ppxscal/embedding_network huggingface dataset:
iProver --- An Instantiation-Based Theorem Prover for First-Order Logic (System Description)
Authors: Konstantin Korovin
Venue: Proceedings of the 4th international joint conference on Automated Reasoning (2008)

iProver is an instantiation-based theorem prover which is based on Inst-Gen calculus, complete for first-order logic. One of the distinctive features of iProver is a modular combination of instantiation and propositional reasoning. In particular, any state-of-the art SAT solver can be integrated into our framework. iProver incorporates state-of-the-art implementation techniques such as indexing, redundancy elimination, semantic selection and saturation algorithms. Redundancy elimination implemented in iProver include: dismatching constraints, blocking non-proper instantiations and propositional-based simplifications. In addition to instantiation, iProver implements ordered resolution calculus and a combination of instantiation and ordered resolution. In this paper we discuss the design of iProver and related implementation issues.
Capsules: Expressing Composable Computations in a Parallel Programming Model
Authors: Hasnain A. Mandviwala, Umakishore Ramachandran, Kathleen Knobe
Venue: Languages and Compilers for Parallel Computing: 20th International Workshop, LCPC 2007, Urbana, IL, USA, October 11-13, 2007, Revised Selected Papers (2007)

A well-known problem in designing high-level parallel programming models and languages is the "granularity problem", where the execution of parallel task instances that are too fine-grain incur large overheads in the parallel run-time and decrease the speed-up achieved by parallel execution. On the other hand, tasks that are too coarse-grain create load-imbalance and do not adequately utilize the parallel machine. In this work we attempt to address this issue with a concept of expressing "composable computations" in a parallel programming model called "Capsules". Such composability allows adjustment of execution granularity at run-time.In Capsules, we provide a unifying framework that allows composition and adjustment of granularity for both data and computation over iteration space and computation space. We show that this concept not only allows the user to express the decision on granularity of execution, but also the decision on the granularity of garbage collection, and other features that may be supported by the programming model.We argue that this adaptability of execution granularity leads to efficient parallel execution by matching the available application concurrency to the available hardware concurrency, thereby reducing parallelization overhead. By matching, we refer to creating coarse-grain Computation Capsules, that encompass multiple instances of fine-grain computation instances. In effect, creating coarse-grain computations reduces overhead by simply reducing the number of parallel computations. This leads to: (1) Reduced synchronization cost such as for blocked searches in shared data-structures; (2) Reduced distribution and scheduling cost for parallel computation instances; and (3) Reduced book-keeping cost maintain data-structures such as for unfulfilled data requests.Capsules builds on our prior work, TStreams, a data-flow oriented parallel programming framework. Our results on an SMP machine using the Cascade Face Detector, and the Stereo Vision Depth applications show that adjusting execution granularity through profiling helps determine optimal coarse-grain serial execution granularity, reduces parallelization overhead and yields maximum application performance.
Decision Making Graphical Tool for Multiobjective Optimization Problems
Authors: X. Blasco, J. M. Herrero, J. Sanchis, M. Martínez
Venue: Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks (2007)

Multiobjective optimization problems have become an important issue at many engineering problems. A tradeoff between several design criteria is required and important efforts are made for the development of Multiobjective Optimization Techniques and, in particular, Evolutionary Multiobjective Optimization. Usually these algorithms produce a set of optimum solutions in Pareto sense, there is not a unique solution. The designer (Decision Maker) has to finally select one solution for each particular problem, then he has to select from a set of Pareto solutions, the most adequate solution according to his preferences. It is widely accepted that visualization tools are valuable tools to provide the Decision Maker (DM) with a meaningful way to analyze Pareto set and then to help to select an adequate solution. This work describes a new graphical way to represent high dimensional and large sets of Pareto solutions, allowing an easier analysis, and helping the DM to select an adequate solution.
Speaker Segmentation for Air Traffic Control
Authors: Michael Neffe, Tuan Pham, Horst Hering, Gernot Kubin
Venue: Speaker Classification II: Selected Projects (2007)

In this contribution a novel system of speaker segmentation has been designed for improving safety on voice communication in air traffic control. In addition to the usage of the aircraft identification tag to assign speaker turns on the shared communication channel to aircrafts, speaker verification is investigated as an add-on attribute to improve security level effectively for the air traffic control. The verification task is done by training universal background models and speaker dependent models based on Gaussian mixture model approach. The feature extraction and normalization units are especially optimized to deal with small bandwidth restrictions and very short speaker turns. To enhance the robustness of the verification system, a cross verification unit is further applied. The designed system is tested with SPEECHDAT-AT and WSJ0 database to demonstrate its superior performance.
Prediction of Chaotic Time Series Based on Kernel Function and Multi-scales Wavelet Transform
Authors: Lan Gao, Qing Hua, Yixiang Fu, Jinyong Zhou, Qingguo Song
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

