ppxscal/embedding_network huggingface dataset:
Heuristiscs-Based High-Level Strategy for Multi-agent Systems
Authors: Péter Gasztonyi, István Harmati
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part I (2008)

In this paper, a high-level strategy concept is presented for robot soccer, based on low level heuristic inference methods, rather than explicit rule-based strategy. During tactical positioning, no strict role set is assigned for the agents, instead a fitting point of the role-space is selected dynamically. The algorithm for this approach applies fuzzy logic. We compute fields-of-quality, regarding some relevant aspects of the scenario, and integrate them into one decision-field, according to given strategic parameters (used as weights). The most relevant locations are derived from the decision-field through subtractive clustering, and the agents are allocated to these locations, as their desired positions, according to their significance and their cost of reaching the given target. If an agent is in a position to manipulate the ball, an appropriate action is being selected for it. The simulation and experiments prove that the proposed approach can be efficient in dynamically changing environment or against opponents of different strategies.
Rational Design of atomic gaussian basis sets for Ab initio calculations of the dipole polarizabilities and hyperpolarizabilities. I. Optimized polarization sets for the first-row atoms from B to F
Authors: Mikhail B. Zuev, Sergey E. Nefediev
Venue: Journal of Computational Methods in Sciences and Engineering (2004)

The rational design rules for atomic Gaussian basis sets, tailored for the calculations of molecular polarizabilities &alpha;$_{ij}$ and hyperpolarizabilities &beta;$_{ijk}$ at the SCF level, are elaborated. The sequence of the optimized polarization sets (2d), (3d), (4d), (3d1f), (4d3f) for the first-row atoms from B to F is constructed, the optimization technique is supposed to provide a rapid convergence of both &alpha;$_{ij}$ and &beta;$_{ijk}$ with the size of the polarization set. Adding of these polarization sets to the [6s4p] and [6s5p] substrates, derived from the energy-optimized (10s6p) primitive set of Huzinaga, gives the [6s4pNdMf] and [6s5pNdMf] bases families. They are combined with the corresponding [4sNpMd] and [5sNpMd] bases for Hydrogen built up in the same manner. The convergence of SCF results with the size of the basis set has been thoroughly investigated by the examples of H$_{2}$CO, CH$_{3}$CN, and HCN molecules. It has been established that both &alpha;$_{ij}$ and &beta;$_{ijk}$ values converge to their saturation limits at the [6s4p3d/4s3p] level. This basis set provides the results, which are very close to near HF limit values, estimated with the most sophisticated polarized Gaussian basis sets used up to now.
An explanation-based tools for debugging constraint satisfaction problems
Authors: Samir Ouis, Mohamed Tounsi
Venue: Applied Soft Computing (2008)

This paper describes an explanation-based tools for constraint programming system. These tools provide to the user the conflicts when it raise during solving process. Our tools simulate constraint additions and/or constraint relaxations without any propagation; it also determine if a given constraint belongs to a conflict and it provide diagnosis tool (e.g. why variable v cannot take value val?). With more user-friendly representation of conflicts and explanations, our proposed tools give better problem understanding to the user. We prooved that the proposed tools are efficient, and while there is no debugging system that allow the user to interact with the solver, our explanation-based tools could be used for many other applications.
A Causal Modeling Framework for Generating Clinical Practice Guidelines from Data
Authors: Subramani Mani, Constantin Aliferis
Venue: Proceedings of the 11th conference on Artificial Intelligence in Medicine (2007)

The practice of medicine is becoming increasingly evidence-based and clinical practice guidelines (CPGs) are necessary for advancing evidence-based medicine (EBM). We hypothesize that machine learning methods can play an important role in learning CPGs automatically from data . Automatically induced CPGs can then be used for further manual refinement and deployment, for automated guideline compliance checking, for better understanding of disease processes, and for improved physician education. We discuss why learning CPGs is a special form of computational causal discovery and why simply predictive (i.e., non-causal) methods may not be appropriate for this task.
Expressing Ignorance or Indifference
Authors: Maria Aloni
Venue: Logic, Language, and Computation: 6th International Tbilisi Symposium on Logic, Language, and Computation,TbiLLC 2005 Batumi, Georgia, September 12-16, 2005. Revised Selected Papers (2007)

The article presents a formal analysis in the framework of bi-directional optimality theory of the free choice, ignorance and indifference implicatures conveyed by the use of indefinite expressions or disjunctions. Ignorance is expressed by standard means of epistemic logic. To express indifference we use Groenendijk and Stokhof's question meanings. To derive implicature, Grice's conversational maxims, and an additional principle expressing preferences for minimal models, are formulated as violable constraints used to select optimal candidates out of a set of alternative sentence-context pairs. The implicatures of an utterance of ¿ are then defined as the sentences which are entailed by any optimal context for ¿ (but not by ¿ itself). Entailment is defined in a version of update semantics where contextual updates are derived by competition among contexts. Free choice and other modal implicatures of disjunctions and indefinites will follow, but also scalar implicatures and exhaustification.
Generation of Multiple Background Model by Estimated Camera Motion Using Edge Segments
Authors: Taeho Kim, Kang-Hyun Jo
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

