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• Discretionary or ex gratia payouts are widespread in parametric insurance programs. • Payouts attempt to secure social license to operate despite contractual basis risk. • Discretionary payouts obscure and entrench insurers' market power. • Attempts at conciliation often stoke further demands from aggrieved collectivities. • Geospatial monitoring alone cannot resolve questions of post-disaster distribution. The past decade has seen the dramatic proliferation of geospatial tools to remotely monitor, measure, and transfer weather and climate risk, particularly in the Global South. These "parametric" tools are calculative market devices used to commodify risk and facilitate its distantiated exchange via insurance markets. Yet the environmental estimates they generate are frequently wrong, sometimes grossly so. This paper investigates what happens after calculative market devices fail, analyzing so-called ex gratia ("from grace") practices in which insurers make payouts to aggrieved clients even when none are due under contractual terms. Though these payments are commonly deployed to dispel discontent, these "public secrets" of the industry have gone unnoticed in insurance scholarship. This paper documents numerous ex gratia payouts in parametric programs across Africa, disputing the myth of autonomous insurance markets that function according to formal, transparent rules administered by dispassionate calculative devices. Instead, these "discretionary" monetary transfers reintroduce human decision making in informal, black-boxed ways, cloaking the inadequacy of the parametric contracts they shore up. Far from incidental, I argue that these practices have been constitutive of the parametric insurance market's persistence and expansion. Though ex gratia payments sometimes give rise to negotiations over indemnification with aggrieved collectivities, these unfold on insurers' terms, informalizing power and delegitimizing complaint. The growing emphasis on parametric insurance as a tool for climate adaptation makes it all the more important to excavate the mechanics and politics of ex gratia, and to relinquish expectations that geospatial algorithms alone can resolve questions of post-disaster redistribution. [ABSTRACT FROM AUTHOR]

Time is a crucial factor for decision making, especially in environments where it is necessary to provide a high quality of experience for the users and at the same time make efficient use of their resources; functions that in some cases may conflict. That is one of the challenges that mobile operators have now; the unceasing growth in terms of users and service requirements that generate not only technical challenges. In an initial approach to this problem, we proposed a multi-objective model for selecting the cooperation scheme between base stations and an evolutionary algorithm that provides a set of optimal solutions. However, to find the optimal set of solutions, the convergence time of the proposed algorithm varies between a few seconds to hours, which makes it feasible only for network planning exercises. In this paper, we present an algorithm that allows us to find an efficient solution to the multi-objective problem in a reasonable time for operational decision-making. [ABSTRACT FROM AUTHOR]

• Support vector machine and tree model for degradation analysis in power transformers. • A cluster analysis is proposed for partial discharge analysis and to improve the evaluation. • Remaining life is created with tree model technique associated to decision making process. The power transformer analysis focused internal fault identification is important for the energy efficiency in all the countries, the partial discharge is one of the main failure modes, it affects the remaining life until the failure. In this research article, the new approach by using soft techniques as support vector machine (SVM) and tree models (TM) allow to detect partial discharge with high accuracy (97.55%); dissolved gas analysis (DGA) for the quality of the oil-Kraft, degradation and evaluate the remaining life. An important achieve is the highest accuracy for the thermal influence in oil degradation (100%). Finally, this research article has provided an algorithm for the highest accuracy obtained from the DGA for oil and Kraft degradation with a crosscheck process by using the cluster analysis. [ABSTRACT FROM AUTHOR]

The paging problem is that of deciding which pages to keep in a memory of k pages in order to minimize the number of page faults in a two-level store. It is a special case of the famous k -server problem. In this paper, we firstly show that WFA is k M / m -competitive for the k -server problem where the distances between two points are all in [ m , M ] (0 < m ≤ M), and thus WFA is k -competitive for the paging problem. And we further show that the well known F I F O for the paging problem is just a special case of WFA. [ABSTRACT FROM AUTHOR]

A set S of vertices of a graph G is P 3 ⁎ -convex if there is no vertex outside S having two non-adjacent neighbors in S. The P 3 ⁎ -convex hull of S is the minimum P 3 ⁎ -convex set containing S. If the P 3 ⁎ -convex hull of S is V (G) , then S is a P 3 ⁎ -hull set. The minimum size of a P 3 ⁎ -hull set is the P 3 ⁎ -hull number of G. In this paper, we show that the problem of deciding whether the P 3 ⁎ -hull number of a chordal graph is at most k is NP -complete and present a linear-time algorithm to determine this parameter and provide a minimum P 3 ⁎ -hull set for unit interval graphs. [ABSTRACT FROM AUTHOR]

Abstract Target searching in an unknown environment is a traditional research issue in the multiagent area. In some real cases, the agents do not only discover the targets; instead, they have subsequent tasks that must be completed before a deadline. In this paper, these cases are abstracted as the agents searching for target locations in an unknown environment and then occupying these target locations within a limited time. The agents can obtain rewards by occupying the target locations, and the goal of this problem is to maximize net income, defined as total reward minus the moving cost of the agents. This problem can be transformed into the traditional problems, and then be solved by previous related algorithms. However, this approach is not optimal. In this paper, we present a method that combines previous algorithms and a decision-making algorithm. The experiments demonstrate that the method containing our decision-making algorithm can lead to higher net income than simply using previous algorithms. Highlights • The multiagent target searching and occupancy problem is analyzed. • Previous algorithms are combined with a new decision-making algorithm. • The method containing our decision-making algorithm can lead to better results. [ABSTRACT FROM AUTHOR]

