ppxscal/embedding_network huggingface dataset:
The geometry of the uncontrollability set for standardizable second order generalized linear systems on parameters
Authors: M. Isabel García-Planas, J. Clotet
Venue: Proceedings of the WSEAS International Conference on Applied Computing Conference (2008)

Let (E, A1, A2, B) be a quadruple of matrices representing a second order generalized time-invariant linear system, Ex = A1x + A2x + Bu. In this paper we study the controllability of second order generalized systems by means the rank of a certain constant matrix that we will call "the controllability matrix" of second order generalized linear systems. We use this matrix to study the geometry of the set of uncontrollable systems contained in the set of standardizable ones.
Stella: Growing Upward, Downward, and Outward
Authors: Norman Chonacky
Venue: Computing in Science and Engineering (2004)

In this article, I'll review Stella's modeling capabilities for both research and instruction. I'll describe the basic modeling tools using my student's quest as a simple, illustrative case study, exploring how these tools contribute to speed and efficiency in creating models for concept testing. I will also examine some of Stella's broader research capabilities in the context of how they support and connect with more specific and scalable modeling systems implemented in high-end systems. Finally, I will comment on Stella's range of educational applications.
Acoustic Speaker Identification: The LIMSI CLEAR'07 System
Authors: Claude Barras, Xuan Zhu, Cheung-Chi Leung, Jean-Luc Gauvain, Lori Lamel
Venue: Multimodal Technologies for Perception of Humans: International Evaluation Workshops CLEAR 2007 and RT 2007, Baltimore, MD, USA, May 8-11, 2007, Revised Selected Papers (2008)

The CLEAR 2007 acoustic speaker identification task aims to identify speakers in CHIL seminars via the acoustic channel. The LIMSI system for this task consists of a standard Gaussian mixture model based system working on cepstral coefficients, with MAP adaptation of a Universal Background Model (UBM). It builds upon the LIMSI CLEAR'06 system with several modifications: removal of feature normalization and frames filtering, and pooling of all speaker enrollment data for UBM training. The primary system uses a beamforming of all audio channels, while a single channel is selected for the contrastive system. This latter system performs the best and improves the baseline system by 50% relative for the 1 second and 5 seconds test conditions.
GoGoBot: group collaboration, multi-agent modeling, and robots
Authors: William Rand, Paulo Blikstein, Uri Wilensky
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers (2008)

Multi-agent simulation is a powerful technique used to encode real-world simple rules in virtual agents and then simulate their interactions [1]. Participatory simulations are similar to multi-agent simulation except individuals play the role of virtual agents, sometimes in combination with these virtual agents [2]. Finally, the bifocal modeling framework has enabled the examination of agents embodied in physical entities using sensors and actuators [3]. All three of these technologies are concerned with the creation, manipulation, and development of agents in one form or another. Thus combining these three disparate systems in to one unified platform would be useful. Multi-agent simulation platforms, participatory simulations, and bifocal modeling have all been demonstrated separately in the past. However many extant systems are difficult if not impossible to integrate.
Garden Variety Pervasive Computing
Authors: Marc Bohlen
Venue: IEEE Pervasive Computing (2004)

Can pervasive computing help us build systems that give us a new perspective on our environment? Can we hope for deeper and alternative interactions with animals, insects, and plants under pervasive and ubiquitous computing? UNSEEN, an experience machine for outdoor spaces, continuously collects real-time data on plants, insects, and animals through a multicamera vision system; it interprets this data and shares the results with visitors. The system visualizes change and builds hypotheses on the life forms it observes over an entire summer. The information it shares is meant to inspire and provoke questions rather than just answer them.
Gem #19: XML streaming of Ada objects
Authors: Pascal Obry
Venue: ACM SIGAda Ada Letters (2008)

Ada Gem #19 -- XML streaming of Ada objects.
Middleware in Modern High Performance Computing System Architectures
Authors: Christian Engelmann, Hong Ong, Stephen L. Scott
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

A recent trend in modern high performance computing (HPC) system architectures employs "lean" compute nodes running a lightweight operating system (OS). Certain parts of the OS as well as other system software services are moved to service nodes in order to increase performance and scalability. This paper examines the impact of this HPC system architecture trend on HPC "middleware" software solutions, which traditionally equip HPC systems with advanced features, such as parallel and distributed programming models, appropriate system resource management mechanisms, remote application steering and user interaction techniques. Since the approach of keeping the compute node software stack small and simple is orthogonal to the middleware concept of adding missing OS features between OS and application, the role and architecture of middleware in modern HPC systems needs to be revisited. The result is a paradigm shift in HPC middleware design, where single middleware services are moved to service nodes, while runtime environments (RTEs) continue to reside on compute nodes.
Towards a Monitoring Framework for Agent-Based Contract Systems
Authors: Noura Faci, Sanjay Modgil, Nir Oren, Felipe Meneguzzi, Simon Miles, Michael Luck
Venue: Proceedings of the 12th international workshop on Cooperative Information Agents XII (2008)

