Tasks management is a very interesting research topic for various application domains. Tasks may have the form of analytics or any other processing activities over the available data. One of the main concerns is to efficiently allocate and execute tasks to produce meaningful results that will facilitate any decision making. The advent of the Internet of Things (IoT) and Edge Computing (EC) defines new requirements for tasks management. Such requirements are related to the dynamic environment where IoT devices and EC nodes act and process the collected data. The statistics of data and the status of IoT/EC nodes are continuously updated. In this paper, we propose a demand- and uncertainty-driven tasks management scheme with the target to allocate the computational burden to the appropriate places. As the proper place, we consider the local execution of a task in an EC node or its offloading to a peer node. We provide the description of the problem and give details for its solution. The proposed mechanism models the demand for each task and efficiently selects the place where it will be executed. We adopt statistical learning and fuzzy logic to support the appropriate decision when tasks’ execution is requested by EC nodes. Our experimental evaluation involves extensive simulations for a set of parameters defined in our model. We provide numerical results and reveal that the proposed scheme is capable of deciding on the fly while concluding the most efficient allocation.

Video transcoding is the process of producing from an original (already encoded) input video sequence, multiple output sequences, each at potentially different bitrate, resolution and/or format. Transcoding is essential to support video delivery towards clients that use different players and have different network access capabilities. In the most basic scheme the input sequence is decoded and then re-encoded at the desired levels. Although significant research on fast transcoding schemes exists, the transcoding process is still computationally intensive. For this reason efficient scheduling methods that allocate resources to transcoding jobs are necessary in order to achieve good overall performance. Such policies usually aim at allocating transcoding jobs over co-located servers, thus, they typically overlook parameters such as network traffic. Motivated by the case of transcoding in the Cloud, in this paper we investigate the problem of scheduling transcoding jobs over a distributed system comprising of processing nodes that are geographically dispersed and might be whole clusters or even separate data centers. We propose algorithms to minimize both the inter-node network traffic and the intra-node energy consumption, while meeting the deadlines and quality requirements. Through simulation experiments we conclude on the best alternatives.

Dyscalculia is a neurodevelopmental disorder that affects the ability of a child to learn arithmetic. Dyscalculia appears despite normal intelligence, proper schooling, adequate environment, socioeconomic status and motivation. The first aim of the present research protocol was to construct a battery of tests that can be delivered by computer in order to screen children's arithmetic skills. Our second aim was to develop a Web application screener for dyscalculia that assesses children aged from 8-11 years old and that, to the best of our knowledge, does not exist. The hypothesis of the present study was that Greek students that are already diagnosed by paper-and-pencil tests as dyscalculic, will present lower performance and higher time latencies in the tasks of the aforementioned Web application screener. A total of sixty, right handed children (30 male and 30 female, age range 8-11 years old) participated in this study. The students with disorders in mathematics (N=30, 15 male and 15 female) had a statement of dyscalculia issued after assessment at a Centre of Diagnosis, Assessment and Support, as required by Greek Law. The comparison group (N=30) was formed by pupils who attended the same classes with dyscalculics, presented typical academic performance according to their teachers' ratings and had been matched for age and gender with the children with disorder in mathematics. Three tasks were used for evaluating children's arithmetic ability: a calculation task, a task that evaluated their skills in understanding mathematical terminology, and an arithmetic problem solving task. Statistical analysis revealed that children with dyscalculia had statistically significant lower mean scores of correct answers and larger time latencies in all tasks compared to their average peers that participated in the comparison group. In conclusion, it must be highlighted that the Web application screener for dyscalculia used in this study was found to be a feasible instrument for first-pass screening services and referral.

The multi-layered negative effects caused by pollutants released into the atmosphere as a result of fires served as the stimulus for the development of a system that protects the health of firefighters operating in the affected area. A collaborative network comprising mobile and stationary Internet of Things (IoT) devices that are furnished with gas sensors, along with a remote server, constructs a resilient framework that monitors the concentrations of harmful emissions, characterizes the ambient air quality of the vicinity where the fire transpires, adopting European Air Quality levels, and communicates the outcomes via suitable applications (RESTful APIs and visualizations) to the stakeholders responsible for fire management decision making. Different experimental evaluations adopting separate contexts illustrate the operation of the infrastructure.

The task of classification using intelligent methods and learning algorithms is a difficult task leading the research community on finding new classifications techniques to solve it. In this work, a new approach based on particle swarm optimization (PSO) clustering is proposed to perform the fuzzy cognitive map learning for classification performance. Fuzzy cognitive map (FCM) is a simple, but also powerful computational intelligent technique which is used for the adoption of the human knowledge and/or historical data, into a simple mathematical model for system modeling and analysis. The aim of this study is to investigate a new classification algorithm for the autism disorder problem by integrating the Particle Swarm Optimization method (PSO) in FCM learning, thus producing a higher performance classification tool regarding the accuracy of the classification, and overcoming the limitations of FCMs in the pattern analysis area.