High-Utility Itemset Mining (HUIM) is considered a major issue in recent decades since it reveals profit strategies for use in industry for decision-making. Most existing works have focused on mining high-utility itemsets from databases showing large amount of patterns; however exact decisions are still challenging to make from that large amounts of discovered knowledge. Closed High-utility itemset mining (CHUIM) provides a smart way to present concise high-utility itemsets that can be more effective for making correct decisions. However, none of the existing works have focused on handling large-scale databases to integrate discovered knowledge from several distributed databases. In this paper, we first present a large-scale information fusion architecture to integrate discovered closed high-utility patterns from several distributed databases. The generic composite model is used to cluster transactions regarding their relevant correlation that can ensure correctness and completeness of the fusion model. The well-known MapReduce framework is then deployed in the developed DFM-Miner algorithm to handle big datasets for information fusion and integration. Experiments are then compared to the state-of-the-art CHUI-Miner and CLS-Miner algorithms for mining closed high-utility patterns and the results indicated that the designed model is well designed for handling large-scale databases with less memory usage. Moreover, the designed MapReduce framework can speed up the mining performance of closed high-utility patterns in the developed fusion system. • Mine required CHUIs in parallel and distributed environments. • Use HG-k-means to cluster highly relevant transactions in a group that ensures fusion. • An interactive three-phases MapReduce model with a DFM-Miner algorithm is presented. • Experiments on various large-scale datasets are conducted to achieve better performance. [ABSTRACT FROM AUTHOR]

Ridepooling is expected to become more and more important in people's mobility. At the same time, autonomous vehicles are expected to be widely used in such applications in the future. While the driver as a person of trust currently provides security, passengers are alone with strangers in an autonomous ridepooling vehicle. In the paper, we address this aspect by including incompatibilities between customers, meaning that incompatible customers cannot share the vehicle at the same time. Although incompatibilities can be defined in any way by the provider, they can be set, for example, if a customer feels unsafe in sharing the vehicle with customers of a certain type. As other compatible customers can replace the driver as a person of trust, we present a second setting where all customers are allowed to share the vehicle at the same time, but customers are never alone in the vehicle with incompatible customers. We present Branch-and-Cut algorithms for both problem settings including methods to improve the search with respect to the incompatibilities. We show in a computational study that the introduced methods improve the search significantly and evaluate the Branch-and-Cut algorithms on realistic instances with up to 120 customers and 10 vehicles. Moreover, our results suggest that it can be a beneficial option for the provider to allow their customers to participate in the decision process by indicating incompatibilities for types of customers, meaning that they do not want to share a vehicle with a customer of an incompatible type. • A Dial-a-Ride Problem with incompatibilities is investigated. • Two types of incompatibilities are considered. • Branch-and-Cut algorithms are developed for both types. • Zero distance chains where several events occur simultaneously are included. [ABSTRACT FROM AUTHOR]

Global warming, as many other threats, requires more activity from the government, and people inquire about this, rightfully so. However, the public and private sectors nowadays have a joint responsibility with society. Global warming has come out of the scientific community and shows that climate change is an important social, economic, and political issue. Currently the carbon dioxide (CO 2 ) emissions by transportation sector are the main polluter of the planet. For this reason, in this paper we propose a reflection on transport, which is an important sector of the economy, responsible for the generation of many jobs and wealth but, at the same time, it has negative impacts on people, companies, and the environment. Recent investigations demonstrate that mobility problems affects the life quality of the people, business productivity and gross domestic product (GDP) performance. In the face of the changes that we are experiencing, it is important to find alternatives that assistance governments in the decision-making to solve the problem of traffic in large and medium-size cities and bring well-being to the population. The goal is to provide accurate tool to help take the best decisions in situations that most of the time has a lack of economic resources and transparency. Another motivation is to increase the productivity of companies and achieve sustainable development by reducing mobility problems and CO 2 emissions. As outcomes of the paper, an in-depth study is presented on the subject that indicates a gap in the literature with fuzzy logic applied to sustainability. We indicate a sustainable option for the transport sector using algorithms for decision-making, and broaden the discussion on climate change and solutions to reduce the impacts caused by humanity. When the models were applied through a conceptual study, it was possible to provide an instrument generated on the use of algorithms that can aid decision-making by getting qualitative data from a discussion with stakeholders on a topic. Finally, the document identifies the importance of the participation of stakeholder, reinforces legitimacy and transparency in decision-making processes and above all present a tool that can help governments to solve this problem. In short, this work proposes an original and valuable tool to be applied in the decision-making process for the sustainable transport sector based on the stakeholder engagement. [ABSTRACT FROM AUTHOR]

Autonomous robots facing a diversity of open environments and performing a variety of tasks and interactions need explicit deliberation in order to fulfill their missions. Deliberation is meant to endow a robotic system with extended, more adaptable and robust functionalities, as well as reduce its deployment cost. The ambition of this survey is to present a global overview of deliberation functions in robotics and to discuss the state of the art in this area. The following five deliberation functions are identified and analyzed: planning, acting, monitoring, observing, and learning. The paper introduces a global perspective on these deliberation functions and discusses their main characteristics, design choices and constraints. The reviewed contributions are discussed with respect to this perspective. The survey focuses as much as possible on papers with a clear robotics content and with a concern on integrating several deliberation functions. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to describe all available evidence on the distinctive features of a group of 4 life-threatening acute aortic pathologies gathered under the name of acute aortic syndrome (AAS). The epidemiology, diagnostic strategy, and management of these patients has been updated. The authors propose a new and simple diagnostic algorithm to support clinical decision making in cases of suspected AAS, thereby minimizing diagnostic delays, misdiagnoses, and unnecessary advanced imaging. AAS-related entities are reviewed, and a guideline to avoid imaging misinterpretation is provided. Centralization of patients with AAS in high-volume centers with high-volume surgeons is key to improving clinical outcomes. Thus, the role of multidisciplinary teams, an "aorta code" (streamlined emergent care pathway), and aortic centers in the management of these patients is boosted. A tailored patient treatment approach for each of these acute aortic entities is needed, and as such has been summarized. Finally, a set of prevention measures against AAS is discussed. [ABSTRACT FROM AUTHOR]

