10.3390/a10020062

Authors: Hanbing Liu Mengsu Zhang Xianqiang Wang Shuai Tian Yubo Jiao

In order to relieve traffic congestion, irregularly-shaped bridges have been widely used in urban overpasses. However, the analysis on modal characteristics of irregularly-shaped bridges is not exhaustive, and the effect of design parameters on modal characteristics will be deeply investigated in future studies. In this paper, a novel strategy based on a free-interface mode synthesis algorithm is proposed to evaluate the parameters’ effect on the modal characteristics of irregularly-shaped bridges. First, a complicated, irregularly-shaped bridge is divided into several substructures based on its properties. Then, the modal characteristics of the overall structure can be obtained, only by a few low-order modal parameters of each substructure, using a free-interface mode synthesis method. A numerical model of a typical irregularly-shaped bridge is employed to verify the effectiveness of the proposed strategy. Simulation results reveal that the free-interface mode synthesis method possesses favorable calculation accuracy for analyzing the modal characteristics of irregularly-shaped bridges. The effect of design parameters such as ramp curve radius, diaphragm beam stiffness, cross-section feature, and bearing conditions on the modal characteristics of an irregularly-shaped bridge is evaluated in detail. Analysis results can provide references for further research into and the design of irregularly-shaped bridges.

]]>Algorithms doi: 10.3390/a10020061

Authors: Elisabetta Stefani Carlo Ferrari

This paper is about the design, implementation, and deployment of a multi-modal biometric system to grant access to a company structure and to internal zones in the company itself. Face and iris have been chosen as biometric traits. Face is feasible for non-intrusive checking with a minimum cooperation from the subject, while iris supports very accurate recognition procedure at a higher grade of invasivity. The recognition of the face trait is based on the Local Binary Patterns histograms, and the Daughman’s method is implemented for the analysis of the iris data. The recognition process may require either the acquisition of the user’s face only or the serial acquisition of both the user’s face and iris, depending on the confidence level of the decision with respect to the set of security levels and requirements, stated in a formal way in the Service Level Agreement at a negotiation phase. The quality of the decision depends on the setting of proper different thresholds in the decision modules for the two biometric traits. Any time the quality of the decision is not good enough, the system activates proper rules, which ask for new acquisitions (and decisions), possibly with different threshold values, resulting in a system not with a fixed and predefined behaviour, but one which complies with the actual acquisition context. Rules are formalized as deduction rules and grouped together to represent “response behaviors” according to the previous analysis. Therefore, there are different possible working flows, since the actual response of the recognition process depends on the output of the decision making modules that compose the system. Finally, the deployment phase is described, together with the results from the testing, based on the AT&amp;T Face Database and the UBIRIS database.

]]>Algorithms doi: 10.3390/a10020060

Authors: Yan Fu Shengchun Wang

We would like to make the following change to our article [1]. [...]

]]>Algorithms doi: 10.3390/a10020058

Authors: Chun-Liang Lee Tzu-Hao Yang

As part of network security processes, network intrusion detection systems (NIDSs) determine whether incoming packets contain malicious patterns. Pattern matching, the key NIDS component, consumes large amounts of execution time. One of several trends involving general-purpose processors (GPPs) is their use in software-based NIDSs. In this paper, we describe our proposal for an efficient and flexible pattern-matching algorithm for inspecting packet payloads using a head-body finite automaton (HBFA). The proposed algorithm takes advantage of multi-core GPP parallelism and single-instruction multiple-data operations to achieve higher throughput compared to that resulting from traditional deterministic finite automata (DFA) using the Aho-Corasick algorithm. Whereas the head-body matching (HBM) algorithm is based on pre-defined DFA depth value, our HBFA algorithm is based on head size. Experimental results using Snort and ClamAV pattern sets indicate that the proposed algorithm achieves up to 58% higher throughput compared to its HBM counterpart.

]]>Algorithms doi: 10.3390/a10020059

Authors: Luyang Li Bing Qin Ting Liu

Contradiction detection is a task to recognize contradiction relations between a pair of sentences. Despite the effectiveness of traditional context-based word embedding learning algorithms in many natural language processing tasks, such algorithms are not powerful enough for contradiction detection. Contrasting words such as “overfull” and “empty” are mostly mapped into close vectors in such embedding space. To solve this problem, we develop a tailored neural network to learn contradiction-specific word embedding (CWE). The method can separate antonyms in the opposite ends of a spectrum. CWE is learned from a training corpus which is automatically generated from the paraphrase database, and is naturally applied as features to carry out contradiction detection in SemEval 2014 benchmark dataset. Experimental results show that CWE outperforms traditional context-based word embedding in contradiction detection. The proposed model for contradiction detection performs comparably with the top-performing system in accuracy of three-category classification and enhances the accuracy from 75.97% to 82.08% in the contradiction category.

]]>Algorithms doi: 10.3390/a10020057

Authors: Jinglin Du Yayun Liu Yanan Yu Weilan Yan

Precipitation is a very important topic in weather forecasts. Weather forecasts, especially precipitation prediction, poses complex tasks because they depend on various parameters to predict the dependent variables like temperature, humidity, wind speed and direction, which are changing from time to time and weather calculation varies with the geographical location along with its atmospheric variables. To improve the prediction accuracy of precipitation, this context proposes a prediction model for rainfall forecast based on Support Vector Machine with Particle Swarm Optimization (PSO-SVM) to replace the linear threshold used in traditional precipitation. Parameter selection has a critical impact on the predictive accuracy of SVM, and PSO is proposed to find the optimal parameters for SVM. The PSO-SVM algorithm was used for the training of a model by using the historical data for precipitation prediction, which can be useful information and used by people of all walks of life in making wise and intelligent decisions. The simulations demonstrate that prediction models indicate that the performance of the proposed algorithm has much better accuracy than the direct prediction model based on a set of experimental data if other things are equal. On the other hand, simulation results demonstrate the effectiveness and advantages of the SVM-PSO model used in machine learning and further promises the scope for improvement as more and more relevant attributes can be used in predicting the dependent variables.

]]>Algorithms doi: 10.3390/a10020056

Authors: Qin Li Bo Liu

In recent years, metaheuristic algorithms have been widely used in solving clustering problems because of their good performance and application effects. Krill herd algorithm (KHA) is a new effective algorithm to solve optimization problems based on the imitation of krill individual behavior, and it is proven to perform better than other swarm intelligence algorithms. However, there are some weaknesses yet. In this paper, an improved krill herd algorithm (IKHA) is studied. Modified mutation operators and updated mechanisms are applied to improve global optimization, and the proposed IKHA can overcome the weakness of KHA and performs better than KHA in optimization problems. Then, KHA and IKHA are introduced into the clustering problem. In our proposed clustering algorithm, KHA and IKHA are used to find appropriate cluster centers. Experiments were conducted on University of California Irvine (UCI) standard datasets, and the results showed that the IKHA clustering algorithm is the most effective.

]]>Algorithms doi: 10.3390/a10020054

Authors: Ioannis Argyros Janak Sharma Deepak Kumar

We present the semilocal convergence of a multi-step modified Newton-Hermitian and Skew-Hermitian Splitting method (MMN-HSS method) to approximate a solution of a nonlinear equation. Earlier studies show convergence under only Lipschitz conditions limiting the applicability of this method. The convergence in this study is shown under generalized Lipschitz-type conditions and restricted convergence domains. Hence, the applicability of the method is extended. Moreover, numerical examples are also provided to show that our results can be applied to solve equations in cases where earlier study cannot be applied. Furthermore, in the cases where both old and new results are applicable, the latter provides a larger domain of convergence and tighter error bounds on the distances involved.

]]>Algorithms doi: 10.3390/a10020055

Authors: Fayyaz Ahmad Toseef Bhutta Umar Shoaib Malik Ullah Ali Alshomrani Shamshad Ahmad Shahid Ahmad

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]]>Algorithms doi: 10.3390/a10020053

Authors: Huiru Zhao Yuwei Wang Mingrui Zhao Chuyu Sun Qingkun Tan

The bilateral spot electricity market is very complicated because all generation units and demands must strategically bid in this market. Considering renewable resource penetration, the high variability and the non-dispatchable nature of these intermittent resources make it more difficult to model and simulate the dynamic bidding process and the equilibrium in the bilateral spot electricity market, which makes developing fast and reliable market modeling approaches a matter of urgency nowadays. In this paper, a Gradient Descent Continuous Actor-Critic algorithm is proposed for hour-ahead bilateral electricity market modeling in the presence of renewable resources because this algorithm can solve electricity market modeling problems with continuous state and action spaces without causing the “curse of dimensionality” and has low time complexity. In our simulation, the proposed approach is implemented on an IEEE 30-bus test system. The adequate performance of our proposed approach—such as reaching Nash Equilibrium results after enough iterations of training are tested and verified, and some conclusions about the relationship between increasing the renewable power output and participants’ bidding strategy, locational marginal prices, and social welfare—is also evaluated. Moreover, the comparison of our proposed approach with the fuzzy Q-learning-based electricity market approach implemented in this paper confirms the superiority of our proposed approach in terms of participants’ profits, social welfare, average locational marginal prices, etc.

