ppxscal/embedding_network huggingface dataset:
Cautious inference in collective classification
Authors: Luke K. McDowell, Kalyan Moy Gupta, David W. Aha
Venue: Proceedings of the 22nd national conference on Artificial intelligence - Volume 1 (2007)

Collective classification can significantly improve accuracy by exploiting relationships among instances. Although several collective inference procedures have been reported, they have not been thoroughly evaluated for their commonalities and differences. We introduce novel generalizations of three existing algorithms that allow such algorithmic and empirical comparisons. Our generalizations permit us to examine how cautiously or aggressively each algorithm exploits intermediate relational data, which can be noisy. We conjecture that cautious approaches that identify and preferentially exploit the more reliable intermediate data should outperform aggressive approaches. We explain why caution is useful and introduce three parameters to control the degree of caution. An empirical evaluation of collective classification algorithms, using two base classifiers on three data sets, supports our conjecture.
Emerging Trends in Health Care Delivery: Towards Collaborative Security for NIST RBAC
Authors: Solomon Berhe, Steven Demurjian, Thomas Agresta
Venue: Proceedings of the 23rd Annual IFIP WG 11.3 Working Conference on Data and Applications Security XXIII (2009)

In the next 10 years there will be rapid adoption of health information technology - electronic medical records by providers and personal health records by patients - linked via health information exchange. There is an emergent need to provide secure access to information spread across multiple repositories for health care providers (e.g., physicians, nurses, home health aides, etc.) who collaborate with one another across cyberspace to deliver patient care. Are available security models capable of supporting collaborative access where providers are simultaneously modifying a patient's medical record? To address this question, this paper details collaborative security extensions to NIST RBAC.
Learning and inference with constraints
Authors: Ming-Wei Chang, Lev Ratinov, Nicholas Rizzolo, Dan Roth
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 3 (2008)

Probabilistic modeling has been a dominant approach in Machine Learning research. As the field evolves, thc problems of interest become increasingly challenging and complex. Making complex decisions in real world problems often involves assigning values to sets of interdependent variables where the expressive dependency structure can influence, or even dictate, what assignments are possible. However, incorporating nonlocal depcndencies in a probabilistic model can lead to intractable training and inference. This paper presents Constraints Conditional Models (CCMs), a framework that augments probabilistic models with declarative constraints as a way to support decisions in an expressive output space while maintaining modularity and tractability of training. We further show that declarative constraints can be used to take advantage of unlabeled data when training the probabilistic model.
3-D Extraction of Fibres from Microtomographic Images of Fibre-Reinforced Composite Materials
Authors: Petr Dokládal, Dominique Jeulin
Venue: Proceedings of the 9th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing (2009)

Mechanical properties of molded components made from fibre-reinforced composite materials locally depend of the orientation of the fibres. The evaluation of the properties is done by sampling the component at known positions. The samples (of size of 1 mm3) are scanned in a tomograph which yields 3-D images. We are interested in extracting the individual fibres, to analyze their length and local orientation.The segmentation of the fibres is a challenging task. First, the resolution of the reconstruction being at the limits of the capabilities of the device (optics, sensor, wavelength), the images are noisy and fuzzy. Second, the fibres have a non uniform length and are heavily tangled. This paper describes the segmentation process.
An Efficient Algorithm for the Shortest Path Problem with Forbidden Paths
Authors: Chiun-Chieh Hsu, Da-Ren Chen, Hua-Yuan Ding
Venue: Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Processing (2009)

In this paper, we discuss the shortest path problem with forbidden paths (SPPFP), where the constraints come from a set of forbidden paths (arc sequences) that cannot be part of any feasible solution. SPPFP can be used to solve the problems with hard-to-modeled path constraints or to implement exact branching scheme.The method proposed by Villeneuve and Desaulniers first transfer the SPPFP problem to the k-shortest paths problem. We propose an algorithm which reduces execution time of solving the SPPFP problem via a nodes combination process. It improves the approach proposed by Villeneuve and Desaulniers. In addition, their algorithm for generating augmented paths could cause the inconsistency in particular situations. The proposed method also improves the weakness without increasing its time complexity.
WordTree: Results of a Word Prediction System Presented Thanks to a Tree
Authors: Georges Badr, Mathieu Raynal
Venue: Proceedings of the 5th International Conference on Universal Access in Human-Computer Interaction. Part III: Applications and Services (2009)