We investigate new approach for segmentation of moving objects and generation of MBM (Multiple Background Model) from an image sequence by a mobile robot. For generating MBM from unstable camera, we have to know the camera motion. When we correlate two consecutive images to calculate the similarity, we use edge segments to reduce computational cost. Because the regions, neighbors of edge segments, have distinctive spatial features while some regions like blue sky, empty road, etc. have ambiguity. Based on the similarity result, we obtain best matched regions, their centroids and displacement vector between two centroids. The highest density of displacement vector histogram, named motion vector, indicates camera motion between consecutive frames. We generate MBM based on motion vector and MBM algorithm classifies each matched pixel to several clusters. The experimental results shows that proposed algorithm successfully detect moving objects with MBM when camera has 2-D translation.
The Boolean Column and Column-Row Matrix Decompositions
Authors: Pauli Miettinen
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

Matrix decompositions are used for many data mining purposes. One of these purposes is to find a concise but interpretable representation of a given data matrix. Different decomposition formulations have been proposed for this task, many of which assume a certain property of the input data (e.g., nonnegativity) and aim at preserving that property in the decomposition.
Critical Information Infrastructures: Should Models Represent Structures or Functions?
Authors: Erik Hollnagel
Venue: Proceedings of the 27th international conference on Computer Safety, Reliability, and Security (2008)

The common approaches to modeling and analyzing complex socio-technical systems, of which Critical Information Infrastructures is one example, assumes that they can be completely specified. The methods emphasize how systems are composed or structured and how component failures propagate. Since socio-technical systems always are underspecified, they cannot be analyzed in the same way. The alternative is to focus on their functions, and how the variability of functions can combine to create non-linear effects. An example of that is the Functional Resonance Analysis method (FRAM).
A Double-Sampling and Hold Based Approach for Accurate and Efficient Network Flow Monitoring
Authors: Guang Cheng, Yongning Tang, Wei Ding
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

One crucial challenge in network flow monitoring is how to accurately and efficiently monitor the large volume of network flows. Several approaches proposed to address this challenge either lack flexibility adapting to greatly varying network traffic (e.g. sNetFlow), or require intensive computing resources (e.g. ANF). In this paper, we propose a novel double-sampling and hold approach for net work flow monitoring to tackle this challenge. We take a coarse-grained packet sampling to initially reduce the total number of monitored packets; then, an enhanced fine-grained sample and hold algorithm (ESHA) is adopted to selectively add packets into flow cache. By optimally adjusting the ESHA sampling rate and taking Early Removal flow cache management scheme, the flow information can be maximized with given limited system resources. Extensive simulation and experiment studies show that our approach can significantly improve both the accuracy and efficiency in network flow monitoring than other methods.
Self-Organizing Neuro-Fuzzy Inference System
Authors: Héctor Allende-Cid, Alejandro Veloz, Rodrigo Salas, Steren Chabert, Héctor Allende
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

The architectural design of neuro-fuzzy models is one of the major concern in many important applications. In this work we propose an extension to Rogers's ANFIS model by providing it with a selforganizing mechanism. The main purpose of this mechanism is to adapt the architecture during the training process by identifying the optimal number of premises and consequents needed to satisfy a user's performance criterion. Using both synthetic and real data, our proposal yields remarkable results compared to the classical ANFIS.
Learning Aggregate Functions with Neural Networks Using a Cascade-Correlation Approach
Authors: Werner Uwents, Hendrik Blockeel
Venue: Proceedings of the 18th international conference on Inductive Logic Programming (2008)

In various application domains, data can be represented as bags of vectors. Learning functions over such bags is a challenging problem. In this paper, a neural network approach, based on cascade-correlation networks, is proposed to handle this kind of data. By defining special aggregation units that are integrated in the network, a general framework to learn functions over bags is obtained. Results on both artificially created and real-world data sets are reported.
Sound Localization Based on Excitation Source Information for Intelligent Home Service Robots
Authors: Keun-Chang Kwak
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

