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With the development of economic globalization, distributed hybrid flow shop scheduling problem (DHFSSP) has become prevalent in realistic manufacturing systems. Moreover, to accord with the actual production scenarios and satisfy the requirement of manufacturing market, it is imperative to comprehensively explore various complex manufacturing scenarios (e.g., production assembly) and production-constrained resources (e.g., worker resources) in DHFSSP. However, the integration mode of DHFSSP, assembly shop problem (ASP), and dual-resource constraints (DRC) has not been reported in existing literature. Thus, to fill out this research gap, this paper first attempts to investigate DAHFSSP-DRC with minimization the total tardiness (T T D). To solve this problem, a mixed-integer linear programming (MILP) model and a knowledge-based iterated greedy algorithm (KBIG) are presented. The novelties of KBIG are as follows: (1) An efficient decoding is developed to improve the solution's quality; (2) A knowledge-based NEH (KB-NEH) initialization strategy is presented to generate an initial solution; (3) A knowledge-based destruction and construction is designed to improve the exploration capability; (4) A product-based local search is proposed to enhance the exploitation capability. Additionally, to validate the proposed model, we implement CPLEX to solve it on 24 small-sized instances. To verify the effectiveness of the proposed KBIG, extensive experiments are conducted to compare with other 7 comparison algorithms on 405 large-sized instances. Experimental results illustrate that KBIG is superior to its competitors. • This paper first investigates DAHFSSP-DRC with minimization TTD. • A MILP model and a KBIG are proposed for DAHFSSP-DRC. • The importance level of different knowledge is considered in KBIG. • Experimental results prove the effectiveness of MILP model and KBIG. [ABSTRACT FROM AUTHOR]

Grey prediction evolutionary algorithm (GPE) is an emerging category of meta-heuristic algorithm based on mathematical model. It generates offspring by predicting the macroscopic evolutionary trend of population series. On the contrary, the mining of local evolutionary trend are flawed. To address this imperfection, this paper introduces a quadratic function as a surrogate model to mine the local evolutionary trend of population series for the first time. Thereby an improved grey prediction evolutionary algorithm called grey prediction evolutionary algorithm with a surrogate model based on quadratic interpolation (GPE-QI) is designed. The surrogate model based on quadratic interpolation is regarded as an external auxiliary tool to mine the local evolutionary trend of population series by using currently superior individuals. Through utilizing the mined local trend information, a donor population closer to the optimal solution is formed to improve the prediction direction of the algorithm. The performance of GPE-QI is tested on CEC2019, CEC2020 benchmark functions and six engineering design problems. The experimental results demonstrate the stronger competitiveness of GPE-QI to well-established algorithms in terms of solving accuracy and convergence rate. If this paper is accepted, MATLAB codes associated with this paper will be uploaded to https://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE. • Improvement can be made by enhancing local search of grey prediction evolution. • Mine local evolutionary trend of population series by quadratic interpolation. • Construct a surrogate model by using the local evolutionary trend. • Design an improved grey prediction evolutionary algorithm with a surrogate model. • The proposed algorithm is more competitive than the well-established. [ABSTRACT FROM AUTHOR]

In the last years, many areas in science, business, and engineering have experienced an enormous growth in the amount of data that they are required to analyze. In many cases, this analysis relies intimately on data visualization and, as a result, graph drawing has emerged as a new field of research. This paper addresses the challenge of drawing hierarchical graphs, which is one of the most widely used drawing standards. We introduce a new mathematical model to automatically represent a graph based on the alignment of long arcs, which we combine with the classic arc crossing minimization objective in hierarchical drawings. We complement our proposal with a heuristic algorithm that can obtain high-quality results in the short computational time required by graph drawing systems. Our algorithm joins two methodologies, tabu search and strategic oscillation (SOS), to perform a fast and effective exploration of the search space. We conduct extensive experimentation that integrates our new mathematical programming formulation and the SOS tabu search that targets large instances. Our statistical analysis confirms the effectiveness of this proposal. [ABSTRACT FROM AUTHOR]

Due to increasing air pollution, which is a consequence of the environmental effects of production in various industries, green supply chain management (GSCM) has attracted the attention of both scholars and practitioners. Green supplier selection is an important problem in GSCM and seeks to satisfy a firm's environmental goals as well as its economic targets. In this paper, for the first time, a green supplier selection problem considering both green and non-green evaluation criteria in a closed-loop supply chain is studied, and a cap-and-trade mechanism as a way of controlling the air pollution caused by manufacturers is proposed. To solve this particular problem, we propose a multi-objective robust optimization (RO) model. This specific model is an effective approach to handle uncertainty. A numerical example using randomly generated data, accompanied by subsequent discussion of the proposed approach, is deployed to validate the model. The results prove that the developed model for green supplier selection is able to effectively enhance the decision-making process of the experts. By illustrating the trade-off in robustness between the model and proposed solutions, as well as the effect of the deviation penalty on the closeness of results to the achieved solution, we show how firms can make optimal decisions when assigning the parameters. Furthermore, analyses show that decreasing the allowance amount (cap) and increasing the allowance prices in the cap-and-trade system escalate the firms' costs, but lower the amount of carbon released. Finally, we show that the cap-and-trade mechanism results in a better solution in terms of the total utility of the supply chain compared to the penalty-based system for a specific range of carbon allowance. • This paper generalizes the green supplier selection and order allocation problem. • A mathematical model for the problem is developed. • A robust optimization approach to handle the model uncertainty is developed. • The impact of changes in model parameters on the objective functions is analyzed. • The cap-and-trade mechanism is compared with a penalty-based mechanism. [ABSTRACT FROM AUTHOR]

The occurrence of dynamic events such as machine breakdown during workshop production can make the original scheduling scheme infeasible. Therefore, this paper establishes a mathematical model for a multi-objective dynamic flexible job shop scheduling problem with machine breakdown, and proposes a multi-objective migrating birds optimization algorithm based on game theory. Firstly, in order to solve the problem of difficult to determine the weight in weighted multi-objective optimization, this paper introduces game theory to balance the Pareto optimality and fairness between the two objectives of production efficiency and stability. When solving the solution of the game model, there may be no perfect Nash equilibrium solution, so a solution method that approximates the Nash equilibrium solution is designed. In the improved migrating algorithm, neighborhood operators based on path relinking and machine age are designed to improve the search ability. Based on the attributes of multi-objective problems, a multiple similarity measure method is designed to select and replace solutions. The experiment part proves the effectiveness of the game strategy in multi-objective optimality and fairness, and concludes that the algorithm has good performance by comparing with the advanced algorithms in recent years. • A multi-objective game model is designed to solve dynamic scheduling problems. • The approximate Nash equilibrium solution is designed to solve the game matrix. • The neighborhood operators of path relinking and machine age are designed. • A multiple similarity measurement method is designed for multi-objective problem. [ABSTRACT FROM AUTHOR]

