ppxscal/embedding_network huggingface dataset:
Learning from Collaboratively Observing Videos during Problem Solving with Andes
Authors: Scotty D. Craig, Kurt Vanlehn, Soniya Gadgil, Micki Chi
Venue: Proceeding of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work (2007)

Learning by observation has long been a traditional method of learning. Recent work has pointed toward collaboratively observing tutoring as a promising new method for observational learning. Our current study tested this new method in the PSLC physics LearnLab where students were introduced two topics of rotational kinematics by observing videos while problem solving in Andes. The students were randomly assigned to a pair condition that collaboratively observed a video of an expert tutoring or providing an example, or to a solo condition that observed a video of an expert worked example. Several robust and normal learning measures were collected, however, to date only multiple choice measures have been analyzed. Students' performance on the multiple choice questionnaires revealed significant pretest to posttest gains for all conditions. However, no differences have been found among conditions for normal learning measures.
REPETITA
Authors: Luca Marsella, Francesco Sirocco, Antonio Trovato, Flavio Seno, Silvio C.E. Tosatto
Venue: Bioinformatics (2009)

Motivation: Proteins with solenoid repeats evolve more quickly than non-repetitive ones and their periodicity may be rapidly hidden at sequence level, while still evident in structure. In order to identify these repeats, we propose here a novel method based on a metric characterizing amino-acid properties (polarity, secondary structure, molecular volume, codon diversity, electric charge) using five previously derived numerical functions. Results: The five spectra of the candidate sequences coding for structural repeats, obtained by Discrete Fourier Transform (DFT), show common features allowing determination of repeat periodicity with excellent results. Moreover it is possible to introduce a phase space parameterized by two quantities related to the Fourier spectra which allow for a clear distinction between a non-homologous set of globular proteins and proteins with solenoid repeats. The DFT method is shown to be competitive with other state of the art methods in the detection of solenoid structures, while improving its performance especially in the identification of periodicities, since it is able to recognize the actual repeat length in most cases. Moreover it highlights the relevance of local structural propensities in determining solenoid repeats. Availability: A web tool implementing the algorithm presented in the article (REPETITA) is available with additional details on the data sets at the URL: http://protein.bio.unipd.it/repetita/. Contact: silvio.tosatto@unipd.it
CoCo: coding cost for parameter-free outlier detection
Authors: Christian Böhm, Katrin Haegler, Nikola S. Müller, Claudia Plant
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

How can we automatically spot all outstanding observations in a data set? This question arises in a large variety of applications, e.g. in economy, biology and medicine. Existing approaches to outlier detection suffer from one or more of the following drawbacks: The results of many methods strongly depend on suitable parameter settings being very difficult to estimate without background knowledge on the data, e.g. the minimum cluster size or the number of desired outliers. Many methods implicitly assume Gaussian or uniformly distributed data, and/or their result is difficult to interpret. To cope with these problems, we propose CoCo, a technique for parameter-free outlier detection. The basic idea of our technique relates outlier detection to data compression: Outliers are objects which can not be effectively compressed given the data set. To avoid the assumption of a certain data distribution, CoCo relies on a very general data model combining the Exponential Power Distribution with Independent Components. We define an intuitive outlier factor based on the principle of the Minimum Description Length together with an novel algorithm for outlier detection. An extensive experimental evaluation on synthetic and real world data demonstrates the benefits of our technique. Availability: The source code of CoCo and the data sets used in the experiments are available at: http://www.dbs.ifi.lmu.de/Forschung/KDD/Boehm/CoCo.
Minors of simplicial complexes
Authors: Tomáš Kaiser
Venue: Discrete Applied Mathematics (2009)

We extend the notion of a minor from matroids to simplicial complexes. We show that the class of matroids, as well as the class of independence complexes, is characterized by a single forbidden minor. Inspired by a recent result of Aharoni and Berger, we investigate possible ways to extend the matroid intersection theorem to simplicial complexes.
Modelling time-varying higher moments with maximum entropy density
Authors: Felix Chan
Venue: Mathematics and Computers in Simulation (2009)

