ppxscal/embedding_network huggingface dataset:
Learning spectral graph transformations for link prediction
Authors: Jérôme Kunegis, Andreas Lommatzsch
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

We present a unified framework for learning link prediction and edge weight prediction functions in large networks, based on the transformation of a graph's algebraic spectrum. Our approach generalizes several graph kernels and dimensionality reduction methods and provides a method to estimate their parameters efficiently. We show how the parameters of these prediction functions can be learned by reducing the problem to a one-dimensional regression problem whose runtime only depends on the method's reduced rank and that can be inspected visually. We derive variants that apply to undirected, weighted, unweighted, unipartite and bipartite graphs. We evaluate our method experimentally using examples from social networks, collaborative filtering, trust networks, citation networks, authorship graphs and hyperlink networks.
Plenary lecture IV: use of artificial intelligent techniques to fault diagnosis in analog systems
Authors: Damian E. Grzechca
Venue: Proceedings of the 2nd conference on European computing conference (2008)

Basic concepts of fault diagnosis in analog circuits and devices by means of the simulation-before-test approach, the so called dictionary approach, have been presented. Special attention has been paid to application of artificial intelligence tools, such as: artificial neural networks, fuzzy sets and evolutionary computing.
From dialogic to trialogic learning
Authors: Jakko Van Der Pol
Venue: Proceedings of the 8th international conference on International conference for the learning sciences - Volume 3 (2008)

This poster looks at dialogic learning in academic practice with the specific aim of the collaborative processing of texts. In identifying some shortcomings of these dialogic practices it introduces the notion of trialogic learning, and presents the results from a comparative study on the affordances of different asynchronous discussion tools to support a more content-oriented and trialogic form of conversation.
Numerical investigation of the flow in the bladed spaces of radial-flow fan-impeller
Authors: László Kalmár
Venue: Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering (2008)

The aim of this paper is to introduce numerical procedures to calculate the flow parameters in the bladed space of a cylindrically bladed radial-flow fan-impeller. The characteristics of the flow belonging to different duty points of the fan were determined by the numerical procedure. In this calculation, the blade effects as well as the effects of the fluid friction and the turbulence were taken into consideration separately. The effects of the blade were represented hydro-dynamically by a constrain force field. The frictional effect of the fluid and the turbulence of the flow were reflected by the analogy between the flow in a rectangular channel and the flow in the bladed space of the impeller. The distributions of the average relative velocity, pressure and energy losses were calculated by solving the governing equations of the viscous flow.
Object-based Place Recognition for Mobile Robots Using Panoramas
Authors: Arturo Ribes, Arnau Ramisa, Ramon Lopez de Mantaras, Ricardo Toledo
Venue: Proceeding of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence (2008)

Object recognition has been widely researched for several decades and in the recent years new methods capable of general object classification have appeared. However very few work has been done to adapt these methods to the challenges raised by mobile robotics. In this article we discuss the data sources (appearence information, temporal context, etc.) that such methods could use and we review several state of the art object recognition methods that build in one or more of these sources. Finally we run an object based robot localization experiment using an state of the art object recognition method and we show that good results are obtained even with a naïve place descriptor.
Some mathematical problems of the Geoid determination in the coastal areas of the Gulf of Mexico
Authors: Alexey L. Sadovski, Gary Jeffress, Hong-Zhi Song, Natalya Warner
Venue: Proceedings of the 15th american conference on Applied mathematics (2009)

This paper deals with the outline of some mathematical problems of the Geoid determination. It is the report of the ongoing long term research, which includes some results from statistical models and Stokes-Helmert approach as well as description of the expected future results.
COFALE: An Adaptive Learning Environment Supporting Cognitive Flexibility
Authors: Vu Minh Chieu, Elie Milgrom
Venue: Proceeding of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology (2005)

Constructivism is a learning theory that states that people learn best when they actively construct their own knowledge. Various forms of “constructivist” learning systems have been proposed in recent years. According to our analysis, those systems exhibit only a few constructivist principles, and few of them support adaptation to different kinds of students. Our research aims to design truly constructivist and adaptive learning environments. Our approach is based on a set of operational criteria for certain aspects of constructivism: We use these criteria both as guidelines for designing our learning system and for evaluating the conformity of our learning system with constructivist principles. One of the facets often mentioned as being strongly relevant to constructivism is cognitive flexibility. This paper presents COFALE, a domain-independent adaptive e-Learning platform that supports cognitive flexibility, and an example of its use.
Robust Face Recognition Using Color Information
Authors: Zhiming Liu, Chengjun Liu
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