• The paper focuses on a pick-up routing problem with a three-dimensional (3D-PRP) loading constraint. • The paper combines genetic algorithm for pick-up routing with machine learning for three-dimensional loading problem. • K-means algorithm is developed for clustering phase. • The paper includes four expert systems. • Results of a real dataset are highly promising. This paper presents a combinatorial problem called a pick-up routing problem with a three-dimensional (3D-PRP) loading constraint, clustered backhauls at the operational level, and train loading at the tactical level for an intermodal transportation network. A two-phase approach, called clustering first, packing-routing second, is proposed for use during the first stage. The clustering of backhauls is carried out using the k-means algorithm. A hybrid approach is provided, which combines the packing of orders by first solving a 3D loading problem for each cluster using machine learning with a best-fit-first strategy, with routing using a genetic algorithm. During the second stage, the train-loading problem is solved using a mixed integer programming approach to minimise the total costs by incorporating various cost types, in which detention and demurrage costs are taken into account. All solution approaches are computationally evaluated on real-world data provided by an international logistics firm and new randomly generated instances. Comparisons are carried out using both exact solution methods and heuristic approaches, and the proposed approach was shown to be more effective for real-world problems. [ABSTRACT FROM AUTHOR]

With the extensive application of laser cutting, the proper planning of the cutting path has a significate impact on industrial production, like clothing, metalware, and furniture. This paper proposes heuristic approaches for the two-dimensional cutting path problem, which aims to find the shortest path to cut pieces from the master plate. Two cutting methods, the arc routing method, and the node routing method, are studied based on whether the piece must be cut separately. A two-step heuristic approach is proposed for the arc routing method. A model based on a generalized traveling salesman problem (GTSP) and a variable neighborhood search (VNS) is introduced for the node routing method. The results show that the proposed two-step heuristic quickly finds the optimal or near-optimal solution. CPLEX using the GTSP model can solve small-size instances, while the VNS can find the solution with good quality in a reasonable time for medium-size instances. In addition, the model and approaches are also tested on larger instances with more than 250 pieces and 20,000 nodes. The results show that the proposed heuristics can handle these instances within a reasonable time. • A two-step heuristic is proposed for the arc routing method. • A new model is proposed for the node routing method. • A variable neighborhood search is introduced for the node routing method. • The two-step heuristic quickly finds the optimal or near-optimal solution. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid classifier method using a mixed-integer linear programming (MILP) model and an improved online learning algorithm for general fuzzy min–max neural networks (IOL_GFMM). IOL_GFMM is used to generate initial hyper-boxes for the MILP model in order to improve its efficiency. This paper is the first attempt that integrates the fuzzy min–max neural networks and the MILP model. The applicability of the proposed hybrid model is proven on both real and artificial datasets. Performance evaluation parameters of the novel hybrid classifier are classification accuracy, training time, number of iterations, and number of misclassified data. Results show that the proposed hybrid method provides satisfying improvements in performance evaluation parameters compared with the pure MILP model. Our proposed approach achieves to speed the training time up to 76 times and decreases the number of misclassified data by up to 17 times. Results of artificial datasets show another contribution that the proposed hybrid approach is very efficient in order to classify data samples not only when they belong to separate regions but also when there exists significant overlap. [ABSTRACT FROM AUTHOR]

Sugarcane is one of the main commodities in the world economy. An important step in the sugarcane supply chain is the harvest stage, requiring the support of mathematical modeling and optimization tools. Integrating the process of choosing the areas for harvesting according to the sugarcane maturation with the formation of the harvesting fronts and associating them with machine relocation costs is challenging. This paper contributes with a novel integer mathematical model that considers these concerns using three conflicting objectives. The first is that the sugarcane should be harvested as close as possible to its maturity, considering the sugarcane should be harvested as close as possible to its maturity by considering a combined measure of temperature and time since planting. The second aims to minimize the number of harvest fronts. The third objective is to minimize the costs involved with the relocation of the harvesters that cut the cane. Different strategies for obtaining Pareto-optimal solutions were proposed using exact methods, including MIP start and a decomposition technique. In the worst case, solutions with tolerable integrality gaps were obtained (inferior to 3%) in less than 1.5 h. Computational tests in real case studies were conducted in the sugarcane industry in two countries, indicating strong trade-offs in the determined solutions. In the Brazilian case, harvesting as close to maturation as possible, for example, causes an increase of 158% in the number of harvest fronts and 47% in the transportation costs, considering the minimum values for these objectives. Other trade-offs were seen and discussed when one of the other objectives was prioritized. These different compromise solutions help the decision-makers choose the most appropriate harvesting schedule according to their preferences. • We propose a new MIP optimization model to determine the harvesting of sugarcane. • The model aims to optimize three conflicting objectives. • A new method of computing cane maturation is used: the concept of degree-days. • A procedure for determining Pareto solutions by exact methods is proposed. [ABSTRACT FROM AUTHOR]

Independent interactive medical applications cooperate closely with user systems through independent information and data exchange, which realizes digital and intelligent communication for health monitoring system. Due to extensive connectivity and application services, heavy data traffic and limited energy consumption reduce the quality of application services. In this paper, a mathematical model for efficient energy harvesting resource allocation based on cognitive radio sensor network (CRSN) infrastructure is constructed to address energy limitation issues in health monitoring system applications. Because of the highly dynamic nature of available network resources, energy efficiency and communication reliability can be improved. Since the proposed optimization problem is a complex and NP hard problem, coevolutionary theory is adopted, and then a stochastic optimization algorithm based on polynomial approximation and quantum particle swarm optimization is proposed. Simulation results show that the proposed CQPSO algorithm has better performance in terms of re-transmission probability, system throughput, and energy consumption compared to other similar algorithms. Efficient resource allocation improves the performance of the health monitoring system, more effectively and real-time monitoring of human physiological and motion information, providing testers with high-precision quantitative information, and achieving early monitoring and intervention of common diseases. • The additional antenna provides more space freedom and improves throughput. • The modeling problem is to maximize the network utility under uncertainty. • Bernstein approximation technique is used to control the approximation error. • Coevolution theory is adopted to deal with the large-scale character of problems. [ABSTRACT FROM AUTHOR]

Modern organizations execute processes to deliver product and services, whose enactment needs to adhere to laws, regulations and standards. Conformance checking is the problem of pinpointing where deviations are observed. This paper shows how instances of the conformance checking problem can be represented as planning problems in PDDL (Planning Domain Definition Language) for which planners can find a correct solution in a finite amount of time. If conformance checking problems are converted into planning problems, one can seamlessly update to the recent versions of the best performing automated planners, with evident advantages in term of versatility and customization. The paper also reports on results of experiments conducted on two real-life case studies and on eight larger synthetic ones, mainly using the Fast-downward planner framework to solve the planning problems due to its performances. Some experiments were also repeated though other planners to concretely showcase the versatility of our approach. The results show that, when process models and event logs are of considerable size, our approach outperforms existing ones even by several orders of magnitude. Even more remarkably, when process models are extremely large and event log traces very long, the existing approaches are unable to terminate because they run out of memory, while our approach is able to properly complete the alignment task. [ABSTRACT FROM AUTHOR]

