Search

This paper provides new insight into the theoretical principle and the practical applicability of the ranked nodes method (RNM) that is used to construct conditional probability tables (CPTs) for Bayesian networks (BNs) by expert elicitation. RNM is designed for specific types of discrete random variables called ranked nodes that are common in real-world applications of BNs. Despite its active use in recent years, there remains ambiguity about the exact theoretical basis of RNM which can hamper its effective employment. In addition, there are a lack of studies about the general ability of CPTs generated with RNM to represent probabilistic relationships in real-world applications. In this paper, it is shown how the generation of probabilities with RNM is underpinned by a regression model of continuous random variables. Then, it is experimentally determined that in typical applications of RNM, one can generate in a matter of seconds CPTs whose elements reflect well probabilities given by the underlying regression model. Another experiment discovers that CPTs generated with RNM provide a good average fit to a large portion of various real-world CPTs investigated. This confirms the usefulness of RNM in practical applications. The results of the experiment also indicate that choices made by the user of RNM can considerably impact the ability of a generated CPT to represent a given probabilistic relationship. This paper then provides practical advice on the efficient use of RNM with regard to the user-controlled features explored in the experiment. [ABSTRACT FROM AUTHOR]

A fundamental challenge for system engineers is to capture a problem with an effective model or framework and then facilitate transferring the information of that captured problem to practical systems engineering tools and methods. The early problem definition phase requires an application of systems thinking with adequate modeling tools and methods. Then, the later problem definition phase and early system architecting phase requires transferring the captured problem to systems engineering tools and methods through emerging techniques such as model-based systems engineering (MBSE) using SysML (MBSE is the practice of using a modeling tools to capture systems engineering diagrams). This paper presents a method for capturing a problem through systemigrams and the Boardman soft systems methodology and then directly translating the systemigrams into SysML diagrams. With MBSE increasing in usage, this method could provide a time savings opportunity during model development along with the possibility of lowering information distortion or loss that can occur during transformation of systems thinking to systems engineering activities. This paper includes a case study which demonstrates how the proposed approach was applied on a problem being considered by the U.S. Army—Contingency Basing for Small Combat Units. Finally, this paper will provide the conclusion on the development of the method and describe future research directions that can allow systems thinking and MBSE to function in a congruent methodology. [ABSTRACT FROM AUTHOR]

Nowadays, the increased amount of users’ devices produce huge volumes of data that should be efficiently managed by modern applications. Streams are adopted to deliver data that, usually, are stored into a number of partitions. Splitting the data offers a lot of advantages as applications can process them in parallel, thus, they increase the speed of processing. Progressive analytics are also adopted to deliver partial responses, during processing, thus, saving time in the execution of applications. Data exploration and analytics queries are very significant for future applications. Usually, such queries demand for an ordered set of objects as a response and require intelligent predictive schemes to deliver the responses on top of the partial results retrieved by the distributed data partitions. A finite set of query processors are adopted to produce these partial results. Processors are placed in front of each partition and report progressive analytics to a central entity. In this paper, we envision the query controller (QC) as the central entity that collects progressive analytics and return the final response to users/applications. The QC receives partial ordered sets of objects and aggregates them to derive the final outcome. We focus on a QC that applies time-optimized techniques and aggregation operators to deliver every response, i.e., ordered sets, over streams of partial ordered subsets. We perform a comprehensive performance assessment with synthetic data and report on the performance of the QC. Our experimental evaluation reveals the pros and cons of the proposed model and a comparison assessment places this paper in the respective literature. [ABSTRACT FROM AUTHOR]

Role assignment is an important and complex task in collaboration and management. Group role assignment (GRA) pursues the optimal team performance by assigning pertinent roles to agents in a team from the team’s viewpoint. There are many factors that should be considered when conducting role assignment. Budgets are such a factor. This paper presents a challenging role assignment problem in collaboration, called GRA with budget constraints (GRABC). This paper contributes a thorough investigation of the GRABC problem including: 1) the first formalization of the proposed problem; 2) a set of practical solutions to different forms of this problem by using the IBM ILOG CPLEX optimization package (CPLEX); 3) a set of necessary conditions for these problems to possess feasible solutions to improve the CPLEX solutions; and 4) a synthesized process to deal with GRABC problems. Contribution 2) actually clarifies a list of requirements in solving the GRABC problem. Simulations, experiments and a case study are used to verify the practicability and efficiency of the proposed solutions. [ABSTRACT FROM AUTHOR]

Decision support is a vital function in electronic commerce (e-commerce). The purpose of this paper is to construct a review-based decision support model for items comparison in e-commerce. The proposed model uses probability multivalued neutrosophic linguistic numbers (PMVNLNs) to characterize online reviews. It overcomes the limitation of existing models by considering neutral information and hesitancy in text reviews. The fuzzy characterization of reviews (i.e., PMVNLN) can reflect similarities and differences in positive (negative) information. In addition, the model considers consumers’ bounded rational behaviors by combining the regret theory with an outranking method. We empirically compare the proposed model with models in http://PConline.com and four existing models with data from http://PConline.com. The performance of these models in terms of accuracy is measured by the total relative difference metric. Results indicate the good performance of the proposed model. Our model is a promising option for e-commerce to provide consumers with good decision support service. [ABSTRACT FROM AUTHOR]