This paper presents a robust face recognition method using color information with the following three-fold contributions. First, a novel hybrid color space, the RCrQ color space, is constructed out of three different color spaces: the RGB, YCbCr, and YIQ color spaces. The RCrQ hybrid color space, whose component images possess complementary characteristics, enhances the discriminating power for face recognition. Second, three effective image encoding methods are proposed for the component images in the RCrQ hybrid color space: (i) a patch-based Gabor image representation for the R component image, (ii) a multi-resolution LBP feature fusion scheme for the Cr component image, and (iii) a component-based DCT multiple face encoding for the Q component image. Finally, at the decision level, the similarity matrices generated using the three component images in the RCrQ hybrid color space are fused using a weighted sum rule. The most challenging Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 shows that the proposed method, which achieves the face verification rate of 92.43% at the false accept rate of 0.1%, performs better than the state-of-the-art face recognition methods.
Organizational Ambidexterity: Balancing Exploitation and Exploration for Sustained Performance
Authors: Sebastian Raisch, Julian Birkinshaw, Gilbert Probst, Michael L. Tushman
Venue: Organization Science (2009)

Organizational ambidexterity has emerged as a new research paradigm in organization theory, yet several issues fundamental to this debate remain controversial. We explore four central tensions here: Should organizations achieve ambidexterity through differentiation or through integration? Does ambidexterity occur at the individual or organizational level? Must organizations take a static or dynamic perspective on ambidexterity? Finally, can ambidexterity arise internally, or do firms have to externalize some processes? We provide an overview of the seven articles included in this special issue and suggest several avenues for future research.
A one-way nested tsunami computation model for the Penang Island in Peninsular Malaysia
Authors: Md. Fazlul Karim, Ahmad Izani M. Ismail, Mohammed Ashaque Meah
Venue: Proceedings of the 7th WSEAS international conference on System science and simulation in engineering (2008)

A one-way nested tsunami computation model is developed and applied to compute the response of the 2004 Indonesian tsunami along the coastal belts of Penang Island in Peninsular Malaysia. In this study, a one-way nested finite difference scheme is used to solve the depth averaged shallow water equations in Cartesian coordinates. A fine mesh numerical scheme (inner scheme) for the west coast of Malaysia covering 5°10′-5°35′N and 100°-100°30′E has been nested into a coarse mesh scheme (outer scheme) enclosing 2°-14°N and 90°-100°30′E. The fine mesh scheme incorporates the Penang Island more accurately as the grid size is considered small. In a one-way nested grid model, information (velocity components and sea surface elevation) from the coarse mesh scheme could enter and affect the fine mesh scheme through the interface between them in each time step of the solution process, while disturbances from the fine mesh do not feed back to the coarse one. The outer scheme is thus running completely independent in each time step of the solution process. A simulation experiment shows that the performance of the inner scheme of the one-way nested model is superior in accuracy compared to that of outer scheme. Different aspects of tsunami along the coastal belts of Penang Island have been computed through outer and inner schemes and the results are found to be satisfactory.
T-S fuzzy model identification based on a novel fuzzy c-regression model clustering algorithm
Authors: Chaoshun Li, Jianzhong Zhou, Xiuqiao Xiang, Qingqing Li, Xueli An
Venue: Engineering Applications of Artificial Intelligence (2009)

This paper proposes a novel approach for identification of Takagi-Sugeno (T-S) fuzzy model, which is based on a new fuzzy c-regression model (FCRM) clustering algorithm. The clustering prototype in fuzzy space partition is hyper-plane, so FCRM clustering technique is more suitable to be applied in premise parameters identification of T-S fuzzy model. A new FCRM clustering algorithm (NFCRMA) is presented, which is deduced from the fuzzy clustering objective function of FCRM with Lagrange multiplier rule, possessing integrative and concise structure. The proposed approach consists mainly of two steps: premise parameter identification and consequent parameter identification. The NFCRMA is utilized to partition the input-output data and identify the premise parameters, which can discover the real structure of the training data; on the other hand, orthogonal least square is exploited to identify the consequent parameters. Finally, some examples are given to verify the validity of the proposed modeling approach, and the results show the new approach is very efficient and of high accuracy.
Signal processing in measuring electromagnetic interference
Authors: Eleonora Darie, Costin Cepisca, Emanuel Darie
Venue: Proceedings of the 8th WSEAS international conference on Signal processing, robotics and automation (2009)

This work analyzes the advantages of broad-band time-domain the electrical measurement applied to electromagnetic interference (EMI) problems. The digital signal processing of EMI measurements allows to emulate in real-time the various modes of conventional analogous equipment. Since time-domain techniques allow processing the amplitude and phasing information of the whole signal spectrum in parallel, the measurement time may be reduced by at least one order of magnitude. On details the signal processing algorithms and the measurement results obtained with the time-domain electromagnetic interference system.
Automatic annotation of context and speech acts for dialogue corpora
Authors: Kallirroi Georgila, Oliver Lemon, James Henderson, Johanna d. Moore
Venue: Natural Language Engineering (2009)