• Design of a dynamic mathematical model for blood assignment problem. • Proposal of a symbiotic optimization search algorithm for the BAP. • Implementation of two hybrid SOSGA and SOSSA algorithms to solve the BAP. • Suggestions for further development and possible areas of improvement. The shortage and wastage of blood products have been identified as the major contending factors that are frequently encountered in the management of blood supply chain processes. In general, the blood which is considered an essential product for which human existence relies on, has perishability characteristics that allow it to be stored up to a limited number of days. Therefore, this feature constrains the quantity of blood that can be retained in hospitals and blood centers, because keeping excessive number of blood units on inventory may result in blood product wastage. On the other hand, failure to stockpile on inventory can lead to shortage of this resource, and as a result may cause the cancellation of important activities such as treatment of special cases like surgery, accident, disaster circumstance and, in a worst case scenario increases the fatality rates at hospitals. This paper presents a dynamic mathematical model with the goal of improving the efficiency of blood related activities that occur at the blood centers. The model also caters for the assignment of whole blood units of available blood types to various requests. A set of equations that incorporate both the ABO and Rhesus blood groups are derived and presented subsequently. This further extends the initial work where only the ABO blood group was considered. In an effort to implement the developed model, three metaheuristic algorithms namely, symbiotic organisms search, symbiotic organisms search genetic algorithm, and symbiotic organisms search simulated annealing algorithms are proposed to identify the optimal routing for each of the blood types. An extensive numerical study was carried out using datasets from a synthetic blood sample collection process to illustrate the potential of the three metaheuristic algorithms to solve the developed blood assignment model. Furthermore, experimental results show that the hybrid symbiotic organisms search algorithms not only achieve superior accuracy, but also exhibits a higher level of stability, with the hybrid symbiotic organisms search genetic algorithm having the overall best superior performance. [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]

The main aim of this paper is to connect R-fuzzy sets and type-2 fuzzy sets, so as to provide a practical means to express complex uncertainty without the associated difficulty of a type-2 fuzzy set. The paper puts forward a significance measure, to provide a means for understanding the importance of the membership values contained within an R-fuzzy set. The pairing of an R-fuzzy set and the significance measure allows for an intermediary approach to that of a type-2 fuzzy set. By inspecting the returned significance degree of a particular membership value, one is able to ascertain its true significance in relation, relative to other encapsulated membership values. An R-fuzzy set coupled with the proposed significance measure allows for a type-2 fuzzy equivalence, an intermediary, all the while retaining the underlying sentiment of individual and general perspectives, and with the adage of a significantly reduced computational burden. Several human based perception examples are presented, wherein the significance degree is implemented, from which a higher level of detail can be garnered. The results demonstrate that the proposed research method combines the high capacity in uncertainty representation of type-2 fuzzy sets, together with the simplicity and objectiveness of type-1 fuzzy sets. This in turn provides a practical means for problem domains where a type-2 fuzzy set is preferred but difficult to construct due to the subjective type-2 fuzzy membership. [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]

Narrative complexity should be managed systematically during the creation process because it heavily influences the levels of understanding and the interest of recipients. Existing research has tended to depend on impressionist criticism or to deal with narrative complexity at general and superficial levels. In this paper, we consider the creation and acceptance of narrative as information processing mediated by a narrative text. Under this assumption, we propose a method to quantitatively evaluate narrative complexity at the recipient's cognitive level, and to effectively utilize the evaluation to aid in the author's narrative creation process. Within our knowledge distribution model, a narrative is represented as a knowledge structure , and the knowledge state and knowledge flow of narrative agents are evaluated using a probabilistic reasoning model. From the knowledge flows, the amount of information processed and required by the recipient is calculated as a measure of the narrative complexity using entropy theory. As a case study, we conduct a comparative analysis of an actual cinematic narrative and a structurally manipulated version of that narrative to show how the method captures characteristics and changes of the narrative; we also discuss the improvements presented in this paper as compared to our previous research. [ABSTRACT FROM AUTHOR]

• Mathematical notations and constraints of DHHFSP with blocking constraints is formulated. • NEH-Increase heuristic strategy is designed to allocate the jobs to heterogeneous factories. • The neighborhood search strategies cooperatively optimize the scheduling sequence. • A local intensification strategy is presented to reinforce the exploration ability. The hybrid flow shop and distributed flow shop problems have been extensively studied due to their wide industrial applications. However, the distributed heterogeneous hybrid flow shop problems (DHHFSP) with blocking constraints have not yet been well studied up to date. This paper considers how to arrange a variety of jobs to different heterogeneous factories, and each factory has a minimal makespan. The innovations of this paper lie in presenting a mathematical model of the DHHFSP with blocking constraints and designing a collaborative iterative greedy (CIG) algorithm. The CIG contains the problem-specific initialization strategy, the neighborhood search strategy, the destruction-reconstruction strategy, and the local intensification strategy. The cross-factory and inner-factory neighborhood search strategies based on two swap operators are adopted to reduce the blocking time. The local intensification strategy is developed to optimize the scheduling sequence of each factory. The proposed algorithm is empirically compared with five state-of-the-art algorithms on 60 different instance sets. The experimental results show that the proposed algorithm significantly outperforms the compared ones in terms of objective values and relative percentage deviation values. [ABSTRACT FROM AUTHOR]

Highlights • The efficiency improvement in network representation learning. • Combination of network structure and topic similarity evaluation. • High performance in network random walk mechanism. • Capabilities in large-scaled information network handling. Abstract Recently, heterogeneous network representation learning has attracted a lot of attentions due to its potential applications. Our works in this paper are concentrated on how to leverage the output of network representation learning by combining with the topic similarity between nodes in content-based heterogeneous information network (C HIN). These unique challenges come from the shortage of topic similarity evaluation between text-based nodes which limit the accuracy of the similarity search as well other network mining tasks. Moreover, the massive sizes of current real-world network also raises challenges for traditional standalone-based heterogeneous network analysis models. Different from previous network representation learning models, such as: Node2Vec or Metapath2Vec, our proposed W-MethPath2Vec model uses the topic-driven meta-path-based random walk mechanism for generating heterogeneous neighborhood of nodes as the learning features. Then, these learning nodes' features are used to train the learning model which is used for solving various heterogeneous network mining tasks such as: node similarity search, clustering, classification, link prediction, etc. The W-MethPath2Vec model enables the simultaneous modeling of structural and topic correlations between nodes in heterogeneous networks. Moreover, the W-MethPath2Vec model is implemented in the Apache Spark-based distributed framework which enables the capability of handling large-scaled networks. We tested our W-MethPath2Vec model with the previous state-of-the-art approaches in the real-world datasets to demonstrate the effectiveness of our proposed model. [ABSTRACT FROM AUTHOR]

