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This paper innovatively proposes a high-sensitive cross-channel color image encryption algorithm (SFHM-IEA) by using two-dimensional hyperchaotic map (2D-SFHM) derived from Sine map and mathematical function. The performance of the proposed map is numerically investigated, and the results show that 2D-SFHM has larger Lyapunov exponents (up to 8.766), more stable sample entropy, C0 complexity and permutation entropy (all almost 1) than other existing chaotic maps, which indicates that the chaotic sequences generated by 2D-SFHM are highly stochastic. Then a cross-channel color image encryption algorithm combining peripheral-pixel extension technique is proposed, which uses circular-shift permutation and bidirectional-parallel diffusion to obtain strong confusion and diffusion properties. The experimental result with chi-square=229.2843, Pearson correlation coefficient and mutual information are almost 0, information entropy can reach 7.9998, average NPCR=99.6098% and average UACI=33.4632%, which proves the ability of the proposed algorithm to securely resist various illegal attacks. • A new large-scale hyperchaotic map is constructed and its performance is tested. • A new chaos-based cross-channel color image encryption algorithm is designed. • The secure performance of the proposed image algorithm is thoroughly analyzed. [ABSTRACT FROM AUTHOR]

• The significant and impressive performance of the Chimp Optimization Algorithm (ChOA) has been a great inspiration for this paper to study on its multi-objective version called MOChOA. • A memory structure has been exploited as an archive to store the non-dominated solutions alongside a leader selection strategy and grid mechanism. The leader selection procedure consists of analyzing the archive contents to choose the best candidates for the leader of independent groups, which are drivers, barriers, chasers, and attackers. The strategy also guarantees the exploration of search space. • Twelve test problems with various shapes and different dimensions have opted to evaluate MOChOA. The results of coverage, generational distance, averaged Hausdorff distance, spacing, diversity, and other metrics are considered in the performance evaluation of the algorithm. The Multi-Objective Chimp Optimization Algorithm (MOChOA), a multi-objective variation of the recently proposed ChOA, is developed in this research to address multi-objective optimization issues in various engineering problems. The optimization community has recently been offered numerous evolutionary and meta -heuristic optimization strategies for tackling optimization challenges. When analyzing multi-objective optimization (MOO) problems, these approaches frequently produce poor solutions because they do not accurately estimate the Pareto optimal solutions or increase the distribution across all objectives. The significant and impressive performance of the ChOA has been a great inspiration for this paper to study on its multi-objective version called MOChOA. In this approach, a memory structure has been exploited as an archive to store the non-dominated solutions alongside a leader selection strategy and grid mechanism. The leader selection procedure consists of analyzing the archive contents to choose the best candidates for the leader of independent groups, which are drivers, barriers, chasers, and attackers. The strategy also guarantees the exploration of search space. Twelve test problems with various shapes and different dimensions have opted to evaluate MOChOA. The results of coverage, generational distance, averaged Hausdorff distance, spacing, diversity, and other metrics are considered in the performance evaluation of the algorithm. According to results, MOChOA can provide competitive results and outperform well-known intelligent algorithms such as ANSGAIII, ARMOEA, dMOPSO, EFRRR, IBEA, SPEA2SDE, SPEAR, and tDEA. Alongside the remarkable performance of the proposed algorithm, its light-weight structure causes a fast execution. The proposed MOChOA can be applied in various multi-objective optimization application, including engineering, science, chemical processes, economics and logistics when appropriate decisions must be made in the face of competing objectives. [ABSTRACT FROM AUTHOR]

Main impact of the paper is proposing a family of algorithms for the online learning and classification. These algorithms work in rounds, where at each round a new instance is given and the algorithm makes a prediction. After the true class of the instance is revealed, the learning algorithm updates its internal hypothesis. The proposed algorithms are based on fuzzy C-means clustering and kernel-based fuzzy C-means clustering, followed by a calculation of distances between cluster centroids and the incoming instance for which the class label is to be predicted. In one of the proposed variants, simple distance-based classifiers thus obtained serve as basic classifiers for the implemented Rotation Forest ensemble classifier, which increases the accuracy of classification. In the paper we also propose using kernelized fuzzy C-means clustering method as an alternative approach to constructing distance based online classifiers. The approach allows to construct online classifiers of the polynomial computational complexity which is a significant feature considering potential application to the big data analysis. Using the kernelized clustering is advantageous since it allows for automatic estimation of the number of clusters maintaining the number of the user-defined parameters. The proposed classification algorithms are validated experimentally. Experiment results show that the approach assures good quality of classification, extending the range of the available online approaches. [ABSTRACT FROM AUTHOR]

Artificial Immune System (AIS) is inspired from Biological Immune System (BIS) and demonstrates a lot of interesting facets and intelligence that include self-learning, self adaption, self regulatory, distributed with self/non-self detection capabilities. Due to these astonishing qualities AIS are predominantly used in anomaly detection where anomalies are treated as non-self that needs to be detected. Therefore, AIS appears appropriate for development of a proactive system to identify and prevent novel and unseen anomalies. This paper presents “An Efficient Proactive Artificial Immune System based Anomaly Detection and Prevention System (EPAADPS)” which embodies immune attributes to distinguish self and non-self in quest to identify and prevent novel, unseen anomalies. Negative Selection Algorithm (NSA) is a key AIS concept and is used for anomaly detection in various publications. Despite its relative success, detector selection and thereafter anomaly detection demands a more effective algorithm. This paper put forwards concept of self-tuning of detectors and detector power in NSA with the intension to make a detector evolve and facilitate better and correct self and non-self coverage. Thereafter, agents accompanying detectors collaborate and communicate between themselves to proactively discover correct anomalies and then take appropriate preventive measures. The performance of EPAADPS is contrasted with closely related state of art RNS algorithm using real valued representation and Euclidean distance. Experimental results revels promising EPAADPS performance which very comfortably outperforms the RNS. Furthermore, these results also demonstrate that EPAADPS shows remarkable resilience and intelligence in detecting novel unseen anomalies and with preventive measures to overcome the threat perception. [ABSTRACT FROM AUTHOR]

Fuzzy numbers and intuitionistic fuzzy numbers are introduced in the literature to model problems involving incomplete and imprecise information in expert and intelligent systems. Ranking of TrIFNs plays an important role in an information system (Decision Making) with imprecise and inadequate information and the complete ranking on the class of trapezoidal intuitionistic fuzzy number is an open problem worldwide. Researchers from all over the world have been working in ranking of intuitionistic fuzzy numbers since 1985, but till date there is no common methodology that ranks any two arbitrary intuitionistic fuzzy numbers due to the partial ordering of TraIFNs. Different algorithms are available in the literature for solving intuitionistic fuzzy decision (or information system) problem, but each and every algorithm failed to give better result in some places due to the ranking procedure of TrIFNs. Intuitionistic fuzzy decision algorithm works better when it have a complete ranking procedure that ranks arbitrary intuitionistic fuzzy numbers. In this paper a linear (total) ordering on the class of trapezoidal intuitionistic fuzzy numbers using axiomatic set of eight different scores is introduced. The main idea of this paper is to classify and study the properties of eight different sub classes of the set of TrIFNs. Further new total order relations are defined on each of the subclasses of TrIFNs and they are extended to a complete ranking procedure on the set of TrIFNs. Finally the significance of the proposed method over existing methods is studied by illustrative examples. [ABSTRACT FROM AUTHOR]

This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which are novel in this study. The Coral Reefs Optimization algorithm (CRO) is a recently-proposed bio-inspired approach for optimization, based on the simulation of the processes that occur in coral reefs, including reproduction, fight for space or depredation. This paper presents a grouping version of the CRO, which has not previously evaluated before. Grouping meta-heuristics are characterized by variable-length encoding solutions, and have been successfully applied to a number of different optimization and assignment problems. The GCRO proposed is a novel contribution to the intelligent systems field, which is able to improve results obtained by two alternative grouping algorithms such as grouping genetic algorithms and grouping Harmony Search. The performance of the proposed GCRO and the algorithms for comparison has been tested with real data in a case study of a MNDP in Alcalá de Henares, Madrid, Spain. [ABSTRACT FROM AUTHOR]