Many applications, such as robotics, are increasingly utilizing Reinforcement Learning (RL). The current implementation of RL typically assumes that the state update from the previous actions happens instantaneously, and thus can be used for making future decisions. However, this may not always be true. When the state update is not available, the decision is taken partly in the blind since it cannot rely on the current state information. This paper proposes an approach, where the delay in the knowledge of the state can be used, and the decisions are made based on the available information which may not include the current state information. One approach could be to include the actions after the last-known state as a part of the state information, however, that leads to an increased state-space making the problem complex and slower in convergence. The proposed algorithm gives an alternate approach where the state space is not enlarged, as compared to the case when there is no delay in the state update. Evaluations on the control environments further illustrate the improved performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In recent years, we have witnessed the growing use of the Internet of Things (IoT) to automate and improve activities of Business Processes (BPs), especially in critical systems. Within these contexts, uncertainties and their impacts cannot be ignored, justifying the interest for risk assessment. Risk assessment in BPs should rely on a meaningful computation of the risk level considering the individual risk of BP elements. Moreover, decision makers need to have risk information at different granularity levels so they can focus on some specific parts of BPs and identify which process fragments contribute most to the BP risk level. Current proposals only analyse risk at the process level, without taking into account different BP granularity levels, and the way they calculate the risk level can prevent the analysis of worst case scenarios. This paper proposes an algorithm to calculate the process risk level that reveals the BP elements that contribute most to risk, thus supporting risk analysis at different BP granularity levels. In addition, our proposal enriches BP models with information about risk likelihood, business value of BP elements, and risk acceptance criteria. These contributions are as relevant as the increasing use of IoT aware BPs in critical systems, promoting the improvement of BPs with a risk perspective and well informed decision making. [ABSTRACT FROM AUTHOR]

Social robots—such as autonomous vehicles, service robots, or healthcare robots—are designed to support tasks in a broad range of human activities. However, these robots face moral dilemmas because they must make decisions that may do good for one human but potentially inflict harm on another. We argue that Kant's categorical imperative provides a framework for algorithm-based moral decision-making. By systematically addressing ethical concerns from the outset in the development of the algorithms that steer social robots, their designers can help ensure that such robots promote the well-being of individuals, communities, and society. We conclude that those involved in the development of social robots need to embed ethics into their design and functioning. The solutions to the ethical dilemmas we advance in this paper can help improve the adoption and impact of social robots. The presented insights contribute to research, practice, and policy. • Social robots face moral dilemmas because they must decide whom to do good/harm. • Kant's categorical imperative provides a framework for algorithm-based moral decision-making. • Algorithms based on maxims can ensure that such robots promote the well-being. • Development of social robots need to embed ethics into their design and functioning. [ABSTRACT FROM AUTHOR]

In this paper, Tangled String, a method for visualizing sequential data is summarized and exemplified for log text of histories given as timeline lists. Valuable events are detected according to the results, on the assumption that the value of an event or an item in history means the essential impact on the past trend or on the future events that can be understood by qualitative explanation. According to the results, valuable events differ for different granularity in segmenting history into subsequences of events, corresponding to our intuitive sense of the impacts of events for human decisions and actions. This paper also aims to suggest evaluation criteria about the value of events and information in general, where their quality cannot be easily replaced by quantity. Humans’ thoughts should be involved in the process of quality evaluation, so a method such as Tangled String is desired to support this process and improve efficiency. [ABSTRACT FROM AUTHOR]

Energy consumption monitoring and regulation on large-scale public buildings are always an indispensable part in building energy conservation. With the mounting establishment of the building sub-metering platform, massive amounts of historical power consumption data are provided. In this paper, different building types of six large-scale public buildings in Shanghai are selected, with their sub-metering data deeply analyzed. The concept of CDHs/HDHs (cooling/heating degree hours) is introduced and weekly prediction models of total building power consumption are proposed by the way of multiple linear regression algorithm which is relatively simple and easy to understand. The prediction models are validated to have great accuracy and general applicability in the paper, offering reliable instructions to the building facility manager and relevant competent authorities in terms of decision making and policy implementation. [ABSTRACT FROM AUTHOR]

To meet the urgent needs of emergency observation missions, a well-designed constellation is key to ensuring system performance. This paper presents a multi-objective optimization framework which is well suited for constellation designers to identify key trade-offs and make decisions. Firstly, this work investigates the metrics of responsiveness and coverage simultaneously to approximate to the actual conditions of emergencies, and a multi-objective mathematical model is constructed to explore solutions to enhance the overall system performance. Then, according to the characteristics of emergency missions, a hierarchical chromosome encoding method is proposed in the framework to encode a solution that explores different sized constellations without constraining its specific geometry. Finally, we propose the use of radial axes plots to help constellation designers to gain pragmatic insights for informed decision making. The simulation of disaster management verifies that the proposed chromosome encoding method effectively promotes the convergence of the algorithm, and the multi-objective optimization framework helps to generate a rapid and adequate observation satellite constellation to provide maximum coverage performance while ensuring timeliness. [ABSTRACT FROM AUTHOR]

