ppxscal/embedding_network huggingface dataset:
Research on Symmetric NAT Traversal in P2P applications
Authors: Yong Wang, Zhao Lu, Junzhong Gu
Venue: Proceedings of the International Multi-Conference on Computing in the Global Information Technology (2006)

For security and share of Internet IP addresses, NAT (Network Address Translation) firewalls are widely used to isolate the private networks from Internet in some enterprises/organizations. Unfortunately, NATs block the data transmitted directly between two clients under different private networks in many P2P (Peer to Peer) applications. A protocol, STUN, has been proposed by IETF to solve the problem. But if the NAT is Symmetric NAT, the STUN can't tackle it effectively. Another protocol of TURN has been proposed to complement the limitation of STUN, however, it brings the additional cost of resources. Now, almost all the research into the problem are concentrated on using middle server to transmit data among clients although this way brings the additional consumption of network bandwidth and transmission delays at the same time. To solve this problem, this paper makes many practices in Symmetric NAT traversal and proposes a new algorithm PS-STUN that can traversal Symmetric NAT and transmits data directly between the clients in the situations where the STUN isn't enabling. Through emulation tests, we prove that the algorithm is efficient and can solve the Symmetric NAT traversal trouble about STUN.
Intelligent Ontology Learning based on Context: Answering Crucial Questions
Authors: Lobna Karoui
Venue: Proceedings of the International Conference on Computational Inteligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (2006)

A "Context" is an elusive concept, that, it could not be defined automatically by a machine. During the last fifteen years, a strong motivation for research on context appeared in different disciplines such as natural language semantics, linguistics, cognitive psychology and artificial intelligence (AI). In this paper, we focus our attention on the ontology learning field. Till now, the concept of "Context" was not really used for the ontology building task rather for its reuse, evaluation or formalization. In this theoretical study, we carry our interest on the use of context in the ontology learning process. We answer three crucial questions treating the impact of the context in the ontology learning task, its importance in relation with the intelligent interpretation and the utility of reasoning in multiple contexts instead of only one context. These theoretical argumentations will serve to enhance our existing contextual models either for the ontology building or evaluation, in the following research.
Control and stabilization of a rotating planar body with flexible attachments
Authors: A. L. Zuyev
Venue: Proceedings of the 2003 International Conference on Physics and Control - Volume 4 (2003)

A control system describing rotation of rigid body with an arbitrary number of elastic beams is considered. We characterize the approximate controllability property in terms of the eigenvalues of the associated homogeneous beam equation. It is proved that boundary controllability of the whole infinite dimensional dynamics is possible if there is no response in the system. Under this assumption we construct a feedback law that strongly stabilizes the equilibrium. 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
Towards a Multimodal Framework for Human Behavior Recognition
Authors: Sidi O. Soueina, Jocelyne Kiss, Pascal Chaudeyrac, Patrice Bouvier, Ahmed H. Salem, Adel S. Elmaghraby
Venue: Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology (2006)

This paper proposes a framework of an interface that is capable of detecting and classifying human intentions and personality types. The framework relies on a conventional set of multimodal components, namely an eye-tracking system, a mouse and a keyboard, where a large number of real time data analysis will take place, in order to detect behavioral patterns. The system gradually learns from the players and builds a collection of patterns of actions. The personalities of the players are detected through ontological comparisons of known personality types with the newly discovered patterns of actions. We further layout the ground of our initial assumption -- appearing in other publications, that the sub-conscious controls the eyes movements during the game, on those elements or words that are related to the fears and desire, i.e., the personality of the player.
Learning sparse metrics via linear programming
Authors: Rómer Rosales, Glenn Fung
Venue: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (2006)

Calculation of object similarity, for example through a distance function, is a common part of data mining and machine learning algorithms. This calculation is crucial for efficiency since distances are usually evaluated a large number of times, the classical example being query-by-example (find objects that are similar to a given query object). Moreover, the performance of these algorithms depends critically on choosing a good distance function. However, it is often the case that (1) the correct distance is unknown or chosen by hand, and (2) its calculation is computationally expensive (e.g., such as for large dimensional objects). In this paper, we propose a method for constructing relative-distance preserving low-dimensional mapping (sparse mappings). This method allows learning unknown distance functions (or approximating known functions) with the additional property of reducing distance computation time. We present an algorithm that given examples of proximity comparisons among triples of objects (object i is more like object j than object k), learns a distance function, in as few dimensions as possible, that preserves these distance relationships. The formulation is based on solving a linear programming optimization problem that finds an optimal mapping for the given dataset and distance relationships. Unlike other popular embedding algorithms, this method can easily generalize to new points, does not have local minima, and explicitly models computational efficiency by finding a mapping that is sparse, i.e. one that depends on a small subset of features or dimensions. Experimental evaluation shows that the proposed formulation compares favorably with a state-of-the art method in several publicly available datasets.
Web Services Selection Based on Multiple-Aspect Similarity Function
Authors: Hui-Na Chua, S. M. F. D. Syed Mustapha
Venue: Proceedings of the 2006 IEEE/WIC/ACM international conference on Web Intelligence and Intelligent Agent Technology (2006)

