ppxscal/embedding_network huggingface dataset:
Peirce on Icons and Cognition
Authors: Christopher Hookway
Venue: Proceedings of the 15th international conference on Conceptual Structures: Knowledge Architectures for Smart Applications (2007)

Charles Sanders Peirce often emphasized the importance of iconic representations in all cognition. After addressing some general issues about the analysis of iconic representation, the paper examines the cognitive role of diagrammatic representations, and explores the implications for his theory of perception of Peirce's claim that ideas are like composite photographs. The discussions also consider the role of imagination in cognition.
Why Automatic Understanding?
Authors: Ryszard Tadeusiewicz, Marek R. Ogiela
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II (2007)

In the paper a new way of intelligent medical pattern analysis directed for automatic semantic categorization and merit content understanding will be presented. Such an understanding will be based on the linguistic mechanisms of pattern interpretation and categorisation and is aimed at facilitation of in-depth analysis of the meaning for some classes of medical patterns, especially in the form of planar images or spatial reconstructions of selected organs. The approach presented in this paper will show the great possibilities of automatic lesion detection in the analysed structures using the grammar approach to the interpretation and classification tasks, based on cognitive resonance processes. Cognitive methods imitate the psychological and neurophysiological processes of understanding the analysed patterns or cases, as they take place in the brain of a qualified professional.
Fuzzy Association Rule Mining from Spatio-temporal Data
Authors: Seda Unal Calargun, Adnan Yazici
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

The use of fuzzy sets in mining association rules from spatio-temporal databases is useful since fuzzy sets are able to model the uncertainty embedded in the meaning of data. There are several fuzzy association rule mining techniques that can work on spatio-temporal data. Their ability to mine fuzzy association rules has to be compared on a realistic scenario. Besides the performance criteria, other criteria that can express the quality of an association rule discovered shall be specified. In this paper, fuzzy association rule mining is performed with spatio-temporal data cubes and Apriori algorithm. A real life application is developed to compare data cubes and Apriori algorithm according to the following criteria: interpretability, precision, utility, novelty, direct-to-the-point, performance and visualization, which are defined within the scope of this paper.
Pedagogical Protocols Selection Automatic Assistance
Authors: Paola Britos, Zulma Cataldi, Enrique Sierra, Ramón García-Martínez
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

The preliminary results presented in this paper corresponds to a research project oriented to the search of the relationship between the predilection of students concerning learning style and the pedagogical protocols used by the human tutors (professors during the first courses of the Computer Engineering undergraduate Program) by using intelligent systems tools.
Document-Oriented Views of Guideline Knowledge Bases
Authors: Samson W. Tu, Shantha Condamoor, Tim Mather, Richard Hall, Neill Jones, Mark A. Musen
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

A computer-interpretable guideline knowledge base can be a very large network whose information content is difficult for developers and clinicians to comprehend and review. We created a method to annotate a guideline model and use the annotations to export the guideline knowledge base in an XML format that can be transformed into a readable document. We applied this method to knowledge bases developed in three different guideline modeling projects to analyze uses and limitations of this approach. We demonstrate the promise of creating such document-oriented views, but conclude that guideline models and knowledge bases should be constructed with the goal of creating such human-comprehensible views from the beginning.
Stabilizing highly dynamic locomotion in planar bipedal robots with dimension reducing control
Authors: Jessy W. Grizzle
Venue:  (2008)

In the field of robotic locomotion, the method of hybrid zero dynamics (HZD) proposed by Westervelt, Grizzle, and Koditschek provided a new solution to the canonical problem of stabilizing walking in planar bipeds. Original walking experiments on the French biped RABBIT were very successful, with gaits that were robust to external disturbances and to parameter mismatch. Initial running experiments on RABBIT were cut short before a stable gait could be achieved, but helped to identify performance limiting aspects of both the physical hardware of RABBIT and the method of hybrid zero dynamics. To improve upon RABBIT, a new robot called MABEL was designed and constructed in collaboration between the University of Michigan and Carnegie Mellon University. In light of experiments on RABBIT and in preparation for experiments on MABEL, this thesis provides a theoretical foundation that extends the method of hybrid zero dynamics to address walking in a class of robots with series compliance. Extensive new design tools address two main performance limiting aspects of previous HZD controllers: the dependence on non-Lipschitz finite time convergence and the lack of a constructive procedure for achieving impact invariance when outputs have relative degree greater than two. An analytically rigorous set of solutions&mdash;an arbitrarily smooth stabilizing controller and a constructive parameter update scheme&mdash;is derived using the method of Poincar&eacute; sections. Additional contributions of this thesis include the development of sample-based virtual constraints, analysis of walking on a slope, and identification of dynamic singularities that can arise from poorly chosen virtual constraints.
A New Narrow Block Mode of Operations for Disk Encryption
Authors: Mohamed Abo El-Fotouh, Klaus Diepold
Venue: Proceedings of the 2008 The Fourth International Conference on Information Assurance and Security (2008)

