ppxscal/embedding_network huggingface dataset:
A methodology for automated fuzzy model generation
Authors: Markos G. Tsipouras, Themis P. Exarchos, Dimitrios I. Fotiadis
Venue: Fuzzy Sets and Systems (2008)

In this paper we propose a generic methodology for the automated generation of fuzzy models. The methodology is realized in three stages. Initially, a crisp model is created and in the second stage it is transformed to a fuzzy one. In the third stage, all parameters entering the fuzzy model are optimized. The proposed methodology is novel and generic since it can integrate alternative techniques in each of its stages. A specific realization of this methodology is implemented, using decision trees for the creation of the crisp model, the sigmoid function, the min-max operators and the maximum defuzzifier, for the transformation of the crisp model into a fuzzy one, and four different optimization strategies, including global and local optimization techniques, as well as, hybrid approaches. The proposed methodology presents several advantages and novelties: the transformation of the crisp model to the respective fuzzy one is straightforward ensuring its full automated nature and it introduces a set of parameters, expressing the importance of each fuzzy rule. The above realization is extensively evaluated using several benchmark data sets from the UCI machine learning repository and the obtained results indicate its high efficiency.
Test Case Generation from QR Models
Authors: Harald Brandl, Franz Wotawa
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

In this paper we present the application of AI to automated test case generation. In particular we introduce a technique for deriving abstract test cases from qualitative models. In the case of reactive systems, the behavior of the device depends on its internal state and the perception of the environment via its sensors. Such systems are well suited for modeling with using Qualitative Reasoning methods. Qualitative Reasoning enables one to specify system behavior that also acquires changing environmental conditions and hence provides a good foundation to derive more realistic test cases. In the first part of this paper we give a short introduction to Qualitative Reasoning and Garp3, the tool we use for model creation and simulation. We also present a method for modeling differential equations within Garp3. In the second part we deal with abstract test case generation from QR models and present first results obtained.
Situating Cognitive Agents in GOLEM
Authors: Stefano Bromuri, Kostas Stathis
Venue: Engineering Environment-Mediated Multi-Agent Systems: International Workshop, EEMMAS 2007, Dresden, Germany, October 5, 2007. Selected Revised and Invited Papers (2008)

We investigate the application of a logic-based framework representing an agent environment as a composite structure that evolves over time. Such a complex structure contains the interaction between two main classes of entities: agents and objects. Interactions between these entities are specified in term of events whose occurrence is governed by a set of physical laws specifying the possible evolutions of the agent environment, including how these evolutions are perceived by agents and affect objects and processes in the agent environment. We illustrate the work using GOLEM, a protype platform whose aim is to implement the framework to build situated cognitive agents in a distributed agent environment.
Two-Stage Ant Colony Optimization for Solving the Traveling Salesman Problem
Authors: Amilkar Puris, Rafael Bello, Yailen Martínez, Ann Nowe
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)

In this paper, a multilevel approach of Ant Colony Optimization to solve the Traveling Salesman Problem is introduced. The basic idea is to split the heuristic search performed by ants into two stages; in this case we use both the Ant System and Ant Colony System algorithms. Also, the effect of using local search was analyzed. We have studied the performance of this new algorithm for several Traveling Salesman Problem instances. Experimental results obtained conclude that the Two-Stage approach significantly improves the Ant System and Ant Colony System in terms of the computation time needed.
Text region extraction algorithm on steel making process
Authors: SungHoo Choi, Jong Pil Yun, KeunHwi Koo, JongHyun Choi, Sang Woo Kim
Venue: Proceedings of the 8th WSEAS International Conference on Robotics, Control and Manufacturing Technology (2008)

This paper describes about the extraction of slab management numbers (SMNs) from captured still images for automation of steel making process. In order to prevent the product mixing in each steel making stage, automatic recognition system of the SMNs is essential. Moreover, the SMNs extraction algorithm which has robust performance is necessary because it affects seriously to the performance of the entire recognition system. For several decades, many researchers have researched about the scene text extraction from images and thus many algorithms exist. But, these algorithms are not well suited for our application because captured still images have much noise and especially there is not enough time to apply the time-consuming state-of-the-art algorithms. In this paper, we propose a localization method of text region candidates by using text features and selection method of true text among the text candidates. Finally, experimental results show that our fast algorithm is operates reliably.
Resolving Inconsistencies in Probabilistic Knowledge Bases
Authors: Marc Finthammer, Gabriele Kern-Isberner, Manuela Ritterskamp
Venue: Proceedings of the 30th annual German conference on Advances in Artificial Intelligence (2007)

