ppxscal/embedding_network huggingface dataset:
Optimal Experimental Design in the Modelling of Pattern Formation
Authors: Adrián López García De Lomana, Àlex Gómez-Garrido, David Sportouch, Jordi Villà-Freixa
Venue: Proceedings of the 8th international conference on Computational Science, Part I (2008)

Gene regulation plays a major role in the control of developmental processes. Pattern formation, for example, is thought to be regulated by a limited number genes translated into transcription factors that control the differential expression of other genes in different cells in a given tissue. We focused on the Notch pathway during the formation of chess-like patterns along development. Simplified models exist of the patterning by lateral inhibition due to the Notch-Delta signalling cascade. We show here how parameters from the literature are able to explain the steady-state behavior of model tissues of several sizes, although they are not able to reproduce time series of experiments. In order to refine the parameters set for data from real experiments we propose a practical implementation of an optimal experimental design protocol that combines parameter estimation tools with sensitivity analysis, in order to minimize the number of additional experiments to perform.
Autonomic multi-agent management of power and performance in data centers
Authors: Rajarshi Das, Jeffrey O. Kephart, Charles Lefurgy, Gerald Tesauro, David W. Levine, Hoi Chan
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track (2008)

The rapidly rising cost and environmental impact of energy consumption in data centers has become a multi-billion dollar concern globally. In response, the IT Industry is actively engaged in a first-to-market race to develop energy-conserving hardware and software solutions that do not sacrifice performance objectives. In this work we demonstrate a prototype of an integrated data center power management solution that employs server management tools, appropriate sensors and monitors, and an agent-based approach to achieve specified power and performance objectives. By intelligently turning off servers under low-load conditions, we can achieve over 25% power savings over the unmanaged case without incurring SLA penalties for typical daily and weekly periodic demands seen in webserver farms.
Communicability Criteria of Law Equations Discovery
Authors: Takashi Washio, Hiroshi Motoda
Venue: Computational Discovery of Scientific Knowledge: Introduction, Techniques, and Applications in Environmental and Life Sciences (2007)

The "laws" in science are not the relations established by only the objective features of the nature. They have to be consistent with the assumptions and the operations commonly used in the study of scientists identifying these relations. Upon this consistency, they become communicable among the scientists. The objectives of this literature are to discuss a mathematical foundation of the communicability of the "scientific law equation" and to demonstrate "Smart Discovery System (SDS)" to discover the law equations based on the foundation. First, the studies of the scientific law equation discovery are briefly reviewed, and the need to introduce an important communicability criterion called "Mathematical Admissibility" is pointed out. Second, the axiomatic foundation of the mathematical admissibility in terms of measurement processes and quantity scale-types are discussed. Third, the strong constraints on the admissible formulae of the law equations are shown based on the criterion. Forth, the SDS is demonstrated to discover law equations by successively composing the relations that are derived from the criterion and the experimental data. Fifth, the generic criteria to discover communicable law equations for scientists are discussed in wider view, and the consideration of these criteria in the SDS is reviewed.
Reinforcement Learning in Fine Time Discretization
Authors: Paweł Wawrzyński
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

Reinforcement Learning (RL) is analyzed here as a tool for control system optimization. State and action spaces are assumed to be continuous. Time is assumed to be discrete, yet the discretization may be arbitrarily fine. It is shown here that stationary policies, applied by most RL methods, are improper in control applications, since for fine time discretization they can not assure bounded variance of policy gradient estimators. As a remedy to that difficulty, we propose the use of piecewise non-Markov policies. Policies of this type can be optimized by means of most RL algorithms, namely those based on likelihood ratio.
Timed Parity Games: Complexity and Robustness
Authors: Krishnendu Chatterjee, Thomas A. Henzinger, Vinayak S. Prabhu
Venue: Proceedings of the 6th international conference on Formal Modeling and Analysis of Timed Systems (2008)

We consider two-player games played in real time on game structures with clocks and parity objectives. The games are concurrent in that at each turn, both players independently propose a time delay and an action, and the action with the shorter delay is chosen. To prevent a player from winning by blocking time, we restrict each player to strategies that ensure that the player cannot be responsible for causing a zeno run. First, we present an efficient reduction of these games to turn-based (i.e., nonconcurrent) finite-state (i.e., untimed) parity games. The states of the resulting game are pairs of clock regions of the original game. Our reduction improves the best known complexity for solving timed parity games. Moreover, the rich class of algorithms for classical parity games can now be applied to timed parity games.Second, we consider two restricted classes of strategies for the player that represents the controller in a real-time synthesis problem, namely, limit-robust and bounded-robust strategies. Using a limit-robust strategy, the controller cannot choose an exact real-valued time delay but must allow for some nonzero jitter in each of its actions. If there is a given lower bound on the jitter, then the strategy is bounded-robust. We show that exact strategies are more powerful than limit-robust strategies, which are more powerful than bounded-robust strategies for any bound. For both kinds of robust strategies, we present efficient reductions to standard timed automaton games. These reductions provide algorithms for the synthesis of robust real-time controllers.
Chemical Workbench for Blind People --- Accessing the Structure of Chemical Formula
Authors: Federsel Stephan, Klaus Miesenberger
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