Highlights • We propose a real time view of an attributed graph for a peer recommendation system. • We used dynamic community search on an attributed graph for peer recommendation. • We assign weight to the attributes of a node in dynamic graphs. • Skip-gram model is used to expand the attribute sets. Abstract In this paper, we propose a novel model to recommend possible research peers to a user efficiently. We model all the authors along with their attributes as an attributed graph and then perform community search on this attributed graph to find the most appropriate peer for a user. We propose algorithms to recommend a dynamic attributed graph efficiently and create a real-time community search model by deploying an incremental training algorithm. We also propose dynamic weighted attributes for each node (peer). Given a node and set of attributes (query), the proposed model is capable of self-expansion of the attribute set leading to a more significant match between two nodes. Our experimental results show that our proposed model achieved substantial performance gains over the existing models. In a nutshell, we present an intelligent system that incorporates the changes going on in the research world and suggest up-to-date recommendations. The proposed model is also robust enough to ensure that the recommendations do not suffer due to poor queries. [ABSTRACT FROM AUTHOR]

Highlights • An improvement-based method is proposed to identify the feedback types. • A novel negative feedback model which captures negative information is proposed. • Integrating positive and negative feedback model is explored to improve retrieval. • A content-based representativeness criterion is proposed and evaluated. • Performance of the proposed models is evaluated on several TREC datasets. Abstract Pseudo-relevance feedback (PRF) has evident potential for enriching the representation of short queries. Traditional PRF methods treat top-ranked documents as feedback, since they are assumed to be relevant to the query. However, some of these feedback documents may actually distract from the query topic for a range of reasons and accordingly downgrade PRF system performance. Such documents constitute negative examples (negative feedback) but could also be valuable in retrieval. In this paper, a novel framework of query language model construction is proposed in order to improve retrieval performance by integrating both positive and negative feedback. First, an improvement-based method is proposed to automatically identify the types of feedback documents (i.e. positive or negative) according to whether the document enhances the retrieval's effectiveness. Subsequently, based on the learned positive and negative examples, the positive feedback models and the negative feedback models are estimated using an Expectation-Maximization algorithm with the assumptions: the positive term distribution is affected by the context term distribution and the negative term distribution is affected by both the positive term distribution and the context term distribution (such that the positive feedback model upgrades the rankings of relevant documents and the negative feedback model prunes the irrelevant documents from a query). Finally, a content-based representativeness criterion is proposed in order to obtain the representative negative feedback documents. Experiments conducted on the TREC collections demonstrate that our proposed approach results in better retrieval accuracy and robustness than baseline methods. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid model using modular neural networks and fuzzy logic was designed to provide the hypertension risk diagnosis of a person. This model considers age, risk factors and behavior of the blood pressure in a period of 24 h, using as a basis the Framingham Heart Study. Records of blood pressure are collected with the ambulatory blood pressure monitoring (ABPM), a device which takes readings for a period of time of 24 h. A modular neural network was designed, with three modules, of which the first and second modules correspond to the systolic and diastolic pressures and the last one to the heart rate. Each module is trained with the data obtained by the ABPM of different patients, this in order that the neural network learns the different behaviors that the blood pressure may have. Also, different architectures and learning methods are considered to obtain the best possible architecture. In addition, two fuzzy inference systems (FISs) for classification purpose are proposed, the first one for the heart rate level and the second one for the night profile of the patient. These were tested with different types of membership functions and then selecting the FIS that obtained the best results. Furthermore, a third FIS as a blood pressure classifier is also used. The different proposed methodologies were tested, in the case of the modular neural network to find the architecture that produces better results and in the fuzzy inference systems to find which membership functions were the ideal ones for the case study, in this way obtaining overall good results. For the case of the modular neural network, the learning accuracy in the first module is 98%, in the second module is 97.62% and the third module is 97.83% respectively. For the night profile, the fuzzy system is compared to a traditional system of production rules, and it is noted that the first one gives all correct outputs and the second one just gives 53% of the outputs, this is due to the uncertainty handling that fuzzy systems can provide, which the traditional system cannot because its rules are very strict. Hybrid intelligent systems for the solution of this kind of complex problems have excellent performance, due to the good learning in each module of the neural network and the classification uncertainty that is well managed by the fuzzy systems, obtaining with this a hybrid combination for achieving good results. [ABSTRACT FROM AUTHOR]

An intensive analysis of the dependence structure among stock markets is invaluable to financial experts, policy makers, and academic researchers, providing them with important implications for portfolio management, policy-making, and risk assessment. This paper proposes a novel spatiotemporal model to both examine global stock market linkages and investigate what drives stock returns. The newly introduced model allows us to go beyond conventional correlation analyses confined to studying pairwise relationships and seems to be more suitable for detecting the dependence structure of high-dimensional financial time series. Moreover, a new copula-based approach to define the spatial weight matrix is presented that is based on the construction of a dissimilarity matrix using the Spearman's contagion index. To the best of our knowledge, this paper is the first to incorporate copulas into the definition of the spatial weight matrix. In addition, the maximum likelihood estimator of our model is derived, together with a Monte Carlo simulation study evaluating its performance compared to two other methods. Finally, the results demonstrate that our proposed measure of the spatial weight matrix, coupled with our model, performs very well in terms of capturing spatial and temporal dependencies among global stock markets, and that the relative values of conditional volatilities are also important factors in determining stock returns. [ABSTRACT FROM AUTHOR]

Benthem’s correspondence theory is one of the most important tools of the theory of modal logics developed in the last three decades. Correspondence theory, a subfield of the model theory, reflects a systematic study of relations between classes of frames and modal language. In this paper, we use correspondence theory for modal logics to solve a problem not addressed yet in the literature, namely the soundness and completeness of a logic combining two different, yet related modalities: agents’ knowledge and commitments. The paper proves the soundness and completeness of this logic called CTLKC + . This work is highly significant as it proves that combining the two agents’ modalities resulted in a consistent logic that can be used to design reliable systems. The methodology is as follows: we develop a set of reasoning postulates (axioms) that reflect the interaction between agents’ knowledge and social commitments in multi-agent system (MAS) using the CTLKC + logic and correspond them to certain classes of frames. In particular, we first give a name, formalization and meaning for each postulate. Then, we correspond the postulates to certain classes of frames and provide the required proofs. Thereafter, we present a discussion that illustrates the importance of the proposed postulates in MASs using a concrete application example called the NetBill protocol taken from the business domain. Finally, we show how the postulates were addressed in the literature. The existence of such a correspondence allows us to prove that the logic generated by any subset of these postulates is sound and complete with respect to models that are based on the corresponding frames. The ultimate goal of this paper is to further assess the logic of knowledge and commitments (CTLKC + ) from a new perspective (i.e., the soundness and completeness). Consequently, this work advances the literature of logics in MASs and closes a gap that has not been explored before. [ABSTRACT FROM AUTHOR]