In this paper, we present a new narrow block mode of operation, the Masked Code Book (MCB), that can be efficiently deployed in disk encryption applications. MCB is characterized by its high-speed in comparison to current state of the art narrow block modes of operation. It is about 80% faster than XTS (when AES with 128-bits key is the underlying cipher).
An Empirical Study of Bringing Audience into the Movie
Authors: Tao Lin, Akinobu Maejima, Shigeo Morishima
Venue: Proceedings of the 9th international symposium on Smart Graphics (2008)

In this paper we first present an audience-participating movie experience DIM, in which the photo-realistic 3D virtual actor of audience is constructed by computer graphic technologies, and then evaluate the effects of DIM on audience experience using physiological and subjective methods. The empirical results suggest that the participation of virtual actors causes increased subjective sense of presence and engagement, and more intensive emotional responses as compared to traditional movie form; interestingly, there also significantly different physiological responses caused by the participation of virtual actors, objectively indicating the improvement of interaction between audience and movie.
Discrimination of Coronary Microcirculatory Dysfunction Based on Generalized Relevance LVQ
Authors: Qi Zhang, Yuanyuan Wang, Weiqi Wang, Jianying Ma, Juying Qian, Junbo Ge
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

There are fewer effective methods to accurately discriminate the coronary microcirculatory dysfunction from the normal coronary microcirculation. Rather than traditional approaches only considering a single hemodynamic parameter, a novel scheme is proposed based on the generalized relevance learning vector quantization (GRLVQ) using multiple parameters (features). Naturally integrating the tasks of feature selection and classification, this scheme circularly adopts GRLVQ to gradually prune the unimportant features according to their weighting factors. In each circulation, the prototypes are generated for classification and the classification accuracy is obtained. Finally, the feature subset with the highest classification accuracy is selected and the corresponding classifier is also achieved. This approach not only simplifies the classifier but also enhances the classification performance. The method is verified on the physiological data collected from animals, and proved to be superior to the traditional single-parameter method.
PEACHES and Peers
Authors: Massimiliano Pala, Sean W. Smith
Venue: Proceedings of the 5th European PKI workshop on Public Key Infrastructure: Theory and Practice (2008)

How to distribute resource locators is a fundamental problem in PKI. Our PKI Resource Query Protocol (PRQP), recently presented at IETF, provides a standard method to query for PKI resources locators. However the distribution of locators across PKIs is still an unsolved problem. In this paper, we propose an extension to PRQP in order to distribute PRQP messages over a Peer-to-Peer (P2P) network. In this work, we combine PRQP with Distributed Hash Tables (DHTs) to efficiently distribute contents over a dynamic P2P overlay network. In particular we present the PEACH protocol and a PEACH Enabled System (PEACHES) which are specifically targeted toward solving the PKI resources discovery problem. Our work enhances interoperability between existing PKIs and allows for easy configuration of applications, thus augmenting usability of PKI technology.
Mechanising a Proof of Craig's Interpolation Theorem for Intuitionistic Logic in Nominal Isabelle
Authors: Peter Chapman, James Mckinna, Christian Urban
Venue: Proceedings of the 9th AISC international conference, the 15th Calculemas symposium, and the 7th international MKM conference on Intelligent Computer Mathematics (2008)

Craig's Interpolation Theorem is an important meta- theoretical result for several logics. Here we describe a formalisation of the result for first-order intuitionistic logic without function symbols or equality, with the intention of giving insight into how other such results in proof theory might be mechanically verified, notable cut-admissibility. We use the package Nominal Isabelle, which easily deals with the binding issues in the quantifier cases of the proof.
Finite Dedekind Sums
Authors: Yoshinori Hamahata
Venue: Proceedings of the 2nd international workshop on Arithmetic of Finite Fields (2008)

In this paper, we introduce Dedekind sums associated to lattices defined over finite fields. We establish the reciprocity law for them.
Visual-model-based, real-time 3D pose tracking for autonomous navigation: methodology and experiments
Authors: Hans Ruiter, Beno Benhabib
Venue: Autonomous Robots (2008)

