ppxscal/embedding_network huggingface dataset:
Resource Restricted Computability Theoretic Learning: Illustrative Topics and Problems
Authors: John Case
Venue: Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World (2007)

Computability theoretic learning theory (machine inductive inference) typically involves learning programs for languages or functions from a stream of complete data about them and, importantly, allows mind changes as to conjectured programs. This theory takes into account algorithmicity but typically does not take into account feasibility of computational resources. This paper provides some example results and problems for three ways this theory can be constrained by computational feasibility. Considered are: the learner has memory limitations, the learned programs are desired to be optimal, and there are feasibility constraints on obtaining each output program and on the number of mind changes.
On RDF/S Ontology Evolution
Authors: George Konstantinidis, Giorgos Flouris, Grigoris Antoniou, Vassilis Christophides
Venue: Semantic Web, Ontologies and Databases: VLDB Workshop, SWDB-ODBIS 2007, Vienna, Austria, September 24, 2007, Revised Selected Papers (2008)

The algorithms dealing with the incorporation of new knowledge in an ontology (ontology evolution) often share a rather standard process of dealing with changes. This process consists of the specification of the language, the determination of the allowed update operations, the identification of the invalidities that could be caused by each such operation, the determination of the various alternatives to deal with each such invalidity, and, finally, some selection mechanism for singling out the "best" of these alternatives. Unfortunately, most ontology evolution algorithms implement these steps using a case-based, ad-hoc methodology, which is cumbersome and error-prone. The first goal of this paper is to present, justify and make explicit the five steps of the process. The second goal is to propose a general framework for ontology change management that captures this process, in effect generalizing the methodology employed by existing tools. The introduction of this framework allows us to devise a whole class of ontology evolution algorithms, which, due to their formal underpinnings, avoid many of the problems exhibited by ad-hoc frameworks. We exploit this framework by implementing a specific ontology evolution algorithm for RDF ontologies as part of the FORTH-ICS Semantic Web Knowledge Middleware (SWKM).
Impact of Digital Mammography in Breast Cancer Screening: Initial Experience in a National Breast Screening Program
Authors: N. Hambly, N. Phelan, G. Hargaden, A. O'Doherty, F. Flanagan
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

Full field digital mammography (FFDM) was introduced into the Irish National Breast Screening Program (INBSP) in 2005. The aim of this study is to review the use of FFDM in a National Breast Screening Program and to compare the results to standard screen-film mammography with respect to recall rate and cancer detection rate. All women who underwent breast cancer screening between Jan 2005 and Sep 2007 were reviewed. The recall and cancer detection rates were determined and compared to the gold standard of SFM. Over the study period 16% of women were screened using FFDM. Both the recall rate and the cancer detection rate were higher in the FFDM group. Initial results from the use of FFDM in a National Breast Screening Program indicate that it is comparable to the gold standard of SFM with respect to recall rate and cancer detection rate.
Relevance Measures Using Geographic Scopes and Types
Authors: Geoffrey Andogah, Gosse Bouma
Venue: Advances in Multilingual and Multimodal Information Retrieval: 8th Workshop of the Cross-Language Evaluation Forum, CLEF 2007, Budapest, Hungary, September 19-21, 2007, Revised Selected Papers (2008)

This paper proposes two kinds of relevance measures to rank documents by geographic restriction: scope-based and type-based. The non-geographic and geographic relevance scores are combined using a weighted harmonic mean. The proposed relevance measures and weighting schemes are evaluated on GeoCLEF 2007 dataset with encouraging performance over the standard IR performance. The best performance is achieved when the importance of non-geographic relevance scores outweigh the importance of geographic relevance scores.
Modified q-State Potts Model with Binarized Synaptic Coefficients
Authors: Vladimir Kryzhanovsky
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

Practical applications of q-state Potts models are complicated, as they require very large RAM (32N 2 q 2 bits, where N is the number of neurons and q is the number of the states of a neuron). In this work we examine a modified Potts model with binarized synaptic coefficients. The procedure of binarization allows one to make the required RAM 32 times smaller (N 2 q 2 bits), and the algorithm more than q times faster. One would expect that the binarization worsens the recognizing properties. However our analysis shows an unexpected result: the binarization procedure leads to the increase of the storage capacity by a factor of 2. The obtained results are in a good agreement with the results of computer simulations.
Model Based Refinement and the Tools of Tomorrow
Authors: Richard Banach
Venue: Proceedings of the 1st international conference on Abstract State Machines, B and Z (2008)

