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The integration of semantic technologies and TV services is a substantial innovation to improve the services to users in an environment that is extended beyond the fixed home environment. But currently, this integration is mainly limited to provide personalized recommendation services and systems by matching user static preferences. Designing and development of interactive TV (iTV) applications using semantic technologies are not realized yet. In this work, we explore the potential of introduction semantic technologies and smart spaces in design and development of iTV applications. We use an example scenario to show how future iTV applications include the mesh-up of information from different sources. We proposed a methodology and show how ontology-driven approach can help to design and develop these iTV applications. We demonstrate the suitability of our ontology-driven application development tools and rule-based approach for the development of highly dynamic context-aware iTV applications. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we present a general framework for estimating the energy consumption of an embedded Java virtual machine (JVM). We have designed a number of experiments to find the constant overhead and establish an energy consumption cost for individual Java opcodes for two JVMs. The results show that there is a basic constant overhead for every Java program, and that a subset of Java opcodes have an almost constant energy cost. We also show that memory access is a crucial energy consumption component. [Copyright &y& Elsevier]

The main aim of this article is to discuss how the functional and the object-oriented views can be inter-played to represent the various modeling perspectives of embedded systems. We discuss whether the object-oriented modeling paradigm, the predominant one to develop software at the present time, is also adequate for modeling embedded software and how it can be used with the functional paradigm. More specifically, we present how the main modeling tool of the traditional structured methods, data flow diagrams, can be integrated in an object-oriented development strategy based on the unified modeling language. The rationale behind the approach is that both views are important for modeling purposes in embedded systems environments, and thus a combined and integrated model is not only useful, but also fundamental for developing complex systems. The approach was integrated in a model-driven engineering process, where tool support for the models used was provided. In addition, model transformations have been specified and implemented to automate the process. We exemplify the approach with an IPv6 router case study. [ABSTRACT FROM AUTHOR]

The B method is one of the few formal methods with commerciallly available tool support. It has been succesfully applied in the development of several industrial applications. In this paper we present an application of the B-Method to a new domain, namely language development. We have developed an approach for translating a language with a structural operational semantics into B specifications. The language semantics is specified in separate B machines, that act as an interpreter for programs in the language. The programs, represented as abstract syntax trees, can then automatically be translated into B specifications. We have used the approach in the context of Rialto, a language for multiple models of computation. We show how Rialto programs are translated into B machines, and how the different scheduling policies that are used to interpret concurrency can be also be represented as B machines. We also discuss some shortcomings of the approach and the B-Tools that we have available. [ABSTRACT FROM AUTHOR]

Highlights: [•] Human activity modelling ontologies for imprecise knowledge reasoning are inexistent. [•] We develop a fuzzy ontology to deal with uncertain or vague knowledge representation. [•] We show examples of use of the ontology in different scenarios within office domain. [•] We create a dataset with rules and queries and compare fuzzy and crisp reasoning. [•] We demonstrate that fuzzy ontological reasoning improves accuracy and it is scalable. [Copyright &y& Elsevier]

Describing user activity plays an essential role in ambient intelligence. In this work, we review different methods for human activity recognition, classified as data-driven and knowledge-based techniques.We focus on context ontologieswhose ultimate goal is the tracking of human behavior. After studying upper and domain ontologies, both useful for human activity representation and inference, we establish an evaluation criterion to assess the suitability of the different candidate ontologies for this purpose. As a result, any missing features, which are relevant for modeling daily human behaviors, are identified as future challenges. [ABSTRACT FROM AUTHOR]

Background: Self-quantification of health parameters is becoming more popular; thus, the validity of the devices requires assessments. The aim of this study was to evaluate the validity of Fitbit One step counts (Fitbit Inc., San Francisco, CA, USA) against Actigraph wActisleep-BT step counts (ActiGraph, LLC, Pensacola, FL, USA) for measuring habitual physical activity among children. Design: The study was implemented as a cross-sectional experimental design in which participants carried two waist-worn activity monitors for five consecutive days. Methods: The participants were chosen with a purposive sampling from three fourth grade classes (9-10 year olds) in two comprehensive schools. Altogether, there were 34 participants in the study. From these, eight participants were excluded from the analysis due to erroneous data. Primary outcome measures for step counts were Fitbit One and Actigraph wActisleep-BT. The supporting outcome measures were based on activity diaries and initial information sheets. Classical Bland-Altman plots were used for reporting the results. Results: The average per-participant daily difference between the step counts from the two devices was 1937. The range was [116, 5052]. Fitbit One gave higher step counts for all but the least active participant. According to a Bland-Altman plot, the hourly step counts had a relative large mean bias across participants (161 step counts). The differences were partially explained by activity intensity: higher intensity denoted higher differences, and light intensity denoted lower differences. Conclusions: Fitbit One step counts are comparable to Actigraph step counts in a sample of 9-10-year-old children engaged in habitual physical activity in sedentary and light physical activity intensities. However, in moderate-to-vigorous physical activity, Fitbit One gives higher step counts when compared to Actigraph. [ABSTRACT FROM AUTHOR]

