Algorithms, Volume 12, Issue 1 (January 2019) – 27 articles

Marine transportation in Aegean Sea, a part of the Mediterranean Sea that serves as gateway between three continents has recently seen a significant increase. Despite the commercial benefits to the region, there are certain issues related to the preservation of the local ecosystem and safety. This danger is further deteriorated by the absence of regulations on allowed waterways. Marine accidents could cause a major ecological disaster in the area and pose big socio-economic impacts in Greece. Monitoring marine traffic data is of major importance and one of the primary goals of the current research. Real-time monitoring and alerting can be extremely useful to local authorities, companies, NGO’s and the public in general. Apart from real-time applications, the knowledge discovery from historical data is also significant. Towards this direction, a data analysis and simulation framework for maritime data has been designed and developed. The framework analyzes historical data about ships and area conditions, of varying time and space granularity, measures critical parameters that could influence the levels of hazard in certain regions and clusters such data according to their similarity. Upon this unsupervised step, the degree of hazard is estimated and along with other important parameters is fed into a special type of Bayesian network, in order to infer on future situations, thus, simulating future data based on past conditions. Another innovative aspect of this work is the modeling of shipping traffic as a social network, whose analysis could provide useful and informative visualizations. The use of such a system is particularly beneficial for multiple stakeholders, such as the port authorities, the ministry of Mercantile Marine, etc. mainly due to the fact that specific policy options can be evaluated and re-designed based on feedback from our framework. Full article

Machine translation is used in many applications in everyday life. Due to the increase of translated documents that need to be organized as useful or not (for building a translation model), the automated categorization of texts (classification), is a popular research field of machine learning. This kind of information can be quite helpful for machine translation. Our parallel corpora (English-Greek and English-Italian) are based on educational data, which are quite difficult to translate. We apply two state of the art architectures, Random Forest (RF) and Deeplearnig4j (DL4J), to our data (which constitute three translation outputs). To our knowledge, this is the first time that deep learning architectures are applied to the automatic selection of parallel data. We also propose new string-based features that seem to be effective for the classifier, and we investigate whether an attribute selection method could be used for better classification accuracy. Experimental results indicate an increase of up to 4% (compared to our previous work) using RF and rather satisfactory results using DL4J. Full article

For an energy-harvesting wireless transmission system, considering that a transmitter which can harvest energy from nature has two kinds of extra energy consumption, circuit consumption and storage losses, the optimization models are set up in this paper for the purpose of maximizing the average throughput of the system within a certain period of time for both a time-invariant channel and time-varying channel. Convex optimization methods such as the Lagrange multiplier method and the KKT (Karush–Kuhn–Tucker) condition are used to solve the optimization problem; then, an optimal offline power allocation algorithm which has a three-threshold structure is proposed. In the three-threshold algorithm, two thresholds can be achieved by using a linear search method while the third threshold is calculated according to the channel state information and energy losses; then, the offline power allocation is based on the three thresholds and energy arrivals. Furthermore, inspired by the optimal offline algorithm, a low-complexity online algorithm with adaptive thresholds is derived. Finally, the simulation results show that the offline power allocation algorithms proposed in this paper are better than other algorithms, the performance of the online algorithm proposed is close to the offline one, and these algorithms can help improve the average throughput of the system. Full article

A lot of research work has studied the auction mechanism of uncertain advertising cooperation between the e-commerce platform and advertisers, but little has focused on pricing strategy in stable advertising cooperation under a certain market power structure. To fill this gap, this paper makes a study of the deep interest distribution of two parties in such cooperation. We propose a pricing strategy by building two stackelberg master-slave models when the e-commerce platform and the advertiser are respectively the leader in the cooperation. It is analyzed that the optimization solution of the profits of both parties and the total system are affected by some main decision factors including the income commission proportion, the advertising product price and the cost of advertising effort of both parties’ brand in different dominant models. Then, some numerical studies are used to verify the effectiveness of the models. Finally, we draw a conclusion and make some suggestions to the platforms and the advertisers in the e-commerce advertising cooperation. Full article

Let k denote an integer greater than 2, let G denote a k-partite graph, and let S denote the set of all maximal k-partite cliques in G. Several open questions concerning the computation of S are resolved. A straightforward and highly-scalable modification to the classic recursive backtracking approach of Bron and Kerbosch is first described and shown to run in O(3^n/3) time. A series of novel graph constructions is then used to prove that this bound is best possible in the sense that it matches an asymptotically tight upper limit on |S|. The task of identifying a vertex-maximum element of S is also considered and, in contrast with the k = 2 case, shown to be NP-hard for every k ≥ 3. A special class of k-partite graphs that arises in the context of functional genomics and other problem domains is studied as well and shown to be more readily solvable via a polynomial-time transformation to bipartite graphs. Applications, limitations, potentials for faster methods, heuristic approaches, and alternate formulations are also addressed. Full article