In group decision making, it is inevitable that the individual decision maker’s subjectivity is involved, which causes difficulty in reaching a group decision. One of the difficulties is to aggregate a small set of expert opinions with the individual subjectivity or uncertainty modeled with probability theory. This difficult problem is called probability distribution function aggregation (DFA). This paper presents a simple and efficient approach to the DFA problem. The main idea of the proposed approach is that the DFA problem is modeled as a nonlinear function of a set of probability distribution functions, and then a linear feedback iteration scheme is proposed to solve the nonlinear function, leading to a group judgment or decision. Illustration of this new approach is given by a well-known DFA example which was solved with the Delphi method. The DFA problem is a part of the group decision problem. Therefore, the proposed algorithm is also useful to the decision making problem in general. Another contribution of the this paper is the proposed notation of systematically representing the imprecise group decision problem with the classification of imprecise information into three classes, namely incomplete information, vague information, and uncertain information. The particular DFA problem dealt with in this paper is then characterized with this general notation. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of fault detection for sampled-data systems, which are based on multirate sampling, with frequency specifications. A general multirate system is considered in this paper, where not only the inputs and outputs but also their different channels have different sampling rates. The purpose of this paper is to make this residual system with multirate sampling satisfy a given disturbance attenuation level over a restricted frequency range. With the use of the lifting technique, this paper reformulates a single-rate linear time-invariant system, which is equivalent to the multirate time-varying system. For a given restricted frequency range, convex conditions are obtained in designing a required fault detection filter. Then, the restricted frequency ranges problem are also solved specifically via the generalized Kalman–Yakubovic–Popov lemma. Finally, this paper uses a continuous-stirred tank reactor system to illustrate the effectiveness and advantages of the fault detection filter design method. [ABSTRACT FROM AUTHOR]

The aim of this paper is to construct a theoretical framework for stability analysis relevant to standing balance of humanoids on top of the linear inverted pendulum model, in which their dynamics between the center of mass (CoM) and the zero moment point (ZMP) is dealt with. Based on the well-known sufficient condition that the contact between the ground and the support leg is stable if the corresponding ZMP is always inside the supporting region, this paper aims at characterizing three types of the associated stability regions. More precisely, assuming no external force disturbances affecting the motion of the humanoids, the stability region of the initial CoM position and velocity values can be explicitly computed by solving a finite number of linear inequalities. The stability regions of time-invariant force disturbances such as impulsive force and constant force disturbances are also dealt with in this paper, where the former is exactly obtained through a finite number of linear inequalities while the latter is approximately derived by using an idea of truncation. Furthermore, time-varying force disturbances of finite energy and finite amplitude are concerned with, and their maximum admissible ${l} _{2}$ and ${l} _{\infty }$ norms are computed in this paper, where the former can be exactly obtained by solving the discrete-time Lyapunov equation while the latter is approximately derived through an idea of truncation. It is further shown for both the truncation ideas that the approximately obtained stability regions converge to the exact stability regions with an exponential order of ${N}$ , where ${N}$ is the truncation parameter. Finally, the effectiveness of the computation methods proposed in this paper is demonstrated through some simulation results. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of reducing the conservatism for stabilization of discrete-time Takagi–Sugeno fuzzy systems via a new extended representation approach. Different from previous slack variable methods, an advanced slack variable method without limitations is proposed for further making good use of available fuzzy weighting functions. Owing to the introduction of extended representations for homogenous polynomials, those strong limitations in recent literature are relaxed and thus the primary and secondary sequences of all normalized fuzzy weighting functions can be mapped to one augmented multi-indexed matrix for each homogeneous polynomial. Therefore, less conservative stabilization conditions can be provided as a result of these effective measures and thus a part of the online computational cost is transferred to the offline implementation. Finally, the benefits of the proposed method are illustrated by means of numerical experiments. [ABSTRACT FROM AUTHOR]

Role negotiation is a pivotal step in the role-based collaboration (RBC) process. There are many factors to be considered when decision makers set up criteria to evaluate group performance and agents’ abilities. In this paper, we investigate the applications of three traditional multicriteria decision making methods into role negotiation of RBC, i.e., simple additive weighting, multiplication exponent weighting, and weighted distance. The goal is to acquire ideal and satisfactory methods to set up the evaluation criteria for the role negotiation task. The main contributions of this paper include: 1) a method of generating the matrix of agent qualifications, with a discrete, usually, limited, and finite set of prespecified attributes and values; 2) the upper and lower thresholds of a role’s ability requirements, which are critical parameters for a workable team and its performance; and 3) the potential influences of specialist agents and generalist agents on the group performance. The proposed methods are verified by simulation experiments. The simulation results show that the proposed methods are reasonable, feasible, and practical. [ABSTRACT FROM AUTHOR]

It is an important topic to organize a team efficiently and keep it in a good state. In most cases, administrators try to avoid critical members. With this requirement, administrators prefer that their team members are able to be good at many things and expert in one (GMEO). However, that too many people are “good at many things” is definitely a waste. Role-based collaboration and its environments—classes, agents, roles, groups, and objects (E-CARGO) model are a good means to provide modeling and solutions to such a challenging problem. This paper formalizes the problem of GMEO into GMEO-1 (the fundamental form of GMEO) with the support of E-CARGO, clarifies two different forms of GMEO-1 and provides two highly practical solutions. The proposed solutions are verified by comparing with initial solutions using a linear programming solver, i.e., the IBM ILOG CPLEX Optimization Platform. The contributions of this paper are a thorough investigation of GMEO-1 that has no exact solutions even for a small group (e.g., ten people) and is normally managed by highly qualified administrators. The proposed solutions provide digital results with algorithms that form a solid foundation for decision making in dealing with similar issues. [ABSTRACT FROM AUTHOR]

