ppxscal/embedding_network huggingface dataset:
An economic model for resource management in a Grid-based content distribution network
Authors: Antonella Di Stefano, Corrado Santoro
Venue: Future Generation Computer Systems (2008)

This paper presents an architecture to federate Content Distribution Networks (CDNs) in order to share computational resources, thus building an infrastructure that we call a Content Distribution Grid (CDG). The purpose of a CDG is to use a community policy allowing each CDN to put together a portion of its own resources to meet each other's requirements, and therefore to guarantee a stronger quality of service to users. Since CDNs of a CDG belong to different organisations, the interaction scenario can be considered competitive, that is, organizations are mainly self-interested and are aiming at maximizing the performances of its own system. For this reason, the resource sharing policy proposed here is based on a offer/demand competitive model, in which resources are purchased by paying for them a certain amount of (virtual or real) money. An economic model is thus derived to guide such a sale; here the CDN requesting resources (buyer) and the CDN offering resources (seller) agree on the quantity and the price by means of a utility-based negotiation approach. A multi-agent system is then proposed to realise the software architecture supporting this model of CDG.
Robust feature detection for facial expression recognition
Authors: Spiros Ioannou, George Caridakis, Kostas Karpouzis, Stefanos Kollias
Venue: Journal on Image and Video Processing (2007)

This paper presents a robust and adaptable facial feature extraction system used for facial expression recognition in human-computer interaction (HCI) environments. Such environments are usually uncontrolled in terms of lighting and color quality, as well as human expressivity and movement; as a result, using a single feature extraction technique may fail in some parts of a video sequence, while performing well in others. The proposed system is based on a multicue feature extraction and fusion technique, which provides MPEG-4-compatible features assorted with a confidence measure. This confidence measure is used to pinpoint cases where detection of individual features may be wrong and reduce their contribution to the training phase or their importance in deducing the observed facial expression, while the fusion process ensures that the final result regarding the features will be based on the extraction technique that performed better given the particular lighting or color conditions. Real data and results are presented, involving both extreme and intermediate expression/emotional states, obtained within the sensitive artificial listener HCI environment that was generated in the framework of related European projects.
Active target particle swarm optimization: Research Articles
Authors: Ying-Nan Zhang, Qing-Ni Hu, Hong-Fei Teng
Venue: Concurrency and Computation: Practice Experience (2008)

We propose an active target particle swarm optimization (APSO). APSO uses a new three-target velocity updating formula, i.e. the best previous position, the global best position and a new target position (called active target). In this study, we distinguish APSO from EPSO (extended PSO)&#x002F;PSOPC (PSO with passive congregation) by the different methods of getting the active target. Note that here EPSO and PSOPC are the two existing methods for using three-target velocity updating formula, and getting the third (active) target from the obtained positions by the swarm. The proposed APSO gets the active (third) target using complex method, where the active target does not belong to the existing positions. We find that the APSO has the advantages of jumping out of the local optimum and keeping diversity; however, it also has the disadvantages of adding some extra computation expenses. The experimental results show the competitive performance of APSO when compared with PSO, EPSO, and PSOPC. Copyright &copy; 2007 John Wiley & Sons, Ltd.
Loop Corrections for Approximate Inference on Factor Graphs
Authors: Joris M. Mooij, Hilbert J. Kappen
Venue: The Journal of Machine Learning Research (2007)

We propose a method to improve approximate inference methods by correcting for the influence of loops in the graphical model. The method is a generalization and alternative implementation of a recent idea from Montanari and Rizzo (2005). It is applicable to arbitrary factor graphs, provided that the size of the Markov blankets is not too large. It consists of two steps: (i) an approximate inference method, for example, belief propagation, is used to approximate cavity distributions for each variable (i.e., probability distributions on the Markov blanket of a variable for a modified graphical model in which the factors involving that variable have been removed); (ii) all cavity distributions are improved by a message-passing algorithm that cancels out approximation errors by imposing certain consistency constraints. This loop correction (LC) method usually gives significantly better results than the original, uncorrected, approximate inference algorithm that is used to estimate the effect of loops. Indeed, we often observe that the loop-corrected error is approximately the square of the error of the uncorrected approximate inference method. In this article, we compare different variants of the loop correction method with other approximate inference methods on a variety of graphical models, including "real world" networks, and conclude that the LC method generally obtains the most accurate results.
A framework for clustering evolving data streams
Authors: Charu C. Aggarwal, Jiawei Han, Jianyong Wang, Philip S. Yu
Venue: Proceedings of the 29th international conference on Very large data bases - Volume 29 (2003)