Our paper introduces a new methodology to solve group decision-making problems under fuzzy and dynamic environment. The methodology takes group members’ linguistically defined pair wise preferences of alternatives in different time intervals and aggregates them across the intervals to obtain each member's net preference levels. Each member's net preference levels are again aggregated across the members to obtain the group's preference. Our paper attaches higher importance to the members whose involvement in the decision process is more recent than the members who opined their views in the past. The fuzzy aggregation operator, IOWA (Induced Ordered Weighted Average) is used to aggregate their views in accordance to their importance in the group. The Ranked_List algorithm, introduced in our paper, inputs the aggregated views of the members in pair wise form and produces the set of sequences of ranked list of alternatives representing the group's consensus view as output. The Ranked_List algorithm is validated and analyzed through a series of synthetic data sets and its results are compared with a movie selection case study. The methodology is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field. [ABSTRACT FROM AUTHOR]

This paper studies a heterogeneous task allocation problem with the budget constraint. Existing works on task allocation mainly tackle this well-known NP-hard problem from an optimisation perspective. They have not been able to cater to the extra needs of scalability and robustness in large-scale systems. Furthermore, some general allocation mechanisms do not consider system budget and agent cost. Thus, they cannot guarantee to obtain valid solutions when the budget is constrained. This paper models the task allocation problem as a game whose players are the agents to be assigned to the teams working on the tasks, and align the task allocation objective (i.e., system optimality) with the game-theoretic solution concept of Nash equilibrium. Based on this formulation, a novel algorithm, called CF, is proposed in this paper. CF searches for a valid Nash equilibrium solution using a greedy strategy that aims to improve system utility while takes into consideration of the overall system budget constraint. CF is a scalable, anytime, and monotonic algorithm, which in turn, makes it robust for the deployment in large-scale systems. CF can also be used as a local search algorithm for improving the quality of any existing valid allocation solution. Comprehensive empirical studies have been carried out in this paper to demonstrate that CF is effective in all budget states and achieves a solution quality better than the state-of-the-art algorithms. • The objective in task allocation is aligned with Nash equilibrium in game theory. • The proposed algorithm can be used as a local search algorithm. • The proposed algorithm guarantees the return of a valid Nash equilibrium solution. • The proposed algorithm is an "anytime" and "monotonic" algorithm. • The proposed algorithm is computationally efficient and highly scalable. [ABSTRACT FROM AUTHOR]

Max-cut is one of the most classic NP-hard combinatorial optimization problems. The symmetry nature of it leads to special difficulty in extracting meaningful configuration information for learning; none of the state-of-the-art algorithms has employed any learning operators. This paper proposes an original learning method for max-cut, namely post-flip edge-state learning (PF-ESL). Different from previous algorithms, PF-ESL regards edge-states (cut or not cut) rather than vertex-positions as the critical information of a configuration, and extracts their statistics over a population for learning. It is based on following observations. 1) Edges are the only factors considered by the objective function. 2) Edge-states keep invariant when rotating a local configuration to its symmetry position, but vertex-positions do not. These suggest that edge-states contain more meaningful information about a configuration than vertex-positions do. It is impossible to set the state of an edge without influencing some other edges' states due to their dependencies. Therefore, instead of setting edge-states directly, PF-ESL samples the flips on vertices. Flips on vertices are sampled according to their capacities in increasing the similarity on edge-states between the given solution and a population. PF-ESL is employed in an EDA (Estimation of Distribution Algorithm) perturbation operator and a path-relinking operator. Experimental results show that our algorithm is competitive, and show that edge-state learning is value-added for both the two operators. The main contributions of this paper are as follows. Firstly, previous state-of-the-art evolutionary algorithms for max-cut focus on vertex positions in their evolutionary operation, this paper proposes a new and more reasonable perspective suggesting that edge-states are the critical information of divided graphs rather than vertex positions, and introduces a novel method to measure and utilize their similarities based on it. Such a perspective is fundamental to learning based algorithms design for max-cut and other graph partitioning problems, and can shed lights on future researches. Furthermore, since max-cut is one of the most classic and fundamental NP hard problems, many real-world problems involve dividing graph data into different parts to optimize certain functions, this new perspective may inspire related or similar problems. Secondly, besides the original edge-states based perspective, and the post-flip edge-states learning (PFESL) operator based on it, our memetic algorithm also incorporates a novel evolutionary framework which alternates between EDA based Iterated Tabu search (ITS) and path relinking based genetic algorithm. Finally, the proposed algorithm provides competitive results on two mostly used benchmark sets and improves the best-known results of 6 most challenging instances. • An original edge-state perspective for design learning operators for max-cut. • A novel learning operator to learn edge states. • A new evolutionary framework for max-cut. • It provides competitive results on two mostly used benchmark sets. [ABSTRACT FROM AUTHOR]

A Meta-evolutionary Selection of Constituents in Ensemble DE (MeSCEDE) framework is proposed in this paper to automate the design of high-level multi-population ensemble Differential Evolution (DE) algorithms. The automated design of high-level multi-population ensemble DE algorithms involves both automated selection of constituent DE algorithms for the ensemble and automated configuration of ensemble related parameters. Grammatical Evolution has been used as the meta-evolutionary algorithm in MeSCEDE to search the space of design choices so as to evolve effective ensemble design(s) for given problem(s). The simulation experiments carried out in this paper involve applying MeSCEDE to evolve ensemble DE designs for solving 30-dimensional CEC'17 benchmark functions. The effectiveness of the evolved designs are validated on 30 and 50-dimensional CEC'14 functions as well as on 22 real-world problem instances from CEC'11 benchmark suite. The MeSCEDE evolved designs have exhibited a competitive performance against state-of-the-art ensemble DE algorithms. In addition, the potential of MeSCEDE has been demonstrated against irace , a state-of-the-art algorithm configurator. All simulation experiments reiterate the potential of MeSCEDE towards evolving effective and robust ensemble DE designs. • Automated design of ensemble differential evolution (DE) algorithm is presented. • The proposed approach is Meta-evolutionary Selection of Constituents in Ensemble DE. • The approach uses grammatical evolution as the meta evolutionary algorithm. • The approach is demonstrated on CEC'17, CEC'14 and CEC'11 benchmark suites. • Relieves the practitioners from the difficult task of algorithm configuration. [ABSTRACT FROM AUTHOR]

• Non-linear Marine Predator Algorithm (NMPA) is introduced in this paper. • NMPA mimics two foraging techniques by marine predators: Levy and Brownian movements. • Fair power allocation in NOMA-VLC-B5G Networks is solved using NMPA algorithm. This paper is an influential attempt to identify and alleviate some of the issues with the recently proposed optimization technique called the Marine Predator Algorithm (MPA). With a visual investigation of its exploratory and exploitative behavior, it is observed that the transition of search from being global to local can be further improved. As an extremely cost-effective method, a set of nonlinear functions is used to change the search patterns of the MPA algorithm. The proposed algorithm, called Nonlinear Marin Predator Algorithm (NMPA), is tested on a set of benchmark functions. A comprehensive comparative study shows the superiority of the proposed method compared to the original MPA and even other recent meta -heuristics. The paper also considers solving a real-world case study around power allocation in non-orthogonal multiple access (NOMA) and visible light communications (VLC) for Beyond 5G (B5G) networks to showcase the applicability of the NMPA algorithm. NMPA algorithm also shows its superiority in solving a wide range of benchmark functions as well as obtaining fair power allocation for multiple users in NOMA-VLC-B5G systems compared with the state-of-the-art algorithms. 1 1 For the source code and the application, you can download the related files via the following links: GitHub and MathWorks: https://github.com/alisafaa12/nmpa https://uk.mathworks.com/matlabcentral/fileexchange/111135-nonlinear-marine-predator-algorithm-nmpa. [ABSTRACT FROM AUTHOR]

