117,662 research outputs found
Dissipative Replaceable Bracing Connections (DRBrC) for earthquake protection of steel and composite structures
No full textThe article describes the development of a novel dissipative bracing connection device (identified by the acronym DRBrC) for concentrically braced frames in steel and composite structures. The origins of the device trace back to the seminal work of Kelly, Skinner and Heine (1972), and, more directly related, to the PIN-INERD device, overcoming some of its limitations and greatly improving the replaceability characteristics. The connection device is composed of a rigid housing, connected to both the brace and the beam-column connection (or just the column), in which the axial force transfer is achieved by four-point bending of a dissipative pin. The experimental validation stages, presented in detail, consisted of a preliminary testing campaign, resulting in successive improvements of the original device design, followed by a systematic parametric testing campaign. That final campaign was devised to study the influence of the constituent materials (S235 and Stainless Steel, for the pin, and S355 and High Strength Steel, for the housing), of the geometry (four-point bending intermediate spans) and of the loading history (constant amplitude or increasing cyclic alternate). The main conclusions point to the most promising DRBrC device configurations, also presenting some suggestions in terms of the replaceability requirements
Hysteretic behavior of dissipative welded fuses for earthquake resistant composite steel and concrete frames
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Visual and textual explainability for a biometric verification system based on piecewise facial attribute analysis
No full textThe decisions behind the mechanics of a biometric verification system based on Machine Learning (ML) are difficult to comprehend. Although there is now well-established research in various fields of application, such as health or justice, the use of ML-based methods is accompanied by a lack of confidence that results in their limited use. The explainability of a ML system and the comprehension of what lies behind its prediction is one of the numerous characteristics that define "trust" in these systems. Over the years, face-based biometric authen-tication has been the subject of extensive research in both academia and industry. However, existing biometric authentication systems still have problems regarding accuracy, robustness and, explainability. Still lacking in the literature is a comprehensive examination of the use of post-hoc explainability techniques for such systems. Cognitive neuroscience has always been interested in the method by which people perceive faces; local elements such as the nose, eyes, and mouth are critical to the perception and recognition of a face. In this work, starting from this assumption, we propose a framework of visual and textual explainability based on the parts of a face by analyzing them with respect to the facial attributes reported in the CelebA dataset. The primary objective is to be able to explain why two pictures of different subjects are distinct. This is done by sinthesizing pairs of images that illustrate how dissimilar the various parts of the face under investigation are and incisive and direct textual explanations of the distinguishing features are generated. A further study analyzes an interpretable mapping between the semantic space of the text and the space of the image. (c) 2023 Elsevier B.V. All rights reserved
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Square Dancing with the Stars to Enhance Dynamic Hirschman Linkages?
No full textIn this Presidential Address, the author takes the reader on a reconnaissance of his life and time as a regional scientist. He points out scenery he found scintillating along the way, hoping that some may pick up the banner and chew on a few of the ideas for a while. He suggests a revisit to Albert O. Hirschman’s notion of key sectors and more empirical analysis related to Marcus Berliant’s and Masahisa Fujita’s notion of knowledge creation and transfer.Presidential Address, San Antonio, Texas, March 29, 2014 (53rd Meetings of the Southern Regional Science Association
Appropriate Similarity Measures for Author Cocitation Analysis
No full textWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Unsupervised online anomaly detection in Software Defined Network environments
No full textSoftware Defined Networking (SDN) simplifies network management and significantly reduces operational costs. SDN removes the control plane from forwarding devices (e.g., routers and switches) and centralizes this plane in a controller, enabling the management of the network forwarding decisions by programming the control plane with a high-level language. However, its centralized architecture may be compromised by flooding attacks, such as Distributed Denial of Service (DDoS) and portscan. Facing this challenge, we propose an Intrusion Detection System (IDS) based on online clustering to detect attacks in an evolving SDN network taking advantage of the entropy of source and destination IP addresses and ports. Our proposal is focused on avoiding the demand for labeling and previous knowledge to provide a practical and accurate method to address real-life online scenarios. Moreover, our proposal paves the way for a comprehensive analysis by projecting the cluster's structure over the feature space, providing insights on intensity, seasonality, and attack type. Our experiments were carried out with the DenStream algorithm in several databases attacked by DDoS and portscan with different intensities, durations, and overlapping patterns. When comparing DenStream performance to Half-Space-Trees, an accurate online one-class classification algorithm for anomaly detection, it was possible to expose the capacity of our unsupervised proposal, overcoming the one-class solution, and reaching f-measure rates above 99.60%
Artificial Immune Systems and Fuzzy Logic to Detect Flooding Attacks in Software-Defined Networks
No full textSoftware-defined Networking (SDN) has been discovered as an architecture that uses applications to make networks flexible and centrally controlled. Although SDN provides innovative management, it still susceptible to attacks daily. Traditional detection approaches may not be sufficient to contain these threats. In this paper, we present an Artificial Immune System based IDS named AIS-IDS, which is inspired by the human body's defense cells. AIS-IDS can detect variations in network behavior and identify attacks without prior knowledge about them. Along with AIS, the fuzzy logic is applied on detection to minimize the uncertainty when there is no clear boundary between anomalous and normal traffic behavior. We have simulated portscan and flooding attacks as well as used a public dataset with several types of DDoS attacks to assess our proposal. We compared the AIS-IDS performance with Naive Bayes, k-nearest neighbors, and the Local Outlier Factor. The AIS-IDS outperformed the compared algorithms, achieving f-measure rates 99.97% and 92.28% when submitted to a simulated and a public dataset, respectively
Aspidodera vazi Proenca
No full textAspidodera vazi Proença Tolypeutes tricinctus (Linnaeus), unspecified site of infection, Piauí, NHR (CHIOC 4446 c).Published as part of Muniz-Pereira, Luís C., Vieira, Fabiano M. & Luque, José L., 2009, Checklist of helminth parasites of threatened vertebrate species from Brazil, pp. 1-45 in Zootaxa 2123 on page 10, DOI: 10.5281/zenodo.18817
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