Abstract
We consider the problem of reliable gossip/epidemic dissemination in a network of n processes using push and pull algorithms. We generalize the random phone call model so that processes can refuse to push a rumor or answer pull requests. With this relaxation, we show that it is possible to disseminate a rumor to all processes with high probability using T(ln n) rounds of communication and only n + O(n/ln n) messages, both of which are optimal and achievable with push-pull and pull-only algorithms. Our algorithms are strikingly simple, address-oblivious and thus fully distributed. This contradicts a well-known result of Karp et al. [3] stating that any address-oblivious algorithm requires O(n ln lnn) messages. We also develop precise estimates of the number of rounds required in the push and pull phases of our algorithms to guarantee dissemination to all processes with a certain probability. For the push phase, we focus on a practical infect upon contagion approach that balances the load evenly across all processes. As an example, our push-pull algorithm requires 17 rounds to disseminate a rumor to all processes with probability 1 - 10-100 in a network of one million processes with a communication overhead of only 0:4%. © 2017 Association for Computing Machinery.

Abstract
Today any OEM automotive industry that wants a status of a world-class organization has to follow a proper supplier quality management based on the worldwide recognized international standards. With this, the customer within a supply chain should have the sureness that the company has supplier capabilities in place to provide a service that consistently meets its needs and expectations. Although some of this Supplier Quality Management Systems (SQMS) are deeply integrated and used inside of an organization, sometimes they are implemented using inappropriate or limited tools, making the work of employees harder and acting often as entropy sources in those systems. Having the right tools to handle Inspections as well as Nonconformance, Complaint, Corrective Action and Concession processes is key to successfully track the supplier performance. This paper presents a platform to support a SQMS, that fits the technical specification ISO/TS 16949 requirements.

Abstract
The increasing operational complexity of power systems considering the higher renewable energy penetration and changing load characteristics, together with the recent developments in the ICT field have led to more research and implementation efforts related to the activation of the demand side. In this manner, different direct load control (DLC) and indirect load control concepts have been developed and DLC strategies are considered as an effective tool for load serving entities (LSEs) with several real-world application examples. In this study, a new DLC strategy tailored for residential air-conditioners (ACs) participating in the day-ahead planning, based on offering energy credits to the enrolled end-users is proposed. The mentioned energy credits are then used by residential end-users to lower their energy procurement costs during peak-price periods. The strategy is formulated as a stochastic mixed-integer linear programming (MILP) model considering uncertainties related to weather conditions. The outcomes regarding the end-user comfort level and economic benefits are also analyzed.

Abstract

Abstract
Augmented reality has seen a fast-growing gain of interest in the last few decades. Due to technological advances, smartphones are now devices that allow to experience augmented reality systems, anytime and anywhere. Although its emerging success among users, some problems AR have been reported inhibiting its full acceptance in our digital society. The aim of this paper is to present a study about techniques to implement augmented reality systems in the cultural heritage context, including a prototype to test the technology which, based on physical objects that belong to the landscape, present an effective and accurate augmented reality approach, overlaying 3D virtual models aligned over the real images, using a smartphone's camera.

Abstract
The use of Social Media applications in educational settings has gained attention ever since educators became aware of their growing role in student's daily routine. These arise as privileged tools for social interactions, information exchange, collaborative knowledge building, immediate communication and persistent attention retaining, among others. Consequently, these tools impose themselves as complements to the profoundly established use of the traditional LMS, either being propelled by educators or requested by students. In previous research, we have already identified Facebook groups as one of the social media applications with the highest potential to foster the development of social learning communities. We have acknowledged the need to integrate Facebook groups and corresponding learning analytics into formal learning environments, such as the institutional LMS, and we have developed and presented a system which performs that integration. However, as the educational settings diversify in terms of pedagogy, coursework and student's profile and cultural background, we have identified the need to extend this integration to other social media tools, such as the instant messaging app WhatsApp, and to provide valuable learning analytics on its usage. Mobile, instant messaging based learning communities differ a lot from forum-alike communities, where threads, topics, conversations and interactions are easily trackable and, for instance, social network analysis can be conducted to profile members, roles and relationships. Therefore, research presented in this paper adds to previous consolidated work both on the technological and analytical dimensions. We address the challenges posed by the integration of WhatsApp based learning analytics in the LMS Moodle, starting by the fact that, unlike Facebook groups, WhatsApp does not provide an API for developers, nor any stream of structured data that can feed a real-time monitoring system. We then focus research on revealing an actual set of visual learning analytics that characterize a learning community of about thirty foreign master students, who used WhatsApp as a complementary tool during a semester. We discuss which type of learning analytics and corresponding visualizations best suit WhatsApp learning communities; what can educators draw from the analytics of such communities; and how that information can strengthen student assessment and profiling.