• The modeling of fog/cloud offloading using replicator dynamics is scalable. • It saves 180% of the time compared to the best state-of-the-art methods. • It saves 380% of the energy compared to the best state-of-the-art methods. • The proposed method gains more users' latency satisfaction. • The proposed method is promising for energy-constraint applications. Due to the growing demand for delay-sensitive applications, partial offloading is very important in fog/cloud computing. In partial offloading, different parts of each task are run in parallel on the end device, fog device, and remote cloud. Generally, a fog/cloud environment consists of independent, selfish agents who want to get more resources. Game theory is a well-known mathematical tool for modeling the interactions of these selfish agents. In traditional game-theoretical models, the mobile user is considered a player. A significant problem with such modeling is scalability. When the number of players is very large, the algorithm may require a long time to converge. The reason is that with the increase in the number of users, the search space for strategies becomes larger. The player must have complete knowledge about competitors to make the best decision. Moreover, the dynamic changes in the fog environment have been ignored in almost all previous studies. This paper presents dual-population modeling using replicator dynamics considering the interaction between the user and the fog environment. Unlike classical game theory, here, the offloading decision (strategy) is viewed as the player. The proposed method can very efficiently model the growing/shrinking behavior of two populations of CPU cycles on both the user and the network sides. It is scalable and converges to a stable equilibrium point regardless of the number of mobile users. We derive conditions under which agents can update their decisions according to the dynamics of the environment. The simulation results reveal the superiority of the proposed method over the full local execution, full offloading, evolutionary algorithms, and the Stackelberg methods. It saves 180% and 380% of the time and energy compared to the best state-of-the-art and baseline methods. [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]

This paper proposes an intuitionistic fuzzy decision method based on prospect theory and the evidential reasoning approach, aiming at analyzing multi-attribute decision making problems in which the criteria values are intuitionistic fuzzy numbers and the information of attributes weights is unknown. Firstly, the measures of entropy and cross entropy are defined for intuitionistic fuzzy sets by taking into consideration the preference of decision maker towards hesitancy degree. Secondly, combined with bounded rationality, the prospect decision matrix is calculated in the light of prospect theory and intuitionistic fuzzy distance. Thirdly, the correlational analyses are conducted between the attribute weights and three indicators which are entropy, cross entropy and prospect value, and optimization models for identifying attribute weights are built under the circumstances that the weights are incomplete and unknown. Finally, in order to avoid the loss of decision making information, the evidential reasoning approach is applied to the calculation of comprehensive prospective values for all alternatives. Following the value calculation, the ranking and the optimal alternative are determined based on the comprehensive prospective values. Illustrating examples demonstrate that the proposed method is reasonable and feasible. [ABSTRACT FROM AUTHOR]

Traditional cellular systems may not be appropriate to support machine-type communications (MTC) due to a large number of devices and relatively small, infrequent data transmissions. The 3GPP has identified the MTC as an important area of the LTE system and has discussed several mechanisms that control random access (RA) overload caused by massive MTC devices. In this paper, we show that a retransmission mechanism of RA may lead to performance degradation in an overload situation, and propose two RA solutions that relieve the RA overload. Since the RA success probability is closely related with the number of simultaneous RA attempts, the first solution adjusts the maximum number of RA retransmissions to control the amount of RA attempts. The second solution separates the RA resources into two subsets that MTC devices can access according to the number of consecutive RA failures and distributes the RA traffic over the two subsets. The two proposed solutions are analyzed by a mathematical model assuming a simplified operation, and a more realistic environment is considered by protocol-level simulations. Since the performance of the proposed solutions depends on the system configurations and parameters, the base station may adaptively adjust them for an optimal operation. [ABSTRACT FROM AUTHOR]

Recommender systems have been developed to overcome the information overload problem by retrieving the most relevant resources. Constructing an appropriate model to estimate the user interests is the major task of recommender systems. The profile matching and latent factors are two main approaches for user modeling. Although a notion of timestamps has already been applied to address the temporary nature of recommender systems, the evolutionary behavior of such systems is less studied. In this paper, we introduce the concept of trend to capture the interests of user in selecting items among different group of similar items. The trend based user model is constructed by incorporating user profile into a new extension of Distance Dependent Chines Restaurant Process (dd-CRP). dd-CRP which is a Bayesian Nonparametric model, provides a framework for constructing an evolutionary user model that captures the dynamics of user interests. We evaluate the proposed method using a real-world data-set that contains news tweets of three news agencies (New York Times, BBC and Associated Press). The experimental results and comparisons show the superior recommendation accuracy of the proposed approach, and its ability to effectively evolve over time. [ABSTRACT FROM AUTHOR]

Recommender systems have gained much attention due to their great commercial benefits in electronic markets. The quality of the recommendations depends on the quality of the preference model extracted by the recommender system. Recently, latent factor models based on probabilistic matrix factorisation have gained much attention, owing to their superior accuracy over traditional recommender systems and their great efficiency. Although latent factor models are very efficient, they mostly ignore the user preferences over different item feature values. For example, they assume that lower prices are preferred by all users. However, there may be users who believe that a high price comes with better quality or more prestige. Furthermore, according to homophily and social influence in social sciences, similar users in a social network tend to acquire similar tastes through social interactions. Therefore, all components of human preferences including feature value discrepancies are subject to social influence. Moreover, most of the latent factor models ignore the possible dependencies that naturally exist between item features. To tackle these problems, in this paper we propose two novel latent factor models incorporating socially-influenced feature value discrepancies, and socially-influenced conditional feature value discrepancies. We test the accuracy of the proposed methods on three well-known benchmark datasets. The extensive experiments show that the proposed method achieves significantly higher accuracies than all state of the art traditional, latent factor, and social recommendation models. [ABSTRACT FROM AUTHOR]

Classifiers deployed in the real world operate in a dynamic environment, where the data distribution can change over time. These changes, referred to as concept drift, can cause the predictive performance of the classifier to drop over time, thereby making it obsolete. To be of any real use, these classifiers need to detect drifts and be able to adapt to them, over time. Detecting drifts has traditionally been approached as a supervised task, with labeled data constantly being used for validating the learned model. Although effective in detecting drifts, these techniques are impractical, as labeling is a difficult, costly and time consuming activity. On the other hand, unsupervised change detection techniques are unreliable, as they produce a large number of false alarms. The inefficacy of the unsupervised techniques stems from the exclusion of the characteristics of the learned classifier, from the detection process. In this paper, we propose the Margin Density Drift Detection (MD3) algorithm, which tracks the number of samples in the uncertainty region of a classifier, as a metric to detect drift. The MD3 algorithm is a distribution independent, application independent, model independent, unsupervised and incremental algorithm for reliably detecting drifts from data streams. Experimental evaluation on 6 drift induced datasets and 4 additional datasets from the cybersecurity domain demonstrates that the MD3 approach can reliably detect drifts, with significantly fewer false alarms compared to unsupervised feature based drift detectors. At the same time, it produces performance comparable to that of a fully labeled drift detector. The reduced false alarms enables the signaling of drifts only when they are most likely to affect classification performance. As such, the MD3 approach leads to a detection scheme which is credible, label efficient and general in its applicability. [ABSTRACT FROM AUTHOR]