]]>Algorithms doi: 10.3390/a10020051

Authors: Raffaele Pizzolante Bruno Carpentieri Sergio De Agostino

In this work, we present a scheme for the lossy compression of image sequences, based on the Adaptive Vector Quantization (AVQ) algorithm. The AVQ algorithm is a lossy compression algorithm for grayscale images, which processes the input data in a single-pass, by using the properties of the vector quantization to approximate data. First, we review the key aspects of the AVQ algorithm and, subsequently, we outline the basic concepts and the design choices behind the proposed scheme. Finally, we report the experimental results, which highlight an improvement in compression performances when our scheme is compared with the AVQ algorithm.

]]>Algorithms doi: 10.3390/a10020052

Authors: Geong Poh Vishnu Baskaran Ji-Jian Chin Moesfa Mohamad Kay Win Lee Dharmadharshni Maniam Muhammad Z’aba

Cloud storage services allow users to efficiently outsource their documents anytime and anywhere. Such convenience, however, leads to privacy concerns. While storage providers may not read users’ documents, attackers may possibly gain access by exploiting vulnerabilities in the storage system. Documents may also be leaked by curious administrators. A simple solution is for the user to encrypt all documents before submitting them. This method, however, makes it impossible to efficiently search for documents as they are all encrypted. To resolve this problem, we propose a multi-server searchable symmetric encryption (SSE) scheme and construct a system called the searchable data vault (SDV). A unique feature of the scheme is that it allows an encrypted document to be divided into blocks and distributed to different storage servers so that no single storage provider has a complete document. By incorporating the scheme, the SDV protects the privacy of documents while allowing for efficient private queries. It utilizes a web interface and a controller that manages user credentials, query indexes and submission of encrypted documents to cloud storage services. It is also the first system that enables a user to simultaneously outsource and privately query documents from a few cloud storage services. Our preliminary performance evaluation shows that this feature introduces acceptable computation overheads when compared to submitting documents directly to a cloud storage service.

]]>Algorithms doi: 10.3390/a10020050

Authors: Shmuel Klein Dana Shapira

The Hamming code is a well-known error correction code and can correct a single error in an input vector of size n bits by adding logn parity checks. A new parallel implementation of the code is presented, using a hierarchical structure of n processors in logn layers. All the processors perform similar simple tasks, and need only a few bytes of internal memory.

]]>Algorithms doi: 10.3390/a10020049

Authors: Khaled Bouzenad Messaoud Ramdani

Monitoring process upsets and malfunctions as early as possible and then finding and removing the factors causing the respective events is of great importance for safe operation and improved productivity. Conventional process monitoring using principal component analysis (PCA) often supposes that process data follow a Gaussian distribution. However, this kind of constraint cannot be satisfied in practice because many industrial processes frequently span multiple operating states. To overcome this difficulty, PCA can be combined with nonparametric control charts for which there is no assumption need on the distribution. However, this approach still uses a constant confidence limit where a relatively high rate of false alarms are generated. Although nonlinear PCA (NLPCA) using autoassociative bottle-neck neural networks plays an important role in the monitoring of industrial processes, it is difficult to design correct monitoring statistics and confidence limits that check new performance. In this work, a new monitoring strategy using an enhanced bottleneck neural network (EBNN) with an adaptive confidence limit for non Gaussian data is proposed. The basic idea behind it is to extract internally homogeneous segments from the historical normal data sets by filling a Gaussian mixture model (GMM). Based on the assumption that process data follow a Gaussian distribution within an operating mode, a local confidence limit can be established. The EBNN is used to reconstruct input data and estimate probabilities of belonging to the various local operating regimes, as modelled by GMM. An abnormal event for an input measurement vector is detected if the squared prediction error (SPE) is too large, or above a certain threshold which is made adaptive. Moreover, the sensor validity index (SVI) is employed successfully to identify the detected faulty variable. The results demonstrate that, compared with NLPCA, the proposed approach can effectively reduce the number of false alarms, and is hence expected to better monitor many practical processes.

]]>Algorithms doi: 10.3390/a10020048

Authors: Xi-Guang Li Shou-Fei Han Liang Zhao Chang-Qing Gong Xiao-Jing Liu

The Dynamic Search Fireworks Algorithm (dynFWA) is an effective algorithm for solving optimization problems. However, dynFWA easily falls into local optimal solutions prematurely and it also has a slow convergence rate. In order to improve these problems, an adaptive mutation dynamic search fireworks algorithm (AMdynFWA) is introduced in this paper. The proposed algorithm applies the Gaussian mutation or the Levy mutation for the core firework (CF) with mutation probability. Our simulation compares the proposed algorithm with the FWA-Based algorithms and other swarm intelligence algorithms. The results show that the proposed algorithm achieves better overall performance on the standard test functions.

]]>Algorithms doi: 10.3390/a10020047

Authors: Darra Hofman Luciana Duranti Elissa How

A popular bumper sticker states: “There is no cloud. It’s just someone else’s computer.” Despite the loss of control that comes with its use, critical records are increasingly being entrusted to the cloud, generating ever-growing concern about the privacy and security of those records. Ultimately, privacy and security constitute an attempt to balance competing needs: privacy balances the need to use information against the need to protect personal data, while security balances the need to provide access to records against the need to stop unauthorized access. The importance of these issues has led to a multitude of legal and regulatory efforts to find a balance and, ultimately, to ensure trust in both digital records and their storage in the cloud. Adding a particular challenge is the fact that distinct jurisdictions approach privacy differently and an in-depth understanding of what a jurisdiction’s laws may be, or even under what jurisdiction particular data might be, requires a Herculean effort. And yet, in order to protect privacy and enhance security, this effort is required. This article examines two legal tools for ensuring the privacy and security of records in the cloud, data protection laws, and data localization laws, through the framework of “trust” as understood in archival science. This framework of trust provides new directions for algorithmic research, identifying those areas of digital record creation and preservation most in need of novel solutions.

]]>Algorithms doi: 10.3390/a10020046

Authors: Chunhua Jia Hong Zhu

In this paper, we propose a new approach to raise the performance of multiobjective particle swam optimization. The personal guide and global guide are updated using three kinds of knowledge extracted from the population based on cultural algorithms. An epsilon domination criterion has been employed to enhance the convergence and diversity of the approximate Pareto front. Moreover, a simple polynomial mutation operator has been applied to both the population and the non-dominated archive. Experiments on two series of bench test suites have shown the effectiveness of the proposed approach. A comparison with several other algorithms that are considered good representatives of particle swarm optimization solutions has also been conducted, in order to verify the competitive performance of the proposed algorithm in solve multiobjective optimization problems.

]]>Algorithms doi: 10.3390/a10020045

Authors: Xiaofeng Wang Yang Li

In this paper, we present a new sixth-order iterative method for solving nonlinear systems and prove a local convergence result. The new method requires solving five linear systems per iteration. An important feature of the new method is that the LU (lower upper, also called LU factorization) decomposition of the Jacobian matrix is computed only once in each iteration. The computational efficiency index of the new method is compared to that of some known methods. Numerical results are given to show that the convergence behavior of the new method is similar to the existing methods. The new method can be applied to small- and medium-sized nonlinear systems.

]]>Algorithms doi: 10.3390/a10020044

Authors: Danilo Pelusi Raffaele Mascella Luca Tallini

The choice of the best optimization algorithm is a hard issue, and it sometime depends on specific problem. The Gravitational Search Algorithm (GSA) is a search algorithm based on the law of gravity, which states that each particle attracts every other particle with a force called gravitational force. Some revised versions of GSA have been proposed by using intelligent techniques. This work proposes some GSA versions based on fuzzy techniques powered by evolutionary methods, such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE), to improve GSA. The designed algorithms tune a suitable parameter of GSA through a fuzzy controller whose membership functions are optimized by GA, PSO and DE. The results show that Fuzzy Gravitational Search Algorithm (FGSA) optimized by DE is optimal for unimodal functions, whereas FGSA optimized through GA is good for multimodal functions.

