ppxscal/embedding_network huggingface dataset:
Action awareness: enabling agents to optimize, transform, and coordinate plans
Authors: Freek Stulp, Michael Beetz
Venue: Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems (2006)

As agent systems are solving more and more complex tasks in increasingly challenging domains, the systems themselves are becoming more complex too, often compromising their adaptivity and robustness. A promising approach to solve this problem is to provide agents with reflective capabilities. Agents that can reflect on the effects and expected performance of their actions, are more aware and knowledgeable of their capabilities and shortcomings.In this paper, we introduce a computational model for what we call action awareness. To achieve this awareness, agents learn predictive action models from observed experience. This knowledge is then used to optimize, transform and coordinate plans. We apply this computational model to a number of typical scenarios from robotic soccer. Various experiments on real robots demonstrate that action awareness enables the robots to improve the performance of their plans substantially.
Fair E-cash Payment Model on Credit Overdraft
Authors: Shaobin Wang, Na Su, Lei Hu
Venue: Proceedings of the 2006 IEEE Asia-Pacific Conference on Services Computing (2006)

It proposes a fair e-cash payment model on credit outdrawing, which can avoid the odd change and division of E-cash, Users, according to the bill of credit issued by the bank, get the E-cash to pay their consumption to sellers after verification by the sellers without the participation of bank at each time. The role of banks is to solve the disputes between the users and sellers to ensure the fairness. It can avoid the problem of partition e-cash for changes, prevent from reusing e-cash and faking e-cash. It fits justice, anonymity, non-deny and impartiality.
Systolic product-sum circuit for GF((22)m) using neuron MOSFETs
Authors: Nabil Abu-Khader, Pepe Siy
Venue: Integration, the VLSI Journal (2005)

A quaternary systolic product-sum computation circuit for GF((22)m) using voltage-mode vMOSFETs is presented. The design is composed of four basic cells connected in a pipelined fashion. Each basic cell is composed of 2 Galois field adders, 2 Galois field multipliers, and 7 flip-flops. The circuit was simulated using Affirma Analog Circuit Design Environment tool supplied by Cadence, and it has shown to perform correctly. The quaternary circuit for GF((22)2) shows a significant amount of savings in both transistor count and number of connections compared to the one that uses the binary field GF(24).
hLCGA: A Hybrid Competitive Coevolutionary Genetic Algorithm
Authors: Gregoire Danoy, Pascal Bouvry, Tomy Martins
Venue: Proceedings of the Sixth International Conference on Hybrid Intelligent Systems (2006)

We introduce in this article a new hybrid coevolutionary algorithm called hLCGA (hybrid Loosely Coupled Genetic Algorithm) that consists in combining a competitive coevolutionary genetic algorithm and a local search algorithm. We apply it to the Rosenbrock function optimization problem and compare the results of five hybrid variants to the original LCGA. We show the advantages of hybridizing a coevolutionary algorithm with local search algorithms in terms of solution quality and convergence speed.
A Grid-based Information Integration System for Drug Discovery
Authors: Gen Kawamura, Masato Kitajima, Kentaro Wakatsuki, Takehiro Furudate, Takahiro Kosaka, Kazuto Yamazaki, Reiji Teramoto, Susumu Date, Shinji Shimojo, Hideo Matsuda
Venue: Proceedings of the 21st International Conference on Data Engineering Workshops (2005)

Effective integration of heterogeneous databases and information sources has been studied as the most pressing challenge in various fields; such as, corporate data management and life sciences. In this paper, we present an information integration system for drug discovery by employing grid technology. Drug discovery involves a number of stages including disease modeling, target protein identification, lead compound identification, and clinical trials. Each stage requires its own specific type of data and many of them are stored in their databases. However, those databases are independently operated and their schema and data description are much diverged since they are based on various data domains and research backgrounds. Furthermore, the amounts of the databases have been increased very rapidly due to the completion of the human genome project. To cope with these issues, we utilize grid technology for integrating various information and develop metadata for describing relationships between heterogeneous data that belong to different domains (such as, protein-compound interactions). The effectiveness of our system is demonstrated by evaluating results of several example queries and by measuring processing times against geographically separated databases.
On level set regularization for highly ill-posed distributed parameter estimation problems
Authors: K. van den Doel, U. M. Ascher
Venue: Journal of Computational Physics (2006)