The ingredients of typical model based development via refinement are re-examined, and some well known frameworks are reviewed in that light, drawing out commonalities and differences. It is observed that alterations in semantics take place de facto due to applications pressures and for other reasons. This leads to a perspective on tools for such methods in which the proof obligations become programmable and/or configurable, permitting easier co-operation between techniques and interaction with an Evidential Tool Bus. This is of intrinsic interest, and also relevant to the Verification Grand Challenge.
A Design and Implementation of Ubiquitous Campus Environment Information Service
Authors: Jeon Chan-Sik, Lee Min-Jae, Oh Seung-Hyun
Venue: Proceedings of the 2008 International Conference on Advanced Language Processing and Web Information Technology (2008)

Recently, many researches of various fields that used a ubiquitous sensor network are going on. Specially, various experiment had a function of monitoring without human is preceded in the environmental fields where access of a person is difficult. For example, in Korea, various pilot u-city projects are driven by nation. This paper designed a service as it used a wireless sensor network to measure and monitor environmental information in the campus
Position control of a robotic manipulator using neural network and a simple vision system
Authors: Bach H. Dinh, Matthew W. Dunnigan, Donald S. Reay
Venue: Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases (2008)

This paper describes a new approach for approximating the inverse kinematics of a manipulator using a RBFN (Radial Basis Function Network). In fact, there are several traditional methods based on the known geometry of the manipulator to calculate the relationship between the joint variable space and the world coordinate space. However, these traditional methods are impractical if the manipulator geometry cannot be determined, in a robot-vision system for example. Therefore, a neural network with its inherent learning ability can be an effective alternative solution for the inverse kinematics problem. In this paper, a training solution using the strict interpolation method and the LMS (Least Mean Square) algorithm is presented in a two phase training procedure. The strict interpolation method with regularly-spaced-position training patterns in the workspace can produce an appropriate approximation of the inverse kinematic function. However, this solution has the main difficulty in how to collect accurate training patterns whose inputs are selected at pre-defined positions in the workspace. Additionally, the LMS algorithm can improve the approximate function iteratively through on-line training with arbitrary position patterns. A training approach combining two mentioned techniques can be applied for the inverse kinematics problem even with an inaccurate training set. It consists of two steps, firstly producing an inaccurate inverse kinematic approximation by strict interpolation (reflecting the situation that the initial setup and application environments are different) and then re-correcting the network through on-line training by the LMS. The advantages of both training methods can deal with the difficulty of collecting training patterns for practical applications. To verify the performance of the proposed approach, a practical experiment has been performed using a Mitsubishi PA10-6CE manipulator observed by a webcam. All application programmes, such as robot servo control, neural network, and image processing were written in C/C++ and run in a real robotic system. The experimental results prove that the proposed approach is effective.
The Choice of Communication Media and the Use of Mobile Phone among Senior Users and Young Users
Authors: Ayako Hashizume, Masaaki Kurosu, Takao Kaneko
Venue: Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction (2008)

The use of communication media including the land line, the cell phone, the letter, the telegram, the cell phone mail, the PC mail, asking somebody to convey the message, and the face-to-face meeting was analyzed in accordance with the situation comparing senior users and young users. Senior users showed the tendency to stick to the old media such as the land line.
So tell me what happened: turning agent-based interactive drama into comics
Authors: Tiago Alves, Ana Simões, Rui Figueiredo, Marco Vala, Ana Paiva, Ruth Aylett
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

As virtual characters become more autonomous, their use in interactive drama is growing. By creating interesting and well authored personalities, these characters are able to interact with each other and create appealing non-scripted stories. As with any other story, we may want to re-tell it or create a summary of the essential parts. Our goal was to create comic-like summaries of these stories. This paper presents a system that analyses story logs, looks at the characters' emotional information to understand their actions and their importance in the story, selects the most important events and creates comic strips. Forty users evaluated the system and the results show that the summaries driven by the characters' emotional information are effective when compared with showing the stories as they unfold.
Dynamic Probabilistic Atlas of Functional Brain Regions for Transcranial Magnetic Stimulation
Authors: Juha Koikkalainen, Mervi Könönen, Jari Karhu, Jarmo Ruohonen, Eini Niskanen, Jyrki Lötjönen
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