]]>Algorithms doi: 10.3390/a10020043

Authors: Yuan Feng Li Wang Xinhong Liu

This paper investigates a fuzzy portfolio selection problem with guaranteed reliability, in which the fuzzy variables are used to capture the uncertain returns of different securities. To effectively handle the fuzziness in a mathematical way, a new expected value operator and variance of fuzzy variables are defined based on the m λ measure that is a linear combination of the possibility measure and necessity measure to balance the pessimism and optimism in the decision-making process. To formulate the reliable portfolio selection problem, we particularly adopt the expected total return and standard variance of the total return to evaluate the reliability of the investment strategies, producing three risk-guaranteed reliable portfolio selection models. To solve the proposed models, an effective genetic algorithm is designed to generate the approximate optimal solution to the considered problem. Finally, the numerical examples are given to show the performance of the proposed models and algorithm.

]]>Algorithms doi: 10.3390/a10020042

Authors: Ziyan Chen Yu Huang Yuexian Liang Yang Wang Xingyu Fu Kun Fu

Most of the previous works on relation extraction between named entities are often limited to extracting the pre-defined types; which are inefficient for massive unlabeled text data. Recently; with the appearance of various distributional word representations; unsupervised methods for many natural language processing (NLP) tasks have been widely researched. In this paper; we focus on a new finding of unsupervised relation extraction; which is called distributional relation representation. Without requiring the pre-defined types; distributional relation representation aims to automatically learn entity vectors and further estimate semantic similarity between these entities. We choose global vectors (GloVe) as our original model to train entity vectors because of its excellent balance between local context and global statistics in the whole corpus. In order to train model more efficiently; we improve the traditional GloVe model by using cosine similarity between entity vectors to approximate the entity occurrences instead of dot product. Because cosine similarity can convert vector to unit vector; it is intuitively more reasonable and more easily converge to a local optimum. We call the improved model RGloVe. Experimental results on a massive corpus of Sina News show that our proposed model outperforms the traditional global vectors. Finally; a graph database of Neo4j is introduced to store these relationships between named entities. The most competitive advantage of Neo4j is that it provides a highly accessible way to query the direct and indirect relationships between entities.

]]>Algorithms doi: 10.3390/a10020041

Authors: Yunpeng Zhang Chengyou Wang Xiao Zhou

Currently, most digital image watermarking schemes are affected by geometric attacks like rotation, scaling, and translation (RST). In the watermark embedding process, a robust watermarking scheme is proposed against RST attacks. In this paper, three-level discrete wavelet transform (DWT) is applied to the original image. The three-level low frequency sub-band is decomposed by the singular value decomposition (SVD), and its singular values matrix is extracted for watermarking embedding. Before the watermarking extraction, the keypoints are selected by scale-invariant feature transform (SIFT) in the original image and attacked image. By matching the keypoints in two images, the RST attacks can be precisely corrected and the better performance can be obtained. The experimental results show that the proposed scheme achieves good performance of imperceptibility and robustness against common image processing and malicious attacks, especially geometric attacks.

]]>Algorithms doi: 10.3390/a10020040

Authors: Georgios Drakopoulos Andreas Kanavos Konstantinos Tsakalidis

Edge-fuzzy graphs constitute an essential modeling paradigm across a broad spectrum of domains ranging from artificial intelligence to computational neuroscience and social network analysis. Under this model, fundamental graph properties such as edge length and graph diameter become stochastic and as such they are consequently expressed in probabilistic terms. Thus, algorithms for fuzzy graph analysis must rely on non-deterministic design principles. One such principle is Random Walker, which is based on a virtual entity and selects either edges or, like in this case, vertices of a fuzzy graph to visit. This allows the estimation of global graph properties through a long sequence of local decisions, making it a viable strategy candidate for graph processing software relying on native graph databases such as Neo4j. As a concrete example, Chebyshev Walktrap, a heuristic fuzzy community discovery algorithm relying on second order statistics and on the teleportation of the Random Walker, is proposed and its performance, expressed in terms of community coherence and number of vertex visits, is compared to the previously proposed algorithms of Markov Walktrap, Fuzzy Walktrap, and Fuzzy Newman–Girvan. In order to facilitate this comparison, a metric based on the asymmetric metrics of Tversky index and Kullback–Leibler divergence is used.

]]>Algorithms doi: 10.3390/a10020039

Authors: Shahid Anwar Jasni Mohamad Zain Mohamad Fadli Zolkipli Zakira Inayat Suleman Khan Bokolo Anthony Victor Chang

In the past few decades, the rise in attacks on communication devices in networks has resulted in a reduction of network functionality, throughput, and performance. To detect and mitigate these network attacks, researchers, academicians, and practitioners developed Intrusion Detection Systems (IDSs) with automatic response systems. The response system is considered an important component of IDS, since without a timely response IDSs may not function properly in countering various attacks, especially on a real-time basis. To respond appropriately, IDSs should select the optimal response option according to the type of network attack. This research study provides a complete survey of IDSs and Intrusion Response Systems (IRSs) on the basis of our in-depth understanding of the response option for different types of network attacks. Knowledge of the path from IDS to IRS can assist network administrators and network staffs in understanding how to tackle different attacks with state-of-the-art technologies.

]]>Algorithms doi: 10.3390/a10020037

Authors: Fan Wu Kun Fu Yang Wang Zhibin Xiao Xingyu Fu

Location prediction has attracted much attention due to its important role in many location-based services, such as food delivery, taxi-service, real-time bus system, and advertisement posting. Traditional prediction methods often cluster track points into regions and mine movement patterns within the regions. Such methods lose information of points along the road and cannot meet the demand of specific services. Moreover, traditional methods utilizing classic models may not perform well with long location sequences. In this paper, a spatial-temporal-semantic neural network algorithm (STS-LSTM) has been proposed, which includes two steps. First, the spatial-temporal-semantic feature extraction algorithm (STS) is used to convert the trajectory to location sequences with fixed and discrete points in the road networks. The method can take advantage of points along the road and can transform trajectory into model-friendly sequences. Then, a long short-term memory (LSTM)-based model is constructed to make further predictions, which can better deal with long location sequences. Experimental results on two real-world datasets show that STS-LSTM has stable and higher prediction accuracy over traditional feature extraction and model building methods, and the application scenarios of the algorithm are illustrated.

]]>Algorithms doi: 10.3390/a10020038

Authors: Sayaka Kamei Hirotsugu Kakugawa

This paper discusses the generalized local version of critical section problems including mutual exclusion, mutual inclusion, k-mutual exclusion and l-mutual inclusion. When a pair of numbers (li, ki) is given for each process Pi, it is the problem of controlling the system in such a way that the number of processes that can execute their critical sections at a time is at least li and at most ki among its neighboring processes and Pi itself. We propose the first solution for the generalized local (li, |Ni| + 1)-critical section problem (i.e., the generalized local li-mutual inclusion problem). Additionally, we show the relationship between the generalized local (li, ki)-critical section problem and the generalized local (|Ni| + 1 − ki, |Ni| + 1 − li)-critical section problem. Finally, we propose the first solution for the generalized local (li, ki)-critical section problem for arbitrary (li, ki), where 0 ≤ li &lt; ki + |Ni| + 1 for each process Pi.

]]>Algorithms doi: 10.3390/a10020036

Authors: J. Quiroz-Ibarra Guillermo Mallén-Fullerton Guillermo Fernández-Anaya

DNA fragment assembly requirements have generated an important computational problem created by their structure and the volume of data. Therefore, it is important to develop algorithms able to produce high-quality information that use computer resources efficiently. Such an algorithm, using graph theory, is introduced in the present article. We first determine the overlaps between DNA fragments, obtaining the edges of a directed graph; with this information, the next step is to construct an adjacency list with some particularities. Using the adjacency list, it is possible to obtain the DNA contigs (group of assembled fragments building a contiguous element) using graph theory. We performed a set of experiments on real DNA data and compared our results to those obtained with common assemblers (Edena and Velvet). Finally, we searched the contigs in the original genome, in our results and in those of Edena and Velvet.

]]>Algorithms doi: 10.3390/a10010035

Authors: Evaggelos Spyrou Michalis Korakakis Vasileios Charalampidis Apostolos Psallas Phivos Mylonas

Living in the “era of social networking”, we are experiencing a data revolution, generating an astonishing amount of digital information every single day. Due to this proliferation of data volume, there has been an explosion of new application domains for information mined from social networks. In this paper, we leverage this “socially-generated knowledge” (i.e., user-generated content derived from social networks) towards the detection of areas-of-interest within an urban region. These large and homogeneous areas contain multiple points-of-interest which are of special interest to particular groups of people (e.g., tourists and/or consumers). In order to identify them, we exploit two types of metadata, namely location-based information included within geo-tagged photos that we collect from Flickr, along with plain simple textual information from user-generated tags. We propose an algorithm that divides a predefined geographical area (i.e., the center of Athens, Greece) into “tile”-shaped sub-regions and based on an iterative merging procedure, it aims to detect larger, cohesive areas. We examine the performance of the algorithm both in a qualitative and quantitative manner. Our experiments demonstrate that the proposed geo-clustering algorithm is able to correctly detect regions that contain popular tourist attractions within them with very promising results.

