ppxscal/embedding_network huggingface dataset:
Deterministic Approximation Algorithms for the Nearest Codeword Problem
Authors: Noga Alon, Rina Panigrahy, Sergey Yekhanin
Venue: Proceedings of the 12th International Workshop and 13th International Workshop on Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques (2009)

The Nearest Codeword Problem (NCP) is a basic algorithmic question in the theory of error-correcting codes. Given a point $v \in \mathbb{F}_2^n$ and a linear space $L\subseteq \mathbb{F}_2^n$ of dimension k NCP asks to find a point l ¿ L that minimizes the (Hamming) distance from v. It is well-known that the nearest codeword problem is NP-hard. Therefore approximation algorithms are of interest. The best efficient approximation algorithms for the NCP to date are due to Berman and Karpinski. They are a deterministic algorithm that achieves an approximation ratio of O(k/c) for an arbitrary constant c, and a randomized algorithm that achieves an approximation ratio of O(k/logn).In this paper we present new deterministic algorithms for approximating the NCP that improve substantially upon the earlier work. Specifically, we obtain: A polynomial time O(n/logn)-approximation algorithm; An n O(s) time O(k log(s) n / logn)-approximation algorithm, where log(s) n stands for s iterations of log, e.g., log(2) n = loglogn; An $n^{O(\log^* n)}$ time O(k/logn)-approximation algorithm. We also initiate a study of the following Remote Point Problem (RPP). Given a linear space $L\subseteq \mathbb{F}_2^n$ of dimension k RPP asks to find a point $v\in \mathbb{F}_2^n$ that is far from L. We say that an algorithm achieves a remoteness of r for the RPP if it always outputs a point v that is at least r-far from L. In this paper we present a deterministic polynomial time algorithm that achieves a remoteness of ¿(nlogk / k) for all k ≤ n/2. We motivate the remote point problem by relating it to both the nearest codeword problem and the matrix rigidity approach to circuit lower bounds in computational complexity theory.
On Askey-scheme and d-orthogonality, I: A characterization theorem
Authors: Y. Ben Cheikh, I. Lamiri, A. Ouni
Venue: Journal of Computational and Applied Mathematics (2009)

This is the first in a series of papers dealing with generalized hypergeometric d-orthogonal polynomials extending the polynomial families in the Askey-scheme. In this paper, we give a characterization theorem to introduce new examples of generalized hypergeometricd-orthogonal polynomials to be studied in the forthcoming works. For d=1, we obtain an unification of some known characterization theorems in the orthogonal polynomials theory.
A discriminative model for tree-to-tree translation
Authors: Brooke Cowan, Ivona Kučerová, Michael Collins
Venue: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (2006)

This paper proposes a statistical, tree-to-tree model for producing translations. Two main contributions are as follows: (1) a method for the extraction of syntactic structures with alignment information from a parallel corpus of translations, and (2) use of a discriminative, feature-based model for prediction of these target-language syntactic structures---which we call aligned extended projections, or AEPs. An evaluation of the method on translation from German to English shows similar performance to the phrase-based model of Koehn et al. (2003).
Research on Structural Performance Simulation of Heavy Duty Freight Car Based on Virtual Prototyping Technology
Authors: Siqun Ma, Wenzhong Zhao
Venue: Proceedings of the 2009 International Conference on Measuring Technology and Mechatronics Automation - Volume 02 (2009)

The concept of virtual prototyping is discussed and the makeup of unit modules of virtual prototyping system of railway vehicle and its function frame are given, as a example, the structural performance simulation integration and light weight design of D45 Clamping Rolling Stock is researched, the method how to create simulation frame, analysis and evaluation system supported by virtual prototyping system is introduced when develop complicated products. Practical application indicated this system can supply effective technology support for the development course of product.
The Jamming problem in IEEE 802.11-based mobile ad hoc networks with hidden terminals: Performance analysis and enhancement
Authors: Yun Li, Chonggang Wang, Weiliang Zhao, Xiaohu You
Venue: International Journal of Communication Systems (2009)

The hidden-terminal problem significantly degrades the performance of IEEE 802.11 DCF. Many previous works have investigated its influence on the throughput of CSMA-based medium access control (MAC) protocols, especially IEEE 802.11 DCF. In this paper, we introduce a new Jamming problem for IEEE 802.11-based mobile ad hoc networks, which is caused by hidden terminals. An analytical model is established for this problem. Based on this model, an adaptive DCF (ADCF), is designed to solve the jamming problem through adaptively adjusting the minimum contention window of hidden terminals. Simulation results effectively demonstrate that the proposed A-DCF can avoid the jamming and in turn greatly improve channel utilization and throughput. Copyright &copy; 2009 John Wiley & Sons, Ltd. In this paper, we introduce a new Jamming problem for IEEE 802.11-based mobile ad hoc networks, which is caused by hidden terminals. An analytical model is established for this problem. Based on this model, an adaptive DCF (ADCF), is designed to solve the jamming problem through adaptively adjusting the minimum contention window of hidden terminals. Simulation results effectively demonstrate that the proposed A-DCF can avoid the jamming and in turn greatly improve channel utilization and throughput. Copyright &copy; 2009 John Wiley & Sons, Ltd.
On the classification performance of TAN and general Bayesian networks
Authors: Michael G. Madden
Venue: Knowledge-Based Systems (2009)

