ppxscal/embedding_network huggingface dataset:
Characterization of Artificial Muscles Using Image Processing
Authors: Rafael Berenguer Vidal, Rafael Verdú Monedero, Juan Morales Sánchez, Jorge Larrey Ruiz
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

Artificial muscles are bio-inspired devices formed by several layers of conducting polymers. These devices have the ability of transform electrical energy into mechanical energy through an electrochemical reaction, which is produced by an oxidation or reduction of the polymer due to an electric current. Since the device have a strip shape, this reaction results in a macroscopic swelling and shrinking movement. This movement is similar to the biological muscles and it has several applications as motor prostheses and as part of complex biomaterials. In this paper we describe a computer vision system developed to analyze and characterize these devices through their cycle of life. The method includes cameras for tracking the movement of the muscle from different angles and a set of algorithms to characterize the motion of the device through its use. By means of active contours it is determined the instantaneous position of the muscle in the space. From these contours other parameters like the parametric motion and energy of curvature are calculated. These data are compared with the physical parameters of the device, like the tension and energy consumption, providing a way for performing automatic testing on the research of artificial muscles.
Tutorial on Model Checking: Modelling and Verification in Computer Science
Authors: Armin Biere
Venue: Proceedings of the 3rd international conference on Algebraic Biology (2008)

This paper serves as background material for an invited tutorial on model checking given at the Third International Conference on Algebraic Biology (AB 2008). The intended audience of the tutorial were researchers in natural science, particularly life science, but this paper may also serve as a light-weight introduction into model checking techniques in general.
White Blood Cell Automatic Counting System Based on Support Vector Machine
Authors: Tomasz Markiewicz, Stanisław Osowski, Bożena Mariańska
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

The paper presents the automatic system for white blood cell recognition on the basis of the image of bone marrow smear. The paper proposes the complete system solving all problems, beginning from cell extraction using watershed algorithm, generation of different features based on texture, geometry and the statistical description of image intensity, feature selection using Linear Support Vector Machine and final classification by applying Gaussian kernel Support Vector Machine. The results of numerical experiments of recognition of 10 classes of blood cells of patients suffering from leukaemia have shown that the proposed system is sufficiently accurate so as to find practical application in the hospital practice.
ProtCV: A Tool for Extracting, Visualizing and Validating Protein Clusters Using Mass Spectra Peak-Lists
Authors: Stavroula Ventoura, Eugenia G. Giannopoulou, Elias S. Manolakos
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

We present ProtCV (Protein Clustering and Visualization) a new software tool for grouping samples (mass spectra peak-lists) emanating from a high throughput proteomics analysis based on their spectral similarities, and summarizing effectively for the user the clustering results using advanced visualization methods. A unique feature of ProtCV is that it can compare clustering methods applied to the same data set based on several validity in dices to assist the user identify groupings that seem to capture best the underlying structure of the data set. Moreover, ProtCV can assist in formulating hypotheses about the potential role of unidentified proteins in a cluster, identify sets of proteins which act jointly at a specific biological state etc. All these data set mining and exploration operations are very useful for interpreting the results of high throughput MS based proteomics analyses that are commonly used for biomarkers discovery.
Latent variable framework for modeling and separating single-channel acoustic sources
Authors: Barbara G. Shinn-Cunningham
Venue:  (2008)

Auditory Scene Analysis refers to the human ability to extract different perceptual objects from a sound mixture. Replicating this ability in artificial systems has been an active area of research, related both to how one characterizes acoustic sources and separates sources from mixtures. The focus of this thesis is to develop models and algorithms that provide a framework to address these questions. The framework comprises latent variable models that employ hidden variables to model unobservable quantities. Such models are appropriate for obtaining representations of data that make hidden structure explicit. This work shows how one can utilize these ideas for the problem of source separation using single-channel audio signals. The proposed framework focuses on learning the time-frequency (TF) structure in a data-driven manner. TF representations of sounds are modeled by treating the energy in every TF bin as histogram counts of multiple draws. This formulation allows the extraction of the characteristic frequency structure of individual sources as latent components and models the sources as additive combinations of these components. The framework is then extended to incorporate the idea of sparse coding to overcome an important limitation of the basic model: an upper bound on the number of extractable components. Sparsity, imposed in the form of an entropic prior distribution, allows extraction of overcomplete sets of components that are more expressive and better characterize the sources. The statistical foundation of the framework makes it amenable to other extensions where known or hypothesized structure about the data can be easily incorporated by imposing appropriate prior distributions. Theoretical analysis of the proposed methods and algorithms for parameter inference are presented. Applications of the models to real-world problems are evaluated and discussed. The latent components learned from acoustic sources are used in a supervised setting for source separation and in a semi-supervised setting for denoising. Unlike approaches based on time-frequency masks that reconstruct partial spectral descriptions of sources by identifying time-frequency bins in which a source dominates, this approach reconstructs entire spectral descriptions of all sources. Various experimental results demonstrate the utility of the proposed framework.
Real Time Monocular Depth from Defocus
Authors: Jean-Vincent Leroy, Thierry Simon, François Deschenes
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

