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The purpose of this article is to propose and evaluate a novel system architecture for Smart City applications which uses ontology reasoning and a distributed stream processing framework on the cloud. In the domain of Smart City, often methodologies of semantic modeling and automated inference are applied. However, semantic models often face performance problems when applied in large scale.

The problem domain is addressed by using methods from Big Data processing in combination with semantic models. The architecture is designed in a way that for the Smart City model still traditional semantic models and rule engines can be used. However, sensor data occurring at such Smart Cities are pre-processed by a Big Data streaming platform to lower the workload to be processed by the rule engine.

By creating a real-world implementation of the proposed architecture and running simulations of Smart Cities of different sizes, on top of this implementation, the authors found that the combination of Big Data streaming platforms with semantic reasoning is a valid approach to the problem.

In this article, real-world sensor data from only two buildings were extrapolated for the simulations. Obviously, real-world scenarios will have a more complex set of sensor input values, which needs to be addressed in future work.

The simulations show that merely using a streaming platform as a buffer for sensor input values already increases the sensor data throughput and that by applying intelligent filtering in the streaming platform, the actual number of rule executions can be limited to a minimum.

The purpose of this paper is to propose an automatic method to generate semantic annotations of football transfer in the news. The current automatic news integration systems on the Web are constantly faced with the challenge of diversity, heterogeneity of sources. The approaches for information representation and storage based on syntax have some certain limitations in news searching, sorting, organizing and linking it appropriately. The models of semantic representation are promising to be the key to solving these problems.

The approach of the author leverages Semantic Web technologies to improve the performance of detection of hidden annotations in the news. The paper proposes an automatic method to generate semantic annotations based on named entity recognition and rule-based information extraction. The authors have built a domain ontology and knowledge base integrated with the knowledge and information management (KIM) platform to implement the former task (named entity recognition). The semantic extraction rules are constructed based on defined language models and the developed ontology.

The proposed method is implemented as a part of the sport news semantic annotations-generating prototype BKAnnotation. This component is a part of the sport integration system based on Web Semantics BKSport. The semantic annotations generated are used for improving features of news searching – sorting – association. The experiments on the news data from SkySport (2014) channel showed positive results. The precisions achieved in both cases, with and without integration of the pronoun recognition method, are both over 80 per cent. In particular, the latter helps increase the recall value to around 10 per cent.

This is one of the initial proposals in automatic creation of semantic data about news, football news in particular and sport news in general. The combination of ontology, knowledge base and patterns of language model allows detection of not only entities with corresponding types but also semantic triples. At the same time, the authors propose a pronoun recognition method using extraction rules to improve the relation recognition process.

The purpose of this paper is to enhance the quality of new reducing sentence in sentence-generation-based summarizing method by establishing consequence relationship between two action, state or process Vietnamese sentences.

First, types of pairs of Vietnamese sentences based on presupposition about the consequence relationship is classified: the verb indicating action or state at the first sentence is considered as the consequence of the verb indicating action, state or process at the second sentence. Then main predicates in Discourse Representation Structure – a logical form which represents the semantic of a given pair of sentences – is analyzed and inner- and inter-sentential relationships are determined. The next step is to generate the syntactic structure of the new reducing sentence. Finally, a combination with the built set of lexicons is done to complete the new meaning-summarizing Vietnamese sentence.

This method makes the new meaning-summarizing Vietnamese sentence satisfy two requirements: summarize the semantic of the given pair of Vietnamese sentences and have naturalism in common Vietnamese communication. In addition, it is possible to extend the method and apply for the purpose of summarizing the more complex Vietnamese paragraphs as well as paragraphs in other languages.

At the first step, only inter-sentential consequence relationship is considered and this is applied to the limit types of pairs of Vietnamese sentences which have a simple structure.

This study presents improvements in sentence-generation-based summarization method to enhance the quality of new meaning-summarizing Vietnamese sentences. This method proves effective in summarizing the considered pairs of sentences.

The purpose of this paper is to determine the effectiveness of using fully homomorphic encryption (FHE) to preserve the privacy of biometric data in an authentication system. Biometrics offers higher accuracy for personal recognition than traditional methods because of its properties. Biometric data are permanently linked with an individual and cannot be revoked or cancelled, especially when biometric data are compromised, leading to privacy issues.

By reviewing current approaches, FHE is considered as a promising solution for the privacy issue because of its ability to perform computations in the encrypted domain. The authors studied the effectiveness of FHE in biometric authentication systems. In doing so, the authors undertake the study by implementing a protocol for biometric authentication system using iris.

The security analysis of the implementation scheme demonstrates the effectiveness of FHE to protect the privacy of biometric data, as unlimited operations can be performed in the encrypted domain, and the FHE secret key is not shared with any other party during the authentication protocol.

The use of malicious model in the design of the authentication protocol to improve the privacy, packing methods and use of low-level programming language to enhance performance of the system needs to be further investigated.

The main contributions of this paper are the implementation of a privacy-preserving iris biometric authentication protocol adapted to lattice-based FHE and a sound security analysis of authentication and privacy.

