Technical Paper Recommendation: A Study in Combining Multiple Information Sources

The growing need to manage and exploit the proliferation of online data sources is opening up new opportunities for bringing people closer to the resources they need. For instance, consider a recommendation service through which researchers can receive daily pointers to journal papers in their fields of interest. We survey some of the known approaches to the problem of technical paper recommendation and ask how they can be extended to deal with multiple information sources. More specifically, we focus on a variant of this problem - recommending conference paper submissions to reviewing committee members - which offers us a testbed to try different approaches. Using WHIRL - an information integration system - we are able to implement different recommendation algorithms derived from information retrieval principles. We also use a novel autonomous procedure for gathering reviewer interest information from the Web. We evaluate our approach and compare it to other methods using preference data provided by members of the AAAI-98 conference reviewing committee along with data about the actual submissions

Semantic and generative models for lossy text compression

The apparent divergence between the research paradigms of text and image compression has led us to consider the potential for applying methods developed for one domain to the other. This paper examines the idea of "lossy" text compression, which transmits an approximation to the input text rather than the text itself. In image coding, lossy techniques have proven to yield compression factors that are vastly superior to those of the best lossless schemes, and we show that this a also the case for text. Two different methods are described here, one inspired by the use of fractals in image compression. They can be combined into an extremely effective technique that provides much better compression than the present state of the art and yet preserves a reasonable degree of match between the original and received text. The major challenge for lossy text compression is identified as the reliable evaluation of the quality of this match

Mining, compressing and classifying with extensible motifs

BACKGROUND: Motif patterns of maximal saturation emerged originally in contexts of pattern discovery in biomolecular sequences and have recently proven a valuable notion also in the design of data compression schemes. Informally, a motif is a string of intermittently solid and wild characters that recurs more or less frequently in an input sequence or family of sequences. Motif discovery techniques and tools tend to be computationally imposing, however, special classes of "rigid" motifs have been identified of which the discovery is affordable in low polynomial time. RESULTS: In the present work, "extensible" motifs are considered such that each sequence of gaps comes endowed with some elasticity, whereby the same pattern may be stretched to fit segments of the source that match all the solid characters but are otherwise of different lengths. A few applications of this notion are then described. In applications of data compression by textual substitution, extensible motifs are seen to bring savings on the size of the codebook, and hence to improve compression. In germane contexts, in which compressibility is used in its dual role as a basis for structural inference and classification, extensible motifs are seen to support unsupervised classification and phylogeny reconstruction. CONCLUSION: Off-line compression based on extensible motifs can be used advantageously to compress and classify biological sequences

Financial Time series: motif discovery and analysis using VALMOD

Motif discovery and analysis in time series data-sets have a wide-range of applications from genomics to finance. In consequence, development and critical evaluation of these algorithms is required with the focus not just detection but rather evaluation and interpretation of overall significance. Our focus here is the specific algorithm, VALMOD, but algorithms in wide use for motif discovery are summarised and briefly compared, as well as typical evaluation methods with strengths. Additionally, Taxonomy diagrams for motif discovery and evaluation techniques are constructed to illustrate the relationship between different approaches as well as inter-dependencies. Finally evaluation measures based upon results obtained from VALMOD analysis of a GBP-USD foreign exchange (F/X) rate data-set are presented, in illustration

On Hilberg's Law and Its Links with Guiraud's Law

Hilberg (1990) supposed that finite-order excess entropy of a random human text is proportional to the square root of the text length. Assuming that Hilberg's hypothesis is true, we derive Guiraud's law, which states that the number of word types in a text is greater than proportional to the square root of the text length. Our derivation is based on some mathematical conjecture in coding theory and on several experiments suggesting that words can be defined approximately as the nonterminals of the shortest context-free grammar for the text. Such operational definition of words can be applied even to texts deprived of spaces, which do not allow for Mandelbrot's ``intermittent silence'' explanation of Zipf's and Guiraud's laws. In contrast to Mandelbrot's, our model assumes some probabilistic long-memory effects in human narration and might be capable of explaining Menzerath's law.Comment: To appear in Journal of Quantitative Linguistic

