ppxscal/embedding_network huggingface dataset:
Semantic Decomposition for Question Answering
Authors: Sven Hartrumpf
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

In this paper, we develop and evaluate methods for decomposing complex questions for a question answering system to less complex questions. This aims at increasing the number of correct answers, especially in (deep) semantic question answering systems. For example, an event that occurs as a temporal restriction of a question can be queried for its date and the resulting answer can be substituted in the original question leading to a simpler, revised question. We present six decomposition classes, which are employed for annotating the 996 different German QA@CLEF questions from 2004 till 2008 and trigger different decomposition methods. Most methods work on the level of semantic representations, thereby avoiding natural language generation, a second parsing step, and possible errors in these two steps. The decomposition classes are not equally distributed, but three of them occur frequently in the questions. In the evaluation, the precision and recall for automatically classifying questions with respect to the decomposition classes are investigated. Then, the impact on a deep question answering system is determined. On the QA@CLEF questions, which by construction prefer questions that can be answered from single sentences, the performance gain in number of correct answers is not large, but significant. This encourages us to develop and test further decomposition classes and methods as future work.
DB2 9 for Linux, UNIX, and Windows Database Administration Upgrade: Certification Study Guide
Authors: Roger E. Sanders
Venue:  (2007)

In DB2 9 for Linux, UNIX, and Windows Database Administration Upgrade Certification Study Guide, Roger E. Sandersone of the worlds leading DB2 authors and an active participant in the development of IBMs DB2 certification examscovers everything a reader needs to know to pass the shortbut notoriously challengingDB2 9 for LUW DBA Certification Upgrade exam (Exam 736).This specialized study guide steps you through all of the topics that are covered on the exam, including server management, data placement, XML concepts, activity analysis, high availability, database security, and much more. Everything new to DB2 9 that you will need to know in order to successfully pass the exam is covered in this book.Taking and passing the DB2 9 for LUW DBA Certification Upgrade exam (Exam 736) provides validation that you have mastered DB2 9. Passing this exam also earns you the IBM Certified Database Administrator or Advanced Database Administrator certification.This concentrated guide includes an extensive set of practice questions in each chapter that closely models the actual exam, along with an answer key with a full description of why the answer is the correct one. No other source gives you this much help in passing the exam.With the DB2 9 for Linux, UNIX, and Windows Database Administration Upgrade Certification Study Guide, you will:Gain the knowledge necessary to pass the DB2 9 for LUW DBA UpgradeCertification exam (Exam 736) Discover the changes to DB2 9 thatyoull need to know in order to be successful when taking the exam Receive an explanation of every topic included on the testby someoneinvolved in the creation of the actual exam Find 60 practice questionsbased on the actual exams format and approach, along withcomprehensive answers to the test questions to help you gainunderstanding
Column by column image-signal enhancement
Authors: Gabor T. Herman
Venue:  (2008)

The general problem that we consider is the real-time enhancement of a noisy image as it arrives column by column (time is associated with the horizontal axis). The enhancement is modeled by a column of real values between 0 and 1 (with as many pixels as in the noisy image column), which is used as a mask on the noisy image column. That is, the clean image column is estimated by multiplying the noisy image column by the mask column, pixel by pixel. In this work, the real-valued column is attained by "fuzzifying" a binary column, which is estimated from the noisy image column using a prior model and a noise model. The prior model is specified by a statistical distribution that assigns to binary columns a probability of occurrence. The noise model is specified by a statistical distribution that assigns the likelihood of occurrence of the noisy image column given the binary column. These statistics should either be known or estimated from a training set of typical images from the application area. Given models of this type, our task is to design an algorithm that will produce the sought-after binary and "fuzzified" columns in "real-time," that is as the noisy image arrives column by column.In many applications signals are transformed into images by taking the squared norm of the Short-Time Fourier Transform of the signal, called the spectrogram. These spectrograms are extremely redundant; columns represent spectral information of overlapping time intervals. We, therefore, tested the possible applicability of image processing algorithms to the problem of increasing recognition in the hearing aid application. It was our hypothesis that for every spectrogram image of noisy speech there exist a binary image and its &ldquo;fuzzified&rdquo; image that (along with the noisy spectrogram image) contain the information essential for estimating the clean speech signal. The proposed processing was tested on 24 normal hearing subjects using the Modified Rhyme Test. Unfortunately, the specific choices that we made in the development of our processing methodology decreased the recognition of noisy speech signals.
splitSVM: fast, space-efficient, non-heuristic, polynomial kernel computation for NLP applications
Authors: Yoav Goldberg, Michael Elhadad
Venue: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers (2008)