The behaviours of autonomous agents may deviate from those deemed to be for the good of the societal systems of which they are a part. Norms have therefore been proposed as a means to regulate agent behaviours in open and dynamic systems, and may be encoded in electronic contracts in order to specify the obliged, permitted and prohibited behaviours of agents that are signatories to such contracts. Enactment and management of electronic contracts thus enables the use of regulatory mechanisms to ensure that agent behaviours comply with the encoded norms. To facilitate such mechanisms requires monitoring in order to detect and explain violation of norms. In this paper we propose a framework for monitoring that is to be implemented and integrated into a suite of contract enactment and management tools. The framework adopts a non-intrusive approach to monitoring, whereby the states of a contract with respect to its contained norms can be inferred on the basis of messages exchanged. Specifically, the framework deploys agents that observe messages sent between contract signatories, where these messages correspond to agent behaviours and therefore indicate whether norms are, or are in danger of, being violated.
Physical parameter optimization in swarms of ultra-low complexity agents
Authors: Ryan Connaughton, Paul Schermerhorn, Matthias Scheutz
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3 (2008)

Physical agents (such as wheeled vehicles, UAVs, hovercraft, etc.) with simple control systems are often sensitive to changes in their physical design and control parameters. As such, it is crucial to evaluate the agent's control systems together with the agent's physical implementation. This can consequently lead to an explosion in the parameter space to be considered. In this paper we investigate the use of swarms of ultra-low complexity agents, and address the issue of finding workable physical agent parameters. We describe a technique for reducing the dimensionality of the search space by performing evaluation tasks that can be used to predict near-optimal parameter values for agents in related multi-agent tasks. We validate our approach on an example task, and demonstrate that this technique can greatly reduce the computational resources required to design a multi-agent system.
Maximum of Marginal Likelihood Criterion instead of Cross-Validation for Designing of Artificial Neural Networks
Authors: Zenon Waszczyszyn, Marek Słoński
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

The cross-validation method is commonly applied in the design of Artificial Neural Networks (ANNs). In the paper the design of ANN is related to searching for an optimal value of the regularization coefficient or the number of neurons in the hidden layer of network. Instead of the cross-validation procedure, the Maximum of Marginal Likelihood (MML) criterion, taken from Bayesian approach, can be used. The MML criterion, applied to searching for the optimal values of design parameters of neural networks, is illustrated on two examples. The obtained results enable us to formulate conclusions that the MML criterion can be used instead of the cross-validation method (especially for small data sets), since it permits the design of ANNs without formulation of a validation set of patterns.
Autonomic communication services: a new challenge for software agents
Authors: Raffaele Quitadamo, Franco Zambonelli
Venue: Autonomous Agents and Multi-Agent Systems (2008)

The continuous growth in ubiquitous and mobile network connectivity, together with the increasing number of networked devices populating our everyday environments, call for a deep rethinking of traditional communication and service architectures. The emerging area of autonomic communication addresses such challenging issues by trying to identify novel flexible network architectures, and by conceiving novel conceptual and practical tools for the design, development, and execution of "autonomic" (i.e., self-organizing, self-adaptive and context-aware) communication services. In this paper, after having introduced the general concepts behind autonomic communication and autonomic communication services, we analyze the key issue of defining suitable "component" models for autonomic communication services, and discuss the strict relation between such models and agent models. On this basis, we survey and compare different approaches, and eventually try to synthesize the key desirable characteristics that one should expect from a general-purpose component model for autonomic communication services. The key message we will try to deliver is that current research in software agents and multi-agent systems have the potential for playing a major role in inspiring and driving the identification of such a model, and more in general for influencing and advancing the whole area of autonomic communication.
A Hybrid Econometric-AI Ensemble Learning Model for Chinese Foreign Trade Prediction
Authors: Lean Yu, Shouyang Wang, Kin Keung Lai
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