The clustering problem is a difficult problem for the data stream domain. This is because the large volumes of data arriving in a stream renders most traditional algorithms too inefficient. In recent years, a few one-pass clustering algorithms have been developed for the data stream problem. Although such methods address the scalability issues of the clustering problem, they are generally blind to the evolution of the data and do not address the following issues: (1) The quality of the clusters is poor when the data evolves considerably over time. (2) A data stream clustering algorithm requires much greater functionality in discovering and exploring clusters over different portions of the stream. The widely used practice of viewing data stream clustering algorithms as a class of one-pass clustering algorithms is not very useful from an application point of view. For example, a simple one-pass clustering algorithm over an entire data stream of a few years is dominated by the outdated history of the stream. The exploration of the stream over different time windows can provide the users with a much deeper understanding of the evolving behavior of the clusters. At the same time, it is not possible to simultaneously perform dynamic clustering over all possible time horizons for a data stream of even moderately large volume. This paper discusses a fundamentally different philosophy for data stream clustering which is guided by application-centered requirements. The idea is divide the clustering process into an online component which periodically stores detailed summary statistics and an offine component which uses only this summary statistics. The offine component is utilized by the analyst who can use a wide variety of inputs (such as time horizon or number of clusters) in order to provide a quick understanding of the broad clusters in the data stream. The problems of efficient choice, storage, and use of this statistical data for a fast data stream turns out to be quite tricky. For this purpose, we use the concepts of a pyramidal time frame in conjunction with a microclustering approach. Our performance experiments over a number of real and synthetic data sets illustrate the effectiveness, efficiency, and insights provided by our approach.
Towards the development of a rigorous model-driven domain-specific software engineering environment
Authors: Emanuel S. Grant, Hassan Reza
Venue: Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology (2007)

Over the past decades, the software development community has increasingly embraced the principles of a model-driven approach, resulting in the definition of many such methodologies. It is now an appropriate time to consolidate the many software model-driven approaches into a set of well-defined and related techniques, and processes. This set of techniques and processes, would be the building block for subsequent model-driven development methodologies. Such an accomplishment would provide a framework from which rigorously defined domain-specific methodologies are crafted. It would also provide a platform from which the next significant advancement in software development may be launched. The objective of this research is the definition and implementation of a platform independent environment for model-driven software development that incorporates formal specification techniques with informal graphical modeling notations. The methodologies of the environment will amalgamate some of the best practices in model-driven development from academia and industry. The success and viability of this defined environment will be manifested in its adaptation by industrial partners.
OnRipple: A Distributed Overlay Framework for Targeted Immunization in Large-Scale Networks
Authors: Sirui Yang, Hai Jin, Xiaofei Liao, Hong Yao
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 03 (2007)

Targeted immunization has been studied as an applicable measure for network epidemics. Unless global information is collected, existing targeted strategies are short of strength in organizing a potent structure to spread the vaccines fleetly. We propose a novel dynamic immunization mechanism, called OnRipple, to propagate vaccines in Susceptible- Infected-Removed (SIR) epidemiological model. With a light-weighted, scalable and distributed framework and an "axis" structure, OnRipple buildsup an effective overlay network. Thus, nodes have essential sampling information of the whole network and targeted immunization can be easily implemented. Nodes are also able to radiate vaccines to both nearby and distant participants almost at the same time. We analyze the performance of this strategy by simulations on small-world networks. On average, OnRipple is 34% faster than traditional targeted immunization. It is proven to be flexible and effective in large-scale peer-to- peer (P2P) networks and scale-free networks.
Airline Operations: The Real-Time Environment
Authors: M. J. Fenello
Venue: Computer (1972)

State-of-the-art" real-time computer system techniques and their impact on airline operations and the "bottom line" are reviewed. 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
Application of Time-varying Coherence to Coordinative Connectivity Based on Event Related EEG
Authors: Xiaotong Wen, Kai Yin, Dongzhi Sun, Li Yao, Xiaojie Zhao
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 02 (2007)

Classic coherence analysis can detect coordination of EEG rhythms between brain areas. However, it could not reflect the dynamic properties changing with time. Time-varying coherence analysis is a method developed on base of classic coherence analysis and signal's joint time-frequency representations in recent years. It was used to extract transient characteristics of the interactions among brain areas and describe the temporal, spatial and frequency relationships of brain activities. This paper discussed the probability of applying time-varying coherence to event related EEG to study the coordination mechanism of brain. Aimed at the applications of time-varying coherence analysis methods based on non-parameter estimation and multi-variant parameter model to EEG, we constructed the simulated EEG data, looked for the suitable approach for cognitive EEG and tried to apply it to the EEG data from classic Stroop effect. The results showed that appropriate time-varying analysis methods were effective tools for event related EEG to study the coordination between brain areas.
A comparative study of pairwise regression techniques for problem determination
Authors: Mohammad A. Munawar, Paul A. S. Ward
Venue: Proceedings of the 2007 conference of the center for advanced studies on Collaborative research (2007)

