ppxscal/embedding_network huggingface dataset:
THOR: A Tool for Reasoning about Shape and Arithmetic
Authors: Stephen Magill, Ming-Hsien Tsai, Peter Lee, Yih-Kuen Tsay
Venue: Proceedings of the 20th international conference on Computer Aided Verification (2008)

We describe Thor (Tool for Heap-Oriented Reasoning), a tool based on separation logic that is capable of reasoning automatically about heap-manipulating programs. There are several such systems in development now. However, Thor is unique in that it provides not only shape analysis, but also arithmetic reasoning via a novel combination procedure. Also, considerable effort has been put into making the output clear and easy to understand. Thor uses Javascript and HTML to produce an interactive representation of the analysis results.
Towards Emotionally-Intelligent Pedagogical Agents
Authors: Konstantin Zakharov, Antonija Mitrovic, Lucy Johnston
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

Research shows that emotions play an important role in learning. Human tutors are capable of identifying and responding to the affective states of their students; therefore, for ITSs to be truly affective, they should also be capable of tracking and appropriately responding to the emotional state of their users. We report on a project aimed at developing an affect-aware pedagogical agent persona for an ITS for teaching database design skills. We use the dimensional approach to affective modeling, and track the users' affective state along the valence dimension as identified from tracking the users' facial features. We describe the facial-feature tracking application we developed, as well as the set of rules that control the agent's behavior. The agent's response to the student's action depends on the student's cognitive state (as determined from the session history) as well as on the student's affective state. The experimental study of the agent shows the general preference towards the affective agent over the non-affective agent.
Using Distributed Data Mining and Distributed Artificial Intelligence for Knowledge Integration
Authors: Ana C. Paula, Bráulio C. Ávila, Edson Scalabrin, Fabrício Enembreck
Venue: Proceedings of the 11th international workshop on Cooperative Information Agents XI (2007)

In this paper we study Distributed Data Mining from a Distributed Artificial Intelligence perspective. Very often, databases are very large to be mined. Then Distributed Data Mining can be used for discovering knowledge (rule sets) generated from parts of the entire training data set. This process requires cooperation and coordination between the processors because incon-sistent, incomplete and useless knowledge can be generated, since each processor uses partial data. Cooperation and coordination are important issues in Distributed Artificial Intelligence and can be accomplished with different techniques: planning (centralized, partially distributed and distributed), negotiation, reaction, etc. In this work we discuss a coordination protocol for cooperative learning agents of a MAS developed previously, comparing it conceptually with other learning systems. This cooperative process is hierarchical and works under the coordination of a manager agent. The proposed model aims to select the best rules for integration into the global model without, however, decreasing its accuracy rate. We have also done experiments comparing accuracy and complexity of the knowledge generated by the cooperative agents.
An Heuristic Method for GPS Surveying Problem
Authors: Stefka Fidanova
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

This paper describes metaheuristic algorithm based on simulated annealing method,which is a nature-inspired method, to analyze and improve the efficiency of the design of Global Positioning System (GPS) surveying networks. Within the context of satellite surveying, a positioning network can be defined as a set of points which are coordinated by placing receivers on these point to determine sessions between them. The problem is to search for the best order in which these sessions can be observed to give the best possible schedule. The same problem arise in Mobile Phone surveying networks. Several case studies have been used to experimentally asses the performance of the proposed approach in terms of solution quality and computational effort.
Modeling Web Server Traffic with Session-Based Arrival Streams
Authors: Laurence Hoflack, Stijn Vuyst, Sabine Wittevrongel, Herwig Bruneel
Venue: Proceedings of the 15th international conference on Analytical and Stochastic Modeling Techniques and Applications (2008)

In this paper, we consider a discrete-time queueing model with infinite waiting room and one single output channel. Users from an infinite user population can start and end sessions during which they are active and send packets to a buffer system. Each active user generates a random but strictly positive number of packets per time slot. The queueing model is applied to study the traffic of a web server, where each session corresponds to one file download. This approach has the advantage of leading to analytical expressions for the main performance measures (buffer occupancy, delay, ...) of the output buffer of the web server. Specifically, in this paper, a closed-form expression for the mean session delay is obtained. We then apply the model to a web server, based on a trace of actual web traffic. Using the model, we can rapidly study the influence of the different system parameters and compare various alternatives.
Integrating Epistemic Action (Active Vision) and Pragmatic Action (Reaching): A Neural Architecture for Camera-Arm Robots
Authors: Dimitri Ognibene, Christian Balkenius, Gianluca Baldassarre
Venue: Proceedings of the 10th international conference on Simulation of Adaptive Behavior: From Animals to Animats (2008)