Since the introduction of the Autoregressive Conditional Heteroscedasticity (ARCH) model of Engle [R. Engle, Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation, Econometrica 50 (1982) 987-1007], the literature of modelling the conditional second moment has become increasingly popular in the last two decades. Many extensions and alternate models of the original ARCH have been proposed in the literature aiming to capture the dynamics of volatility more accurately. Interestingly, the Quasi Maximum Likelihood Estimator (QMLE) with normal density is typically used to estimate the parameters in these models. As such, the higher moments of the underlying distribution are assumed to be the same as those of the normal distribution. However, various studies reveal that the higher moments, such as skewness and kurtosis of the distribution of financial returns are not likely to be the same as the normal distribution, and in some cases, they are not even constant over time. These have significant implications in risk management, especially in the calculation of Value-at-Risk (VaR) which focuses on the negative quantile of the return distribution. Failed to accurately capture the shape of the negative quantile would produce inaccurate measure of risk, and subsequently lead to misleading decision in risk management. This paper proposes a solution to model the distribution of financial returns more accurately by introducing a general framework to model the distribution of financial returns using maximum entropy density (MED). The main advantage of MED is that it provides a general framework to estimate the distribution function directly based on a given set of data, and it provides a convenient framework to model higher order moments up to any arbitrary finite order k. However this flexibility comes with a high cost in computational time as k increases, therefore this paper proposes an alternative model that would reduce computation time substantially. Moreover, the sensitivity of the parameters in the MED with respect to the dynamic changes of moments is derived analytically. This result is important as it relates the dynamic structure of the moments to the parameters in the MED. The usefulness of this approach will be demonstrated using 5min intra-daily returns of the Euro/USD exchange rate.
COE: A General Approach for Efficient Genome-Wide Two-Locus Epistasis Test in Disease Association Study
Authors: Xiang Zhang, Feng Pan, Yuying Xie, Fei Zou, Wei Wang
Venue: Proceedings of the 13th Annual International Conference on Research in Computational Molecular Biology (2009)

The availability of high density single nucleotide polymorphisms (SNPs) data has made genome-wide association study computationally challenging. Two-locus epistasis (gene-gene interaction) detection has attracted great research interest as a promising method for genetic analysis of complex diseases. In this paper, we propose a general approach, COE, for efficient large scale gene-gene interaction analysis, which supports a wide range of tests. In particular, we show that many commonly used statistics are convex functions. From the observed values of the events in two-locus association test, we can develop an upper bound of the test value. Such an upper bound only depends on single-locus test and the genotype of the SNP-pair. We thus group and index SNP-pairs by their genotypes. This indexing structure can benefit the computation of all convex statistics. Utilizing the upper bound and the indexing structure, we can prune most of the SNP-pairs without compromising the optimality of the result. Our approach is especially efficient for large permutation test. Extensive experiments demonstrate that our approach provides orders of magnitude performance improvement over the brute force approach.
Stochastic search using the natural gradient
Authors: Sun Yi, Daan Wierstra, Tom Schaul, Jürgen Schmidhuber
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

To optimize unknown 'fitness' functions, we present Natural Evolution Strategies, a novel algorithm that constitutes a principled alternative to standard stochastic search methods. It maintains a multinormal distribution on the set of solution candidates. The Natural Gradient is used to update the distribution's parameters in the direction of higher expected fitness, by efficiently calculating the inverse of the exact Fisher information matrix whereas previous methods had to use approximations. Other novel aspects of our method include optimal fitness baselines and importance mixing, a procedure adjusting batches with minimal numbers of fitness evaluations. The algorithm yields competitive results on a number of benchmarks.
Influence of workload imbalances on the need for worker flexibility
Authors: Darwin J. Davis, Hemant V. Kher, Bret J. Wagner
Venue: Computers and Industrial Engineering (2009)

The benefits of worker flexibility have been well recognized both in practice and in academic journals. Prior literature suggests that most of the benefits of worker flexibility accrue with a moderate level of cross training. Because of factors such as MRP planning systems, seasonality and budgeting cycles, many companies experience workload imbalances that are more structured than the workload variability modeled in prior studies. Using factors based on industry examples we model structured workload imbalances. Our premise is that workload imbalances create the ''need'' for worker flexibility, while the available work center capacity in the shop influences the extent to which worker flexibility can be utilized to counter the workload imbalances. Our results confirm that under low workload imbalance conditions, most benefits associated with worker cross training are obtained with a moderate level of training. However, in the presence of high workload imbalances we find that more extensive worker cross training significantly enhances shop performance. A sufficient level of work center capacity is required to realize the benefits of extensive cross training in countering high workload imbalances.
A new paradigm of humanoid robot motion programming based on touch interpretation
Authors: Fabio Dalla Libera, Takashi Minato, Ian Fasel, Hiroshi Ishiguro, Enrico Pagello, Emanuele Menegatti
Venue: Robotics and Autonomous Systems (2009)