In this paper, we aim at developing a new collaborative filtering recommender system using soft ratings, which is capable of dealing with both imperfect information about user preferences and the sparsity problem. On the one hand, Dempster–Shafer theory is employed for handling the imperfect information due to its advantage in providing not only a flexible framework for modeling uncertain, imprecise, and incomplete information, but also powerful operations for fusion of information from multiple sources. On the other hand, in dealing with the sparsity problem, community context information that is extracted from the social network containing all users is used for predicting unprovided ratings. As predicted ratings are not a hundred percent accurate, while the provided ratings are actually evaluated by users, we also develop a new method for calculating user–user similarities, in which provided ratings are considered to be more significant than predicted ones. In the experiments, the developed recommender system is tested on two different data sets; and the experiment results indicate that this system is more effective than CoFiDS, a typical recommender system offering soft ratings. [ABSTRACT FROM AUTHOR]

In this paper, we modify the graph model for conflict resolution (GMCR) to model interactive unawareness of decision makers (DMs) about the options available to them in the conflict. More specifically, we consider a GMCR with two DMs, where a DM, in some given state, can be unconscious about some of his options, or about the options of his opponent, and therefore, may have only a partial knowledge of the state space. By interactive unawareness, it is meant that a DM can reason about the awareness level of the other DM and about the awareness level of the other DM regarding his or her own awareness level and so on. We generalize standard solution concepts for this model and illustrate its usefulness by means of a hypothetical war conflict. [ABSTRACT FROM AUTHOR]

The finite-iteration tracking for singular coupled systems with packet-dropping learning controllers is discussed in this paper. The singular coupled systems are constructed to describe systems with some algebraic constraints, and iterative learning controllers are then designed to achieve the finite-iteration tracking of singular systems. The definition of the finite-iteration tracking is first proposed and the settling iteration is explicitly calculated. Moreover, the iterative learning controllers are designed to consider the case of packet losses. Simulation results are given to elaborate the correctness of the given theorems. [ABSTRACT FROM AUTHOR]

In this paper, we provide a novel framework to assess the vulnerability/robustness of a network with respect to pair-wise nodes’ connectivity. In particular, we consider attackers that aim, at the same time, at dealing the maximum possible damage to the network in terms of the residual connectivity after the attack and at keeping the cost of the attack (e.g., the number of attacked nodes) at a minimum. Differently from the previous literature, we consider the attacker perspective using a multiobjective formulation and, rather than making hypotheses on the mindset of the attacker in terms of a particular tradeoff between the objectives, we consider the entire Pareto front of nondominated solutions. Based on that, we define novel global and local robustness/vulnerability indicators and we show that such indices can be the base for the implementation of effective protection strategies. Specifically, we propose two different problem formulations and we assess their performances. We conclude this paper by analyzing, as case studies, the IEEE118 power network and the U.S. Airline Network as it was in 1997, comparing the proposed approach against centrality measures. [ABSTRACT FROM AUTHOR]

Collaboration is complex. To solve a problem occurring in collaboration, using computers, we must first define and specify the problem. This paper presents a challenging problem in collaboration, called group role assignment with cooperation and conflict factors (GRACCFs). This problem’s solution aims at creating a high-performance group by role assignment with consideration of cooperation and conflicts between agents. The contribution of this paper is the formalization of the proposed problem, a confirmation of the complexity of the problem, a practical solution that uses the IBM ILOG CPLEX optimization package (ILOG), a verification of the benefits of solving the GRACCF problem by simulations and a practical way of collecting the required factors to support decision makers in solving such a problem within a real-world scenario. Experiments are used to verify the efficiency of the proposed ILOG solution. [ABSTRACT FROM AUTHOR]

Link similarity is widely applied in measuring the similarity between such objects as Web pages, scientific papers, and social networks. However, there are some deficiencies in the existing methods to measure it. For example, they cannot handle some semantic-similar contents. Their computation may not lead to accurate results in some cases. This paper presents a novel method to do so. It introduces the semantic similarity to calculate the similarity between two given objects, and overcomes the drawback caused by the fact that the existing methods ignore the semantic information of objects. It also gives a novel computation function to make the computing result of similarity more accurate. [ABSTRACT FROM PUBLISHER]

Recently, a measure of “total uncertainty” (TU) in Dempster–Shafer theory, based on the pignistic distribution called ambiguity measure (AM), have been modified. The resulting new measure has been simply referred as modified AM (MAM). In the literature, it has been shown that AM, in addition to showing some undesirable behaviors, has important drawbacks related to two essential properties for such measures: 1) subadditivity and 2) monotonicity. The MAM measure has been developed to solve the AM subadditivity problem, but this paper demonstrates that MAM suffers the same drawback as AM with respect to monotonicity. A measure of uncertainty that cannot meet the monotonicity requirement has an important drawback for its exploitation in operational contexts such as in analytics, information fusion, and decision support. This paper aims at identifying and discussing drawbacks of this type of measures (AM, MAM). Our main motivation is to insist upon the important requirement of monotonicity that a TU measure should possess to improve its potential of being used and trusted in applications. This discussion is due time since the monotonicity problem needs first to be solved to avoid building too high expectations for usefulness and potential exploitation of such measures in operational communities. [ABSTRACT FROM PUBLISHER]