The focus of this paper is on the practical aspects of efficiently resolving inconsistencies when merging probabilistic rule sets. We consider the problem of prioritized merging, when one core knowledge base is to be used without modifications and to be extended by information from other sources. This problem is addressed by our flexible system Heureka that aims at restoring consistency by finding those parts of the additional rule bases which are compatible with the core base and are considered most valuable by the user. We give an overview on the methodological framework of the system and describe some details of its main techniques. In particular, Heureka offers a convenient interface to inductive probabilistic reasoning on maximum entropy. An example from the domain of auditing illustrates the problem and the practical applicability of our framework.
Recurrent Boosting for Classification of Natural and Synthetic Time-Series Data
Authors: Robert D. Vincent, Joelle Pineau, Philip Guzman, Massimo Avoli
Venue: Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence (2007)

Boosted ensemble classifiers have a demonstrated ability to discover regularities in large, poorly modeled datasets. In this paper we present an application of multi-hypothesis AdaBoost to detect epileptiform activity from electrophysiological recordings. While existing boosting methods do not account automatically for the sequence information that is available when analyzing time-series data, we present a recurrent extension to AdaBoost, and show that it improves classification accuracy in our application domain.
Optimal Quantum Adversary Lower Bounds for Ordered Search
Authors: Andrew M. Childs, Troy Lee
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

The goal of the ordered search problem is to find a particular item in an ordered list of n items. Using the adversary method, Høyer, Neerbek, and Shi proved a quantum lower bound for this problem of $\frac{1}{\pi} \ln n + {\it \Theta}(1)$. Here, we find the exact value of the best possible quantum adversary lower bound for a symmetrized version of ordered search (whose query complexity differs from that of the original problem by at most 1). Thus we show that the best lower bound for ordered search that can be proved by the adversary method is $\frac{1}{\pi} \ln n + O(1)$. Furthermore, we show that this remains true for the generalized adversary method allowing negative weights.
TALP at GeoCLEF 2007: Results of a Geographical Knowledge Filtering Approach with Terrier
Authors: Daniel Ferrés, Horacio Rodríguez
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 describes and analyzes the results of our experiments in Geographical Information Retrieval (GIR) in the context of our participation in the CLEF 2007 GeoCLEF Monolingual English task.Our system uses Linguistic and Geographical Analysis to process topics and document collections. Geographical Document Retrieval is performed with Terrier and Geographical Knowledge Bases.Our experiments show that Geographical Knowledge Bases can be used to improve the retrieval results of the Terrier state-of-the-art IR system by filtering out non geographically relevant documents.
Representing Spatial Activities by Spatially Contextualised Motion Patterns
Authors: Björn Gottfried, Jörn Witte
Venue: RoboCup 2006: Robot Soccer World Cup X (2006)

The interpretation of spatial activities plays a fundamental role in several areas, ranging from the analysis of animal behaviour to location-based assistance applications. One important aspect when interpreting spatial activities consists in relating them to their environment. A problem arises insofar propositional representations lack an appropriate attention mechanism to comprehend the spatiotemporal development of spatial activities. Therefore, we propose a diagrammatic formalism which allows spatial activities to get classified depending on their spatial context and provide a link to propositional formalisms. It shows that RoboCup soccer is particularly suitable for investigating these issues. In fact, alone the spatial activity of the ball teaches us to a considerable degree much about a game.
DECONcert: Making Waves with Water, EEG, and Music
Authors: Steve Mann, James Fung, Ariel Garten
Venue: Computer Music Modeling and Retrieval. Sense of Sounds: 4th International Symposium, CMMR 2007, Copenhagen, Denmark, August 27-31, 2007. Revised Papers (2008)

We describe events in which music, water and the brain form an immersive environment for human-computer and human-computer-human collective engagement. The theme of sound wave production, regeneration and audition from water waves and brain waves is our central exploration, beginning with our DECONcerts in which participants, immersed in water and connected to EEG equipment, regeneratively create or affect live music by varying their alpha wave output. We explored the five states-of-matter (Classical Elements) of solid ("Earth"), liquid ("Water"), gas ("Air"), plasma ("Fire"), and quintessence ("Idea"), in the context of immersive media (e.g. when the surrounding state-of-matter was liquid). Some of these immersive environments spanned multiple countries, by way of networked connectivity. We also expanded from philosophical to therapeutic contexts by including Parkinson's patients in our immersed environments.
Optimizing Threshold Protocols in Adversarial Structures
Authors: Maurice Herlihy, Flavio P. Junqueira, Keith Marzullo, Lucia Draque Penso
Venue: Proceedings of the 22nd international symposium on Distributed Computing (2008)