Most humanoid soccer robot teams design the basic movements of their robots, like walking and kicking, off-line and manually. Once these motions are considered satisfactory, they are stored in the robot's memory and played according to a high level behavioral strategy. Much time is spent in the development of the movements, and despite the significant progress made in humanoid soccer robots, the interfaces employed for the development of motions are still quite primitive. In order to accelerate development, an intuitive instruction method is desired. We propose the development of robot motions through physical interaction. In this paper we propose a ''teaching by touching'' approach; the human operator teaches a motion by directly touching the robot's body parts like a dance instructor. Teaching by directly touching is intuitive for instructors. However, the robot needs to interpret the instructor's intention since tactile communication can be ambiguous. This paper presents a method to learn the interpretation of the touch meaning and investigates, through experiments, a general (shared among different users) and intuitive touch manner.
The Role of Blood Vessels in Rabbit Propagation Dynamics and Cardiac Arrhythmias
Authors: Matthew Gibb, Martin Bishop, Rebecca Burton, Peter Kohl, Vicente Grau, Gernot Plank, Blanca Rodriguez
Venue: Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart (2009)

Recent experimental findings have suggested the important role played by blood vessels within the heart in stabilising arrhythmias. However, this link has yet to be explored computationally. In this paper, we develop a computational framework to model fibre orientation around structural inhomogeneities in myocardial tissue based on information obtained from high-resolution histological and MRI images. This framework allows the simulation of cardiac wavefront propagation for a generalised vessel orientation and position within the ventricular wall and transmural fibre architecture around it. We simulate propagation following different stimulation protocols around a transmural and a sub-epicardial vessel, using both bidomain and monodomain representations. We demonstrate the importance of accurately modelling the fibre structure around blood vessels relative to a simplistic transmurally varying fibre orientation model and suggest how this may impact pro-arrhythmic electrical dynamics.
Defect Prediction in Hot Strip Rolling Using ANN and SVM
Authors: Manu Hietaniemi, Ulla Elsilä, Perttu Laurinen, Juha Röning
Venue: Proceeding of the 2008 conference on Tenth Scandinavian Conference on Artificial Intelligence: SCAI 2008 (2008)

One of the largest factors affecting the loss for steel manufacturing are defects in the steel strips produced. Therefore the prediction of these defects forehand would be very important. In this study we used classifiers --feedforward neural networks and a support vector machine --to solve this problem. We also used different kinds of feature selection methods such as a preprocessing step for the classifiers. As a result, these two classifiers confirmed the same grade of classification error in this study.
DTS: A Decentralized Tracing System
Authors: Kenji Masui, Benoit Donnet
Venue: Proceedings of the First International Workshop on Traffic Monitoring and Analysis (2009)

A new generation of widely distributed systems to measure the Internet topology at the interface level is currently being deployed. Cooperation between monitors in these systems is required in order to avoid over-consumption of network resources. This paper proposes an architecture for a distributed topology measurement (DTM) system that, for the first time, decentralizes probing information. The key idea of our proposal is that, by utilizing a shared database as a communication method among monitors and taking advantage of the characteristics of the Doubletree algorithm, we can get rid of a specific control point, and a DTM system can be constructed in a decentralized manner. In this paper, we describe our implementation of a DTM, called Decentralized Tracing System (DTS). Decentralization within DTS is achieved using various distributed hash tables (DHTs), each one being dedicated to a particular plane (i.e., control or data). We also provide preliminary evaluation results.
Predicting Upgrade Project Defects Based on Enhancement Requirements: An Empirical Study
Authors: Lei He, Juan Li, Qing Wang, Ye Yang
Venue: Proceedings of the International Conference on Software Process: Trustworthy Software Development Processes (2009)

In upgrade project development, Enhancement Requirements (ER, e.g. requirement additions and modifications) introduce new defects to the project. We need to evaluate this impact to help plan later project schedule and resources. Typically, many of the existing prediction technologies estimate defects based on software size or process performance baselines. However, they are limited in estimating the impact of ER on product quality. This paper proposes a novel ER-based defect prediction method using information retrieval (IR) technique and support vector machines (SVM). We analyze the historical data of defects and requirement specifications of actual upgrade projects to establish multiple prediction models to estimate new defects introduced by ER. Then we design two experiments to validate the method and report some preliminary results. The results indicate that our method can provide useful support for impact analysis of requirement evolution in upgrade projects.
An ontology-based hierarchical semantic modeling approach to clinical pathway workflows
Authors: Yan Ye, Zhibin Jiang, Xiaodi Diao, Dong Yang, Gang Du
Venue: Computers in Biology and Medicine (2009)

This paper proposes an ontology-based approach of modeling clinical pathway workflows at the semantic level for facilitating computerized clinical pathway implementation and efficient delivery of high-quality healthcare services. A clinical pathway ontology (CPO) is formally defined in OWL web ontology language (OWL) to provide common semantic foundation for meaningful representation and exchange of pathway-related knowledge. A CPO-based semantic modeling method is then presented to describe clinical pathways as interconnected hierarchical models including the top-level outcome flow and intervention workflow level along a care timeline. Furthermore, relevant temporal knowledge can be fully represented by combing temporal entities in CPO and temporal rules based on semantic web rule language (SWRL). An illustrative example about a clinical pathway for cesarean section shows the applicability of the proposed methodology in enabling structured semantic descriptions of any real clinical pathway.
Stability analysis of frame structures by bubble-function method
Authors: Xuanneng Gao, Haoming Zhu, Renhui Wang
Venue: Finite Elements in Analysis and Design (2009)