This paper is concerned with the finite-time tracking for linear and nonlinear systems, with respect to the reference signal (to be tracked by the system state) obeying some uniformity. Before the control design of finite-time tracking, an interesting and basic problem is whether or not such control exists for the system concerned. This paper aims to this basic problem. First, the sufficient and necessary conditions on finite-time tracking are proposed for linear systems. Such conditions are then extended to those for affine nonlinear systems. It should be pointed out that the sufficient and necessary conditions proposed in the paper essentially reveal the relationship between the structure of the linear/nonlinear systems and the existence of the finite-time tracking control. [ABSTRACT FROM PUBLISHER]

Yahoo! Answers is an online platform where users can post questions and answer other users’ questions. Our previous work studied the online social network (OSN) of Yahoo! Answers by analyzing information from the profiles (including fans, contacts, and interests) of top contributors and their related users. Rather than using the static profile information from the top-contributor-centered dataset, in this paper, we particularly analyze the actual questioning and answering (Q/A) behaviors of normal users. We build a Q/A network that unidirectionally connects each asker to his/her answerers. We analyze the structural characteristics of the Q/A network, user Q/A activities, and knowledge base of all users. In addition to the observations similar to our previous study, which indicates that the OSN of Yahoo! Answers can reflect user Q/A activities to a certain extent, we additionally observe that: 1) a large portion of users only ask questions without answering others’ questions; 2) users are active in more knowledge categories than those indicated in their profiles; and 3) the knowledge categories of the top-contributor-related users cannot represent those of normal users. Finally, we analyze the characteristics of questions and answers in different knowledge categories. This paper not only provides an understanding of actual Q/A activities of users but also showcases the aspects of Q/A activities that the OSN of Yahoo! Answers can and cannot accurately reflect. Based on the insights gained from this paper, we propose a few methods to help improve the quality of service of Yahoo! Answers. [ABSTRACT FROM PUBLISHER]

Data backups play an important role in effective recovery of system operations, especially, those in computing and Information Technology-related applications. By saving copies of information associated with the completed part of a mission task, a failed system, upon being repaired, can resume its operation from the latest backup point instead of having to repeat the entire work from scratch. This paper considers a repairable, real-time system performing a sequence of nonperiodic full backup (FB) and incremental backup (IB) procedures during its mission. The mission succeeds if a specified amount of work can be accomplished by the system within a deadline. New contributions of this paper are twofold. First, a numerical recursive method is proposed to model effects of the nonperiodic, mixed backup strategy in assessing mission success probability, and expected completion time of the considered system. The method is applicable to any distribution of FBs and IBs and has no limitation on system time-to-failure distributions. Second, the optimal backup policy problem is formulated and solved, which finds the distribution of FBs and IBs maximizing the mission success probability. Examples are provided to illustrate the proposed methodology as well as influence of different parameters on the optimal solution. Advantages of adopting nonperiodic backups over periodic ones are also illustrated. [ABSTRACT FROM PUBLISHER]

A general deep learning (DL) mechanism for a multiple hidden layer feed-forward neural network contains two parts, i.e., 1) an unsupervised greedy layer-wise training and 2) a supervised fine-tuning which is usually an iterative process. Although this mechanism has been demonstrated in many fields to be able to significantly improve the generalization of neural network, there is no clear evidence to show which one of the two parts plays the essential role for the generalization improvement, resulting in an argument within the DL community. Focusing on this argument, this paper proposes a new DL approach to train multilayer feed-forward neural networks. This approach uses restricted Boltzmann machine (RBM) as the layer-wise training and uses the generalized inverse of a matrix as the supervised fine-tuning. Different from the general deep training mechanism like back-propagation (BP), the proposed approach does not need to iteratively tune the weights, and therefore, has many advantages such as quick training, better generalization, and high understandability, etc. Experimentally, the proposed approach demonstrates an excellent performance in comparison with BP-based DL and the traditional training method for multilayer random weight neural networks. To a great extent, this paper demonstrates that the supervised part plays a more important role than the unsupervised part in DL, which provides some new viewpoints for exploring the essence of DL. [ABSTRACT FROM AUTHOR]

This paper focuses on the complexity of the (weak) soundness problem of acyclic workflow (WF) nets, and two main results are established: 1) soundness of 1-bounded acyclic WF nets is co-NP-complete and 2) weak soundness of 3-bounded acyclic asymmetric-choice WF nets is co-NP-complete. [ABSTRACT FROM AUTHOR]

The efficacy of the multimodel framework (MMF) in modeling and identification of complex, nonlinear, and uncertain systems has been widely recognized in the literature owing to its simplicity, transparency, and mathematical tractability, allowing the use of well-known modeling analysis and control design techniques. The approach proved to be effective in addressing some of the shortcomings of other modeling techniques such as those based on a single nonlinear autoregressive network with exogenous inputs model or neural networks. A great number of researchers have contributed to this active field. Due to the significant amount of contributions and the lack of a recent survey, the review of recent developments in this field is vital. In this two-part paper, we attempt to provide a comprehensive coverage of the multimodel approach for modeling and identification of complex systems. The study contains a classification of different methods, the challenges encountered, as well as recent applications of MMF in various fields. Part 1 of this paper presents an overview of MMF for modeling and identification of nonlinear systems as well as the review of recent developments in the partitioning strategies employed. [ABSTRACT FROM AUTHOR]

