title: Online estimation of discrete, continuous, and conditional joint densities using classifier chains

authors: Geilke, Michael

subjects: Science & Technology

abstract: We address the problem of estimating discrete, continuous, and conditional joint densities online, i.e., the algorithm is only provided the current example and its current estimate for its update. The family of proposed online density estimators, estimation of densities online (EDO), uses classifier chains to model dependencies among features, where each classifier in the chain estimates the probability of one particular feature. Because a single chain may not provide a reliable estimate, we also consider ensembles of classifier chains and ensembles of weighted classifier chains. For all density estimators, we provide consistency proofs and propose algorithms to perform certain inference tasks. The empirical evaluation of the estimators is conducted in several experiments and on datasets of up to several millions of instances. In the discrete case, we compare our estimators to density estimates computed by Bayesian structure learners. In the continuous case, we compare them to a state-of-the-art online density estimator. Our experiments demonstrate that, even though designed to work online, EDO delivers estimators of competitive accuracy compared to other density estimators (batch Bayesian structure learners on discrete datasets and the state-of-the-art online density estimator on continuous datasets). Besides achieving similar performance in these cases, EDO is also able to estimate densities with mixed types of variables, i.e., discrete and continuous random variables.

text: Online estimation of discrete, continuous, and conditional joint densities using classifier chains We address the problem of estimating discrete, continuous, and conditional joint densities online, i.e., the algorithm is only provided the current example and its current estimate for its update. The family of proposed online density estimators, estimation of densities online (EDO), uses classifier chains to model dependencies among features, where each classifier in the chain estimates the probability of one particular feature. Because a single chain may not provide a reliable estimate, we also consider ensembles of classifier chains and ensembles of weighted classifier chains. For all density estimators, we provide consistency proofs and propose algorithms to perform certain inference tasks. The empirical evaluation of the estimators is conducted in several experiments and on datasets of up to several millions of instances. In the discrete case, we compare our estimators to density estimates computed by Bayesian structure learners. In the continuous case, we compare them to a state-of-the-art online density estimator. Our experiments demonstrate that, even though designed to work online, EDO delivers estimators of competitive accuracy compared to other density estimators (batch Bayesian structure learners on discrete datasets and the state-of-the-art online density estimator on continuous datasets). Besides achieving similar performance in these cases, EDO is also able to estimate densities with mixed types of variables, i.e., discrete and continuous random variables.

year: 2018

title: DOCODE 3.0 (DOcument COpy DEtector): A system for plagiarism detection by applying an information fusion process from multiple documental data sources

authors: Velásquez, Juan D.

subjects: Plagiarism detection

abstract: Plagiarism refers to the act of presenting external words, thoughts, or ideas as one’s own, without providing references to the sources from which they were taken. The exponential growth of different digital document sources available on the Web has facilitated the spread of this practice, making the accurate detection of it a crucial task for educational institutions. In this article, we present DOCODE 3.0, a Web system for educational institutions that performs automatic analysis of large quantities of digital documents in relation to their degree of originality. Since plagiarism is a complex problem, frequently tackled at different levels, our system applies algorithms in order to perform an information fusion process from multi data source to all these levels. These algorithms have been successfully tested in the scientific community in solving tasks like the identification of plagiarized passages and the retrieval of source candidates from the Web, among other multi data sources as digital libraries, and have proven to be very effective. We integrate these algorithms into a multi-tier, robust and scalable JEE architecture, allowing many different types of clients with different requirements to consume our services. For users, DOCODE produces a number of visualizations and reports from the different outputs to let teachers and professors gain insights on the originality of the documents they review, allowing them to discover, understand and handle possible plagiarism cases and making it easier and much faster to analyze a vast number of documents. Our experience here is so far focused on the Chilean situation and the Spanish language, offering solutions to Chilean educational institutions in any of their preferred Virtual Learning Environments. However, DOCODE can easily be adapted to increase language coverage.