In the stability analysis of frame structures, the results by conventional finite element method (FEM) in which one member is taken as one element are sometimes unavailable. This paper took a new basic function system with bubble functions as the shape function of a bar element to develop a bubble function finite element method (BFEM), in which the bending and the geometric stiffness matrices were derived from the principle of virtual work. Bubble functions are finite element modes that are located entirely within a single element and are zero on boundaries of the element, but are nonzero at the other points. BFEM is as concise as conventional bar FEM but has better accuracy, and is adaptable to the buckling analysis of all kinds of frame structures. The use of bubble functions significantly improves the convergence of finite element analysis, and efficiently reduces the computation cost for the buckling analysis of frame structures. Numerical results show that using bubble functions in finite element for the stability analysis of structures is very efficient, especially for high-rise and large-scale frame structures.
Law and technologyKeeping track of telecommunications surveillance
Authors: Paul M. Schwartz
Venue: Communications of the ACM (2009)

The creation of a statistical index of U.S. telecommunications surveillance activities and their results will benefit both civil liberties and law enforcement.
Handwritten document retrieval strategies
Authors: Venu Govindaraju, Huaigu Cao, Anurag Bhardwaj
Venue: Proceedings of The Third Workshop on Analytics for Noisy Unstructured Text Data (2009)

With the continuous growth of the World Wide Web, there is an urgent need for an efficient information retrieval system which can search and retrieve handwritten documents when presented with user queries. However, unconstrained handwriting recognition remains to be a challenging task with inadequate performance (around 30%, accuracy) thus proving to be a major hurdle in providing a robust search experience in the domain of handwritten documents. In this paper, we describe our recent research with focus on information retrieval from noisy text output by imperfect recognizers applied to handwritten document images. We describe three techniques each exploring a different approach for solving the noisy text retrieval task. The first method uses a novel bootstrapping mechanism to refine the OCR'ed text and uses the cleaned text for retrieval. The second method uses the uncorrected or raw OCR'ed text but modifies the standard vector space model for handling noisy text issues. The third method employs robust image features to index the documents instead of using noisy OCR'ed text. We describe these approaches in detail and also present their performance using standard IR evaluation metrics.
Tracking Closed Curves with Non-linear Stochastic Filters
Authors: Christophe Avenel, Etienne Mémin, Patrick Pérez
Venue: Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision (2009)

The joint analysis of motions and deformations is crucial in a number of computer vision applications. In this paper, we introduce a non-linear stochastic filtering technique to track the state of a free curve. The approach we propose is implemented through a particle filter which includes color measurements characterizing the target and the background respectively. We design a continuous-time dynamics that allows us to infer inter-frame deformations. The curve is defined by an implicit level-set representation and the stochastic dynamics is expressed on the level-set function. It takes the form of a stochastic differential equation with Brownian motion of low dimension. Specific noise models lead to traditional evolution laws based on mean curvature motions, while other forms lead to new evolution laws with different smoothing behaviors. In these evolution models, we propose to combine local motion information extracted from the images and an incertitude modeling of the dynamics. The associated filter we propose for curve tracking thus belongs to the family of conditional particle filters. Its capabilities are demonstrated on various sequences with highly deformable objects.
The role of item fixation in haptic search
Authors: Myrthe A. Plaisier, Irene A. Kuling, Wouter M. Bergmann Tiest, Astrid M. L. Kappers
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

Enclosing objects in the hand is a common and efficient way of haptic exploration. Recently, the importance of grasping for more realistic haptic perception of virtual objects has been recognised in haptic interface design. While several studies on haptic perception have addressed haptic exploration of a single object, perception of several objects grasped together in the hand has received almost no attention yet. In this study we focus on the importance of freedom to manipulate the objects in the hand for three-dimensional shape perception. Furthermore, we investigate differences in detection speed for different positions in the grasping hand. Subjects were asked to search for a cube among spheres or for a sphere among cubes. Response times were measured for different locations of target shape in the hand. Also, the way in which the items were fixed was varied from allowing small displacements and rotation of the shapes to rigidly fixed. There were only differences in search times between the different positions in the hand, when the centre item was difficult to access because of the surrounding items. Finally, we show that search was faster when the items were rigidly fixed than when displacement and rotation was possible. This shows that more exploratory freedom does not necessarily make search for a three-dimensional shape faster.