The recovery of a distributed parameter function with discontinuities from inverse problems with elliptic forward PDEs is fraught with theoretical and practical difficulties. Better results are obtained for problems where the solution may take on at each point only one of two values, thus yielding a shape recovery problem.This article considers level set regularization for such problems. However, rather than explicitly integrating a time embedded PDE to steady state, which typically requires thousands of iterations, methods based on Gauss-Newton are applied more directly. One of these can be viewed as damped Gauss-Newton utilized to approximate the steady state equations which in turn are viewed as the necessary conditions of a Tikhonov-type regularization with a sharpening sub-step at each iteration. In practice this method is eclipsed, however, by a special "finite time" or Levenberg-Marquardt-type method which we call dynamic regularization applied to the output least squares formulation. Our stopping criterion for the iteration does not involve knowledge of the true solution.The regularization functional is applied to the (smooth) level set function rather than to the discontinuous function to be recovered, and the second focus of this article is on selecting this functional. Typical choices may lead to flat level sets that in turn cause ill-conditioning. We propose a new, quartic, non-local regularization term that penalizes flatness and produces a smooth level set evolution, and compare its performance to more usual choices.Two numerical test cases are considered: a potential problem and the classical EIT/DC resistivity problem.
Robust Moving Object Detection at Distance in the Visible Spectrum and Beyond Using A Moving Camera
Authors: Yan Zhang, Stephen J. Kiselewich, William A. Bauson, Riad Hammoud
Venue: Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop (2006)

Automatic detection of moving objects at distance and in all weather conditions is a critical task in many visionbased safety applications such as video surveillance and vehicle forewarn collision warning. In such applications, prior knowledge about the object class (vehicle, pedestrian, tree, etc.) and imaging conditions (shadow, depth) is unavailable. What makes the task even more challenging is when the camera is non-stationary, e.g., mounted on a moving vehicle. The essential problem in this case lies in distinguishing between camera-induced motion and independent motion. This paper proposes a robust algorithm for automatic moving object detection at distance. The camera is mounted on a moving vehicle and operates in both day and night time. Through the utilization of the focus of expansion (FOE) and its associated residual map, the proposed method is able to detect and separate independently moving objects (IMOs) from the "moving" background caused by the camera motion. Experimentations on numerous realworld driving videos have shown the effectiveness of the proposed technique. Moving objects such as pedestrians and vehicles up to 40 meters away from the camera have been reliably detected at 10 frames per second on a 1.8GHz PC.
A Remote IDS Based on Multi-Agent Systems, Web Services and MDA
Authors: Mauro Silva, Denivaldo Lopes, Zair Abdelouahab
Venue: Proceedings of the International Conference on Software Engineering Advances (2006)

In this paper, we present a model, an architecture and an implementation of a Remote IDS (Intrusion Detection System) using the technology of Multi-agent Systems, Web Services and MDA (Model-Driven Architecture). This model adapts and extends the NIDIA (Network Intrusion Detection System based on Intelligent Agents) to provide a remote IDS on the Internet. The purpose is that users that do not have a local IDS can use the services provided by our remote IDS. NIDIA is an IDS whose architecture consists of a set of cooperative agents. The IDS functionalities are provided as a set of accessible services on the Internet through Web Services. The architecture of our IDS uses MDA to support metadata management such as profiles of machines, profiles of users and profiles of services. An illustrative example shows our IDS.
Providing Context Aware Learning Services to Learners with Portable Devices
Authors: Stephen J. H. Yang, Irene Y. L. Chen
Venue: Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies (2006)

Context aware learning service is an interactive model between learners and learning services based on learners' surrounding learning environments. We envision that providing context aware learning services is an important step to enhance current Web and mobile based e-learning by finding right collaborator, right information and right learning services in the right place at the right time. The major contributions of this paper are the provision of context-aware learning services, namely contextaware content access for learners with portable devices.
A survey of credibility theory
Authors: Baoding Liu
Venue: Fuzzy Optimization and Decision Making (2006)

This paper provides a survey of credibility theory that is a new branch of mathematics for studying the behavior of fuzzy phenomena. Some basic concepts and fundamental theorems are introduced, including credibility measure, fuzzy variable, membership function, credibility distribution, expected value, variance, critical value, entropy, distance, credibility subadditivity theorem, credibility extension theorem, credibility semicontinuity law, product credibility theorem, and credibility inversion theorem. Recent developments and applications of credibility theory are summarized. A new idea on chance space and hybrid variable is also documented.
A Dynamic Bayesian Network Model for Autonomous 3D Reconstruction from a Single Indoor Image
Venue: Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (2006)

When we look at a picture, our prior knowledge about the world allows us to resolve some of the ambiguities that are inherent to monocular vision, and thereby infer 3d information about the scene. We also recognize different objects, decide on their orientations, and identify how they are connected to their environment. Focusing on the problem of autonomous 3d reconstruction of indoor scenes, in this paper we present a dynamic Bayesian network model capable of resolving some of these ambiguities and recovering 3d information for many images. Our model assumes a "floorwall" geometry on the scene and is trained to recognize the floor-wall boundary in each column of the image. When the image is produced under perspective geometry, we show that this model can be used for 3d reconstruction from a single image. To our knowledge, this was the first monocular approach to automatically recover 3d reconstructions from single indoor images.
Improving Delivery Ratio and Power Efficiency in Unicast Geographic Routing with a Realistic Physical Layer for Wireless Sensor Networks
Authors: Juan A. Sanchez, Pedro M. Ruiz
Venue: Proceedings of the 9th EUROMICRO Conference on Digital System Design (2006)