Over a decade ago, Friedman et al. introduced the Tree Augmented Naive Bayes (TAN) classifier, with experiments indicating that it significantly outperformed Naive Bayes (NB) in terms of classification accuracy, whereas general Bayesian network (GBN) classifiers performed no better than NB. This paper challenges those claims, using a careful experimental analysis to show that GBN classifiers significantly outperform NB on datasets analyzed, and are comparable to TAN performance. It is found that the poor performance reported by Friedman et al. are not attributable to the GBN per se, but rather to their use of simple empirical frequencies to estimate GBN parameters, whereas basic parameter smoothing (used in their TAN analyses but not their GBN analyses) improves GBN performance significantly. It is concluded that, while GBN classifiers may have some limitations, they deserve greater attention, particularly in domains where insight into classification decisions, as well as good accuracy, is required.
A Study on Real-Time Scheduling for Holonic Manufacturing Systems --- Determination of Utility Values Based on Multi-agent Reinforcement Learning
Authors: Koji Iwamura, Norihisa Mayumi, Yoshitaka Tanimizu, Nobuhiro Sugimura
Venue: Proceedings of the 4th International Conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing (2009)

This paper deals with a real-time scheduling method for holonic manufacturing systems (HMS). In the previous paper, a real-time scheduling method based on utility values has been proposed and applied to the HMS. In the proposed method, all the job holons and the resource holons firstly evaluate the utility values for the cases where the holon selects the individual candidate holons for the next machining operations. The coordination holon secondly determine a suitable combination of the resource holons and the job holons which carry out the next machining operations, based on the utility values. Multi-agent reinforcement learning is newly proposed and implemented to the job holons and the resource holons, in order to improve their capabilities for evaluating the utility values of the candidate holons. The individual job holons and resource holons evaluate the suitable utility values according to the status of the HMS, by applying the proposed learning method.
A Visual Brain Chip Based on Selective Attention for Robot Vision Application
Authors: Tao Wang, Nanning Zheng, Kuizhi Mei
Venue: Proceedings of the Third IEEE International Conference on Space Mission Challenges for Information Technology (2009)

Due to huge computational loads and the state of art of current hardware, nowadays how to implement real-time vision tasks on a signal chip is still a great interesting work. It is critical for eventually accomplishing biointelligence on robots, whatever humanoid robots, insect robots or even space robots. This paper presents a design of a so-called visual brain chip for robotic vision applications in order to explore biological visual information processing mechanisms.Biological vision systems explore their environments via allocating their visual resources to only the interesting parts of a scene. This is achieved by a selective visual attention mechanism that controls eye movements. Such mechanism is useful to reduce the complexity of calculation. The computational process was divided into three stages for achieving real-time visual computation. The whole system for the chip design used a PTZ TV camera, ASIC technology altogether with a LEON RISC processor and was verified on the FPGA development board. It is a good attempt for future robot vision system applications, especially for space robot, due to limited onboard computing resources, limited viewpoints and mobility.
The Challenges of Using Wireless Mesh Networks for Earthquake Early Warning Systems
Authors: Jens Nachtigall, Anatolij Zubow, Robert Sombrutzki, Matteo Picozzi
Venue: Proceedings of the 2009 Second International Conference on Advances in Mesh Networks (2009)

The total cost of an Earthquake Early Warning System (EEWS) can be substantially decreased by using Wireless Mesh Networks (WMNs), which are inexpensive computer networks whose nodes communicate wirelessly using a license-free spectrum in a self-organized manner. The Early Warning System triggers on the small-amplitude, but fast P-wave in order to shutdown critical infrastructures before the destructive, but slow S-waves arrive only a few seconds later. It demands low-latency communications of high robustness. We conducted shakeboard-based measurements using IEEE 802.11a/b. Innovatively, our tests show that already for the slight shaking related to P-waves representative for strong (Mw > 6) and nearby (epicentral distance
Portfolio selection problems with random fuzzy variable returns
Authors: Takashi Hasuike, Hideki Katagiri, Hiroaki Ishii
Venue: Fuzzy Sets and Systems (2009)