Passenger car equivalents for heavy vehicles are required to carry out capacity calculations and perform operational analysis of any road entity (roadway segments or intersections). At single-lane roundabouts, the constraints to the vehicular trajectories imposed by the curvilinear geometric design and the driver's gap acceptance behaviour are expected to produce an impact of the heavy vehicles on the quality of traffic flow different from that produced on freeways and two-lane highways or other at-grade intersections. This is also because entering flow is opposed by the circulating flow which has priority and travels in an anticlockwise direction on a single-lane path around the central island. This paper addresses the question of how to estimate the passenger car equivalents for heavy vehicles on single-lane roundabouts. First, a comparison was performed between the empirical capacity functions based on a meta-analytic estimation of the critical and the follow up headways and the simulation outputs manually obtained for a single-lane roundabout built in Aimsun microscopic simulator. A genetic algorithm-based calibration procedure, therefore, was used to reach a better convergence between the simulation outputs and the empirical capacities. Based on the calibrated model, the passenger car equivalents were determined by comparing the capacity functions built for a fleet of passenger cars with the capacity functions calculated for different percentages of heavy vehicles. Differently from HCM 2010 which assumes a heavy vehicle to be equivalent to two passenger cars and sets as 2.0 the passenger car equivalents for heavy vehicles for roundabouts, a higher PCE effect would be expected on the quality of traffic conditions when the traffic stream contains a high number of heavy vehicles; this effect should be accounted for when calculating capacity and level-of-services. [ABSTRACT FROM AUTHOR]

In the evidential reasoning approach of decision theory, different evidence weights can generate different combined results. Consequently, evidence weights can significantly influence solutions. In terms of the “psychology of economic man,” decision-makers may tend to seek similar pieces of evidence to support their own evidence and thereby form alliances. In this paper, we extend the concept of evidential reasoning (ER) to evidential reasoning based on alliances (ERBA) to obtain the weights of evidence. In the main concept of ERBA, pieces of evidence that are easy for decision-makers to negotiate are classified in the same group or “alliance.” On the other hand, if the pieces of evidence are not easy to negotiate, they are classified in different alliances. In this study, two negotiation optimization models were developed to provide relative importance weights based on intra- and inter-alliance evidence features. The proposed models enable weighted evidence to be combined using the ER rule. Experimental results showed that the proposed approach is rational and effective. [ABSTRACT FROM AUTHOR]

• Proposing a novel MCDM method. • Developing the BWM for considering fuzzy pairwise comparisons. • Obtaining crisp weights from a fuzzy pairwise comparison matrix. • Superiority to the two well-known previous BWMs. One of the latest multi-criteria decision-making methods is best-worst method (BWM). In the procedure of BWM, decision maker (DM) identifies the most and the least important criteria namely, best and worst. Thereafter, DM identifies the degrees which he believes the best criterion is better than the other criteria, and identifies the degrees which he believes the other criteria are better than the worst criterion. Because of uncertainties in comparisons due to using linguistic variables for pairwise comparisons and also the lack of complete information, the crisp values of pairwise comparisons cannot appropriately model the problems. This paper develops the BWM for considering fuzzy pairwise comparisons (FBWM) by proposing a new fuzzy mathematical model which yields crisp weights from a fuzzy pairwise comparison matrix. Unlike to some previous papers that obtains fuzzy weights from fuzzy pairwise comparison matrix, the crisp weights of the proposed method of this paper eliminates the supplementary aggregation of fuzzy weights and ranking procedures. Moreover, the proposed method avoids obtaining the different ranking results due to the different ranking procedures of fuzzy numbers. Another outstanding advantage of this paper is that the obtained weights of the proposed method better satisfy the initial judgments compared to previous methods, while as we know, the satisfaction of the initial judgments is essential for pairwise comparison judgments. This paper presents several numerical examples to prove the good performance and the merit of the proposed method. According to the provided numerical examples, the proposed method of this paper absolutely outperforms the two well-known previous methods. [ABSTRACT FROM AUTHOR]

Extractive summarization aims to automatically produce a short summary of a document by concatenating several sentences taken exactly from the original material. Due to its simplicity and easy-to-use, the extractive summarization methods have become the dominant paradigm in the realm of text summarization. In this paper, we address the sentence scoring technique, a key step of the extractive summarization. Specifically, we propose a novel word-sentence co-ranking model named CoRank, which combines the word-sentence relationship with the graph-based unsupervised ranking model. CoRank is quite concise in the view of matrix operations, and its convergence can be theoretically guaranteed. Moreover, a redundancy elimination technique is presented as a supplement to CoRank, so that the quality of automatic summarization can be further enhanced. As a result, CoRank can serve as an important building-block of the intelligent summarization systems. Experimental results on two real-life datasets including nearly 600 documents demonstrate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Speech signals and glottal signals convey speakers’ emotional state along with linguistic information. To recognize speakers’ emotions and respond to it expressively is very much important for human-machine interaction. To develop a subject independent speech emotion/stress recognition system, by identifying speaker's emotion from their voices, features from OpenSmile toolbox, higher order spectral features and feature selection algorithm, is proposed in this work. Feature selection plays an important role in overcoming the challenge of dimensionality in several applications. This paper proposes a new particle swarm optimization assisted Biogeography-based algorithm for feature selection. The simulations were conducted using Berlin Emotional Speech Database (BES), Surrey Audio-Visual Expressed Emotion Database (SAVEE), Speech under Simulated and Actual Stress (SUSAS) and also validated using eight benchmark datasets. These datasets are of different dimensions and classes. Totally eight different experiments were conducted and obtained the recognition rates in range of 90.31%–99.47% (BES database), 62.50%–78.44% (SAVEE database) and 85.83%–98.70% (SUSAS database). The obtained results convincingly prove the effectiveness of the proposed feature selection algorithm when compared to the previous works and other metaheuristic algorithms (BBO and PSO). [ABSTRACT FROM AUTHOR]

Image thresholding is a process for separating interesting objects within an image from their background. An optimal threshold’s selection can be regarded as a single objective optimization problem, where obtaining a solution can be computationally expensive and time-consuming, especially when the number of thresholds increases greatly. This paper proposes a novel hybrid differential evolution algorithm for selecting the optimal threshold values for a given gray-level input image, using the criterion defined by Otsu. The hybridization is done by adding a reset strategy, adopted from the Cuckoo Search, within the evolutionary loop of differential evolution. Additionally a study of different evolutionary or swarm-based intelligence algorithms for the purpose of thresholding, with a higher number of thresholds was performed, since many real-world applications require more than just a few thresholds for further processing. Experiments were performed on eleven real world images. The efficiency of the hybrid was compared to the cuckoo search and self-adaptive differential evolution, the original differential evolution, particle swarm optimization, and artificial bee colony where the results showed the superiority of the hybrid in terms of better segmentation results with the increased number of thresholds. Since the proposed method needs only two parameters adjusted, it is by far a better choice for real-life applications. [ABSTRACT FROM AUTHOR]