The purpose of this paper is to review the state-of-the-art in adaptive user interface systems by studying their historical development over the past 20 years. Moreover, this paper contributes with a specific model combining three main entities (users, context and devices) that have been demonstrated to be always represented in these environments. Novel concepts that should be taken into account in these systems are also presented.

The authors first provide a review and a comparison of current user interface adaptive systems. Next, the authors detail the most significant models and the set of techniques used to, finally, propose a novel model based on the studied literature.

Literature solutions for adaptive user interface systems tend to be very domain dependant. This situation restricts the possibility of sharing and exporting the information between such systems. Furthermore, the studied approaches barely highlight the dynamism of these models.

The paper is a review of adaptive user interface systems and models. Although there are several reviews in this area, there is a lack of research for modelling users, context and devices simultaneously in this domain. The paper also presents several significant concepts that should be taken into account to bring an adaptive and dynamic perspective to the studied models.

Scalability is a fundamental problem in mobile ad hoc networks (MANETs), where network topology includes large number of nodes and demands a large number of packets in network that characterized by dynamic topologies, existence of bandwidth constrained, variable capacity links, energy constraint and nodes are highly prone to security threats. The key purpose of this paper is to overview the efficiency of the proposed clustering scheme for large-scale MANETs and its performance evaluation and especially in the case of a large number of nodes in the network.

Designing clustering schemes for MANETs, which are efficient and scalable in the case of large number of mobile nodes, has received a great attention in the last few years. It is widely used to improve resources management, hierarchical routing protocol design, quality of service, network performance parameters such as routing delay, bandwidth consumption, throughput and security. MANETs are characterized by limited wireless bandwidth, nodes mobility that results in a high frequency of failure regarding wireless links, energy constraint and nodes are highly prone to security threats. Due to all these features, the design of a scalable and efficient clustering scheme is quite complex. Many clustering schemes have been proposed to divide nodes into clusters, focusing on different metrics and purposes.

To the best of the author's knowledge, the different proposed clustering schemes are not scalable when the network size increases to a very large number. The paper presents the clustering scheme in detail and its performance evaluation by simulating MANETs composed of a large number of mobile nodes. The authors compare the performance of the scheme with a number of existing clustering schemes such as lowest-ID, highest degree, and weighted clustering algorithm, based on a number of performance metrics. Simulation results show that the scheme performs better than other clustering schemes, based on the performance metrics considered, for large-scale MANETs.

This paper addresses the problem of scalability in MANETs when there are high numbers of node in the network. The paper analyses the performance of the proposed clustering scheme for large-scale MANETs. The obtained results show that the different proposed clustering schemes do not allow the scalability when the network size is very large. The scheme supports scalability efficiently when the number of nodes increases in the network (more than 2,000 nodes).

The authors present a low-cost unmanned aerial vehicle (UAV) for autonomous flight and navigation in GPS-denied environments using an off-the-shelf smartphone as its core on-board processing unit. Thereby, the approach is independent from additional ground hardware and the UAV core unit can be easily replaced with more powerful hardware that simplifies setup updates as well as maintenance. The paper aims to discuss these issues.

The UAV is able to map, locate and navigate in an unknown indoor environment fusing vision-based tracking with inertial and attitude measurements. The authors choose an algorithmic approach for mapping and localization that does not require GPS coverage of the target area; therefore autonomous indoor navigation is made possible.

The authors demonstrate the UAVs capabilities of mapping, localization and navigation in an unknown 2D marker environment. The promising results enable future research on 3D self-localization and dense mapping using mobile hardware as the only on-board processing unit.

The proposed autonomous flight processing pipeline robustly tracks and maps planar markers that need to be distributed throughout the tracking volume.

Due to the cost-effective platform and the flexibility of the software architecture, the approach can play an important role in areas with poor infrastructure (e.g. developing countries) to autonomously perform tasks for search and rescue, inspection and measurements.

The authors provide a low-cost off-the-shelf flight platform that only requires a commercially available mobile device as core processing unit for autonomous flight in GPS-denied areas.

Coexistence of various wireless access networks and the ability of mobile terminals to switch between them make an optimal selection of serving networks for multicast groups a challenging problem. Since optimal network selection requires large dimensions of data to be collected from several network locations and sent between several network components, the scalability can easily become a bottleneck in large-scale systems. Therefore, reducing data exchange within heterogeneous wireless networks is important. The paper aims to discuss these issues.

The authors study the decision-making process and the data that need to be sent between different network components. To analyze the operation of the wireless heterogeneous network, the authors built a mathematical model of the network. The objective is defined as a minimization of multicast streams in the system. To evaluate the heuristic solutions, the authors define the upper and lower bounds to their operation.

The proposed heuristic solutions substantially reduce the usage of bandwidth in mobile networks and exchange of information between the network components.

The authors proposed the approach that allows network selection in a decentralized manner with only limited information shared among the decision makers. The authors studied how different sets of information available to decision makers influenced the performance of the system. The work also investigates the usage of multiple paths for multicast in heterogeneous mobile environments.