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Grasping and object manipulation is a key element of intelligent behavior. Many innovative cyberphysical systems involve some kind of object grasp and manipulation, to the extent that grasping has been recognized as a critical technology for the next generation industrial systems. In this survey, we aim to draw a broad landscape of applications and current research trends and topics relating to grasping techniques and tools. Applications range from biomedical and surgical to industrial warehouse pick and place tasks, covering a wide range of spatial scales, from micro to macro scales. The resources involved and research lines under development include the latest computational intelligence tools as well as the research on new materials and devices for sensing and actuation. [ABSTRACT FROM AUTHOR]

Emergency Departments (ED) suffer heavy overload due to lack of primary attention service. Increasingly geriatric admissions pose specific problems contributing to this overload. A consequence is the increase of patient returning short time after discharge, i.e., readmissions, sometimes requiring hospitalization. In this latter case the patient problem was not solved in the first admission and the condition has aggravated. The time threshold defining a patient comeback as readmission varies; therefore we have considered several such thresholds in our prediction experiments. Prediction of hospitalization following ED readmission is posed over a heavily imbalanced class distribution, so we have considered several approaches to deal with imbalanced datasets and several base classifiers, as well as performance measures that enhance the critical comparison between approaches. Experimental works are carried out on real data from a university hospital in Santiago, Chile, corresponding to a period of 3 years, including pediatric and adult admissions to the ED. We achieve results that encourage the development of real life application of the data balancing and classification approach for prediction of hospitalization after readmission. [ABSTRACT FROM PUBLISHER]

Electronic Health Records are clinical information repositories that have been proposed primarily to provide access to all clinical data of a patient. They have been formally defined by a dual model composed of a reference model and an archetype model. Such dual approach allows semantic interoperability, thus making different systems understand each other. In this work we extend the current structure with a third Decisional Model that will allow reasoning over the embedded clinical contents. Such reasoning will be based on the reuse of the clinical experience gained by the corresponding clinical professionals during different decision procedures. [ABSTRACT FROM PUBLISHER]

We present a cooperative aerial robot system for the transportation of hoses. The hose–robot attachment makes the whole system physically interconnected but not rigid, so that control design becomes a difficult nonlinear optimization problem. The hose in quasistationary state can be modeled by sections of catenary curves. We use proportional integral derivative (PID) controllers for both quadrotor attitude and trajectory control, tuned by particle swarm optimization (PSO). In this work we test PSO minimizing an energy function to achieve the PID controller tuning for horizontal motion of quadrotor teams transporting hoses under different stress conditions. [ABSTRACT FROM PUBLISHER]

Social network online services are growing at an exponential pace, both in quantity of users and diversity of services; thus, the evaluation of trust in the interaction among users and toward the system is a central issue from the user point of view. Trust can be grounded in past direct experience or in the indirect information provided by trusted third-party users shaping the trustee reputation. When there is no previous history of interactions, the truster must resort to some form of prediction in order to establish Trust or Distrust on a potential trustee. In this study, we deal with the prediction of trust relationships on the basis of reputation information. Trust can be positive or negative (Distrust), hence, we have a two-class problem. Feature vectors for the classification have binary-valued components. Artificial neural network and statistical classifiers provide state-of-the-art results with these features on a benchmarking trust database. In this article, we propose the application of a sample generation method for the minority class in order to reduce some of the effect of class imbalance among Trust and Distrust classes. Specifically, the approach shows high resiliency to system growth. [ABSTRACT FROM PUBLISHER]

Decision recommendations are a set of alternative options for clinical decisions (e.g., diagnosis, prognosis, treatment selection, follow-up, and prevention) that are provided to decision makers by knowledge-based Clinical Decision Support Systems (k-CDSS) as aids. We propose to follow a “reasoning over domain” approach for the generation of decision recommendations by gathering and inferring conclusions from production rules. In order to rationalize our approach, we present a specification that will sustain the logic models supported in the knowledge bases we use for persistence. We introduce first the underlying knowledge model and then the necessary extensions that will convey toward the solution of the reported needs. The starting point of our approach is the proposition of Reflexive Ontologies (RO). Here, we go a step further, proposing an extension of RO that includes the handling and reasoning that production rules provide. Our approach speeds up the recommendation generation process. [ABSTRACT FROM PUBLISHER]

