ABSTRACTCurrently, security breaches in public places like airports and official buildings are a major concern by both governmental and corporative organizations. In these situations, X-ray devices must scan a vast amount of baggage in a short time frame. Hence, deploying scanners that automatize the task of detecting suspicious artefacts becomes of vital importance to prevent from threats. In this paper we present FORTIFIER, a formal distributed framework designed to detect suspicious artefacts. This approach consists in the integration of several image detection algorithms executed in a distributed environment, which are aimed to detect a wide spectrum of weapons like guns, knifes and bombs. The main core of our proposed framework for recognizing suspicious artefacts is divided in different phases, where each one is modelled with a specific finite state machine (FSM). Several FSMs are combined to detect different artefacts. We also present a case of study where some performance experiments are carried out for analysing the scalability of FORTIFIER. Initially, FORTIFIER is deployed in a single cloud environment. Once the main features that have a significant impact on the overall system performance are analysed, our proposed framework is deployed in a multi-cloud environment.

ABSTRACTIn recent years, the emerging development of Internet of things (IoT) has created new opportunities for IoT service provider, device manufacturer and customer (end user). In agriculture, employing IoT can help reducing manpower as well as standardizing agricultural processes, thus increasing the benefit for customer who does agricultural business. Beside the opportunities, there are also issues while doing maintenance for an agricultural IoT system. One of them is how to automate the process to detect faults of IoT devices in a large deployment area. While previous studies focus on investigating algorithms as well as theoretical model to detect faults for specific IoT devices, automatically expanding of IoT management process for detecting faults of sensor/actuator has not been investigated yet. To achieve this, we should be able to deal with the relationship between IoT device manufacturer, service provider and customer. In this study, we conceptualize the knowledge of IoT devices, management process and fault detection requirement. To realize this model, we introduce a runtime system to expand the IoT management process for detecting sensor/actuator faults which is able to provide the above features. We do several experiments to show the effectiveness and usefulness of the proposed solution.

ABSTRACTMining closed high utility itemsets (CHUIs) serves as a compact and lossless representation of high utility itemsets (HUIs). CHUIs and their generators are useful in analytical and recommendation systems. In this paper, we introduce a lattice approach to extract CHUIs and their generators from a set of HUIs quickly. The experimental results show that mining CHUIs and their generators from a lattice of HUIs is efficient in both runtime and memory usage.

ABSTRACTIn general, a common approach to enhance the diversity, an important criterion of intelligent collectives, is to enlarge the collective cardinality. The objective of this study was to analyse some issues with collective cardinality in the effectiveness of collective performance. For this aim, first, we investigate the impact of collective cardinality on collective performance. Subsequently, a comparison among collective performance measures is presented. The simulation results have qualified the positive impact of collective cardinality on the effectiveness of collective performance. Moreover, some issues on processing large collectives; some further challenges of the impact of diversity on collective prediction are discussed.

ABSTRACTIndoor navigation provides the positioning service to the indoor users, where the GPS coverage is not available. The challenges for most signal-based indoor positioning systems are the unpredictable signal propagation caused by the complex building interiors, and the dynamic of the environment caused by the peoples' movements. However, most existing systems made no assumption about the quality of their predictions, which is crucial in such noisy indoor environment. To address this challenge, this article proposes a confidence measure to reflect the uncertainty of the positioning prediction. More importantly, the users may control the size of the prediction set by setting the confidence level tailoring to their personal requirement. The proposed approach in this article has been validated in three real office buildings with challenging indoor environments, which indicated that it performed up to 20% more accurate than traditional Naïve Bayes and Weighted K-nearest neighbours (W-KNN) algorithms.