Due to the complexity of economic system, the interactive effects of economic variables or factors on Chinese foreign trade make the prediction of China's foreign trade extremely difficult. To analyze the relationship between economic variables and foreign trade, this study proposes a novel nonlinear ensemble learning methodology hybridizing nonlinear econometric model and artificial neural networks (ANN) for Chinese foreign trade prediction. In this proposed learning approach, an important econometrical model, the co-integration-based error correction vector auto-regression (EC-VAR) model is first used to capture the impacts of the economic variables on Chinese foreign trade from a multivariate analysis perspective. Then an ANN-based EC-VAR model is used to capture the nonlinear patterns hidden between foreign trade and economic factors. Subsequently, for introducing the effects of irregular events on foreign trade, the text mining and expert's judgmental adjustments are also incorporated into the nonlinear ANN-based EC-VAR model. Finally, all economic variables, the outputs of linear and nonlinear EC-VAR models and judgmental adjustment model are used as another neural network inputs for ensemble prediction purpose. For illustration, the proposed ensemble learning methodology integrating econometric techniques and artificial intelligence (AI) methods is applied to Chinese export trade prediction problem.
A Branch and Bound Algorithm for Numerical MAX-CSP
Authors: Jean-Marie Normand, Alexandre Goldsztejn, Marc Christie, Frédéric Benhamou
Venue: Proceedings of the 14th international conference on Principles and Practice of Constraint Programming (2008)

The Constraint Satisfaction Problem (CSP) framework allows users to define problems in a declarative way, quite independently from the solving process. However, when the problem is over-constrained, the answer "no solution" is generally unsatisfactory. A Max-CSP $\mathcal{P}_m = \langle V, \textbf{D}, C \rangle$ is a triple defining a constraint problem whose solutions maximise constraint satisfaction. In this paper, we focus on numerical CSPs, which are defined on real variables represented as floating point intervals and which constraints are numerical relations defined in extension. Solving such a problem (i.e., exactly characterizing its solution set) is generally undecidable and thus consists in providing approximations. We propose a branch and bound algorithm that computes under and over approximations of its solution set and determines the maximum number of satisfied constraints. The technique is applied on three numeric applications and provides promising results.
A Quality Adaptation Scheme for Internet Video Streams
Authors: Panagiotis Papadimitriou, Vassilis Tsaoussidis
Venue: Proceedings of the 5th international conference on Wired/Wireless Internet Communications (2007)

We propose a layered quality adaptation scheme for video streams to smooth the short-term oscillations induced by Additive Increase Multiplicative Decrease (AIMD) mechanisms, and eventually refine the perceptual video quality. The layered scheme utilizes receiver buffering, adapting the video quality along with long-term variations in the available bandwidth. The allocation of a new layer is based on explicit criteria that consider the available bandwidth, as well as the amount of buffering at the receiver. Consequently, the adaptation mechanism prevents wasteful layer changes that have an adverse effect on user-perceived quality. In the sequel, we concentrate on the interactions of the layered approach with Scalable Streaming Video Protocol (SSVP). Exploiting performance measures related to the perceived quality of rate-adaptive video streams, we quantify the combination of SSVP rate control and receiver-buffered layered adaptation.
Expedited Breast Care: A New Model in Breast Health
Authors: A. M. Lopez, G. Barker, A. Bhattacharryya, K. Scott, M. Descour, L. Richter, J. Davenport, S. Lazarus, L. Kreykes, A. Valencia, R. Weinstein
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

Women in the U.S. have a 1 in 7 lifetime risk for developing breast cancer. The breast cancer detection process from mammography to clinical consultation with the medical oncologist is often at minimum 28 days. The pathway of diagnosis to treatment generally begins with either an abnormal screening test or an abnormal self or clinical breast examination. This initial warning sign may be followed by diagnostic imaging which may lead to a diagnostic biopsy performed by a breast radiologist under mammography, ultrasound or MRI guidance by a breast surgeon. Once the biopsy is performed, the tissue is processed and read by a pathologist who generates a report and sends it to the clinical site where the biopsy was obtained. If the diagnosis is malignant, the patient is informed and surgical consultation is scheduled. Consultation with the medical oncologist is generally scheduled after the definitive surgery has been performed. The medical oncology consultation is set up to define further treatment planning. Increasingly, the appointment for medical oncology is scheduled prior to definitive surgery to assure a more multidisciplinary approach to breast cancer care.
Evacuation Simulation in Floor Field by Real-Coded Cellular Automata
Authors: Kazuhiro Yamamoto
Venue: Proceedings of the 8th international conference on Cellular Automata for Reseach and Industry (2008)