This paper exposes some ideas how blind people can get better access to the structure of chemical formulas. The first chapters outline the problems of blind people, who work in chemical areas. A further part of this paper discusses the state of the art in access to chemistry. Starting form this level of information ideas on a new approach will be shown how a blind user can navigate through chemical formulas.
Linear Arithmetic with Stars
Authors: Ruzica Piskac, Viktor Kuncak
Venue: Proceedings of the 20th international conference on Computer Aided Verification (2008)

We consider an extension of integer linear arithmetic with a "star" operator takes closure under vector addition of the solution set of a linear arithmetic subformula. We show that the satisfiability problem for this extended language remains in NP (and therefore NP-complete). Our proof uses semilinear set characterization of solutions of integer linear arithmetic formulas, as well as a generalization of a recent result on sparse solutions of integer linear programming problems. As a consequence of our result, we present worst-case optimal decision procedures for two NP-hard problems that were previously not known to be in NP. The first is the satisfiability problem for a logic of sets, multisets (bags), and cardinality constraints, which has applications in verification, interactive theorem proving, and description logics. The second is the reachability problem for a class of transition systems whose transitions increment the state vector by solutions of integer linear arithmetic formulas.
The Role of Conceptual Structure in Designing Cellular Automata to Perform Collective Computation
Authors: Manuel Marques-Pita, Melanie Mitchell, Luis M. Rocha
Venue: Proceedings of the 7th international conference on Unconventional Computing (2008)

The notion of conceptual structure in CA rules that perform the density classification task (DCT) was introduced by [1]. Here we investigate the role of process-symmetry in CAs that solve the DCT, in particular the idea of conceptual similarity, which defines a novel search space for CA rules. We report on two new process-symmetric one-dimensional rules for the DCT which have the highest "balanced" performance observed to date on this task, as well as the highest-performing CA known to perform the DCT in two dimensions. Finally, we investigate the more general problem of assessing how different learning strategies (based on evolution and coevolution, with and without spatial distribution), previously compared by [2], are suited to exploit conceptual structure in learning CAs to perform collective computation.
c-Means Clustering on the Multinomial Manifold
Authors: Ryo Inokuchi, Sadaaki Miyamoto
Venue: Proceedings of the 4th international conference on Modeling Decisions for Artificial Intelligence (2007)

In this paper, we discuss c-means clustering algorithms on the multinomial manifold. Data forms a Riemannian manifold with the Fisher information metric via the probabilistic mapping from datum to a probability distribution. For discrete data, the statistical manifold of the multinomial distribution is appropriate. In general, The euclidean distance is not appropriate on the manifold because the parameter space of the distribution is not flat. We apply the Kullback-Leibler (KL) divergence or the Hellinger distance as approximations of the geodesic distance to hard c-means and fuzzy c-means.
Accuracy in Privacy-Preserving Data Mining Using the Paradigm of Cryptographic Elections
Authors: Emmanouil Magkos, Manolis Maragoudakis, Vassilis Chrissikopoulos, Stefanos Gridzalis
Venue: Proceedings of the UNESCO Chair in data privacy international conference on Privacy in Statistical Databases (2008)

Data mining technology raises concerns about the handling and use of sensitive information, especially in highly distributed environments where the participants in the system may by mutually mistrustful. In this paper we argue in favor of using some well-known cryptographic primitives, borrowed from the literature on large-scale Internet elections, in order to preserve accuracy in privacy-preserving data mining (PPDM) systems. Our approach is based on the classical homomorphic model for online elections, and more particularly on some extensions of the model for supporting multi-candidate elections. We also describe some weaknesses and present an attack on a recent scheme [1] which was the first to use a variation of the homomorphic model in the PPDM setting. In addition, we show how PPDM can be used as a building block to obtain a Random Forests classification algorithm over a set of homogeneous databases with horizontally partitioned data.
Autonomous Virtual Agents for Performance Evaluation of Tracking Algorithms
Authors: Pau Baiget, Xavier Roca, Jordi Gonzàlez
Venue: Proceedings of the 5th international conference on Articulated Motion and Deformable Objects (2008)