]]>Algorithms doi: 10.3390/a10010034

Authors: Vasileios Athanasiou Manolis Maragoudakis

Sentiment analysis has played a primary role in text classification. It is an undoubted fact that some years ago, textual information was spreading in manageable rates; however, nowadays, such information has overcome even the most ambiguous expectations and constantly grows within seconds. It is therefore quite complex to cope with the vast amount of textual data particularly if we also take the incremental production speed into account. Social media, e-commerce, news articles, comments and opinions are broadcasted on a daily basis. A rational solution, in order to handle the abundance of data, would be to build automated information processing systems, for analyzing and extracting meaningful patterns from text. The present paper focuses on sentiment analysis applied in Greek texts. Thus far, there is no wide availability of natural language processing tools for Modern Greek. Hence, a thorough analysis of Greek, from the lexical to the syntactical level, is difficult to perform. This paper attempts a different approach, based on the proven capabilities of gradient boosting, a well-known technique for dealing with high-dimensional data. The main rationale is that since English has dominated the area of preprocessing tools and there are also quite reliable translation services, we could exploit them to transform Greek tokens into English, thus assuring the precision of the translation, since the translation of large texts is not always reliable and meaningful. The new feature set of English tokens is augmented with the original set of Greek, consequently producing a high dimensional dataset that poses certain difficulties for any traditional classifier. Accordingly, we apply gradient boosting machines, an ensemble algorithm that can learn with different loss functions providing the ability to work efficiently with high dimensional data. Moreover, for the task at hand, we deal with a class imbalance issues since the distribution of sentiments in real-world applications often displays issues of inequality. For example, in political forums or electronic discussions about immigration or religion, negative comments overwhelm the positive ones. The class imbalance problem was confronted using a hybrid technique that performs a variation of under-sampling the majority class and over-sampling the minority class, respectively. Experimental results, considering different settings, such as translation of tokens against translation of sentences, consideration of limited Greek text preprocessing and omission of the translation phase, demonstrated that the proposed gradient boosting framework can effectively cope with both high-dimensional and imbalanced datasets and performs significantly better than a plethora of traditional machine learning classification approaches in terms of precision and recall measures.

]]>Algorithms doi: 10.3390/a10010033

Authors: Andreas Kanavos Nikolaos Nodarakis Spyros Sioutas Athanasios Tsakalidis Dimitrios Tsolis Giannis Tzimas

Sentiment Analysis on Twitter Data is indeed a challenging problem due to the nature, diversity and volume of the data. People tend to express their feelings freely, which makes Twitter an ideal source for accumulating a vast amount of opinions towards a wide spectrum of topics. This amount of information offers huge potential and can be harnessed to receive the sentiment tendency towards these topics. However, since no one can invest an infinite amount of time to read through these tweets, an automated decision making approach is necessary. Nevertheless, most existing solutions are limited in centralized environments only. Thus, they can only process at most a few thousand tweets. Such a sample is not representative in order to define the sentiment polarity towards a topic due to the massive number of tweets published daily. In this work, we develop two systems: the first in the MapReduce and the second in the Apache Spark framework for programming with Big Data. The algorithm exploits all hashtags and emoticons inside a tweet, as sentiment labels, and proceeds to a classification method of diverse sentiment types in a parallel and distributed manner. Moreover, the sentiment analysis tool is based on Machine Learning methodologies alongside Natural Language Processing techniques and utilizes Apache Spark’s Machine learning library, MLlib. In order to address the nature of Big Data, we introduce some pre-processing steps for achieving better results in Sentiment Analysis as well as Bloom filters to compact the storage size of intermediate data and boost the performance of our algorithm. Finally, the proposed system was trained and validated with real data crawled by Twitter, and, through an extensive experimental evaluation, we prove that our solution is efficient, robust and scalable while confirming the quality of our sentiment identification.

]]>Algorithms doi: 10.3390/a10010032

Authors: Feng-Gong Lang

In this paper, to overcome the innate drawbacks of some old methods, we present a new quintic spline method for integro interpolation. The method is free of any exact end conditions, and it can reconstruct a function and its first order to fifth order derivatives with high accuracy by only using the given integral values of the original function. The approximation properties of the obtained integro quintic spline are well studied and examined. The theoretical analysis and the numerical tests show that the new method is very effective for integro interpolation.

]]>Algorithms doi: 10.3390/a10010031

Authors: Koichi Kobayashi Kunihiko Hiraishi

Control of gene regulatory networks is one of the fundamental topics in systems biology. In the last decade, control theory of Boolean networks (BNs), which is well known as a model of gene regulatory networks, has been widely studied. In this review paper, our previously proposed methods on optimal control of probabilistic Boolean networks (PBNs) are introduced. First, the outline of PBNs is explained. Next, an optimal control method using polynomial optimization is explained. The finite-time optimal control problem is reduced to a polynomial optimization problem. Furthermore, another finite-time optimal control problem, which can be reduced to an integer programming problem, is also explained.

]]>Algorithms doi: 10.3390/a10010030

Authors: Petr Procházka Jan Holub

We address the problem of positional indexing in the natural language domain. The positional inverted index contains the information of the word positions. Thus, it is able to recover the original text file, which implies that it is not necessary to store the original file. Our Positional Inverted Self-Index (PISI) stores the word position gaps encoded by variable byte code. Inverted lists of single terms are combined into one inverted list that represents the backbone of the text file since it stores the sequence of the indexed words of the original file. The inverted list is synchronized with a presentation layer that stores separators, stop words, as well as variants of the indexed words. The Huffman coding is used to encode the presentation layer. The space complexity of the PISI inverted list is O ( ( N − n ) ⌈ log 2 b N ⌉ + ( ⌊ N − n α ⌋ + n ) × ( ⌈ log 2 b n ⌉ + 1 ) ) where N is a number of stems, n is a number of unique stems, α is a step/period of the back pointers in the inverted list and b is the size of the word of computer memory given in bits. The space complexity of the presentation layer is O ( − ∑ i = 1 N ⌈ log 2 p i n ( i ) ⌉ − ∑ j = 1 N ′ ⌈ log 2 p j ′ ⌉ + N ) with respect to p i n ( i ) as a probability of a stem variant at position i, p j ′ as the probability of separator or stop word at position j and N ′ as the number of separators and stop words.

]]>Algorithms doi: 10.3390/a10010029

Authors: Qingshan You Qun Wan

Compressive principal component pursuit (CPCP) recovers a target matrix that is a superposition of low-complexity structures from a small set of linear measurements. Pervious works mainly focus on the analysis of the existence and uniqueness. In this paper, we address its stability. We prove that the solution to the related convex programming of CPCP gives an estimate that is stable to small entry-wise noise. We also provide numerical simulation results to support our result. Numerical results show that the solution to the related convex program is stable to small entry-wise noise under board condition.

]]>Algorithms doi: 10.3390/a10010028

Authors: Vassiliki Gkantouna Giannis Tzimas

Domain-specific design patterns provide developers with proven solutions to common design problems that arise, particularly in a target application domain, facilitating them to produce quality designs in the domain contexts. However, research in this area is not mature and there are no techniques to support their detection. Towards this end, we propose a methodology which, when applied on a collection of websites in a specific domain, facilitates the automated identification of domain-specific design patterns. The methodology automatically extracts the conceptual models of the websites, which are subsequently analyzed in terms of all of the reusable design fragments used in them for supporting common domain functionalities. At the conceptual level, we consider these fragments as recurrent patterns consisting of a configuration of front-end interface components that interrelate each other and interact with end-users to support certain functionality. By performing a pattern-based analysis of the models, we locate the occurrences of all the recurrent patterns in the various website designs which are then evaluated towards their consistent use. The detected patterns can be used as building blocks in future designs, assisting developers to produce consistent and quality designs in the target domain. To support our case, we present a case study for the educational domain.

]]>Algorithms doi: 10.3390/a10010027

Authors: Heng Zhang Chengyou Wang Xiao Zhou

Digital image authentication has become a hot topic in the last few years. In this paper, a pixel-based fragile watermarking method is presented for image tamper identification and localization. By analyzing the left and right singular matrices of SVD, it is found that the matrix product between the first column of the left singular matrix and the transposition of the first column in the right singular matrix is closely related to the image texture features. Based on this characteristic, a binary watermark consisting of image texture information is generated and inserted into the least significant bit (LSB) of the original host image. To improve the security of the presented algorithm, the Arnold transform is applied twice in the watermark embedding process. Experimental results indicate that the proposed watermarking algorithm has high security and perceptual invisibility. Moreover, it can detect and locate the tampered region effectively for various malicious attacks.