Many replication protocols are using a threshold model in which failures are independent and identically distributed (IID). In this model, one assumes that no more than t out of n components can fail. In many real systems, however, failures are not IID, and a straightforward application of threshold protocols yields suboptimal results.Here, we examine the problem of optimally transforming threshold protocols into survivor-set protocols tolerating dependent failures. In particular, we are interested in threshold protocols where the number of components n and the number of failures t are related by n > k ·t, where k is a positive integer constant k. We develop an optimal transformation that translates any such threshold protocol to a survivor-set dependent failure model, and hence, to adversarial structures. Our transformation does not require authentication, self-verification or encryption. We characterize equivalence classes of adversarial structures, regarding solvability, using certain hierarchical properties based on set intersection.
A New Combinatorial Approach for Sparse Graph Problems
Authors: Guy E. Blelloch, Virginia Vassilevska, Ryan Williams
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

We give a new combinatorial data structure for representing arbitrary Boolean matrices. After a short preprocessing phase, the data structure can perform fast vector multiplications with a given matrix, where the runtime depends on the sparsity of the input vector. The data structure can also return minimum witnesses for the matrix-vector product. Our approach is simple and implementable: the data structure works by precomputing small problems and recombining them in a novel way. It can be easily plugged into existing algorithms, achieving an asymptotic speedup over previous results. As a consequence, we achieve new running time bounds for computing the transitive closure of a graph, all pairs shortest paths on unweighted undirected graphs, and finding a maximum node-weighted triangle. Furthermore, any asymptotic improvement on our algorithms would imply a o(n 3/log2 n) combinatorial algorithm for Boolean matrix multiplication, a longstanding open problem in the area. We also use the data structure to give the first asymptotic improvement over O(mn) for all pairs least common ancestors on directed acyclic graphs.
A Method for Enlargement of Digital Images Based on Neural Network
Authors: Zhao Jiufen, Zhang Xurong, Li Qingzhen
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks, Part III (2007)

The enlargement of the digital image implies the improvement of the image resolution, where the high frequency components lost in sampling must be estimated. In this paper, an image enlargement method using a high resolution neural network is proposed, corresponding to the region with rapid change (high local variance) and the region requiring a smooth interpolation (low local variance). It is shown that the high resolution NN has high potential ability.
Distortion-Free Nonlinear Dimensionality Reduction
Authors: Yangqing Jia, Zheng Wang, Changshui Zhang
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

Nonlinear Dimensionality Reduction is an important issue in many machine learning areas where essentially low-dimensional data is nonlinearly embedded in some high-dimensional space. In this paper, we show that the existing Laplacian Eigenmaps method suffers from the distortion problem, and propose a new distortion-free dimensionality reduction method by adopting a local linear model to encode the local information. We introduce a new loss function that can be seen as a different way to construct the Laplacian matrix, and a new way to impose scaling constraints under the local linear model. Better low-dimensional embeddings are obtained via constrained concave convex procedure. Empirical studies and real-world applications have shown the effectiveness of our method.
Enhancing Prediction on Non-dedicated Clusters
Authors: Joseph Ll. Lérida, F. Solsona, F. Giné, J. R. García, M. Hanzich, P. Hernández
Venue: Proceedings of the 14th international Euro-Par conference on Parallel Processing (2008)

In this paper, we present a scheduling scheme to estimate the turnaround time of parallel jobs on a heterogeneous and non-dedicated cluster or NoW (Network of Workstations). This scheme is based on an analytical prediction model that establishes the processing and communication slowdown of the execution times of the jobs based on the cluster nodes and links powerful and occupancy. Preservation of the local application responsiveness is also a goal.We address the impact of inaccuracies in these estimates on the overall system performance. Furthermore, we demonstrate that job scheduling benefits from the accuracy of these estimates. The applicability of our proposal has been proved by measuring the efficiency of our method by comparing the predicted deviations of the parallel jobs in a real environment with respect to the most representative ones of the literature.The additional cost of obtaining these was also evaluated and compared. The present work is implemented within the CISNE project, a previously developed scheduling framework for non-dedicated and heterogeneous cluster environments.
Countering Poisonous Inputs with Memetic Neuroevolution
Authors: Julian Togelius, Tom Schaul, Jürgen Schmidhuber, Faustino Gomez
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