Physical infrastructures are commonly composed of interconnected and intra- and interdependent subsystems, which in their essence constitute systems-of-systems (S-o-S) with multiple functions, operations, and stakeholders. Their complexity is characterized by highly interconnected and interdependent physical, cyber, organizational, and economic subsystems through shared resources, decisions, and states. This paper explores potential sources of systemic risks in complex S-o-S by analyzing unique failure modes of such systems in a nonlinear dynamic multiobjective sequential decision-making process. We posit that certain preferences of decision-makers when choosing among multiple competing objectives may reduce the safety margin of a specific subsystem within the S-o-S to withstand unexpected external perturbations due to the interdependencies manifested by shared states among the subsystems. This can contribute to the failure of an entire S-o-S even though all decisions are made based on Pareto-optimality. By quantifying the level of subsystem interdependency caused by shared states, this paper develops a method to mitigate such systemic risks through decomposing and coordinating the interconnected subsystems of an S-o-S in a decentralized way. The implication of the proposed theory is demonstrated using an illustrative example of a highway bridge S-o-S. [ABSTRACT FROM PUBLISHER]

The vast quantity of visual data generated by the rapid expansion of large scale distributed multicamera networks, makes automated person detection and reidentification (RE-ID) essential components of modern surveillance systems. However, the integration of automated person detection and RE-ID algorithms is not without problems, and the errors arising in this integration must be measured (e.g., detection failures that may hamper the RE-ID performance). In this paper, we present a window-based classifier based on a recently proposed architecture for the integration of pedestrian detectors and RE-ID algorithms, that takes the output of any bounding-box RE-ID classifier and exploits the temporal continuity of persons in video streams. We evaluate our contributions on a recently proposed dataset featuring 13 high-definition cameras and over 80 people, acquired during 30 min at rush hour in an office space scenario. We expect our contributions to drive research in integrated pedestrian detection and RE-ID systems, bringing them closer to practical applications. [ABSTRACT FROM PUBLISHER]

The combat system-of-systems (CSoSs) in high-tech information warfare, composed of different types of combat systems connected through multiple interconnections, can be abstracted as a type of complex heterogeneous network. Research on the disintegration of the operational capability of a heterogeneous combat network (HCN) is of significant military value for optimizing the planning of operation process and improving network survivability in a complex electromagnetic battlefield. Accordingly, this paper presents an integrated framework called HCN operational capability disintegration based on link prediction (OCDLP) for modeling and solving the disintegration problem regarding the operational capability of an HCN. More specifically, a heterogeneous network model of a combat network under incomplete information is first established, considering different types of functional entities and information flows. Based on this model, a link prediction model is then applied to recover the original combat network structure while taking advantage of the observed information. Next, a disintegration evaluation metric of the operational capability of an HCN is elicited to evaluate the attack efficiency after a link prediction. Last, the reliability and effectiveness of the OCDLP are illustrated through a case study. The results provide useful insight into the operation guidance and design of a more resilient CSoSs. [ABSTRACT FROM AUTHOR]

The problem of ${\mathcal {H}}_{\infty }$ filtering to estimate the unmeasurable states is investigated in this paper and the most general multirate measurements condition is taken into account. The main result of this paper is to give a method to design a filter, which can guarantee the stability of the resultant filtering error system with ${\mathcal {H}}_{\infty }$ performance. For a given system with multirate measurements, this paper first provides a method to convert this multirate design problem into an equivalent single-rate design problem. Then, it can be proved that the design approach of a required filter based on a linear matrix inequality is a sufficient and necessary condition. Lastly, two examples are utilized to demonstrate that this design approach is effective and applicable to estimate the unmeasurable states for the given system with multirate measurements. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of designing a control strategy to guarantee stability in probability for high-order stochastic systems. The control hinges on the sliding-mode scheme. We first prove that the control design can stabilize a fast terminal sliding-mode function. We further show that the stable sliding mode is able to assure the stability in probability for the high-order stochastic system. The resulting system may account nonvanishing diffusion terms, which is regarded as a novelty. The derived control strategy has implications for applications—experiments are carried out to control the pitch angle of a one-degree-of-freedom aerial device. The corresponding experimental results demonstrate the benefits and effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This paper focuses on the behavior similarity of workflow nets (WF-nets). The similarity of two WF-nets reflects their consistent degree in behaviors. It explores the behavioral relations of subsets of transitions based on the interleaving semantics, and more accurate relations are defined than the existing work. Therefore, a more accurate similarity of two WF-nets (in their behaviors) can be obtained than that in the existing work that usually do not consider the loop and complex correspondence. By refining the interleaving relation in a behavioral profile into six types, this paper proposes the notion of a relation profile based on behavioral profile. Based on the relation profile of a WF-net, behavioral relation matrix can be constructed. Additionally, we refine the complex correspondence and generate a group of behavioral relation submatrices from the behavioral relation matrix. By using them we present a new formula to measure the behavior similarity of two WF-nets. Finally, examples illustrate that our method can measure the similarity degree more accurately. [ABSTRACT FROM AUTHOR]