Highlights • An architecture for numerical design of experiment configuration is proposed. • A bayesian network with a "multi-net" strategy is proposed. • Models are trained from historical data and expert knowledge. • The performances of the proposed method are validated through a case study. Abstract This paper describes a specific knowledge-based system (KBS) to assist designers in configuring numerical design of experiments (NDoE) processes efficiently. NDoE processes are applied in product design to improve the quality of product, by taking into account variabilities and uncertainties. NDoE processes are defined by various and complex methodologies to achieve several objectives, as optimization, surrogate modeling or sensitivity analysis. On the other hand, NDoE processes may demand huge computing resources to execute hundreds simulations, and also advanced expert knowledge to set the best configuration amongst numerous possibilities. Designers aim to obtain most useful results with a minimal computational cost as soon as possible. Thus, the configuration step must be as fast as possible, and it must lead to an efficient combination of complex methods, algorithms and hyper-parameters, to obtain valuable information on the product. The proposed KBS and its inference engine, a bayesian network, is detailed and applied to a product developed by automotive industry. The KBS propose new efficient configurations to achieve designers' goal. This application shorten the configuration step of the NDoE process, and enables designers to use more complex methods. It also allows designers to capitalize knowledge and learn from each past NDoE process. [ABSTRACT FROM AUTHOR]

Highlights • Developing a new Spiking Neural P Grey Wolf Optimization System. • Novel strategies for solving non-determinism problem of Spiking Neural P systems. • Design a new mathematical model of the grey wolf optimization algorithm. • Control the copying spikes process among neurons is developed. • Consider the time control approach to avoid non-determinism inside neurons. Abstract Spiking neural P systems (SN P systems, in short) are the latest branch of membrane computing; inspired by the biological behavior of spiking neurons. They are considered true distributed and parallel systems; modeled to solve time consumption problem and presented the concept of parallelism usage in the computing field. This paper proposes novel strategies for solving non-determinism problem of SN P systems. The proposed algorithm relies on the parallelism feature to simulate the social hierarchy, tracking, encircling, and attacking behaviors in the grey wolf optimizer. It is modeled by collaboration between a set of SN P systems to get a feasible solution in polynomial time. Moreover, a new method named the power of signal is proposed to control the copying spikes process between neurons and differentiate between the arithmetic operations. Additionally, a time control approach is proposed to avoid non-determinism inside neurons that applied the determinism feature during firing rules. The theoretical and empirical experiments proved that the algorithm can successfully halt and in addition to the effectiveness of the proposed neural systems in getting an optimal solution in a reasonable time. As a result, this study is counted as a significant advancement in intelligent and optimization systems, whereas it has a direct impact on enhancing the performance of these systems and their applications. [ABSTRACT FROM AUTHOR]

Highlights • The proposed algorithm considers a double pair of evolutionary operators. • The proposed approach uses local consensus formulations with reactive responses. • The proposed method guarantees leaderless movement decisions. • The proposed algorithm outperforms several evolutionary methods. Abstract Evolutionary Computation (EC) algorithms have been proposed as stochastic methods to solve complex optimization problems. The design of EC methods typically involves the construction of empirical operators based on abstractions of animal behaviors or physical and biological phenomena. Through its operators, every EC approach proposes a particular solution to the exploration-exploitation balance which is currently considered an unsolved problem within EC literature. On the other hand, multi-agent systems have been utilized as intelligent, cooperative and self-organized structures where the synergy of simple rules creates complex interactions among agents. In this paper, a novel EC algorithm called Neighborhood-based Consensus for Continuous Optimization (NCCO) is presented. NCCO is based on typical processes present in multi-agent systems, such as local consensus formulations and reactive responses. These operations are conducted by using appropriate operators that are applied in each evolutionary stage. A traditional EC algorithm considers in its operation the application of every operator without examining its final impact in the searching process. In contrast to other EC techniques, the proposed method uses additional operators to avoid the undesirable effects produced by the over-exploitation or suboptimal exploration of conventional operations. In order to illustrate the performance and accuracy of the proposed NCCO approach, it is compared to several well-known, state-of-the-art algorithms over a set of benchmark functions and real-world design applications. The experimental results demonstrate that NCCO's performance is superior to the test algorithms. [ABSTRACT FROM AUTHOR]

Highlights • Feature selection over high-dimensional colon cancer Microarray Datasets. • Features selected from both Gene Expression and DNA-Methylation Microarray datasets. • Resultant six biomarker genes for colon cancer validated using Enrichment Analysis. • Biomarker genes validated on independent datasets with 99.9% classification accuracy. Abstract Cancer is a dangerous disease that causes death worldwide. Discovering few genes relevant to one cancer disease can result in effective treatments. The challenge associated with the Microarray datasets is its high dimensionality; the huge number of features compared to the modest number of samples in these datasets. Recent research efforts attempted to reduce this high-dimensionality using different feature selection techniques. This paper presents an ensemble feature selection technique based on t -test and genetic algorithm. After preprocessing the data using t -test, a Nested Genetic Algorithm, namely Nested-GA, is used to get the optimal subset of features by combining data from two different datasets. Nested-GA consists of two Nested Genetic Algorithms (outer and inner) that run on two different kinds of datasets. The Outer Genetic Algorithm (OGA-SVM) works on Microarray gene expression datasets, whereas the Inner Genetic Algorithm (IGA-NNW) runs on DNA Methylation datasets. Nested-GA is performed on a colon cancer dataset with 5-fold cross validation. After applying Nested-GA, the Incremental Feature Selection (IFS) strategy is used to get the smallest optimal genes subset. The genes subset has been validated on an independent dataset resulting in 99.9% classification accuracy. Consequently, the biological significance of the resulting optimal genes is validated using Enrichment Analysis. Moreover, the results of Nested-GA have been compared to the results of other feature selection algorithms that have been run on either Gene Expression or DNA Methylation datasets. From the experimental results, Nested-GA showed the highest classification performance with a small optimal feature subset compared to the other algorithms. Furthermore, by running Nested-GA on lung cancer datasets that contain two different cancer subtypes, it resulted in significantly better classification accuracy (98.4%) compared to the accuracy of a previous research (84.6%) that utilized lung cancer DNA-Methylation data only. [ABSTRACT FROM AUTHOR]

Highlights • A hybrid semantic enrichment framework to classify event-related tweets • Combines semantic enrichment, external documents and named-entity recognition • A semantic feature pruning algorithm and feature selection to deal with huge volume of features • Assessment of each framework component using a broad experimental setup • Superior results compared to a textual baseline and a word embeddings baseline Abstract Twitter has become instrumental as a means of spreading information, opinions or awareness about real-world events. The classification of event-related tweets is a challenging problem since tweets are noisy and sparse pieces of text that lack contextual information. Related work proposes contextual enrichment techniques using external sources (e.g. semantic web, external documents), often considering underlying assumptions about the target events. However, they lack guidelines for determining the textual features to enrich, the external sources to use, the properties to explore, and how to prevent the inclusion of unrelated information. In this paper, we propose a hybrid semantic enrichment framework for the classification of event-related tweets. We contribute to this field by leveraging different contextual enrichment strategies into a unifying framework targeted at a broad range of event types, where each enrichment technique has a role in the improvement of event classification. The framework also encompasses a solution to deal with the huge number of features that result from semantic enrichment, which combines a pruning method to select domain relevant semantic features and general-purpose feature selection techniques. We assessed the contribution of each framework component to event classification improvement using a broad experimental setting. Using seven events of distinct natures, we outperformed a word embeddings baseline in 93.6% of cases, and a textual baseline in 60.3% of cases. In most cases, we improved the recall, with no significant impact on the precision. [ABSTRACT FROM AUTHOR]