As an important angle sensor of the opto-electric platform, gyro output accuracy plays a vital role in the stabilization and track accuracy of the whole system. It is known that the generally used fixed-bandwidth filters, single neural network models, or linear models cannot compensate for gyro error well, and so they cannot meet engineering needs satisfactorily. In this paper, a novel hybrid ARIMA-Elman model is proposed. For the reason that it can fully combine the strong linear approximation capability of the ARIMA model and the superior nonlinear compensation capability of a neural network, the proposed model is suitable for handling gyro error, especially for its non-stationary random component. Then, to solve the problem that the parameters of ARIMA model and the initial weights of the Elman neural network are difficult to determine, a differential algorithm is initially utilized for parameter selection. Compared with other commonly used optimization algorithms (e.g., the traditional least-squares identification method and the genetic algorithm method), the intelligence differential algorithm can overcome the shortcomings of premature convergence and has higher optimization speed and accuracy. In addition, the drift error is obtained based on the technique of lift-wavelet separation and reconstruction, and, in order to weaken the randomness of the data sequence, an ashing operation and Jarque-Bear test have been added to the handle process. In this study, actual gyro data is collected and the experimental results show that the proposed method has higher compensation accuracy and faster network convergence, when compared with other commonly used error-compensation methods. Finally, the hybrid method is used to compensate for gyro error collected in other states. The test results illustrate that the proposed algorithm can effectively improve error compensation accuracy, and has good generalization performance. Full article

In this paper, a robust guaranteed-cost preview repetitive controller is proposed for a class of polytopic uncertain discrete-time systems. In order to improve the tracking performance, a repetitive controller, combined with preview compensator, is inserted in the forward channel. By using the L-order forward difference operator, an augmented dynamic system is constructed. Then, the guaranteed-cost preview repetitive control problem is transformed into a guaranteed-cost control problem for the augmented dynamic system. For a given performance index, the sufficient condition of asymptotic stability for the closed-loop system is derived by using a parameter-dependent Lyapunov function method and linear matrix inequality (LMI) techniques. Incorporating the controller obtained into the original system, the guaranteed-cost preview repetitive controller is derived. A numerical example is also included, to show the effectiveness of the proposed method. Full article

This paper develops an automated algorithm to process input data for segmented string relative rankings (SSRRs). The purpose of the SSRR methodology is to create rankings of countries, companies, or any other units based on surveys of expert opinion. This is done without the use of grading systems, which can distort the results due to varying degrees of strictness among experts. However, the original SSRR approach relies on manual application, which is highly laborious and also carries a risk of human error. This paper seeks to solve this problem by further developing the SSRR approach by employing link analysis, which is based on network theory and is similar to the PageRank algorithm used by the Google search engine. The ranking data are treated as part of a linear, hierarchical network and each unit receives a score according to how many units are positioned below it in the network. This approach makes it possible to efficiently resolve contradictions among experts providing input for a ranking. A hypertext preprocessor (PHP) script for the algorithm is included in the article’s appendix. The proposed methodology is suitable for use across a range of social science disciplines, especially economics, sociology, and political science. Full article

Quality of service multicast routing is an important research topic in networks. Research has sought to obtain a multicast routing tree at the lowest cost that satisfies bandwidth, delay and delay jitter constraints. Due to its non-deterministic polynomial complete problem, many meta-heuristic algorithms have been adopted to solve this kind of problem. The paper presents a new hybrid algorithm, namely ACO&CM, to solve the problem. The primary innovative point is to combine the solution generation process of ant colony optimization (ACO) algorithm with the Cloud model (CM). Moreover, within the framework structure of the ACO, we embed the cloud model in the ACO algorithm to enhance the performance of the ACO algorithm by adjusting the pheromone trail on the edges. Although a high pheromone trail intensity on some edges may trap into local optimum, the pheromone updating strategy based on the CM is used to search for high-quality areas. In order to avoid the possibility of loop formation, we devise a memory detection search (MDS) strategy, and integrate it into the path construction process. Finally, computational results demonstrate that the hybrid algorithm has advantages of an efficient and excellent performance for the solution quality. Full article