For mobility and portability reasons, written communication devices are getting smaller and physical keyboards are replaced with virtual or on-screen one (PDA, pocket pc, GPS, etc.). Text entry has become a tiring job for people (ordinary and motor disabled people) because of the low entry speed. A list of predicted words can be presented to reduce the number of clicks to complete the word. However, these techniques present a disadvantage: let's consider the situation where the word the user wants to enter is not presented in the list. He has then to continue his writing. The prediction system narrows the possibilities in order to display the wanted word. In this paper we present a novel interaction technique in which the user can select a substring of the word. The user clicks on any letter, and the substring to this letter is inserted in the text. We also present the simulated experiments to compare our system to the classical list. The results are then discussed and analyzed.
A linear programming model for determining ordered weighted averaging operator weights with maximal Yager's entropy
Authors: Jian Wu, Bo-Liang Sun, Chang-Yong Liang, Shan-Lin Yang
Venue: Computers and Industrial Engineering (2009)

It has a wide attention about the methods for determining OWA operator weights. At the beginning of this dissertation, we provide a briefly overview of the main approaches for obtaining the OWA weights with a predefined degree of orness. Along this line, we next make an important generalization of these approaches as a special case of the well-known and more general problem of calculation of the probability distribution in the presence of uncertainty. All these existed methods for dealing these kinds of problems are quite complex. In order to simplify the process of computation, we introduce Yager's entropy based on Minkowski metric. By analyzing its desirable properties and utilizing this measure of entropy, a linear programming (LP) model for the problem of OWA weight calculation with a predefined degree of orness has been built and can be calculated much easier. Then, this result is further extended to the more realistic case of only having partial information on the range of OWA weights except a predefined degree of orness. In the end, two numerical examples are provided to illustrate the application of the proposed approach.
Consolidation of unorganized point clouds for surface reconstruction
Authors: Hui Huang, Dan Li, Hao Zhang, Uri Ascher, Daniel Cohen-Or
Venue: ACM Transactions on Graphics (TOG) (2009)

We consolidate an unorganized point cloud with noise, outliers, non-uniformities, and in particular interference between close-by surface sheets as a preprocess to surface generation, focusing on reliable normal estimation. Our algorithm includes two new developments. First, a weighted locally optimal projection operator produces a set of denoised, outlier-free and evenly distributed particles over the original dense point cloud, so as to improve the reliability of local PCA for initial estimate of normals. Next, an iterative framework for robust normal estimation is introduced, where a priority-driven normal propagation scheme based on a new priority measure and an orientation-aware PCA work complementarily and iteratively to consolidate particle normals. The priority setting is reinforced with front stopping at thin surface features and normal flipping to enable robust handling of the close-by surface sheet problem. We demonstrate how a point cloud that is well-consolidated by our method steers conventional surface generation schemes towards a proper interpretation of the input data.
SiMPE: Fourth Workshop on Speech in Mobile and Pervasive Environments
Authors: A. A. Nanavati, N. Rajput, A. I. Rudnicky, M. Turunen, A. L. Kun, T. Paek, I. Tashev
Venue: Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services (2009)

With the proliferation of pervasive devices and the increase in their processing capabilities, client-side speech processing has been emerging as a viable alternative. SiMPE 2009, the fourth in the series, will continue to explore issues, possibilities, and approaches for enabling speech processing as well as convenient and effective speech and multimodal user interfaces. One of our major goals for SiMPE 2009 is to increase the participation of speech/multimodal HCI designers, and increase their interactions with speech processing experts. Multimodality got more attention in SiMPE 2008 than it has received in the previous years. In SiMPE 2007 [3], the focus was on developing regions. Given the importance of speech in developing regions, SiMPE 2008 had "SiMPE for developing regions" as a topic of interest. We think of this as a key emerging area for mobile speech applications, and will continue this in 2009 as well.
Design of thermal imprinting system with uniform residual thickness
Authors: Won-Ho Shin
Venue: Microelectronic Engineering (2009)

A new thermal imprinting system for the printed circuit boards (PCBs) with both large areas and fine conducting lines was developed adopting hot airs with a high pressure. Several small nickel stamps were used to cover the large area, and the stamps were replicated from an electroforming process, in addition, a vacuum jig was utilized to avoid bubbles captured in resins or imprinted interfaces. Stefan's equation was used to estimate residual thicknesses of the imprinted resins, and effects of imprinting conditions on the residual thickness were investigated from numerical analyses to confirm process profiles and specifications of the developed equipment. The results show that the developed imprinting system can remarkably improve the uniformity of the residual thickness after imprinting, as compared with those of the conventional press, in spite of the thickness difference between the used stamps.
Layered shape synthesis: automatic generation of control maps for non-stationary textures
Authors: Amir Rosenberger, Daniel Cohen-Or, Dani Lischinski
Venue: ACM Transactions on Graphics (TOG) (2009)