The method proposed in this paper uses two blurred images, acquired from the same point of view with different focus settings, in order to estimate depth in real time. It falls under the group "Depth from defocus" methods. The blur is modelled by the convolution of a Gaussian Point Spread Function (PSF) which with the theoretical sharp image. We calculate the gradients and Laplacians of the two blurred images and according to the type of contour, step, ramp, roof o line, we compute the differences of these images or their derivative until order 2 to obtain the difference in blur, difference of the variances of the Gaussian ones, which we connect to the depth by taking account of the parameters of the optical system. We use then this difference to the depth by tacking account of the parameters of the optical system. We present a set of results on real images showing the performance of the method and its limits. The computing times which we measured make possible the use of our algorithm at video rate.
An Algebra Subsystem for Diagnosing Students' Input in a Physics Tutoring System
Authors: Joel A. Shapiro
Venue: International Journal of Artificial Intelligence in Education (2005)

To help a student in an introductory physics course do quantitative homework problems, an intelligent tutoring system must determine information of an algebraic nature. This paper describes a subsystem which resolves such questions for Andes2. The capabilities of the subsystem would be useful for any ITS which deals with problems involving complex systems of equations. This subsystem is capable of 1) solving the systems of equations at the level of introductory physics problems, 2) checking the validity of equations the students enter, 3) investigating whether an equation is independent from a set of other equations, and if not, determining on which equations it does depend, and finally 4) providing tools to help the student with algebraic manipulations, including a "solve-tool" that solves her equations. The ability to determine dependence of equations is first used by Andes during problem generation, by providing information to that component of the ITS which generates correct solutions to the problem. Later, during tutoring, it enables the help module to model which equations the student appears to know. One new feature of this algebra subsystem is that it deals with the dimensional units of physical quantities throughout. An important change from a previous approach is in the meaning of "correctness" of an equation and in the method of determining which equations it can be derived from. The theoretical differences between the two methods, and the pros and cons of each, are discussed. Then we evaluate how the capabilities of the subsystem have affected the Andes tutor's effectiveness, with a particular emphasis on the effects of the changed method.
Weighted Association Rule Mining from Binary and Fuzzy Data
Authors: M. Sulaiman Khan, Maybin Muyeba, Frans Coenen
Venue: Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects (2008)

A novel approach is presented for mining weighted association rules (ARs) from binary and fuzzy data. We address the issue of invalidation of downward closure property (DCP) in weighted association rule mining where each item is assigned a weight according to its significance w.r.t some user defined criteria. Most works on weighted association rule mining so far struggle with invalid downward closure property and some assumptions are made to validate the property. We generalize the weighted association rule mining problem for databases with binary and quantitative attributes with weighted settings. Our methodology follows an Apriori approach [9] and avoids pre and post processing as opposed to most weighted association rule mining algorithms, thus eliminating the extra steps during rules generation. The paper concludes with experimental results and discussion on evaluating the proposed approach.
The GPS Identification Scheme Using Frobenius Expansions
Authors: Waldyr D. Benits,  Jr., Steven D. Galbraith
Venue: Research in Cryptology: Second Western European Workshop, WEWoRC 2007, Bochum, Germany, July 4-6, 2007, Revised Selected Papers (2008)

The Girault-Poupard-Stern (GPS) identification scheme is designed for public key cryptography on very restricted devices. We propose a variant of GPS for Koblitz elliptic curves using Frobenius expansions. The idea is to use Frobenius expansions throughout the protocol, so there is no need to convert between integers and Frobenius expansions. We give a security analysis of the proposed scheme.
Multi-player Go
Authors: Tristan Cazenave
Venue: Proceedings of the 6th international conference on Computers and Games (2008)

Multi-player Go is Go played with more than two colors. Monte-Carlo Tree Search is an adequate algorithm to program the game of Go with two players. We address the application of Monte-Carlo Tree Search to multi-player Go.
Integrated approach for the exploration of geospatial datasets: the interaction of concepts, methods and data
Authors: Xiping Dai
Venue:  (2007)