We present a fast, space efficient and non-heuristic method for calculating the decision function of polynomial kernel classifiers for NLP applications. We apply the method to the MaltParser system, resulting in a Java parser that parses over 50 sentences per second on modest hardware without loss of accuracy (a 30 time speedup over existing methods). The method implementation is available as the open-source splitSVM Java library.
Realization of button click feeling by use of ultrasonic vibration and force feedback
Authors: Kaoru Tashiro, Yuta Shiokawa, Tomotake Aono, Takashi Maeno
Venue: Proceedings of the World Haptics 2009 - Third Joint EuroHaptics conference and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems (2009)

This paper presents a displaying method of button like click feeling using ultrasonic vibration with amplitude of a few micrometers. Artificial click feeling is displayed by recreating rapid change in force arising from buckling of a mechanical push button utilizing squeeze film effect. First, important factors for displaying click feeling were extracted by analyzing the relationship among force-stroke curve of mechanical buttons and perceived click feeling. Then, click feeling display system was constructed. In the system, stimulation are applied to the operators at both buckling and restitution point. Finally, by conducting several sensory evaluation experiments, it was verified that button like feeling can be displayed without recreating the stroke of mechanical buttons by use of ultrasonic vibration. It was also verified that various artificial click feeling can be displayed by controlling the multiple parameters of the system.
Children's programming, reconsidered: settings, stuff, and surfaces
Authors: Michael Eisenberg, Nwanua Elumeze, Michael MacFerrin, Leah Buechley
Venue: Proceedings of the 8th International Conference on Interaction Design and Children (2009)

The subject of children's programming has long been a vexed and controversial one in the field of educational technology. Debates in this area have typically focused on issues such as how to create a child-friendly programming language; or whether children can learn particular topics (e.g., recursion) in programming; or indeed, whether it is worthwhile for children to encounter programming at all. For the most part, these debates have taken place against an implicit background of assumptions about what children's programming looks like--namely, an activity focused on creating effects on a desktop screen or, occasionally, robotic toy. This paper argues that the cultural and anthropological contexts of children's programming are now poised to change: that new programming materials, physical settings, and unorthodox display surfaces are likely to shift the nature of the children's-programming debate in profound ways, and to make programming a far more informal, approachable, and natural activity than heretofore. We illustrate this argument with projects underway in our own research.
A Hybrid Tissue-Level Model of the Left Ventricle: Application to the Analysis of the Regional Cardiac Function in Heart Failure
Authors: Julien Fleureau, Mireille Garreau, Erwan Donal, Christophe Leclercq, Alfredo Hernández
Venue: Proceedings of the 5th International Conference on Functional Imaging and Modeling of the Heart (2009)

This work contributes to the systemic interpretation of clinical data for the analysis of the regional cardiac function in the context of heart failure. A two-step patient-specific approach, combining a realistic geometry and a hybrid, tissue-level electromechanical model of the left ventricle is proposed. For the first step, a fast framework to extract a realistic geometry of the left ventricle from MSCT data is proposed. This geometry is then applied to a tissue-level model of the left ventricle, coupling a discrete electrical model, a mechanical model integrating a visco-elastic law, solved by a finite element method and a hydraulic model. A set of simulations carried out with the model are shown and preliminary results of the parameter identification approach, based on real patient data, are presented and discussed.
The Fekete-Szegö problem for certain classes of parabolic starlike and uniformly convex functions
Authors: Oqlah Al-Refai, Maslina Darus
Venue: Proceedings of the 13th WSEAS international conference on Applied mathematics (2008)

The purpose of the present paper is to investigate the Fekete-Szegö problem for certain classes of parabolic starlike and uniformly convex functions which involving certain generalized fractional operator defined in [1]. Sharp estimates for the first few coefficients of the inverse functions are investigated as well.
Koko: architecture and methodology for engineering social affective applications
Authors: Derek J. Sollenberger, Munindar P. Singh
Venue: Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (2009)

Representing and reasoning about affect (e.g. emotion and mood) is essential for producing believable characters and empathic interactions with users, both of which are necessary for effective entertainment and education. Leading applications of interest include pedagogical tools [1], military training simulations [2], and educational games [3].
Revisiting the Potentialities of a Mechanical Thesaurus
Authors: Uta Priss, L. John Old
Venue: Proceedings of the 7th International Conference on Formal Concept Analysis (2009)

This paper revisits the lattice-based thesaurus models which Margaret Masterman used for machine translation in the 1950's and 60's. Masterman's notions are mapped onto modern, Formal Concept Analysis (FCA) terminology and three of her thesaurus algorithms are formalised with FCA methods. The impact of the historical and social situatedness of Roget's Thesaurus on such algorithms is considered. The paper concludes by discussing connections between Masterman's research and modern research.
A Novel Fuzzy-Based Automatic Speaker Clustering Algorithm
Authors: Haipeng Wang, Xiang Zhang, Hongbin Suo, Qingwei Zhao, Yonghong Yan
Venue: Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II (2009)