This paper describes a framework which exploits the use of computer animation to evaluate the performance of tracking algorithms. This can be achieved in two different, complementary strategies. On the one hand, augmented reality allows to gradually increasing the scene complexity by adding virtual agents into a real image sequence. On the other hand, the simulation of virtual environments involving autonomous agents provides with synthetic image sequences. These are used to evaluate several difficult tracking problems which are under research nowadays, such as performance processing long---time runs and the evaluation of sequences containing crowds of people and numerous occlusions. Finally, a general event---based evaluation metric is defined to measure whether the agents and actions in the scene given by the ground truth were correctly tracked by comparing two event lists. This metric is suitable to evaluate different tracking approaches where the underlying algorithm may be completely different.
Converting ECG and Other Paper Legated Biomedical Maps into Digital Signals
Authors: A. R. Silva, H. M. Oliveira, R. D. Lins
Venue: Graphics Recognition. Recent Advances and New Opportunities: 7th International Workshop, GREC 2007, Curitiba, Brazil, September 20-21, 2007. Selected Papers (2008)

This paper presents a digital signal processing tool developed using MatlabTM, which provides a very low-cost and effective strategy for analog-to-digital conversion of legated paper biomedical maps without requiring dedicated hardware. This software-based approach is particularly helpful for digitalizing biomedical signals acquired from analogical devices equipped with a plottingter. Albeit signals used in biomedical diagnosis are the primary concern, this imaging processing tool is suitable to modernize facilities in a non-expensive way. Legated paper ECG and EEG charts can be fast and efficiently digitalized in order to be added in existing up-to-date medical data banks, improving the follow-up of patients.
The 9 + -Intersection: A Universal Framework for Modeling Topological Relations
Authors: Yohei Kurata
Venue: Proceedings of the 5th international conference on Geographic Information Science (2008)

The 9 + -intersection is an extension of the 9-intersection, which distinguishes the topological relations between various spatial objects by the pattern of a nested matrix. This paper develops a small set of constraints on this matrix, which is applicable to arbitrary pairs of spatial objects in various spaces. Based on this set of universal constraints, the sets of matrix patterns, each representing a candidate for topological relations, are derived for every possible pair of basic objects (points, directed/non-directed line segments, regions, and bodies) embedded in R 1, R 2, R 3, S 1, and S 2. The derived sets of candidates are consistent with the sets of topological relations ever identified, as well as yield the identification of some missing sets of topological relations. Finally, the topological relations between a region and a region with a hole in R 2 and S 2 are identified to demonstrate the applicability of our approach to deriving topological relations between more complicated objects.
Cooperative CG-Wrappers for Web Content Extraction
Authors: Fotis Kokkoras, Nick Bassiliades, Ioannis Vlahavas
Venue: Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications (2007)

We use Conceptual Graphs (CGs) to model web content extraction rules (CG-Wrappers). The approach presented incorporates all major existing extraction techniques and allows the definition of synergies of cooperative wrappers for handling complex extraction task, without requiring programming.
Experimenting with Small Changes in Conflict-Driven Clause Learning Algorithms
Authors: Gilles Audemard, Laurent Simon
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

Experimentation of new algorithms is the usual companion section of papers dealing with SAT. However, the behavior of those algorithms is so unpredictable that even strong experiments (hundreds of benchmarks, dozen of solvers) can be still misleading. We present here a set of experiments of very small changes of a canonical Conflict Driven Clause Learning (CDCL) solver and show that even very close versions can lead to very different behaviors. In some cases, the best of them could perfectly have been used to convince the reader of the efficiency of a new method for SAT. This observation can be explained by the lack of real experimental studies of CDCL solvers.
A visual approach for modeling spatiotemporal relations
Authors: Rodrigo Laiola Guimarães, Carlos de Salles Soares Neto, Luiz Fernando Gomes Soares
Venue: Proceeding of the eighth ACM symposium on Document engineering (2008)

Textual programming languages have proven to be difficult to learn and to use effectively for many people. For this sake, visual tools can be useful to abstract the complexity of such textual languages, minimizing the specification efforts. In this paper we present a visual approach for high level specification of spatiotemporal relations. In order to accomplish this task, our visual representation provides an intuitive way to specify complex synchronization events amongst media. Finally, to validate our work, the visual specification is mapped to NCL (Nested Context Language), the standard declarative language of the Brazilian Terrestrial Digital TV System.
Intuitionistic Fuzzy Histogram Hyperbolization for Color Images
Authors: Ioannis K. Vlachos, George D. Sergiadis
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