Nowadays, dynamic parameter adaptation has been shown to provide a significant improvement in several metaheuristic optimization methods, and one of the main ways to realize this dynamic adaptation is the implementation of Fuzzy Inference Systems. The main reason for this is because Fuzzy Inference Systems can be designed based on human knowledge, and this can provide an intelligent dynamic adaptation of parameters in metaheuristics. In addition, with the coming forth of Type-2 Fuzzy Logic, the capability of uncertainty handling offers an attractive improvement for dynamic parameter adaptation in metaheuristic methods, and, in fact, the use of Interval Type-2 Fuzzy Inference Systems (IT2 FIS) has been shown to provide better results with respect to Type-1 Fuzzy Inference Systems (T1 FIS) in recent works. Based on the performance improvement exhibited by IT2 FIS, the present paper aims to implement the Shadowed Type-2 Fuzzy Inference System (ST2 FIS) for further improvements in dynamic parameter adaptation in Harmony Search and Differential Evolution optimization methods. The ST2 FIS is an approximation of General Type-2 Fuzzy Inference Systems (GT2 FIS), and is based on the principles of Shadowed Fuzzy Sets. The main reason for using ST2 FIS and not GT2 FIS is because the computational cost of GT2 FIS represents a time limitation in this application. The paper presents a comparison of the conventional methods with static parameters and the dynamic parameter adaptation based on ST2 FIS, and the approaches are compared in solving mathematical functions and in controller optimization. Full article

Pansharpening is a domain-specific task of satellite imagery processing, which aims at fusing a multispectral image with a corresponding panchromatic one to enhance the spatial resolution of multispectral image. Most existing traditional methods fuse multispectral and panchromatic images in linear manners, which greatly restrict the fusion accuracy. In this paper, we propose a highly efficient inference network to cope with pansharpening, which breaks the linear limitation of traditional methods. In the network, we adopt a dilated multilevel block coupled with a skip connection to perform local and overall compensation. By using dilated multilevel block, the proposed model can make full use of the extracted features and enlarge the receptive field without introducing extra computational burden. Experiment results reveal that our network tends to induce competitive even superior pansharpening performance compared with deeper models. As our network is shallow and trained with several techniques to prevent overfitting, our model is robust to the inconsistencies across different satellites. Full article

This paper proposes total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization (MP-GMBSO). Efficient utilization of energy is necessary for reduction of CO₂ emission, and smart city demonstration projects have been conducted around the world in order to reduce total energies and the amount of CO₂ emission. The problem can be formulated as a mixed integer nonlinear programming (MINLP) problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolution (DE), Differential Evolutionary Particle Swarm Optimization (DEEPSO), Brain Storm Optimization (BSO), Modified BSO (MBSO), Global-best BSO (BSO), and Global-best Modified Brain Storm Optimization (GMBSO) have been applied to the problem. However, there is still room for improving solution quality. Multi-population based evolutionary computation methods have been verified to improve solution quality and the approach has a possibility for improving solution quality. The proposed MS-GMBSO utilizes only migration for multi-population models instead of abest, which is the best individual among all of sub-populations so far, and both migration and abest. Various multi-population models, migration topologies, migration policies, and the number of sub-populations are also investigated. It is verified that the proposed MP-GMBSO based method with ring topology, the W-B policy, and 320 individuals is the most effective among all of multi-population parameters. Full article

Diabetic retinopathy (DR) is a complication of diabetes and is known as visual impairment, and is diagnosed in various ethnicities of the working-age population worldwide. Fundus angiography is a widely applicable modality used by ophthalmologists and computerized applications to detect DR-based clinical features such as microaneurysms (MAs), hemorrhages (HEMs), and exudates (EXs) for early screening of DR. Fundus images are usually acquired using funduscopic cameras in varied light conditions and angles. Therefore, these images are prone to non-uniform illumination, poor contrast, transmission error, low brightness, and noise problems. This paper presents a novel and real-time mechanism of fundus image enhancement used for early grading of diabetic retinopathy, macular degeneration, retinal neoplasms, and choroid disruptions. The proposed system is based on two folds: (i) An RGB fundus image is initially taken and converted into a color appearance module (called lightness and denoted as J) of the CIECAM02 color space model to obtain image information in grayscale with bright light. Afterwards, in step (ii), the achieved J component is processed using a nonlinear contrast enhancement approach to improve the textural and color features of the fundus image without any further extraction steps. To test and evaluate the strength of the proposed technique, several performance and quality parameters—namely peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), entropy (content information), histograms (intensity variation), and a structure similarity index measure (SSIM)—were applied to 1240 fundus images comprised of two publicly available datasets, DRIVE and MESSIDOR. It was determined from the experiments that the proposed enhancement procedure outperformed histogram-based approaches in terms of contrast, sharpness of fundus features, and brightness. This further revealed that it can be a suitable preprocessing tool for segmentation and classification of DR-related features algorithms. Full article