So far, Cellular Automata (CA) have been applied in a variety of scientific fields, including traffic problems and biological researches. Especially, as one of the excellent achievements, CA models for pedestrian dynamics have been developed. Since the pedestrian flows are caused by collective crowd behavior, it is difficult to model the pedestrian dynamics based on differential equations. Although the social force model has been proposed [1], CA approach could be more appropriate to describe pedestrian dynamics, because pedestrian flows are naturally emerged in a collective behavior of pedestrians [2-5].In our previous study, we have proposed a new approach for pedestrian dynamics. We call it a Real-coded Cellular Automata (RCA) [6,7]. The idea is based on the Real-coded Lattice Gas (RLG) developed for fluid simulation [8]. We applied this scheme to the CA model for pedestrian dynamics. The numerical procedure is not explained here. Similar to RLG, the position and velocity of the pedestrian can be freely given, independent of grid points. It has been confirmed that the movement of pedestrians in an oblique direction is successfully described by RCA, which was not taken into account in the former CA models. Some benchmark simulations including room evacuation and lane formation in the street have been conducted.
Random k-Labelsets: An Ensemble Method for Multilabel Classification
Authors: Grigorios Tsoumakas, Ioannis Vlahavas
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

This paper proposes an ensemble method for multilabel classification. The RAndom k-labELsets (RAKEL) algorithm constructs each member of the ensemble by considering a small random subset of labels and learning a single-label classifier for the prediction of each element in the powerset of this subset. In this way, the proposed algorithm aims to take into account label correlations using single-label classifiers that are applied on subtasks with manageable number of labels and adequate number of examples per label. Experimental results on common multilabel domains involving protein, document and scene classification show that better performance can be achieved compared to popular multilabel classification approaches.
Experimental Assessment of the Practicality of a Fault-Tolerant System
Authors: Jai Wug Kim, Jongpil Lee, Heon Y. Yeom
Venue: Proceedings of the 33rd conference on Current Trends in Theory and Practice of Computer Science (2007)

Fault-tolerance has gained renewed importance with the proliferation of high-performance clusters. However, fault-tolerant systems have not yet been widely adopted commercially because they are either hard to deploy, hard to use, hard to manage, hard to maintain, or hard to justify. We have developed M 3, a practical and easily-deployable multiple fault-tolerant MPI system for Myrinet, to satisfy the demand for a fault-tolerant system.In this paper, we run rigorous tests using real-world applications to validate that M 3 can be used in commercial clusters. We also describe improvements made to our system to solve various problems that arose when deploying it on a commercial cluster.This paper models our system's checkpoint overhead and presents the results of a series of tests using computation- and communication-intensive MPI applications used commercially in various fields of science. The experimental results show that not only does our system conform to various types of running environment well, but that it can also be practically deployed in commercial clusters.
An Empirical Study for the Multi-class Imbalance Problem with Neural Networks
Authors: R. Alejo, J. M. Sotoca, G. A. Casañ
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

The latest research in neural networks demonstrates that the class imbalance problem is a critical factor in the classifiers performance when working with multi-class datasets. This occurs when the number of samples of some classes is much smaller compared to other classes. In this work, four different options to reduce the influence of the class imbalance problem in the neural networks are studied. These options consist of introducing several cost functions in the learning algorithm in order to improve the generalization ability of the networks and speed up the convergence process.
Long-Run Cost Analysis by Approximation of Linear Operators over Dioids
Authors: David Cachera, Thomas Jensen, Arnaud Jobin, Pascal Sotin
Venue: Proceedings of the 12th international conference on Algebraic Methodology and Software Technology (2008)

We present a static analysis technique for modeling and approximating the long-run resource usage of programs. The approach is based on a quantitative semantic framework where programs are represented as linear operators over dioids. We provide abstraction techniques for such linear operators which make it feasible to compute safe over-approximations of the long-run cost of a program. A theorem is proved stating that such abstractions yield correct approximations of the program's long-run cost. These approximations are effectively computed as the eigenvalue of the matrix representation of the abstract semantics. The theoretical developments are illustrated on a concrete example taken from the analysis of the cache behaviour of a simple bytecode language.
Measuring the performance of interactive applications with listener latency profiling
Authors: Milan Jovic, Matthias Hauswirth
Venue: Proceedings of the 6th international symposium on Principles and practice of programming in Java (2008)

When Java developers need to improve the performance of their applications, they usually use one of the many existing profilers for Java. These profilers generally capture a profile that represents the execution time spent in each method. The developer can thus focus her optimization efforts on the methods that consume the most time. In this paper we argue that this type of profile is insufficient for tuning interactive applications. Interactive applications respond to user events, such as mouse clicks and key presses. The perceived performance of interactive applications is directly related to the response time of the program. In this paper we present listener latency profiling, a profiling approach with two distinctive characteristics. First, we call it latency profiling because it helps developers to find long latency operations. Second, we call it listener profiling because it abstracts away from method-level profiles to compute the latency of the various listeners. This allows a developer to reason about performance with respect to listeners, also called observers, the high level abstraction at the core of any interactive Java application. We present our listener latency profiling approach, describe LiLa, our implementation, validate it on a set of micro-benchmarks, and evaluate it on a complex real-world interactive application.