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Experimental approaches are widely employed to benchmark the performance of an information retrieval (IR) system. Measurements in terms of recall and precision are computed as performance indicators. Although they are good at assessing the retrieval effectiveness of an IR system, they fail to explore deeper aspects such as its underlying functionality and explain why the system shows such performance. Recently, inductive (i.e., theoretical) evaluation of IR systems has been proposed to circumvent the controversies of the experimental methods. Several studies have adopted the inductive approach, but they mostly focus on theoretical modeling of IR properties by using some metalogic. In this article, we propose to use inductive evaluation for functional benchmarking of IR models as a complement of the traditional experiment-based performance benchmarking. We define a functional benchmark suite in two stages: the evaluation criteria based on the notion of "aboutness," and the formal evaluation methodology using the criteria. The proposed benchmark has been successfully applied to evaluate various well-known classical and logic-based IR models. The functional benchmarking results allow us to compare and analyze the functionality of the different IR models.

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Cryptographic hashes such as MD5 and SHA-1 are used for many data mining and security applications -they are used as an identifier for files and documents. However, if a single byte of a file is changed, then cryptographic hashes result in a completely different hash value. It would be very useful to work with hashes which identify that files were similar based on their hash values. The security field has proposed similarity digests, and the data mining community has proposed locality sensitive hashes. Some proposals include the Nilsimsa hash (a locality sensitive hash), Ssdeep and Sdhash (both Ssdeep and Sdhash are similarity digests). Here, we describe a new locality sensitive hashing scheme the TLSH. We provide algorithms for evaluating and comparing hash values and provide a reference to its open source code. We do an empirical evaluation of publically available similarity digest schemes. The empirical evaluation highlights significant problems with previously proposed schemes; the TLSH scheme does not suffer from the flaws identified.

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Abstract-This paper provides a solution based on LinearProgramming to the problem of designing observers that ensures guaranteed bounds on the estimated states. Firstly, considering linear systems without uncertainties, we provide a complete solution for the existence of interval observers having minimal l1-norm of the interval error. Secondly, new type of observers involving dilatation functions, are introduced in order to deal with the robust estimation of systems that are possibly nonlinear and subject to uncertainties. A new methodology is provided for the design and characterization of tight robust interval observers. All the proposed conditions are expressed in term of Linear programming.

It has been over a decade since Rosenblatt published his seminal paper on modelling the dynamic facility layout problem (DFLP). Since then, there have been improvements to Rosenblatt s original dynamic programming model. Alternate solution methods have also been proposed. However, no comprehensive review of the research in the DFLP has been undertaken. In this paper we categorize the different works of research that have followed and discuss them. They include improved and more flexible solution methods, fathoming procedures, bound determinations and, method comparisons.