ppxscal/embedding_network huggingface dataset:
Towards a standardised framework for a multi-agent system approach for cooperation in an original design manufacturing company
Authors: Jiun-Shiung Lin, Chao Ou-Yang, Yeh-Chun Juan
Venue: International Journal of Computer Integrated Manufacturing (2008)

Design chain cooperation has attracted the attention of original design manufacturing (ODM) companies. Promoting design chain cooperation needs ODM companies and their design chain members to communicate and cooperate in a computer network environment. Owing to the characteristics of agents, the multi-agent system (MAS) has been considered a promising enabling technology for design chain cooperation and several all-purpose MAS development approaches have been proposed. Additionally, MAS have been applied to design chain cooperation in many studies. However, most of these studies focus on a specific design issue and lead to a particular solution. None provides a standardised MAS development framework for ODM companies to implement their design chain cooperation. Considering the industry standard, design chain operations reference-model (DCOR), newly proposed by Supply Chain Council has been recognised as a good tool for design process analysis, this research has integrated this industry standard into an existing MAS development approach to develop a standardised MAS development framework for ODM companies to implement design chain cooperation. The framework is composed of three phases. In the design chain cooperation requirements modelling phase, the desired design chain cooperation operations are described with DCOR. Next, the MAS specification development phase determines the specification of agents by using an existing MAS approach. Finally, the MAS implementation and test phase implements the MAS specification on JADE platform to support the cooperation requirements of the ODM company.
Multiresolution-based bilinear recurrent neural network
Authors: Dong-Chul Park
Venue: Knowledge and Information Systems (2009)

A multiresolution-based bilinear recurrent neural network (MBLRNN) is proposed in this paper. The proposed MBLRNN is based on the BLRNN that has robust abilities in modeling and predicting time series. The learning process is further improved by using a multiresolution-based learning algorithm for training the BLRNN so as to make it more robust for the prediction of time series data. The proposed MBLRNN is applied to the problems of network traffic prediction and electric load forecasting. Experiments and results on both practical problems show that the proposed MBLRNN outperforms both the traditional multilayer perceptron type neural network (MLPNN) and the BLRNN in the prediction accuracy.
An architecture for virtual solution composition and deployment in infrastructure clouds
Authors: Alexander V. Konstantinou, Tamar Eilam, