On the definition of <Emphasis Type="Italic">definition</Emphasis>

An attempt is undertaken to define the word “definition.“ The correlation of concepts, such as definition, term, discourse, and frame is discussed and a systematic definition of the term “definition” is given.     

The Modified <Emphasis Type="Italic">h</Emphasis>-<Emphasis Type="Italic">Index</Emphasis> of Scopus: A New Way in Fair Scientometrics

The h-index of Scopus is used as an important and practical instrument in analyzing the scientific level of researchers. In this research, 6 effective items (including author’s order, quality of publication source, quality of citation source, type of citation, research scopes of the authors and the regular scope of citation source) were considered in order to increase the accuracy and equity of research and also the optimization of scientometrics. In order to find the actual impact of each item, a relation with a default coefficient was considered, and all effective items were presented after evaluating the influence of them on the desired coefficients. Here, the introduced effective factors were separately investigated for 20 Iranian authors who are randomly selected. The analysis of the results showed that by applying the effective factors, the h-index of authors was obtained on the basis of real and scientific criteria; thus, the mean of all effective factors is introduced as modified h-index. Among considered authors, the lowest reduction in h-index was 10.67% and the highest reduction was 37.25% (SD was 6.5). This reduction was obtained in comparison with their h-index of Scopus, and it demonstrates that the authors, who have less reduction in their h-index value, have more scientific and honest approach to research.     

The Dutch <Emphasis Type="Italic">comptoir</Emphasis> as information centre

While sitting in their comptoir (office), merchants in early modern Holland were able to manage their plantations in the West Indies or, nearby, their seigniories in the Netherlands because they could make use of records spanning space and time. The merchants knew that information was not only instrumental in running their own business, but was also effective on a larger scale in exercising knowledge, control, and power. The performative power of records—that they may make, and in fact do make a difference in status before and after—was used in the management at a distance. From the seventeenth century, women became involved in business. They got access to the office where records supported business outside the home, but as part of everyday life.     

(<Emphasis Type="Italic">CF</Emphasis>)<Superscript>2</Superscript> architecture: contextual collaborative filtering

Recommender systems have dramatically changed the way we consume content. Internet applications rely on these systems to help users navigate among the ever-increasing number of choices available. However, most current systems ignore the fact that user preferences can change according to context, resulting in recommendations that do not fit user interests. This research addresses these issues by proposing the \(({ CF})^2\) architecture, which uses local learning techniques to embed contextual awareness into collaborative filtering models. The proposed architecture is demonstrated on two large-scale case studies involving over 130 million and over 7 million unique samples, respectively. Results show that contextual models trained with a small fraction of the data provided similar accuracy to collaborative filtering models trained with the complete dataset. Moreover, the impact of taking into account context in real-world datasets has been demonstrated by higher accuracy of context-based models in comparison to random selection models.     

Data Driven Similarity Measures for <Emphasis Type="Italic">k</Emphasis>-Means Like Clustering Algorithms

We present an optimization approach that generates k-means like clustering algorithms. The batch k-means and the incremental k-means are two well known versions of the classical k-means clustering algorithm (Duda et al. 2000). To benefit from the speed of the batch version and the accuracy of the incremental version we combine the two in a ping–pong fashion. We use a distance-like function that combines the squared Euclidean distance with relative entropy. In the extreme cases our algorithm recovers the classical k-means clustering algorithm and generalizes the Divisive Information Theoretic clustering algorithm recently reported independently by Berkhin and Becher (2002) and Dhillon1 et al. (2002). Results of numerical experiments that demonstrate the viability of our approach are reported.This research was supported in part by the US Department of Defense, the United States–Israel Binational Science Foundation (BSF), and Northrop Grumman Mission Systems (NG/MS).     

Learning to rank for<Emphasis Type="Italic"> why</Emphasis>-question answering

In this paper, we evaluate a number of machine learning techniques for the task of ranking answers to why-questions. We use TF-IDF together with a set of 36 linguistically motivated features that characterize questions and answers. We experiment with a number of machine learning techniques (among which several classifiers and regression techniques, Ranking SVM and SVM ^map ) in various settings. The purpose of the experiments is to assess how the different machine learning approaches can cope with our highly imbalanced binary relevance data, with and without hyperparameter tuning. We find that with all machine learning techniques, we can obtain an MRR score that is significantly above the TF-IDF baseline of 0.25 and not significantly lower than the best score of 0.35. We provide an in-depth analysis of the effect of data imbalance and hyperparameter tuning, and we relate our findings to previous research on learning to rank for Information Retrieval.     

<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.     

The scientometric analysis of publications based on the materials of the peer-reviewed journal <Emphasis Type="Italic">The Physics of Nanoobjects and Nanotechnologies</Emphasis> of the All-Union Institute for Scientific and Technical Information of the Russian Academy of Sciences (VINITI RAS): Graphene

A comparative analysis of the thematic scope of publications in the literature focusing on graphene in the period between 2010 and the first half of 2011 is conducted. The development patterns for physics of graphene are highlighted. Hotspots and emerging trends are outlined in this area, based on the analysis of publications in volumes 1–12 of the 2010 edition and volumes 1–6 of the 2011 edition of the peer-reviewed journal The Physics of Nanoobjects and Nanotechnologies published by the All Union Institute for Scientific and Technical Information of the Russian Academy of Sciences (VINITI RAS).     

<Emphasis Type="Italic">Managing their own past</Emphasis>. German archivists between national socialism and democracy

The article examines the transition of (West) German archivists from the Nazi period to the time of Allied occupation and on into the early years of the Federal Republic of Germany. After considering the extent of the profession’s nazification, the article focuses on Allied denazification efforts after the war and discusses the strategies archivists devised in order to maneuver through these dangerous times. In the end, the archival profession mastered the transition with only minor “denazification damage.” The article closes with an examination of the consequences of the continuity of personnel especially among the leading archivists of the former Prussian Archival Administration (Preussische Archivverwaltung) for the reconstruction of the archival profession in West Germany.

Waves: a fast multi-tier top-<Emphasis Type="Italic">k</Emphasis> query processing algorithm

In this paper, we present Waves, a novel document-at-a-time algorithm for fast computing of top-k query results in search systems. The Waves algorithm uses multi-tier indexes for processing queries. It performs successive tentative evaluations of results which we call waves. Each wave traverses the index, starting from a specific tier level i. Each wave i may insert only those documents that occur in that tier level into the answer. After processing a wave, the algorithm checks whether the answer achieved might be changed by successive waves or not. A new wave is started only if it has a chance of changing the top-k scores. We show through experiments that such lazy query processing strategy results in smaller query processing times when compared to previous approaches proposed in the literature. We present experiments to compare Waves’ performance to the state-of-the-art document-at-a-time query processing methods that preserve top-k results and show scenarios where the method can be a good alternative algorithm for computing top-k results.