Highlights • We recommend courses to users of Massive open online courses (MOOCs). • We perform Multi-Modal Matrix Factorization with Side Information. • We outperform the classic MF algorithm and other novel algorithms. Abstract Massive open online courses (MOOCs) have recently gained a huge users' attention on the Web. They are considered as a highly promising form of teaching from leading universities such as Stanford and Berkeley. However, users confront the problem of choosing among thousands of offered MOOCs. In such a scenario of severe "information overload", recommender systems can be very useful to recommend the right course to a user, since they base their operation on past user's log history. For example, Coursera recommends courses to users so that, they can acquire those skills, that are expected from their ideal job. These user's preferences are not expected to be independent from others choices, as users follow trends of similar behaviour. In this paper, we propose, xSVD++, where the "x" means that it is a multi-dimensional Matrix Factorization (MMF) model combined with Collaborative Filtering (CF) algorithm, which exploits information from external resources (i.e., users' skills, courses' characteristics, etc.) to predict course trends and to perform rating predictions according to them. Our experimental results indicate that xSVD++ is superior over classic and non-negative matrix factorization algorithms and the state-of-the-art CMF, and SVD++ algorithms. [ABSTRACT FROM AUTHOR]

Highlights • Depending on clustering algorithm used, the results may change. • Based on Multi-Algorithm Voting, robustness of cluster solutions can be improved. • OMAV is developed that uses an optimization algorithm as integrative method. • OMAV is applied to the example of country clustering using GLOBE data. • Increased robustness and reduced subjectivity are demonstrated. Abstract Currently, the influence of a single statistical cluster algorithm on the results of clustering procedures represents a major threat to the objectivity in clustering. To exemplify this question, this paper refers to country clustering in cross-cultural research. In this field, previous research has determined differing numbers of clusters, depending on choices available for the clustering procedure, leading to a high number of inconsistent results. Hence, it is argued that the variety in cluster solutions induced by the choice of different statistical cluster algorithms should be reduced. To this end, this study builds on Multi-Algorithm Voting (MAV) procedure introduced by Bittmann and Gelbard (2007) and presents an advancement to the MAV method. Specifically, MAV procedure is refined for the analysis of larger data sets using the simulated annealing algorithm for optimization. The use of this Optimized MAV (OMAV) is then demonstrated for country clustering in cross-cultural research. Specifically, a set of 57 countries is divided into 12 clusters based on work-related values obtained from GLOBE database reported in House et al. (2004). Thus, results clearly show that the objectivity of clustering results can be significantly improved based on OMAV. Implications for expert and intelligent systems on the use of OMAV are discussed. Namely, OMAV represents a powerful tool supporting the decision-making process in cluster analysis reducing the number of subjective and arbitrary decisions. Taken together, this study contributes to existing literature by providing an integrative and robust method of country clustering using OMAV and by presenting country clusters applicable to various settings. [ABSTRACT FROM AUTHOR]

Abstract In recommender systems, human preferences are identified by a number of individual components with complicated interactions and properties. Recently, the dynamicity of preferences has been the focus of several studies. The changes in user preferences can originate from substantial reasons, like personality shift, or transient and circumstantial ones, like seasonal changes in item popularities. Disregarding these temporal drifts in modelling user preferences can result in unhelpful recommendations. Moreover, different temporal patterns can be associated with various preference domains, and preference components and their combinations. These components comprise preferences over features, preferences over feature values, conditional dependencies between features, socially-influenced preferences, and bias. For example, in the movies domain, the user can change his rating behaviour (bias shift), her preference for genre over language (feature preference shift), or start favouring drama over comedy (feature value preference shift). In this paper, we first propose a novel latent factor model to capture the domain-dependent component-specific temporal patterns in preferences. The component-based approach followed in modelling the aspects of preferences and their temporal effects enables us to arbitrarily switch components on and off. We evaluate the proposed method on three popular recommendation datasets and show that it significantly outperforms the most accurate state-of-the-art static models. The experiments also demonstrate the greater robustness and stability of the proposed dynamic model in comparison with the most successful models to date. We also analyse the temporal behaviour of different preference components and their combinations and show that the dynamic behaviour of preference components is highly dependent on the preference dataset and domain. Therefore, the results also highlight the importance of modelling temporal effects but also underline the advantages of a component-based architecture that is better suited to capture domain-specific balances in the contributions of the aspects. [ABSTRACT FROM AUTHOR]

Highlights • Two binary variants of the butterfly optimization algorithm (BOA) are proposed. • Two transfer functions are used to map the continuous search space to discrete one. • Twenty one UCI datasets are utilized in the experiments. • A superior performance of the proposed binary variants is proved in the experiments. Abstract In this paper, binary variants of the Butterfly Optimization Algorithm (BOA) are proposed and used to select the optimal feature subset for classification purposes in a wrapper-mode. BOA is a recently proposed algorithm that has not been systematically applied to feature selection problems yet. BOA can efficiently explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The two proposed binary variants of BOA are applied to select the optimal feature combination that maximizes classification accuracy while minimizing the number of selected features. In these variants, the native BOA is utilized while its continuous steps are bounded in a threshold using a suitable threshold function after squashing them. The proposed binary algorithms are compared with five state-of-the-art approaches and four latest high performing optimization algorithms. A number of assessment indicators are utilized to properly assess and compare the performance of these algorithms over 21 datasets from the UCI repository. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which proves the ability of BOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks. [ABSTRACT FROM AUTHOR]

Highlights • An ensemble-based co-training approach, named ECoRec, is proposed. • ECoRec process data from two or more different views to create a more robust model. • Our approach provide an enriched matrix that alleviate the sparsity and cold-start problems. • Results show that our strategy improves the overall system's performance. Abstract Collaborative Filtering (CF) is one of the best performing and most widely used approaches for recommender systems. Although significant progress has been made in this area, current CF methods still suffer from cold-start and sparsity problems. A primary issue is that the fraction of users willing to rate items tends to be very small in most practical applications, which causes the number of users and/or items with few or no interactions in recommendation databases to be large. As a direct consequence of ratings sparsity, recommender algorithms may provide poor recommendations (reducing accuracy) or decline recommendations (reducing coverage). This paper proposes an ensemble scheme based on a co-training approach, named ECoRec, that drives two or more recommenders to agree with each others' predictions to generate their own. The experiments on eight real-life public databases show that better accuracy can be obtained when recommender algorithms are simultaneously trained from multiple views and combined into an ensemble to make predictions. [ABSTRACT FROM AUTHOR]

Highlights • A BDI agent system that allows the comparison of ways to merge plans. • Evaluation of the actions to be included in the plan domain-independent. • A way to weight and merge recommendations on the plans. • Tested performance of the merging algorithms defined. • Observed that the contribution from recommendations vs. from the way plans are merged. Abstract In this paper, we propose and compare alternative ways to merge plans formed of sequences of actions with unknown similarities between the goals and actions. Plans are formed of actions and are executed by several operator agents, which cooperate through recommendations. The operator agents apply the plan actions to passive elements (which we call node agents) that will require additional future executions of other plans after some time. The ignorance of the similarities between the plan actions and the goals justifies the use of a distributed recommendation system to produce a useful plan for a given operator agent to apply towards a certain goal. This plan is generated from the known results of previous executions of various plans by other operator agents. Here, we present the general framework of execution (the agent system) and the results of applying various merging algorithms to this problem. [ABSTRACT FROM AUTHOR]

