ppxscal/embedding_network huggingface dataset:
Validation of operational AVHRR subpixel snow retrievals over the European Alps based on ASTER data
Authors: N. Foppa, A. Hauser, D. Oesch, S. Wunderle, R. Meister
Venue: International Journal of Remote Sensing (2007)

Snow is of great economic and social importance for the European Alps. Accurate monitoring of the alpine snow cover is a key component in studying regional climate change as well as in daily weather forecasting and snowmelt run-off modelling. These applications require snow cover information on a high temporal resolution in near-real time. For the European Alps, operational snow cover fraction maps are generated on a daily basis using data from the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) platforms. Snow cover distribution is inherently discontinuous and heterogeneous in this mountainous region. We have therefore implemented a straightforward multiple endmember unmixing approach to estimate fractional snow cover. Subpixel proportions are difficult to validate because similar products are not available and appropriate ground-based observations do not exist. In this study, we validate AVHRR subpixel snow retrievals using binary classified data sets from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to establish absolute errors of our operational approach at three test sites. Our analysis indicates that the AVHRR subpixel maps compare well with the aggregated ASTER data, showing an overall correlation of 0.78 and providing subpixel estimates with a mean absolute error of 10.4% fractional snow cover. Discrepancies between AVHRR and ASTER snow fraction maps can be attributed to varying snow conditions, terrain effects and density in forest cover.
High-performance computing of 1/√xi and exp(±xi) for a vector of inputs xi on Alpha and IA-64 CPUs
Authors: Md. Haidar Sharif, Achim Basermann, Christian Seidel, Axel Hunger
Venue: Journal of Systems Architecture: the EUROMICRO Journal (2008)

The modern microprocessors have become more sophisticated, the performance of software on modern architectures has grown more and more difficult to dissect and prognosticate. The execution of a program nowadays entails the complex interaction of code, compiler and processor micro-architecture. The built-in functions to compute 1/x or exp(+/-x) of math library and hardware are often incapable of achieving the challenging performance of high-performance numerical computing. To meet this demand, the current trend in constructing high-performance numerical computing for specific processors Alpha 21264 & 21364, and IA-64 has been optimized for 1/x"i and exp(+/-x"i) for a vector of inputs x"i which is significantly faster than optimized library routines. A detailed deliberation of how the processor micro-architecture as well as the manual optimization techniques improve the computing performance has been developed.
DRIVE: a reconfigurable testbed for advanced vehicular services and communications
Authors: Carolina Pinart, Pilar Sanz, Iván Lequerica, Daniel García, Isaac Barona, Diego Sánchez-Aparisi
Venue: Proceedings of the 4th International Conference on Testbeds and research infrastructures for the development of networks & communities (2008)

This paper introduces DRIVE, a reconfigurable demonstrator that will allow for experimental validation of vehicular networking research, as well as proof of concept and realistic performance studies of advanced services for vehicular users. The testbed features an OSGi-based software platform that allows easy service development through bundles. DRIVE's hardware platform is based on a carPC, wireless sensors and rooftop antennae and uses wireless and mobile technologies, e.g., WiFi, UMTS and Bluetooth. On top of these platforms, a hybrid communication node enables always-on, QoS-aware connectivity by using the Always Best Connected concept, and a service platform enables seamless car, driver and passenger services.
Distributed Data Aggregation in Multi-sink Sensor Networks Using a Graph Coloring Algorithm
Authors: Ryouhei Kawano, Toshiaki Miyazaki
Venue: Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops (2008)

In this paper, we propose an algorithm to minimize the multihop route from each sensor node to a sink node in wireless sensor networks with multiple sink nodes. In addition to solving the shortest path problem, the algorithm solves a time slot problem to support TDMA (time division multiple access) communication. TDMA is effective for avoiding packet collisions; however, the time slot allocation itself is a difficult problem to solve. We introduce a distributed coloring algorithm to solve it. After describing the algorithm, some experimental results proving its effectiveness are presented.
A QoS Broker Based Architecture for Dynamic Web Service Selection
Authors: Demian Antony D'Mello, V. S. Ananthanarayana, Santhi Thilagam
Venue: Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS) (2008)