text: DOCODE 3.0 (DOcument COpy DEtector): A system for plagiarism detection by applying an information fusion process from multiple documental data sources Plagiarism refers to the act of presenting external words, thoughts, or ideas as one’s own, without providing references to the sources from which they were taken. The exponential growth of different digital document sources available on the Web has facilitated the spread of this practice, making the accurate detection of it a crucial task for educational institutions. In this article, we present DOCODE 3.0, a Web system for educational institutions that performs automatic analysis of large quantities of digital documents in relation to their degree of originality. Since plagiarism is a complex problem, frequently tackled at different levels, our system applies algorithms in order to perform an information fusion process from multi data source to all these levels. These algorithms have been successfully tested in the scientific community in solving tasks like the identification of plagiarized passages and the retrieval of source candidates from the Web, among other multi data sources as digital libraries, and have proven to be very effective. We integrate these algorithms into a multi-tier, robust and scalable JEE architecture, allowing many different types of clients with different requirements to consume our services. For users, DOCODE produces a number of visualizations and reports from the different outputs to let teachers and professors gain insights on the originality of the documents they review, allowing them to discover, understand and handle possible plagiarism cases and making it easier and much faster to analyze a vast number of documents. Our experience here is so far focused on the Chilean situation and the Spanish language, offering solutions to Chilean educational institutions in any of their preferred Virtual Learning Environments. However, DOCODE can easily be adapted to increase language coverage.

year: 2016-01

title: Introduction to tephra-derived soils, North Island, New Zealand: University of Waikato and University of Wisconsin-Platteville post-conference Andisol excursion, 21-23 December, 2016

authors: Lowe, David J.

abstract: Provisional itinerary DAY 1 Wednesday 21 December: Mamaku Plateau-Rerewhakaaitu-Lake Tarawera: 1 Goodwin Farm, Tapapa Rd, Tapapa: welded ignimbrite, tephras, loess, buried soils (Tirau soil) - stratigraphy of sequence (230 ka and younger) and upbuilding pedogenesis - Tirau silt loam 2 Brett Rd, Rerewhakaaitu: Holocene tephras and buried soil (Rotomahana soil) - Volcanic landscape, historical importance of area for NZ soil survey - Stratigraphy of sequence (~9.5 cal ka to 10 June 1886 Tarawera eruption) and upbuilding pedogenesis - Rotomahana sandy loam, 3 Ash Pit Rd, Rerewhakaaitu: tephras and buried soils (Matahina soil) - Matahina gravel - buried spodic/podzol soil features, 4 Okareka Loop Rd tephra section with proximal Rotorua Tephra and buried soils, loess, Buried Village Museum, Te Wairoa, Lake Tarawera (Stoney Point). DAY 2 Thursday 22 December: Otorohanga-Waitomo Caves -Pirongia Mountain: 1 Raynes Road section: Ultisol in composite tephra sequence (Kainui soil), 2 Otorohanga Kiwi House, 3 Waitomo Caves,, 4 Pirongia Mountain: Mangakara forest walk, 5 Evening meal on an Andisol. DAY 3 Friday 23 December: Hobbiton, Hamilton gardens including Te Parapara (ancient Maori garden): 1. Hobbiton tour 10.15 am (2 hrs), 2. Hamilton Gardens incl. Te Parapara Garden and human-modified soils (Tamahere soil).

text: Introduction to tephra-derived soils, North Island, New Zealand: University of Waikato and University of Wisconsin-Platteville post-conference Andisol excursion, 21-23 December, 2016 Provisional itinerary DAY 1 Wednesday 21 December: Mamaku Plateau-Rerewhakaaitu-Lake Tarawera: 1 Goodwin Farm, Tapapa Rd, Tapapa: welded ignimbrite, tephras, loess, buried soils (Tirau soil) - stratigraphy of sequence (230 ka and younger) and upbuilding pedogenesis - Tirau silt loam 2 Brett Rd, Rerewhakaaitu: Holocene tephras and buried soil (Rotomahana soil) - Volcanic landscape, historical importance of area for NZ soil survey - Stratigraphy of sequence (~9.5 cal ka to 10 June 1886 Tarawera eruption) and upbuilding pedogenesis - Rotomahana sandy loam, 3 Ash Pit Rd, Rerewhakaaitu: tephras and buried soils (Matahina soil) - Matahina gravel - buried spodic/podzol soil features, 4 Okareka Loop Rd tephra section with proximal Rotorua Tephra and buried soils, loess, Buried Village Museum, Te Wairoa, Lake Tarawera (Stoney Point). DAY 2 Thursday 22 December: Otorohanga-Waitomo Caves -Pirongia Mountain: 1 Raynes Road section: Ultisol in composite tephra sequence (Kainui soil), 2 Otorohanga Kiwi House, 3 Waitomo Caves,, 4 Pirongia Mountain: Mangakara forest walk, 5 Evening meal on an Andisol. DAY 3 Friday 23 December: Hobbiton, Hamilton gardens including Te Parapara (ancient Maori garden): 1. Hobbiton tour 10.15 am (2 hrs), 2. Hamilton Gardens incl. Te Parapara Garden and human-modified soils (Tamahere soil).

year: 2016-12-21