Highlights • A new hybrid method for the imputation of missing values is proposed. • The method is based on a novel fuzzy c-means, mutual information, and regression. • Performance of imputation increases by using Grey in the fuzzy c-means algorithm. • The proposed method outperforms five existing imputation methods, in most cases. • The proposed method can also provide high classification accuracies. Abstract The presence of missing values in real-world data is not only a prevalent problem but also an inevitable one. Therefore, missing values should be handled carefully before the mining or learning process. This paper proposes a novel technique to impute missing data. It employs a new version of Fuzzy c-Means clustering algorithm which benefits from advantages of Grey Relational Grade over Minkowski-like similarity measures. To impute a missing value more accurately, it also performs a local mutual information based feature selection in each cluster to select only highly relevant features. Briefly, missing values are imputed in the following steps. First, the algorithm finds the importance of each missing attribute. Next, input instances are separated into several fuzzy clusters. Then, the algorithm selects clusters which satisfy a minimum condition. After that, it chooses highly dependent features of instances within each cluster using a mutual information based feature selection approach. When the features are selected, regression models will be applied to the selected features of the selected clusters to provide estimations for a missing value. Finally, the missing value is imputed through a weighted average of estimated values obtained from the previous step. Three well-known evaluation criteria and the accuracy of classification task are used to assess the performance of the proposed method. The experimental results for seven UCI data sets with different missing ratios and strategies indicate that the proposed algorithm outperforms five other imputation methods in general. [ABSTRACT FROM AUTHOR]

Transforming unstructured text into a formal representation is an important goal of the Semantic Web in order to facilitate the integration and retrieval of information. The construction of Knowledge Graphs (KGs) pursues such an idea, where named entities (real world things) and their relations are extracted from text. In recent years, many approaches for the construction of KGs have been proposed by exploiting Discourse Analysis, Semantic Frames, or Machine Learning algorithms with existing Semantic Web data. Although such approaches are useful for processing taxonomies and connecting beliefs, they provide several linguistic descriptions, which lead to semantic data heterogeneity and thus, complicating data consumption. Moreover, Open Information Extraction (OpenIE) approaches have been slightly explored for the construction of KGs, which provide binary relations representing atomic units of information that could simplify the querying and representation of data. In this paper, we propose an approach to generate KGs using binary relations produced by an OpenIE approach. For such purpose, we present strategies for favoring the extraction and linking of named entities with KG individuals, and additionally, their association with grammatical units that lead to producing more coherent facts. We also provide decisions for selecting the extracted information elements for creating potentially useful RDF triples for the KG. Our results demonstrate that the integration of information extraction units with grammatical structures provides a better understanding of proposition-based representations provided by OpenIE for supporting the construction of KGs. [ABSTRACT FROM AUTHOR]

The flow-shop scheduling problem with blocking constraints has received an increasing concern recently. However, multiple scheduling criteria are rarely considered simultaneously in most research. Therefore, in this paper, a multi-objective blocking flow-shop scheduling problem (MOBFSP) that minimizes the makespan and total tardiness simultaneously is investigated. To address this problem, a multi-objective discrete invasive weed optimization (MODIWO) algorithm is proposed. In the proposed MODIWO, a high-quality and diversified initial population is firstly constructed via two heuristics and varying weighed values. Then, a reference line-based reproduction and a sliding insertion-based spatial dispersal are developed to guide the global exploration and local exploitation of algorithm. Meanwhile, to enhance intensification search in local region, a self-adaption phase is introduced, which is implemented by a Pareto-based two stage local search with speedup mechanism. Furthermore, a new competitive exclusion strategy is also embedded to construct a superior population for the next generation. Finally, extensive computational experiments and comparisons with several recent state-of-the-art algorithms are carried out based on the well-known benchmark instances. Experimental results demonstrate the efficiency and effectiveness of the proposed MODIWO in solving the considered MOBFSP. [ABSTRACT FROM AUTHOR]

Background: Classification and localization of focal epileptic seizures provide a proper diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long-term electroencephalography (EEG) is tedious, time-consuming and leads to human error. Therefore, there is a need for an automated classification system. Methods: In this paper, we introduce a tool called CADFES: computerized automated detection of focal epileptic seizures. For the study, total 41.66 hours of EEG data from the Bern-Barcelona database was used. Set of 28 features were extracted from time, frequency, and statistical domain and significant features were selected using neighborhood component analysis (NCA). In NCA, optimization of regularization parameter ensured better classification accuracy (less classification loss) with seven features. The performance of the algorithm was assessed using support vector machine (SVM), K-nearest neighbor (K-NN), random forest and adaptive boosting (AdaBoost) classifiers. Results: Experimental results revealed sensitivity, specificity, accuracy, positive predictive rate, negative predictive rate, and area under the curve of 97.6%, 94.4%, 96.1%, 92.9%, 98.8% and 0.96 respectively using the SVM classifier. Finally, MATLAB based software tool referred to as CADFES was introduced for automated classification of focal and non-focal seizures. Comparison results ensure that proposed study is superior to existing methods. Hence, it is expected to perform better at the hospitals for automated classification of focal epileptic seizures in real-time. [ABSTRACT FROM AUTHOR]

This paper proposes an improved version of the grasshopper optimization algorithm (GOA) based on the opposition-based learning (OBL) strategy called OBLGOA for solving benchmark optimization functions and engineering problems. The proposed OBLGOA algorithm consists of two stages: the first stage generates an initial population and its opposite using the OBL strategy; and the second stage uses the OBL as an additional phase to update the GOA population in each iteration. However, the OBL is applied to only half of the solutions to reduce the time complexity. To investigate the performance of the proposed OBLGOA, six sets of experiment series are performed, and they include twenty-three benchmark functions and four engineering problems. The experiments revealed that the results of the proposed algorithm were superior to those of ten well-known algorithms in this domain. Eventually, the obtained results proved that the OBLGOA algorithm can provide competitive results for optimization engineering problems compared with state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This paper considers the problem of mining probabilistic frequent itemsets in the sliding window of an uncertain data stream. We design an effective in-memory index named PFIT to store the data synopsis, so the current probabilistic frequent itemsets can be output in real time. We also propose a depth-first algorithm, PFIMoS , to bottom-up build and maintain the PFIT dynamically. Because computing the probabilistic support is time consuming, we propose a method to estimate the range of probabilistic support by using the support and expected support, which can greatly reduce the runtime and memory usage. Nevertheless, massive probabilistic supports have to be computed when the minimum support is low over dense data, which may result in a drastic reduction of computing speed. We further address this problem with a heuristic rule-based algorithm, PFIMoS+ , in which an error parameter is introduced to decrease the probabilistic support computing count. Theoretical analysis and experimental studies demonstrate that our proposed algorithms can efficiently reduce computing time and memory, ensure fast and exact mining of probabilistic data streams, and markedly outperform the state-of-the-art algorithms TODIS-Stream (Sun et al., 2010) and FEMP (Akbarinia & Masseglia, 2013). [ABSTRACT FROM AUTHOR]

The outlier relies on its distinctive mechanism and valuable information to play an important role in expert and intelligent systems, and thus outlier detection has already been extensively applied in relevant fields including the fraud detection, medical diagnosis, public security, etc. The outlier detection methods of rough sets recently gain in-depth research, because they are data-driven and never require additional knowledge. However, classical rough set-based methods consider only categorical data; furthermore, neighborhood rough sets adhere to numeric and heterogeneous data, but their outlier detection is mainly restricted to numeric data now. According to the hybrid data-driving, this paper investigates outlier detection by the neighborhood information entropy and its developmental measures, and the applicable data sets widely concern categorical, numeric, and mixed data; as a result, the new method extends both the traditional distance-based and rough set-based methods to enrich outlier detection. Concretely, the neighborhood information system is first determined by the heterogeneous distance and self-adapting radius, the neighborhood information entropy is then defined to implement whole uncertainty measurement, three gradual information measures are further constructed to describe each single object, and finally the neighborhood entropy-based outlier factor (NEOF) is integratedly established to detect outliers; moreover, the NEOF-based outlier detection algorithm (called the NIEOD algorithm) is designed and applied. By virtue of UCI data experiments, the NIEOD algorithm is compared with six existing detection algorithms (including the NED, IE, SEQ, FindCBLOF, DIS, KNN algorithms), and the concrete results generally reflect the better effectiveness and adaptability of the new method. [ABSTRACT FROM AUTHOR]

