Networks are used as highly expressive tools in different disciplines. In recent years, the analysis and mining of temporal networks have attracted substantial attention. Frequent pattern mining is considered an essential task in the network science literature. In addition to the numerous applications, the investigation of frequent pattern mining in networks directly impacts other analytical approaches, such as clustering, quasi-clique and clique mining, and link prediction. In nearly all the algorithms proposed for frequent pattern mining in temporal networks, the networks are represented as sequences of static networks. Then, the inter- or intra-network patterns are mined. This type of representation imposes a computation-expressiveness trade-off to the mining problem. In this paper, we propose a novel representation that can preserve the temporal aspects of the network losslessly. Then, we introduce the concept of constrained interval graphs ( CIGs). Next, we develop a series of algorithms for mining the complete set of frequent temporal patterns in a temporal network data set. We also consider four different definitions of isomorphism for accommodating minor variations in temporal data of networks. Implementing the algorithm for three real-world data sets proves the practicality of the proposed approach and its capability to discover unknown patterns in various settings.

Networks are known as perfect tools for modeling various types of systems. In the literature of network mining, frequent subgraph mining is considered as the essence of mining network data. In this problem, the dataset is composed of networks representing multiple independent systems or one system at multiple time stamps. The cores of mining frequent subgraphs are graph and subgraph isomorphism. Due to the complexities of these problems, the frequent subgraph mining algorithms proposed in the literature employ various heuristics for candidate generation, duplicate subgraphs pruning, and support computation. In this survey, we provide a classification of proposed algorithms in the literature. The algorithms for static networks have found numerous applications. Therefore, these algorithms will be reviewed in detail. Besides, it is discussed that consideration of temporality of data can impact the derived insight and attracted substantial attention in recent years. However, prior surveys have not comprehensively examined the algorithms of frequent subgraph mining in a database of temporal networks represented as network snapshots. Therefore, the algorithms proposed for mining frequent subgraphs in temporal networks are reviewed. Moreover, most of the surveys have focused on main-memory algorithms. Here, we review disk-based, parallel, and distributed algorithms proposed for mining frequent subgraphs.

Sepsis is a devastating multi-stage health condition with a high mortality rate. Its complexity, prevalence, and dependency of its outcomes on early detection have attracted substantial attention from data science and machine learning communities. Previous studies rely on individual cellular and physiological responses representing organ system failures to predict health outcomes or the onset of different sepsis stages. However, it is known that organ systems' failures and dynamics are not independent events. In this study, we identify the dependency patterns of significant proximate sepsis-related failures of cellular and physiological responses using data from 12,223 adult patients hospitalized between July 2013 and December 2015. The results show that proximate failures of cellular and physiological responses create better feature sets for outcome prediction than individual responses. Our findings reveal the few significant proximate failures that play the major roles in predicting patients' outcomes. This study's results can be simply translated into clinical practices and inform the prediction and improvement of patients' conditions and outcomes.

Multi-agent systems and agent-oriented methodologies support analysis, characterization and development of complex software systems. These methodologies introduce different definitions for the essential components of multi-agent systems and cover different phases of the system development life cycle. Therefore, appropriate frameworks for evaluation and comparison of different methodologies would support developers to adopt the best methodology, or a combination of different methodologies, based on the project requirements. This review covers the system development phases and the main conceptual components in the context of multi-agent systems. Then, the evaluation frameworks proposed in the literature for comparison and evaluation of agent-oriented methodologies are reviewed. Evaluation frameworks proposed in the literature are categorized into three categories: methodology-based, phase-based and feature-based evaluation frameworks. The paper concludes with the agent-oriented methodologies’ usage challenges, their current limitations and potential future directions.

Conventional approaches for stability analysis of bilateral teleoperation systems assume that the human operator does not inject energy into the system and behaves in a passive manner. Does this assumption hold for various tasks the human operator may execute in a teleoperation context? To answer this question, in this paper we measure the endpoint impedance (inertia, viscosity, and stiffness) of the human arm during two tasks: (1) relaxed grasping of a haptic device while the device imposes position perturbations, and (2) rigid grasping of a haptic device (posture maintenance) while the device imposes force perturbations. The human arm impedance is identified as a 2 × 2 transfer function matrix and assessed for passivity over the frequency range characteristic of human motion. Our results agree with previous findings that the relaxed human arm behaves as a passive system. However, whether the rigid arm behaves as an active or passive system is found to depend on the magnitude of the force perturbations. We discuss why the passivity of the human operator is task dependent.

A bilateral teleoperation system includes a human operator and an environment, which make the system stability analysis complicated due to their unknown, time-varying and nonlinear nature. Unable to have exact models for the human operator and the environment, it is typically assumed that they are passive but otherwise arbitrary. In this paper, through a set of experiments, first we show that a human operator's relaxed arm is strictly passive while voluntary motions of the human operator's arm involve non-passive characteristics. Then, we adjust the passivity assumption of the human operator's arm (by tightening it for an input-strictly-passive arm and relaxing it for a non-passive arm) in order to enable a more precise stability analysis of the teleoperation system. Inspired by Llewellyn's absolute stability criterion, a powerful stability analysis approach is developed to investigate the stability of a two-port network when it is coupled to an input-strictly-passive or a non-passive termination. Although this new stability criterion is applicable to any two-port network system, we apply it to a position-error-based bilateral teleoperation system as a case study.

Stability of a haptic teleoperation system is influenced by the typically uncertain, time-varying and/or unknown dynamics of the operator and the environment. For a stability analysis that is independent of the operator and the environment dynamics, Llewellyn's absolute stability criterion proposes certain conditions on the two-port network representing the teleoperator (comprising the master, the controller and communication channel, and the slave) assuming that the terminations (i.e., the operator and the environment) are passive. These are less-than-accurate assumptions. It is desirable to extend Llewellyn's result to the cases where the operator or the environment is non-passive. This paper revisits Llewellyn's criterion and relaxes the assumption of passivity for one of the terminations. The possibly non-passive termination is realistically assumed to have a complex impedance with an upper or lower bound on its amplitude or real part, respectively. Although the proposed stability criteria are useful for any application of two-port network systems, we specifically apply them on bilateral teleoperation systems and find the stability conditions when the operator or the environment is not passive; this is a result that Llewellyn's absolute stability criterion cannot afford.

In a bilateral teleoperation system, conditions involving open-loop model parameters and controller parameters for ensuring teleoperator passivity are useful as control design guidelines to attain maximum teleoperation transparency (due to passivity/transparency tradeoffs). By teleoperator, we mean the teleoperation system excluding the human operator and the remote environment. The rationale behind considering teleoperator passivity instead of teleoperation system stability is that, unlike the former, the latter is influenced by the dynamics of the human operator and the remote environment, which are typically uncertain, time-varying, and/or nonlinear. In this paper, a condition for the passivity of a teleoperator is found when the teleoperation controllers are implemented in the discrete-time domain. Such as new passivity analysis is necessary because discretization causes energy leaks and does not necessarily preserve passivity. We show that the passivity criterion for the sampled-data teleoperator imposes a lower bound on the robot damping and upper bounds on the control gains and the sampling time. The criterion has been verified through computer simulations as well as experimental tests involving a bilateral teleoperation system consisting of a pair of Phantom Omni robots.