This paper considers leader-following bipartite consensus of single-integrator multiagent systems in the presence of measurement noise. To attenuate the noise, a time-varying consensus gain ${q}$ (${t}$) is introduced into the stochastic approximation-type protocol. Necessary and sufficient conditions for ensuring a strong mean square leader-following bipartite consensus are given. In particular, in the absence of measurement noise, the convergence speed of error dynamics is dependent on the eigenvalues of Laplacian and the rate of ${\int ^{{t}}_{0}{q}({s})\text {d}{s}}$ approaching infinity. By appropriately choosing ${q}$ (${t}$), the speed of leader-following bipartite consensus convergence can be improved in a fixed communication topology. It is proven that conditions for the signed digraph to be structurally balanced and having a spanning tree are necessary and sufficient to ensure leader-following bipartite consensus, regardless of measurement noise. [ABSTRACT FROM AUTHOR]

This paper investigates the debt clearing problem in a debt network with complicated debt relations among various debtors. Each pair of debt relations is modeled with both forward debt chain and backward debt chain to simplify the whole debt network with a developed algorithm. A debt clearing strategy is derived and its benefits and managerial implications are shown in risk management. This paper further demonstrates the validation of the approach for sovereign debt analysis and risk analytics using real debt data from ten developed countries. [ABSTRACT FROM AUTHOR]

A fast retransmit, which reduces the sender’s waiting time before retransmitting a lost segment, is applied to guarantee data integrity with no data loss in transmission. Based on fast retransmit, many application protocols have been enhanced and evolved to ensure quality of service and reduce data transmission time. One of application protocols is the multipath transmission control protocol which is catholically applied in modern computer networks. Communication lines used in this kind of network have different states, namely, failure, partial failure, and maintenance. Therefore, a computer network with a fast retransmit can be classified as stochastic and is called a stochastic-flow computer network with a fast retransmit (SCNFR). This paper assesses the system reliability of SCNFR for the successful transmission of demand d through ${k}$ (${k} \,\, >1$) separate minimal paths (${k}$ SMiP) under the latency. An algorithm is proposed to find all minimal capacity vectors (MCVs) that satisfy the demand and latency. Then, the system reliability is computed based on all MCVs. We adopt two practical cases of the pan-European research and education network and the Taiwan academic network to explore the effectiveness of the algorithm. The results show that the system reliability can be classified as decision reference while the manager decides the better ${k}$ SMiP with higher system reliability. [ABSTRACT FROM AUTHOR]

This paper aims to design a dissipative fuzzy filter for a class of discrete-time nonlinear networked systems. In order to adopt the limited communication links, we employ an event-triggered scheme to reduce the number of transmitted data in the network by preventing the unnecessary ones from releasing. Due to the digital channel, the data to be transmitted should be quantized and a logarithmic quantizer is employed. Then, when the part of released data is transmitted in the network, data losses is captured by a Bernoulli process. Consequently, the uncomplete data sequence is compensated by the buffer and then send to the filter. During this process, a new random series is developed to help constructing the filtering systems. Thus, a novel method is presented to guarantee the filter error system to be dissipative based on the T–S fuzzy model approach. Finally, an example concerned with Henon mapping system is provided to verify the validity of the proposed design method. [ABSTRACT FROM AUTHOR]

In this paper, the problems of nonfragile and nonsynchronous synthesis for Bernoulli switched systems are studied. The disturbances in the systems are assumed to have bounded peak in the sense of mean square. With the interval additive gain variations consideration, a nonfragile and nonsynchronous controller is designed subject to a nonsynchronous assumption that the switching signals of the system are not accurately gained for synthesis. A sufficient condition that is dependent on the known sojourn probabilities of switching signals is proposed to ascertain the reachable set of the closed-loop system, which is a hidden Bernoulli model, mean square contained in an ellipsoid-like set. Several nonfragile and nonsynchronous controllers are parameterized to guarantee the corresponding requirements to be satisfied for the closed-loop systems. The effectiveness and advantages of the proposed new design techniques are illustrated by a numerical simulation example. [ABSTRACT FROM AUTHOR]

Bitcoin is a cryptocurrency and a financial transaction network implemented using blockchain technology. Users in the Bitcoin network use pseudonymous Bitcoin addresses and conduct transactions with others without revealing their real identities. In order to further enhance their privacy and convenience, users often use a large number of different addresses. In this paper, we analyze different patterns of transactions occurring in the Bitcoin network in order to cluster addresses that share the same ownership. In order to evaluate the proposed clustering approach, Bitcoin addresses belonging to known entities are tagged and these are used in conjunction with the Gini impurity index to test the accuracy of the recovered identity-based clusters. The results show that our heuristic was able to detect relationships between Bitcoin addresses that were missed by the existing heuristics. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel method for the discovery of value functions for Markov decision processes (MDPs). This method, which we call value function discovery (VFD), is based on ideas from the evolutionary algorithm field. VFDs key feature is that it discovers descriptions of value functions that are algebraic in nature. This feature is unique, because the descriptions include the model parameters of the MDP. The algebraic expression of the value function discovered by VFD can be used in several scenarios, e.g., conversion to a policy (with one-step policy improvement) or control of systems with time-varying parameters. The work in this paper is a first step toward exploring potential usage scenarios of discovered value functions. We give a detailed description of VFD and illustrate its application on an example MDP. For this MDP, we let VFD discover an algebraic description of a value function that closely resembles the optimal value function. The discovered value function is then used to obtain a policy, which we compare numerically to the optimal policy of the MDP. The resulting policy shows near-optimal performance on a wide range of model parameters. Finally, we identify and discuss future application scenarios of discovered value functions. [ABSTRACT FROM PUBLISHER]

