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We address the problem of detecting highly raised crowd density in situations such as indoor dance events.We propose a new method for estimating crowd density by anonymous, non-participatory, indoor Wi-Fi localization of smart phones. Using a probabilistic model inspired by statistical mechanics, and relying only on big data analytics, we tackle three challenges: (1) the ambiguity of Wi-Fi based indoor positioning, which appears regardless of whether the latter is performed with machine learning or with optimization, (2) the MAC address randomization when a device is not connected, and (3) the volatility of packet interarrival times. The main result is that our estimation becomes more -- rather than less -- accurate when the crowd size increases. This property is crucial for detection of dangerous crowd density.

In this paper we present a methodology for gaining a better understanding of the contribution behavior, interests and expertise of communities of Wikipedia users. Starting from a list of core articles and their main editors, we identify which other articles (outside of the initial list) they contributed to ‘significantly’. The ordering is based on (empirical) Bayesian estimates of the contribution probabilities for each of the articles. By constructing a co-contribution network, we can identify the general themes the community expresses exceptional interest (or disinterest) in. In order to show what type of insights one might gain from employing the proposed method, we use the editors that contributed to the articles on designer drugs as a case study. We find that the users in this community contribute significantly to articles on pharmaceuticals, popular party drugs, chemistry, mental illnesses, diseases, medicine and cell biology.

The abundance and heterogeneity of IT resources available, together with the ability to dynamically scale applications poses significant usability issues to users. Without understanding the performance profile of available resources users are unable to efficiently scale their applications in order to meet performance objectives. High quality media collaborations, like CineGrid, are one example of such diverse environments where users can leverage dynamic infrastructures to move and process large amounts of data. This paper describes our user-centric approach to executing high quality media processing workloads over dynamic infrastructures. Our main contribution is the CGtoolkit ?environment, an integrated system which aids users cope with the infrastructure complexity and large data sets specific to the digital cinema domain. We describe the general challenges user face in heterogeneous it infrastructure environments and identify their instance in the case of CineGrid.We characterize the most frequent workloads in the CineGrid collaboration including their infrastructure resource requirements.We present the design and implementation of a system which simplifies workload resource definition by leveraging semantic web description languages for infrastructure.We provide Pareto-optimal resource configurations for workload execution.

Software abstractions are crucial to effectively program heterogeneous Multi-Processor Systems on Chip (MPSoCs). Prime examples of such abstractions are Kahn Process Networks (KPNs) and execution traces. When modeling computation as a KPN, one of the key challenges is to obtain a good mapping, i.e., an assignment of logical computation and communication to physical resources. In this paper we compare two system-level frameworks for solving the mapping problem: Sesame and MAPS. These frameworks, while superficially similar, embody different approaches. Sesame, motivated by modeling and design-space exploration, uses evolutionary algorithms for mapping. MAPS, being a compiler framework, uses simple and fast heuristics instead. In this work we highlight the value of common abstractions, such as KPNs and traces, as a vehicle to enable comparisons between large independent frameworks. These types of comparisons are fundamental for advancing research in the area. At the same time, we illustrate how the lack of formalized models at the hardware level are an obstacle to achieving fair comparisons. Additionally, using a set of applications from the embedded systems domain, we observe that genetic algorithms tend to outperform heuristics by a factor between 1× and 5×, with notable exceptions. This performance comes at the cost of a longer computation time, between 0 and 2 orders of magnitude in our experiments.

This paper presents results of the ongoing development of the Cloud Services Delivery Infrastructure (CSDI) that provides a basis for infrastructure centric cloud services provisioning, operation and management in multi-cloud multi-provider environment defined as a Zero Touch Provisioning, Operation and Management (ZTP/ZTPOM) model. The presented work refers to use cases from data intensive research that require high performance computation resources and large storage volumes that are typically distributed between datacenters often involving multiple cloud providers. Automation for large scale scientific (and industrial) applications should include provisioning of both inter-cloud network infrastructure and intra-cloud application resources. It should provide support for the complete application operation workflow together with the possible application infrastructure and resources changes that can occur during the application lifecycle. The authors investigate existing technologies for automation of the service provisioning and management processes aiming to cross-pollinate best practices from currently disconnected domains such as cloud based applications provisioning and multi-domain high-performance network provisioning. The paper refers to the previous and legacy research by authors, the Open Cloud eXchange (OCX), that has been proposed to address the last mile problem in cloud services delivery to campuses over trans-national backbone networks such as GEANT. OCX will serve as an integral component of the prospective ZTP infrastructure over the GEANT network. Another important component, the Marketplace, is defined for providing cloud services and applications discovery (in generally intercloud environment) and may also support additional services such as services composition and trust brokering for establishing customer-provider federations.

In the contemporary cloud computing environment, the various cloud providers adopt different architectures for their systems and usually these are not compatible. The scalable exchange of information about these heterogeneous resources is often not addressed; still this represents an interesting challenge, since different Cloud systems and vendors have different ways to describe and invoke their services, to specify and communicate requirements. Hence a way to provide a common access to Cloud services and to discover and use required services in Cloud federations is highly desirable. The IEEE P2302 Intercloud approach is to define a scalable architecture and to adopt the OMN (Open-Multinet) ontology for supporting its operation. Our work specifically describes the inner workings of the Intercloud system in terms of the Intercloud Federation Application Programmers Interface (API), which are in turn based on the semantic definition of resources, Service Level Agreements (SLAs), and Bearer Network Provisioning Metadata.

This discussion paper introduces the concept of the Virtual Artery as a multiscale model for arterial physiology and pathologies at the physics–chemistry–biology (PCB) interface. The cellular level is identified as the mesoscopic level, and we argue that by coupling cell-based models with other relevant models on the macro- and microscale, a versatile model of arterial health and disease can be composed. We review the necessary ingredients, both models of arteries at many different scales, as well as generic methods to compose multiscale models. Next, we discuss how this can be combined into the virtual artery. Finally, we argue that the concept of models at the PCB interface could or perhaps should become a powerful paradigm, not only as in our case for studying physiology, but also for many other systems that have such PCB interfaces. This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’.