Bayesian online algorithms for learning in discrete Hidden Markov Models

We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.

Entrepreneurship and the informal economy: a study of Ukraine's hidden enterprise culture

How many entrepreneurs start-up their business ventures conducting some or all of their trade in the informal economy? The aim of this paper is to answer this key question that has been seldom addressed using data from 600 face-to-face structured interviews conducted in Ukraine in late 2005 and early 2006. Analyzing the 331 entrepreneurs identified (i.e., individuals starting-up an enterprise in the past three years), just 10 percent operate on a wholly legitimate basis, while 39 percent have a license to trade and/or have registered their business but conduct a portion of their trade in the informal economy, and 51 percent operate unregistered enterprises and conduct all of their trade on an off-the-books basis. Given that some 90 percent of all business start-ups operate partially or wholly in the informal economy, and that 40 percent of all respondents depend on the informal economy as either their principal or secondary contributor to their livelihoods, the paper concludes by considering the wider implications of these findings both for further research and public policy.

Hidden state models for time series

Amongst all the objectives in the study of time series, uncovering the dynamic law of its generation is probably the most important. When the underlying dynamics are not available, time series modelling consists of developing a model which best explains a sequence of observations. In this thesis, we consider hidden space models for analysing and describing time series. We first provide an introduction to the principal concepts of hidden state models and draw an analogy between hidden Markov models and state space models. Central ideas such as hidden state inference or parameter estimation are reviewed in detail. A key part of multivariate time series analysis is identifying the delay between different variables. We present a novel approach for time delay estimating in a non-stationary environment. The technique makes use of hidden Markov models and we demonstrate its application for estimating a crucial parameter in the oil industry. We then focus on hybrid models that we call dynamical local models. These models combine and generalise hidden Markov models and state space models. Probabilistic inference is unfortunately computationally intractable and we show how to make use of variational techniques for approximating the posterior distribution over the hidden state variables. Experimental simulations on synthetic and real-world data demonstrate the application of dynamical local models for segmenting a time series into regimes and providing predictive distributions.

Biomedical events extraction using the hidden vector state model

Objective: Biomedical events extraction concerns about events describing changes on the state of bio-molecules from literature. Comparing to the protein-protein interactions (PPIs) extraction task which often only involves the extraction of binary relations between two proteins, biomedical events extraction is much harder since it needs to deal with complex events consisting of embedded or hierarchical relations among proteins, events, and their textual triggers. In this paper, we propose an information extraction system based on the hidden vector state (HVS) model, called HVS-BioEvent, for biomedical events extraction, and investigate its capability in extracting complex events. Methods and material: HVS has been previously employed for extracting PPIs. In HVS-BioEvent, we propose an automated way to generate abstract annotations for HVS training and further propose novel machine learning approaches for event trigger words identification, and for biomedical events extraction from the HVS parse results. Results: Our proposed system achieves an F-score of 49.57% on the corpus used in the BioNLP'09 shared task, which is only 2.38% lower than the best performing system by UTurku in the BioNLP'09 shared task. Nevertheless, HVS-BioEvent outperforms UTurku's system on complex events extraction with 36.57% vs. 30.52% being achieved for extracting regulation events, and 40.61% vs. 38.99% for negative regulation events. Conclusions: The results suggest that the HVS model with the hierarchical hidden state structure is indeed more suitable for complex event extraction since it could naturally model embedded structural context in sentences.

Extracting protein-protein interactions from MEDLINE using the hidden vector state model

A major challenge in text mining for biomedicine is automatically extracting protein-protein interactions from the vast amount of biomedical literature. We have constructed an information extraction system based on the Hidden Vector State (HVS) model for protein-protein interactions. The HVS model can be trained using only lightly annotated data whilst simultaneously retaining sufficient ability to capture the hierarchical structure. When applied in extracting protein-protein interactions, we found that it performed better than other established statistical methods and achieved 61.5% in F-score with balanced recall and precision values. Moreover, the statistical nature of the pure data-driven HVS model makes it intrinsically robust and it can be easily adapted to other domains.

Discriminative training of the hidden vector state model for semantic parsing

In this paper, we discuss how discriminative training can be applied to the hidden vector state (HVS) model in different task domains. The HVS model is a discrete hidden Markov model (HMM) in which each HMM state represents the state of a push-down automaton with a finite stack size. In previous applications, maximum-likelihood estimation (MLE) is used to derive the parameters of the HVS model. However, MLE makes a number of assumptions and unfortunately some of these assumptions do not hold. Discriminative training, without making such assumptions, can improve the performance of the HVS model by discriminating the correct hypothesis from the competing hypotheses. Experiments have been conducted in two domains: the travel domain for the semantic parsing task using the DARPA Communicator data and the Air Travel Information Services (ATIS) data and the bioinformatics domain for the information extraction task using the GENIA corpus. The results demonstrate modest improvements of the performance of the HVS model using discriminative training. In the travel domain, discriminative training of the HVS model gives a relative error reduction rate of 31 percent in F-measure when compared with MLE on the DARPA Communicator data and 9 percent on the ATIS data. In the bioinformatics domain, a relative error reduction rate of 4 percent in F-measure is achieved on the GENIA corpus.

Ontology-based protein-protein interactions extraction from literature using the hidden vector state model

This paper proposes a novel framework of incorporating protein-protein interactions (PPI) ontology knowledge into PPI extraction from biomedical literature in order to address the emerging challenges of deep natural language understanding. It is built upon the existing work on relation extraction using the Hidden Vector State (HVS) model. The HVS model belongs to the category of statistical learning methods. It can be trained directly from un-annotated data in a constrained way whilst at the same time being able to capture the underlying named entity relationships. However, it is difficult to incorporate background knowledge or non-local information into the HVS model. This paper proposes to represent the HVS model as a conditionally trained undirected graphical model in which non-local features derived from PPI ontology through inference would be easily incorporated. The seamless fusion of ontology inference with statistical learning produces a new paradigm to information extraction.