Categories are the basic concepts and building blocks for human knowledge in understanding, exploring and describing the world. Geographers use categories to conceptualize, interpret and communicate phenomena, such as land cover, urbanization, regions of economic growth, etc. Inductive machine-learning enabled by computers has proved to be a powerful tool for categorization in increasingly complex geographical datasets. Machine-learning tools are able to locate clusters of patterns or partitions in the space constructed from variables, which, however, are often hard for humans to understand and interpret. Furthermore, the data-driven clusters or patterns provided as results are not guaranteed to be appropriate in terms of &ldquo;information classes&rdquo; which are meaningful and important to users. This research provides a two-part solution to this problem. In the first part, a cognitive model of category development is synthesized to emphasize the integration of data-driven and theory/knowledge-based categories. Inductive learning from data examples provides data-driven classes or clusters. Human knowledge is employed in supervised category development, and develops generalized category models for GIS communities. The construction of categories is a problem of the integration between &ldquo;information classes&rdquo; (categories) and data-driven classes provided by machine learning. This model provides an integrated approach combining machine learning and human expert knowledge, and then improves communication, representation and sharing of categories during their development. In the second part, the integrated category development model is structured and optimized by combining visualization techniques and exploratory statistics with machine-learning tools. The visualization interface enables preprocessing of examples, facilitates an examination of the uncertainty in category design, and allows users to visually explore feature space. The combination of visualization and machine-learning supports the construction of categories, including the exploration for appropriate methods in category development, e.g. choosing between different types of classifiers, selection of appropriate training examples, rejection of outliers. This combined method of category development incorporates human expertise into the data-driven machine-learning by the visual interface, which allows controls on machine-learning tools and coordination among tools. The communications between the examples, description, categories and human expertise are, thus, enhanced and an integrated category development system is achieved. The integrated category development model is implemented as a series of visual and computation components and connected into a workflow design using GeoVISTA Studio.
Temporal-Bounded CBR for the Management of Commitments in RT-Agents
Authors: Marti Navarro, Stella Heras, Vicente Botti, Vicente Julián
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

In MAS, agents sometimes require services that other agents provide. In addition, agents could ask for the performance of the requested services in a bounded time. The late fulfilment of some services could reduce the quality of the response offered by a multi-agent system. Therefore, before committing itself to performing a service, an agent must analyse if it is able to complete the service within a specific time. This article proposes the use of the case-based reasoning methodology to carry out this analysis. A framework featuring a Commitment Manager that takes commitment decisions on the basis of previous experiences is presented in the paper.
Acoustic NDT on Building Materials Using Features Extraction Techniques
Authors: M. Camplani, B. Cannas, F. Cau, G. Concu, M. Usai
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

Ultrasonic materials analysis is based on the principle that the propagation of any wave is affected by the medium through which it travels. Thus, changes in measurable parameters associated with the passage of a wave through a material can be correlated with changes in physical properties of the material. In this paper, an ultrasonic technique has been experimented for non destructive testing of building structures. In particular, several parameters associated with acoustic waves propagating through a trachite masonry and a concrete pillar have been analyzed and discussed making reference to a Finite Element model of the structures.
Learning from Animated Diagrams: How Are Mental Models Built?
Authors: Richard Lowe, Jean-Michel Boucheix
Venue: Proceedings of the 5th international conference on Diagrammatic Representation and Inference (2008)

Current approaches to the design of educational animations too often appear to be largely founded upon intuition rather than research-based principles. Animated diagrams designed to be behaviourally realistic run the risk of learners overlooking vital high relevance information that has low intrinsic perceptual salience. The information that learners extract from such representations is a poor basis upon which to build high quality dynamic mental models. For animated diagrams to be effective as tools for learning, their design should be based upon explicit and principled modeling of the way learners process such depictions. This paper synthesizes recent research to propose a theoretical framework for learners' perceptual and conceptual processing of animated diagrams. A five-stage model is presented that characterizes the role of different levels of processing in building dynamic mental models of the depicted content.
Spatially Constrained Grammars for Mobile Intention Recognition
Authors: Peter Kiefer
Venue: Proceedings of the international conference on Spatial Cognition VI: Learning, Reasoning, and Talking about Space (2008)

