ppxscal/embedding_network huggingface dataset:
Object Tracking in Video Sequences by Unsupervised Learning
Authors: R. M. Luque, J. M. Ortiz-De-Lazcano-Lobato, Ezequiel Lopez-Rubio, E. J. Palomo
Venue: Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns (2009)

A Growing Competitive Neural Network system is presented as a precise method to track moving objects for video-surveillance. The number of neurons in this neural model can be automatically increased or decreased in order to get a one-to-one association between objects currently in the scene and neurons. This association is kept in each frame, what constitutes the foundations of this tracking system. Experiments show that our method is capable to accurately track objects in real-world video sequences. 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
On Social Networks Reduction
Authors: Václav Snášel, Zdeněk Horák, Jana Kočíbová, Ajith Abraham
Venue: Proceedings of the 18th International Symposium on Foundations of Intelligent Systems (2009)

Since the availability of social networks data and the range of these data have significantly grown in recent years, new aspects have to be considered. In this paper, we use combination of Formal Concept Analysis and well-known matrix factorization methods to address computational complexity of social networks analysis and clarity of their visualization. The goal is to reduce the dimension of social network data and to measure the amount of information, which has been lost during the reduction. Presented example containing real data proves the feasibility of our approach.
Bayesian word sense induction
Authors: Samuel Brody, Mirella Lapata
Venue: Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics (2009)

Sense induction seeks to automatically identify word senses directly from a corpus. A key assumption underlying previous work is that the context surrounding an ambiguous word is indicative of its meaning. Sense induction is thus typically viewed as an unsupervised clustering problem where the aim is to partition a word's contexts into different classes, each representing a word sense. Our work places sense induction in a Bayesian context by modeling the contexts of the ambiguous word as samples from a multinomial distribution over senses which are in turn characterized as distributions over words. The Bayesian framework provides a principled way to incorporate a wide range of features beyond lexical co-occurrences and to systematically assess their utility on the sense induction task. The proposed approach yields improvements over state-of-the-art systems on a benchmark dataset.
A Replication Strategy in Data Grid Environment
Authors: Jing Li
Venue: Proceedings of the 2009 IITA International Conference on Services Science, Management and Engineering (2009)

A major challenge facing grid applications is the appropriate handling of failures. Data replication is a key technique of data grid, from which, we can get better access performance. It is a fault-tolerant approach, but at the same time bringing some hidden trouble for security, so security and fault-tolerance should be colligated to research. Combining data replication with security, we propose a mathematic model which can determine data replication quantity. In this model, taking economic interest and reputation index of service supplier into account, we simplify an optimization problem of two targets under hypothesis in reason. Through numeric calculation and analysis, we show there is an optimal point for the data replication.
Induced disjoint paths problem in a planar digraph
Authors: Yusuke Kobayashi
Venue: Discrete Applied Mathematics (2009)

As an extension of the disjoint paths problem, we introduce a new problem which we call the induced disjoint paths problem. In this problem we are given a graph G and a collection of vertex pairs {(s"1,t"1),...,(s"k,t"k)}. The objective is to find k paths P"1,...,P"k such that P"i is a path from s"i to t"i and P"i and P"j have neither common vertices nor adjacent vertices for any distinct i,j. The induced disjoint paths problem has several variants depending on whether k is a fixed constant or a part of the input, whether the graph is directed or undirected, and whether the graph is planar or not. We investigate the computational complexity of several variants of the induced disjoint paths problem. We show that the induced disjoint paths problem is (i) solvable in polynomial time when k is fixed and G is a directed (or undirected) planar graph, (ii) NP-hard when k=2 and G is an acyclic directed graph, (iii) NP-hard when k=2 and G is an undirected general graph. As an application of our first result, we show that we can find in polynomial time certain structures called a ''hole'' and a ''theta'' in a planar graph.
A Scalable Optical Interconnection Network
Authors: Qi Xingyun, Feng Quanyou, Han Gang, Dou Wenhua
Venue: Proceedings of the 2009 International Forum on Information Technology and Applications - Volume 01 (2009)

With the rapid development of the high performance computer system, the high speed interconnection technology has become a hotspot of this field. Traditional electrical interconnection cannot meet the needs of the high speed data transmission. Therefore, optical interconnection technology was brought forward. Based on the study and analysis of the present optical interconnection networks, a new inter-processor optical interconnection fabric named PIBOS was put forward, which included the single-stage PIBOS and the multi-stage PIBOS. Simulation results showed that compared to other optical interconnection networks, the PIBOS interconnection network can reduce the packet latency and improve the throughput.
A General Attack against Several Group Signatures
Authors: Zhengjun Cao, Lihua Liu
Venue: Proceedings of the 2009 Eigth IEEE/ACIS International Conference on Computer and Information Science (2009)