Fuzzy clustering has been proved successful in various fields in the recent past. In this paper, we introduce fuzzy clustering algorithms into the domain of automatic speaker clustering, and present a novel fuzzy-based hierarchical speaker clustering algorithm by applying fuzzy theory into the state-of-the-art agglomerative hierarchical clustering. This method follows a bottom-up strategy, and determines the fuzzy memberships according to a membership propagation strategy, which propagates fuzzy memberships in the iterative process of hierarchical clustering. Further analysis reveals that this method is an extension of conventional hierarchical clustering algorithm. Experiment results show that our method exhibits quite competitive performances compared to conventional k-means, fuzzy c-means and agglomerative hierarchical clustering algorithms.
Combinatorial markov random fields and their applications to information organization
Authors: James Allan
Venue:  (2008)

We propose a new type of undirected graphical models called a Combinatorial Markov Random Field (Comraf) and discuss its advantages over existing graphical models. We develop an efficient inference methodology for Comrafs based on combinatorial optimization of information-theoretic objective functions; both global and local optimization schema are discussed. We apply Comrafs to multi-modal clustering tasks: standard (unsupervised) clustering, semi-supervised clustering, interactive clustering, and one-class clustering. For the one-class clustering task, we analytically show that the proposed optimization method is optimal under certain simplifying assumptions. We empirically demonstrate the power of Comraf models by comparing them to other state-of-the-art machine learning techniques, both in text clustering and image clustering domains. For unsupervised clustering, we show that Comrafs consistently and significantly outperform three previous state-of-the-art clustering techniques on six real-world textual datasets. For semi-supervised clustering, we show that the Comraf model is superior to a well-known constrained optimization method. For interactive clustering, Comraf obtains higher accuracy than a Support Vector Machine, trained on a large amount of labeled data. For one-class clustering, Comrafs demonstrate superior performance over two previously proposed methods. We summarize our thesis by giving a comprehensive recipe for machine learning modeling with Comrafs.
Blind motion deblurring using multiple images
Authors: Jian-Feng Cai, Hui Ji, Chaoqiang Liu, Zuowei Shen
Venue: Journal of Computational Physics (2009)

Recovery of degraded images due to motion blurring is a challenging problem in digital imaging. Most existing techniques on blind deblurring are not capable of removing complex motion blurring from the blurred images of complex structures. One promising approach is to recover the clear image using multiple images captured for the scene. However, in practice it is observed that such a multi-frame approach can recover a high-quality clear image of the scene only after multiple blurred image frames are accurately aligned during pre-processing, which is a very challenging task even with user interactions. In this paper, by exploring the sparsity of the motion blur kernel and the clear image under certain domains, we propose an alternative iteration approach to simultaneously identify the blur kernels of given blurred images and restore a clear image. Our proposed approach is not only robust to image formation noises, but is also robust to the alignment errors among multiple images. A modified version of linearized Bregman iteration is then developed to efficiently solve the resulting minimization problem. Experiments show that our proposed algorithm is capable of accurately estimating the blur kernels of complex camera motions with minimal requirements on the accuracy of image alignment. As a result, our method is capable of automatically recovering a high-quality clear image from multiple blurred images.
Bayesian learning of Bayesian networks with informative priors
Authors: Nicos Angelopoulos, James Cussens
Venue: Annals of Mathematics and Artificial Intelligence (2008)

This paper presents and evaluates an approach to Bayesian model averaging where the models are Bayesian nets (BNs). A comprehensive study of the literature on structural priors for BNs is conducted. A number of prior distributions are defined using stochastic logic programs and the MCMC Metropolis-Hastings algorithm is used to (approximately) sample from the posterior. We use proposals which are tightly coupled to the priors which give rise to cheaply computable acceptance probabilities. Experiments using data generated from known BNs have been conducted to evaluate the method. The experiments used 6 different BNs and varied: the structural prior, the parameter prior, the Metropolis-Hasting proposal and the data size. Each experiment was repeated three times with different random seeds to test the robustness of the MCMC-produced results. Our results show that with effective priors (i) robust results are produced and (ii) informative priors improve results significantly.
CSLML: a markup language for expressive Chinese sign language synthesis
Authors: Kejia Ye, Baocai Yin, Lichun Wang
Venue: Computer Animation and Virtual Worlds (2009)