The increasing number of Web services over the Web makes the requester to use tools to search for suitable Web services available throughout the globe. UDDI is the first step towards meeting these demands. However the requester’s demand may include not only functional aspects of Web services but also non-functional aspects like Quality of Service (QoS). There is a need to select the most suitable (qualitatively optimal) Web service based on the requester’s QoS requirements and preferences. In this paper we explore the different types of requester’s QoS requirements (demands) with illustrations. We propose the QoS broker based architecture for dynamic Web service selection which facilitates the requester to specify his/her QoS requirements along with functional requirements. The paper presents the Web service selection mechanism which selects the best (most suitable) Web service based on the requester’s functional and quality requirements.
Investigation of Process Impact on Soft Error Susceptibility of Nanometric SRAMs Using a Compact Critical Charge Model
Authors: Shah M. Jahinuzzaman, Mohammad Sharifkhani, Manoj Sachdev
Venue: Proceedings of the 9th international symposium on Quality Electronic Design (2008)

Nanometric SRAMs are more vulnerable to experiencing particle induced soft error due to lower operating voltages coupled with higher packing density and increased process variations. In this paper, we present a compact model for critical charge of a 6T SRAM cell for estimating the effects of process variations on its soft error susceptibility. The model is based on dynamic behavior of the cell and a simple decoupling technique for the non-linearly coupled storage nodes. The model describes the critical charge in terms of transistor parameters, cell supply voltage, and injected current parameters. Consequently, it enables investigating the spread of critical charge due to process induced variations in these parameters and to manufacturing defects, such as, resistive contacts or vias. In addition, the model can estimate the improvement in critical charge when MIM capacitors are added to the cell in order to improve the soft error robustness. The critical charge calculated by the model is in good agreement with SPICE simulations for a commercial 90nm CMOS process with a maximum discrepancy of less than 5%.
Integrating Real and Financial Markets in an Agent-Based Economic Model: An Application to Monetary Policy Design
Authors: Marco Raberto, Andrea Teglio, Silvano Cincotti
Venue: Computational Economics (2008)

This article presents an agent-based integrated model of a real, financial, and monetary economy. The model is characterized by a monopolist firm that supplies a single homogeneous product in the goods market, hires workers in the labor market, and demands loans in the credit market; a trade union that sets the nominal wage; N heterogeneous households that buy the consumption good, provide the labor force, and trade the firm's equity in the stock market; and a bank that lends money to the firm at an interest rate set according to a monetary policy strategy. The model is used to perform monetary policy experiments. A monetary policy rule which targets the gap between the current output and the potential output in the full employment case is investigated, studying the effects on the economy for different degrees of policy tightness. The monetary policy rule is compared to a random policy rule that conserves a similar structure. Results show that a tight monetary policy clearly over performs the random policy rule. Moreover, results corroborate the effectiveness of monetary policy in limiting inflation and increasing welfare.
netEmbed: a service for embedding distributed applications
Authors: Jorge Londoño, Azer Bestavros
Venue: Proceedings of the 2007 ACM/IFIP/USENIX international conference on Middleware companion (2007)

An increased number of applications, such as computational grids, testbeds, peer-to-peer networks, and sensor networks (among many others) rely on finding a set of resources that meet certain criteria for their operation. In particular, in many of these cases their requirements may be described as a labeled graph where nodes represent computational resources and links represent connectivity/communication requirements. Similarly, the infrastructure where the service will be deployed is also described by a labeled graph, where the attributes of nodes and links represent their capabilities. The problem of finding a feasible set of links and nodes on which to deploy the service is what we call the embedding problem.
Using Fuzzy Cognitive Maps to Reduce False Alerts in SOM-Based Intrusion Detection Sensors
Authors: Mahmoud Jazzar, Aman Bin Jantan
Venue: Proceedings of the 2008 Second Asia International Conference on Modelling & Simulation (AMS) (2008)

Most of the intrusion detection sensors suffer from the high rate of fake alerts that the sensor produce. In this paper, we propose a new approach based on fuzzy cognitive maps (FCM) to reduce false alerts in SOM-based intrusion detection sensors. Initially, each neuron is mapped to its best matching unit in the self organizing map and then updated by the fuzzy cognitive map framework. This updating is achieved through the weights of the neighboring neurons. Based on the domain knowledge of network data (network packets) the SOM/FCM combination presents quantitative and qualitative matching correspondences which in turn reduce the number of suspicious neurons i.e. reduce the number of false alerts. This method work as a unique fuzzy clustering approach and we demonstrate its performance using DARPA 1999 network traffic data set.
3-D Representation and Volumetric Measurement of Human Heart from a Cylindrical B-Spline Surface Model
Authors: Ting-ting Jiang, Shengyong Chen, Yiqiang Xu
Venue: Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 01 (2008)