Web service selection has recently emerged as one of the potential research area in the web service composition. Selection of web service has become a challenge when the composition of the web service is to be done automatically. The capability of the multiple-aspects of similarity function is the advance feature to the web service selection capability.
A view from the beginning: when does a description become a taxonomy
Authors: Philip J. Kiviat
Venue: Proceedings of the 36th conference on Winter simulation (2004)

This paper describes the author's career leading up to the publication of his 1969 paper Digital Computer Simulation: Computer Programming Languages, how it influenced the paper, and why the paper has endured as a taxonomy for discrete-event simulation programming languages.
Lightweight key management for IEEE 802.11 wireless LANs with key refresh and host revocation
Authors: Avishai Wool
Venue: Wireless Networks (2005)

The IEEE 802.11 Wireless LAN standard has been designed with very limited key management capabilities, using up to 4 static, hong term, keys, shared by all the stations on the LAN. This design makes it quite difficult to fully revoke access from previously-authorized hosts. A host is fully revoked when it can no longer eavesdrop and decrypt traffic generated by other hosts on the wireless LAN.This paper proposes WEP*, a lightweight solution to the host-revocation problem. The key management in WEP* is in the style of pay-TV systems: The Access Point periodically generates new keys, and these keys are transferred to the hosts at authentication time. The fact that the keys are only valid for one re-key period makes host revocation possible, and scalable: A revoked host will simply not receive the new keys.Clearly, WEP* is not an ideal solution, and does not address all the security problems that IEEE 802.11 suffers from. However, what makes WEP* worthwhile is that it is 100% compatible with the existing standard. And, unlike other solutions, WEP* does not rely on external authentication servers. Therefore, WEP* is suitable for use even in the most basic IEEE 802.11 LAN configurations, such as those deployed in small or home offices. A WEP* prototype has been partially implemented using free, open-source tools.
R-D Optimized Frame-Layer Bit Allocation for H.264
Authors: Do-Kyoung Kwon, Mei-Yin Shen, C.-C. Jay Kuo
Venue: Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia (2006)

The frame-layer bit allocation problem for H.264 is studied in this work. We first impose a GOP (Group of Pictures) structure on the underlying H.264 video, and then develop a two-pass rate control algorithm using the Lagrange optimization framework. The proposed algorithm estimates the rate-distortion (R-D) information of all frames in a GOP via pre-encoding in the first pass. The target number of bits of each frame is determined by searching the Lagrange multiplier that meets the bit budget for the GOP with the estimated R-D information. The proposed algorithm is useful for non-conversational applications such as video streaming where video sources are encoded off-line. Experimental results demonstrate that the proposed algorithm achieves substantially higher picture quality than that used in the H.264 JM8.1a reference code.
Code torrent: content distribution using network coding in VANET
Authors: Uichin Lee, Joon-Sang Park, Joseph Yeh, Giovanni Pau, Mario Gerla
Venue: Proceedings of the 1st international workshop on Decentralized resource sharing in mobile computing and networking (2006)

Mobile peer-to-peer systems have recently got in the lime-light of the research community that is striving to build efficient and effective mobile content addressable networks. Along this line of research, we propose a network coding based file swarming protocol targeting vehicular ad hoc net-works (VANET). We argue that file swarming protocols in VANET should deal with typical mobile network issues such as dynamic topology and intermittent connectivity as well as various other issues that have been disregarded in previous mobile peer-to-peer researches such as addressing, node/user density, non-cooperativeness, and unreliable channel. Through simulation, we show that the efficiency and effectiveness of our protocol allows shorter file downloading time compared to an existing VANET file swarming protocol.
Depth from Familiar Objects: A Hierarchical Model for 3D Scenes
Authors: Erik B. Sudderth, Antonio Torralba, William T. Freeman, Alan S. Willsky
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (2006)

