Common Purpose

The relationship between performance and curation has shift ed. A new attitude of fluid and pragmatic alliance has evolved as the sense of an essential antagonism between performance and curation recedes and the two fields discover a shared focus on aspects of social engagement and agency. This article considers an Australian socially engaged art project, the Kandos School of Cultural Adaptation (KSCA), which meshes curatorial and artistic practices in its efforts to reimagine and reanimate the future of a small country town. Employing a wide range of strategies, KSCA works closely with the local community to facilitate collective memory, reflection and social and environmental transformation. Deliberately avoiding traditional lines of artistic and institutional tension, KSCA employs an impure and inclusive approach that is emblematic of emerging forms of activist contemporary art.

A Comparison between Term-Independence Retrieval Models for Ad Hoc Retrieval

In Information Retrieval, numerous retrieval models or document ranking functions have been developed in the quest for better retrieval effectiveness. Apart from some formal retrieval models formulated on a theoretical basis, various recent works have applied heuristic constraints to guide the derivation of document ranking functions. While many recent methods are shown to improve over established and successful models, comparison among these new methods under a common environment is often missing. To address this issue, we perform an extensive and up-to-date comparison of leading term-independence retrieval models implemented in our own retrieval system. Our study focuses on the following questions: (RQ1) Is there a retrieval model that consistently outperforms all other models across multiple collections; (RQ2) What are the important features of an effective document ranking function? Our retrieval experiments performed on several TREC test collections of a wide range of sizes (up to the terabyte-sized Clueweb09 Category B) enable us to answer these research questions. This work also serves as a reproducibility study for leading retrieval models. While our experiments show that no single retrieval model outperforms all others across all tested collections, some recent retrieval models, such as MATF and MVD, consistently perform better than the common baselines.

Analysis of Trending Topics and Text-based Channels of Information Delivery in Cybersecurity

Computer users are generally faced with difficulties in making correct security decisions. While an increasingly fewer number of people are trying or willing to take formal security training, online sources including news, security blogs, and websites are continuously making security knowledge more accessible. Analysis of cybersecurity texts from this grey literature can provide insights into the trending topics and identify current security issues as well as how cyber attacks evolve over time. These in turn can support researchers and practitioners in predicting and preparing for these attacks. Comparing different sources may facilitate the learning process for normal users by creating the patterns of the security knowledge gained from different sources. Prior studies neither systematically analysed the wide range of digital sources nor provided any standardisation in analysing the trending topics from recent security texts. Moreover, existing topic modelling methods are not capable of identifying the cybersecurity concepts completely and the generated topics considerably overlap. To address this issue, we propose a semi-automated classification method to generate comprehensive security categories to analyse trending topics. We further compare the identified 16 security categories across different sources based on their popularity and impact. We have revealed several surprising findings as follows: (1) The impact reflected from cybersecurity texts strongly correlates with the monetary loss caused by cybercrimes, (2) security blogs have produced the context of cybersecurity most intensively, and (3) websites deliver security information without caring about timeliness much.

Toward Taming the Overhead Monster for Data-flow Integrity

Data-Flow Integrity (DFI) is a well-known approach to effectively detecting a wide range of software attacks. However, its real-world application has been quite limited so far because of the prohibitive performance overhead it incurs. Moreover, the overhead is enormously difficult to overcome without substantially lowering the DFI criterion. In this work, an analysis is performed to understand the main factors contributing to the overhead. Accordingly, a hardware-assisted parallel approach is proposed to tackle the overhead challenge. Simulations on SPEC CPU 2006 benchmark show that the proposed approach can completely enforce the DFI defined in the original seminal work while reducing performance overhead by 4×, on average.

Hipernetch: High-Performance FPGA Network Switch

We present Hipernetch, a novel FPGA-based design for performing high-bandwidth network switching. FPGAs have recently become more popular in data centers due to their promising capabilities for a wide range of applications. With the recent surge in transceiver bandwidth, they could further benefit the implementation and refinement of network switches used in data centers. Hipernetch replaces the crossbar with a “combined parallel round-robin arbiter”. Unlike a crossbar, the combined parallel round-robin arbiter is easy to pipeline, and does not require centralised iterative scheduling algorithms that try to fit too many steps in a single or a few FPGA cycles. The result is a network switch implementation on FPGAs operating at a high frequency and with a low port-to-port latency. Our proposed Hipernetch architecture additionally provides a competitive switching performance approaching output-queued crossbar switches. Our implemented Hipernetch designs exhibit a throughput that exceeds 100 Gbps per port for switches of up to 16 ports, reaching an aggregate throughput of around 1.7 Tbps.

An insect’s-eye view

This article is concerned with representations of insects and insect habitats in late nineteenth- and early twentieth-century Dutch art and print culture. It adopts an eco-critical approach, with an eye toward multispecies studies. The article considers the ecologically conceived image of bees, butterflies, and other insects gathering pollen from a wide range of flowering plant life in Theo van Hoytema’s lithograph announcing the Biological Exhibition: the Life of Plants and Animals held in 1910 at the Royal Zoological Botanical Gardens in The Hague. This closely observed water’s-edge environment is considered in the context of the wider body of works on paper done by Van Huitema especially during the seminal period of the 1890s, and within the growing print culture surrounding the Dutch naturalist and environmental movements in the early years of the twentieth century.

SGN: Sparse Gauss-Newton for Accelerated Sensitivity Analysis

We present a sparse Gauss-Newton solver for accelerated sensitivity analysis with applications to a wide range of equilibrium-constrained optimization problems. Dense Gauss-Newton solvers have shown promising convergence rates for inverse problems, but the cost of assembling and factorizing the associated matrices has so far been a major stumbling block. In this work, we show how the dense Gauss-Newton Hessian can be transformed into an equivalent sparse matrix that can be assembled and factorized much more efficiently. This leads to drastically reduced computation times for many inverse problems, which we demonstrate on a diverse set of examples. We furthermore show links between sensitivity analysis and nonlinear programming approaches based on Lagrange multipliers and prove equivalence under specific assumptions that apply for our problem setting.

Authorship Attribution for a Resource Poor Language—Urdu

Authorship attribution refers to examining the writing style of authors to determine the likelihood of the original author of a document from a given set of potential authors. Due to the wide range of authorship attribution applications, a plethora of studies have been conducted for various Western, as well as Asian, languages. However, authorship attribution research in the Urdu language has just begun, although Urdu is widely acknowledged as a prominent South Asian language. Furthermore, the existing studies on authorship attribution in Urdu have addressed a considerably easier problem of having less than 20 candidate authors, which is far from the real-world settings. Therefore, the findings from these studies may not be applicable to the real-world settings. To that end, we have made three key contributions: First, we have developed a large authorship attribution corpus for Urdu, which is a low-resource language. The corpus is composed of over 2.6 million tokens and 21,938 news articles by 94 authors, which makes it a closer substitute to the real-world settings. Second, we have analyzed hundreds of stylometry features used in the literature to identify 194 features that are applicable to the Urdu language and developed a taxonomy of these features. Finally, we have performed 66 experiments using two heterogeneous datasets to evaluate the effectiveness of four traditional and three deep learning techniques. The experimental results show the following: (a) Our developed corpus is many folds larger than the existing corpora, and it is more challenging than its counterparts for the authorship attribution task, and (b) Convolutional Neutral Networks is the most effective technique, as it achieved a nearly perfect F1 score of 0.989 for an existing corpus and 0.910 for our newly developed corpus.