Based on the NTCIR-4 test-collection, our first objective is to present an overview of the retrieval effectiveness of nine vector-space and two probabilistic models when performing monolingual searches in the Chinese, Japanese, Korean and English languages. Our second goal is to analyze the relative merits of using various automated and freely available tools to translate English-language topics into Chinese, Japanese or Korean, and then submit the resultant query to retrieve pertinent documents written in one of these three Asian languages. We also demonstrate how bilingual searches could be improved by applying both combined query translation strategies and data fusion approaches. Finally, we address basic problems related to multilingual searches in which queries written in English are used to search documents written in the English, Chinese, Japanese and Korean languages.

Abstract. Keystroke dynamics—the analysis of individuals ’ distinctive typing rhythms—has been proposed as a biometric to discriminate legitimate users from impostors (whether insiders or external attackers). Anomaly detectors have reportedly performed well at this discrimination task, but there is room for improvement. Detector performance might be constrained by the widespread use of comparatively low-resolution clocks (typically 10–15 milliseconds). This paper investigates the effect of clock resolution on detector performance. Using a high-resolution clock, we collected keystroke timestamps from 51 subjects typing 400 passwords each. We derived the timestamps that would have been generated by lower-resolution clocks. Using these data, we evaluated three types of detectors from the keystroke-dynamics literature, finding that detector performance is slightly worse at typical clock resolutions than at higher ones (e.g., a 4.2 % increase in equal-error rate). None of the detectors achieved a practically useful level of performance, but we suggest opportunities for progress through additional, controlled experimentation. Keywords: Anomaly detection; Insider-attack detection; Keystroke dynamics; Digital biometrics.

reference set of international guidelines and recommendations for the reporting and presentation of statistical data and metadata. Although, the Handbook was prepared primarily for short-term economic statistics, many, if not most of the recommendations presented are also relevant for annual (structural) statistics and for social and population statistics. The Handbook draws heavily on existing international statistical guidelines and recommendations that have been developed over the last twenty years by international organisations and national statistical agencies. These international standards have been developed for implementation by international organisations themselves and by national agencies in both developed and developing economies. The need for the articulation of a comprehensive set of recommendations on the reporting and presentation of statistical data and metadata is one aspect of the overall demand for improved international comparability and consistency of statistics compiled and published by national agencies and international organisations. This requirement has itself been driven by closer economic and social linkages between both the 30 OECD Member countries and between those countries and large emerging non-member economies. As discussed in the Handbook, the major causes of differences in statistics compiled in different countries flow from the use of different definitions and concepts and

The Proceedings of the Winter Simulation Conference is the permanent record of the papers presented at the conference. As an aid to authors who seek to improve the readability of their Proceedings papers, this note summarizes some useful guidelines on technical writing, including key references on each topic that is discussed. For questions about these guidelines, please send e-mail to <jwilson@ncsu.edu> or contact the Proceedings Editors. I. Organizing the paper (what to do before beginning to write) A. Analyze the situation—that is, the problem, the solution, and the target audience. 1. Formulate the objectives of the paper. 2. Specify the scope of the paper’s coverage of the subject and the results to be discussed. Orient the paper toward the theme of your session as indicated either by the title of your session or by the instructions of your session chair. Also take into account the type of track—tutorial, methodology, or application. 3. Identify the target audience and determine the background knowledge that you can assume for this particular group of people. Introductory tutorials are generally attended by newcomers who are interested in the basics of simulation. Advanced tutorials are designed to provide more experienced professionals with a thorough discussion of special topics of much current interest; and some specialfocus sessions in this track are designed to provide experts with an overview of recent fundamental

this paper we consider the general partitioned linear (or Gau--Markov) model y = X 1 # 1 +X 2 # 2 + #, (1.1) or in another notation, 12 := X 12 # 12 , V} := X 1 # 1 +X 2 # 2 , V}, (1.2) 166 K. L. Chu et al. with E(y) = X 12 # 12 , E(#) = 0, cov(y) = cov(#) = V, (1.3) where E() denotes expectation (or expected value) and cov() denotes the covariance (or dispersion) matrix. The vector y is an n 1 observable random vector, # is an n unobservable random error vector, and (1.4) is a p 12 1 vector of unknown parameters with p 12 = p 1 + p 2 = p, with # 1 (p 1 1) and # 2 (p 2 1). The model (or design) matrix X 12 is n p 12 and is partitioned columnwise as X 12 = (X 1 : X 2 ) , (1.5) with p 12 = p 1 + p 2 , X 1 (n p 1 ) and X 2 (n p 2 ). Both the model matrix X 12 and the covariance matrix V are known. Usually the model matrix X 12 is denoted by just X and the number of its columns by just p

College of Business of the University of Illinois at Urbana-Champaign and the Deloitte Foundation. This draft is preliminary and incomplete; please do not quote or cite without the author’s permission. Estimate-related Disclosures, Investor Mindset and the Illusion of

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