We develop an integrated, probabilistic model for the appearance and three-dimensional geometry of cluttered scenes. Object categories are modeled via distributions over the 3D location and appearance of visual features. Uncertainty in the number of object instances depicted in a particular image is then achieved via a transformed Dirichlet process. In contrast with image-based approaches to object recognition, we model scale variations as the perspective projection of objects in different 3D poses. To calibrate the underlying geometry, we incorporate binocular stereo images into the training process. A robust likelihood model accounts for outliers in matched stereo features, allowing effective learning of 3D object structure from partial 2D segmentations. Applied to a dataset of office scenes, our model detects objects at multiple scales via a coarse reconstruction of the corresponding 3D geometry.
Utilising Evolutionary Approaches and Object Oriented Techniques for Design Space Exploration
Authors: Liam Noonan, Colin Flanagan
Venue: Proceedings of the 9th EUROMICRO Conference on Digital System Design (2006)

In this paper we examine how multi-objective evolutionary algorithms can assist in design space exploration. In this approach an engineer specifies certain hard and soft performance requirements for a multi-processor system, and allows it to be generated automatically by competitive evolution/optimisation, thus obviating the need for detailed design. To make the proposal concrete, we use the Intel IXP1200 network processor as a baseline complex system design and show how various improvements can be make to this architecture by evolutionary/competitive design. Various approaches to multi-objective optimisation (Darwin, Lamarck Baldwin, etc.) are compared and contrasted in their ability to generate architectures meeting various constraints. Our paper highlights the flexibility that this approach bestows on the designer, along with the potential to achieve cost savings and performance improvement.
Hybrid Genetic Algorithm Applied to the Determination of the Optimal Operation of Hydrothermal Systems
Authors: Patricia T. Leite, Adriano A. F. M. Carneiro, Andre C. P. L. F. Carvalho
Venue: Proceedings of the Ninth Brazilian Symposium on Neural Networks (2006)

The Brazilian Southeast Hydrothermal System is a large scale and complex system, with 36 GW of installed capacity system and composed of 35 hydro plants. The definition of a efficient policy to use the available resources minimizing the operation costs is an optimization problem. Traditional optimization techniques have difficulties to deal with problems of this dimension. A Hybrid Genetic Algorithm based on gradient calculus developed for Hydrothermal System Scheduling is described in this paper. The algorithm employs new genetic operators, gradient mutation and gradient directed mutation. This paper evaluates the performance of this algorithm in the determination of hydrothermal systems optimal operation. Experimental results show the potential of this new algorithm, not only because the gain obtained by using the new operators but also for providing a new tool for large scale hydrothermal systems scheduling. The algorithm can be applied not only to the southeast, but to any part of the Brazilian Hydrothermal System.
On the structure of the adjacency matrix of the line digraph of a regular digraph
Authors: Simone Severini
Venue: Discrete Applied Mathematics (2006)

We show that the adjacency matrix M of the line digraph of a d-regular digraph D on n vertices can be written as M = AB, where the matrix A is the Kronecker product of the all-ones matrix of dimension d with the identity matrix of dimension n and the matrix B is the direct sum of the adjacency matrices of the factors in a dicycle factorization of D.
Illumination invariant recognition of three-dimensional texture in color images
Authors: Jie Yang, Mohammed Al-Rawi
Venue: Journal of Computer Science and Technology (2005)

In this paper, illumination-affine invariant methods are presented based on affine moment normalization techniques, Zernike moments, and multiband correlation functions. The methods are suitable for the illumination invariant recognition of 3D color texture. Complex valued moments (i.e., Zernike moments) and affine moment normalization are used in the derivation of illumination affine invariants where the real valued affine moment invariants fail to provide affine invariants that are independent of illumination changes. Three different moment normalization methods have been used, two of which are based on affine moment normalization technique and the third is based on reducing the affine transformation to a Euclidian transform. It is shown that for a change of illumination and orientation, the affinely normalized Zernike moment matrices are related by a linear transform. Experimental results are obtained in two tests: the first is used with textures of outdoor scenes while the second is performed on the well-known CUReT texture database. Both tests show high recognition efficiency of the proposed recognition methods.
Realising Common Knowledge Assumptions in Agent Auctions
Authors: Frank Guerin, Emmanuel M. Tadjouddine
Venue: Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology (2006)

Game theory is popular in agent systems for designing auctions with desirable properties. However, many of these properties will only hold if the game and its properties are common knowledge among the agents. For example, in an auction where truthful bidding is an equilibrium strategy, unless this is common knowledge, it may not be rational for an agent to bid truthfully. It is currently not clear how this state of common knowledge can be achieved, especially in open agent societies where agents may encounter previously unseen auction specifications. We need a method for communicating the rules of the game to the agents, and the agents need to be able to determine its properties. We present a machine-readable language in which the rules of the game can be written. We show that it is not feasible for an agent to determine the properties of any arbitrary specification, unless information about the properties is communicated and/or certain restrictions are placed on the specification. We look at two special cases where common knowledge is achievable: auctions with identical players where the two highest bidders determine the price, and Groves mechanisms with a restriction on the pricing rule.
Peer-to-peer form based web information systems
Authors: Stijn Dekeyser, Jan Hidders, Richard Watson, Ron Addie
Venue: Proceedings of the 17th Australasian Database Conference - Volume 49 (2006)