Richly annotated dialogue corpora are essential for new research directions in statistical learning approaches to dialogue management, context-sensitive interpretation, and context-sensitive speech recognition. In particular, large dialogue corpora annotated with contextual information and speech acts are urgently required. We explore how existing dialogue corpora (usually consisting of utterance transcriptions) can be automatically processed to yield new corpora where dialogue context and speech acts are accurately represented. We present a conceptual and computational framework for generating such corpora. As an example, we present and evaluate an automatic annotation system which builds ‘Information State Update’ (ISU) representations of dialogue context for the Communicator (2000 and 2001) corpora of human–machine dialogues (2,331 dialogues). The purposes of this annotation are to generate corpora for reinforcement learning of dialogue policies, for building user simulations, for evaluating different dialogue strategies against a baseline, and for training models for context-dependent interpretation and speech recognition. The automatic annotation system parses system and user utterances into speech acts and builds up sequences of dialogue context representations using an ISU dialogue manager. We present the architecture of the automatic annotation system and a detailed example to illustrate how the system components interact to produce the annotations. We also evaluate the annotations, with respect to the task completion metrics of the original corpus and in comparison to hand-annotated data and annotations produced by a baseline automatic system. The automatic annotations perform well and largely outperform the baseline automatic annotations in all measures. The resulting annotated corpus has been used to train high-quality user simulations and to learn successful dialogue strategies. The final corpus will be made publicly available.
Automatic Frame Extraction from Sentences
Authors: Martin Scaiano, Diana Inkpen
Venue: Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence (2009)

We present a method for automatic extraction of frames from a dependency graph. Our method uses machine learning applied to a dependency tree to identify frames and assign frame elements. The system is evaluated by cross-validation on FrameNet sentences, and also on the test data from the SemEval 2007 task 19. Our system is intended for use in natural language processing applications such as summarization, entailment, and novelty detection.
Nonassociative Lambek Calculus with Additives and Context-Free Languages
Authors: Wojciech Buszkowski, Maciej Farulewski
Venue: Languages: From Formal to Natural: Essays Dedicated to Nissim Francez on the Occasion of His 65th Birthday (2009)

We study Nonassociative Lambek Calculus with additives ¿ , ¿, satisfying the distributive law (Distributive Full Nonassociative Lambek Calculus DFNL). We prove that categorial grammars based on DFNL, also enriched with assumptions, generate context-free languages. The proof uses proof-theoretic tools (interpolation) and a construction of a finite model, earlier employed in [11] in the proof of Finite Embeddability Property (FEP) of DFNL; our paper is self-contained, since we provide a simplified version of the latter proof. We obtain analogous results for different variants of DFNL, e.g. BFNL, which admits negation ¬ such that ¿ , ¿ ,¬ satisfy the laws of boolean algebra, and HFNL, corresponding to Heyting algebras with an additional residuation structure. Our proof also yields Finite Embeddability Property of boolean-ordered and Heyting-ordered residuated groupoids. The paper joins proof-theoretic and model-theoretic techniques of modern logic with standard tools of mathematical linguistics.
Scores Selection for Emotional Speaker Recognition
Authors: Zhenyu Shan, Yingchun Yang
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

Emotion variability of the training and testing utterances is one of the largest challenges in speaker recognition. It is a common situation where training data is the neutral speech and testing data is the mixture of neutral and emotional speech. In this paper, we experimentally analyzed the performance of the GMM-based verification system with the utterances in this situation. It reveals that the verification performance improves as the emotion ratio decreases and the scores of neutral features against his/her model are distributed in the upper area than other three scores(neutral against the model of other speakers, and non-neutral speech against the model of himself/herself and other speakers). Based on these, we propose a scores selection method to reduce the emotion ratio of the testing utterance by eliminating the non-neutral features. It is applicable to the GMM-based recognition system without labeling the emotion state in the testing process. The experiments are carried on the MASC Corpus and the performance of the system with scores selection is improved with an EER reduction from 13.52% to 10.17%.
Learning Expressive Ontologies
Authors: Johanna Völker, Peter Haase, Pascal Hitzler
Venue: Proceeding of the 2008 conference on Ontology Learning and Population: Bridging the Gap between Text and Knowledge (2008)