This paper is concerned with Sound Localization (SL) using Excitation Source Information (ESI) and effective angle estimation for intelligent home service robots that are equipped with multi-channel sound board and three low-cost condenser microphones. The main goal is to localize a caller by estimating time-delay with features obtained from the excitation source based on Linear Prediction (LP) residual and Hilbert envelop, when the speaker calls robot's name in all directions. For performance analysis, we collected SL-DB (sound localization database) with the variation of distance and angle under test-bed environments like home. Here the localization success rate (LSR) and average localization error (ALE) from field of view (FOV) range of robot camera are used as localization performance criterion. The experimental results reveal that the presented method shows a good performance in comparison with the well-known Time Delay of Arrival (TDOA) and Generalized Cross Correlation- Phase Transform (GCC-PHAT) method.
An Improved Succinct Representation for Dynamic k-ary Trees
Authors: Diego Arroyuelo
Venue: Proceedings of the 19th annual symposium on Combinatorial Pattern Matching (2008)

k-ary trees are a fundamental data structure in many text-processing algorithms (e.g., text searching). The traditional pointer-based representation of trees is space consuming, and hence only relatively small trees can be kept in main memory. Nowadays, however, many applications need to store a huge amount of information. In this paper we present a succinct representation for dynamic k-ary trees of n nodes, requiring 2n + nlogk + o(nlogk) bits of space, which is close to the information-theoretic lower bound. Unlike alternative representations where the operations on the tree can be usually computed in O(logn) time, our data structure is able to take advantage of asymptotically smaller values of k, supporting the basic operations parent and child in O(logk + loglogn) time, which is o(logn) time whenever logk = o(logn). Insertions and deletions of leaves in the tree are supported in $O((\log{k}+\log\log{n})(1+\frac{\log{k}}{\log{(\log{k} + \log\log{n})}}))$ amortized time. Our representation also supports more specialized operations (like subtreesize, depth, etc.), and provides a new trade-off when k = O(1) allowing faster updates (in O(loglogn) amortized time, versus the amortized time of O((loglogn)1 + ¿ ), for ¿> 0, from Raman and Rao [21]), at the cost of slower basic operations (in O(loglogn) time, versus O(1) time of [21]). The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Law and technologyThe end of the generative internet
Authors: Jonathan Zittrain
Venue: Communications of the ACM (2009)

Exploring the expectations and implications for version 2.0 of the Net's new gated communities.
Significant Characteristics to Abstract Content: Long Term Preservation of Information
Authors: Manfred Thaller, Volker Heydegger, Jan Schnasse, Sebastian Beyl, Elona Chudobkaite
Venue: Proceedings of the 12th European conference on Research and Advanced Technology for Digital Libraries (2008)

The (automatic) extraction of significant characteristics of files is an important feature of all long term preservation activities. We propose, however, that for the necessary automatic evaluation of the outcomes of certain preservation actions --- notably migration --- an approach is necessary, which follows other traditions in the abstraction of format descriptions. To implement a strategy for the automatic evaluation of various actions within a preservation environment, we define two formal, XML base languages: One allowing to define the content of a specific file, the other describing a file format in such a way, that it can be handled by multi-purpose software.
An Improved On-Line Sequential Learning Algorithm for Extreme Learning Machine
Authors: Bin Li, Jingming Wang, Yibin Li, Yong Song
Venue: Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks (2007)

This paper presents an efficient online sequential learning algorithm for extreme learning machine, which can learn data one by one. In this algorithm, the parameters of hidden nodes (the input weights and biases of additive nodes or the centers and impact factors of RBF nodes) are randomly selected and the output weights are analytically determined based on the sequentially arriving data. In the online sequence, the algorithm updates the output-layer weights with a Givens QR decomposition based on the orthogonalized least square algorithm. Simulations on benchmark problems demonstrate that the algorithm produces much better generalization performance than another online sequential extreme learning machine algorithm, or sometimes it has good performance than primitive extreme learning machine algorithm.
Optimal admission and pricing control problems in service industries with multiple servers and sideline profit
Authors: Jae-Dong Son, Yaghoub Khojasteh Ghamari
Venue: Applied Stochastic Models in Business and Industry (2008)

This paper deals with the problem of selecting profitable customer orders sequentially arriving at a company operating in service industries with multiple servers in which two classes of services are provided. The first class of service is designed to meet the particular needs of customers; and the company (1) makes a decision on whether to accept or to reject the order for this service (admission control) and (2) decides a price of the order and offers it to an arriving customer (pricing control). The second class of service is provided as a sideline, which prevents servers from being idle when the number of customer orders for the first class is less than the number of servers. This yields the sideline profit. A cost is paid to search for customer orders, which is called the search cost. In the context of search cost, the company has an option whether to conduct the search or not. In this paper, we discuss both admission control and pricing control problems within an identical framework as well as examine the structure of the optimal policies to maximize the total expected net profit gained over an infinite planning horizon. We show that when the sideline profit is large, the optimal policies may not be monotone in the number of customer orders in the system. Copyright &copy; 2008 John Wiley & Sons, Ltd.
Inverse-Halftoning for Error Diffusion Based on Statistical Mechanics of the Spin System
Authors: Yohei Saika
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