A novel framework of training hidden Markov support vector machines from lightly-annotated data

Natural language understanding (NLU) aims to map sentences to their semantic mean representations. Statistical approaches to NLU normally require fully-annotated training data where each sentence is paired with its word-level semantic annotations. In this paper, we propose a novel learning framework which trains the Hidden Markov Support Vector Machines (HM-SVMs) without the use of expensive fully-annotated data. In particular, our learning approach takes as input a training set of sentences labeled with abstract semantic annotations encoding underlying embedded structural relations and automatically induces derivation rules that map sentences to their semantic meaning representations. The proposed approach has been tested on the DARPA Communicator Data and achieved 93.18% in F-measure, which outperforms the previously proposed approaches of training the hidden vector state model or conditional random fields from unaligned data, with a relative error reduction rate of 43.3% and 10.6% being achieved.

Hidden in plain sight:low-literacy adults in a developed country overcoming social and educational challenges through mobile learning support tools

Illiteracy is often associated with people in developing countries. However, an estimated 50 % of adults in a developed country such as Canada lack the literacy skills required to cope with the challenges of today's society; for them, tasks such as reading, understanding, basic arithmetic, and using everyday items are a challenge. Many community-based organizations offer resources and support for these adults, yet overall functional literacy rates are not improving. This is due to a wide range of factors, such as poor retention of adult learners in literacy programs, obstacles in transferring the acquired skills from the classroom to the real life, personal attitudes toward learning, and the stigma of functional illiteracy. In our research we examined the opportunities afforded by personal mobile devices in providing learning and functional support to low-literacy adults. We present the findings of an exploratory study aimed at investigating the reception and adoption of a technological solution for adult learners. ALEX© is a mobile application designed for use both in the classroom and in daily life in order to help low-literacy adults become increasingly literate and independent. Such a solution complements literacy programs by increasing users' motivation and interest in learning, and raising their confidence levels both in their education pursuits and in facing the challenges of their daily lives. We also reflect on the challenges we faced in designing and conducting our research with two user groups (adults enrolled in literacy classes and in an essential skills program) and contrast the educational impact and attitudes toward such technology between these. Our conclusions present the lessons learned from our evaluations and the impact of the studies' specific challenges on the outcome and uptake of such mobile assistive technologies in providing practical support to low-literacy adults in conjunction with literacy and essential skills training. © 2013 Her Majesty the Queen in Right of Canada.

Computational mechanics analysis of the hidden conformational dynamics within a molecular trajectory

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Online learning in discrete hidden Markov models

We present and analyze three different online algorithms for learning in discrete Hidden Markov Models (HMMs) and compare their performance with the Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of the generalization error we draw learning curves in simplified situations and compare the results. The performance for learning drifting concepts of one of the presented algorithms is analyzed and compared with the Baldi-Chauvin algorithm in the same situations. A brief discussion about learning and symmetry breaking based on our results is also presented. © 2006 American Institute of Physics.

Gender, borrowing patterns and self-employment:some evidence for England

The purpose of this paper is twofold. First, we try to identify whether women have different borrowing patterns from men when trying to get into self-employment along with the factors that explain these different propensities. Second, we try to quantify the impact of these differences in borrowing propensity on women's willingness to become self-employed. The empirical analysis is carried out on a sample of individuals drawn from the English Household Survey of Entrepreneurship, 2003. Our results show that (1) women are less likely than men to seek external finance and that (2) gender differences in access to finance are affecting adversely the transition into self-employment. © 2010 Springer Science+Business Media, LLC.

A rejtett gazdaság és az ellene való fellépés tényezői = The hidden economy and the factors involved in combating it

A szerzők közgazdasági modellek áttekintése segítségével elemzik a rejtett gazdaságban való részvétel tényezőit. Bemutatják, hogy a haszonmaximáló cselekvők a rejtett gazdaság egyes megjelenési formáival kapcsolatos döntésük meghozatala során (például adócsalás esetében) számot vetnek a rejtett gazdaságban való részvétel, illetve az attól való tartózkodás költségeivel és hasznaival. A tanulmány az adócsalás társadalmilag optimális szintjének meghatározásával foglalkozik a rejtett gazdasággal szembeni kormányzati lépések egyes típusaival, ezek hatásaival és a rejtett gazdasággal szembeni optimális kormányzati politika lehetőségeivel. _______ The authors analyse the factors behind participation in the hidden economy, through a review of the models of economic theory. They show that those seeking to maximize profits weigh various forms of the hidden economy (tax evasion, for instance), in the search for the optimum solution, calculate the costs and benefits of participating in the hidden economy or refraining from doing so. Taking the ‘socially optimum level’ of tax evasion, the study covers the various types of government measures that can be taken against the hidden economy, the effects of them, and the scope for an optimum government policy to combat the hidden economy.

Borrowing services from nature. Methodologies to evaluate ecosystem services focusing on Hungarian case studies.

In modern societies, humans are largely managing the world's natu­ral resources as if they were limitless. ln fact, even the so-called renewable resources require time tor renewal once they have been used. This kind of human attitude puts pressure on Earth's ecosystems at an unprecedented scale. In order to reverse the recent trend, policies shall tackle the roots of the problems and the drivers behind them must be addressed in order to reduce the pressure on ecosystems, including the overuse of natural resources and ecological space. This publication aims to show good examples tor evaluating natural values and tor showing possible environmental and social impacts of various policies. Considering carefully the examples presented in this publication could play fundamental role in proper decision making that considers environmental and overall sustainability aspects.