In this paper an extension of the Intuitionistic Fuzzy Image Processing (IFIP) framework from gray-scale to color images is presented. Analysis and synthesis of images into their corresponding intuitionistic fuzzy components is demonstrated using a suitable color model. Additionally, application of the proposed framework to histogram hyperbolization is also shown. Finally, experimental results demonstrate the efficiency of the proposed scheme.
A Hierarchical Self-organizing Associative Memory for Machine Learning
Authors: Janusz A. Starzyk, Haibo He, Yue Li
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

This paper proposes novel hierarchical self-organizing associative memory architecture for machine learning. This memory architecture is characterized with sparse and local interconnections, self-organizing processing elements (PE), and probabilistic synaptic transmission. Each PE in the network dynamically estimates its output value from the observed input data distribution and remembers the statistical correlations between its inputs. Both feed forward and feedback signal propagation is used to transfer signals and make associations. Feed forward processing is used to discover relationships in the input patterns, while feedback processing is used to make associations and predict missing signal values. Classification and image recovery applications are used to demonstrate the effectiveness of the proposed memory for both hetero-associative and auto-associative learning.
Characterizing Emotion in the Soundtrack of an Animated Film: Credible or Incredible?
Authors: Noam Amir, Rachel Cohen
Venue: Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (2007)

In this study we present a novel emotional speech corpus, consisting of dialog that was extracted from an animated film. This type of corpus presents an interesting compromise between the sparsity of emotion found in spontaneous speech, and the contrived emotion found in speech acted solely for research purposes. The dialog was segmented into 453 short units and judged for emotional content by native and non-native English speakers. Emotion was rated on two scales: Activation and Valence. Acoustic analysis gave a comprehensive set of 100 features covering F0, intensity, voice quality and spectrum. We found that Activation is more strongly correlated to our acoustic features than Valence. Activat-ion was correlated to several types of features, whereas Valence was correlated mainly to intensity related features. Further, ANOVA analysis showed some interesting contrasts between the two scales, and interesting differences in the judgments of native vs. non-native English speakers.
Tracking and analysis of caenorhabditis elegans behavior using machine vision
Authors: Pamela Cosman
Venue:  (2008)

The behavior of the nematode C. elegans has proven increasingly useful for the genetic dissection of neurobiological signaling pathways and for investigating the neural and molecular basis of nervous system function. Locomotion is among the most complex aspects of C. elegans behavior, and involves a number of discrete motor activities such as omega bends (deep bends typically on the ventral side of the body which reorient the direction of forward locomotion), reversals (changes in the direction of the locomotion wave that cause a switch from forward to backward crawling), and foraging (a rapid, side-to-side movement of the nose). Here we use automated methods to automatically detect these activities, which rely in part on a new method for obtaining a morphological skeleton describing the body posture of coiled worms. These new methods have made it possible to reliably detect events that are time-consuming and laborious to detect by real-time observation or human video analysis. We also present an algorithm for tracking and distinguishing multiple C. elegans in a video sequence, including when they are in physical contact with one another. Our method makes it possible to identify two worms correctly before and after they touch each other, and to find the body poses for further feature extraction. The algorithm has many applications in the study of physical interactions between C. elegans.
Virtual id routing: a scalable routing framework with support for mobility and routing efficiency
Authors: Guor-Huar Lu, Sourabh Jain, Shanzhen Chen, Zhi-Li Zhang
Venue: Proceedings of the 3rd international workshop on Mobility in the evolving internet architecture (2008)

Current flat-id based routing schemes promise support for mobility and scalability. However, providing efficient routing with minimal overheads for mobility is still a challenge. In this paper we provide a solution to these problems by introducing Virtual Id Routing (VIR). VIR meets these basic requirements of future id-based routing schemes: namely, i) scalability - by using distributed hash table based routing framework; ii) mobility support - by separating the node-identifier from the network location; and iii) routing efficiency - by exploiting network topology information, which is done by introducing a dynamic, self-organizing virtual id (vid) space layer in between the node id space (uid) and the network topology. Preliminary evaluation of the protocol shows promising results, specifically routing stretch for VIR is very close to 1, and the overheads due to mobility are also much smaller than existing schemes such as VRR.
Design of an Auto-associative Neural Network by Using Design of Experiments Approach
Authors: Božidar Bratina, Nenad Muškinja, Boris Tovornik
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I (2008)

Data driven computational intelligence methods have become popular in Fault detection and isolation (FDI) due to relatively quick design and not so difficult implementation on real systems. In this paper a research work on a Taguchi DoE approach for training the auto-associative neural network to extract non-linear principal components of a system, is presented. Design of such network was first proposed by Kramer however for achieving robustness to unspecified parameters such as noise level and disturbances, a design of experiments methodology can be used to optimally define network structure and parameters.