In recent years, cyber-attacks against businesses and organizations have increased. Such attacks usually result in significant damage to the organization, such as the loss and/or leakage of sensitive and confidential information. Because email communication is an integral part of daily business operations, attackers frequently leverage email as an attack vector in order to initially penetrate the targeted organization. Email message allows the attacker to deliver dangerous content to the victim, such as malicious attachments or links to malicious websites. Existing email analysis solutions analyze only specific parts of the email using rule-based methods, while other important parts remain unanalyzed. Existing anti-virus engines primarily use signature-based detection methods, and therefore are insufficient for detecting new unknown malicious emails. Machine learning methods have been shown to be effective at detecting maliciousness in various domains and particularly in email. Previous works which used machine learning methods suggested sets of features which offer a limited perspective over the whole email message. In this paper, we propose a novel set of general descriptive features extracted from all email components (header, body, and attachments) for enhanced detection of malicious emails using machine learning methods. The proposed features are extracted just from the email itself; therefore, our features are independent, since the extraction process does not require an Internet connection or the use of external services or other tools, thereby meeting the needs of real-time detection systems. We conducted an extensive evaluation of our new novel features against sets of features suggested by previous academic work using a collection of 33,142 emails which contains 38.73% malicious and 61.27% benign emails. The results show that malicious emails can be detected effectively when using our novel features with machine learning algorithms. Moreover, our novel features enhance the detection of malicious emails when used in conjunction with features suggested by related work. The Random Forest classifier achieved the highest detection rates, with an AUC of 0.929, true positive rate (TPR) of 0.947, and false positive rate (FPR) of 0.03. We also present the IDR (integrated detection rate), a new measure which helps calibrate the threshold of a machine learning classifier in order to achieve the optimal TP and FP rates, which are the most important measures for a real-time and practical cyber-security application. [ABSTRACT FROM AUTHOR]

Voice activity detection (VAD) is an essential task in expert systems that rely on oral interfaces. The VAD module detects the presence of human speech and separates speech segments from silences and non-speech noises. The most popular current on-line VAD systems are based on adaptive parameters which seek to cope with varying channel and noise conditions. The main disadvantages of this approach are the need for some initialisation time to properly adjust the parameters to the incoming signal and uncertain performance in the case of poor estimation of the initial parameters. In this paper we propose a novel on-line VAD based only on previous training which does not introduce any delay. The technique is based on a strategy that we have called Multi-Normalisation Scoring (MNS). It consists of obtaining a vector of multiple observation likelihood scores from normalised mel-cepstral coefficients previously computed from different databases. A classifier is then used to label the incoming observation likelihood vector. Encouraging results have been obtained with a Multi-Layer Perceptron (MLP). This technique can generalise for unseen noise levels and types. A validation experiment with two current standard ITU-T VAD algorithms demonstrates the good performance of the method. Indeed, lower classification error rates are obtained for non-speech frames, while results for speech frames are similar. [ABSTRACT FROM AUTHOR]

Community detection technologies have the general research significance in complex networks, in which the topology information of network is worthy to be the focus for its widely application. It is the definition of community structure that the connection of nodes in the community is dense with the connection of nodes outside the community is sparse, which is corresponding to the core structure in the complex real networks is represented by a compact and dense set of connected nodes. While all the notes in the network are considered by the traditional topology, it is hard to extract the core structure with the continuous, exponential growth of community networks. In this paper, a novel network core structure extraction algorithm utilized variational autoencoder for community detection(CSEA) is proposed for finding the community structure more accurately. Firstly, the K-truss algorithm is used to find the core structure information in the network, and the similarity matrix is generated by similarity mapping combined with local information. Secondly, the variational autoencoder is used to extract and reduce the dimension of the similarity matrix containing the core structure of the network, and the low-dimensional feature matrix is obtained. Finally, the K-means clustering algorithm is utilized to obtain the community structure information. We compare CSEA algorithm with 18 different types of community detection algorithms using 4 evaluation metrics on 19 complex real networks. By extensively evaluating our algorithm on large real-world datasets, we show that CSEA algorithm has an excellent community division effect in dense complex real networks, especially in small and medium-sized networks, and it can accurately divide the complex real networks with unknown community structure. Simultaneously, CSEA algorithm also reveals some efficiency advantage in its on-line test. • Core structure in complex networks is meaningful for community detection. • A new variational autoencoder model is designed to speed up the clustering process. • A novel structure is proposed combines the logic analysis with neural networks. [ABSTRACT FROM AUTHOR]

The Asymmetric Traveling Salesman Problem (ATSP), as a branch of the traveling salesman problem, aims to find a group of circuits when a salesman travels all cities at the minimum cost and returns to the starting city under the condition of different round-trip costs between the two cities. Mayfly Algorithm (MA) is a swarm-based metaheuristic algorithm, which has been applied to many fields successfully. In this paper, we arrange all cities on the sphere. A Discrete Mayfly Algorithm (DMA) for solving spherical asymmetric traveling salesman problem is proposed. The algorithm applies inver-over operator, crossover operator and 3-opt operator to simplify the parameters of the algorithm, enrich the diversity of the population and enhance the local search ability. By experimenting, analyzing and comparing the effects of DMA and other metaheuristic algorithms in twelve scales, this study proves that the proposed algorithm is superior to others for the spherical ATSP, especially in high-dimensional cases. [ABSTRACT FROM AUTHOR]

The Local Binary Pattern (LBP) is a commonly used method for texture classification that performs well in terms of feature discrimination. However, (1) LBP can misclassify some important edge-located textures as non-uniform patterns with only one bin in the feature histogram, thus losing their discrimination capability. (2) When center pixel is contaminated by noise, a uniform pattern may be transformed into a non-uniform pattern, which can seriously affect the obtained LBP, thus degrading the classification results. To overcome these drawbacks, an edge-located uniform pattern recovery mechanism using statistical feature-based optimal center pixel selection strategy (SFB-OCPS) is proposed in this paper. To extract the correct edge pixels, we divide the whole texture image into 16 = 4 × 4 sub-images and propose an edge pixel selection strategy (EPSS) based on adaptive quantization with local threshold on each sub-image. Then 3 candidate center pixels constructed by statistical features of the local sampling neighborhood are generated for each edge-located center pixel. After the steps above, the SFB-OCPS strategy is introduced into the LBP-based algorithms. It is possible to recover some important edge-located non-uniform patterns to uniform patterns with an optimal center pixel selection, thus improving feature discrimination capability of the LBP-based algorithms. It should be emphasized that any LBP variants can introduce the proposed SFB-OCPS strategy to achieve the recovery of the edge-located uniform patterns. To validate the effectiveness of the proposed SFB-OCPS strategy, we introduce the SFB-OCPS strategy into the original LBP and 5 representative LBP-based algorithms. Experiments are conducted on 6 representative texture databases. Classification comparison reveals that the introduction of SFB-OCPS strategy can significantly improve the texture classification performance of LBP-based algorithms. Additionally, the noise-robustness of the proposed SFB-OCPS strategy is also verified through a series of experiments. [ABSTRACT FROM AUTHOR]