This paper explores supply chain systems coordination challenges in the presence of multiple heterogeneous risk sensitive retail buyers using the commonly seen markdown contract under both information symmetric and asymmetric settings. For each setting, we explore two scenarios. The first scenario allows the upstream manufacturer to freely set a separate contract to each risk averse retailer, whereas the second scenario specifies that the manufacturer has to grant the same contract to each risk averse retailer under the fair trade rule. We analytically show that the markdown contract which can achieve “perfect coordination” only exists in the first scenario (without the fair trade rule) under the information symmetric setting. For all the other scenarios, we find that perfect coordination cannot be achieved by the markdown contract, and hence we develop the computational algorithms to help identify the markdown contract parameter(s) which can achieve the “best possible coordination.” In addition, we reveal that the manufacturer’s risk attitude does significantly affect the achievability of perfect coordination. The findings of this paper also provide analytical evidence to show that the fair trade rule would do more harm than good for supply chain systems optimization under simple supply contracts. [ABSTRACT FROM PUBLISHER]

Role assignment is a critical element in the role-based collaboration process. There are many constraints to be considered when undertaking this task. This paper formalizes the group role assignment problem when faced with the constraint of conflicting agents, verifies the benefits of solving the problem, proves that such a problem is a subproblem of the extended integer linear programming (x-ILP) problem, proposes a practical approach to the solution, and assures performance based on the results of experiments. The contributions of this paper include: 1) formalization of the proposed problem; 2) verification of the benefit achieved by avoiding conflicts in role assignment through simulation; 3) theoretical proof that conflict avoidance is a subproblem of the x-ILP problem that is nonpolynomial-complete; and 4) a practical solution based on the IBM ILOG CPLEX optimization package (ILOG) and verification of the scale of problems that can be solved with ILOG. The proposed approach is validated by simulation experiments. Its efficiency is verified by comparison with the previous exhaustive search-based approach. [ABSTRACT FROM PUBLISHER]

In this correspondence paper, an exact method is developed to guarantee asymptotic stability of a bilateral teleoperation system that is subjected for a time-delayed communication. This extends the prior art of searching for the maximum upper bound of time delay. In order to improve the flexibility in controller design and obtain better performance, a fractional-order PD ^ \alpha controller is proposed. The exactly stable regions of delays are explored for both integral-order and fractional-order controllers. Compared with conditions in most previous works which are deduced by the Lyapunov–Krasovskii functional and rely on the solution of some linear matrix inequalities, the stability conditions proposed in this paper are established from the frequency domain point of view, and thus, the results are not only sufficient but also necessary. To illustrate accuracy of the conditions, they are simulated on a delayed teleoperation system composed of a pair of robots. [ABSTRACT FROM PUBLISHER]

In network-centric warfare, the interconnections among various combat resources enable an advanced operational pattern of cooperative engagement. The operational effectiveness and outcome strongly depends on the reasonable utilization of available sensors and weapons. In this paper, a mathematical model for the coallocation of sensors and weapons is built, taking into account the interdependencies between weapons and sensors, the resource constraints, the capability constraints, as well as the strategy constraints. A marginal-return-based constructive heuristic (MRBCH) is proposed to solve the formulated sensor–weapon–target assignment (S-WTA) problem. MRBCH exploits the marginal return of each sensor–weapon–target triplet and dynamically updates the threat value of all targets. It relies only on simple lookup operations to choose each assignment triplet, thus resulting in very low computational complexity. For performance evaluation, we build a general Monte Carlo simulation-based S-WTA framework. Furthermore, we employ a random sampling method and an extension of the state-of-the-art algorithm Swt_opt as competitors. The computational results show that MRBCH consistently performs very well in solving S-WTA instances of different scales, and it can generate assignment schemes much more efficiently than its competitors. [ABSTRACT FROM AUTHOR]

In this paper, a general-purpose double auction mechanism that is specifically relevant for energy transactions among competing buyers and sellers, i.e., agents, in a microgrid has been investigated. This auction algorithm is implemented by means of a microgrid controller (MC), and is individually rational, weakly budget balanced, and efficient (social welfare maximizing). Furthermore, the mechanism does not violate any physical grid constraints and does not require agents’ private information. These characteristics of the auction are accomplished by a suitable surrogate objective function which implicitly maximizes the social welfare while the agents place bids in a selfish manner and can sell or procure energy. The sellers of energy are modeled as prosumers that are equipped with photovoltaic panels as well as advanced smart meters and are willing to participate in selling their excess energy due to saturation in their energy consumption utility curves. Buyers of energy are assumed to be conventional consumers with smart meter capabilities to communicate with the MC and place bids to get optimal allocations. Buyers aim to maximize their energy usage utility plus their payment amount whereas sellers maximize their own energy consumption utility plus the reimbursement that they receive for selling their excess generation. Furthermore, the mechanism allows the possibility of sellers’ coalition where a fair redistribution of the energy supply is possible in a competitive market with less demand and more supply. [ABSTRACT FROM AUTHOR]