On the basis of statistical mechanics of the Q-Ising model with ferromagnetic interactions under the random fields, we formulate the problem of inverse-halftoning for the error diffusion using the Floyd-Steinburg kernel. Then using the Monte Carlo simulation for a set of the snapshots of the Q-Ising model and a standard image, we estimate the performance of our method based on the mean square error and edge structures of the reconstructed image, such as the edge length and the gradient of the gray-level. We clarify that the optimal performance of the MPM estimate is achieved by suppressing the gradient of the gray-level on the edges of the halftone image and by removing a part of the halftone image if we set parameters appropriately.
Emergent Sorting in Networks of Router Agents
Authors: Alexander Scheidler, Christian Blum, Daniel Merkle, Martin Middendorf
Venue: Proceedings of the 6th international conference on Ant Colony Optimization and Swarm Intelligence (2008)

In this paper we study basic properties of so called Emergent Sorting Networks. These are directed networks which consist of router agents and buffer sites that are located between every two neighbouring agents. The agents can move objects from their input buffer sites to their output buffers. It is assumed that the objects may differ in type and are inserted randomly at an input agent. Brueckner (2000) presented a set of simple local policies for the agents which lead to a sorted outflow (batches of objects of the same type) of the objects out of an output agent. In this paper we introduce a pheromone based variant of the routing policies. The different policies are studied on quadratic and linear network topologies in terms of sorting performance and fairness.
Cognitive Tutoring System with "Consciousness"
Authors: Daniel Dubois, Mohamed Gaha, Roger Nkambou, Pierre Poirier
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

Developing a learning system with the intent of optimizing human knowledge acquisition is a complex endeavor. CTS (Conscious Tutoring System) is meant to help astronauts learn the manipulation of Canadarm2. Our proposition relies on a cognitive architecture involving "consciousness" mechanisms. We give a partial account of CTS' cognitive architecture in its current state of development, and how it compares to other cognitive architectures and agents.
Motivations as an Abstraction of Meta-level Reasoning
Authors: Felipe Meneguzzi, Michael Luck
Venue: Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V (2007)

In agent systems, meta-level reasoning is commonly used in enforcing rationality in the choice of goals and actions performed by an agent, ensuring that an agent behaves as effectively and efficiently as possible. Through meta-reasoning an agent is able to explicitly consider goals before committing to them, and consider courses of action before executing plans. In this paper, we argue that although seldom considered, a flexible meta-level reasoning component is a valuable addition to any agent architecture. We describe such a component for use in BDI architectures, underpinned by a model of motivation and a motivation-based description language, and demonstrate its effectiveness empirically.
Linking patterns in European Union countries: geographical maps of the European academic web space
Authors: José Luis Ortega, Isidro F. Aguillo
Venue: Journal of Information Science (2008)

This national level study intends to describe the existing relationships between the number of web pages, inlinks to and outlinks from Europe and national or internal links in the European Higher Education Area through a sample of 535 European universities' web domains. Several geographical maps are introduced to summarize and visualize this statistical information. The main result shows that larger countries, in number of web pages, link less to the remaining European countries, while the smaller ones are characterized by their link profusion to the European zone. The great presence of national links in large and medium size countries confirms that the European academic web space is shaped by the aggregation of national sub-networks, while the similar low presence in small countries suggests that these are linked to another large country.
DataJewel: Integrating Visualization with Temporal Data Mining
Authors: Mihael Ankerst, Anne Kao, Rodney Tjoelker, Changzhou Wang
Venue: Visual Data Mining: Theory, Techniques and Tools for Visual Analytics (2008)

In this chapter we describe DataJewel, a new temporal data mining architecture. DataJewel tightly integrates a visualization component, an algorithmic component and a database component. We introduce a new visualization technique called CalendarView as an implementation of the visualization component, and we introduce a data structure that supports temporal mining of large databases. In our architecture, algorithms can be tightly integrated with the visualization component and most existing temporal data mining algorithms can be leveraged by embedding them into DataJewel. This integration is achieved by an interface that is used by both the user and the algorithms to assign colors to events. The user interactively assigns colors to incorporate domain knowledge or to formulate hypotheses. The algorithm assigns colors based on discovered patterns. The same visualization technique is used for displaying both data and patterns to make it more intuitive for the user to identify useful patterns while exploring data interactively or while using algorithms to search for patterns. Our experiments in analyzing several large datasets from the airplane maintenance domain demonstrate the usefulness of our approach and we discuss its applicability to domains like homeland security, market basket analysis and web mining.