In this paper, a cylindrical 3-D B-Spline model of heart’s surfaces is proposed. Since volume is a basic parameter in cardiac image analysis, an effectively integral algorithm for volumetric measurement of the B-Spline surface model represented by matrix forms is presented in this article as well. This method makes the volumetric measurement convenient and its result is more accurate than the results counted by the traditional methods. In order to validate B-Spine surface fitting and advantage of our method for volumetric measurement, several experiments were done. Finally, the results show that the cylindrical B-Spline with a given number of control points can well fit the surfaces of the heart and left ventricle. Experiments also show the algorithm can get satisfactory measurement accuracy and efficiency.
Modeling the Latency on Production Grids with Respect to the Execution Context
Authors: Diane Lingrand, Johan Montagnat, Tristan Glatard
Venue: Proceedings of the 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (2008)

In this paper, we study grid jobs submission latency. The latency highly impacts performances on production grids, due to its high values and variations. It is particularly prejudicial for determining the status and expected duration of jobs and it makes outliers detection difficult. In previous work, a probabilistic model of the latency has been presented. It allows to estimate the best timeout value considering agiven distribution of jobs latencies. This timeout value is then used in a job resubmission strategy. The purpose of this paper is to evaluate to what extent updating this model with relevant contextual parameters can help to refine the latency estimation. Experiments on the EGEE production grid show thatthe choice of the resource broker or the computing site has astatistically significant influence on the jobs latency. We exploit this contextual information to propose a reliable job submission strategy.
How has volatility in metals markets changed?
Authors: Clinton Watkins, Michael McAleer
Venue: Mathematics and Computers in Simulation (2008)

Within the industrial metals industry, there has been a great deal of interest surrounding trends in metals market volatility over time. This paper uses a rolling AR(1)-GARCH(1,1) model to estimate and forecast the volatility processes for daily returns on the futures prices of two important non-ferrous metals, namely aluminium and copper. The rolling models are used to examine how the processes driving aluminium and copper returns volatility have evolved over a long sample. The variation over time seen in the volatility processes, as modelled by GARCH, suggest that, while volatility in returns has not necessarily increased, the conditional volatility process in metals markets is itself time-varying when analysed over a long horizon.
Detection of wave fronts in the Indian Ocean from geostationary sunglint satellite imagery
Authors: A. Turiel, J. Isern-Fontanet, E. Garcia-Ladona, J. A. Young
Venue: International Journal of Remote Sensing (2007)

We demonstrate the potential of monitoring coherent wave fronts on the tropical ocean surface using an advanced detection algorithm applied to visible sunglint-affected imagery from geostationary satellites. Data from MeteoSat are processed with the advanced analysis technique to reveal transient wave activity near and to the west of the Mascarene Ridge of the western Indian Ocean. The technique is based on a wavelet-based singularity analysis which enhances the subtle but highly coherent wave patterns in the meteorological satellite data. It is likely that the patterns represent internal ocean waves, a pathway to ocean mixing and climate. The prospect for using this detection algorithm in greatly expanded satellite global surveys of wave activity is outlined.
Runtime support for region-based memory management in Mercury
Authors: Quan Phan, Zoltan Somogyi, Gerda Janssens
Venue: Proceedings of the 7th international symposium on Memory management (2008)

Applying region-based memory management (RBMM) to logic programming languages poses a special challenge: backtracking can require regions removed during forward execution to be "resurrected", and any memory allocated during a computation that has been backtracked over must be recovered promptly, without waiting for the regions involved to come to the end of their life. In this paper, we describe how we implemented runtime support for RBMM in the logic programming language Mercury, whose specialized implementation of the language constructs involved in backtracking required equally specialized support. Our benchmark Mercury programs run about 25% faster on average with RBMM than with the usual Boehm garbage collector, and for some programs, RBMM achieves optimal memory consumption.
Dynamic word problems
Authors: G. S. Frandsen, P. B. Miltersen, S. Skyum
Venue: Proceedings of the 1993 IEEE 34th Annual Foundations of Computer Science (1993)

Let M be a fixed finite monoid. We consider the problem of implementing a data type containing a vector x=(x/sub 1/,x/sub 2/,...,x/sub n/)/spl isin/M/sup n/, initially (1,1,...,1) with two kinds of operations, for each i/spl isin/{1,...,n}, a/spl isin/M, an operation change/sub i,a/ which changes x/sub i/ to a and a single operation product returning /spl Pi//sub i=1//sup n/x/sub i/. This is the dynamic word problem. If we in addition for each j/spl isin/{1,...,n} have an operation prefix/sub j/ returning /spl Pi//sub i=1//sup j/x/sub i/, we talk about the dynamic prefix problem. We analyze the complexity of these problems in the cell probe or decision assignment tree model for two natural cell sizes, 1 bit and log n bits. We obtain a classification of the complexity based on algebraic properties of M.
Efficient Publish/Subscribe-Based Multicast for Opportunistic Networking with Self-Organized Resource Utilization
Authors: Janico Greifenberg, Dirk Kutscher
Venue: Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops (2008)