This paper presents a novel 3D-model-based computer-vision method for tracking the full six degree-of-freedom (dof) pose (position and orientation) of a rigid body, in real-time. The methodology has been targeted for autonomous navigation tasks, such as interception of or rendezvous with mobile targets. Tracking an object's complete six-dof pose makes the proposed algorithm useful even when targets are not restricted to planar motion (e.g., flying or rough-terrain navigation). Tracking is achieved via a combination of textured model projection and optical flow. The main contribution of our work is the novel combination of optical flow with z-buffer depth information that is produced during model projection. This allows us to achieve six-dof tracking with a single camera.A localized illumination normalization filter also has been developed in order to improve robustness to shading. Real-time operation is achieved using GPU-based filters and a new data-reduction algorithm based on colour-gradient redundancy, which was developed within the framework of our project. Colour-gradient redundancy is an important property of colour images, namely, that the gradients of all colour channels are generally aligned. Exploiting this property provides a threefold increase in speed. A processing rate of approximately 80 to 100 fps has been obtained in our work when utilizing synthetic and real target-motion sequences. Sub-pixel accuracies were obtained in tests performed under different lighting conditions.
Automatic Segmentation of Cardiac MRI Using Snakes and Genetic Algorithms
Authors: Gustavo Miranda Teixeira, Igor Ramalho Pommeranzembaum, Bernardo Lino Oliveira, Marcelo Lobosco, Rodrigo Weber Dos Santos
Venue: Proceedings of the 8th international conference on Computational Science, Part III (2008)

In this work we study and implement techniques for the automatic segmentation of cardiac Magnetic Resonance Images. The methods are based on the active contours algorithm called Snakes, which are adapted and tailored to the specific task of automatic segmentation of the left ventricle of the heart in Magnetic Resonance Images. We propose a new external force to improve the convergence of the Snakes method. In addition, a genetic algorithm is used to find the best set of configuration parameters for the Snakes method. The algorithms are implemented in Java and threads are used to explore data parallelism on shared-memory machines. Tests are performed on 150 short-axis images acquired from two healthy volunteers. Preliminary results suggest the proposed methods are promising and with further development and validation may be used, for instance, for the automatic calculation of cardiac ejection fraction.
Supply chain risk management: models and algorithms
Authors: Dennis L. Bricker
Venue:  (2007)

Optimization models have been used in making operational decisions for supply chains. However, supply chains are subject to risk in the form of random disruptive events that invalidate such decisions. These may be natural, e.g., earthquakes and floods, or man-made, e.g., labor strikes and terrorist attacks. Assuming a finite number of the possible future events (scenarios) have been identified, we describe a two-stage stochastic linear programming model in which the first stage consists of the operational decision-making for the supply chain, and the second stage consists of selecting recourses for alleviating each of the possible disruptions. The model makes the first-stage decisions so as to minimize the maximum regret, i.e., the difference between the maximum return which would have been possible with perfect foresight and the actual return which can be attained using the recourses available. Decomposition strategies, including cross-decomposition, are described, analogous to strategies which have been used for the more typical stochastic programming models for maximizing the expected return. These optimize the model by solving a sequence of deterministic problems, each of which plans for a specific scenario. Supply chain models may include binary decisions, e.g., the selection of customer orders to be accepted and the suppliers for component parts required to fill the orders. Our model allows such decisions in the first stage, at the expense of a computational burden which may make finding the exact optimum impractical. Therefore, a two-phase strategy is described, in which the decomposition algorithm terminates with a sub-optimal solution, which is improved upon by a genetic algorithm (GA) in the second phase. The genetic algorithm makes use of information gathered during the first phase, in order to generate near-optimal solutions, with a bound on the error. The two-phase algorithm is illustrated, using a small example, and computational experience in solving larger problems is described.
Holistic computer architectures based on application, user, and process characteristics
Authors: Gokhan Memik
Venue:  (2007)

As we move into deeper sub-micron technologies, the complexity of pushing the circuit performance further is becoming an important obstacle. To achieve better performance, there is an increasing need for collaboration of higher level (e.g. microarchitecture-level) and circuit level optimizations. Traditionally for a computer system, applications lie at the top of the whole spectrum. Previously, researchers have looked into application characteristics to optimize the performance of the system. The ever-increasing need for improvement in system performance and power utilization led me to believe that we need to look beyond the application level to utilize the system resources more intelligently. Note that, the primary objective of a computing system is to satisfy the user's expectations. Previous researchers have worked into user satisfaction while interacting with a system. But their objective was to dynamically optimize the operating system behavior to satisfy the user. We believe that including individual user's preferences to optimize system hardware utilization would lead to better performance and power. The results presented in this work support this hypothesis. Additionally, analysis of the materials that lies at the lowermost end of the system spectrum opens up a number of opportunities to optimize the system performance. Every individual piece of hardware possesses unique properties even after going through the same manufacturing technology. Therefore, the "one-size-fits-all" approach of current DVFS schemes is suboptimal in the presence of process variations. We have proposed architectural optimization based on process characteristics of individual CPU. Circuit designers typically consider the worst case scenario to predict the default voltage properties of a processor chip. The hard constraint of reliability has created a gap between the default value and the threshold where a circuit can work flawlessly. We have shown that treating the correctness as an objective can improve the system performance with noted reductions in power consumption. The results obtained from these research works have led us to propose the idea of the "holistic architecture".
Neighbourhood Broadcasting and Broadcasting on the (n, k)-Star Graph
Authors: L. He, K. Qiu, Z. Z. Shen
Venue: Proceedings of the 8th international conference on Algorithms and Architectures for Parallel Processing (2008)