This paper presents a decision-making algorithm for ship collision avoidance. First, an algorithm for the planar distribution of collision risk is described. The level of collision risk was calculated as a function of the plane coordinates based on the predicted encounter state. It enables the visualization of all the potential collision risks as a collision risk map and the calculation of the integrative collision risk against several ships, considering COLREGs. Second, an optimal route-planning algorithm that can consider the maneuvering characteristics of the ship is proposed. Because a ship cannot change its course and speed suddenly, the nodes and their connections can be set to limit the course and speed change amounts to appropriate ranges. The optimal route was obtained as the minimum-cost route along node connections. Finally, a decision-making algorithm for ship collision avoidance was developed by combining the proposed collision risk mapping and optimal route-planning algorithm. It was applied to representative encounter situations and evaluated by an evaluation method. Further, the proposed algorithm was compared with state-of-art algorithms. Thus, safe and efficient decision-making for collision avoidance can be achieved even in congested situations by appropriately changing both a course and a speed depending on the encounter situation. • An algorithm to calculate a planar distribution of collision risk considering COLREGs is proposed. • An algorithm for optimal routing for collision avoidance by both course and speed changes is proposed. • A decision-making method for collision avoidance is developed by combining the proposed two algorithms. • The developed method can provide a safer and more efficient collision avoidance plan even in congested situations. [ABSTRACT FROM AUTHOR]

Binary Decision Diagram (BDD) based fault tree analysis algorithms are among the most efficient ones. They allow performing exact probabilistic analyses, as well as to derive a Zero-suppressed BDD (ZBDD) to efficiently encode Significant Prime Implicants (PI) or Minimal Cut Sets (MCS). The present paper describes a dynamic labelling method for BDD/ZBDD to analyse non-coherent fault trees. An L-BDD is a BDD in which the information about the variable type is associated to each node. This information is useful to select, for each node, the corresponding algorithms for performing the probabilistic analysis and for determining PI or MCS. When the computational resources are not sufficient to complete the BDD construction, it is convenient to construct the ZBDD directly from the fault tree. The second part of this paper describes rules for constructing a Truncated Labelled ZBDD (TL-ZBDD) of non-coherent fault trees. Results of the analysis of some non-coherent fault trees by means of L-BDD and TL-ZBDD are provided. [ABSTRACT FROM AUTHOR]

Objectives: Early recognition of breast cancer, the most commonly diagnosed form of cancer in women, is of crucial importance, given that it leads to significantly improved chances of survival. Medical thermography, which uses an infrared camera for thermal imaging, has been demonstrated as a particularly useful technique for early diagnosis, because it detects smaller tumors than the standard modality of mammography.Methods and Material: In this paper, we analyse breast thermograms by extracting features describing bilateral symmetries between the two breast areas, and present a classification system for decision making. Clearly, the costs associated with missing a cancer case are much higher than those for mislabelling a benign case. At the same time, datasets contain significantly fewer malignant cases than benign ones. Standard classification approaches fail to consider either of these aspects. In this paper, we introduce a hybrid cost-sensitive classifier ensemble to address this challenging problem. Our approach entails a pool of cost-sensitive decision trees which assign a higher misclassification cost to the malignant class, thereby boosting its recognition rate. A genetic algorithm is employed for simultaneous feature selection and classifier fusion. As an optimisation criterion, we use a combination of misclassification cost and diversity to achieve both a high sensitivity and a heterogeneous ensemble. Furthermore, we prune our ensemble by discarding classifiers that contribute minimally to the decision making.Results: For a challenging dataset of about 150 thermograms, our approach achieves an excellent sensitivity of 83.10%, while maintaining a high specificity of 89.44%. This not only signifies improved recognition of malignant cases, it also statistically outperforms other state-of-the-art algorithms designed for imbalanced classification, and hence provides an effective approach for analysing breast thermograms.Conclusions: Our proposed hybrid cost-sensitive ensemble can facilitate a highly accurate early diagnostic of breast cancer based on thermogram features. It overcomes the difficulties posed by the imbalanced distribution of patients in the two analysed groups. [ABSTRACT FROM AUTHOR]

The use of Bayesian Belief Networks (BBNs) in risk analysis (and in particular Human Reliability Analysis, HRA) is fostered by a number of features, attractive in fields with shortage of data and consequent reliance on subjective judgments: the intuitive graphical representation, the possibility of combining diverse sources of information, the use the probabilistic framework to characterize uncertainties. In HRA, BBN applications are steadily increasing, each emphasizing a different BBN feature or a different HRA aspect to improve. This paper aims at a critical review of these features as well as at suggesting research needs. Five groups of BBN applications are analysed: modelling of organizational factors, analysis of the relationships among failure influencing factors, BBN-based extensions of existing HRA methods, dependency assessment among human failure events, assessment of situation awareness. Further, the paper analyses the process for building BBNs and in particular how expert judgment is used in the assessment of the BBN conditional probability distributions. The gaps identified in the review suggest the need for establishing more systematic frameworks to integrate the different sources of information relevant for HRA (cognitive models, empirical data, and expert judgment) and to investigate algorithms to avoid elicitation of many relationships via expert judgment. [ABSTRACT FROM AUTHOR]

Bi-level optimization has gained a lot of interest during the last decade. This framework is suitable to model several real-life situations. Bi-level optimization problems refer to two related optimization tasks, each one is assigned to a decision level (i.e., upper and lower levels). In this way, the evaluation of an upper level solution requires the evaluation of the lower level. This hierarchical decision making necessitates the execution of a significant number of Function Evaluations (FEs). When dealing with a multi-objective optimization context, new complexities are added and imposed by the conflicting objectives and their evaluation techniques. In this paper, we aim to reduce the induced complexity using approximation techniques in order to obtain the lower level optimality. To this end, ideas from multi-objective optimization have been extracted, improved, and hybridized with evolutionary methods to build an efficient approach for Multi-objective Bi-Level Optimization Problems (MBLOPs). In this work, three techniques are suggested: (1) a complete lower level approximation Pareto front procedure, (2) a reference-based approximation selection procedure, and (3) a sub-set reference-based approximation selection one. The proposed variants are applied to a new multi-objective formulation of a well-known combinatorial problem integrating two systems in the supply chain management, namely, the Bi-level Multi Depot Vehicle Routing Problem (Bi-MDVRP). The statistical analysis demonstrates the efficiency of each algorithm according to a set of metrics. Indeed, a large number of savings are detected which confirms the efficiency of our proposals for solving combinatorial optimization problems. [ABSTRACT FROM AUTHOR]

