ppxscal/embedding_network huggingface dataset:
Software Engineering with Formal Methods: Experiences with the Development of a Storm Surge Barrier Control System
Authors: Klaas Wijbrans, Franc Buve, Robin Rijkers, Wouter Geurts
Venue: Proceedings of the 15th international symposium on Formal Methods (2008)

This paper revisits the experiences with the use of formal methods in the development of the control system for the Maeslant Kering. The Maeslant Kering is the movable barrier which has to protect Rotterdam from floodings while, at almost the same time, not restricting shipping traffic to the port of Rotterdam. The control system, called BOS, completely autonomously decides about closing and opening of the barrier and, when necessary, also performs these tasks without human intervention. BOS is a safety-critical software system of the highest Safety Integrity Level according to the IEC 61508 standard. One of the reliability increasing techniques used during its development is formal methods. This paper revisits the earlier published experiences with the project after the system is in operation for ten years and has performed its first autonomous barrier operation on November 11th, 2007.
Goal-Oriented Interaction Protocols
Authors: Lars Braubach, Alexander Pokahr
Venue: Proceedings of the 5th German conference on Multiagent System Technologies (2007)

Developing agent applications is a complex and difficult task due to a variety of reasons. One key aspect making multi-agent systems more complicated than traditional applications is that interaction behavior is based on elaborate communication forms such as negotiations instead of simple method calls. Aimed at facilitating the specification and usage of agent communication, agent research resulted e.g. in the definition and standardization of several general purpose interaction protocols such as contract-net or English auction. Nevertheless, the usage of these valuable interaction patterns currently forces developers to concentrate on the details of message passing instead of thinking in terms of the application domain. To alleviate this problem in this paper a goal-oriented approach is proposed, which hides message passing details allowing developers to concentrate on the domain aspects of protocols. The new approach is based on the BDI agent model and is implemented within the Jadex agent framework. The advantages of the goal-based interaction handling are further illustrated by an example application.
Memetic Algorithm Based on a Constraint Satisfaction Technique for VRPTW
Authors: Marco A. Cruz-Chávez, Ocotlán Díaz-Parra, David Juárez-Romero, Martín G. Martínez-Rangel
Venue: Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing (2006)

In this paper a Memetic Algorithm (MA) is proposed for solving the Vehicles Routing Problem with Time Windows (VRPTW) multi-objective, using a constraint satisfaction heuristic that allows pruning of the search space to direct a search towards good solutions. An evolutionary heuristic is applied in order to establish the crossover and mutation between sub-routes. The results of MA demonstrate that the use of Constraints Satisfaction Technique permits MA to work more efficiently in the VRPTW.
Leakage-Aware Energy Efficient Scheduling for Fixed-Priority Tasks with Preemption Thresholds
Authors: Xiaochuan He, Yan Jia
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Dynamic Voltage Scaling (DVS), which adjusts the clock speed and supply voltage dynamically, is an effective technique in reducing the energy consumption of embedded real-time systems. However, most existing DVS algorithms focus on reducing the energy consumption of CPU only, ignoring their negative impacts on task scheduling and system wide energy consumption. In this paper, we address one of such side effects, an increase in task preemptions due to DVS. We present energy-efficient Fixed-priority with preemption threshold (EE-FPPT) scheduling algorithm to solve this problem. First, we propose an appropriate schedulability analysis, based on response time analysis, for supporting energy-efficient FPPT scheduling in hard real-time systems. Second, we prove that a task set achieves the minimal energy consumptions under Maximal Preemption Threshold Assignment (MPTA).
Computers Analyze Energy Problems
Authors: W. Myers
Venue: Computer (1978)

The energy crisis of the 1970's, perhaps fortunately, is occurring at a time when the computational power of the computer is far enough advanced to contribute significantly to the solution of the resulting problems. Four technical sessions at NCC last month sampled some of these contributions and forecast some of the requirements energy problems will place on computers in the decade ahead. 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
Growing Algorithm of Laguerre Orthogonal Basis Neural Network with Weights Directly Determined
Authors: Yunong Zhang, Tongke Zhong, Wei Li, Xiuchun Xiao, Chenfu Yi
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence (2008)

Determination of appropriate neural-network (NN) structure is an important issue for a given learning or training task since the NN performance depends much on it. To remedy the weakness of conventional BP neural networks and learning algorithms, a new Laguerre orthogonal basis neural network is constructed. Based on this special structure, a weights-direct-determination method is derived, which could obtain the optimal weights of such a neural network directly (or to say, just in one step). Furthermore, a growing algorithm is presented for determining immediately the smallest number of hidden-layer neurons. Theoretical analysis and simulation results substantiate the efficacy of such a Laguerre-orthogonal-basis neural network and its growing algorithm based on the weights-direct-determination method.
Domain Account Model
Authors: Giulio Galiero, Paolo Roccetti, Andrea Turli
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