]]>Algorithms doi: 10.3390/a10010026

Authors: Xi-Guang Li Shou-Fei Han Chang-Qing Gong

The Fireworks Algorithm is a recently developed swarm intelligence algorithm to simulate the explosion process of fireworks. Based on the analysis of each operator of Fireworks Algorithm (FWA), this paper improves the FWA and proves that the improved algorithm converges to the global optimal solution with probability 1. The proposed algorithm improves the goal of further boosting performance and achieving global optimization where mainly include the following strategies. Firstly using the opposition-based learning initialization population. Secondly a new explosion amplitude mechanism for the optimal firework is proposed. In addition, the adaptive t-distribution mutation for non-optimal individuals and elite opposition-based learning for the optimal individual are used. Finally, a new selection strategy, namely Disruptive Selection, is proposed to reduce the running time of the algorithm compared with FWA. In our simulation, we apply the CEC2013 standard functions and compare the proposed algorithm (IFWA) with SPSO2011, FWA, EFWA and dynFWA. The results show that the proposed algorithm has better overall performance on the test functions.

]]>Algorithms doi: 10.3390/a10010025

Authors: Tseng-Hsu Chien Yeong-Chin Chen

This is the first paper to present such a digital redesign method for the (conventional) OKID system and apply this novel technique for nonlinear system identification. First, the Observer/Kalman filter Identification (OKID) method is used to obtain the lower-order state-space model for a stochastic chaos system. Then, a digital redesign approach with the high-gain property is applied to improve and replace the observer identified by OKID. Therefore, the proposed OKID combined with an observer-based digital redesign novel tracker not only suppresses the uncertainties and the nonlinear perturbations, but also improves more accurate observation parameters of OKID for complex Multi-Input Multi-Output systems. In this research, Chen’s stochastic chaotic system is used as an illustrative example to demonstrate the effectiveness and excellence of the proposed methodology.

]]>Algorithms doi: 10.3390/a10010024

Authors: Henning Fernau Andreas Krebs

We study several classical decision problems on finite automata under the (Strong) Exponential Time Hypothesis. We focus on three types of problems: universality, equivalence, and emptiness of intersection. All these problems are known to be CoNP-hard for nondeterministic finite automata, even when restricted to unary input alphabets. A different type of problems on finite automata relates to aperiodicity and to synchronizing words. We also consider finite automata that work on commutative alphabets and those working on two-dimensional words.

]]>Algorithms doi: 10.3390/a10010023

Authors: James Lee David Rowlands Nicholas Jackson Raymond Leadbetter Tomohito Wada Daniel James

The increasing professionalism of sports persons and desire of consumers to imitate this has led to an increased metrification of sport. This has been driven in no small part by the widespread availability of comparatively cheap assessment technologies and, more recently, wearable technologies. Historically, whilst these have produced large data sets, often only the most rudimentary analysis has taken place (Wisbey et al in: “Quantifying movement demands of AFL football using GPS tracking”). This paucity of analysis is due in no small part to the challenges of analysing large sets of data that are often from disparate data sources to glean useful key performance indicators, which has been a largely a labour intensive process. This paper presents a framework that can be cloud based for the gathering, storing and algorithmic interpretation of large and inhomogeneous time series data sets. The framework is architecture based and technology agnostic in the data sources it can gather, and presents a model for multi set analysis for inter- and intra- devices and individual subject matter. A sample implementation demonstrates the utility of the framework for sports performance data collected from distributed inertial sensors in the sport of swimming.

]]>Algorithms doi: 10.3390/a10010022

Authors: Marios Koniaris Ioannis Anagnostopoulos Yannis Vassiliou

“Public legal information from all countries and international institutions is part of the common heritage of humanity. Maximizing access to this information promotes justice and the rule of law”. In accordance with the aforementioned declaration on free access to law by legal information institutes of the world, a plethora of legal information is available through the Internet, while the provision of legal information has never before been easier. Given that law is accessed by a much wider group of people, the majority of whom are not legally trained or qualified, diversification techniques should be employed in the context of legal information retrieval, as to increase user satisfaction. We address the diversification of results in legal search by adopting several state of the art methods from the web search, network analysis and text summarization domains. We provide an exhaustive evaluation of the methods, using a standard dataset from the common law domain that we objectively annotated with relevance judgments for this purpose. Our results: (i) reveal that users receive broader insights across the results they get from a legal information retrieval system; (ii) demonstrate that web search diversification techniques outperform other approaches (e.g., summarization-based, graph-based methods) in the context of legal diversification; and (iii) offer balance boundaries between reinforcing relevant documents or sampling the information space around the legal query.

]]>Algorithms doi: 10.3390/a10010021

Authors: Sergio Agostino Bruno Carpentieri Raffaele Pizzolante

Broadcasting a message from one to many processors in a network corresponds to concurrent reading on a random access shared memory parallel machine. Computing the trees of a forest, the level of each node in its tree and the path between two nodes are problems that can easily be solved with concurrent reading in a time logarithmic in the maximum height of a tree. Solving such problems with exclusive reading requires a time logarithmic in the number of nodes, implying message passing between disjoint pairs of processors on a distributed system. Allowing concurrent reading in parallel algorithm design for distributed computing might be advantageous in practice if these problems are faced on shallow trees with some specific constraints. We show an application to LZC (Lempel-Ziv-Compress)-compressed file decoding, whose parallelization employs these computations on such trees for realistic data. On the other hand, zipped files do not have this advantage, since they are compressed by the Lempel–Ziv sliding window technique.

]]>Algorithms doi: 10.3390/a10010020

Authors: Anne Berry Geneviève Simonet

The algorithm MLS (Maximal Label Search) is a graph search algorithm that generalizes the algorithms Maximum Cardinality Search (MCS), Lexicographic Breadth-First Search (LexBFS), Lexicographic Depth-First Search (LexDFS) and Maximal Neighborhood Search (MNS). On a chordal graph, MLS computes a PEO (perfect elimination ordering) of the graph. We show how the algorithm MLS can be modified to compute a PMO (perfect moplex ordering), as well as a clique tree and the minimal separators of a chordal graph. We give a necessary and sufficient condition on the labeling structure of MLS for the beginning of a new clique in the clique tree to be detected by a condition on labels. MLS is also used to compute a clique tree of the complement graph, and new cliques in the complement graph can be detected by a condition on labels for any labeling structure. We provide a linear time algorithm computing a PMO and the corresponding generators of the maximal cliques and minimal separators of the complement graph. On a non-chordal graph, the algorithm MLSM, a graph search algorithm computing an MEO and a minimal triangulation of the graph, is used to compute an atom tree of the clique minimal separator decomposition of any graph.

]]>Algorithms doi: 10.3390/a10010019

Authors: Ru Wang Chao Tan Jing Xu Zhongbin Wang Jingfei Jin Yiqiao Man

In order to improve the performance of the hydraulic support electro-hydraulic control system test platform, a self-tuning proportion integration differentiation (PID) controller is proposed to imitate the actual pressure of the hydraulic support. To avoid the premature convergence and to improve the convergence velocity for tuning PID parameters, the PID controller is optimized with a hybrid optimization algorithm integrated with the particle swarm algorithm (PSO) and genetic algorithm (GA). A selection probability and an adaptive cross probability are introduced into the PSO to enhance the diversity of particles. The proportional overflow valve is installed to control the pressure of the pillar cylinder. The data of the control voltage of the proportional relief valve amplifier and pillar pressure are collected to acquire the system transfer function. Several simulations with different methods are performed on the hydraulic cylinder pressure system. The results demonstrate that the hybrid algorithm for a PID controller has comparatively better global search ability and faster convergence velocity on the pressure control of the hydraulic cylinder. Finally, an experiment is conducted to verify the validity of the proposed method.

]]>Algorithms doi: 10.3390/a10010018

Authors: Emer Bernal Oscar Castillo José Soria Fevrier Valdez

In this paper we are presenting a method using fuzzy logic for dynamic parameter adaptation in the imperialist competitive algorithm, which is usually known by its acronym ICA. The ICA algorithm was initially studied in its original form to find out how it works and what parameters have more effect upon its results. Based on this study, several designs of fuzzy systems for dynamic adjustment of the ICA parameters are proposed. The experiments were performed on the basis of solving complex optimization problems, particularly applied to benchmark mathematical functions. A comparison of the original imperialist competitive algorithm and our proposed fuzzy imperialist competitive algorithm was performed. In addition, the fuzzy ICA was compared with another metaheuristic using a statistical test to measure the advantage of the proposed fuzzy approach for dynamic parameter adaptation.