We put forward a general attack against several group signatures. By checking whether schemes satisfy traceability, we show that the several group signatures are not secure. In contrast to those attacks against theseschemes, the new attack is very understandable and simple. It's also the first time to show thescheme is insecure.
Constructing a DC decomposition for ordered median problems
Authors: Zvi Drezner, Stefan Nickel
Venue: Journal of Global Optimization (2009)

In this paper we show how to express ordered median problems as a difference between two convex functions (DC). Such an expression can be exploited in solving ordered median problems by using the special methodology available for DC optimization. The approach is demonstrated for solving ordered one median problems in the plane. Computational experiments demonstrated the effectiveness of the approach.
A Method for Identifying Malicious Activity in Collaborative Systems with Maps
Authors: Vasco Furtado, Thiago Assunção, Marcos de Oliveira, Mairon Belchior, Jonathan D'Orleans
Venue: Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining (2009)

In this paper we describe the method we have created for the purpose of identifying misbehavior of users who intends to generate false trends in digital maps. Basically, the idea is to identify patterns of communities of users who strongly contribute with reports that lead a particular geographic area to be considered a hot spot. The association between hot spots, computed from Kernel Density Estimation techniques, and the methods for identifying communities in social networks is the main innovation of the method proposed. A multi-agent system was built in order to simulate several scenarios of malicious activities. This method has shown to be effective for alerting the possibility of malicious activity in a real system.
Syntactic models for structural word insertion and deletion
Authors: Arul Menezes, Chris Quirk
Venue: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2008)

An important problem in translation neglected by most recent statistical machine translation systems is insertion and deletion of words, such as function words, motivated by linguistic structure rather than adjacent lexical context. Phrasal and hierarchical systems can only insert or delete words in the context of a larger phrase or rule. While this may suffice when translating in-domain, it performs poorly when trying to translate broad domains such as web text. Various syntactic approaches have been proposed that begin to address this problem by learning lexicalized and unlexicalized rules. Among these, the treelet approach uses unlexicalized order templates to model ordering separately from lexical choice. We introduce an extension to the latter that allows for structural word insertion and deletion, without requiring a lexical anchor, and show that it produces gains of more than 1.0% BLEU over both phrasal and baseline treelet systems on broad domain text.
Fast Invariant Contour-Based Classification of Hand Symbols for HCI
Authors: Thomas Bader, René Räpple, Jürgen Beyerer
Venue: Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns (2009)

Video-based recognition of hand symbols is a promising technology for designing new interaction techniques for multi-user environments of the future. However, most approaches still lack performance for direct application for human-computer interaction (HCI).In this paper we propose a novel approach to contour-based recognition of hand symbols for HCI. We present adequate methods for normalization and representation of signatures extracted from boundary contours, which allow for efficient recognition of hand poses invariant to translation, rotation, scale and viewpoint variations, which are relevant for many applications in HCI. The developed classification system is evaluated on a dataset containing 13 hand symbols captured from four different persons.
Texture Sensitive Denoising for Single Sensor Color Imaging Devices
Authors: Angelo Bosco, Sebastiano Battiato, Arcangelo Bruna, Rosetta Rizzo
Venue: Computational Color Imaging: Second International Workshop, CCIW 2009, Saint-Etienne, France, March 26-27, 2009. Revised Selected Papers (2009)

This paper presents a spatial noise reduction technique designed to work on CFA (Color Filter Array) data acquired by CCD/CMOS image sensors. The overall processing preserves image details by using heuristics related to HVS (Human Visual System) and texture detection. The estimated amount of texture and HVS sensitivity are combined to regulate the filter strength. Experimental results confirm the effectiveness of the proposed technique.
Giving advice about preferred actions to reinforcement learners via knowledge-based kernel regression
Authors: Richard Maclin, Jude Shavlik, Lisa Torrey, Trevor Walker, Edward Wild
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 2 (2005)