The use of gateways as a user-friendly way to access the Grid is increasing, as evidenced, for example, by the popularity of TeraGrid Science Gateways. Such gateways, however, imply additional layers of software abstraction, which in turn implies more levels of trust delegation - thus compounding security problems of enforcing trust at different layers.In this paper we present the Domain Account Model (DAM), extending the Shibboleth and GridShib ones to enable interoperability between identity-based (i.e. GSI) and attribute-based (i.e. SAML) Grid authorization mechanisms, to ease the administration of user attributes by allowing domain separation between Real and Virtual Organizations (VO), and to improve the trust management by means of the OAuth protocol.
Policy-based management of middleware for distributed sensor applications
Authors: Nelson Matthys, Sam Michiels, Wouter Joosen, Pierre Verbaeten
Venue: Proceedings of the 6th workshop on Middleware for network eccentric and mobile applications (2008)

Contemporary distributed software systems have become extremely heterogeneous, dynamic and large-scale. They include back-end servers, regular PCs and various mobile and embedded devices, as well as diverse network infrastructures, such as sensor networks. However, these factors make manual management of the distributed system and its installed software extremely complex. In this position paper we seek for an architectural solution for a middleware that supports autonomic management of these distributed software systems through a policy-based approach. This middleware architecture suggests the introduction of a closed control loop on top of a highly customizable middleware architecture. We describe the key research challenges that still remain open for discussion and propose a plan to tackle them.
Horseshoe Dynamics in a Small Hyperchaotic Neural Network
Authors: Qingdu Li, Xiao-Song Yang
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

This paper studies the hyperchaotic dynamics in a four dimensional Hopfield neural network. A topological horseshoe on a three dimensional block is found in a carefully chosen Poincaré section hyperplane of the ordinary differential equations. Numerical studies show that there exist two-directional expansions in this horseshoe map. In this way, a computer-assisted verification of hyperchaoticity of this neural network is presented by virtue of topological horseshoe theory.
Fault Tolerance in the Biswapped Network
Authors: Wenhong Wei, Wenjun Xiao
Venue: Proceedings of the 8th international conference on Algorithms and Architectures for Parallel Processing (2008)

Biswapped network (BSN) is a new topology for interconnection networks in multiprocessor systems. BSN is built of 2n copies of an n-node basic network and total nodes are 2n 2, and its basic network may be hypercube, mesh and other networks, hence we can construct BSN-Hypercube and BSN-Mesh by using hypercube and mesh as basic network. Some topological properties of BSN have been investigated, and some algorithms have been developed on the BSN such as sorting and matrix multiplication etc. In this paper, we discuss the fault tolerant issue of the BSN including network connectivity and fault diameter.
Automatic Labeling of Colonoscopy Video for Cancer Detection
Authors: Fernando Vilariño, Gerard Lacey, Jiang Zhou, Hugh Mulcahy, Stephen Patchett
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

The labeling of large quantities of medical video data by clinicians is a tedious and time consuming task. In addition, the labeling process itself is rigid, since it requires the expert's interaction to classify image contents into a limited number of predetermined categories. This paper describes an architecture to accelerate the labeling step using eye movement tracking data. We report some initial results in training a Support Vector Machine (SVM) to detect cancer polyps in colonoscopy video, and a further analysis of their categories in the feature space using Self Organizing Maps (SOM). Our overall hypothesis is that the clinician's eye will be drawn to the salient features of the image and that sustained fixations will be associated with those features that are associated with disease states.
ReCrash: Making Software Failures Reproducible by Preserving Object States
Authors: Shay Artzi, Sunghun Kim, Michael D. Ernst
Venue: Proceedings of the 22nd European conference on Object-Oriented Programming (2008)

It is very hard to fix a software failure without being able to reproduce it. However, reproducing a failure is often difficult and time-consuming. This paper proposes a novel technique, ReCrash, that generates multiple unit tests that reproduce a given program failure. During every execution of the target program, ReCrash stores partial copies of method arguments in memory. If the program fails (e.g., crashes), ReCrash uses the saved information to create unit tests reproducing the failure.We present ReCrashJ, an implementation of ReCrash for Java. ReCrashJ reproduced real crashes from Javac, SVNKit, Eclipsec, and BST. ReCrashJ is efficient, incurring 13%---64% performance overhead. If this overhead is unacceptable, then ReCrashJ has another mode that has negligible overhead until a crash occurs and 0%---1.7% overhead until the crash occurs for a second time, at which point the test cases are generated.
The ACMS, a model-driven approach to new interactive television services production and delivery
Authors: Roberto Del Pero, Fulvio Negro, Luca Vignaroli
Venue: Proceedings of the 2008 Ambi-Sys workshop on Ambient media delivery and interactive television (2008)