This paper considers several portfolio selection problems including probabilistic future returns with ambiguous expected returns assumed as random fuzzy variables. Random fuzzy portfolio selection problems are formulated as nonlinear programming problems based on both stochastic and fuzzy programming approaches Since there is no efficient solution method to solve these problems directly, main problems are transformed into equivalent deterministic quadratic programming problems using probabilistic chance constraints, possibility measure and fuzzy goals, and their efficient solution methods to find a global optimal solution of each problem is constructed. Furthermore, numerical examples of portfolio selection problems are provided to illustrate our proposed models and solution methods compared with several previous basic models and to show that our proposed model is a versatile model to be applicable to various unexpected conditions.
Value-based policy teaching with active indirect elicitation
Authors: Haoqi Zhang, David Parkes
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 1 (2008)

Many situations arise in which an interested party's utility is dependent on the actions of an agent; e.g., a teacher is interested in a student learning effectively and a firm is interested in a consumer's behavior. We consider an environment in which the interested party can provide incentives to affect the agent's actions but cannot otherwise enforce actions. In value-based policy teaching, we situate this within the framework of sequential decision tasks modeled by Markov Decision Processes, and seek to associate limited rewards with states that induce the agent to follow a policy that maximizes the total expected value of the interested party. We show value-based policy teaching is NP-hard and provide a mixed integer program formulation. Focusing in particular on environments in which the agent's reward is unknown to the interested party, we provide a method for active indirect elicitation wherein the agent's reward function is inferred from observations about its response to incentives. Experimental results suggest that we can generally find the optimal incentive provision in a small number of elicitation rounds.
Neural Network Regression for LHF Process Optimization
Authors: Miroslaw Kordos
Venue: Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II (2009)

We present a system for regression using MLP neural networks with hyperbolic tangent functions in the input, hidden and output layer. The activation functions in the input and output layer are adjusted during the network training to fit better the distribution of the underlying data, while the network weights are trained to fit desired input-output mapping. A non-gradient variable step size training algorithm is used since it proved effective for that kind of problems. Finally we present a practical implementation, the system found in the optimization of metallurgical processes. 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
First International Fingerprint Liveness Detection Competition--LivDet 2009
Authors: Gian Luca Marcialis, Aaron Lewicke, Bozhao Tan, Pietro Coli, Dominic Grimberg, Alberto Congiu, Alessandra Tidu, Fabio Roli, Stephanie Schuckers
Venue: Proceedings of the 15th International Conference on Image Analysis and Processing (2009)

Fingerprint recognition systems are vulnerable to artificial spoof fingerprint attacks, like molds made of silicone, gelatin or Play-Doh. "Liveness detection", which is to detect vitality information from the biometric signature itself, has been proposed to defeat these kinds of spoof attacks. The goal for the LivDet 2009 competition is to compare different methodologies for software-based fingerprint liveness detection with a common experimental protocol and large dataset of spoof and live images. This competition is open to all academic and industrial institutions which have a solution for software-based fingerprint vitality detection problem. Four submissions resulted in successful completion: Dermalog, ATVS, and two anonymous participants (one industrial and one academic). Each participant submitted an algorithm as a Win32 console application. The performance was evaluated for three datasets, from three different optical scanners, each with over 1500 images of "fake" and over 1500 images of "live" fingerprints. The best results were from the algorithm submitted by Dermalog with a performance of 2.7% FRR and 2.8% FAR for the Identix (L-1) dataset. The competition goal is to become a reference event for academic and industrial research in software-based fingerprint liveness detection and to raise the visibility of this important research area in order to decrease risk of fingerprint systems to spoof attacks.
RVM-Based Classification of the Network Video Surveillance System
Authors: Ouyang Yi, Zhang Sanyuan
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 01 (2009)

In this paper, a new based on RVM target recognition algorithm is proposed. The algorithm used color and texture characteristics of each frame of video in the time domain modeling, the use of the video frame sequence of images to enhance the relationship between the space division of the accuracy of the classification algorithm to achieve through the RVM MAP, in order to achieve in complex cases, the background video to identify the precise objective of the human body. In strong light case, as well as complex multi-objective circumstances, such as background on the video sequence of the human body target partition, the algorithm used in this article referred to the division better than the cumulative background subtraction as well as the segmentation approach Gaussian Mixture Model.
How optimized environmental sensing helps address information overload on the web
Authors: Carlos Guestrin
Venue: Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data (2009)