According to the noise in the nonlinear systems and shortage of chaotic prediction method at present, this paper presents a local linear adaptive prediction algorithm based on the kernel function of wavelet decomposition. This method using wavelet transformation has a unique multi-scale analysis capability, decomposed the singular into low frequency part and high frequency part, thereby it can reduce the degree of nonlinear time series and make the issue easy to analyze and predict. Analysis of each part indicates that there exists a chaos feature. Then novel local linear predicting models based on kernel function are established, this model is equivalent to estimate high-complicated nonlinear chaotic series by high-complicated nonlinear function in the origin phase space, and can predict chaotic sequence more exactly. At last, forecasting results of the chaotic models are reconstructed which is based on wavelet theory, so as to forecast the system feature reference data series. The following simulation results show the effectiveness of the method described.
The utility of temporal abstraction in reinforcement learning
Authors: Nicholas K. Jong, Todd Hester, Peter Stone
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1 (2008)

The hierarchical structure of real-world problems has motivated extensive research into temporal abstractions for reinforcement learning, but precisely how these abstractions allow agents to improve their learning performance is not well understood. This paper investigates the connection between temporal abstraction and an agent's exploration policy, which determines how the agent's performance improves over time. Experimental results with standard methods for incorporating temporal abstractions show that these methods benefit learning only in limited contexts. The primary contribution of this paper is a clearer understanding of how hierarchical decompositions interact with reinforcement learning algorithms, with important consequences for the manual design or automatic discovery of action hierarchies.
Synergy by Integrating New Functionality
Authors: S. Becker, M. Heller, M. Jarke, W. Marquardt, M. Nagl, O. Spaniol, D. Thißen
Venue: Collaborative and Distributed Chemical Engineering. From Understanding to Substantial Design Process Support: Results of the IMPROVE Project (2008)

The novel informatics concepts presented in Sects. 3.1 to 3.4 can be integrated again to fully exploit their synergistic potential. This section shows three interesting examples of such synergistic integration. The examples bridge different roles or companies in the design process. They also bridge between the efforts of different research groups within IMPROVE.
Certificate-Based Signature Schemes without Pairings or Random Oracles
Authors: Joseph K. Liu, Joonsang Baek, Willy Susilo, Jianying Zhou
Venue: Proceedings of the 11th international conference on Information Security (2008)

In this paper, we propose two new certificate-based signature (CBS) schemes with new features and advantages. The first one is very efficient as it does not require any pairing computation and its security can be proven using Discrete Logarithm assumption in the random oracle model. We also propose another scheme whose security can be proven in the standard model without random oracles. To the best of our knowledge, these are the first CBS schemes in the literature that have such kind of features.
Potentials of precision machining processes for the manufacture of micro forming molds
Authors: Ekkard Brinksmeier, Ralf Gl&#x00e4;be, Oltmann Riemer, Sven Twardy
Venue: Microsystem Technologies (2008)

Molds for micro forming processes require specific functional surfaces to provide optimized tribological condition for forming processes. Such topographies with microscopic dimensions can for instance be generated by lithographical methods. However, a main disadvantage of these processes is their low flexibility, e.g. changing the structures to be generated causes extensive adaptations in the manufacturing process. Therefore, mechanical precision machining processes like micro milling, micro grinding and micro polishing are appropriate processes for the manufacture of micro molds of manifold shapes and with well suited micro topographies.
DELAY: A Lazy Approach for Mining Frequent Patterns over High Speed Data Streams
Authors: Hui Yang, Hongyan Liu, Jun He
Venue: Proceedings of the 3rd international conference on Advanced Data Mining and Applications (2007)

Frequent pattern mining has emerged as an important mining task in data stream mining. A number of algorithms have been proposed. These algorithms usually use a method of two steps: one is calculating the frequency of itemsets while monitoring each arrival of the data stream, and the other is to output the frequent itemsets according to user's requirement. Due to the large number of item combinations for each transaction occurred in data stream, the first step costs lots of time. Therefore, for high speed long transaction data streams, there may be not enough time to process every transactions arrived in stream, which will reduce the mining accuracy. In this paper, we propose a new approach to deal with this issue. Our new approach is a kind of lazy approach, which delays calculation of the frequency of each itemset to the second step. So, the first step only stores necessary information for each transaction, which can avoid missing any transaction arrival in data stream. In order to improve accuracy, we propose monitoring items which are most likely to be frequent. By this method, many candidate itemsets can be pruned, which leads to the good performance of the algorithm, DELAY, designed based on this method. A comprehensive experimental study shows that our algorithm achieves some improvements over existing algorithms, LossyCounting and FDPM, especially for long transaction data streams.
An analysis of data distribution in the ClassAge system: An agent-based system for classification tasks
Authors: Anne M. P. Canuto, Laura Emmanuella A. Santana, Marjory C. C. Abreu, João Carlos Xavier,  Jr.
Venue: Neurocomputing (2008)

