HIGGINS: where knowledge acquisition meets the crowds

We present HIGGINS, an engine for high quality Knowl- edge Acquisition (KA), placing special emphasis on its ar- chitecture. The distinguishing characteristic and novelty of HIGGINS lies in its special blending of two engines: An automated Information Extraction (IE) engine, aided by semantic resources, and a game-based, Human Computing engine (HC). We focus on KA from web data and text sources and, in particular, on deriving relationships between enti- ties. As a running application we utilise movie narratives, using which we wish to derive relationships among movie characters

HIGGINS: where knowledge acquisition meets the crowds

We present HIGGINS, an engine for high quality Knowl- edge Acquisition (KA), placing special emphasis on its ar- chitecture. The distinguishing characteristic and novelty of HIGGINS lies in its special blending of two engines: An automated Information Extraction (IE) engine, aided by semantic resources, and a game-based, Human Computing engine (HC). We focus on KA from web data and text sources and, in particular, on deriving relationships between enti- ties. As a running application we utilise movie narratives, using which we wish to derive relationships among movie characters

Assessing the effectiveness of embedding CFRP laminates in the near surface for structural strengthening

The authors of the present work wish to acknowledge the support provided by the S&P, Bettor MBT Portugal, Secil, Nordesfer, Ferseque, Casais, Solusel, VSL, Unibetão (Braga) and the colaboration of Cemacom.Near Surface Mounted (NSM) is a recent strengthening technique based on bonding Carbon Fiber Reinforced Polymer (CFRP) bars (rods or laminate strips) into pre-cut grooves on the concrete cover of the elements to strength. To assess the effectiveness of the NSM technique, an experimental program is carried out involving reinforced concrete (RC) columns, RC beams and masonry panels. In columns failing in bending the present work shows that the failure strain of the (CFRP) laminates can be attained using the NSM technique. Beams failing in bending are also strengthened with CFRP laminates in order to double their load carrying capacity. This goal was attained and maximum strain levels of about 90% of the CFRP failure strain were recorded in this composite material, revealing that the NSM technique is also very effective to increase the flexural resistance of RC beams. The effectiveness of externally bonded reinforcing (EBR) and NSM techniques to increase the flexural resistance of masonry panels is also assessed. In the EBR technique the CFRP laminates are externally bonded to the concrete joints of the panel, while in the NSM technique the CFRP laminates are fixed into precut slits on the panel concrete joints. The NSM technique provided a higher increase on the panel load carrying capacity, as well as, a larger deflection at the failure of the panel. The performance of EBR and NSM techniques for the strengthening of RC beams failing in shear is also analyzed. The NMS technique was much more effective in terms of increasing the beam load carrying capacity, as well as, the beam deformability at its failure. The NSM technique was easier and faster to apply than the EBR technique.The first author wishes to acknowledge the grant SFRH/BSAB/291/2002-POCTI, provided by FCT and FSE

Distributed top-k aggregation queries at large

Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network

Polymer Shape Anisotropy and the Depletion Interaction

We calculate the second and third virial coefficients of the effective sphere-sphere interaction due to polymer depletion. By utilizing the anisotropy of a typical polymer conformation, we can consider polymers that are roughly the same size as the spherical inclusions. We argue that recent experiments can confirm this anisotropy.Comment: 4 pages, 4 eps figures, RevTe

EU-NICE, Eurasian University Network for International Cooperation in Earthquakes

Despite the remarkable scientific advancements of earthquake engineering and seismology in many countries, seismic risk is still growing at a high rate in the world’s most vulnerable communities. Successful practices have shown that a community’s capacity to manage and reduce its seismic risk relies on capitalization on policies, on technology and research results. An important role is played by education, than contribute to strengthening technical curricula of future practitioners and researchers through university and higher education programmes. In recent years an increasing number of initiatives have been launched in this field at the international and global cooperation level. Cooperative international academic research and training is key to reducing the gap between advanced and more vulnerable regions. EU-NICE is a European Commission funded higher education partnership for international development cooperation with the objective to build capacity of individuals who will operate at institutions located in seismic prone Asian Countries. The project involves five European Universities, eight Asian universities and four associations and NGOs active in advanced research on seismic mitigation, disaster risk management and international development. The project consists of a comprehensive mobility scheme open to nationals from Afghanistan, Bangladesh, China, Nepal, Pakistan, Thailand, Bhutan, India, Indonesia, Malaysia, Maldives, North Korea, Philippines, and Sri Lanka who plan to enrol in school or conduct research at one of five European partner universities in Italy, Greece and Portugal. During the 2010-14 time span a total number of 104 mobilities are being involved in scientific activities at the undergraduate, masters, PhD, postdoctoral and academic-staff exchange levels. This high number of mobilities and activities is selected and designed so as to produce an overall increase of knowledge that can result in an impact on earthquake mitigation. Researchers, future policymakers and practitioners build up their curricula over a range of disciplines in the fields of engineering, seismology, disaster risk management and urban planning. Specific educational and research activities focus on earthquake risk mitigation related topics such as: anti-seismic structural design, structural engineering, advanced computer structural collapse analysis, seismology, experimental laboratory studies, international and development issues in disaster risk management, social-economical impact studies, international relations and conflict resolution

A development cooperation Erasmus Mundus partnership for capacity building in earthquake mitigation science and higher education

Successful practices have shown that a community’s capacity to manage and reduce its seismic risk relies on capitalization on policies, on technology and research results. An important role is played by education, than contribute to strengthening technical curricula of future practitioners and researchers through university and higher education programs. EUNICE is a European Commission funded higher education partnership for international development cooperation with the objective to build capacity of individuals who will operate at institutions located in seismic prone Asian Countries. The project involves five European Universities, eight Asian universities and four associations and NGOs active in advanced research on seismic mitigation, disaster risk management and international development. The project consists of a comprehensive mobility scheme open to nationals from Afghanistan, Bangladesh, China, Nepal, Pakistan, Thailand, Bhutan, India, Indonesia, Malaysia, Maldives, North Korea, Philippines, and Sri Lanka who plan to enroll in school or conduct research at one of five European partner universities in Italy, Greece and Portugal. During the 2010-14 time span a total number of 104 mobilities are being involved in scientific activities at the undergraduate, masters, PhD, postdoctoral and academic-staff exchange levels. Researchers, future policymakers and practitioners build up their curricula over a range of disciplines in the fields of earthquake engineering, seismology, disaster risk management and urban planning

Few-Shot Attribute Learning

Semantic concepts are frequently defined by combinations of underlying attributes. As mappings from attributes to classes are often simple, attribute-based representations facilitate novel concept learning with zero or few examples. A significant limitation of existing attribute-based learning paradigms, such as zero-shot learning, is that the attributes are assumed to be known and fixed. In this work we study the rapid learning of attributes that were not previously labeled. Compared to standard few-shot learning of semantic classes, in which novel classes may be defined by attributes that were relevant at training time, learning new attributes imposes a stiffer challenge. We found that supervised learning with training attributes does not generalize well to new test attributes, whereas self-supervised pre-training brings significant improvement. We further experimented with random splits of the attribute space and found that predictability of test attributes provides an informative estimate of a model's generalization ability.Comment: Technical report, 25 page