]]>Algorithms doi: 10.3390/a10010016

Authors: Yi-Shan Lin Chun-Liang Lee Yaw-Chung Chen

Since frequent communication between applications takes place in high speed networks, deep packet inspection (DPI) plays an important role in the network application awareness. The signature-based network intrusion detection system (NIDS) contains a DPI technique that examines the incoming packet payloads by employing a pattern matching algorithm that dominates the overall inspection performance. Existing studies focused on implementing efficient pattern matching algorithms by parallel programming on software platforms because of the advantages of lower cost and higher scalability. Either the central processing unit (CPU) or the graphic processing unit (GPU) were involved. Our studies focused on designing a pattern matching algorithm based on the cooperation between both CPU and GPU. In this paper, we present an enhanced design for our previous work, a length-bounded hybrid CPU/GPU pattern matching algorithm (LHPMA). In the preliminary experiment, the performance and comparison with the previous work are displayed, and the experimental results show that the LHPMA can achieve not only effective CPU/GPU cooperation but also higher throughput than the previous method.

]]>Algorithms doi: 10.3390/a10010017

Authors: Fayyaz Ahmad Toseef Bhutta Umar Shoaib Malik Zaka Ullah Ali Alshomrani Shamshad Ahmad Shahid Ahmad

A modification to an existing iterative method for computing zeros with unknown multiplicities of nonlinear equations or a system of nonlinear equations is presented. We introduce preconditioners to nonlinear equations or a system of nonlinear equations and their corresponding Jacobians. The inclusion of preconditioners provides numerical stability and accuracy. The different selection of preconditioner offers a family of iterative methods. We modified an existing method in a way that we do not alter its inherited quadratic convergence. Numerical simulations confirm the quadratic convergence of the preconditioned iterative method. The influence of preconditioners is clearly reflected in the numerically achieved accuracy of computed solutions.

]]>Algorithms doi: 10.3390/a10010015

Authors: Olga Santos

Personal tracking technologies allow sensing of the physical activity carried out by people. Data flows collected with these sensors are calling for big data techniques to support data collection, integration and analysis, aimed to provide personalized support when learning motor skills through varied multisensorial feedback. In particular, this paper focuses on vibrotactile feedback as it can take advantage of the haptic sense when supporting the physical interaction to be learnt. Despite each user having different needs, when providing this vibrotactile support, personalization issues are hardly taken into account, but the same response is delivered to each and every user of the system. The challenge here is how to design vibrotactile user interfaces for adaptive learning of motor skills. TORMES methodology is proposed to facilitate the elicitation of this personalized support. The resulting systems are expected to dynamically adapt to each individual user’s needs by monitoring, comparing and, when appropriate, correcting in a personalized way how the user should move when practicing a predefined movement, for instance, when performing a sport technique or playing a musical instrument.

]]>Algorithms doi: 10.3390/a10010013

Authors: Paolo Mercorelli

This article is focused on the detection of errors using an approach that is signal based. The proposed algorithm considers several criteria: soft, hard and very hard recognition error. After the recognition of the error, the error is replaced. In this sense, different strategies for data reconciliation are associated with the proposed criteria error detection. Algorithms in several industrial software platforms are used for detecting errors of sensors. Computer simulations confirm the validation of the presented applications. Results with actual sensor measurements in industrial processes are presented.

]]>Algorithms doi: 10.3390/a10010014

Authors: Yu Feng Jianzhong Zhou Muhammad Tayyab

This paper presents a kernel fuzzy clustering with a novel differential harmony search algorithm to coordinate with the diversion scheduling scheme classification. First, we employed a self-adaptive solution generation strategy and differential evolution-based population update strategy to improve the classical harmony search. Second, we applied the differential harmony search algorithm to the kernel fuzzy clustering to help the clustering method obtain better solutions. Finally, the combination of the kernel fuzzy clustering and the differential harmony search is applied for water diversion scheduling in East Lake. A comparison of the proposed method with other methods has been carried out. The results show that the kernel clustering with the differential harmony search algorithm has good performance to cooperate with the water diversion scheduling problems.

]]>Algorithms doi: 10.3390/a10010012

Authors: Wu Huang Feng Ding

This paper focuses on the recursive identification problems for a multivariate output-error system. By decomposing the system into several subsystems and by forming a coupled relationship between the parameter estimation vectors of the subsystems, two coupled auxiliary model based recursive least squares (RLS) algorithms are presented. Moreover, in contrast to the auxiliary model based recursive least squares algorithm, the proposed algorithms provide a reference to improve the identification accuracy of the multivariate output-error system. The simulation results confirm the effectiveness of the proposed algorithms.

]]>Algorithms doi: 10.3390/a10010011

Authors: Algorithms Editorial Office

The editors of Algorithms would like to express their sincere gratitude to the following reviewers for assessing manuscripts in 2016.[...]

]]>Algorithms doi: 10.3390/a10010008

Authors: Emna Ben Abdallah Tony Ribeiro Morgan Magnin Olivier Roux Katsumi Inoue

Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. Results: We propose a new methodology for models expressed through a timed extension of the automata networks (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. Conclusion: The originality of our work is three-fold: (i) identifying the sign of the interaction; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems’ dynamics. We show the benefits of such an automatic approach on dynamical biological models, the DREAM4(in silico) and DREAM8 (breast cancer) datasets, popular reverse-engineering challenges, in order to discuss the precision and the computational performances of our modeling method.

]]>Algorithms doi: 10.3390/a10010010

Authors: Ştefan Măruşter

In this paper, we propose an algorithm to estimate the radius of convergence for the Picard iteration in the setting of a real Hilbert space. Numerical experiments show that the proposed algorithm provides convergence balls close to or even identical to the best ones. As the algorithm does not require to evaluate the norm of derivatives, the computing effort is relatively low.

]]>Algorithms doi: 10.3390/a10010009

Authors: Ruxin Zhao Qifang Luo Yongquan Zhou

The Social Spider Optimization algorithm (SSO) is a novel metaheuristic optimization algorithm. To enhance the convergence speed and computational accuracy of the algorithm, in this paper, an elite opposition-based Social Spider Optimization algorithm (EOSSO) is proposed; we use an elite opposition-based learning strategy to enhance the convergence speed and computational accuracy of the SSO algorithm. The 23 benchmark functions are tested, and the results show that the proposed elite opposition-based Social Spider Optimization algorithm is able to obtain an accurate solution, and it also has a fast convergence speed and a high degree of stability.

]]>Algorithms doi: 10.3390/a10010007

Authors: Lingjun Liu Zhonghua Xie Jiuchao Feng

This paper presents a variant of the iterative shrinkage-thresholding (IST) algorithm, called backtracking-based adaptive IST (BAIST), for image compressive sensing (CS) reconstruction. For increasing iterations, IST usually yields a smoothing of the solution and runs into prematurity. To add back more details, the BAIST method backtracks to the previous noisy image using L2 norm minimization, i.e., minimizing the Euclidean distance between the current solution and the previous ones. Through this modification, the BAIST method achieves superior performance while maintaining the low complexity of IST-type methods. Also, BAIST takes a nonlocal regularization with an adaptive regularizor to automatically detect the sparsity level of an image. Experimental results show that our algorithm outperforms the original IST method and several excellent CS techniques.

]]>Algorithms doi: 10.3390/a10010005

Authors: Sung Bae Tong-Wook Shinn Tadao Takaoka

We present efficient sequential and parallel algorithms for the maximum sum (MS) problem, which is to maximize the sum of some shape in the data array. We deal with two MS problems; the maximum subarray (MSA) problem and the maximum convex sum (MCS) problem. In the MSA problem, we find a rectangular part within the given data array that maximizes the sum in it. The MCS problem is to find a convex shape rather than a rectangular shape that maximizes the sum. Thus, MCS is a generalization of MSA. For the MSA problem, O ( n ) time parallel algorithms are already known on an ( n , n ) 2D array of processors. We improve the communication steps from 2 n − 1 to n, which is optimal. For the MCS problem, we achieve the asymptotic time bound of O ( n ) on an ( n , n ) 2D array of processors. We provide rigorous proofs for the correctness of our parallel algorithm based on Hoare logic and also provide some experimental results of our algorithm that are gathered from the Blue Gene/P super computer. Furthermore, we briefly describe how to compute the actual shape of the maximum convex sum.