In this talk, we tackle a fundamental problem that arises when using sensors to monitor the ecological condition of rivers and lakes, the network of pipes that bring water to our taps, or the activities of an elderly individual when sitting on a chair: Where should we place the sensors in order to make effective and robust predictions? Such sensing problems are typically NP-hard, and in the past, heuristics without theoretical guarantees about the solution quality have often been used. In this talk, we present algorithms which efficiently find provably near-optimal solutions to large, complex sensing problems. Our algorithms are based on the key insight that many important sensing problems exhibit submodularity, an intuitive diminishing returns property: Adding a sensor helps more the fewer sensors we have placed so far. In addition to identifying most informative locations for placing sensors, our algorithms can handle settings, where sensor nodes need to be able to reliably communicate over lossy links, where mobile robots are used for collecting data or where solutions need to be robust against adversaries and sensor failures. We present results applying our algorithms to several real-world sensing tasks, including environmental monitoring using robotic sensors, activity recognition using a built sensing chair, and a sensor placement competition. We conclude with drawing an interesting connection between sensor placement for water monitoring and addressing the challenges of information overload on the web. As examples of this connection, we address the problem of selecting blogs to read in order to learn about the biggest stories discussed on the web, and personalizing content to turn down the noise in the blogosphere.
The Re-improved GRAP Model of the Fourth Party Logistics Providers
Authors: Zhang Ying
Venue: Proceedings of the 2009 First International Workshop on Database Technology and Applications (2009)

With the fast development of the logistics industry, the fourth party logistics becomes a top issue in this line. The correct evaluation and selection of supply chain cooperation partners is a key problem in the development of the fourth party logistics. This paper constructs an evaluation model of the fourth party logistics provider selection by using analysis process of grey relation, GRAP together with analytic hierarchy process, AHP. This model is a re-improved one based on the traditional GRAP and its first improvement of the Entropy-GRAP model by the author. The grey nature of information is used fully on the basis of the weight defined by AHP. According to the analysis of a true example, it is clear that the model can successfully solve some complex problems such as incomplete information, numerous indices and correlations between indices.
Image Segmentation Using Graph Representations and Local Appearance and Shape Models
Authors: Johannes Keustermans, Dieter Seghers, Wouter Mollemans, Dirk Vandermeulen, Paul Suetens
Venue: Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition (2009)

A generic model-based segmentation algorithm is presented. Based on a set of training data, consisting of images with corresponding object segmentations, a local appearance and local shape model is build. The object is described by a set of landmarks. For each landmark a local appearance model is build. This model describes the local intensity values in the image around each landmark. The local shape model is constructed by considering the landmarks to be vertices in an undirected graph. The edges represent the relations between neighboring landmarks. By implying the markovianity property on the graph, every landmark is only directly dependent upon its neighboring landmarks, leading to a local shape model. The objective function to be minimized is obtained from a maximum a-posteriori approach. To minimize this objective function, the problem is discretized by considering a finite set of possible candidates for each landmark. In this way the segmentation problem is turned into a labeling problem. Mean field annealing is used to optimize this labeling problem. The algorithm is validated for the segmentation of teeth from cone beam computed tomography images and for automated cephalometric analysis.
Noise-Tolerant Analog Circuits for Sensory Segmentation Based on Symmetric STDP Learning
Authors: Gessyca Maria Tovar, Tetsuya Asai, Yoshihito Amemiya
Venue: Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II (2009)

We previously proposed a neural segmentation model suitable for implementation with complementary metal-oxide-semiconductor (CMOS) circuits. The model consists of neural oscillators mutually coupled through synaptic connections. The learning is governed by a symmetric spike-timing-dependent plasticity (STDP). Here we demonstrate and evaluate the circuit operation of the proposed model with a network consisting of six oscillators. Moreover, we explore the effects of mismatch in the threshold voltage of transistors, and demonstrate that the network was tolerant to mismatch (noise).
Unsupervised learning of Bulgarian POS tags
Authors: Derrick Higgins
Venue: Proceedings of the 2003 EACL Workshop on Morphological Processing of Slavic Languages (2003)

This paper presents an approach to the unsupervised learning of parts of speech which uses both morphological and syntactic information. While the model is more complex than those which have been employed for unsupervised learning of POS tags in English, which use only syntactic information, the variety of languages in the world requires that we consider morphology as well. In many languages, morphology provides better clues to a word's category than word order. We present the computational model for POS learning, and present results for applying it to Bulgarian, a Slavic language with relatively free word order and rich morphology.
Context Oriented Information Integration
Authors: Mukesh Mohania, Manish Bhide, Prasan Roy, Venkatesan T. Chakaravarthy, Himanshu Gupta
Venue: Transactions on Large-Scale Data- and Knowledge-Centered Systems I (2009)

Faced with growing knowledge management needs, enterprises are increasingly realizing the importance of seamlessly integrating critical business information distributed across both structured and unstructured data sources. Academicians have focused on this problem but there still remain a lot of obstacles for its widespread use in practice. One of the key problems is the absence of schema in unstructured text. In this paper we present a new paradigm for integrating information which overcomes this problem --- that of Context Oriented Information Integration. The goal is to integrate unstructured data with the structured data present in the enterprise and use the extracted information to generate actionable insights for the enterprise. We present two techniques which enable context oriented information integration and show how they can be used for solving real world problems.