Office documents are used extensively by individuals and organizations. Most users consider these documents safe for use. Unfortunately, Office documents can contain malicious components and perform harmful operations. Attackers increasingly take advantage of naive users and leverage Office documents in order to launch sophisticated advanced persistent threat (APT) and ransomware attacks. Recently, targeted cyber-attacks against organizations have been initiated with emails containing malicious attachments. Since most email servers do not allow the attachment of executable files to emails, attackers prefer to use of non-executable files (e.g., documents) for malicious purposes. Existing anti-virus engines primarily use signature-based detection methods, and therefore fail to detect new unknown malicious code which has been embedded in an Office document. Machine learning methods have been shown to be effective at detecting known and unknown malware in various domains, however, to the best of our knowledge, machine learning methods have not been used for the detection of malicious XML-based Office documents ( * .docx, * .xlsx, * .pptx, * .odt, * .ods, etc.). In this paper we present a novel structural feature extraction methodology ( SFEM ) for XML-based Office documents. SFEM extracts discriminative features from documents, based on their structure. We leveraged SFEM ’s features with machine learning algorithms for effective detection of malicious * .docx documents. We extensively evaluated SFEM with machine learning classifiers using a representative collection (16,938 * .docx documents collected "from the wild") which contains ∼4.9% malicious and ∼95.1% benign documents. We examined 1,600 unique configurations based on different combinations of feature extraction, feature selection, feature representation, top-feature selection methods, and machine learning classifiers. The results show that machine learning algorithms trained on features provided by SFEM successfully detect new unknown malicious * .docx documents. The Random Forest classifier achieves the highest detection rates, with an AUC of 99.12% and true positive rate (TPR) of 97% that is accompanied by a false positive rate (FPR) of 4.9%. In comparison, the best anti-virus engine achieves a TPR which is ∼25% lower. [ABSTRACT FROM AUTHOR]

Many production systems consist of a sequence of processes or stages. For these systems, relational network DEA can be used and an overall system efficiency (equal to the efficiency of the different processes) can be computed. However, there can be alternative solutions that give different estimations of the process efficiencies and therefore lead to different decompositions of the overall system efficiency. It is not obvious which efficiency decomposition to use. In this paper, it is shown how a Nash bargaining game can be used to compute point estimates of the efficiency of the processes for multistage systems. The proposed approach extends and improves over existing approaches for production systems with just two stages. The rationality principles behind the proposed solution approach are presented and an interesting interpretation of the resulting efficiency decomposition is provided. The fact that this rigorous solution approach leads to such a simple and elegant efficiency decomposition should facilitate its adoption by Expert and Intelligent Systems practitioners. [ABSTRACT FROM AUTHOR]

The increasing popularity of Twitter as social network tool for opinion expression as well as information retrieval has resulted in the need to derive computational means to detect and track relevant topics/events in the network. The application of topic detection and tracking methods to tweets enable users to extract newsworthy content from the vast and somehow chaotic Twitter stream. In this paper, we apply our technique named Transaction-based Rule Change Mining to extract newsworthy hashtag keywords present in tweets from two different domains namely; sports (The English FA Cup 2012) and politics (US Presidential Elections 2012 and Super Tuesday 2012). Noting the peculiar nature of event dynamics in these two domains, we apply different time-windows and update rates to each of the datasets in order to study their impact on performance. The performance effectiveness results reveal that our approach is able to accurately detect and track newsworthy content. In addition, the results show that the adaptation of the time-window exhibits better performance especially on the sports dataset, which can be attributed to the usually shorter duration of football events. [ABSTRACT FROM AUTHOR]

The popularity of realized measures and various linear models for volatility forecasting has been the focus of attention in the literature addressing energy markets’ price variability over the past decade. However, there are no studies to help practitioners achieve optimal forecasting accuracy by guiding them to a specific estimator and model. This paper contributes to this literature in two ways. First, to capture the complex patterns hidden in linear models commonly used to forecast realized volatility, we propose a novel framework that couples realized measures with generalized regression based on artificial neural networks. Our second contribution is to comprehensively evaluate multiple-step-ahead volatility forecasts of energy markets using several popular high frequency measures and forecasting models. We compare forecasting performance across models and across realized measures of crude oil, heating oil, and natural gas volatility during three qualitatively distinct periods: the pre-crisis period, the 2008 global financial crisis, and the post-crisis period. We conclude that the newly proposed approach yields both statistical and economic gains, while reducing the tendency to over-predict volatility uniformly during all the tested periods. In addition, the proposed methodology is robust to a substantial structural break induced by the recent financial crisis. Our analysis favors median realized volatility because it delivers the best performance and is a computationally simple alternative for practitioners. [ABSTRACT FROM AUTHOR]

Job shop scheduling problem is very important one in real application of many kind of industries. In the real world applications, there exist many different kinds of disruptions with different sizes. In this situation, beyond the optimality of the final solution, its ability to absorb disruptions is vital, so that the final solution remains feasible in the case of occurring predicted disruptions. In this paper a robust job shop scheduling problem under disruptions is considered. It is intended to generate robust schedules which are able to absorb a predefined range of disruptions. For this reason, two robustness indices are proposed and the related formulas for computing the required buffer times to reach the desired robustness level are proposed. For this purpose, four methods are proposed to solve the variant defined robust job shop scheduling problem, including (1) a mathematical model, (2) a Branch and Bound algorithm, (3) a beam search algorithm, and (4) a meta-heuristic algorithm based on the particle swarm optimization (PSO) method. The first and second methods result in optimum solution. As the size of the problem increases, the required time to find the optimum solutions raises, so that no solutions can be achieved in rational amount of time, e.g. one hour. Therefore, the third and fourth methods are proposed to find good solutions even for large-scale problem in a limited amount of time. Finally, the algorithms are compared using some randomly generated instances. The results show that each algorithm can outperform the others in different conditions. [ABSTRACT FROM AUTHOR]

Recording videos on smartphones and other mobile devices, given their enormous popularity, is currently very common. The portability of these devices facilitates their use for recording videos in a wide variety of situations, including while witnessing criminal activities. These videos can be later used as evidence in legal proceedings. Therefore, the forensic analysis of videos taken with mobile device videos is important, and could serve for legal and also investigative purposes. It is necessary, however, to use techniques that are quite specific to this type of devices, given some peculiar features of their cameras. In this paper, we will address the issue of video source acquisition identification by presenting a technique based on sensor noise and wavelet transform extraction from video key frames. These frames are extracted using an efficient algorithm that takes their content into account, improving the selection of frames to be analyzed over past proposals. The scheme presented consists of four stages: (1) Key frames extraction, (2) sensor pattern noise extraction, (3) feature extraction, and (4) classifier training and prediction. We also present experimental results that support the validity of the techniques used and show promising results. [ABSTRACT FROM AUTHOR]

Metaheuristics have proven to get a good performance solving difficult optimization problems in practice. Despite its success, metaheuristics still suffers from several problems that remains open as the variability of their performance depending on the problem or instance being solved. One of the approaches to deal with these problems is the hybridization of techniques. This paper presents a hybrid metaheuristic that combines a Genetic Algorithm (GA) with a Cross Entropy (CE) method to solve continuous optimization functions. The algorithm divides the population into two sub-populations, in order to apply GA in one sub-population and CE in the other. The proposed method is tested on 24 continuous benchmark functions, with four different dimension configurations. First, a study to find the best parameter configuration is done. The best configuration found is compared with several algorithms in the literature in order to demonstrate the competitiveness of the proposal. The results shows that GACE is the best performing method for instances with high dimensionality. Statistical tests have been applied, to support the conclusions obtained in the experimentation. [ABSTRACT FROM AUTHOR]