This paper contains a brief report of the analysis of new requirements raised by Interactive Television and then gives a possible solution by defining a model-driven environment for the production and delivery of interactive television applications and their content.
A Comparative Study of Synchrony Measures for the Early Detection of Alzheimer's Disease Based on EEG
Authors: Justin Dauwels, François Vialatte, Andrzej Cichocki
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

It has repeatedly been reported in the medical literature that the EEG signals of Alzheimer's disease (AD) patients are less synchronous than in age-matched control patients. This phenomenon, however, does at present not allow to reliably predict AD at an early stage, so-called mild cognitive impairment (MCI), due to the large variability among patients. In recent years, many novel techniques to quantify EEG synchrony have been developed; some of them are believed to be more sensitive to abnormalities in EEG synchrony than traditional measures such as the cross-correlation coefficient. In this paper, a wide variety of synchrony measures is investigated in the context of AD detection, including the cross-correlation coefficient, the mean-square and phase coherence function, Granger causality, the recently proposed corr-entropy coefficient and two novel extensions, phase synchrony indices derived from the Hilbert transform and time-frequency maps, information-theoretic divergence measures in time domain and time-frequency domain, state space based measures (in particular, non-linear interdependence measures and the S-estimator), and at last, the recently proposed stochastic-event synchrony measures. For the data set at hand, only two synchrony measures are able to convincingly distinguish MCI patients from age-matched control patients (p < 0.005), i.e., Granger causality (in particular, full-frequency directed transfer function) and stochastic event synchrony (in particular, the fraction of non-coincident activity). Combining those two measures with additional features may eventually yield a reliable diagnostic tool for MCI and AD.
Fuzzy Cognitive Layer in RoboCupSoccer
Authors: Susana Muñoz-Hernandez, Wiratna Sari Wiguna
Venue: Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing (2007)

RoboCupSoccer domain has several leagues which varies in the rule of play such as specification of players, number of players, field size, and time length. Nevertheless, each RoboCup league is a variant of a soccer league and therefore they are based on some basic rules of soccer. A layered design of agents system presented in [1] shows a modular approach to build control for a team of robots participating in RoboCupSoccer E-League. Based on this design, we propose a generalized architecture offering flexibility to switch between leagues and programming language while maintaining Prolog as cognitive layer. Prolog is a perfect tool to design strategies for soccer players using simple rules close to human reasoning. Sometimes this reasoning needs to deal with uncertainty, fuzziness or incompleteness of the information. In these cases it is useful Fuzzy Prolog [11,8,10,9]. In this paper we propose to use a combination of Prolog (that is crisp) and Fuzzy Prolog to implement the cognitive layer in RoboCupSoccer, which has the advantage of incorporating as conventional logic as fuzzy logic in this layer. A prototype of a team based on this architecture has been build for RoboCup Soccer Simulator, and we show that this approach provides a convenient way of incorporating a team strategy in high level (human-like) manner, where technical details are encapsulated and fuzzy information is represented.
Feature Weighted Rival Penalized EM for Gaussian Mixture Clustering: Automatic Feature and Model Selections in a Single Paradigm
Authors: Yiu-Ming Cheung, Hong Zeng
Venue: Computational Intelligence and Security: International Conference, CIS 2006, Guangzhou, China, November 3-6, 2006, Revised Selected Papers (2007)

Recently, the Rival Penalized Expectation-Maximization (RPEM) algorithm (Cheung 2004 2005) has demonstrated its outstanding capability to perform the model selection automatically in the context of density mixture models. Nevertheless, the RPEM is unable to exclude the irrelevant variables (also called features) from the clustering process, which may degrade the algorithm's performance. In this paper, we adopt the concept of feature salience (Law et al. 2004) as the feature weight to measure the relevance of features to the cluster structure in the subspace, and integrate it into the RPEM algorithm. The proposed algorithm identifies the irrelevant features and estimates the number of clusters automatically and simultaneously in a single learning paradigm. Experiments show the efficacy of the proposed algorithm on both synthetic and benchmark real data sets.
Fuzzy Metrics Application in Video Spatial Deinterlacing
Authors: Julio Riquelme, Samuel Morillas, Guillermo Peris-Fajarnés, Dolores Castro
Venue: Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory (2007)

Spatial methods play a relevant role in the deinterlacing matter. Common spatial algorithms often introduce artifacts like crawling, alias and blur in the output signal. In this paper a new spatial deinterlacing method for color image sequences that introduces less artifacts than other common methods is proposed. It uses fuzzy metrics to select the current pixel from a group of the nearest pixels taking into account how much chromatically similar and spatially close are these pixels to each other. Experimental results show that the proposed algorithm outperforms common spatial algorithms in various video sequences.
Supporting Users in Creating Pedagogically Sound Personalised Learning Objects
Authors: Aoife Brady, Owen Conlan, Vincent Wade, Declan Dagger
Venue: Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems (2008)