Wind resource is one of the most promising renewable energy, which has become a suitable replacement for fossil fuels. Optimizing the transferring wind energy from a wind turbine is essential to obtain the maximum power output as other variables are uncontrollable. This paper presents four different optimization algorithms, namely ant lion optimization (ALO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and crow search optimization (CSO), considering a hybrid decision-making model to compare the performances of wind energy optimization. In the first phase, the evolutionary algorithms are defined based on several factors to meet the need for wind energy based on volumetric and time reliability, reversibility, and vulnerability as well as evaluate optimized energy to the subscriber from the Gansu region. In the second phase, the ordinal priority approach (OPA) is coupled with VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rank the evolutionary algorithms. Then, the results are compared with the absolute optimal response based on the nonlinear programming method obtained from GAMS software. The results demonstrate that an ALO outperforms other algorithms. The average accuracy of ALO is 92%. CSO is the least accurate with 55% of the absolute optimal response. ALO is found to be faster, more efficient, and achieved economy and reliability as compared to other optimization algorithms for solving the problem under consideration. It is shown that the applied models are robust, effective, and able to save costs. [ABSTRACT FROM AUTHOR]

Nesting Problems, which are important subjects in the cutting and packing field, involve convex and nonconvex polygons and are common in several industries. These irregular open dimensional problems have been studied for decades, particularly the variant with one open dimension. However, in real-world applications, situations that are better suited to a two open dimensional model may arise and, in this sense, the literature is very limited. We here propose new separation and compaction algorithms for two-open dimension nesting problem. The paper develops an adaptation of the no-fit polygon to consider the penetration depth of pieces. The approach is based on an iterative compaction scheme, in which the key step is an obstruction map-based separation algorithm. The algorithms proposed found optimal solutions for artificial instances with up to 28 items within a small runtime. The results of benchmark instances indicate that the new algorithm is competitive when compared with other literature algorithms. It improved 14 of 15 benchmark instances when considering literature approaches on two open dimensions. In addition, the new algorithm achieved better occupation for some open dimension instances than the state of the art. • 2 Open Dimension Problem. • Textile Industry Problem. • Separation and Compaction Algorithm. [ABSTRACT FROM AUTHOR]

The paper describes the development of a system for receiving questions from users and providing answers, which is a part of a larger knowledge-based (KB) system based on Node of Knowledge (NOK) conceptual framework for KB system development. A part of NOK conceptual framework is described, as well as a new method of text-expressed knowledge representation – formalized NOK (FNOK). Furthermore, the structure of system for user interaction and NOK based KB system named Question Answering NOK (QANOK) is presented. The paper answers the question whether it is possible to create a part of QANOK over FNOK formalized knowledge which would accept user questions using natural language and provide answers in natural language. Three algorithms were developed – an algorithm which translates text-expressed knowledge in natural language into FMTEK (Formalized Method for Text Expressed Knowledge) formalized notation, an algorithm which translates a question in natural language into qFMTEK (question FMTEK) formalized knowledge notation, and an algorithm which uses qFMTEK formalized question to find an answer within FMTEK formalized knowledge. Algorithms were tested against a predefined set of sentences and questions in English language. Limitations of developed algorithms are also presented, as well as further development of QANOK system. [ABSTRACT FROM AUTHOR]

Technology evolution and energy of particle impact both plays a major role in inducing multi-cycle transient fault (longer duration transient) in a device. However, designing an optimized multi-cycle fault tolerant system is non-trivial. This paper presents a novel design space exploration (DSE) approach for multi-cycle transient fault tolerant datapath based on user power-delay constraints during high level synthesis (HLS). To the best of the authors’ belief, this is the first work to solve this problem in the literature so far. More specifically, the current work in the literature so far utilizes ‘triple modular redundancy (TMR)’ to design a fault tolerant datapath, however, this paper proposes a ‘dual modular redundancy (DMR) design with equivalent circuit’ scheme to achieve the same. The novel equivalent circuit that works with DMR systems performs the function of extracting the correct output from the DMR design. Further, the proposed work is the first work in the literature that handles multi-cycle transient faults during design space exploration of fault tolerant datapath. Therefore, key contributions of this paper are as follows: (a) novel multi-cycle transient fault tolerant algorithm that has capability to isolate original and duplicate units in a DMR with respect to the transient fault; (b) novel DSE approach that combines our fault tolerant algorithm along with user specified conflicting power-performance constraint that guides this intractable search problem to reach an high quality fault tolerant solution without violating the power budget and delay requirement; (c) integrates a heuristic based on bacterial foraging optimization algorithm (BFOA) that performs adaptive searching. Finally, results indicated an average improvement in Quality of Results (QoR) of >24% and reduction in hardware usage of >57% of the final solution compared to similar approach. [ABSTRACT FROM AUTHOR]

Ensemble methods have been shown to be an effective tool for solving multi-label classification tasks. In the RAndom k -labELsets (RAKEL) algorithm, each member of the ensemble is associated with a small randomly-selected subset of k labels. Then, a single label classifier is trained according to each combination of elements in the subset. In this paper we adopt a similar approach, however, instead of randomly choosing subsets, we select the minimum required subsets of k labels that cover all labels and meet additional constraints such as coverage of inter-label correlations. Construction of the cover is achieved by formulating the subset selection as a minimum set covering problem (SCP) and solving it by using approximation algorithms. Every cover needs only to be prepared once by offline algorithms. Once prepared, a cover may be applied to the classification of any given multi-label dataset whose properties conform with those of the cover. The contribution of this paper is two-fold. First, we introduce SCP as a general framework for constructing label covers while allowing the user to incorporate cover construction constraints. We demonstrate the effectiveness of this framework by proposing two construction constraints whose enforcement produces covers that improve the prediction performance of random selection by achieving better coverage of labels and inter-label correlations. Second, we provide theoretical bounds that quantify the probabilities of random selection to produce covers that meet the proposed construction criteria. The experimental results indicate that the proposed methods improve multi-label classification accuracy and stability compared to the RAKEL algorithm and to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we present a multibiometric system based on two different biometric modalities, palmprint and fingerprint. The proposed system is based on a score fusion which can effectively combine the discriminative power of multiple biometric traits. We propose a combined fingerprint image enhancement, which achieves satisfactory results. We also apply two different indexing algorithms on fingerprints and three different indexing algorithms on palmprints. Existing multibiometric systems are not sufficiently fast for practical applications. Indexing and retrieval mechanisms can effectively narrow down the search space in large databases. We propose a fingerprint indexing based on ellipse properties, which reduces false correspondences. The proposal uses a k-means clustering algorithm to index and retrieve fingerprints and palmprints. Furthermore, we combine some minutia cylinder-code (MCC)-based algorithms and improve the MCC-based indexing algorithms. Empirical experiments on some of datasets of the national institute of standards and technology (NIST), fingerprint verification competition (FVC), and THUPALMLAB databases show that the proposed algorithm considerably narrows down the search space in fingerprint and palmprint databases and increase the recognition performance. [ABSTRACT FROM AUTHOR]

Recommender systems use algorithms to provide users with product or service recommendations. Recently, these systems have been using machine learning algorithms from the field of artificial intelligence. However, choosing a suitable machine learning algorithm for a recommender system is difficult because of the number of algorithms described in the literature. Researchers and practitioners developing recommender systems are left with little information about the current approaches in algorithm usage. Moreover, the development of recommender systems using machine learning algorithms often faces problems and raises questions that must be resolved. This paper presents a systematic review of the literature that analyzes the use of machine learning algorithms in recommender systems and identifies new research opportunities. The goals of this study are to (i) identify trends in the use or research of machine learning algorithms in recommender systems; (ii) identify open questions in the use or research of machine learning algorithms; and (iii) assist new researchers to position new research activity in this domain appropriately. The results of this study identify existing classes of recommender systems, characterize adopted machine learning approaches, discuss the use of big data technologies, identify types of machine learning algorithms and their application domains, and analyzes both main and alternative performance metrics. [ABSTRACT FROM AUTHOR]