Many-core systems provide a great performance potential with the massively parallel hardware structure. Yet, these systems are facing increasing challenges such as high operating temperatures, high electrical bills, unpleasant noise levels due to active cooling and high battery drainage in mobile devices; factors caused directly by poor energy efficiency. Furthermore by pushing the power beyond the limits of the power envelope, parts of the chip cannot be used simultaneously – a phenomenon referred to as “dark silicon”. Power management is therefore needed to distribute the resources to the applications on demand. Traditional power management systems have usually been agnostic to the underlying hardware, and voltage and frequency control is mostly driven by the workload. Static schedules, on the other hand, can be a preferable alternative for applications with timing requirements and predictable behavior since the processing resources can be more precisely allocated for the given workload. In order to efficiently implement power management in such systems, an accurate model is important in order to make the appropriate power management decisions at the right time. For making correct decisions, practical issues such as latency for controlling the power saving techniques should be considered when deriving the system model, especially for fine timing granularity. In this paper we present an accurate energy model for many-core systems which includes switching latency of modern power saving techniques. The model is used when calculating an optimal static schedule for many-core task execution on systems with dynamic frequency levels and sleep state mechanisms. We derive the model parameters for an embedded processor with the help of benchmarks, and we validate the model on real hardware with synthetic applications that model streaming applications. We demonstrate that the model accurately forecasts the behavior on an ARM multicore platform, and we also demonstrate that the model is not significantly influenced by variances in common type workloads. [ABSTRACT FROM AUTHOR]

A correction to the article "Exploiting smart spaces for interactive TV applications development" published in a previous issue is presented.

Human activity recognition is a key task in ambient intelligence applications to achieve proper ambient assisted living. There has been remarkable progress in this domain, but some challenges still remain to obtain robust methods. Our goal in this work is to provide a system that allows the modeling and recognition of a set of complex activities in real life scenarios involving interaction with the environment. The proposed framework is a hybrid model that comprises two main modules: a low level sub-activity recognizer, based on data-driven methods, and a high-level activity recognizer, implemented with a fuzzy ontology to include the semantic interpretation of actions performed by users. The fuzzy ontology is fed by the sub-activities recognized by the low level data-driven component and provides fuzzy ontological reasoning to recognize both the activities and their influence in the environment with semantics. An additional benefit of the approach is the ability to handle vagueness and uncertainty in the knowledge-based module, which substantially outperforms the treatment of incomplete and/or imprecise data with respect to classic crisp ontologies. We validate these advantages with the public CAD-120 dataset (Cornell Activity Dataset), achieving an accuracy of 90.1% and 91.07% for low-level and high-level activities, respectively. This entails an improvement over fully data-driven or ontology-based approaches. [ABSTRACT FROM AUTHOR]

Spatial locality of task execution is becoming important in future hardware platforms since the number of cores is steadily increasing. The large amount of cores requires an intelligent power manager and the high chip and core density requires increased thermal awareness to avoid thermal hotspots on the chip. This paper presents a lightweight task migration mechanism explicitly for distributed operating systems running on many-core platforms. As the distributed OS runs one scheduler on each core, the tasks are migrated between OS kernels within the same shared memory platform. The benefits, such as performance and energy efficiency, of task migration are achieved by re-locating running tasks on the most appropriate cores and keeping the overhead of executing such a migration sufficiently low. We investigate the overhead of migrating tasks on a distributed OS running both on a bus-based platform and a many-core NoC-with these means of measures, we can predict the task migration overhead and pinpoint the emerging bottlenecks. With the presented task migration mechanism, we intend to improve the dynamism of power and performance characteristics in distributed many-core operating systems. [ABSTRACT FROM AUTHOR]

Availability of domain-specific datasets is an essential problem in object detection. Datasets of inshore and offshore maritime vessels are no exception, with a limited number of studies addressing maritime vessel detection on such datasets. For that reason, we collected a dataset consisting of images of maritime vessels taking into account different factors: background variation, atmospheric conditions, illumination, visible proportion, occlusion and scale variation. Vessel instances (including nine types of vessels), seamarks and miscellaneous floaters were precisely annotated: we employed a first round of labelling and we subsequently used the CSRT tracker to trace inconsistencies and relabel inadequate label instances. Moreover, we evaluated the out-of-the-box performance of four prevalent object detection algorithms (Faster R-CNN, R-FCN, SSD and EfficientDet). The algorithms were previously trained on the Microsoft COCO dataset. We compared their accuracy based on feature extractor and object size. Our experiments showed that Faster R-CNN with Inception-Resnet v2 outperforms the other algorithms, except in the large object category where EfficientDet surpasses the latter. [ABSTRACT FROM AUTHOR]