Successful eLearning is predicated on the application of pedagogies appropriate to online education that respond to the capabilities and needs of the learners. Typically, designing and assembling personalized learning objects that respond to the pedagogical needs of a variety of different learners is an expensive and time-consuming process requiring both domain and educational expertise. Educators have the domain expertise and formal or informal pedagogical knowledge to create quality learning objects. However, they lack the tools and often the specific knowledge of online pedagogical approaches that make it time efficient for them to do so. This paper describes the motivation behind, the workflow supported by and the evaluation of the LO Generator, a tool that offers personalized support and scaffolding for users, who are not necessarily content creation or pedagogical experts, in assembling pedagogically sound personalized learning objects.
High-Dimensional Clustering Method for High Performance Data Mining
Authors: Jae-Woo Chang, Hyun-Jo Lee
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

Many clustering methods are not suitable as high-dimensional ones because of the so-called `curse of dimensionality' and the limitation of available memory. In this paper, we propose a new high-dimensional clustering method for the high performance data mining. The proposed high-dimensional clustering method provides efficient cell creation and cell insertion algorithms using a space-partitioning technique, as well as makes use of a filtering-based index structure using an approximation technique. In addition, we compare the performance of our high-dimensional clustering method with the CLIQUE method which is well known as an efficient clustering method for highdimensional data. The experimental results show that our high-dimensional clustering method achieves better performance on cluster construction time and retrieval time than the CLIQUE.
Efficient Data Collection and Selective Queries in Sensor Networks
Authors: Lars Kulik, Egemen Tanin, Muhammad Umer
Venue: GeoSensor Networks: Second International Conference, GSN 2006, Boston, MA, USA, October 1-3, 2006, Revised Selected and Invited Papers (2008)

Efficient data collection in wireless sensor networks (SNs) plays a key role in power conservation. It has spurred a number of research projects focusing on effective algorithms that reduce power consumption with effective in-network aggregation techniques. Up to now, most approaches are based on the assumption that data collection involves all nodes of a network. There is a large number of queries that in fact select only a subset of the nodes in a SN. Thus, we concentrate on selective queries, i.e., queries that request data from a subset of a SN. The task of optimal data collection in such queries is an instance of the NP-hard minimal Steiner tree problem. We argue that selective queries are an important class of queries that can benefit from algorithms that are tailored for partial node participation of a SN. We present an algorithm, called Pocket Driven Trajectories (PDT), that optimizes the data collection paths by approximating the global minimal Steiner tree using solely local spatial knowledge. We identify a number of spatial factors that play an important role for efficient data collection, such as the distribution of participating nodes over the network, the location and dispersion of the data clusters, the location of the sink issuing a query, as well as the location and size of communication holes. In a series of experiments, we compare performance of well-known algorithms for aggregate query processing against the PDT algorithm in partial node participation scenarios. To measure the efficiency of all algorithms, we also compute a near-optimal solution, the globally approximated minimal Steiner tree. We outline future research directions for selective queries with varying node participation levels, in particular scenarios in which node participation is the result of changing physical phenomena as well as reconfigurations of the SN itself.
Network-Aware Replica Optimization in the SCoPE Grid Infrastructure
Authors: Francesco Palmieri, Silvio Pardi
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

In a Data Grid, replication of data is critical for maximizing the overall job throughput. Such replication involves the creation of copies of data files at different sites according to specific Replica Optimization strategies that define when and where replicas should be created or deleted on a per-site basis, and which replicas should be used by Grid jobs. To be really effective these strategies have to take into account the available network bandwidth as a primary resource, prior to any consideration about storage or processing power. We present a novel replica management service, integrated within the GlueDomains active network monitoring architecture, designed and implemented within the centralized collective middleware framework of the SCoPE project to provide network-aware replica optimization for data intensive applications.
A note on the paper “Normal based subdivision scheme for curve design” by Xunnian Yang
Authors: Luming Liang, Huanxi Zhao, Beiji Zou
Venue: Computer Aided Geometric Design (2008)

In a recent paper (Computer Aided Geometric Design 23 (3), 243-260), Yang presented a novel kind of nonlinear and geometry driven subdivision scheme for curve interpolation. The author declared that this scheme was shape preserving when all free parameters were explicitly selected by some specific rules. In this note, we demonstrate that the author (a) ignored the consistency of the definition to straight edges, and also (b) did not give a perfect scheme and proof for the shape preserving subdivision.