Mobile intention recognition is the problem of inferring a mobile agent's intentions from her spatio-temporal behavior. The intentions an agent can have in a specific situation depend on the spatial context, and on the spatially contextualized behavior history. We introduce two spatially constrained grammars that allow for modeling of complex constraints between space and intentions, one based on Context-Free, one based on Tree-Adjoining Grammars. We show which of these formalisms is suited best for frequently occurring intentional patterns. We argue that our grammars are cognitively comprehensible, while at the same time helping to prune the search space for intention recognition.
Protein Secondary Structure Prediction Based on Ramachandran Maps
Authors: Yen-Ru Chen, Sheng-Lung Peng, Yu-Wei Tsay
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

High sequence similarity of two proteins does not imply the relation in their structural similarity. In this paper, we propose a protein secondary structure prediction method based on Ramachandran maps. According to the distribution of Ramachandran plot on ¿ and ¿ backbone conformational angles, the series of backbone dihedral angles are regarded as a sequence of input states. Individual residues are classified into different secondary structures. In one of our results, protein hemoglobin 1A6M aligns 2FAM sequence sharing 11.74% similarity. However, 1A6M shares 81.82% of the common structure with 2FAM. Results display that our proposed method provides a promotive advantage on protein secondary structure prediction, without depending on sequence similarity.
Plug-and-Play Architectural Design and Verification
Authors: Shangzhu Wang, George S. Avrunin, Lori A. Clarke
Venue: Architecting Dependable Systems V (2008)

In software architecture, components represent the computational units of a system and connectors represent the interactions among those units. Making decisions about the semantics of these interactions is a key part of the design process. It is often difficult, however, to choose the appropriate interaction semantics due to the wide range of alternatives and the complexity of the system behavior affected by those choices. Techniques such as finite-state verification can be used to evaluate the impact of these design choices on the overall system behavior.This paper presents the Plug-and-Play approach that allows designers to experiment with alternative design choices of component interactions in a plug-and-play manner. With this approach, connectors representing specific interaction semantics are composed from a library of predefined, reusable building blocks. In addition, standard interfaces for components are defined that reduce the impact of interaction changes on the components' computations. This approach facilitates design-time verification by improving the reusability of component models and by providing reusable formal models for the connector building blocks, thereby reducing model-construction time for finite-state verification.
Improved Impossible Differential Attacks on Large-Block Rijndael
Authors: Lei Zhang, Wenling Wu, Je Hong Park, Bon Wook Koo, Yongjin Yeom
Venue: Proceedings of the 11th international conference on Information Security (2008)

In this paper, we present some improved impossible differential attacks on large-block Rijndael whose block sizes are larger than 128 bits. First of all, we present some important observations which help us to significantly improve the impossible differential attacks on large-block Rijndael proposed by Nakahara-Pavão (ISC 2007). Then we introduce some new impossible differentials for large-block Rijndael. Utilizing these longer impossible differential distinguishers, together with the technique of changing the order of MixColumns and AddRoundKey operations proposed by Zhang-Wu-Feng (ICISC 2007), we can apply impossible differential attacks up to 7-round Rijndael-160, 8-round Rijndael-192, and 9-round Rijndael-224/256. As far as we know, except the attack on Rijndael-256, all the other results are the best cryptanalytic results on large-block Rijndael.
Making CAPTCHAs clickable
Authors: Richard Chow, Philippe Golle, Markus Jakobsson, Lusha Wang, XiaoFeng Wang
Venue: Proceedings of the 9th workshop on Mobile computing systems and applications (2008)

We show how to convert regular keyboard-entry CAPTCHAs into clickable CAPTCHAs. The goal of this conversion is to simplify and speed-up the entry of the CAPTCHA solution, to minimize user frustration and permit the use of CAPTCHAs on devices where they would otherwise be unsuitable. We propose a technique for producing secure clickable CAPTCHAs that are well suited for use on cell phones and other mobile devices. We support the practical viability of our approach by results from a user study, and an analysis of its security guarantees.
U-max-statistics
Authors: W. Lao, M. Mayer
Venue: Journal of Multivariate Analysis (2008)

In 1948, W. Hoeffding [W. Hoeffding, A class of statistics with asymptotically normal distribution, Ann. Math. Statist. 19 (1948) 293-325] introduced a large class of unbiased estimators called U-statistics, defined as the average value of a real-valued k-variate function h calculated at all possible sets of k points from a random sample. In the present paper, we investigate the corresponding extreme value analogue which we shall call U-max-statistics. We are concerned with the behavior of the largest value of such a function h instead of its average. Examples of U-max-statistics are the diameter or the largest scalar product within a random sample. U-max-statistics of higher degrees are given by triameters and other metric invariants.