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Current opinion on undergraduate studies has led to a reformulation of teaching methodologies to base them not just on learning, but also on skills and competencies. In this approach, the teaching/learning process should accomplish both knowledge assimilation and skill development. Previous works demonstrated that a strategy that uses continuous evaluation is able to meet both objectives. However, those studies did not evaluate and quantify the additional effort required to implement such strategies. This paper evaluates the additional instructor effort required when implementing continuous evaluation in a first-year Computer Fundamentals course in the Computer Engineering degree program at the Technical University of Valencia, Spain. The experiment quantifies how instructor workload increases under different continuous evaluation strategies and how this affects the overall student grade. Both the "standard" continuous evaluation method and the intensive continuous evaluation method are analyzed, the latter being a proposal that builds upon the standard method by increasing the number of tests and examinations. The results obtained reveal that continuous evaluation improves student grades but that intensive continuous evaluation is liable to generate an excessive instructor overload without having a significant impact on student scores.

This paper presents the extension of a meta-model (MAM5) and a framework based on the model (JaCalIVE) for developing intelligent virtual environments. The goal of this extension is to develop augmented mirror worlds that represent a real and virtual world coupled, so that the virtual world not only reflects the real one, but also complements it. A new component called a smart resource artifact, that enables modelling and developing devices to access the real physical world, and a human in the loop agent to place a human in the system have been included in the meta-model and framework. The proposed extension of MAM5 has been tested by simulating a light control system where agents can access both virtual and real sensor/actuators through the smart resources developed. The results show that the use of real environment interactive elements (smart resource artifacts) in agent-based simulations allows to minimize the error between simulated and real system. [ABSTRACT FROM AUTHOR]

The inclusion of embedded sensors into a networked system provides useful information for many applications. A Distributed Control System (DCS) is one of the clearest examples where processing and communications are constrained by the client's requirements and the capacity of the system. An embedded sensor with advanced processing and communications capabilities supplies high level information, abstracting from the data acquisition process and objects recognition mechanisms. The implementation of an embedded sensor/actuator as a Smart Resource permits clients to access sensor information through distributed network services. Smart resources can offer sensor services as well as computing, communications and peripheral access by implementing a self-aware based adaptation mechanism which adapts the execution profile to the context. On the other hand, information integrity must be ensured when computing processes are dynamically adapted. Therefore, the processing must be adapted to perform tasks in a certain lapse of time but always ensuring a minimum process quality. In the same way, communications must try to reduce the data traffic without excluding relevant information. The main objective of the paper is to present a dynamic configuration mechanism to adapt the sensor processing and communication to the client's requirements in the DCS. This paper describes an implementation of a smart resource based on a Red, Green, Blue, and Depth (RGBD) sensor in order to test the dynamic configuration mechanism presented. [ABSTRACT FROM AUTHOR]

This paper is part of a study of intelligent architectures for distributed control and communications systems. The study focuses on optimizing control systems by evaluating the performance of middleware through quality of service (QoS) parameters and the optimization of control using Quality of Control (QoC) parameters. The main aim of this work is to study, design, develop, and evaluate a distributed control architecture based on the Data-Distribution Service for Real-Time Systems (DDS) communication standard as proposed by the Object Management Group (OMG). As a result of the study, an architecture called Frame-Sensor-Adapter to Control (FSACtrl) has been developed. FSACtrl provides a model to implement an intelligent distributed Event-Based Control (EBC) system with support to measure QoS and QoC parameters. The novelty consists of using, simultaneously, the measured QoS and QoC parameters to make decisions about the control action with a new method called Event Based Quality Integral Cycle. To validate the architecture, the first five Braitenberg vehicles have been implemented using the FSACtrl architecture. The experimental outcomes, demonstrate the convenience of using jointly QoS and QoC parameters in distributed control systems. [ABSTRACT FROM AUTHOR]

In the current context of the Internet of Things, embedded devices can have some intelligence and distribute both data and processed information. This article presents the paradigm shift from a hierarchical pyramid to an inverted pyramid that is the basis for edge, fog, and cloud-based architectures. To support the new paradigm, the article presents a distributed modular architecture. The devices are made up of essential elements, called control nodes, which can communicate to enhance their functionality without sending raw data to the cloud. To validate the architecture, identical control nodes equipped with a distance sensor have been implemented. Each module can read the distance to each vehicle and process these data to provide the vehicle's speed and length. In addition, the article describes how connecting two or more CNs, forming an intelligent device, can increase the accuracy of the parameters measured. Results show that it is possible to reduce the processing load up to 22% in the case of sharing processed information instead of raw data. In addition, when the control nodes collaborate at the edge level, the relative error obtained when measuring the speed and length of a vehicle is reduced by one percentage point. [ABSTRACT FROM AUTHOR]

Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. Smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). Smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2–4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices. [ABSTRACT FROM AUTHOR]

Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2-4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices.