Explore the vast range of titles available.

Modeling with Data fully explains how to execute computationally intensive analyses on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results.

This book lays out the theoretical groundwork for personalized search and reputation management, both on the Web and in peer-to-peer and social networks. Representing much of the foundational research in this field, the book develops scalable algorithms that exploit the graphlike properties underlying personalized search and reputation management, and delves into realistic scenarios regarding Web-scale data. Sep Kamvar focuses on eigenvector-based techniques in Web search, introducing a personalized variant of Google's PageRank algorithm, and he outlines algorithms--such as the now-famous quadratic extrapolation technique--that speed up computation, making personalized PageRank feasible. Kamvar suggests that Power Method-related techniques ultimately should be the basis for improving the PageRank algorithm, and he presents algorithms that exploit the convergence behavior of individual components of the PageRank vector. Kamvar then extends the ideas of reputation management and personalized search to distributed networks like peer-to-peer and social networks. He highlights locality and computational considerations related to the structure of the network, and considers such unique issues as malicious peers. He describes the EigenTrust algorithm and applies various PageRank concepts to P2P settings. Discussion chapters summarizing results conclude the book's two main sections. Clear and thorough, this book provides an authoritative look at central innovations in search for all of those interested in the subject.

Background Although policy providers have outlined minimal metadata guidelines and naming conventions, ontologies of today still display inter- and intra-ontology heterogeneities in class labelling schemes and metadata completeness. This fact is at least partially due to missing or inappropriate tools. Software support can ease this situation and contribute to overall ontology consistency and quality by helping to enforce such conventions. Objective We provide a plugin for the Protégé Ontology editor to allow for easy checks on compliance towards ontology naming conventions and metadata completeness, as well as curation in case of found violations. Implementation In a requirement analysis, derived from a prior standardization approach carried out within the OBO Foundry, we investigate the needed capabilities for software tools to check, curate and maintain class naming conventions. A Protégé tab plugin was implemented accordingly using the Protégé 4.1 libraries. The plugin was tested on six different ontologies. Based on these test results, the plugin could be refined, also by the integration of new functionalities. Results The new Protégé plugin, OntoCheck, allows for ontology tests to be carried out on OWL ontologies. In particular the OntoCheck plugin helps to clean up an ontology with regard to lexical heterogeneity, i.e. enforcing naming conventions and metadata completeness, meeting most of the requirements outlined for such a tool. Found test violations can be corrected to foster consistency in entity naming and meta-annotation within an artefact. Once specified, check constraints like name patterns can be stored and exchanged for later re-use. Here we describe a first version of the software, illustrate its capabilities and use within running ontology development efforts and briefly outline improvements resulting from its application. Further, we discuss OntoChecks capabilities in the context of related tools and highlight potential future expansions. Conclusions The OntoCheck plugin facilitates labelling error detection and curation, contributing to lexical quality assurance in OWL ontologies. Ultimately, we hope this Protégé extension will ease ontology alignments as well as lexical post-processing of annotated data and hence can increase overall secondary data usage by humans and computers.

The engagement with Egypt and the Egyptian gods that transpired in the Hebrew Bible continued into the texts produced by rabbinic Judaism. Rabbinic texts of late antiquity and the early medieval period frequently presented images of Egypt and its religion. One of the critical objectives of these portrayals of Egypt was to set boundaries of Jewish identity by presenting rabbinic Judaism in opposition to Egyptian culture. The Egyptian cultural icons in rabbinic texts also demonstrate that the rabbis were aware of cultures other than their own.1 The presence of Egyptian elements in midrash had previously been noted to a very limited extent by scholars of the Wissenschaft des Judentums (the science of Judaism), and it has not escaped the attention of more recent scholarship.

The evidence for the acknowledgment of Hadrian before Trajan's death is assessed, especially the question of the authenticity of two aurei for Trajan \"Hadrian Ceasar.\" An examination of Hadrian's aurei for the deified Trajan precedes discussion of the very rare coins of Hadrian's reign from Alexandria depicting Hadrian and Trajan, and why the early Alexandrian coins of Hadrian's reign misspell the adopted name 'Traianus.' This is all regarded as an indication of the complete lack of preparation in the mints of Rome and Alexandria for Trajan's death and Hadrian's accession. The empire was saved from civil war and chaos by Hadrian's decisiveness.

Naming conventions are an important concern in large verification projects using proof assistants, such as Coq. In particular, lemma names are used by proof engineers to effectively understand and modify Coq code. However, providing accurate and informative lemma names is a complex task, which is currently often carried out manually. Even when lemma naming is automated using rule-based tools, generated names may fail to adhere to important conventions not specified explicitly. We demonstrate a toolchain, dubbed Roosterize, which automatically suggests lemma names in Coq projects. Roosterize leverages a neural network model trained on existing Coq code, thus avoiding manual specification of naming conventions. To allow proof engineers to conveniently access suggestions from Roosterize during Coq project development, we integrated the toolchain into the popular Visual Studio Code editor. Our evaluation shows that Roosterize substantially outperforms strong baselines for suggesting lemma names and is useful in practice. The demo video for Roosterize can be viewed at: https://youtu.be/HZ5ac7Q14rc.

Practitioners often argue that range names make spreadsheets easier to understand and use, akin to the role of good variable names in traditional programming languages, yet there is no supporting scientific evidence. The authors previously published experiments that disproved this theory in relation to debugging, and now turn their focus to development. This paper presents the results of two iterations of a new experiment, which measure the effect of range names on the correctness of, and the time it takes to develop, simple summation formulas. Our findings, supported by statistically significant results, show that formulas developed by non-experts using range names are more likely to contain errors and take longer to develop. Taking these findings with the findings from previous experiments, we conclude that range names do not improve the quality of spreadsheets developed by novice and intermediate users. This paper is important in that it finds that the choice of naming convention can have a significant impact on novice and intermediate users' performance in formula development, with less structured naming conventions resulting in poorer performance by users.

With the rapid increasing of software project size and maintenance cost, adherence to coding standards especially by managing identifier naming, is attracting a pressing concern from both computer science educators and software managers. Software developers mainly use identifier names to represent the knowledge recorded in source code. However, the popularity and adoption consistency of identifier naming conventions have not been revealed yet in this field. Taking forty-eight popular open source projects written in three top-ranking programming languages Java, C and C++ as examples, an identifier extraction tool based on regular expression matching is developed. In the subsequent investigation, some interesting findings are obtained. For the identifier naming popularity, it is found that Camel and Pascal naming conventions are leading the road while Hungarian notation is vanishing. For the identifier naming consistency, we have found that the projects written in Java have a much better performance than those written in C and C++. Finally, academia and software industry are urged to adopt the most popular naming conventions consistently in their practices so as to lead the identifier naming to a standard, unified and high-quality road.

This paper discusses the encoding of proper names using the TEI Guidelines, describing the practice of the Women Writers Project at Brown University, and the CELT Project at University College, Cork. We argue that such encoding may be necessary to enable historical and literary research, and that the specific approach taken will depend on the needs of the project and the audience to be served. Because the TEI Guidelines provide a fairly flexible system for the encoding of proper names, we conclude that projects may need to collaborate to determine more specific constraints, to ensure consistency of approach and compatibility of data.