The automatic extraction of ontologies from text and lexical resources has become more and more mature. Nowadays, the results of state-of-the-art ontology learning methods are already good enough for many practical applications. However, most of them aim at generating rather inexpressive ontologies, i.e. bare taxonomies and relationships, whereas many reasoning-based applications in domains such as bioinformatics or medicine rely on much more complex axiomatizations. Those are extremely expensive if built by purely manual efforts, and methods for the automatic or semi-automatic construction of expressive ontologies could help to overcome the knowledge acquisition bottleneck. At the same time, a tight integration with ontology evaluation and debugging approaches is required to reduce the amount of manual post-processing which becomes harder the more complex learned ontologies are. Particularly, the treatment of logical inconsistencies, mostly neglected by existing ontology learning frameworks, becomes a great challenge as soon as we start to learn huge and expressive axiomatizations. In this chapter we present several approaches for the automatic generation of expressive ontologies along with a detailed discussion of the key problems and challenges in learning complex OWL ontologies. We also suggest ways to handle different types of inconsistencies in learned ontologies, and conclude with a visionary outlook to future ontology learning and engineering environments.
Dungine: a Java Dung Reasoner
Authors: Matthew South, Gerard Vreeswijk, John Fox
Venue: Proceeding of the 2008 conference on Computational Models of Argument: Proceedings of COMMA 2008 (2008)

Dungine is an open source Argumentation Engine and API implemented in the Java programming language. Dungine uses argument games to evaluate the acceptability of an argument given a constellation of arguments under grounded (sceptical) and preferred credulous semantics. Existing argumentation engines all rely on a companion logic programming language. These companion languages represent a barrier to integrating an engine into existing software as developers must translate their system into the companion logic programming language. Dungine is liberal in its notion of an Argument and this flexible approach overcomes this barrier, and makes integrating argumentation based reasoning into existing software comparatively easy. To demonstrate the usefulness of this approach, and its ease of application, we describe an integration of Dungine with Araucaria-an open source argument mapping tool.
Computational methods for analyzing and modeling gene regulation dynamics
Authors: Ziv Bar-Joseph
Venue:  (2008)

Gene regulation is a central biological process whose disruption can lead to many diseases. This process is largely controlled by a dynamic network of transcription factors interacting with specific genes to control their expression. Time series microarray gene expression experiments have become a widely used technique to study the dynamics of this process. This thesis introduces new computational methods designed to better utilize data from these experiments and to integrate this data with static transcription factor-gene interaction data to analyze and model the dynamics of gene regulation. The first method, STEM (Short Time-series Expression Miner), is a clustering algorithm and software specifically designed for short time series expression experiments, which represent the substantial majority of experiments in this domain. The second method, DREM (Dynamic Regulatory Events Miner), integrates transcription factor-gene interactions with time series expression data to model regulatory networks while taking into account their dynamic nature. The method uses an Input-Output Hidden Markov Model to identify bifurcation points in the time series expression data. While the method can be readily applied to some species, the coverage of experimentally determined transcription factor-gene interactions in most species is limited. To address this we introduce two methods to improve the computational predictions of these interactions. The first of these methods, SEREND (SEmi-supervised REgulatory Network Discoverer), motivated by the species E. coli is a semi-supervised learning method that uses verified transcription factor-gene interactions, DNA sequence binding motifs, and gene expression data to predict new interactions. We also present a method motivated by human genomic data, that combines motif information with a probabilistic prior on transcription factor binding at each location in the organism's genome, which it infers based on a diverse set of genomic properties. We applied these methods to yeast, E. coli, and human cells. Our methods successfully predicted interactions and pathways, many of which have been experimentally validated. Our results indicate that by explicitly addressing the temporal nature of regulatory networks we can obtain accurate models of dynamic interaction networks in the cell.
Understanding design activities through computer simulation
Authors: Yong Zeng, Shengji Yao
Venue: Advanced Engineering Informatics (2009)

In this paper, a new experimental approach - computer simulation - is introduced for understanding design activities and for validating design theories. Following the generic framework of computer simulation, three main components in simulating design activities are introduced: mathematical model, simulation model, and statistical analysis. The mathematical model consists of the design governing equation and Environment-Based Design (EBD), based on which three routes are introduced to look for new design solutions: (1) formulating the design problem differently at the beginning of a design process may get quite different solutions, in which creative design could emerge; (2) extending designer's knowledge and experience can help generate more candidate solutions, and so increasing the probability of generating a good concept; (3) changing the sequence of design problem decomposition may change product requirements, and thus change the generated design concepts. By viewing mesh generation algorithms as design agents, a computer simulation environment is used to study design activities. Statistical analysis is conducted to validate quantitatively the three routes to new design solutions. The results show that computer simulation is an effective approach to studying design activities.