The active vision and attention-for-action frameworks propose that in organisms attention and perception are closely integrated with action and learning. This work proposes a novel bio-inspired integrated neural-network architecture that on one side uses attention to guide and furnish the parameters to action, and on the other side uses the effects of action to train the task-oriented top-down attention components of the system. The architecture is tested both with a simulated and a real camera-arm robot engaged in a reaching task. The results highlight the computational opportunities and difficulties deriving from a close integration of attention, action and learning.
Practical sampling for ray-based rendering
Authors: David Edwards
Venue:  (2008)

Many methods for realistic rendering, including ray tracing, distribution ray tracing, and path tracing, are based on the underlying operation of intersecting geometric rays with objects in a scene. In effect, these programs are light simulators, where each ray represents the path of light between emitters, reflective surfaces, or the virtual camera. Tracing the number of photons that form real images is prohibitive, so rendering programs must select a representative sample of the light within a scene to simulate. Formally, light transport in a scene is described by an integral equation. Since the rendering integrand is multidimensional and discontinuous, point sampling methods such as Monte Carlo and quasi-Monte Carlo integration are commonly used to estimate the value of the integral. A critical component of these methods is the selection of random points in the domain of integration. This dissertation examines effective methods for selecting these points. This dissertation offers the following contributions. We provide an experimental framework for comparing image difference algorithms, and use this framework to show that humans are more likely to agree with advanced perceptually-based methods than simpler methods such as RMS error. We also compare the performance of several point sets commonly used in multidimensional sampling, and show that low-discrepancy points tend to produce better images than blue noise points or other point sets. According to our comparisons, random-edge discrepancy in multiple dimensions is a good predictor of the quality of images rendered with a given point set. We also compute the distortion of various transformation functions, and show that the distortion induced by a transformation has little effect on image quality. Finally, we argue that at low sampling rates, variance may be more important than expected value, and show how inconsistent estimators with low variance may yield better images than unbiased estimators with relatively more variance.
Information Relationship Identification in Team Innovation
Authors: Xinmiao Li, Xinhui Li, Pengzhu Zhang
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

In the 1990s, innovation has become the main resource of competitive advantage for firms. Computer-mediated team innovative activities provide much more information, thus resulting in information overload. In this paper, the method for identifying the affirmative and negative relationship between the solution and its comment is proposed. The identification model is set up and applied in team innovation. In the model, we improve the traditional feature extraction method from three aspects: classifying feature words into six levels according to their affirmative and negative tone, introducing the new feature of the prior comment's attitude and the new feature of the number of sub-sentences. The results show that the method realizes the identification of the affirmative and negative relationship effectively. It can benefit organizing the large amount of team comments effectively and increase the efficiency of information organization.
On Differential Stroke Diagnosis by Neuro-fuzzy Structures
Authors: Krzysztof Cpałka, Olga Rebrova, Tomasz Gałkowski, Leszek Rutkowski
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

In this paper we develop a neuro-fuzzy system for stroke diagnosis. A novel concept of weights describing importance of antecedents and rules will be incorporated into construction of such systems. Simulation results based on 298 real stroke data will be presented.
A Distributed Computational Model of Spatial Memory Anticipation During a Visual Search Task
Authors: Jérémy Fix, Julien Vitay, Nicolas P. Rougier
Venue: Anticipatory Behavior in Adaptive Learning Systems: From Brains to Individual and Social Behavior (2007)

Some visual search tasks require the memorization of the location of stimuli that have been previously focused. Considerations about the eye movements raise the question of how we are able to maintain a coherent memory, despite the frequent drastic changes in the perception. In this article, we present a computational model that is able to anticipate the consequences of eye movements on visual perception in order to update a spatial working memory.
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Venue: International Conference on Mobile Computing and Networking (2008)