Many inhomogeneous real-world textures are non-stationary and exhibit various large scale patterns that are easily perceived by a human observer. Such textures violate the assumptions underlying most state-of-the-art example-based synthesis methods. Consequently, they cannot be properly reproduced by these methods, unless a suitable control map is provided to guide the synthesis process. Such control maps are typically either user specified or generated by a simulation. In this paper, we present an alternative: a method for automatic example-based generation of control maps, geared at synthesis of natural, highly inhomogeneous textures, such as those resulting from natural aging or weathering processes. Our method is based on the observation that an appropriate control map for many of these textures may be modeled as a superposition of several layers, where the visible parts of each layer are occupied by a more homogeneous texture. Thus, given a decomposition of a texture exemplar into a small number of such layers, we employ a novel example-based shape synthesis algorithm to automatically generate a new set of layers. Our shape synthesis algorithm is designed to preserve both local and global characteristics of the exemplar's layer map. This process results in a new control map, which then may be used to guide the subsequent texture synthesis process.
A demonstration of the RADAR personal assistant
Authors: Andrew Faulring, Brad Myers, Ken Mohnkern, Michael Freed
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 3 (2008)

Email clients were not designed to serve as a task management tools, but a high volume of task-relevant information in email leads many people to use email clients for this purpose. Such usage aggravates a user's experience of email overload and reduces productivity. Prior research systems have sought to address this problem by experimentally adding task management capabilities to email client software. RADAR (Reflective Agents with Distributed Adaptive Reasoning) takes a different approach in which a software agent acts like a trusted human assistant. Many RADAR components employ machine learning to improve their performance. Human participant studies showed a clear impact of learning on useI peIformance metrics.
A Novel Camera Calibration Method of Variable Focal Length Based on Single-View
Authors: GuiHua Liu, WenBin Wang, JianYin Yuan, XianYong Liu, QuanYuan Feng
Venue: Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02 (2009)

The key of camera calibration is to calculated the initial value of extrinsic and intrinsic camera parameters. Since the calculation of initial value is not a purely linear problem, 3D measurement products are often using a fixed focal length camera calibration method, but the measuring exists the problem of inconvenience of usage and a limited measurement-range. We have derived a novel camera calibration method which focal length of camera can be variable. By means of using the orthogonal rotation matrix and constraints among camera parameters in the single-view vision, a new algorithm to linearly and exactly calibrate the camera from single-view at least six known feature points is developed. The theoretical analysis and the experiments have demonstrated that the suggested algorithm is fast, exact, efficient and rather robust against noise, and it can be applied to the area of 3D measurement of variable focal length.
Combination of Dynamic Reservoir and Feedforward Neural Network for Time Series Forecasting
Authors: Štefan Babinec, Jiří Pospíchal
Venue: Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II (2009)

Echo State neural networks are a special case of recurrent neural networks. The most important part of Echo State neural networks is so called "dynamic reservoir". Echo State neural networks use dynamics of this massive and randomly initialized dynamic reservoir to extract interesting properties of incoming sequences. A standard training of these neural networks uses pseudo inverse matrix for one-step learning of weights from hidden to output neurons. In this approach, we have merged this dynamic reservoir with standard feedforward neural network, with a goal to achieve greater prediction ability. This approach was tested in laser fluctuations and Mackey-Glass time series prediction. The prediction error achieved by this approach was substantially smaller in comparison with prediction error achieved by standard algorithm or time delay neural network with backpropagation algorithm.
Refinement Proposal of the Goldberg's Theory
Authors: Jérome Gallard, Adrien Lèbre, Geoffroy Vallée, Christine Morin, Pascal Gallard, Stephen L. Scott
Venue: Proceedings of the 9th International Conference on Algorithms and Architectures for Parallel Processing (2009)

Virtual Machines (VM) allow the execution of various operating systems. VM mechanisms provide several functionalities which are nowadays strongly appreciated by developers and administrators (isolation between applications, flexibility of resource management...). So, "virtualization" has become a buzz word and a lot of "virtualization" solutions have been proposed, each providing particular functionalities. Goldberg proposed to classify virtualization techniques in two models (Type-I and Type-II), which does not enable the classification of latest "virtualization" technologies such emulation, partitioning...We propose an extension of the Goldberg model in order to take into account latest "virtualization" mechanisms. After giving general definitions, we show how our proposal enables to rigorously formalize the following terms: virtualization, emulation, abstraction, partitioning, and identity. We show that a single virtualization solution is generally composed by several layers of virtualization capabilities, depending on the granularity of the analysis. In this manner, our model allow us to classify virtualization technologies according to their performance, similarity and portability.
Consciousness: drinking from the firehose of experience
Authors: Benjamin Kuipers
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 3 (2005)