A multi-agent-based energy-coordination control system (MA-ECCS) is designed for grid-connected large-scale wind-photovoltaic energy storage power-generation units (WPS-PGUs) to address the challenges of low operation efficiency, poor stability, and complex decision making. The proposed system adopts the negotiation model of the contract net protocol with the non-fixed client-server cooperative mechanism among agents. When the power supply and demand are imbalanced, the system selects the state-changeable agent as the task initiator according to the load balance principle. In the energy-coordination process, the proposed system solves the global optimal-energy distribution plan of the system by considering the self-constraint and control objective of each agent and by using the improved particle swarm algorithm to achieve the maximum economic benefit on the basis of stable operations. The paper presents the overall design idea of the system, the agent models of different equipment, and the global energy-coordination optimization algorithm. An example is given to discuss the behavior characteristics of the agent and communication negotiation process and to verify that the system designed in this paper can realize the energy-coordination dispatching flexibly and efficiently. The method adopted by the system can improve the overall operation efficiency and economic benefits of WPS-PGUs. [ABSTRACT FROM AUTHOR]

This paper presents innovative approaches for guidance of spacecraft formations during reconfiguration, developed under the paradigms of cluster autonomy and safe maneuverability. A task-assignment algorithm identifies those satellites that need to be rearranged in order to minimize the total propellant consumption. The decision process relies on convex optimization procedures which estimate control laws and maneuvering trajectories, accounting for thruster technology constraints and collision avoidance. The path-planning has a twofold implementation. In the centralized approach, the reconfiguration strategy is elaborated by a single unit: the maneuvering trajectories are generated considering solely collision avoidance constraints at expenses of computational costs. The alternative method is conceived such that each satellite autonomously determines its tasks. This de-centralized architecture allows the algorithm to run faster, but entails a higher traffic on the intersatellite communication link. In addition, collision avoidance is approximated to make each optimization independent from the other. For preliminary validation of the developed routines, a specific mission scenario is considered, with a distributed Synthetic Aperture Radar in a low Earth orbit that has to be reconfigured for tomographic applications. Simulation results show that, once remote-sensing requirements are specified within the limits set by standard products, the developed algorithms can manage autonomously the maneuver towards the corresponding operative pattern. In details, the centralized approach allows the reconfiguration to be handled with mean delta- v savings around 30%. On the contrary, strategies are elaborated by the decentralized planners about 7 times faster. • Innovative approaches are proposed for formation guidance during reconfigurations. • Strategies account for thruster technology and collision avoidance constraints. • The path-planning is implemented depending on the distributed system architecture. • Techniques are compared in terms of total delta-v and time for maneuver elaboration. • The reconfiguration of a small-sat distributed radar is considered as test-case. [ABSTRACT FROM AUTHOR]

Group Recommender Systems are special kinds of Recommender Systems aimed at suggesting items to groups rather than individuals taking into account, at the same time, the preferences of all (or the majority of) members. Most existing models build recommendations for a group by aggregating the preferences for their members without taking into account social aspects like user personality and interpersonal trust, which are capable of affecting the item selection process during interactions. To consider such important factors, we propose in this paper a novel approach to group recommendations based on fuzzy influence-aware models for Group Decision Making. The proposed model calculates the influence strength between group members from the available information on their interpersonal trust and personality traits (possibly estimated from social networks). The estimated influence network is then used to complete and evolve the preferences of group members, initially calculated with standard recommendation algorithms, toward a shared set of group recommendations, simulating in this way the effects of influence on opinion change during social interactions. The proposed model has been experimented and compared with related works. • Group recommender systems suggest items to be consumed by groups of users. • Individual preferences of any group member must be taken into account. • Considering social influence between group members leads to better recommendations. • Influence strength can be estimated from information retrieved in social networks. • Fuzzy decision making allows the influence-based aggregation of individual preferences. [ABSTRACT FROM AUTHOR]

Highlights • We propose a unified framework for developing text mining. • It integrates packages for natural language processing and machine learning. • It has built-in support for standard research evaluation practices. • The design patterns and technical details are given and code is available at GitHub. • The framework has successfully been used in various text mining projects. Abstract Many of today's businesses are driven by data, and while traditionally only quantitative data is considered, the role of textual data in our digital world is rapidly increasing. Text mining allows to extract and aggregate numerical data from textual documents, which in turn can be used to improve key decision processes. In this paper, we propose Heracles, a framework for developing and evaluating text mining algorithms, with a broad range of applications in industry. In contrast to other frameworks, Heracles supports both the development and evaluation stages of text mining algorithms. A practical use case shows the versatility and ease-of-use of the proposed framework in the domain of aspect-based sentiment analysis. [ABSTRACT FROM AUTHOR]