In the last few years, the amount of work in the field of routing algorithms for WSN has increased significantly. The special characteristics ofWSN make a challenge for researchers to design efficient routing protocols. Nevertheles, most of the work done left out of scope an inherent aspect to WSN such as the radio tranmsission errors. The wellknown unit disk graph model has been widely used as a basis for several routing protocol. However as recent experimental research has revealed, real links behave completely different to that model. In this paper we evaluate the problems that arise when considering that messages can be lost and propose a new geographic routing protocol which takes into account the probability of error transmission to achieve a high delivery ratio while reducing the energy consumed to the minimum possible. We present the results of the extensive simulation made using our protocol and compare it against a centralised version and two others previous work. Our protocol, being totally distributed and using only local information, outperforms the previous work and achieves almost the same results as the centralised version.
Components of Switching Intentional Set
Authors: Matthew F. S. Rushworth, R. E. Passingham, A. C. Nobre
Venue: Journal of Cognitive Neuroscience (2002)

Despite the intuition that we can shift cognitive set on instruction, some behavioral studies have suggested that set shifting might only be accomplished once we engage in performance of the new task. It is possible that set switching consists of more than one component cognitive process and that the component processes might segregated in time. We recorded event-related potentials (ERPs) during two set-switching tasks to test whether different component processes were responsible for (i) set initiation and reconfiguration when presented with the instruction to switch, and (ii) the implementation of the new set once subjects engaged in performing the new task. The response switching (RS) task required shifts of intentional set; subjects selected between responses according to one of two conflicting intentional sets. The results demonstrated the existence of more than one constituent process. Some of the processes were linked to the initiation and reconfiguration of the set prior to actual performance of the new task. Other processes were time locked to performance of new task items. Set initiation started with modulation of medial frontal ERPs and was followed by modulation over parietal electrodes. Implementation of intentional set was associated with modulation of response-related ERPs.
Data Management in Production Grids - Challenges and Techniques
Authors: Erwin Laure
Venue: Proceedings of the 18th International Conference on Scientific and Statistical Database Management (2006)

Advances in networking and distributed computing allowed the establishment of production Grid infrastructures during the past few years. Today, large-scale production Grid infrastructures such as EGEE in Europe, OSG in the US, and NAREGI in Japan are offering their services to many scientific and industrial applications, from domains as diverse as Astronomy, Biomedicine, Computational Chemistry, Earth Sciences, Financial Simulations, and High Energy Physics. Grid infrastructures provide these applications a new means for collaborative research by facilitating the sharing of computational and data resources at an unprecedented scale. The efficient and secure sharing of data resources, which can reach several Tera- to Petabytes in some application domains, is one of the main challenges for Grid infrastructures. In this talk we discuss the main challenges for data sharing on Grid infrastructures, present several techniques that are already established on production Grid infrastructures or currently being developed and point out main open research issues. We will also present examples of application usage of one of the larges Grid infrastructures, EGEE.
Extrinsic Cues Suppress the Encoding of Intrinsic Cues
Authors: Bhavin R. Sheth, Shinsuke Shimojo
Venue: Journal of Cognitive Neuroscience (2004)

Remembering where objects are in space is fundamental to adaptive behavior. Little is known about how intact humans combine information from intrinsic (egocentric) and extrinsic (exocentric, allocentric, or landmark-based) coordinate systems to locate objects. Using a simple location estimation paradigm, this study finds that we mostly remember position in extrinsic coordinates. Intrinsic-coordinate-based mapping of space is less precise in the presence of landmarks or extrinsic cues than in their absence. Thus, not only do extrinsic frames of reference dominate internal representations of space, they suppress intrinsic-based representations as well. We speculate that this dominance-suppression hierarchy undercuts intersystem conflicts and underlies a single, undissociated spatial map in intact humans.
A Hierarchical Approach to Internet Distance Prediction
Authors: Rongmei Zhang, Charlie Hu, Xiaojun Lin, Sonia Fahmy
Venue: Proceedings of the 26th IEEE International Conference on Distributed Computing Systems (2006)