The problem of consciousness has captured the imagination of philosophers, neuroscientists, and the general public, but has received little attention within AI. However, concepts from robotics and computer vision hold great promise to account for the major aspects of the phenomenon of consciousness, including philosophically problematical aspects such as the vividness of qualia, the first-person character of conscious experience, and the property of intentionality. This paper presents and evaluates such an account against eleven features of consciousness "that any philosophical-scientific theory should hope to explain", according to the philosopher and prominent AI critic John Searle.
Past and Future Challenges in Creating Emotionally-Engaging Real-Time Digital Actors in Videogames
Authors: Casey Hudson
Venue: Proceedings of the 9th International Conference on Intelligent Virtual Agents (2009)

Evolving beyond their origins as a novel pastime, videogames have developed into a medium with tremendous power to entertain and engage players through emotionally powerful interaction. These emotional connections are often powered by the quality of the digital actors that inhabit game worlds and bring them to life. But as technologies for creating lifelike characters escalate, so do the challenges of the creation process. This discussion examines methods used by cutting-edge games to create deeply compelling digital actors, and explores future challenges and solutions that will help videogames unlock the full potential of emotionally engaging human interaction. 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
Asymptotic Delay Analysis and Timeout-Based Admission Control for Ad Hoc Wireless Networks
Authors: R. El-Azouzi, S. K. Samanta, E. Sabir, R. El-Khoury
Venue: Proceedings of the 8th International Conference on Ad-Hoc, Mobile and Wireless Networks (2009)

In this paper, we present an analytic model for evaluating average packet delay and achievable end-to-end goodput in a collision channel based multihop wireless ad hoc networks. We consider each node operates not only as a host but also as a router, i.e., the packets may have to be forwarded by several intermediate nodes before they reach their destinations. The end-to-end delay of a connection and throughput of this network depend on the number of nodes, the source traffic characteristics, the number of retransmissions at nodes, the forwarding cooperation level and the behavior of the MAC protocol. Our analysis gives expressions for the queue length and the delay in terms of probability generating functions. In addition to address routing and transmission scheduling, we adopt a cross-layer design that allows information sharing across different layers for efficient utilization of network resources, and meeting the end-to-end performance requirements of demanding applications. Furthermore, we propose a packet admission control scheme based on delay timeout mechanism. This guarantees quality of service for multimedia applications such as voice and video streaming. Afterward we conduct extensive simulations in order to verify our analytical results.
A fast arc consistency algorithm for n-ary constraints
Authors: Olivier Lhomme, Jean-Charles Régin
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 1 (2005)

The GAC-Scheme has become a popular general purpose algorithm for solving n-ary constraints, although it may scan an exponential number of supporting tuples. In this paper, we develop a major improvement of this scheme. When searching for a support, our new algorithm is able to skip over a number of tuples exponential in the arity of the constraint by exploiting knowledge about the current domains of the variables. We demonstrate the effectiveness of the method for large table constraints.
The criterion of Kolmogorov-Smirnov for binary decision tree: Application to interval valued variables
Authors: Chérif Mballo, Edwin Diday
Venue: Intelligent Data Analysis (2006)

With the information technology development, data sets often contain a very large number of observations. Symbolic data analysis treats new units that are underlying concepts on the given data base or found by clustering. In this way, it is possible to reduce the size of the data set to be processed by transforming the initial classical variables into variables called symbolic variables. In symbolic data analysis, the values of the variables can be, among others, intervals. The algebraic structure of these variables leads us to adapt criteria to be able to study them. In this paper, we propose the extension of the Kolmogorov-Smirnov's binary splitting criterion to interval data. This criterion is used as a test selection metric for decision tree induction. For this criterion, the values taken by the explanatory variables have to be ordered. We have been interested in different possible orders of these interval values. We present some results using the pure assignment in order to examine the quality and the precision of this criterion. We compare this criterion to some classical criteria (Gini and entropy) in the case of pure assignment. An application in the case where the variable to be explained is a correlation is presented. We end this paper with a probabilistic method of assignment using the criterion of Komogorov-Smirnov.