Abstract: Multiple objective evolutionary algorithms (MOEAs), which are biologically-inspired optimization methods, have become popular approaches to solve problems with multiple objective functions. With the use of MOEAs, multiple objective optimization becomes a two-part problem. First, the multiple objective optimization problem needs to be formulated and successfully solved using an MOEA. Then, a non- dominated set -also known as efficient or Pareto frontier- needs to be analyzed to select a solution to the problem. This can represent a challenging task to the decision-maker because this set can contain a large number of solutions. This decision- making stage is usually known as the post-Pareto analysis stage. This paper presents the generalization of a post-Pareto optimality method known as the non-numerical ranking preferences (NNRP) method originally proposed by Taboada et al. (2007). This method can help decision makers reduce the number of design possibilities to small subsets that clearly reflect their objective function preferences. Previous research has only presented the application of the NNRP method using three and four objective functions but had not been generalized to the case of n objective functions. The present paper expands the NNRP method to be able to consider multiple objective optimization problems with n number of objective functions. [Copyright &y& Elsevier]

Abstract: Web 2.0 technologies have enabled more and more customers to freely comment on different kinds of entities, such as sellers, products and services. The large scale of information poses the need and challenge of automatic summarization. In many cases, each of the user-generated short comments implies the opinions which rate the target entity. In this paper, we aim to mine and to summarize all the customer comments of a product. The algorithm proposed in this researchis more reliable on opinion identification because it is unsupervised and the accuracy of the result improves as the number of comments increases. Our research is performed in four steps: (1) mining the frequent aspects of a product that have been commented on by customers; (2) mining the infrequent aspects of a product which have been commented by customers (3) identifying opinion words in each comment and deciding whether each opinion word is positive, negative or neutral; (4) summarizing the comments. This paper proposes several novel techniques to perform these tasks. Our experimental results using comments of a number of products sold online demonstrate the effectiveness of the techniques. [Copyright &y& Elsevier]

A multi criteria method, in support of the planning of shared human robot assembly tasks is presented in this paper. CAD models are used for the extraction of a product's assembly sequence, whilst an algorithm is applied for the generation and examination of alternative assignment scenarios. The design algorithm performs the joint planning of task-to-resource assignment and cell layout at the same time. The evaluation of each planning scenario against a set of ergonomics, quality and productivity criteria is made for the identification of the most efficient ones. The method's implementation, in the form of a prototype planning tool (Task Planner), enables its application to a pilot case from the automotive industry. The results indicate that the method can provide high quality solutions with respect to the end users' criteria and has potential applicability as a decision making support tool in the planning stage. [ABSTRACT FROM AUTHOR]

In Brazil, industries consume approximately 40% of all national electrical power, and the complexity of the industrial energy system demands an appropriate energy management. Moreover, industries need suitable tools to support their strategies to meet energy efficiency regulations and promote energy gains. Through a case study, this paper proposes a multicriteria model based on the FITradeoff procedure to support a motor replacement problem in a chemical industry, in order to build a replacement plan to meet the minimum energy performance standards established by the Brazilian Energy Efficiency Law. This interactive model requires less cognitive effort from the decision-maker in comparison to the traditional tradeoff procedure, avoiding a tedious and long decision process. In addition, this model deals with the data inaccuracy through a sensitivity analysis thereby providing more robust results. After building the replacement plan, an energy performance analysis has been drawn up to evaluate the benefits of using a multicriteria decision tool to address the motor replacement problem. [ABSTRACT FROM AUTHOR]

Incorporating decision makers' preferences is of great significance in multiobjective optimization. Target region-based multiobjective evolutionary algorithms (TMOEAs), aiming at a well-distributed subset of Pareto optimal solutions within the user-provided region(s), are extensively investigated in this paper. An empirical comparison is performed among three TMOEA instantiations: T-NSGA-II, T-SMS-EMOA and T-R2-EMOA. Experimental results show that T-SMS-EMOA has the best overall performance regarding the hypervolume indicator within the target region, while T-NSGA-II is the fastest algorithm. We also compare TMOEAs with other state-of-the-art preference-based approaches, i.e., DF-SMS-EMOA, RVEA, AS-EMOA and R-NSGA-II to show the advantages of TMOEAs. A case study in the mission planning of earth observation satellite is carried out to verify the capabilities of TMOEAs in the real-world application. Experimental results indicate that preferences can improve the searching ability of MOEAs, and TMOEAs can successfully find nondominated solutions preferred by the decision maker. [ABSTRACT FROM AUTHOR]

Abstract: An adaptation phase is crucial for a good and reasonable case-based design (CBD) process, which is responsible for finding a solution to solve a new problem in the principle of k-nearest neighbors (k-NN). Statistical adaptation method is a classical method for feature-based case adaptation (FCA) because of its domain-independent and easily to be implemented, but with low adaptation accuracy. Therefore, this paper presents a new adaptation method for solution feature values of retrieved cases by introducing the adaptability value to improve the adaptation performance, called as adaptability-based FCA (AFCA). Unlike the classical statistical FCA method (SFCA) based on similarity or distance value, AFCA is performed in terms of the adaptability of old solution feature calculated by the adaptability measurement (AM) mechanism. A new AM method is studied as well in this paper, where the adaptability value for each solution feature is computed by utilizing the decision tree technique and similarity value, and the similarity is derived from the multi-algorithm-oriented hybrid SM strategy. Furthermore, to validate the feasibility and superiority of AFCA, the proposed method was applied to the power transformer design and was compared with the classical SFCAs. Empirical comparison results indicated that AFCA achieves the better adaptation performance under k-NN than other SFCAs on the basis of the adaptation accuracy. [Copyright &y& Elsevier]