Software defect prediction is an important means to guarantee software quality. Because there are no sufficient historical data within a project to train the classifier, cross-project defect prediction (CPDP) has been recognized as a fundamental approach. However, traditional defect prediction methods use feature attributes to represent samples, which cannot avoid negative transferring, may result in poor performance model in CPDP. This paper proposes a multi-source cross-project defect prediction method based on dissimilarity space (DM-CPDP). This method not only retains the original information, but also obtains the relationship with other objects. So it can enhances the discriminant ability of the sample attributes to the class label. This method firstly uses the density-based clustering method to construct the prototype set with the cluster center of samples in the target set. Then, the arc-cosine kernel is used to calculate the sample dissimilarities between the prototype set and the source domain or the target set to form the dissimilarity space. In this space, the training set is obtained with the earth mover’s distance (EMD) method. For the unlabeled samples converted from the target set, the k-Nearest Neighbor (KNN) algorithm is used to label those samples. Finally, the model is learned from training data based on TrAdaBoost method and used to predict new potential defects. The experimental results show that this approach has better performance than other traditional CPDP methods. Full article

Link prediction is a task predicting whether there is a link between two nodes in a network. Traditional link prediction methods that assume handcrafted features (such as common neighbors) as the link’s formation mechanism are not universal. Other popular methods tend to learn the link’s representation, but they cannot represent the link fully. In this paper, we propose Edge-Nodes Representation Neural Machine (ENRNM), a novel method which can learn abundant topological features from the network as the link’s representation to promote the formation of the link. The ENRNM learns the link’s formation mechanism by combining the representation of edge and the representations of nodes on the two sides of the edge as link’s full representation. To predict the link’s existence, we train a fully connected neural network which can learn meaningful and abundant patterns. We prove that the features of edge and two nodes have the same importance in link’s formation. Comprehensive experiments are conducted on eight networks, experiment results demonstrate that the method ENRNM not only exceeds plenty of state-of-the-art link prediction methods but also performs very well on diverse networks with different structures and characteristics. Full article

In this paper, a novel approach to facial expression recognition based on the discrete separable shearlet transform (DSST) and normalized mutual information feature selection is proposed. The approach can be divided into five steps. First, all test and training images are preprocessed. Second, DSST is applied to the preprocessed facial expression images, and all the transformation coefficients are obtained as the original feature set. Third, an improved normalized mutual information feature selection is proposed to find the optimal feature subset of the original feature set, thus we can retain the key classification information of the original data. Fourth, the feature extraction and selection of the feature space is reduced by employing linear discriminant analysis. Finally, a support vector machine is used to recognize the expressions. In this study, experimental verification was carried out on four open facial expression databases. The results show that this method can not only improve the recognition rate of facial expressions, but also significantly reduce the computational complexity and improve the system efficiency. Full article

In this study, fifth-order and sixth-order diagonally implicit Runge–Kutta type (DIRKT) techniques for solving fourth-order ordinary differential equations (ODEs) are derived which are denoted as DIRKT5 and DIRKT6, respectively. The first method has three and the another one has four identical nonzero diagonal elements. A set of test problems are applied to validate the methods and numerical results showed that the proposed methods are more efficient in terms of accuracy and number of function evaluations compared to the existing implicit Runge–Kutta (RK) methods. Full article

This paper presents a comparison among the bee colony optimization (BCO), differential evolution (DE), and harmony search (HS) algorithms. In addition, for each algorithm, a type-1 fuzzy logic system (T1FLS) for the dynamic modification of the main parameters is presented. The dynamic adjustment in the main parameters for each algorithm with the implementation of fuzzy systems aims at enhancing the performance of the corresponding algorithms. Each algorithm (modified and original versions) is analyzed and compared based on the optimal design of fuzzy systems for benchmark control problems, especially in fuzzy controller design. Simulation results provide evidence that the FDE algorithm outperforms the results of the FBCO and FHS algorithms in the optimization of fuzzy controllers. Statistically is demonstrated that the better errors are found with the implementation of the fuzzy systems to enhance each proposed algorithm. Full article

Discriminative correlation filters (DCFs) have been shown to perform superiorly in visual object tracking. However, visual tracking is still challenging when the target objects undergo complex scenarios such as occlusion, deformation, scale changes and illumination changes. In this paper, we utilize the hierarchical features of convolutional neural networks (CNNs) and learn a spatial-temporal context correlation filter on convolutional layers. Then, the translation is estimated by fusing the response score of the filters on the three convolutional layers. In terms of scale estimation, we learn a discriminative correlation filter to estimate scale from the best confidence results. Furthermore, we proposed a re-detection activation discrimination method to improve the robustness of visual tracking in the case of tracking failure and an adaptive model update method to reduce tracking drift caused by noisy updates. We evaluate the proposed tracker with DCFs and deep features on OTB benchmark datasets. The tracking results demonstrated that the proposed algorithm is superior to several state-of-the-art DCF methods in terms of accuracy and robustness. Full article

Images may be corrupted by salt and pepper impulse noise during image acquisitions or transmissions. Although promising denoising performances have been recently obtained with sparse representations, how to restore high-quality images remains challenging and open. In this work, image sparsity is enhanced with a fast multiclass dictionary learning, and then both the sparsity regularization and robust data fidelity are formulated as minimizations of L₀-L₀ norms for salt and pepper impulse noise removal. Additionally, a numerical algorithm of modified alternating direction minimization is derived to solve the proposed denoising model. Experimental results demonstrate that the proposed method outperforms the compared state-of-the-art ones on preserving image details and achieving higher objective evaluation criteria. Full article

Considering the linear motion of particleboards in the production line, the detection of surface defects in particleboards is a major challenge. In this paper, a method based on moving target tracking is proposed for the detection of surface defects in particleboards. To achieve this, the kernel correlation filter (KCF) target tracking algorithm was modified with the median flow algorithm and used to capture the moving targets of surface defects. The defect images were extracted by a Sobel operator, and the defect number, the defect area, and the degree of damage were calculated. The level of surface defect in particleboards was evaluated by fuzzy pattern recognition. Experiments were then carried out to prove the effectiveness and accuracy of the proposed method. Full article

MapReduce is a parallel computing model in which a large dataset is split into smaller parts and executed on multiple machines. Due to its simplicity, MapReduce has been widely used in various applications domains. MapReduce can significantly reduce the processing time of a large amount of data by dividing the dataset into smaller parts and processing them in parallel in multiple machines. However, when data are not uniformly distributed, we have the so called partitioning skew, where the allocation of tasks to machines becomes unbalanced, either by the distribution function splitting the dataset unevenly or because a part of the data is more complex and requires greater computational effort. To solve this problem, we propose an approach based on metaheuristics. For evaluating purposes, three metaheuristics were implemented: Simulated Annealing, Local Beam Search and Stochastic Beam Search. Our experimental evaluation, using a MapReduce implementation of the Bron-Kerbosch Clique Algorithm, shows that the proposed method can find good partitionings while better balancing data among machines. Full article

The amount of information currently generated in the world has been increasing exponentially, raising the question of whether all acquired data is relevant for the learning algorithm process. If a subset of the data does not bring enough innovation, data-selection strategies can be employed to reduce the computational complexity cost and, in many cases, improve the estimation accuracy. In this paper, we explore some adaptive filtering algorithms whose characteristic features are their fast convergence and data selection. These algorithms incorporate a prescribed data-selection strategy and are compared in distinct applications environments. The simulation results include both synthetic and real data. Full article

Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy. Full article

The combination of adaptive filters is an effective approach to improve filtering performance. In this paper, we investigate the performance of an adaptive combined scheme between two adaptive multiple-input single-output (MISO) filters, which can be easily extended to the case of multiple outputs. In order to generalize the analysis, we consider the multichannel affine projection algorithm (APA) to update the coefficients of the MISO filters, which increases the possibility of exploiting the capabilities of the filtering scheme. Using energy conservation relations, we derive a theoretical behavior of the proposed adaptive combination scheme at steady state. Such analysis entails some further theoretical insights with respect to the single channel combination scheme. Simulation results prove both the validity of the theoretical steady-state analysis and the effectiveness of the proposed combined scheme. Full article

An important ingredient in economic policy planning both in the public or the private sector is risk management. In economics and finance, risk manifests through many forms and it is subject to the sector that it entails (financial, fiscal, international, etc.). An under-investigated form is the risk stemming from geopolitical events, such as wars, political tensions, and conflicts. In contrast, the effects of terrorist acts have been thoroughly examined in the relevant literature. In this paper, we examine the potential ability of geopolitical risk of 14 emerging countries to forecast several assets: oil prices, exchange rates, national stock indices, and the price of gold. In doing so, we build forecasting models that are based on machine learning techniques and evaluate the associated out-of-sample forecasting error in various horizons from one to twenty-four months ahead. Our empirical findings suggest that geopolitical events in emerging countries are of little importance to the global economy, since their effect on the assets examined is mainly transitory and only of regional importance. In contrast, gold prices seem to be affected by fluctuation in geopolitical risk. This finding may be justified by the nature of investments in gold, in that they are typically used by economic agents to hedge risk. Full article