It is our great pleasure to welcome you to the 1st ACM International Workshop on Mobile Entity Localization and Tracking in GPS-less Environments -- MELT'08. Location-awareness is a key component for achieving context-awareness. Recent years have witnessed an increasing trend of location-based services and applications. In most cases, however, location information is limited by the accessibility to GPS, which is unavailable for indoor or underground facilities and unreliable in urban environments. Much research has been done, in both the sensor network community and the ubiquitous computing community, to provide techniques for localization and tracking in GPS-less environments. Novel applications based on ad-hoc localization and real-time tracking of mobile entities are growing as a result of these technologies. It is time to bring leaders from both the academic and industrial research communities to discuss challenging and open problems, to evaluate pros and cons of various approaches, to bridge the gap between theory and applications, and to envision new research opportunities in MELT. The call for papers attracted 38 submissions from Asia, Canada, Europe, and the United States. The program committee accepted 14 papers for oral presentations and 9 papers for poster presentations. The workshop consists of four technical sessions and three poster sessions, with topics covering areas of optimization and signal processing techniques, novel radio signal strength based methods, as well as systems and applications in MELT.
Towards a Capability Model for the Software Release Planning Process -- Based on a Multiple Industrial Case Study
Authors: Markus Lindgren, Rikard Land, Christer Norström, Anders Wall
Venue: Proceedings of the 9th international conference on Product-Focused Software Process Improvement (2008)

Software release planning is an important activity for effectively identifying the customer needs generating best business, especially for incremental software development. In this paper we propose a capability model for improving the release planning process of an organization. Using this model it is possible to 1) determine the capabilities of an organization's release planning process, and 2) identify areas for improvement. The model is based on empirical data from a multiple case study involving 7 industrial companies, all being producers of software intensive systems. We also provide examples of how the proposed capability model can be applied using the companies from the study.
Content-based search using self-organizing peer-to-peer network
Authors: Igor Mekterovic, Krešimir Križanovic, Mirta Baranovic
Venue: Proceedings of the 7th WSEAS International Conference on Software Engineering, Parallel and Distributed Systems (2008)

In order for peer-to-peer (P2P) content sharing network to be scalable, it is imperative to efficiently route queries through the network. Semantic based search should, with as little messages (traffic) as possible, return just those relevant documents stored throughout the network thus achieving precision and recall values comparable to those of correspondent centralized system. In this article we propose protocols for self-organizing P2P network that arranges links between peers according to peer's content. In proposed network peers organize themselves into "semantic communities" but without losing links to other semantic communities. Proposed network has no prior knowledge of the semantics of documents that are to be stored in the system.
Improving efficiency and effectiveness of the image distortion model
Authors: Michael Springmann, Andreas Dander, Heiko Schuldt
Venue: Pattern Recognition Letters (2008)

The image distortion model (IDM) is a deformation model that is used for computing the (dis-)similarity between images. Therefore it evaluates displacements of individual pixels between two images within a so-called warp range and also takes into account the surrounding pixels (local context). It can be used with a kNN classifier and has shown good retrieval quality in handwritten character recognition as well as in past runs of the medical automatic annotation task of ImageCLEF workshops. However, one of its limitations is computational complexity and the resulting long query response times, that may limit its use for a wider range of applications and for modifications to further improve retrieval quality. In particular an enlarged local context and warp range are candidates for such improvements, but would even further increase computational complexity. In our approach, we therefore apply several optimizations that reduce the retrieval time without degrading the result quality. First, we use an early termination strategy for the individual distance computations which contribute a speedup of a factor of 4.3-4.9. Second, we make efficient use of multithreading. With these extensions, we are able to perform the IDM in less than 1.5s per query on an 8-way server and 16s on a standard Pentium 4 PC without any degradation of retrieval quality compared to the non-optimized version. We extend the possible displacements to an area of 7x7 pixels, using a local context of either 5x5 or 7x7 pixels. The results of the extended IDM have been submitted to the medical automatic annotation task of ImageCLEF 2007 and were ranked in the upper third. More importantly, the used techniques for reducing the execution time are not limited strictly to IDM but are also applicable to other expensive distance measures.
Reliability and characteristics of the Illiac electrostatic memory
Authors: J. M. Wier
Venue: Papers and discussions presented at the Dec. 8-10, 1953, eastern joint AIEE-IRE computer conference: information processing systems---reliability and requirements (1953)

The Illiac memory was completed in the spring of 1952. It was tested during the early part of the summer and in August was attached to the previously completed arithmetic unit. About three weeks were needed to complete the physical work necessary for this and to eliminate all unsoldered joints, misconnections, etc. Detailed records were kept after this time. This log begins on September 3 and covers every "on" period in detail. Later entries are more specific due to the added experience gained in isolating difficulties.
Obstacle Detection Using a Single Camera Stereo Sensor
Authors: Luc Duvieubourg, Sébastien Ambellouis, Sébastien Lefebvre, François Cabestaing
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

