H2 and H∞ optimal model reduction using genetic algorithms

The mathematical modeling of most physical systems, such as aerospace systems, heat processes, telecommunication systems, transmission lines and chemical reactors, results in complex high order models. The complexity of the models imposes a lot of difficulties in analysis, simulation and control designs. Several analytical model reduction techniques have been proposed in literature over the past few decades to reduce these difficulties. However, most of the optimal techniques follow computationally demanding, time consuming, iterative procedures that usually result in non-robustly stable models with poor frequency response resemblance to the original high order model in some frequency ranges. Genetic Algorithm (GA) has proved to be an excellent optimization tool in the past few years. Therefore, the aim of this paper will be to use GA to solve H₂ and H_∞ norm model reduction problems, and help obtain globally optimized nominal models.     

<Emphasis Type="Italic">EveTAR</Emphasis>: building a large-scale multi-task test collection over Arabic tweets

This article introduces a new language-independent approach for creating a large-scale high-quality test collection of tweets that supports multiple information retrieval (IR) tasks without running a shared-task campaign. The adopted approach (demonstrated over Arabic tweets) designs the collection around significant (i.e., popular) events, which enables the development of topics that represent frequent information needs of Twitter users for which rich content exists. That inherently facilitates the support of multiple tasks that generally revolve around events, namely event detection, ad-hoc search, timeline generation, and real-time summarization. The key highlights of the approach include diversifying the judgment pool via interactive search and multiple manually-crafted queries per topic, collecting high-quality annotations via crowd-workers for relevancy and in-house annotators for novelty, filtering out low-agreement topics and inaccessible tweets, and providing multiple subsets of the collection for better availability. Applying our methodology on Arabic tweets resulted in EveTAR, the first freely-available tweet test collection for multiple IR tasks. EveTAR includes a crawl of 355M Arabic tweets and covers 50 significant events for which about 62K tweets were judged with substantial average inter-annotator agreement (Kappa value of 0.71). We demonstrate the usability of EveTAR by evaluating existing algorithms in the respective tasks. Results indicate that the new collection can support reliable ranking of IR systems that is comparable to similar TREC collections, while providing strong baseline results for future studies over Arabic tweets.     

Semantic and Phonological Ability to Adjust Recoding: A Unique Correlate of Word Reading Skill?

This study addresses whether reading involves a process termed semantic and phonological ability to adjust recoding (SPAAR). It was hypothesized that SPAAR helps readers link inaccurate pronunciations to lexical entries (e.g., spynitch to spinach). Psychometric properties of the Mispronunciation Correction Task (MCT), a measure of SPAAR, were investigated using a sample of 206 elementary-age children. The 25-item MCT appeared to measure a single construct, and the items discriminated well across the range of ability. Based on a sample of 94 children, construct validity analyses indicated that phonological awareness and decoding skill predicted MCT performance. Hierarchical regressions were used to test the relationship between SPAAR and word reading. After controlling for other skills, SPAAR showed a relationship with both timed and untimed word reading. Phonological awareness did not. These data suggest that SPAAR may be a reading-related ability involving phonological processing and some form of semantic processing.     

Facework in Syria and the United States: A cross-cultural comparison

“The first rule when communicating with people from the Arab world is not to let them lose face” said J. Al-Omari. Face or one's social identity is cultural. A face threat is a situation which threatens to create a loss of face. When experiencing face-threats people guard their face with facework – behavioral actions enacted to protect one's face. Since facework varies across cultures, this study analyzed how cultural collectivism, power distance, masculinity, and uncertainty avoidance influence direct, indirect, competitive, cooperative, hostile, and ritualistic facework in Syria and the United States, employing a MANCOVA design with gender as the covariate. Significant findings (n = 336) showed that: (a) US Americans reported using more direct, competitive, and hostile facework strategies than Syrians while (b) Syrians reported using more indirect, cooperative and ritualistic facework strategies than US Americans (c) US American facework strategies corresponded to individualistic, weak power distance, masculine, and low uncertainty avoidance cultural dimensions while Syrian facework corresponded to collectivistic, high-power distance, moderately masculine, and high uncertainty avoidance and (d) VSM 94 results showed Syria to be more individualistic than Hofstede's original rankings.