Expert and intelligent systems understand the underlying information behind the data by relying on a wide range of machine learning techniques. The interpretation of machine learning models is often the key to success in research areas such as business, finance, medical and health science, and bioinformatics; such research areas demand human understanding of the obtained model. The logistic model tree (LMT) algorithm is a popular classification method that combines a decision tree and logistic regression models. The combination of two complementary algorithms produces an accurate and interpretable classifier by combining the advantages of both logistic regression and tree induction. However, LMT has the disadvantage of high computational cost, which makes the algorithm undesirable in practice. In this paper, we propose an efficient method to learn the logistic regression models in the tree. We employ least angle regression to update the regression model in LogitBoost so that the algorithm efficiently learns sparse logistic regression models composed of relevant input variables. We compare the performance of our proposed method with the original LMT algorithm using 14 benchmark datasets and show that the training time dramatically decreases while the accuracy is preserved. Our proposed algorithm is not only accurate and intuitively interpretable but also computationally efficient. It helps users in making the best possible use of the data that are included in expert and intelligent systems. [ABSTRACT FROM AUTHOR]

Frequent pattern mining is considered a key task to discover useful information. Despite the quality of solutions given by frequent pattern mining algorithms, most of them face the challenge of how to reduce the number of frequent patterns without information loss. Frequent itemset mining addresses this problem by discovering a reduced set of frequent itemsets, named closed frequent itemsets , from which the entire frequent pattern set can be recovered. However, for frequent similar pattern mining , where the number of patterns is even larger than for Frequent itemset mining, this problem has not been addressed yet. In this paper, we introduce the concept of closed frequent similar pattern mining to discover a reduced set of frequent similar patterns without information loss. Additionally, a novel closed frequent similar pattern mining algorithm, named CFSP-Miner , is proposed. The algorithm discovers frequent patterns by traversing a tree that contains all the closed frequent similar patterns. To do this efficiently, several lemmas to prune the search space are introduced and proven. The results show that CFSP-Miner is more efficient than the state-of-the-art frequent similar pattern mining algorithms, except in cases where the number of frequent similar patterns and closed frequent similar patterns are almost equal. However, CFSP-Miner is able to find the closed similar patterns, yielding a reduced size of the discovered frequent similar pattern set without information loss. Also, CFSP-Miner shows good scalability while maintaining an acceptable runtime performance. [ABSTRACT FROM AUTHOR]

In recent years, credit scoring has become an efficient tool that allows financial institutions to differentiate their potential default borrowers. Accordingly, researchers have developed a myriad of approaches, including statistical and artificial intelligence techniques, to fulfill the task of credit scoring. Recent studies have shown that ensemble methods, which combine multiple algorithms that process different hypotheses to form a new hypothesis, generally outperform the other credit scoring approaches. In this paper, we propose a novel heterogeneous ensemble credit model that integrates the bagging algorithm with the stacking method. The proposed model differs from the extant ensemble credit models in three aspects, namely, pool generation, selection of base learners, and trainable fuser. Four popular evaluation metrics, including accuracy, area under the curve (AUC), AUC-H measure, and Brier score, are employed to measure the performance of alternative models. To confirm the efficiency of the proposed bstacking approach, a wide range of models, including individual classifiers, homogeneous ensemble model, and heterogeneous ensemble model, are introduced as benchmarks. We also provided a discussion on the accurate yet complex credit scoring model (e.g., bstacking) from a regulatory perspective. [ABSTRACT FROM AUTHOR]

Due to the rapid extension of social networks in recent years, a new potential has emerged for global spreading of messages and effective broadcasting of news. Identification of influential nodes within a network is now seen as a key factor for bringing this potential into action. k -shell is a measure for detection of node influence, and has already been used in some successful algorithms in this field. However, k-shell does not provide enough information about the topological positions of the nodes, and the present paper seeks to present a special hierarchical measure for dealing with this issue. Along with introduction of the above measure, it is shown that it can be used for detecting and ranking node influence. The experiments done on real-world and artificial networks demonstrate that the proposed approach can rank the influence of nodes more accurately than other approaches. [ABSTRACT FROM AUTHOR]

Dimensionality reduction becomes a significant problem due to the proliferation of high dimensional data. Sparse preserving projection (SPP) obtains the intrinsic geometric structure of the data, which contains natural discriminating information, and avoids the selection of parameters as well. However, SPP neglects the global structures since it computes the sparse representation of each data individually. Low rank representation (LRR), another commonly used dimensionality reduction method, finds the lowest rank representation of all data jointly, and is capable of capturing the global structures of data. Therefore in this paper, we propose a method, global and intrinsic geometric structure embedding for unsupervised feature selection (GGEFS), by constructing a low-rank-sparse graph. Our GGEFS method contains the loss of information, the preservation of structural information and the sparse regularization of projection matrix, on which we impose l 2,1/2 -matrix norm to select sparser and discriminative features. An effective iterative algorithm based on Lagrange Multiplier method is described to solve GGEFS. Extensive experimental results demonstrate that the proposed algorithm outperform several state-of-the-art unsupervised feature selection methods. [ABSTRACT FROM AUTHOR]

Due to the continuous growth of social networks the textual information available has increased exponentially. Data warehouses (DW) and online analytical processing (OLAP) are some of the established technologies to process and analyze structured data. However, one of their main limitations is the lack of automatic processing and analysis of unstructured data (specifically, textual data), and its integration with structured data. This paper proposes the creation, integration and implementation of a new dimension called Contextual Dimension from texts obtained from social networks into a multidimensional model. Such a dimension is automatically created after applying hierarchical clustering algorithms and is fully independent from the language of the texts. This dimension allows the inclusion of multidimensional analysis of texts using contexts and topics integrated with conventional dimensions into business decisions. The experiments were carried out by means of a freeware OLAP system (Wonder 3.0) using real data from social networks. [ABSTRACT FROM AUTHOR]

Events prediction in a sequence of events is a challenging task that can be approached with data mining. In this paper, we focus on the specific case of early prediction of distant events. We aim at mining episode rules with a consequent temporally distant from the antecedent and an antecedent as small as possible both in number of events and in occurrence duration. We refer to these rules as essential rules . To reach this goal, we propose an original algorithm, DEER : Distant and Essential Episode Rules. This algorithm differs from traditional algorithms in three points. First, it determines the consequent of episode rules at an early stage in the mining process, which allows to mine rules with an antecedent as small as possible. Second, it applies a minimal gap constraint between the antecedent and the consequent to guarantee a suitable distance between both elements. Third, the stop criterion used considers both the support and the confidence of the rules, at the opposite of traditional algorithms that use only the support. Experiments on both synthetic and real datasets show that DEER runs faster than several algorithms of the state-of-the-art and has also good scalability on large datasets. Furthermore, by studying in details the episode rules mined from a real dataset of blog messages, we demonstrate not only the efficiency of our algorithm for mining interesting essential rules with a distant consequent, but also that these rules can be used to accurately predict distant events. [ABSTRACT FROM AUTHOR]

This paper presents new feature selection algorithms for aggregate data analysis. Data aggregation is commonly used when it is not appropriate to model the relationship between a response and explanatory variables at an individual-level. We investigate substantial challenges in analysis for aggregate data. Then, we propose a groupwise feature selection method that addresses (i) the change in dataset depending on the selection of predictor variables, (ii) the presence of potential missing responses, and (iii) the suitability of model selection criteria when comparing models using different datasets. In application to real auto insurance data, we find a set of important predictors to classify the policyholders into some homogeneous risk groups. Our results clearly demonstrate the potential of the proposed feature selection method for aggregate data analysis in terms of flexibility and computational complexity. We expect that the proposed algorithms would be further applied into a wide range of decision-making tasks using aggregate data as they are applicable to any type of data. [ABSTRACT FROM AUTHOR]