In this paper we present a catadioptric stereovision system based on a single camera, two plane mirrors and a prism mounted on a rotating axis. On the one hand, the system is able to project a real scene on two half images. Thus, contrary to classical stereo system, it deals with the problem of the camera synchronisation. On the other hand, the optical axis of the system can be steered to point a region of interest of the real scene. It has been developed in the context of on-road vehicle applications and more specifically for long-range road surveillance. This degree of liberty is useful when the region of interest is far away in the front of the vehicle because it requires a long focal length to reach a sufficient resolution. This optical system is associated to a dense stereo matching algorithm and a disparity map segmentation process. We present some results we have obtained using synthetic stereo images to illustrate the functionality of this setup.
Stabilized finite element schemes for incompressible flow using Scott--Vogelius elements
Authors: E. Burman, A. Linke
Venue: Applied Numerical Mathematics (2008)

We propose a stabilized mixed finite element method based on the Scott-Vogelius element for the Oseen equation. Here, only convection has to be stabilized since by construction both the discrete pressure and the divergence of the discrete velocities are controlled in the norm L^2. As stabilization we propose either the local projection stabilization or the interior penalty stabilization based on the penalization of the gradient jumps over element edges. We prove a discrete inf-sup condition leading to optimal a priori error estimates. Moreover, convergence of the velocities is completely independent of the pressure regularity, and in the purely incompressible case the discrete velocities are pointwise divergence free. The theoretical considerations are illustrated by some numerical examples.
QoS multicast aggregation under multiple additive constraints
Authors: N. Ben Ali, A. Belghith, J. Moulierac, M. Molnar
Venue: Computer Communications (2008)

IP Multicast has been proposed in order to manage group communications over the Internet in a bandwidth efficient manner. Although such a proposition has been well studied, there are still some inherent problems for its widespread deployment. In this paper, we propose a new algorithm coined mQMA that deals with the two main problems of traditional IP multicast, i.e., multicast forwarding state scalability and multi-constrained QoS routing. The algorithm mQMA is a QoS multicast aggregation algorithm which handles multiple additive QoS constraints. It builds few trees and maintains few forwarding states for the groups thanks to the technique of multicast tree aggregation, which allows several groups to share the same delivery tree. Moreover, the algorithm mQMA builds trees satisfying multiple additive QoS constraints. We show via extensive simulations that mQMA reduces dramatically the number of trees to be maintained and reduces the utilization of the network resources, yet it leverages the same overall QoS performances as Mamcra which is the main known multi-constrained multicast routing algorithm.
Visualization of Hybrid, N-Body and Octree-Based Adaptive Mesh Resolution Parallelized Simulations
Authors: D. Pomarède, Y. Fidaali, R. Teyssier
Venue: Proceedings of the 2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation (2008)

In this paper, we report on the development of a visualization software which adress the issue of the interactive visualization of three-dimensional hybrid simulations combining an N-body module and a grid-based solver with an Octree-based adaptive mesh. The difficulty inherent to the hybrid nature of the data and the complexity of the mesh structure used to describe both scalar and vector fields is enhanced by the fact that simulations are parallelized. Large-scale simulations are conducted on high-performance mainframes with potentially thousands of processors associated with a non-trivial domain decomposition. The software we present is deployed in the framework of IDL Object Graphics as part of the SDvision graphics tool used primarily in the visualization of astrophysical simulations. It combines in a single scene the graphics objects of the N-body system and the grid-based fields, together with a visual representation of the octree mesh, suitable for interactive and immersive navigation.
Generic Probability Density Function Reconstruction for Randomization in Privacy-Preserving Data Mining
Authors: Vincent Yan Tan, See-Kiong Ng
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

Data perturbation with random noise signals has been shown to be useful for data hiding in privacy-preserving data mining. Perturbation methods based on additive randomization allows accurate estimation of the Probability Density Function (PDF) via the Expectation-Maximization (EM) algorithm but it has been shown that noise-filtering techniques can be used to reconstruct the original data in many cases, leading to security breaches. In this paper, we propose a generic PDF reconstruction algorithm that can be used on non-additive (and additive) randomization techiques for the purpose of privacy-preserving data mining. This two-step reconstruction algorithm is based on Parzen-Window reconstruction and Quadratic Programming over a convex set --- the probability simplex. Our algorithm eliminates the usual need for the iterative EM algorithm and it is generic for most randomization models. The simplicity of our two-step reconstruction algorithm, without iteration, also makes it attractive for use when dealing with large datasets.