Applied to certain problems, neuroevolution frequently gets stuck in local optima with very low fitness; in particular, this is true for some reinforcement learning problems where the input to the controller is a high-dimensional and/or ill-chosen state description. Evidently, some controller inputs are "poisonous", and their inclusion induce such local optima. Previously, we proposed the memetic climber, which evolves neural network topology and weights at different timescales, as a solution to this problem. In this paper, we further explore the memetic climber, and introduce its population-based counterpart: the memetic ES. We also explore which types of inputs are poisonous for two different reinforcement learning problems.
Smart Lenses
Authors: Conrad Thiede, Georg Fuchs, Heidrun Schumann
Venue: Proceedings of the 9th international symposium on Smart Graphics (2008)

Focus + context techniques are widely used for the efficient visualization of large data sets. However, the corresponding adaptation of the representation to the task at hand is not trivial, requiring a suitable model of the visualization goal. One type of focus + context technique is the use of lenses, interactive tools that modify the visualization in a locally confined region and can be 'stacked' to create complex filters. In this paper we propose a new approach to intergrate smart lenses into the visualization process based on Chi's Data State Reference Model.This allows us to automatically adapt specific aspects of the visualization based on relevant influence factors, without complex task modeling.
Accelerating Integral Histograms Using an Adaptive Approach
Authors: Thomas Müller, Claus Lenz, Simon Barner, Alois Knoll
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

Many approaches in computer vision require multiple retrievals of histograms for rectangular patches of an input image. In 2005 an algorithm to accelerate these retrievals was presented. The data structure utilized is called Integral Histogram, which was based on the well known Integral Image.In this paper we propose a novel approximating method to obtain these integral histograms that outperforms the original algorithm and reduces computational cost to more than a tenth. Alongside we will show that our adaptive approach still provides reasonable accuracy --- which allows dramatic performance improvements for real-time applications while still being well suited for numerous computer vision tasks.
Contact Model for Haptic Medical Simulations
Authors: Guillaume Saupin, Christian Duriez, Stephane Cotin
Venue: Proceedings of the 4th international symposium on Biomedical Simulation (2008)

In surgery simulation, precise contact modeling is essential to obtain both realistic behavior and convincing haptic feedback. When instruments create deformations on soft tissues, they modify the boundary conditions of the models and will mainly modify their behavior. Yet, most recent work has focused on the more precise modeling of soft tissues while improving efficiency; but this effort is ruined if boundary conditions are ill-defined. In this paper, we propose a novel and very efficient approach for precise computation of the interaction between organs and instruments. The method includes an estimation of the contact compliance of the concerned zones of the organ and of the instrument. This compliance is put in a buffer and is the heart of the multithreaded local model used for haptics. Contact computation is then performed in both simulation and haptic loops. It follows unilateral formulation and allows realistic interactions on non-linear models simulated with stable implicit scheme of time integration. An iterative solver, initialized with the solution found in the simulation, allows for fast computation in the haptic loop. We obtain realistic and physical results for the simulation and stable haptic rendering.
Incremental Neighborhood Graphs Construction for Multidimensional Databases Indexing
Authors: Hakim Hacid, Tetsuya Yoshida
Venue: Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence (2007)

The point location (neighborhood search) is a significant problem in several fields like databases and data mining. Neighborhood graphs are interesting representations of this problem in a multidimensional space. However, several problems related to neighborhood graphs are under research and require detailed work to solve them. These problems are mainly related to their high construction costs and to their updating difficulties. In this article, we deal with the point location problem by considering neighborhood graphs optimization. We propose and compare two strategies able to quickly build and update these structures.
Dataset Complexity and Gene Expression Based Cancer Classification
Authors: Oleg Okun, Helen Priisalu
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

When applied to supervised classification problems, dataset complexity determines how difficult a given dataset to classify. Since complexity is a nontrivial issue, it is typically defined by a number of measures. In this paper, we explore complexity of three gene expression datasets used for two-class cancer classification. We demonstrate that estimating the dataset complexity before performing actual classification may provide a hint whether to apply a single best nearest neighbour classifier or an ensemble of nearest neighbour classifiers.