Transcranial Magnetic Stimulation (TMS) is a technique to stimulate the brain non-invasively. The applications range from accurate localization of the primary motor areas to potential treatment of disorders such as tinnitus, severe depression, and pain. Stereotactic guidance requires individual MR images of the subject's head, which is in some applications typically omitted due to financial motivations. In this paper, we introduce a method that offers improved TMS pulse targeting also to those subjects who do not have MR examinations. A probabilistic brain model was constructed by spatially normalizing the locations of the functional brain areas in a study population, and modeling the distributions and estimates of the locations of the functional brain regions using probabilistic methods. The application of the probabilistic brain model to the target subject was based on a point set determined from the scalp and facial skin of the target subject. The methods were evaluated using data from four functional brain areas from 56 healthy subjects. The accuracy of the estimates of the locations of the functional brain regions was about nine millimeters.
Meta-learning with Machine Generators and Complexity Controlled Exploration
Authors: Krzysztof Grąbczewski, Norbert Jankowski
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

We present a novel approach to meta-learning, which is not just a ranking of methods, not just a strategy for building model committees, but an algorithm performing a search similar to what human experts do when analyzing data, solving full scope of data mining problems. The search through the space of possible solutions is driven by special mechanisms of machine generators based on meta-schemes. The approach facilitates using human experts knowledge to restrict the search space and gaining meta-knowledge in an automated manner. The conclusions help in further search and may also be passed to other meta-learners. All the functionality is included in our new general architecture for data mining, especially eligible for meta-learning tasks.
Ultrafilter and Non-standard Turing Machines
Authors: Petrus H. Potgieter, Elemér E. Rosinger
Venue: Proceedings of the 7th international conference on Unconventional Computing (2008)

We consider several kinds of non-finitary computation, using ordinary Turing machines, as usual, as the reference case. The main problem which this short paper tries to address, is the problem of defining the output, or final message, of a machine which has run for a countably infinite number of steps. A modest scheme, using non-standard numbers, is proposed.
A Heuristic NLP Based Approach for Getting Didactic Resources from Electronic Documents
Authors: Mikel Larrañaga, Jon A. Elorriaga, Ana Arruarte
Venue: Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts (2008)

The development of Computer Supported Learning Systems is a hard task and, therefore, they are not as broadly used as expected yet. Some authors have claimed that tools for generating learning material in automatic or semiautomatic way are needed. This paper describes how didactic resources can be semi automatically generated from electronic documents using ontologies and Natural Language Processing techniques. Gathering atomic didactic resources and combining them is essential to get results that match human instructors' expects. Several didactic resource similarity measuring methods have been implemented and tested.
Graph-Based Domain Mapping for Transfer Learning in General Games
Authors: Gregory Kuhlmann, Peter Stone
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

A general game player is an agent capable of taking as input a description of a game's rules in a formal language and proceeding to play without any subsequent human input. To do well, an agent should learn from experience with past games and transfer the learned knowledge to new problems. We introduce a graph-based method for identifying previously encountered games and prove its robustness formally. We then describe how the same basic approach can be used to identify similar but non-identical games. We apply this technique to automate domain mapping for value function transfer and speed up reinforcement learning on variants of previously played games. Our approach is fully implemented with empirical results in the general game playing system.
An Integrated Approach for Identifying Relevant Factors Influencing Software Development Productivity
Authors: Adam Trendowicz, Michael Ochs, Axel Wickenkamp, Jürgen Münch, Yasushi Ishigai, Takashi Kawaguchi
Venue: Balancing Agility and Formalism in Software Engineering: Second IFIP TC 2 Central and East European Conference on Software Engineering Techniques, CEE-SET 2007, Poznan, Poland, October 10-12, 2007, Revised Selected Papers (2008)

Managing software development productivity and effort are key issues in software organizations. Identifying the most relevant factors influencing project performance is essential for implementing business strategies by selecting and adjusting proper improvement activities. There is, however, a large number of potential influencing factors. This paper proposes a novel approach for identifying the most relevant factors influencing software development productivity. The method elicits relevant factors by integrating data analysis and expert judgment approaches by means of a multi-criteria decision support technique. Empirical evaluation of the method in an industrial context has indicated that it delivers a different set of factors compared to individual data- and expert-based factor selection methods. Moreover, application of the integrated method significantly improves the performance of effort estimation in terms of accuracy and precision. Finally, the study did not replicate the observation of similar investigations regarding improved estimation performance on the factor sets reduced by a data-based selection method.
The Study of Size Distribution and Spatial Distribution of Urban Systems in Guangdong, China
Authors: Jianmei Yang, Dong Zhuang, Minyi Kuang
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

There are three types of urban size distribution: primate city distribution, rank-size distribution and the medium distribution between them. In order to avoid the defect of data, the paper uses both the population and built-up district area data of above county level cities in Guangdong Province in 2002 and 2004. The results prove that urban size of the urban system in the province follows rank-size distribution. In analyzing the value and the change of Zipf exponents, we point out the characteristics of the size distribution of urban system in the province. The paper also conducts spatial distribution analysis for urban system in Guangdong Province. Power law distribution is also observed, and value of the exponent belongs to the same range of value with those in the size distribution analysis. The differences of focal point between fractal dimension (the inverse of Zipf exponent) and power law exponent (prevailing in complex network analysis) are finally discussed.
Proceedings of the 2008 EDBT workshop on Database technologies for handling XML information on the web
Venue: ACM International Conference Proceeding Series; Vol. 353 (2008)

This volume contains papers selected for presentation at the Third International Workshop on Database Technologies for Handling XML Information on the Web (DataX'08), held in conjunction with the International Conference on Extending Database Technology (EDBT'08) in Nantes (France) on March 25, 2008. In response to the call for papers, 21 high quality submissions were received. Each paper was carefully reviewed by at least three members of the program committee and external reviewers. As result of this process, 6 papers have been selected as long papers and 4 as short papers for presentation at the workshop. The accepted papers cover a large variety of topics, ranging from query optimization, semantic interoperability and data sharing to stream processing and retrieval on P2P systems. In addition, Prof. Yannis Papakonstantinou accepted our invitation to discuss new and interesting topics in XML and database research. Among the accepted papers for the workshop, we have further selected the best papers that are included in this post-workshop proceedings (5 long papers and 3 short papers). The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Feature extraction for one-class classification problems: Enhancements to biased discriminant analysis
Authors: Nojun Kwak, Jiyong Oh
Venue: Pattern Recognition (2009)

In many one-class classification problems such as face detection and object verification, the conventional linear discriminant analysis sometimes fails because it makes an inappropriate assumption on negative samples that they are distributed according to a Gaussian distribution. In addition, it sometimes cannot extract sufficient number of features because it merely makes use of the mean value of each class. In order to resolve these problems, in this paper, we extend the biased discriminant analysis (BDA) which was originally developed for one-class classification problems. The BDA makes no assumption on the distribution of negative samples and tries to separate each negative sample as far away from the center of positive samples as possible. The first extension uses a saturation technique to suppress the influence of the samples which are located far away from the decision boundary. The second one utilizes the L1 norm instead of the L2 norm. Also we present a method to extend BDA and its variants to multi-class classification problems. Our approach is considered useful in the sense that without much complexity, it successfully reduces the negative effect of negative samples which are far away from the center of positive samples, resulting in better classification performances. We have applied the proposed methods to several classification problems and compared the performance with conventional methods.
An L (1/3 + ε) Algorithm for the Discrete Logarithm Problem for Low Degree Curves
Authors: Andreas Enge, Pierrick Gaudry
Venue: Proceedings of the 26th annual international conference on Advances in Cryptology (2007)

The discrete logarithm problem in Jacobians of curves of high genus g over finite fields $\mathbb {F}_q$ is known to be computable with subexponential complexity $L_{q^g}(1/2, O(1))$. We present an algorithm for a family of plane curves whose degrees in X and Y are low with respect to the curve genus, and suitably unbalanced. The finite base fields are arbitrary, but their sizes should not grow too fast compared to the genus. For this family, the group structure can be computed in subexponential time of $L_{q^g}(1/3, O(1))$, and a discrete logarithm computation takes subexponential time of $L_{q^g}(1/3+ \varepsilon, o(1))$ for any positive ¿. These runtime bounds rely on heuristics similar to the ones used in the number field sieve or the function field sieve algorithms.
Linmap: Visualizing complexity gradients in evolutionary landscapes
Authors: Nicholas Geard, Janet Wiles
Venue: Artificial Life (2008)

This article describes an interactive visualization tool, LinMap, for exploring the structure of complexity gradients in evolutionary landscapes. LinMap is a computationally efficient and intuitive tool for visualizing and exploring multidimensional parameter spaces. An artificial cell lineage model is presented that allows complexity to be quantified according to several different developmental and phenotypic metrics. LinMap is applied to the evolutionary landscapes generated by this model to demonstrate that different definitions of complexity produce different gradients across the same landscape; that landscapes are characterized by a phase transition between proliferating and quiescent cell lineages where both complexity and diversity are maximized; and that landscapes defined by adaptive fitness and complexity can display different topographical features.