We present a novel formulation for providing advice to a reinforcement learner that employs support-vector regression as its function approximator. Our new method extends a recent advice-giving technique, called Knowledge-Based Kernel Regression (KBKR), that accepts advice concerning a single action of a reinforcement learner. In KBKR, users can say that in some set of states, an action's value should be greater than some linear expression of the current state. In our new technique, which we call Preference KBKR (Pref-KBKR), the user can provide advice in a more natural manner by recommending that some action is preferred over another in the specified set of states. Specifying preferences essentially means that users are giving advice about policies rather than Q values, which is a more natural way for humans to present advice. We present the motivation for preference advice and a proof of the correctness of our extension to KBKR. In addition, we show empirical results that our method can make effective use of advice on a novel reinforcement-learning task, based on the RoboCup simulator, which we call Breakaway. Our work demonstrates the significant potential of advice-giving techniques for addressing complex reinforcement learning problems, while further demonstrating the use of support-vector regression for reinforcement learning.
Texture-based parametric active contour for target detection and tracking
Authors: Ali Reza Vard, Payman Moallem, Ahmad Reza Naghsh Nilchi
Venue: International Journal of Imaging Systems and Technology (2009)

In recent years, active contour models (ACM) have been considered as powerful tools for image segmentation and object tracking in computer vision and image processing applications. This article presents a new tracking method based on parametric active contour models. In the proposed method, a new pressure energy called &ldquo;texture pressure energy&rdquo; is added to the energy function of the parametric active contour model to detect and track a texture target object in a texture background. In this scheme, the texture features of the contour are calculated by a moment-based method. Then, by comparing these features with texture features of the target object, the contour curve is expanded or contracted to be adapted to the object boundaries. Experimental results show that the proposed method is more efficient and accurate in the tracking of objects compare to the traditional ones, when both object and background are textures in nature. &copy; 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 187&ndash;198, 2009
On the progression of situation calculus basic action theories: resolving a 10-year-old conjecture
Authors: Stavros Vassos, Hector J. Levesque
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 2 (2008)

In a seminal paper, Lin and Reiter introduced a model-theoretic definition for the progression of the initial knowledge base of a basic action theory. This definition comes with a strong negative result, namely that for certain kinds of action theories, first-order logic is not expressive enough to correctly characterize this form of progression, and second-order axioms are necessary. However, Lin and Reiter also considered an alternative definition for progression which is always first-order definable. They conjectured that this alternative definition is incorrect in the sense that the progressed theory is too weak and may sometimes lose information. This conjecture, and the status of first-order definable progression, has remained open since then. In this paper we present two significant results about this alternative definition of progression. First, we prove the Lin and Reiter conjecture by presenting a case where the progressed theory indeed does lose information. Second, we prove that the alternative definition is nonetheless correct for reasoning about a large class of sentences, including some that quantify over situations. In this case the alternative definition is a preferred option due to its simplicity and the fact that it is always first-order.
Demand-Driven Normalisation for ACD Term Rewriting
Authors: Leslie Koninck, Gregory J. Duck, Peter J. Stuckey
Venue: Proceedings of the 25th International Conference on Logic Programming (2009)

ACD Term Rewriting (ACDTR) is term rewriting modulo associativity, commutativity, and a limited form of distributivity called conjunctive context. Previous work presented an implementation for ACDTR based on bottom-up eager normalisation, extended to support the conjunctive context. This paper investigates the possibility of using a demand-driven normalisation strategy for ACDTR. Again, dealing with the conjunctive context proves to be challenging. The alternative normalisation strategy is compared with the current form of eager normalisation and potential further improvements on the strategy are investigated.
A Methodology for Evaluating Illumination Artifact Removal for Corresponding Images
Authors: Tobi Vaudrey, Andreas Wedel, Reinhard Klette
Venue: Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns (2009)

Robust stereo and optical flow disparity matching is essential for computer vision applications with varying illumination conditions. Most robust disparity matching algorithms rely on computationally expensive normalized variants of the brightness constancy assumption to compute the matching criterion. In this paper, we reinvestigate the removal of global and large area illumination artifacts, such as vignetting, camera gain, and shading reflections, by directly modifying the input images. We show that this significantly reduces violations of the brightness constancy assumption, while maintaining the information content in the images. In particular, we define metrics and perform a methodical evaluation to identify the loss of information in the images. Next we determine the reduction of brightness constancy violations. Finally, we experimentally validate that modifying the input images yields robustness against illumination artifacts for optical flow disparity matching. 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
A Novel Approach to On-Site Camera Calibration and Tracking for MR Pre-visualization Procedure
Authors: Wataru Toishita, Yutaka Momoda, Ryuhei Tenmoku, Fumihisa Shibata, Hideyuki Tamura, Takafumi Taketomi, Tomokazu Sato, Naokazu Yokoya
Venue: Proceedings of the 3rd International Conference on Virtual and Mixed Reality: Held as Part of HCI International 2009 (2009)

