The focus of this research is given to find a metric and determine the quality of the offered multimedia cloud services from an end users perception. The Quality of Experience (QoE) has been introduced to measure the quality features, which is used to determine the end-to-end user perceived quality of the used multimedia service. In this study, we have used students satisfaction survey, which provides direct subjective data on need, habits, and frequency of using different multimedia services. This data has been used for validation of the proposed Bayesian Network model for interactive estimation of the acceptability of multimedia cloud services based on the user preferences.

The increasing average age of the population in most industrialized countries imposes a necessity for developing advanced and practical services using state-of-the-art technologies, dedicated to personal living spaces. In this paper, we introduce a hierarchical distributed approach for home care systems based on a new paradigm known as Internet of Things (IoT). The proposed generic framework is supported by a three-level data management model composed of dew computing, fog computing, and cloud computing for efficient data flow in IoT based home care systems. We examine the proposed model through a real case scenario of an early fire detection system using a distributed fuzzy logic approach. The obtained results prove that such implementation of dew and fog computing provides high accuracy in fire detection IoT systems, while achieving minimum data latency.

Cloud computing consists of hardware and software resources, available on the Internet as a set of services for users. This technology aims to provide stable, reliable and encapsulated dynamic information and communication environment for end users to be able to simultaneously access shared resources that are available anywhere and at any time. The major benefit of cloud computing is used to improve the perception of quality for the client requests. Commonly in the communications industry, the term Quality of Experience (QoE) is used as a measure for the user perception of service from the user's point of view. In this research, we propose a classification of cloud-based services based on objective and subjective characteristics for perception of quality. The main contribution in this paper is a novel approach based on Bayesian modeling for efficient assessment of QoE perception for cloud-based services considering the level of interactivity, service complexity, usage domain, and multimedia-intensity.

Smart mobile devices are already used for everyday communication between people. They have become ubiquitous devices for receiving and sharing important information, but they poses limited capability of storing and processing multimedia content. Considering that mobile devices will always have these

Cloud environment is an efficient solution for both computing intensive and data intensive applications and it is a technology paradigm that is ideal for deploying a user centered mobile learning environment. Efficient use of cloud based services is a key component of successful task scheduling and

In this paper we propose a model for interactive, collaborative and creative multimedia distance learning in mCloud which goes beyond the existing solutions and provides personalized delivery of multimedia learning content. Our empirical results confirm that the larger groups of students and their usage of Web and mCloud lead to higher creativity of the students.

The 3D conformation of a protein in the space is the main factor which determines its function in living organisms. Due to the huge amount of newly discovered proteins, there is a need for fast and accurate computational methods for retrieving protein structures. Their purpose is to speed up the process of understanding the structure-to-function relationship which is crucial in the development of new drugs. There are many algorithms addressing the problem of protein structure retrieval. In this paper, we present several novel approaches for retrieving protein tertiary structures. We present our voxel-based descriptor. Then we present our protein ray-based descriptors which are applied on the interpolated protein backbone. We introduce five novel wavelet descriptors which perform wavelet transforms on the protein distance matrix. We also propose an efficient algorithm for distance matrix alignment named Matrix Alignment by Sequence Alignment within Sliding Window (MASASW), which has shown as much faster than DALI, CE, and MatAlign. We compared our approaches between themselves and with several existing algorithms, and they generally prove to be fast and accurate. MASASW achieves the highest accuracy. The ray and wavelet-based descriptors as well as MASASW are more accurate than CE.

An adaptation of one popular model of neural-networks algorithm (ART model) in the field of wireless sensor networks is demonstrated in this paper. The important advantages of the ART class algorithms such as simple parallel distributed computation, distributed storage, data robustness and auto-classification of sensor readings are confirmed within the proposed architecture consisting of one clusterhead which collects only classified input data from the other units. This architecture provides a high dimensionality reduction and additional communication savings, since only identification numbers of the classified input data are passed to the clusterhead instead of the whole input samples. We have adapted and implemented the FuzzyART neural-network algorithm and used it for initial clustering of the sensor data as a sort of pattern recognition. This adaptation was made specifically for MicaZ sensor motes by solving mainly problems concerning the small memory capacity of the motes. At the final clusterhead -server, the data are stored in a database and the results of the data processing are continuously presented in a classification graph.

Most of the current in-network data processing algorithms are modified regression techniques like multidimensional data series analysis. In our opinion, several algorithms developed within the artificial neural-networks tradition can be easily adopted to wireless sensor network platforms and meet the requirements for sensor networks like: simple parallel-distributed computation, distributed storage, data robustness and auto-classification of sensor readings. Lower communication costs and energy savings can be obtained as a consequence of the dimensionality reduction achieved by the neural-networks clustering algorithms. In this paper we present three possible implementations of the ART and FuzzyART neural-networks algorithms, which are unsupervised learning methods for categorization of the sensory inputs. They are tested on a data obtained from a set of several motes, equipped with several sensors each. Results from simulations of deliberately made faulty sensors show the data robustness of these architectures.

Some of the algorithms developed within the artificial neural-networks tradition can be easily adopted to wireless sensor network platforms and in the same time they can meet the requirements for sensor networks like: simple parallel distributed computation, distributed storage, data robustness and auto-classification of sensor readings. Dimensionality reduction, obtained simply from the outputs of the neural-networks clustering algorithms, leads to lower communication costs and energy savings. Two different data aggregation architectures are presented, with algorithms which use wavelets for initial data-processing of the sensory inputs and artificial neural-networks which use unsupervised learning methods for categorization of the sensory inputs. They are analyzed on a data obtained from a set of several motes, equipped with several sensors each. Results from deliberately simulated malfunctioning sensors show the data robustness of these architectures.

The authors extend the concept of distance education with adding a new service, that we call Resource Manager. The Resource Manager offers a possibility to the attendees to share different resources out of time and space boundaries. It enables geographically separated users to effectively facilitate remote access to various, presumably diverse, (real) resources. The Resource Manager has to provide an efficient sharing of resources among distance learning students according to the student profiles. We defined several protocols in XML suitable for communication between agents. The first experiments show that users are satisfied with the Resource Manager's usability. They found Resource Manager to be a very convenient service within the distance education systems.

A key requirement for a dynamic organization is the need for precise and formal knowledge management of the available information. The main objective of our work is to provide an agent based model of an agile integrated business system, more specifically, of the marketing sector of the company, where the knowledge has to be acquired, shared both internally and externally, updated, processed, applied, used and reused in order to find correlations among knowledge from different company sectors. Agents are able to suggest certain operations to the users in response to the change in the data structures they are responsible for maintaining. They can adopt using prior knowledge about company objectives and available customer data. An important role of the agent is to learn and change its practice according to previous approved behavior. The use of fuzzy set theory, in order to maintain and analyze the knowledge kept in the company profiles, as well, as to calculate the reliability of the suggested actions, showed the first positive experimental results. We applied this model for the operation of the Center for Distance Learning, for analysis of the different marketing campaigns and as a consultant for future campaigns.

A dynamic weighted voting scheme for consistency and recovery control of replicated files in distributed systems is presented. The purpose of a replicated file is to improve the availability of a logical file in the presence of site failures and network partitions. The accessible physical copies of a replicated file will be mutually consistent and behave as a single copy. The recovery scheme requires no manual intervention. The control scheme tolerates any number of site failures and network partitions as well as repairs. Correctness results are given.>

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