This paper presents an artificial intelligence algorithmic knowledge transfer approach to the models that have been developed throughout the world for smart grid networks. Many nations are moving forward to implement smarter ways to generate, distribute and network energy, while others are expecting the leading countries to take the initiative and then follow suit. Therefore, we theoretically identify three dimensions of experts' competencies—perception, judgment, and decision choice supported by the Throughput Model algorithms for knowledge transfer. Integrating the Throughput Model algorithmic framework and Deming Cycle (i.e., plan, do, check, act), we propose that Information and Communication Technology (ICT) systems influence experts' decision making towards implementation of Smart Grids (SG). This model was backed up with the perspectives of 32 global experts as surveyed using Carnegie Mellon Maturity model questions and analyzed the results using PLS to validate the findings and compare them to our enhanced knowledge transfer developed from Deming's PDCA cycle. Our results suggest that these key algorithmic decision-making components are critical in explaining the successful application of planning, doing, checking/ acting, and planning of renewable energy technology as well as for a greener environment. Throughput Model displaying perceptions (planning) and judgments (doing, checking/acting) regarding smart grid networks Where, P = perception, I = information, J = judgment, and D = decision choice. [Display omitted] • Implementing the Throughput Model to algorithmic pathways of decision choices may provide researchers and practitioners more clarity regarding Smart Grids. • Human decision-making is rife with comparable biases that algorithms might display, the accountability issue is strengthened when employing a model such as the Throughput Model. • ICT competencies in knowledge transfer on experts' planning can be assisted by an artificial intelligence algorithmic decision-making process of perceptions (planning) and judgments (doing, checking/acting) [ABSTRACT FROM AUTHOR]

Highlights • A geosensor data forecasting tensor framework (GDFTF) for significant societal events is proposed. • A rank increasing strategy and a sliding window strategy is used to improve the prediction accuracy. • Extensive experimental evaluations illustrate the superiority of our approach compared with the state-of-the-arts. Abstract Geosensor data forecasting has high practical value in government affairs such as prompt response and decision making. However, the spatial correlation across distinct sites and the temporal correlation within each site pose challenges to accurate forecasting. In this paper, a geosensor data forecasting tensor framework for significant societal events is proposed. Specifically, a tensor pattern is used to model the geosensor data, based on which a tensor decomposition algorithm is then developed to estimate future values of geosensor data. The proposed approach not only combines and utilizes the multi-mode correlations, but also well extracts the underlying factors in each mode of tensor and mines the multi-dimensional structures of geosensor data. In addition, a rank increasing strategy is used to determine tensor rank automatically, and a sliding window strategy is used to improve the prediction accuracy. Extensive experimental evaluations illustrate the superiority of our approach compared with the state-of-the-arts. [ABSTRACT FROM AUTHOR]

Abstract: That the multi-source remote sensing information integrates knowledge-based geospatial constraints to develop efficient and practical Land cover classification algorithm has become one of the most important developing directions in the field of remote sensing ground object classification. Remote sensing classification is a strictly incompatible problem, but the spectra distribution of remote sensing data has compatible attributes especially in mountainous regions, and such contradiction is one of the main reasons leading to uncertainties in remote sensing classification. In this paper, the remote sensing spectra feature compatible information is transformed into the probability of the association degree firstly, and then the matter-element theory is introduced to establish models to achieve the integrated classification of multi-source data to fuse knowledge-based geographical constrained condition probability. Taking the grassland–wetland fragile ecosystem in Ruoergai plateau of China as a case study, this paper selected the multi-source data including images of Landsat TM and CBERS, ASTER-GDEM and MODIS-NDVI to construct a comprehensive classifier, in which the relationship between topography and land cover, and the prior knowledge on vegetation growth difference were taken as constraints to support the decision-making. The classification accuracy was evaluated by a field investigation and existing land cover map. The test result shows that, the overall accuracy (89.89%) and Kappa coefficient (0.8870) are better than those derived by the Maximum Likelihood method. It indicates that the proposed classification method is not subject to the dimensionality and form of data sources, and it can integrate the data source information to improve the classification accuracy, so that it is very useful to apply multi-source data and prior knowledge to land cover classification in mountainous regions. [Copyright &y& Elsevier]

Abstract: This paper aims at probing into the topic of power units’ operation and dispatch based on Carbon Dioxide (CO2) trading scheme. The trading cost of CO2 emission is embedded into the traditional economic dispatch model, which will be solved by the New Particle Swarm Optimization (NPSO). By considering the CO2 trading scheme, the influences of the various strategies for unit’s dispatch are simulated and analyzed in this paper. The proposed method, NPSO is developed in such a way that PSO with Constriction Factor (PSO-CF) algorithm is applied as a based level search. NPSO introduces two operators, “Random Particles” and “Fine-Tuning” into the PSO-CF algorithm to improve the drawback of searching global optimum and make the search method more efficient at the end of search. The efficiency and ability of NPSO is demonstrated by the six generating units. Simulation results indicated that reasonable solutions provide a practical and flexible framework for power sectors. They can be also used for generating alternatives and thus help decision makers to obtain the goals of minimal operation cost under their desired emission’s policies. [Copyright &y& Elsevier]

Abstract: The aim of this paper is to propose an adaptive maintenance model for a gradually deteriorating system. The system considered initially deteriorates with a nominal deterioration rate and at an unknown time the system''s deterioration rate changes and the new deterioration rate is a time-dependent function. To deal with the transition of mode of deterioration in the framework of the maintenance decision rule an adequate online change detection algorithm is used. The maintenance decision rule is chosen in order to minimise the total maintenance cost including the cost of unavailability. The main result of this paper is to point out the interest of using a detection algorithm and hence the appreciation of a decision rule which takes into account transitions in the deterioration rate. [Copyright &y& Elsevier]

Abstract: This paper presents Reinforcement Learning (RL) approaches to Economic Dispatch problem. In this paper, formulation of Economic Dispatch as a multi stage decision making problem is carried out, then two variants of RL algorithms are presented. A third algorithm which takes into consideration the transmission losses is also explained. Efficiency and flexibility of the proposed algorithms are demonstrated through different representative systems: a three generator system with given generation cost table, IEEE 30 bus system with quadratic cost functions, 10 generator system having piecewise quadratic cost functions and a 20 generator system considering transmission losses. A comparison of the computation times of different algorithms is also carried out. [Copyright &y& Elsevier]