Many runtime metrics can be collected from modern software systems. Stable statistical relationships exist among these metrics. Deviation from these stable relationships indicates potential problems, allowing diagnosis of failures. There exist many modeling techniques to represent these relationships. However, which one to use is a question that has yet to be studied. In this paper we compare the use of simple linear regression (SLR) to some of its more complex variants, including autoregressive regression and locally weighted regression. We consider the component coverage, model robustness, accuracy of diagnosis, and computation cost. Our study finds that while more flexible models can improve diagnosis accuracy, they achieve it at the cost of reduced robust-ness. In particular, we found the autoregressive regression model with exogenous input (ARX) to provide the most accurate diagnosis; however, it is the least robust of the techniques considered and the second most expensive. This study also finds that smoothing and other data transformations can noticeably improve results of SLR, thus providing an efficient alternative to ARX.
Online evaluation of regular tree queries
Authors: Alexandru Berlea
Venue: Nordic Journal of Computing (2006)

Regular tree queries (RTQs) are a class of queries considered especially relevant for the expressiveness and evaluation of XML query languages. The algorithms proposed so far for evaluating queries online, while scanning the input data rather than by explicitly building the tree representation of the input beforehand, only cover restricted subsets of RTQs. In contrast, we introduce here an efficient algorithm for the online evaluation of unrestricted RTQs. We prove our algorithm is optimal in the sense that it finds matches at the earliest possible time for the query and the input document at hand. The time complexity of the algorithm is quadratic in the input size in the worst case and linear in many practical cases. Preliminary experimental evaluation of our practical implementation are very encouraging.
Cyberassault on Estonia
Authors: Marc Donner
Venue: IEEE Security and Privacy (2007)

Security & Privacy's associate editor in chief considers the recent cyberattack on Estonia.
Study of the collision of one rapid sphere on 3D packings: Experimental and numerical results
Authors: L. Oger, M. Ammi, A. Valance, D. Beladjine
Venue: Computers Mathematics with Applications (2008)

We report on experimental studies of the collision process between an incident bead and a three-dimensional packing of mono-size beads. The understanding of such a process and the resulting ejection of grains is, in particular, crucial to describe aeolian sand transport. We present here recent experimental results on the collision and ejection process when varying the angle and the speed of the incident bead. We performed numerical simulations of one bead collisions on the surface of a static granular medium. The simulations have been done for two and three dimensional packings of beads. The effects of the incident bead velocity, the shot angle, the mechanical parameters and the packing structure are analyzed for ordered and disordered 2D packings but only disordered 3D packings. The 2D results are in good agreement with available experimental data. The 3D simulations give good preliminary results about the shock wave propagation through the stacking and provides new insights in the ejection process (''splash function'').
Adaptive post-silicon tuning for analog circuits: concept, analysis and optimization
Authors: Xin Li, Brian Taylor, YuTsun Chien, Lawrence T. Pileggi
Venue: Proceedings of the 2007 IEEE/ACM international conference on Computer-aided design (2007)

The well-known Pelgrom model [14] has demonstrated that the variation between two devices on the same die due to random mismatch is inversely proportional to the square root of the device area: &sigma; ~ 1/sqrt(Area). Based on the Pelgrom model, analog devices are sized to be large enough to average out random variations. Importantly, with CMOS scaling, variations due to random doping fluctuations are making it exceedingly difficult to control device mismatches by sizing alone; namely, the devices have to be made so large that the benefits of CMOS scaling are not realized for analog and RF circuits. In this paper we propose a novel post-silicon tuning methodology to reduce random mismatches for analog circuits in sub-90nm CMOS. A novel dynamic programming algorithm is incorporated into a fast Monte Carlo simulation flow for statistical analysis and optimization of the proposed tunable analog circuits. We apply the proposed post-silicon tuning methodology to several commonly-used analog circuit blocks. We demonstrate that with the post-silicon tuning, device mismatch exponentially decreases as area increases: &sigma; ~ exp(---&alpha;&middot;Area).
A Chord-based Recommender System
Authors: Christoph Sorge
Venue: Proceedings of the 32nd IEEE Conference on Local Computer Networks (2007)