Internet distance prediction gives pair-wise latency information with limited measurements. Recent studies have revealed that the quality of existing prediction mechanisms from the application perspective is short of satisfactory. In this paper, we explore the root causes and remedies for this problem. Our experience with different landmark selection schemes shows that although selecting nearby landmarks can increase the prediction accuracy for short distances, it can cause the prediction accuracy for longer distances to degrade. Such uneven prediction quality significantly impacts application performance. Instead of trying to select the landmark nodes in some "intelligent" fashion, we propose a hierarchical prediction approach with straightforward landmark selection. Hierarchical prediction utilizes multiple coordinate sets at multiple distance scales, with the "right" scale being chosen for prediction each time. Experiments with Internet measurement datasets show that this hierarchical approach is extremely promising for increasing the accuracy of network distance prediction.
A Part Release considering Tool Scheduling and Dynamic Tool Allocation in Flexible Manufacturing Systems
Authors: Choong Soo Lee, Hyun Jong Kim
Venue: Proceedings of the International Conference on Autonomic and Autonomous Systems (2006)

In order to adapt to rapid changes of manufacturing environment, a recent trend in flexible manufacturing systems (FMS) is to compose of the identical versatile machines and to use dynamic tool allocation strategy, sharing tools between machines with fast tool delivery system. Unlike a conventional FMS where parts are fed to the machines with the required tools, tools are acquired from other machines using a tool delivery system and parts can be finished on the same machine in these FMS. Therefore it is more important problem to plan tool flow using tool delivery system in these FMS rather than part flow in conventional FMS. In this study, in FMS under dynamic tool allocation strategy, a method of part release considering tool scheduling at the stage of part release is proposed. In order to prove the efficiency of the proposed part release it is compared with other part release through simulation experiments. Performance measures in these experiments are the throughput and the number of tardy parts.
Economy-Based Data Replication Broker
Authors: Henry Lin, J. H. Abawajy, Rajkumar Buyya
Venue: Proceedings of the Second IEEE International Conference on e-Science and Grid Computing (2006)

Data replication is one of the key components in data grid architecture as it enhances data access and reliability and minimises the cost of data transmission. In this paper, we address the problem of reducing the overheads of the replication mechanisms that drive the data management components of a data grid. We propose an approach that extends the resource broker with policies that factor in user quality of service as well as service costs when replicating and transferring data. A realistic model of the data grid was created to simulate and explore the performance of the proposed policy. The policy displayed an effective means of improving the performance of the grid network traffic and is indicated by the improvement of speed and cost of transfers by brokers.
Age invaders
Authors: Eng Tat Khoo, Shang Ping Lee, Adrian David Cheok
Venue: Proceedings of the 2005 international conference on Augmented tele-existence (2005)

The cultural and generation gaps between the young and the old have been growing in our modern age. This paper introduces Age Invaders, a novel social-physical game which allows the elderly to play harmoniously together with children in physical space.
Beyond classification and ranking: constrained optimization of the ROI
Authors: Lian Yan, Patrick Baldasare
Venue: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (2006)

Classification has been commonly used in many data mining projects in the financial service industry. For instance, to predict collectability of accounts receivable, a binary class label is created based on whether a payment is received within a certain period. However, optimization of the classifier does not necessarily lead to maximization of return on investment (ROI), since maximization of the true positive rate is often different from maximization of the collectable amount which determines the ROI under a fixed budget constraint. The typical cost sensitive learning does not solve this problem either since it involves an unknown opportunity cost due to the budget constraint. Learning the ranks of collectable amount would ultimately solve the problem, but it tries to tackle an unnecessarily difficult problem and often results in poorer results for our specific target. We propose a new algorithm that uses gradient descent to directly optimize the related monetary measure under the budget constraint and thus maximizes the ROI. By comparison with several classification, regression, and ranking algorithms, we demonstrate the new algorithm's substantial improvement of the financial impact on our clients in the financial service industry.
An Interactive Mixed Reality Framework for Virtual Humans
Authors: Arjan Egges, George Papagiannakis, Nadia Magnenat-Thalmann
Venue: Proceedings of the 2006 International Conference on Cyberworlds (2006)

In this paper, we present a simple and robust Mixed Reality (MR) framework that allows for real-time interaction with Virtual Humans in real and virtual environments under consistent illumination. We will look at three crucial parts of this system: interaction, animation and global illumination of virtual humans for an integrated and enhanced presence. The interaction system comprises of a dialogue module, which is interfaced with a speech recognition and synthesis system. Next to speech output, the dialogue system generates face and body motions, which are in turn managed by the virtual human animation layer. Our fast animation engine can handle various types of motions, such as normal key-frame animations, or motions that are generated on-the-fly by adapting previously recorded clips. All these different motions are generated and blended on-line, resulting in a flexible and realistic animation. Our robust rendering method operates in accordance with the previous animation layer, based on an extended for virtual humans Precomputed Radiance Transfer (PRT) illumination model, resulting in a realistic display of such interactive virtual characters in mixed reality environments. Finally, we present a scenario that illustrates the interplay and application of our methods, glued under a unique framework for presence and interaction in MR.