Language Model Co-occurrence Linking for Interleaved Activity Discovery

As ubiquitous computer and sensor systems become abundant, the potential for automatic identification and tracking of human behaviours becomes all the more evident. Annotating complex human behaviour datasets to achieve ground truth for supervised training can however be extremely labour-intensive, and error prone. One possible solution to this problem is activity discovery: the identification of activities in an unlabelled dataset by means of an unsupervised algorithm. This paper presents a novel approach to activity discovery that utilises deep learning based language production models to construct a hierarchical, tree-like structure over a sequential vector of sensor events. Our approach differs from previous work in that it explicitly aims to deal with interleaving (switching back and forth between between activities) in a principled manner, by utilising the long-term memory capabilities of a recurrent neural network cell. We present our approach and test it on a realistic dataset to evaluate its performance. Our results show the viability of the approach and that it shows promise for further investigation. We believe this is a useful direction to consider in accounting for the continually changing nature of behaviours

HALO: Post-Link Heap-Layout Optimisation

Today, general-purpose memory allocators dominate the landscape of dynamic memory management. While these so- lutions can provide reasonably good behaviour across a wide range of workloads, it is an unfortunate reality that their behaviour for any particular workload can be highly suboptimal. By catering primarily to average and worst-case usage patterns, these allocators deny programs the advantages of domain-specific optimisations, and thus may inadvertently place data in a manner that hinders performance, generating unnecessary cache misses and load stalls. To help alleviate these issues, we propose HALO: a post-link profile-guided optimisation tool that can improve the layout of heap data to reduce cache misses automatically. Profiling the target binary to understand how allocations made in different contexts are related, we specialise memory-management routines to allocate groups of related objects from separate pools to increase their spatial locality. Unlike other solutions of its kind, HALO employs novel grouping and identification algorithms which allow it to create tight-knit allocation groups using the entire call stack and to identify these efficiently at runtime. Evaluation of HALO on contemporary out-of-order hardware demonstrates speedups of up to 28% over jemalloc, out-performing a state-of-the-art data placement technique from the literature

Artificial Sequences and Complexity Measures

In this paper we exploit concepts of information theory to address the fundamental problem of identifying and defining the most suitable tools to extract, in a automatic and agnostic way, information from a generic string of characters. We introduce in particular a class of methods which use in a crucial way data compression techniques in order to define a measure of remoteness and distance between pairs of sequences of characters (e.g. texts) based on their relative information content. We also discuss in detail how specific features of data compression techniques could be used to introduce the notion of dictionary of a given sequence and of Artificial Text and we show how these new tools can be used for information extraction purposes. We point out the versatility and generality of our method that applies to any kind of corpora of character strings independently of the type of coding behind them. We consider as a case study linguistic motivated problems and we present results for automatic language recognition, authorship attribution and self consistent-classification.Comment: Revised version, with major changes, of previous "Data Compression approach to Information Extraction and Classification" by A. Baronchelli and V. Loreto. 15 pages; 5 figure

Evaluating deterministic motif significance measures in protein databases

<p>Abstract</p> <p>Background</p> <p>Assessing the outcome of motif mining algorithms is an essential task, as the number of reported motifs can be very large. Significance measures play a central role in automatically ranking those motifs, and therefore alleviating the analysis work. Spotting the most interesting and relevant motifs is then dependent on the choice of the right measures. The combined use of several measures may provide more robust results. However caution has to be taken in order to avoid spurious evaluations.</p> <p>Results</p> <p>From the set of conducted experiments, it was verified that several of the selected significance measures show a very similar behavior in a wide range of situations therefore providing redundant information. Some measures have proved to be more appropriate to rank highly conserved motifs, while others are more appropriate for weakly conserved ones. Support appears as a very important feature to be considered for correct motif ranking. We observed that not all the measures are suitable for situations with poorly balanced class information, like for instance, when positive data is significantly less than negative data. Finally, a visualization scheme was proposed that, when several measures are applied, enables an easy identification of high scoring motifs.</p> <p>Conclusion</p> <p>In this work we have surveyed and categorized 14 significance measures for pattern evaluation. Their ability to rank three types of deterministic motifs was evaluated. Measures were applied in different testing conditions, where relations were identified. This study provides some pertinent insights on the choice of the right set of significance measures for the evaluation of deterministic motifs extracted from protein databases.</p