The ClassAge (classifier agents) system has been proposed as an alternative to transform the centralized decision-making process of a multi-classifier system into a distributed, flexible and incremental one. This system has presented good results in some conventional (centralized) classification tasks. Nevertheless, in some classification tasks, relevant features might be distributed over a set of agents. These applications can be classified as distributed classification tasks and a method for distributing data (features or attributes) among the agents is needed. In this paper, an investigation of the impact of using data distribution among the agents in the performance of ClassAge will be performed. In this investigation, the performance of the ClassAge system will be compared with some existing multi-classifier systems. In all combination systems, a feature distribution method based on the Pearson correlation will be used.
Input-Dependence in Function-Learning
Authors: Sanjay Jain, Eric Martin, Frank Stephan
Venue: Proceedings of the 3rd conference on Computability in Europe: Computation and Logic in the Real World (2007)

In the standard literature on inductive inference, a learner sees as input the course of values of the function to be learned. In the present work, it is investigated how reasonable this choice is and how sensitive the model is with respect to variations like the overgraph or undergraph of the function. Several implications and separations are shown and for the basic notions, a complete picture is obtained. Furthermore, relations to oracles, additional information and teams are explored.
On Specifying and Visualising Long-Running Empirical Studies
Authors: Peter Y. Wong, Jeremy Gibbons
Venue: Proceedings of the 1st international conference on Theory and Practice of Model Transformations (2008)

We describe a graphical approach to formally specifying temporally ordered activity routines designed for calendar scheduling. We introduce a workflow model OWorkflow, for constructing specifications of long running empirical studies such as clinical trials in which observations for gathering data are performed at strict specific times. These observations, either manually performed or automated, are often interleaved with scientific procedures, and their descriptions are recorded in a calendar for scheduling and monitoring to ensure each observation is carried out correctly at a specific time. We also describe a bidirectional transformation between OWorkflow and a subset of Business Process Modelling Notation (BPMN), by which graphical specification, simulation, automation and formalisation are made possible.
A Semantic-Based Fully Visual Application for Context-Aware Matchmaking and Request Refinement in Ubiquitous Computing
Authors: Michele Ruta, Tommaso Noia, Eugenio Sciascio, Floriano Scioscia
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

This paper presents a mobile visual application aimed at fully exploiting semantics of request descriptions to enable advanced services. Distinguishing aspects of its underlying framework include semantic ranking of request results and logic-based explanation of matchmaking outcomes as well as the context identification and exploitation to select suitable resources. The GUI has been designed and implemented to be effective for handheld devices with reduced screen capabilities. It requires no knowledge of any logic principle to be fully used. The framework is general-purpose and could be easily adapted to different mobile scenarios. Here it is motivated and presented in a tourism case study.
On the Hardness of Truthful Online Auctions with Multidimensional Constraints
Authors: Rica Gonen
Venue: Proceedings of the 4th conference on Computability in Europe: Logic and Theory of Algorithms (2008)

This paper assess the prospect of creating truthful mechanisms for sponsored search auctions where advertisers have budget and time constraints. While the existing impossibility in this area by [4] addresses the situation where advertisers have budget limitations and static prices but not time limitations; our result applies to the common setting in practice where advertisers have time and budget limitations, prices are dynamic and advertisers act strategically on their time limitation as well as their budget.We show that in cases where advertisers' arrival and departure times are private information, no truthful deterministic mechanism for budgeted sponsored search with time constrained advertisers can perform well with respect to social welfare maximization. Essentially, to protect itself from advertisers' time manipulation a truthful mechanism must give up significant social welfare.It is also shown that even in cases where advertisers' arrival and departure times are known to the mechanism, the existence of advertiser budgets is itself a problem. In this case a budgeted sponsored search mechanism with time constrained advertisers can not achieve non-trivial welfare approximation when using a local pricing scheme (a player pricing history is not taken into account). Also, it is shown that for a dynamic global pricing scheme no such truthful mechanism exists.
Nonlinear Coordinate Unfolding Via Principal Curve Projections with Application to Nonlinear BSS
Authors: Deniz Erdogmus, Umut Ozertem
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