In this article we present the design and implementation of a mobile cardiac monitoring system oriented to patients in Phase II and III of cardiac rehabilitation. The complete monitoring system involves both hardware and software design perspectives. At the hardware level, we present a T-shirt with a 12-lead ECG system and an embedded inertial sensor for the monitoring of activity and energy expenditure. At the software level, a modular cloud platform performs data processing to detect relevant cardiac events and to provide advanced visualization capabilities. As a case study, we have implemented our system at the Cardiac Rehabilitation program at Donostia University Hospital (Spain). Finally, the validation of the 12-lead ECG recording system is also presented and discussed. [ABSTRACT FROM AUTHOR]

This article uses an innovative approach for the identification of groups of users in a social network by solving a graph coloring problem (GCP). We focus on social networks of users of a company's intranet. We want to identify user groups sharing the same behavior while interacting with the enterprise resource planning (ERP) as a means to verify that their roles, work practices, and positions held in the company are correct for each user. From the ERP logs, we generate the internal social network (ISN) graph where each vertex corresponds to a user and edges correspond to relations between users. Two users will be related if they frequently use the same programs. Other user attributes like the role assigned by the administrator, workplace location, or department offer secondary information to create the relations between users. After generating the graph we identify the users' groups solving a GCP on the ISN complementary graph. Moreover, ISN graph topology changes over time, so the composition of the groups of users may also change. New users can enter the intranet and exit it, and the behavior of the users may change. Therefore, we need approaches the quickly provide user clustering solutions to be able to adapt to the ISN time changes. We propose the gravitational swarm for graph coloring (GSGC) for this task and compare it with state-of-the-art approaches. We provide results on real-life (anonymized) cases. [ABSTRACT FROM AUTHOR]

Image interest point extraction and matching across images is a commonplace task in computer vision–based applications, across widely diverse domains, such as 3D reconstruction, augmented reality, or tracking. We present an empirical evaluation of state-of-the-art interest point detection algorithms measuring several parameters, such as efficiency, robustness to image domain geometric transformations—that is, similarity—affine or projective transformations, as well as invariance to photometric transformations such as light intensity or image noise. [ABSTRACT FROM AUTHOR]

Ontology processing is arguably a time-consuming process with high associated computational costs. Query actions constitute a crucial part of the reasoning process and are a primary source of time consumption. Reflexive ontologies (ROs) is a novel approach intended to reduce time consumption problems while providing a fast reaction from ontology-based applications. In this article we present the implementation of a knowledge-based clinical decision support system (CDSS) for the diagnosis of Alzheimer's disease, which was the benchmark used to evaluate the impact of RO in the overall performance of the system. The implementation details and the definition of the implementation methodology are exposed in this article, along with the results of the evaluation. Some novel techniques that aim to optimize the performance of ROs are also presented with highlights of the test application introduced in our previous work. [ABSTRACT FROM AUTHOR]

Single robot hose transport is a limit case of linked multicomponent robotic systems, where one robot moves the tip of a hose to a desired position. The interaction between the passive, flexible hose and the robot introduces highly nonlinear effects in the system's dynamics, requiring innovative control design approaches, such as reinforcement learning. This article improves previous approaches to this problem by introducing a novel reinforcement learning algorithm (TRQ-learning) and a new system state definition for the autonomous derivation of the hose–robot control algorithm. Computational experiments based on accurate geometrically exact dynamic splines hose dynamics simulations show the improvement obtained. [ABSTRACT FROM PUBLISHER]

The use of engineering standards in virtual engineering and their potential as models for the specification of a given domain's ontology are arguably unexplored. The importance of domain modeling in virtual engineering deals directly with the potential benefits that the semantic technologies may bring, allowing to discover implicit knowledge that can be beneficial for engineers. This work presents a state-of-the-art review of the technologies used in our approach, a successful case study where our methodology was applied, and the description and results of an experiment designed to provide a quantitative validation of our methodology. [ABSTRACT FROM PUBLISHER]

In this article we present an experience-based clinical decision support system (CDSS) for the diagnosis of Alzheimer's disease, which enables the discovery of new knowledge in the system and the generation of new rules that drive reasoning. In order to evolve an initial set of production rules given by medical experts we make use of the Set of Experience Knowledge Structure (SOEKS). An illustrative case of our system is also presented. [ABSTRACT FROM PUBLISHER]