The (n, k)-star graph is a generalization of the star graph. We first present an optimal neighbourhood broadcasting algorithm for the (n, k)-star, which is then used to develop an optimal broadcasting algorithm for it. Both algorithms are for the single-port model of the network. While our neighbourhood broadcasting is the first such algorithm designed for the network, our optimal O(log(n!/(n ¿ k)!))-time (=O(k logn)) broadcasting algorithm improves previous algorithms with O(kn) running time. For the all-port model, we first identify a minimum dominating set for the (n, k)-star. We then use it to find an optimal broadcasting algorithm on the all-port model of the (n, k)-star. The running time of this algorithm matches those of previous ones but the algorithm is simpler by using a dominating set instead of spanning trees. In addition, the algorithm has no redundancy in that no node receives the same message more than once.
A Fixed-Point Theorem for Multi-valued Functions with an Application to Multilattice-Based Logic Programming
Authors: Jesús Medina, Manuel Ojeda-Aciego, Jorge Ruiz-Calviño
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

This paper presents a computability theorem for fixed points of multi-valued functions defined on multilattices, which is later used in order to obtain conditions which ensure that the immediate consequence operator computes minimal models of multilattice-based logic programs in at most ¿ iterations.
Proceedings of the 6th workshop on Middleware for network eccentric and mobile applications
Venue: European Conference on Computer Systems (2008)

Following the successful workshops on Middleware for Network Eccentric and Mobile Applications (MiNEMA) which took place in Dublin, Lancaster, Leuven, Lisbon and Magdeburg, this edition will continue to work towards the design, implementation, deployment and evaluation of middleware architectures for mobile computing and communication environments. Middleware has traditionally been used to simplify and coordinate the deployment, configuration, updating of distributed applications. The mobile context of many contemporary distributed applications considerably increases the complexity of middleware and the need for efficient integration of middleware services with the underlying system software. Bridging the gap between applications and underlying system software is the focus for the sixth international workshop on Middleware for Network Eccentric and Mobile Applications, to be held in Glasgow in April 2008.
Plenary lecture III: clustering with an N-dimensional extension of Gielis superformula
Authors: Angel Kuri-Morales
Venue: Proceedings of the 7th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems (2008)

One of the most important issues regarding the analysis of raw data is the adequate identification of groups within such data. There have been many attempts to define a measure of what is "adequate" clustering. In all published cases, however, the adequateness of a cluster depends on a measure of distance chosen a priori. A metric or distance function is a function which defines a distance between elements of a set (for instance, one of the Ln family or Mahalanobis' distances have been utilized). Once a metric has been defined it remains to prove that the resulting clusters do, indeed, correspond to a proper classification. An obvious disadvantage of this methodology is, however, that in the space of the metric all clusters are hyperspherical. The shortcoming is that this fact immediately invalidates the attempt to find irregular shapes in such N-dimensional space. A metric induces a topology on a set but not all topologies can be generated by a metric. Topological unmetrisable spaces correspond, in this case, to a family of formulas developed originally by Gielis which he called a "superformula" (S). By replacing a metric by the elements of the hull defined by S we allow the search for more general shapes for our clusters. We report on the application of Vasconcelos Genetic Algorithm to find the parameters of S whose hulls encompass a large number of observations and the application of this method to data mining.
A Risk-Reward Competitive Analysis for the Recoverable Canadian Traveller Problem
Authors: Bing Su, Yinfeng Xu, Peng Xiao, Lei Tian
Venue: Proceedings of the 2nd international conference on Combinatorial Optimization and Applications (2008)

From the online point of view, we study the Recoverable Canadian Traveller Problem (Recoverable-CTP) in a special network, in which the traveller knows in advance the structure of the network and the travel time of each edge. However, some edges may be blocked and the traveller only observes that upon reaching the vertex of the blocked edge, and the blocked edge may be reopened but the traveller doesn't know its recovery time. The goal is to find a least-cost route from the origin node to the destination node, more precisely, to find an adaptive strategy minimizing the ratio of traversed time to the travel time of the optimal offline shortest path (where all blocked edges and their recovery time are known in advance). We present an optimal online strategy - a comparison strategy and prove its competitive ratio. Moreover, with the different forecasts of the recovery time, some online strategies are given under the risk-reward framework, and the rewards and the risks of the different strategies are analysed.