Nonlinear independent components analysis (NICA) is known to be an ill-posed problem when only the independence of the sources are sought. Additional constraints on the distribution of the sources or the structure of the mixing nonlinearity are imposed to achieve a solution that is unique in a suitable sense. In this paper, we present a technique that tackles nonlinear blind source separation (NBSS) as a nonlinear invertible coordinate unfolding problem utilizing a recently developed definition of maximum-likelihood principal curves. The proposition would be applicable most conveniently to independent unimodal source distributions with mixtures that have diminishing second order derivatives along the source axes. Application to multimodal sources would be possible with some modifications that are not discussed in this paper. The ill-posed nature of NBSS is also discussed from a differential geometric perspective in this context.
Two-Dimensional Bayesian Subspace Analysis for Face Recognition
Authors: Daoqiang Zhang
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

Bayesian subspace analysis (BSA) has been successfully applied in data mining and pattern recognition. However, due to the use of probabilistic measure of similarity, it often needs much more projective vectors for better performance, which makes the compression ratio very low. In this paper, we propose a novel 2D Bayesian subspace analysis (2D-BSA) method for face recognition at high compression ratios. The main difference between the proposed 2D-BSA and BSA is that the former adopts a new Image-as-Matrix representation for face images, opposed to the Image-as-Vector representation in original BSA. Based on the new representation, 2D-BSA seeks two coupled set of projective vectors corresponding to the rows and columns of the difference face images, and then use them for dimensionality reduction. Experimental results on ORL and Yale face databases show that 2D-BSA is much more appropriate than BSA in recognizing faces at high compression ratios. 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
Using Ant's Brood Sorting to Increase Fault Tolerance in Linda's Tuple Distribution Mechanism
Authors: Matteo Casadei, Ronaldo Menezes, Mirko Viroli, Robert Tolksdorf
Venue: Proceedings of the 11th international workshop on Cooperative Information Agents XI (2007)

Coordination systems have been used in a variety of different applications but have never performed well in large scale, faulty settings. The sheer scale and level of complexity of today's applications is enough to make the current ways of thinking about distributed systems (e.g. deterministic decisions about data organization) obsolete. All the same, computer scientists are searching for new approaches and are paying more attention to stochastic approaches that provide good solutions "most of the time". The trade-off here is that by loosening certain requirements the system ends up performing better in other fronts such as adaptiveness to failures. Adaptation is a key component to fault-tolerance and tuple distribution is the center of the fault-tolerance problem in tuple-space systems. Hence, this paper shows how the tuple distribution in Linda-like systems can be solved by using an adaptive self-organized approach à la Swarm Intelligence. The results discussed in this paper demonstrate that efficient and adaptive solutions to this problem can be achieved using simple and inexpensive approaches.
An accurate time advancement algorithm for particle tracking
Authors: Pavel P. Popov, Randall McDermott, Stephen B. Pope
Venue: Journal of Computational Physics (2008)

We describe a particle position time advancement algorithm that is designed for use with several subgrid velocity reconstruction schemes used in LES/FDF methods, and potentially in other applications. These reconstruction schemes yield a subgrid velocity field with desirable divergence properties, but also with discontinuities across cell faces. Therefore, a conventional time advancement algorithm, such as second-order Runge-Kutta (RK2), does not perform as well as it does with a smooth velocity field. The algorithm that we describe, called Multi-Step RK2 (MRK2), builds upon RK2 by breaking up the time step into two or more substeps whenever a particle crosses one or more velocity discontinuities. When used in conjunction with the parabolic edge reconstruction method, MRK2 performs considerably better than RK2: both the final position of an advected particle, and the final area of a 2D infinitesimal area element are second-order accurate in time (as opposed to first-order accurate for RK2). Furthermore, MRK2 has the theoretical advantage that it better preserves the continuity of the mapping between initial and final particle positions.
How an Ensemble Method Can Compute a Comprehensible Model
Authors: José L. Triviño-Rodriguez, Amparo Ruiz-Sepúlveda, Rafael Morales-Bueno
Venue: Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery (2008)

Ensemble machine learning methods have been developed as an easy way to improve accuracy in theoretical and practical machine learning problems. However, hypotheses computed by these methods are often considered difficult to understand. This could be an important drawback in fields such as data mining and knowledge discovery where comprehensibility is a main criterion. This paper aims to explore the area of trade-offs between accuracy and comprehensibility in ensemble machine learning methods by proposing a learning method that combines the accuracy of boosting algorithms with the comprehensibility of decision trees. The approach described in this paper avoids the voting scheme of boosting by computing simple classification rules from the boosting learning approach while achieving the accuracy of AdaBoost learning algorithm in a set of UCI datasets. The comprehensibility of the hypothesis is thus enhanced without any loss of accuracy.