In multiple attribute decision making (MADM), strategic weight manipulation is understood as a deliberate manipulation of attribute weight setting to achieve a desired ranking of alternatives. In this paper, we study the strategic weight manipulation in a group decision making (GDM) context with interval attribute weight information. In GDM, the revision of the decision makers’ original attribute weight information implies a cost. Driven by a desire to minimize the cost, we propose the minimum cost strategic weight manipulation model, which is achieved via optimization approach, with the mixed 0–1 linear programming model being proved appropriate in this context. Meanwhile, some desired properties to manipulate a strategic attribute weight based on the ranking range under interval attribute weight information are proposed. Finally, numerical analysis and simulation experiments are provided with a twofold aim: 1) to verify the validity of the proposed models and 2) to show the effects of interval attribute weights information and the unit cost, respectively, on the cost to manipulate strategic weights in the MADM in a group decision context. [ABSTRACT FROM AUTHOR]

Passive indoor localization is important. Unlike active localization techniques, it does not require for users to carry measuring devices, e.g., smart phones. Thus, it is widely used in applications such as security, smart housing, object tracking, etc. However, in real-world applications, the passive localization accuracy is limited due to the environment noises, multipath effect, etc. To address those problems, in this paper, we propose to use channel state information (CSI) instead. Specifically, we make the following contributions: 1) we design a CSI-based passive indoor localization system; 2) we develop a Naive Bayes classifier enhanced with confidence level information; and 3) we demonstrate the effectiveness of our technique using real-world deployments. The experimental results show that our technique can achieve more than 86% accuracy on average and at least 15% better than the baseline Naive Bayes classifier. [ABSTRACT FROM AUTHOR]

Control of a large-scale automated manufacturing system is an important and challenging issue. Its discrete event system model represented by Petri nets tends to become highly complicated in structure, especially when there exist uncontrollable or unobservable events. The existing approaches are nontrivial to design both efficient and maximally permissive supervisors to impose constraints on an overall system. In this paper, instead of considering the control problem from an overall system perspective, we intend to transform an overall control problem into the one designing multiple controllers in parallel, each of which is much simpler in structure. A Petri net structure is decomposed via integer linear programming or a polynomial decomposition method to obtain multiple state-machine subnets that constitute a decentralized system. A necessary and sufficient condition for preserving the equivalence in terms of states and behaviors between the overall system and its decentralized version is reported. Constraints representing control requirements are further converted and enforced in the respective subnets. Then, supervisors are generated via a generalized mutual exclusion constraint method. By considering the deviations between the subnet control and overall control, this paper formulates a communication mechanism to guarantee that the decentralized system runs in an appropriate manner. Finally, two examples are presented to demonstrate the proposed approach. [ABSTRACT FROM AUTHOR]

This correspondence paper is concerned with observer design problems for a class of unknown input switched descriptor systems under average dwell time switching signals. The switched descriptor system under consideration is first transformed into a general switched linear system, and then a switched reduced-order observer that can asymptotically estimate the system state without suffering the influence from the unknown inputs is developed. Besides, a high-order sliding mode observer is introduced to obtain the estimations of the output derivatives by using the system measured outputs. On the basis of estimations of the states and output derivatives, an unknown input reconstructing method is provided. Finally, two examples are presented to verify the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

This paper evaluates the applicability of command governor (CG) strategies to the optimal management of drinking water supply systems (DWSSs) in both centralized and distributed ways. It will be shown that CG approaches provide an adequate framework for addressing the management of these large-scale interconnected systems in the presence of periodically time-varying disturbances (water demands) that can be anticipated by using time-series forecasting approaches. The proposed centralized and distributed CG schemes are presented, discussed, and compared when applied to the management of DWSS considering the same set of operational goals in all cases. This paper illustrates the effectiveness of all strategies using the Barcelona DWSS as a case study and highlighting the advantages of each approach. [ABSTRACT FROM PUBLISHER]

The vast majority of user authentication in digital applications is based on alphanumeric passwords. Yet, due to severe problems that might arise with this approach, various efforts have been made in the last decade to replace this authentication paradigm. One candidate for the prospective paradigm shift might be found in the field of graphical passwords. The present paper introduces a novel framework for user authentication based on freehand sketches. The basic idea is that during the registration phase a user draws an arbitrary sketch in a specific drawing canvas (rather than typing a password). Registered users can then be authenticated whenever they are able to reproduce their personal sketch with sufficient precision. The major challenge of such a system is twofold. First, it has to provide a certain degree of error-tolerance such that the authentication of genuine users can be smoothly accomplished. Second, the system should detect even subtle forgeries and reject possible intruders. The main contributions of this paper are as follows. First, we formally represent the underlying sketches by means of strings and present a general authentication algorithm that is based on structural pattern recognition. Second, we present a novel cost model that is particularly useful in conjunction with string matching. Third, by means of an exhaustive empirical investigation using both random and skilled forgeries (stemming from several hundreds of users) we empirically confirm the feasibility of this particular authentication framework in a real-world scenario. [ABSTRACT FROM PUBLISHER]

Many problems in the modern world have a decentralized and distributed nature. Irregular cellular learning automata (ICLA) is a powerful mathematical model for decentralized problems and applications. Convergence of ICLA to a compatible point is very important because this convergence can provide efficient solutions for the problems. The local rule of ICLA can play a key role in this convergence. A local rule that simply rewards or punishes learning automata just based on the response of environment and actions of neighbors does not guarantee convergence of ICLA to a compatible point. In this paper, we present a new local rule that guarantees convergence to a compatible point. Formal proofs for the convergence are provided and results of the conducted experiments support our theoretical findings. [ABSTRACT FROM AUTHOR]