This paper presents camera calibration and tracking method for mixed reality based pre-visualization system for filmmaking. The proposed calibration method collects environmental information required for tracking efficiently since the rough camera path and target environment are known before actual shooting. Previous camera tracking methods using natural feature are suitable for outdoor environment. However, it takes large human cost to construct the database. Our proposed method reduces the cost of calibration process by using fiducial markers. Fiducial markers are used as reference points and feature landmark database is constructed automatically. In shooting phase, moreover, the speed and robustness of tracking are improved by using SIFT descriptor.
Solving Linear Programs Using Multiparty Computation
Authors: Tomas Toft
Venue: Financial Cryptography and Data Security: 13th International Conference, FC 2009, Accra Beach, Barbados, February 23-26, 2009. Revised Selected Papers (2009)

Solving linear programming (LP) problems can be used to solve many different types of problems. Immediate examples include certain types of auctions as well as benchmarking. However, the input data may originate from different, mistrusting sources, which implies the need for a privacy preserving solution.We present a protocol solving this problem using black-box access to secure modulo arithmetic. The solution can be instantiated in various settings: Adversaries may be both active and adaptive, but passive and/or static ones can be employed, e.g. for efficiency reasons. Perfect security can be obtained in the information theoretic setting (up to 1/3 corruptions), while corruption-of-all-but-one is possible in the cryptographic setting. The latter allows a two-party protocol.The solution is based on the well known simplex method. Letting n denote the number of initial variables and m the number of constraints, each pivot requires only $\mathcal{O}({\rm loglog}(m))$ rounds in which $\mathcal{O}(m(m+ n))$ multiplication protocols and $\mathcal{O}(m+n)$ comparison protocols are invoked; this is equivalent to the base-algorithm. A constant-rounds variation is also possible, this increases the number of comparisons to $\mathcal{O}(m^2+n)$.
Modeling vocal interaction for text-independent participant characterization in multi-party conversation
Authors: Kornel Laskowski, Mari Ostendorf, Tanja Schultz
Venue: Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue (2008)

An important task in automatic conversation understanding is the inference of social structure governing participant behavior. We explore the dependence between several social dimensions, including assigned role, gender, and seniority, and a set of low-level features descriptive of talkspurt deployment in a multiparticipant context. Experiments conducted on two large, publicly available meeting corpora suggest that our features are quite useful in predicting these dimensions, excepting gender. The classification experiments we present exhibit a relative error rate reduction of 37% to 67% compared to choosing the majority class.
Robust ontology acquisition from machine-readable dictionaries
Authors: Eric Nichols, Francis Bond, Daniel Flickinger
Venue: Proceedings of the 19th international joint conference on Artificial intelligence (2005)

In this paper, we outline the development of a system that automatically constructs ontologies by extracting knowledge from dictionary definition sentences using Robust Minimal Recursion Semantics (RMRS), a semantic formalism that permits underspecification. We show that by combining deep and shallow parsing resources through the common formalism of RMRS, we can extract ontological relations in greater quality and quantity. Our approach also has the advantages of requiring a very small amount of rules and being easily adaptable to any language with RMRS resources.
Reasoning about designs through frequent patterns mining
Authors: B. Strug, G. lusarczyk
Venue: Advanced Engineering Informatics (2009)

This paper deals with reasoning about designs based on their internal structures in the form of attributed hypergraphs. A database containing hypergraphs representing solutions of similar design tasks is searched for frequently occurring subgraphs. On the basis of search results the quality of newly created solutions is evaluated. Moreover the syntactic reasoning rules are specified by the designer on the basis of the design knowledge concerning a given task and used to evaluate hypergraphs representing new solutions.
An Intelligent Method of Detecting Multi-factors Neighborhood Relation Based On Constrained Delaunay Triangulation
Authors: Wei Wei, LI Man-chun, Long Yi, Liu Yong-xue, Cai Dong
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 01 (2009)

Spatial neighborhood relation detecting is the basis of organization, query, analysis and reasoning of spatial data. For the spatial neighborhood relations of the geographic entities are contained in the Delaunay triangulation, in this paper, the spatial neighborhood relations between multi-factors (including points, lines and polygons) are intelligently detected based on the Constrained Delaunay Triangulation (CDT). This approach consists of steps listed below: A matched candidate points index is set up in order to reorganize the related data of multi-factors. Then the CDT, which is constrained by edges of lines and polygons, is established with the source of coordinates in point index. After coding the CDT according to a rule this paper proposing, neighborhood relation of geographic entities can be searched automatically. And a global neighborhood relation (including separation and neighbor relation) among multi-factors is automatically established by spatial reasoning. This intelligent method of spatial neighborhood relation detecting, which is no need for manual intervention and not limited between two kinds of geographic entities, has high precision and great feasibility in practice.