Operational support assists users while process instances are being executed, by making predictions about the instance completion, or recommending suitable actions, resources or routing decisions, on the basis of the already completed instances, stored as execution traces in the event log. In this paper, we propose a case-based retrieval approach to business process management operational support, where log traces are exploited as cases. Once past traces have been retrieved, classical statistical techniques can be applied to them, to support prediction and recommendation. The framework enables the user to submit queries able to express complex patterns exhibited by the current process instance. Such queries can be composed by several simple patterns (i.e., single actions, or direct sequences of actions), separated by delays (i.e., actions we do not care about). Delays can also be imprecise (i.e., the number of actions can be given as a range). The tool also relies on a tree structure, adopted as an index for a quick retrieval from the available event log. Our approach is highly innovative with respect to the existing literature panorama, since it is the first work that exploits case-based retrieval techniques in the operational support context; moreover, the possibility of retrieving traces by querying complex patterns and the indexing strategy are major departures also with respect to other existing trace retrieval tools proposed in the case based reasoning area. Thanks to its characteristics and methodological solutions, the tool implements operational support tasks in a flexible and efficient way, as demonstrated by our experimental results. [ABSTRACT FROM AUTHOR]

Early diagnosis and prevention of problematic behaviors in youth play an important role in reducing treatment costs and decreasing the toll of antisocial behavior. Over the last several years, the science of preventing antisocial behavior in youth has made significant strides, with the development of evidence-based prevention programs (EBP) using randomized clinical trials. In this paper, we use a real case implemented by schools in an urban school district of 80,000 students in a mid-Atlantic state to show how predictive analytics can help to improve the quality of prevention programs and reduce the cost of delivering associated services. Data patterns are extracted from conduct disorder assessments using the Teacher Observation of Classroom Adaptation (TOCA) screening instrument and evaluated using the results of the Diagnostic Interview Schedule for Children (DISC). A mathematical method called Logical Analysis of Data (LAD) is used to analyze data patterns. Experimental results show that up to 91.58% of the cost of administering DISC would be saved by correctly identifying participants without conduct disorder and excluding them from the DISC test. [ABSTRACT FROM AUTHOR]

Finding groups of highly related vertices in undirected graphs has been widely investigated. Nevertheless, a very few strategies are specially designed for dealing with directed networks. In particular, strategies based on the maximization of the modularity adjusted to overcome the resolution limit for directed networks have not been developed. The analysis of the characteristics of the clusters produced by these approaches is highly important since among the most used strategies for detecting communities in directed networks are the modularity maximization-based algorithms for undirected graphs. Towards these remarks, in this paper we propose a consensus-based strategy, named ConClus , for providing partitions for directed networks guided by the adjusted modularity measure. In the computational experiments, we compared ConClus with benchmark strategies, including Infomap and OSLOM, by using hundreds of LFR networks. ConClus outperformed Infomap and was competitive with OSLOM even for graphs with high mixture index and small-sized clusters, to which modularity-based algorithms have limitations. ConClus outperformed all algorithms when considering the networks with the highest average and maximum in-degrees among the networks used in the experiments. [ABSTRACT FROM AUTHOR]

• A multi-objective model is proposed for one-shot systems supplier allocation. • Solution of the supplier selection problem to minimize whole system failure risks. • A numerical example and a case study are provided to validate the proposed model. • A meta -heuristic algorithm (the genetic algorithm NSGA-II) is used to solve the models. A One-shot device is defined as a one-time unit that cannot be subjected to destructive tests or used more than once. Each component/sub-component used in one-shot systems, regardless of how it is supplied, has a specific risk level. On the other hand, each component affects the factors influencing the total system failure. It is the main objective of the present paper to provide a multi-objective model for determining suppliers of components used in one-shot systems. In this model, the risk associated with each supplier is initially estimated using risk assessment indicators. In the second stage, the risks associated with sub-components are assess using cause-and-effect matrices. Based on the likely catastrophic incidents to the system, the fault tree analysis is used and the relevant diagrams are drawn to identify the best composition of equipment suppliers. The results obtained from a case-study are then used in the especially developed multi-objective model to identify the allocations. The optimum values for the first and second objective functions are calculated to be 1489.349 and 0.809, respectively. Finally, the performance of the model is validated by sensitivity analysis. [ABSTRACT FROM AUTHOR]

• The paper addresses the mode change disruption in Project Scheduling Problems. • Reactive heuristics are proposed to quickly repair the initial disrupted schedule. • A reactive mathematical model was proposed. • The proposed heuristics show high performance in terms of CPU time and stability. This paper accounts for the mode change disruption in the Multi-mode Resource-Constrained Project Scheduling Problem (MRCPSP). In fact, during its execution, the project may face some unexpected events, which can lead to the schedule deterioration or even unfeasibility. A reactive mathematical modeling is proposed. Moreover, three heuristics with several variants are suggested to repair the initial disrupted schedule. These heuristics aim to perform a very quick response to provide the project manager with, hopefully, a feasible schedule. An extensive computational study is performed on PSPLIB benchmark instances with complementary generated data. Finally, the experimental results have shown that the three heuristics outperform Cplex in terms of the CPU running time. Moreover, Lexicographic Regret-Based Heuristic (LRBH) outranks the other heuristics in finding feasible solutions and in terms of the makespan deviation. [ABSTRACT FROM AUTHOR]

This paper presents a simple approach to the mathematical modelling of the Integer Order (IO) and Fractional Order (FO) Interval Type-2 Fuzzy Proportional Integral Derivative (IT2FPID) controllers. In this approach, the control effort produced by the IT2FPID controller is obtained by combining the individual control efforts generated by the Interval Type-2 Fuzzy Proportional (IT2FP), Interval Type-2 Fuzzy Integral (IT2FI), and Interval Type-2 Fuzzy Derivative (IT2FD) controllers. The analytical structures of each of the IT2FPID components are unveiled using one-dimensional (1D) input space and without using any AND or OR operator. To the best of the authors' knowledge, such a strategy was never used and is completely new for the modelling of the IT2FPID controllers. Properties, computational issues, and design guidelines of the newly obtained controllers are discussed, and their applicability is delineated using five simulation examples and one experimental case study. A chemical reactor plant, two fractional order plants, a nonlinear plant, and a Twin Rotor Multi Input Multi Output System (TRMS) are considered in the simulation study. The experimental study is conducted on an unstable nonlinear Magnetic Levitation System (MLS). Moreover, to exhibit the usefulness of the newly derived controllers, simulation and real-time results are also compared with the existing results available in the literature. As the proposed controllers are plant model-free, they can be used for other control applications also. • A simple strategy is proposed for the modelling of IT2 fuzzy PID controller. • The modelling is done using 1D input space and without any AND or OR operator. • The new modelling strategy is extended to fractional order IT2 fuzzy PID controller. • Applicability of the controllers is depicted through simulation and real-time. • Usefulness of the controllers is shown through system performance comparison. [ABSTRACT FROM AUTHOR]