A semantic-guided and self-configurable framework for video analysis

The final publication is available at Springer via http://dx.doi.org/10.1007/s00138-011-0397-xThis paper presents a distributed and scalable framework for video analysis that automatically estimates the optimal workflow required for the analysis of different application domains. It integrates several technologies related with data acquisition, visual analysis tools, communication protocols, and data storage. Moreover, hierarchical semantic representations are included in the framework to describe the application domain, the analysis capabilities, and the user preferences. The automatic determination of the analysis workflow is performed by selecting the most appropriate tools for each domain among the available ones in the framework by means of exploiting the relations between the semantic descriptions. The experimental results in the video surveillance domain demonstrate that the proposed approach successfully composes optimal workflows for video analysis applications.This work has been partially supported by the Spanish Government (TEC2011-25995), by the Consejería de Educación of the Comunidad de Madrid and by The European Social Fund

From Keyword Search to Exploration: How Result Visualization Aids Discovery on the Web

A key to the Web's success is the power of search. The elegant way in which search results are returned is usually remarkably effective. However, for exploratory search in which users need to learn, discover, and understand novel or complex topics, there is substantial room for improvement. Human computer interaction researchers and web browser designers have developed novel strategies to improve Web search by enabling users to conveniently visualize, manipulate, and organize their Web search results. This monograph offers fresh ways to think about search-related cognitive processes and describes innovative design approaches to browsers and related tools. For instance, while key word search presents users with results for specific information (e.g., what is the capitol of Peru), other methods may let users see and explore the contexts of their requests for information (related or previous work, conflicting information), or the properties that associate groups of information assets (group legal decisions by lead attorney). We also consider the both traditional and novel ways in which these strategies have been evaluated. From our review of cognitive processes, browser design, and evaluations, we reflect on the future opportunities and new paradigms for exploring and interacting with Web search results

Special Session on Industry 4.0

No abstract available

A throughput-latency co-optimised cascade of convolutional neural network classifiers

Convolutional Neural Networks constitute a promi-nent AI model for classification tasks, serving a broad span ofdiverse application domains. To enable their efficient deploymentin real-world tasks, the inherent redundancy of CNNs is fre-quently exploited to eliminate unnecessary computational costs.Driven by the fact that not all inputs require the same amount ofcomputation to drive a confident prediction, multi-precision cas-cade classifiers have been recently introduced. FPGAs comprise apromising platform for the deployment of such input-dependentcomputation models, due to their enhanced customisation ca-pabilities. Current literature, however, is limited to throughput-optimised cascade implementations, employing large batching atthe expense of a substantial latency aggravation prohibiting theirdeployment on real-time scenarios. In this work, we introduce anovel methodology for throughput-latency co-optimised cascadedCNN classification, deployed on a custom FPGA architecturetailored to the target application and deployment platform,with respect to a set of user-specified requirements on accuracyand performance. Our experiments indicate that the proposedapproach achieves comparable throughput gains with relatedstate-of-the-art works, under substantially reduced overhead inlatency, enabling its deployment on latency-sensitive applications

From Sensor to Observation Web with Environmental Enablers in the Future Internet

This paper outlines the grand challenges in global sustainability research and the objectives of the FP7 Future Internet PPP program within the Digital Agenda for Europe. Large user communities are generating significant amounts of valuable environmental observations at local and regional scales using the devices and services of the Future Internet. These communities’ environmental observations represent a wealth of information which is currently hardly used or used only in isolation and therefore in need of integration with other information sources. Indeed, this very integration will lead to a paradigm shift from a mere Sensor Web to an Observation Web with semantically enriched content emanating from sensors, environmental simulations and citizens. The paper also describes the research challenges to realize the Observation Web and the associated environmental enablers for the Future Internet. Such an environmental enabler could for instance be an electronic sensing device, a web-service application, or even a social networking group affording or facilitating the capability of the Future Internet applications to consume, produce, and use environmental observations in cross-domain applications. The term ?envirofied? Future Internet is coined to describe this overall target that forms a cornerstone of work in the Environmental Usage Area within the Future Internet PPP program. Relevant trends described in the paper are the usage of ubiquitous sensors (anywhere), the provision and generation of information by citizens, and the convergence of real and virtual realities to convey understanding of environmental observations. The paper addresses the technical challenges in the Environmental Usage Area and the need for designing multi-style service oriented architecture. Key topics are the mapping of requirements to capabilities, providing scalability and robustness with implementing context aware information retrieval. Another essential research topic is handling data fusion and model based computation, and the related propagation of information uncertainty. Approaches to security, standardization and harmonization, all essential for sustainable solutions, are summarized from the perspective of the Environmental Usage Area. The paper concludes with an overview of emerging, high impact applications in the environmental areas concerning land ecosystems (biodiversity), air quality (atmospheric conditions) and water ecosystems (marine asset management)

Mapping a guided image filter on the HARP reconfigurable architecture using OpenCL

Intel recently introduced the Heterogeneous Architecture Research Platform, HARP. In this platform, the Central Processing Unit and a Field-Programmable Gate Array are connected through a high-bandwidth, low-latency interconnect and both share DRAM memory. For this platform, Open Computing Language (OpenCL), a High-Level Synthesis (HLS) language, is made available. By making use of HLS, a faster design cycle can be achieved compared to programming in a traditional hardware description language. This, however, comes at the cost of having less control over the hardware implementation. We will investigate how OpenCL can be applied to implement a real-time guided image filter on the HARP platform. In the first phase, the performance-critical parameters of the OpenCL programming model are defined using several specialized benchmarks. In a second phase, the guided image filter algorithm is implemented using the insights gained in the first phase. Both a floating-point and a fixed-point implementation were developed for this algorithm, based on a sliding window implementation. This resulted in a maximum floating-point performance of 135 GFLOPS, a maximum fixed-point performance of 430 GOPS and a throughput of HD color images at 74 frames per second