Abstract: In this paper, application of the rough set theory (RST) to feature selection in customer relationship management (CRM) is introduced. Compared to other methods, the RST approach has the advantage of combining both qualitative and quantitative information in the decision analysis, which is extremely important for CRM. Automated decision support for CRM has been proposed in recent years. However, little work has been devoted to the development of computer-based systems to support CRM in rule induction. This paper presents a novel rough set based algorithm for automated decision support for CRM. Particularly, the approach is capable to handle real numbers instead of integer numbers through introduction of converted numbers involving tolerances. Being unique and useful in solving CRM problems, an alternative rule extraction algorithm (AREA) is presented for discovering preference-based rules according to the reducts which contain the maximum of strength index (SI) in the same case, where the data with tolerance. The empirical data set associated with CRM has proven the validity and reliability of these approaches. This research thus contributes to developing and validating a useful approach to automated decision support for CRM. This paper forms the basis for solving many other similar problems that occur in the service industry. [ABSTRACT FROM AUTHOR]

Abstract: This paper is devoted to automated sequential decision in AI. More precisely, we focus here on the Rank Dependent Utility (RDU) model. This model is able to encompass rational decision behaviors that the Expected Utility model cannot accommodate. However, the non-linearity of RDU makes it difficult to compute an RDU-optimal strategy in sequential decision problems. This has considerably slowed the use of RDU in operational contexts. In this paper, we are interested in providing new algorithmic solutions to compute an RDU-optimal strategy in graphical models. Specifically, we present algorithms for solving decision tree models and influence diagram models of sequential decision problems. For decision tree models, we propose a mixed integer programming formulation that is valid for a subclass of RDU models (corresponding to risk seeking behaviors). This formulation reduces to a linear program when mixed strategies are considered. In the general case (i.e., when there is no particular assumption on the parameters of RDU), we propose a branch and bound procedure to compute an RDU-optimal strategy among the pure ones. After highlighting the difficulties induced by the use of RDU in influence diagram models, we show how this latter procedure can be extended to optimize RDU in an influence diagram. Finally, we provide empirical evaluations of all the presented algorithms. [Copyright &y& Elsevier]

Abstract: Speech is a promising modality for the convenient measurement of cognitive load, and recent years have seen the development of several cognitive load classification systems. Many of these systems have utilised mel frequency cepstral coefficients (MFCC) and prosodic features like pitch and intensity to discriminate between different cognitive load levels. However, the accuracies obtained by these systems are still not high enough to allow for their use outside of laboratory environments. One reason for this might be the imperfect acoustic description of speech provided by MFCCs. Since these features do not characterise the distribution of the spectral energy within subbands, in this paper, we investigate the use of spectral centroid frequency (SCF) and spectral centroid amplitude (SCA) features, applying them to the problem of automatic cognitive load classification. The effect of varying the number of filters and the frequency scale used is also evaluated, in terms of the effectiveness of the resultant spectral centroid features in discriminating between cognitive loads. The results of classification experiments show that the spectral centroid features consistently and significantly outperform a baseline system employing MFCC, pitch, and intensity features. Experimental results reported in this paper indicate that the fusion of an SCF based system with an SCA based system results in a relative reduction in error rate of 39% and 29% for two different cognitive load databases. [Copyright &y& Elsevier]

Abstract: This paper reports the results of an advanced algorithm for the optimization of electron beam parameters in Free Electron Laser (FEL) Linacs. In the novel approach presented in this paper, the system uses state of the art developments in video games to mimic an operator''s decisions to perform an optimization task when no prior knowledge, other than constraints on the actuators is available. The system was tested for the simultaneous optimization of the energy spread and the transmission of the Australian Synchrotron Linac. The proposed system successfully increased the transmission of the machine from 90% to 97% and decreased the energy spread of the beam from 1.04% to 0.91%. Results of a control experiment performed at the new FERMI@Elettra FEL is also reported, suggesting the adaptability of the scheme for beam-based control. [Copyright &y& Elsevier]

Abstract: This paper studies a multi-goal Q-learning algorithm of cooperative teams. Member of the cooperative teams is simulated by an agent. In the virtual cooperative team, agents adapt its knowledge according to cooperative principles. The multi-goal Q-learning algorithm is approached to the multiple learning goals. In the virtual team, agents learn what knowledge to adopt and how much to learn (choosing learning radius). The learning radius is interpreted in Section . Five basic experiments are manipulated proving the validity of the multi-goal Q-learning algorithm. It is found that the learning algorithm causes agents to converge to optimal actions, based on agents’ continually updated cognitive maps of how actions influence learning goals. It is also proved that the learning algorithm is beneficial to the multiple goals. Furthermore, the paper analyzes how sensitive the learning performance is affected by the parameter values of the learning algorithm. [ABSTRACT FROM AUTHOR]

Abstract: The paper 1 [1] Supported by Engineering and Physical Sciences Research Council (EPSRC), U.K., grant EP/F014406/1. develops a technique for computation inference rules admissible in temporal logic . The problem whether there exists an algorithm recognizing inference rules admissible in is a long-standing open problem. The logic has neither the extension property nor the co-cover property which previously were central instruments for construction decision algorithms for admissibility in modal logics (e.g. reflexive and transitive modal logic ). Our paper uses a linear-compression property, a zigzag-ray property and a zigzag stretching property which hold for . The main result of the paper is a sufficient condition for admissibility inference rules in . It is shown that all rules which are valid in special finite models (with an effective upper bound on size) must be admissible in . [ABSTRACT FROM AUTHOR]