This paper presents a publish/subscribe-based multicast distribution infrastructure for DTN-based opportunistic networking environments. The distribution approach is designed to combine an effective distribution of content to interested nodes in the presence of resource constraints, mobility and unstable connectivity. By considering local resource constraints such as limited storage space and limited available bandwidth at opportunistic contacts as well as knowledge about interest for content in the network environment, nodes make local decisions about resource utilization and DTN bundle prioritization. Without further coordination, this approach uses the overall available network resources more effectively compared to other approaches such as epidemic forwarding. We have implemented this approach and have performed a series of measurements in mobile opportunistic networking scenarios under different configurations.
Application service placement in stochastic grid environments using learning and ant-based methods
Authors: Sharath Babu Musunoori, Geir Horn
Venue: Multiagent and Grid Systems (2007)

Achieving acceptable application performance in a grid environment remains a difficult challenge. In particular, this is true for applications composed of services that require certain criteria regarding quality to be fulfilled in order to satisfy users' needs. The problem considered here is the partitioning of application services onto the available execution nodes of a grid environment in such a way that they satisfy certain minimum criteria regarding quality. Fundamentally, this is an NP-hard problem. We propose three algorithms based on the concepts of learning automata and the metaphor of foraging ants. The algorithms naturally follow a decentralised multi-agent method for solving the service partitioning problem. Moreover, they establish a distributed problem-solving mechanism that does not require the use of a central controller. The proposed algorithms have been rigorously tested and evaluated through extensive simulations on randomly generated application services and grid environments. The results indicate that learning is an essential component for achieving scalability and efficiency in nature-inspired systems.
Discriminative probabilistic models for passage based retrieval
Authors: Mengqiu Wang, Luo Si
Venue: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (2008)

The approach of using passage-level evidence for document retrieval has shown mixed results when it is applied to a variety of test beds with different characteristics. One main reason of the inconsistent performance is that there exists no unified framework to model the evidence of individual passages within a document. This paper proposes two probabilistic models to formally model the evidence of a set of top ranked passages in a document. The first probabilistic model follows the retrieval criterion that a document is relevant if any passage in the document is relevant, and models each passage independently. The second probabilistic model goes a step further and incorporates the similarity correlations among the passages. Both models are trained in a discriminative manner. Furthermore, we present a combination approach to combine the ranked lists of document retrieval and passage-based retrieval. An extensive set of experiments have been conducted on four different TREC test beds to show the effectiveness of the proposed discriminative probabilistic models for passage-based retrieval. The proposed algorithms are compared with a state-of-the-art document retrieval algorithm and a language model approach for passage-based retrieval. Furthermore, our combined approach has been shown to provide better results than both document retrieval and passage-based retrieval approaches.
A computational framework for institutional agency
Authors: Guido Governatori, Antonino Rotolo
Venue: Artificial Intelligence and Law (2008)

This paper provides a computational framework, based on defeasible logic, to capture some aspects of institutional agency. Our background is Kanger-Lindahl-Pörn account of organised interaction, which describes this interaction within a multi-modal logical setting. This work focuses in particular on the notions of counts-as link and on those of attempt and of personal and direct action to realise states of affairs. We show how standard defeasible logic (DL) can be extended to represent these concepts: the resulting system preserves some basic properties commonly attributed to them. In addition, the framework enjoys nice computational properties, as it turns out that the extension of any theory can be computed in time linear to the size of the theory itself.
Learning to classify with missing and corrupted features
Authors: Ofer Dekel, Ohad Shamir
Venue: Proceedings of the 25th international conference on Machine learning (2008)

After a classifier is trained using a machine learning algorithm and put to use in a real world system, it often faces noise which did not appear in the training data. Particularly, some subset of features may be missing or may become corrupted. We present two novel machine learning techniques that are robust to this type of classification-time noise. First, we solve an approximation to the learning problem using linear programming. We analyze the tightness of our approximation and prove statistical risk bounds for this approach. Second, we define the online-learning variant of our problem, address this variant using a modified Perceptron, and obtain a statistical learning algorithm using an online-to-batch technique. We conclude with a set of experiments that demonstrate the effectiveness of our algorithms.