The World Wide Web revolutionized the use of forms in everyday private and business life by allowing a move away from paper forms to easily accessible digital forms. Data captured using such HTML forms could be processed using relational databases or other applications that enforce and apply business logic. Lately XForms has been introduced, offering a logical evolution of digital data capture and dissemination using Internet and document technology.This paper introduces two important new ideas. The first one is the main focus of the paper: a novel type of peer-to-peer web information system where forms are first-class citizens containing extended access rules of very fine granularity which govern read and update rights to data objects associated to the forms. The second idea, which we explore in a preliminary section, forms a powerful motivation for the use of such systems: the automatic and dynamic derivation of workfow processes from the access rules contained in forms.As such, the proposed system leverages current forms and Internet technology to liberate the creation and use of forms and reports, facilitating the capture and dissemination of data, while allowing dynamic management of work flows within organizations.
Magnetic Brain Response Mirrors Extraction of Phonological Features from Spoken Vowels
Authors: Jonas Obleser, Aditi Lahiri, Carsten Eulitz
Venue: Journal of Cognitive Neuroscience (2004)

This study further elucidates determinants of vowel perception in the human auditory cortex. The vowel inventory of a given language can be classified on the basis of phonological features which are closely linked to acoustic properties. A cortical representation of speech sounds based on these phonological features might explain the surprisingly inverse correlation between immense variance in the acoustic signal and high accuracy of speech recognition. We investigated timing and mapping of the N100m elicited by 42 tokens of seven natural German vowels varying along the phonological features tongue height (corresponding to the frequency of the first formant) and place of articulation (corresponding to the frequency of the second and third formants). Auditory evoked fields were recorded using a 148-channel whole-head magnetometer while subjects performed target vowel detection tasks. Source location differences appeared to be driven by place of articulation: Vowels with mutually exclusive place of articulation features, namely, coronal and dorsal elicited separate centers of activation along the posterior-anterior axis. Additionally, the time course of activation as reflected in the N100m peak latency distinguished between vowel categories especially when the spatial distinctiveness of cortical activation was low. In sum, results suggest that both N100m latency and source location as well as their interaction reflect properties of speech stimuli that correspond to abstract phonological features.
Negotiating learning cultures at a distance: MIT academics teach corporate engineers
Authors: Stephen B. Gilbert
Venue: Proceedings of the 1999 conference on Computer support for collaborative learning (1999)

MIT's System Design & Management program (SDM) is a distance education master's degree program in engineering and management for industry employees that hopes to overcome some of the typical barriers to organizational learning. The author has collected a variety of data on the SDM program, and this paper discusses some of the results so far. Findings focus on the technology used for distance learning, the students' ability to apply their learning in the workplace, and the difficulty for students and faculty to adapt to the distance learning setting.
Multiscale Modeling and Constraints for Max-flow/Min-cut Problems in Computer Vision
Authors: Matthew W. Turek, Daniel Freedman
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

Multiscale techniques have been used for many years in computer vision. Recently multiscale edges have received attention in spectral graph methods as an important perceptual cue. In this paper multiscale cues are used in the context of max-flow/min-cut energy minimization. We formulate multiscale min-cut versions of three typical computer vision applications, namely interactive segmentation, image restoration, and optical flow. We then solve across all scales simultaneously. This use of multiscale models and constraints leads to quantitatively and qualitatively improved experimental results.
Using Visual Interpretation of Small Ensembles in Microarray Analysis
Authors: Gregor Stiglic, Matej Mertik, Vili Podgorelec, Peter Kokol
Venue: Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems (2006)

Many different classification models and techniques have been employed on gene expression data. These computational methods are in rapid and continuous evolution and there is no clear consensus on which methods are best to cope with the complex microarray data analysis. Currently ensembles of classifiers are regarded as one of the best classification techniques as they can achieve excellent classification accuracy in comparison to single classifiers methods. One of their main drawbacks is their incomprehensibility. This paper addresses the important issue of the tradeoff between accuracy and comprehensibility when building ensembles and proposes a novel visual technique for interactive interpretation of the knowledge from the small ensembles consisting of only a few decision trees. This way we can achieve better accuracy compared to single classifier, but still maintain a certain level of comprehensibility in small ensembles. The results show that our small ensembles outperform the single classifiers and still retain comprehensibility. Our study also points out that in order to take advantage of our proposed method we need more effective small ensemble building techniques.