Traditional recommender systems rely on the use of a central server. This server is a single point of failure; moreover, the need for an expensive central infrastructure may lead to economic dependencies, forming an incentive to manipulate recommendations. Decentralized recommender systems on the basis of Peer-to-Peer networks can help solving this problem. Most existing approaches, however, are not scalable for large networks, or they do not consider security and privacy issues. The article at hand presents a Peer-to-Peer recommender system based on the Chord overlay network and item-based collaborative filtering. Scalability is improved by introducing various optimizations. Additionally, we present an approach that reduces the potential for manipulation by system participants while granting a high degree of privacy.
Spatially explicit individual-based modeling using a fixed super-individual density
Authors: Ferdi L. Hellweger
Venue: Computers Geosciences (2008)

Individual-based models (IBMs) of planktonic microorganisms (e.g., bacterioplankton, phytoplankton) have to simulate large numbers of individuals. Because of computational limitations these models rely on simulating a number of super-individuals that are representative of a larger number of individuals. Using a fixed representative number (the number of individuals one super-individual represents) results in a lower computational resolution (number of super-individuals) at times and in areas of low individual densities, which is undesirable when (a) large temporal and/or spatial gradients exist and (b) variability in state variables or behavior at low densities is important. Various methods exist that fix the number of super-individuals in the global model domain by allowing the representative number to vary in time. Those methods solve the problem introduced by large temporal gradients, but do not address spatial gradients. This paper presents an accounting method that maintains an approximately constant super-individual density in time and space. Each spatial model segment has a local super-individual population that is resampled when the number shrinks or grows outside user-specified bounds, or when the variance of the representative numbers exceeds a user-specified threshold. This local method is compared to a global method and evaluated quantitatively against the analytical solution to an instantaneous input (slug release) into a river, and qualitatively in a biogeochemical phytoplankton model applied to a point source nutrient discharge into a river. Computations are performed using the iAlgae individual-based phytoplankton modeling framework. The applications demonstrate that the local method results in a spatially uniform or density-independent relative error, and it is computationally more efficient at controlling relative error at low densities. However, for the same total number of super-individuals, it is computationally more demanding and therefore less efficient at controlling absolute error. The local method is superior to the global method for the biogeochemical model application, because a significant spatial gradient (front) exists and the dynamics at the low densities affects the model behavior downstream.
A Reinforcement-Learning Approach to Failure-Detection Scheduling
Authors: Fancong Zeng
Venue: Proceedings of the Seventh International Conference on Quality Software (2007)

A failure-detection scheduler for an online production system must strike a tradeoff between performance and reli- ability. If failure-detection processes are run too frequently, valuable system resources are spent checking and recheck- ing for failures. However, if failure-detection processes are run too rarely, a failure can remain undetected for a long time. In both cases, system performability suffers. We present a model-based learning approach that estimates the failure rate and then performs an optimization to find the tradeoff that maximizes system performability. We show that our approach is not only theoretically sound but prac- tically effective, and we demonstrate its use in an imple- mented automated deadlock-detection system for Java.
An Expert Experience Probabilistic Model for Enterprise Expert Finding
Authors: Zhao Ru, Weiran Xu, Jun Guo
Venue: Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 01 (2007)

Finding experts accurately and automatically is becom- ing difficult especially in a large organization. This paper presents a probabilistic model which applies language mod- eling techniques to find experts in enterprise corpora. The expertise of each candidate expert is modeled through the associated experience. We employ a qualification of experi- ence, and validate this qualification as a measure of exper- tise to replace the documents. In our model, the relevance of a candidate's expertise to a given topic is estimated by its likelihood of generating topic words. Our evaluation on TREC test collection indicates the effectiveness of the prob- abilistic model in expert finding. This model is able to be improved by clustering of the relevant experience measures, which can also save retrieval time.
Automotive software and systems engineering
Authors: M. Broy
Venue: Proceedings of the 2nd ACM/IEEE International Conference on Formal Methods and Models for Co-Design (2005)

Information technology has become the driving force of innovation in many areas of technology and, in particular, in the form of embedded systems in vehicles. Embedded hardware/software systems control innovative functions in cars, support and assist the driver and realize new features for information and entertainment. A rapid development brought more and more digital hardware and software into a modern car only within little more than two decades. Systems and software engineering for embedded systems in vehicles is today one of the great scientific, methodological, and technical challenges. In modern cars we find all issues of software systems in a nutshell. This brings a wide spectrum of challenges for the automotive industry, including business, management, and technical issues. In the following we outline the main problems, research challenges and approaches to deal with them.