This paper presents a Chinese Sign Language Markup Language (CSLML), which is developed for expressive Chinese sign language synthesis by introducing features and structure of sign language prosody. The tags of CSLML are divided into two levels: function level and phonetic level. Function level provides abstract information about signed content and prosody, so it facilitates text annotating for text-driven automatic synthetic system and adapts to diversified synthetic methods, such as motion capture animation or image-based synthesis, which may not be good at processing lower-level information. Phonetic level provides detailed behavioral manners based on phonetics and phonology to interpret the meaning in function level. It facilitates creation and edit of any motions. The two levels co-exist in CSLML documents and high-level description can be mapped into corresponding low-level behavior to provide one-to-many variability and expression of synthesis. Therefore, we also introduce a framework for this mapping processing and exhibit results of our animated prototype system based on this framework. Copyright &copy; 2009 John Wiley & Sons, Ltd. In this paper we present a Chinese Sign Language Markup Language (CSLML), which integrates prosodic information into grammatical regulation to provide a more effective way in describing the features of Chinese Sign Language. Besides, CSLML is divided into two layers, function level and phonetic level, the mapping rules between them can be diversified to increase the flexibility and expressivity of synthesis.
Anomalous window discovery through scan statistics for linear intersecting paths (SSLIP)
Authors: Lei Shi, Vandana P. Janeja
Venue: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (2009)

Anomalous windows are the contiguous groupings of data points. In this paper, we propose an approach for discovering anomalous windows using Scan Statistics for Linear Intersecting Paths (SSLIP). A linear path refers to a path represented by a line with a single dimensional spatial coordinate marking an observation point. Our approach for discovering anomalous windows along linear paths comprises of the following distinct steps: (a) Cross Path Discovery: where we identify a subset of intersecting paths to be considered, (b) Anomalous Window Discovery: where we outline three order invariant algorithms, namely SSLIP, Brute Force-SSLIP and Central Brute Force-SSLIP, for the traversal of the cross paths to identify varying size directional windows along the paths. For identifying an anomalous window we compute an unusualness metric, in the form of a likelihood ratio to indicate the degree of unusualness of this window with respect to the rest of the data. We identify the window with the highest likelihood ratio as our anomalous window, and (c) Monte Carlo Simulations: to ascertain whether this window is truly anomalous and not just a random occurrence we perform hypothesis testing by computing a p-value using Monte Carlo Simulations. We present extensive experimental results in real world accident datasets for various highways with known issues(code and data available from [27], [21]). Our results show that our approach indeed is effective in identifying anomalous traffic accident windows along multiple intersecting highways.
Brief paper: Mean square average-consensus under measurement noises and fixed topologies: Necessary and sufficient conditions
Authors: Tao Li, Ji-Feng Zhang
Venue: Automatica (Journal of IFAC) (2009)

In this paper, average-consensus control is considered for networks of continuous-time integrator agents under fixed and directed topologies. The control input of each agent can only use its local state and the states of its neighbors corrupted by white noises. To attenuate the measurement noises, time-varying consensus gains are introduced in the consensus protocol. By combining the tools of algebraic graph theory and stochastic analysis, the convergence of these kinds of protocols is analyzed. Firstly, for noise-free cases, necessary and sufficient conditions are given on the network topology and consensus gains to achieve average-consensus. Secondly, for the cases with measurement noises, necessary and sufficient conditions are given on the consensus gains to achieve asymptotic unbiased mean square average-consensus. It is shown that under the protocol designed, all agents' states converge to a common Gaussian random variable, whose mathematical expectation is just the average of the initial states.
"It's Just Easier with the Phone" --- A Diary Study of Internet Access from Cell Phones
Authors: Stina Nylander, Terés Lundquist, Andreas Brännström, Bo Karlson
Venue: Proceedings of the 7th International Conference on Pervasive Computing (2009)

We conducted a diary study of how 19 experienced users accessed the Internet from cell phones. Our data show that participants often chose the cell phone to access the Internet even though they had access to a computer, and the most common location for Internet access being the home. Reasons for choosing the phone over the computer were speed, convenience and a desire to use the phone for fun. Additionally, the phone is kept close and is always on which makes it convenient to use. The traditional motivation for mobile services "finding out something about where you are" only accounts for 15% of the user activity.
A Semi-supervised Approach to Bengali-English Phrase-Based Statistical Machine Translation
Authors: Maxim Roy
Venue: Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence (2009)

Large amounts of bilingual data and monolingual data in the target language are usually used to train statistical machine translation systems. In this paper we propose several semi-supervised techniques within a Bengali English Phrase-based Statistical Machine Translation (SMT) System in order to improve translation quality. We conduct experiments on a Bengali-English dataset and our initial experimental results show improvement in translation quality.