]]>Algorithms doi: 10.3390/a10010006

Authors: Mihaly Mezei

An algorithm is presented for the simulation of two partially flexible macromolecules where the interaction between the flexible parts and rigid parts is represented by energy grids associated with the rigid part of each macromolecule. The proposed algorithm avoids the transformation of the grid upon molecular movement at the expense of the significantly lesser effect of transforming the flexible part.

]]>Algorithms doi: 10.3390/a10010004

Authors: Atsushi Mizuta Qi-Wei Ge Hiroshi Matsuno

Despite the recent rapid progress in high throughput measurements of biological data, it is still difficult to gather all of the reaction speed data in biological pathways. This paper presents a Petri net-based algorithm that can derive estimated values for non-valid reaction speeds in a signaling pathway from biologically-valid data. In fact, these reaction speeds are reflected based on the delay times in the timed Petri net model of the signaling pathway. We introduce the concept of a “dependency relation” over a transition set of a Petri net and derive the properties of the dependency relation through a structural analysis. Based on the theoretical results, the proposed algorithm can efficiently shrink the transitions with two elementary structures into a single transition repeatedly to reduce the Petri net size in order to eventually discover all transition sets with a dependency relation. Finally, to show the usefulness of our algorithm, we apply our algorithm to the IL-3 Petri net model.

]]>Algorithms doi: 10.3390/a10010003

Authors: Guomin Li Guisheng Liao

An improved pilot pattern algorithm for facilitating the channel estimation in multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems is proposed in this paper. The presented algorithm reconfigures the parameter in the least square (LS) algorithm, which belongs to the space-time block-coded (STBC) category for channel estimation in pilot-based MIMO-OFDM system. Simulation results show that the algorithm has better performance in contrast to the classical single symbol scheme. In contrast to the double symbols scheme, the proposed algorithm can achieve nearly the same performance with only half of the complexity of the double symbols scheme.

]]>Algorithms doi: 10.3390/a10010001

Authors: Klaus Wehmuth Éric Fleury Artur Ziviani

We present the algebraic representation and basic algorithms for MultiAspect Graphs (MAGs). A MAG is a structure capable of representing multilayer and time-varying networks, as well as higher-order networks, while also having the property of being isomorphic to a directed graph. In particular, we show that, as a consequence of the properties associated with the MAG structure, a MAG can be represented in matrix form. Moreover, we also show that any possible MAG function (algorithm) can be obtained from this matrix-based representation. This is an important theoretical result since it paves the way for adapting well-known graph algorithms for application in MAGs. We present a set of basic MAG algorithms, constructed from well-known graph algorithms, such as degree computing, Breadth First Search (BFS), and Depth First Search (DFS). These algorithms adapted to the MAG context can be used as primitives for building other more sophisticated MAG algorithms. Therefore, such examples can be seen as guidelines on how to properly derive MAG algorithms from basic algorithms on directed graphs. We also make available Python implementations of all the algorithms presented in this paper.

]]>Algorithms doi: 10.3390/a10010002

Authors: Yu-Fei Gao Xun-Chao Cong Yue Yang Qun Wan Guan Gui

This paper investigates a structured sparse SAR imaging algorithm for point scattering model based on tensor decomposition. Several SAR imaging schemes have been developed by researchers for improving the imaging quality. For a typical SAR target scenario, the scatterers distribution usually has the feature of structured sparsity. Without considering this feature thoroughly, the existing schemes have still certain drawbacks. The classic matching pursuit algorithms can obtain clearer imaging results, but the cost is resulting in an extreme complexity and a huge computation resource consumption. Therefore, this paper put forward a tensor-based SAR imaging algorithm by means of multiway structured sparsity which makes full use of the above geometrical feature of the scatterers distribution. The spotlight SAR observation signal is formulated as a Tucker model considering the Kronecker constraint, and then a sparse reconstruction algorithm is introduced by utilizing the structured sparsity of the scene. The proposed tensor-based SAR imaging model is able to take advantage of the Kronecker information in each mode, which ensures the robustness for the signal reconstruction. Both the algorithm complexity analysis and numerical simulations show that the proposed method requires less computation than the existing sparsity-driven SAR imaging algorithms. The imaging realizations based on the practical measured data also indicate that the proposed algorithm is superior to the reference methods even in the severe noisy environment, under the condition of multiway structured sparsity.

]]>Algorithms doi: 10.3390/a9040088

Authors: Huanyang Zheng Jie Wu

Human–Machine Cooperations (HMCs) can balance the advantages and disadvantages of human computation (accurate but costly) and machine computation (cheap but inaccurate). This paper studies HMCs in agglomerative hierarchical clusterings, where the machine can ask the human some questions. The human will return the answers to the machine, and the machine will use these answers to correct errors in its current clustering results. We are interested in the machine’s strategy on handling the question operations, in terms of three problems: (1) Which question should the machine ask? (2) When should the machine ask the question (early or late)? (3) How does the machine adjust the clustering result, if the machine’s mistake is found by the human? Based on the insights of these problems, an efficient algorithm is proposed with five implementation variations. Experiments on image clusterings show that the proposed algorithm can improve the clustering accuracy with few question operations.

]]>Algorithms doi: 10.3390/a9040087

Authors: Yan Fu Shengchun Wang

Nowadays, how to evaluate image quality reasonably is a basic and challenging problem. In view of the present no reference evaluation methods, they cannot reflect the human visual perception of image quality accurately. In this paper, we propose an efficient general-purpose no reference image quality assessment (NRIQA) method based on visual perception, and effectively integrates human visual characteristics into the NRIQA fields. First, a novel algorithm for salient region extraction is presented. Two characteristics graphs of texture and edging of the original image are added to the Itti model. Due to the normalized luminance coefficients of natural images obey the generalized Gauss probability distribution, we utilize this characteristic to extract statistical features in the regions of interest (ROI) and regions of non-interest respectively. Then, the extracted features are fused to be an input to establish the support vector regression (SVR) model. Finally, the IQA model obtained by training is used to predict the quality of the image. Experimental results show that this method has good predictive ability, and the evaluation effect is better than existing classical algorithms. Moreover, the predicted results are more consistent with human subjective perception, which can accurately reflect the human visual perception to image quality.

]]>Algorithms doi: 10.3390/a9040084

Authors: Santoso Wibowo Hepu Deng Wei Xu

This paper presents a fuzzy multi-criteria group decision making method for evaluating the performance of Cloud services in an uncertain environment. Intuitionistic fuzzy numbers are used to better model the subjectivity and imprecision in the performance evaluation process. An effective algorithm is developed based on the technique for order preference by similarity to the ideal solution and the Choquet integral operator for adequately solving the performance evaluation problem. An example is presented for demonstrating the applicability of the proposed method for solving the multi-criteria group decision making problem in real situations.

]]>Algorithms doi: 10.3390/a9040086

Authors: Qinjiao Gao Shenggang Zhang

This paper proposes moving mesh strategies for the moving mesh methods when solving the nonlinear time dependent partial differential equations (PDEs). Firstly we analyse Huang’s moving mesh PDEs (MMPDEs) and observe that, after Euler discretion they could be taken as one step of the root searching iteration methods. We improve Huang’s MMPDE by adding one Lagrange speed term. The proposed moving mesh PDE could draw the mesh to equidistribution quickly and stably. The numerical algorithm for the coupled system of the original PDE and the moving mesh equation is proposed and the computational experiments are given to illustrate the validity of the new method.

]]>Algorithms doi: 10.3390/a9040085

Authors: Yuqin Xie Mingchun Zheng

Devising methods to publish social network data in a form that affords utility without compromising privacy remains a longstanding challenge, while many existing methods based on k-anonymity algorithms on social networks may result in nontrivial utility loss without analyzing the social network topological structure and without considering the attributes of sparse distribution. Toward this objective, we explore the impact of the attributes of sparse distribution on data utility. Firstly, we propose a new utility metric that emphasizes network structure distortion and attribute value loss. Furthermore, we design and implement a differentiated k-anonymity l-diversity social network anonymity algorithm, which seeks to protect users’ privacy in social networks and increase the usability of the published anonymized data. Its key idea is that it divides a node into two child nodes and only anonymizes sensitive values to satisfy anonymity requirements. The evaluation results show that our method can effectively improve the data utility as compared to generalized anonymizing algorithms.

]]>Algorithms doi: 10.3390/a9040083

Authors: Zhimin Liu Shouqiang Du Ruiying Wang

Our purpose of this paper is to solve a class of stochastic linear complementarity problems (SLCP) with finitely many elements. Based on a new stochastic linear complementarity problem function, a new semi-smooth least squares reformulation of the stochastic linear complementarity problem is introduced. For solving the semi-smooth least squares reformulation, we propose a feasible nonsmooth Levenberg–Marquardt-type method. The global convergence properties of the nonsmooth Levenberg–Marquardt-type method are also presented. Finally, the related numerical results illustrate that the proposed method is efficient for the related refinery production problem and the large-scale stochastic linear complementarity problems.

]]>Algorithms doi: 10.3390/a9040082

Authors: Luis Pedraza Cesar Hernandez Ingrid Paez Jorge Ortiz E. Rodriguez-Colina

This paper presents the development and evaluation of two linear algorithms for forecasting reception power for different channels at an assigned spectrum band of global systems for mobile communications (GSM), in order to analyze the spatial opportunity for reuse of frequencies by secondary users (SUs) in a cognitive radio (CR) network. The algorithms employed correspond to seasonal autoregressive integrated moving average (SARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH), which allow for a forecast of channel occupancy status. Results are evaluated using the following criteria: availability and occupancy time for channels, different types of mean absolute error, and observation time. The contributions of this work include a more integral forecast as the algorithm not only forecasts reception power but also the occupancy and availability time of a channel to determine its precision percentage during the use by primary users (PUs) and SUs within a CR system. Algorithm analyses demonstrate a better performance for SARIMA over GARCH algorithm in most of the evaluated variables.

]]>Algorithms doi: 10.3390/a9040081

Authors: Puren Ouyang Yuqi Hu Wenhui Yue Deshun Liu

Reduction of contour error is a very important issue for high precise contour tracking applications, and many control systems were proposed to deal with contour tracking problems for two/three axial translational motion systems. However, there is no research on cross-coupled contour tracking control for serial multi-DOF robot manipulators. In this paper, the contouring control of multi-DOF serial manipulators is developed for the first time and a new cross-coupled PD (CC-PD) control law is proposed, based on contour errors of the end-effector and tracking errors of the joints. It is a combination of PD control for trajectory tracking at joint level and PD control for contour tracking at the end-effector level. The contour error of the end-effector is transformed to the equivalent tracking errors of the joints using the Jacobian regulation, and the CC-PD control law is implemented in the joint level. Stability analysis of the proposed CC-PD control system is conducted using the Lyapunov method, followed by some simulation studies for linear and nonlinear contour tracking to verify the effectiveness of the proposed CC-PD control system.

]]>Algorithms doi: 10.3390/a9040080

Authors: Yuancheng Li Panpan Guo Xiang Li

The smart meter is an important part of the smart grid, and in order to take full advantage of smart meter data, this paper mines the electricity behaviors of smart meter users to improve the accuracy of load forecasting. First, the typical day loads of users are calculated separately according to different date types (ordinary workdays, day before holidays, holidays). Second, the similarity between user electricity behaviors is mined and the user electricity loads are clustered to classify the users with similar behaviors into the same cluster. Finally, the load forecasting model based on the Online Sequential Extreme Learning Machine (OS-ELM) is applied to different clusters to conduct load forecasting and the load forecast is summed to obtain the system load. In order to prove the validity of the proposed method, we performed simulation experiments on the MATLAB platform using smart meter data from the Ireland electric power cooperation. The experimental results show that the proposed method is able to mine the user electricity behaviors deeply, improve the accuracy of load forecasting by the reasonable clustering of users, and reveal the relationship between forecasting accuracy and cluster numbers.

]]>Algorithms doi: 10.3390/a9040079

Authors: Qingshan You Yongjie Luo Qun Wan

We propose a two-stage method to test the robustness of the generalized approximate message passing algorithm (GAMP). A pursuit process based on the marginal posterior probability is inserted in the standard GAMP algorithm to find the support of a sparse vector, and a revised GAMP process is used to estimate the amplitudes of the support. The numerical experiments with simulation and real world data confirm the robustness and performance of our proposed algorithm.

]]>Algorithms doi: 10.3390/a9040078

Authors: Wei Li

The issue of exploration-exploitation remains one of the most challenging tasks within the framework of evolutionary algorithms. To effectively balance the exploration and exploitation in the search space, this paper proposes a modified cloud particles differential evolution algorithm (MCPDE) for real-parameter optimization. In contrast to the original Cloud Particles Differential Evolution (CPDE) algorithm, firstly, control parameters adaptation strategies are designed according to the quality of the control parameters. Secondly, the inertia factor is introduced to effectively keep a better balance between exploration and exploitation. Accordingly, this is helpful for maintaining the diversity of the population and discouraging premature convergence. In addition, the opposition mechanism and the orthogonal crossover are used to increase the search ability during the evolutionary process. Finally, CEC2013 contest benchmark functions are selected to verify the feasibility and effectiveness of the proposed algorithm. The experimental results show that the proposed MCPDE is an effective method for global optimization problems.

]]>Algorithms doi: 10.3390/a9040077

Authors: Carlos De Niz Raziur Rahman Xiangyuan Zhao Ranadip Pal

Precision medicine entails the design of therapies that are matched for each individual patient. Thus, predictive modeling of drug responses for specific patients constitutes a significant challenge for personalized therapy. In this article, we consider a review of approaches that have been proposed to tackle the drug sensitivity prediction problem especially with respect to personalized cancer therapy. We first discuss modeling approaches that are based on genomic characterizations alone and further the discussion by including modeling techniques that integrate both genomic and functional information. A comparative analysis of the prediction performance of four representative algorithms, elastic net, random forest, kernelized Bayesian multi-task learning and deep learning, reflecting the broad classes of regularized linear, ensemble, kernelized and neural network-based models, respectively, has been included in the paper. The review also considers the challenges that need to be addressed for successful implementation of the algorithms in clinical practice.

]]>Algorithms doi: 10.3390/a9040075

Authors: Anantachai Padcharoen Poom Kumam Yeol Cho Phatiphat Thounthong

In this paper, we present a new iterative scheme for finding a common element of the solution set F of the split feasibility problem and the fixed point set F ( T ) of a right Bregman strongly quasi-nonexpansive mapping T in p-uniformly convex Banach spaces which are also uniformly smooth. We prove strong convergence theorem of the sequences generated by our scheme under some appropriate conditions in real p-uniformly convex and uniformly smooth Banach spaces. Furthermore, we give some examples and applications to illustrate our main results in this paper. Our results extend and improve the recent ones of some others in the literature.

]]>Algorithms doi: 10.3390/a9040076

Authors: Patrícia Ramos José Oliveira

In this work, a cross-validation procedure is used to identify an appropriate Autoregressive Integrated Moving Average model and an appropriate state space model for a time series. A minimum size for the training set is specified. The procedure is based on one-step forecasts and uses different training sets, each containing one more observation than the previous one. All possible state space models and all ARIMA models where the orders are allowed to range reasonably are fitted considering raw data and log-transformed data with regular differencing (up to second order differences) and, if the time series is seasonal, seasonal differencing (up to first order differences). The value of root mean squared error for each model is calculated averaging the one-step forecasts obtained. The model which has the lowest root mean squared error value and passes the Ljung–Box test using all of the available data with a reasonable significance level is selected among all the ARIMA and state space models considered. The procedure is exemplified in this paper with a case study of retail sales of different categories of women’s footwear from a Portuguese retailer, and its accuracy is compared with three reliable forecasting approaches. The results show that our procedure consistently forecasts more accurately than the other approaches and the improvements in the accuracy are significant.

]]>Algorithms doi: 10.3390/a9040074

Authors: Hanbing Liu Xin He Xianqiang Wang Yubo Jiao Gang Song

Irregularly-shaped bridges are usually adopted to connect the main bridge and ramps in urban overpasses, which are under significant flexion-torsion coupling effects and in complicated stress states. In irregular-shaped bridge design, the parameters such as ramp radius, bifurcation diaphragm stiffness, box girder height, and supporting condition could affect structural performance in different manners. In this paper, the influence of various parameters on three indices, including maximum stress, the stress variation coefficient, and the fundamental frequency of torsional vibration, is investigated and analyzed based on orthogonal test method. Through orthogonal analysis, the major influence parameters and corresponding optimal values for these indices are achieved. Combining with the analytic hierarchy process (AHP), the hierarchical structure model of the multi-indices orthogonal test is established and a comprehensive weight analysis method is proposed to reflect the parameter influence on overall mechanical properties of an irregularly-shaped bridge. Influence order and optimal values of parameters for overall mechanical properties are determined based on the weight of factors and levels calculated by the comprehensive weight analysis method. The results indicate that the comprehensive weight analysis method is superior to the overall balance method, which verifies the effectiveness and accuracy of the comprehensive weight analysis in the parameter optimization of the multi-indices orthogonal test for an irregularly-shaped bridge. Optimal parameters obtained in this paper can provide reference and guidance for parameter control in irregularly-shaped bridge design.

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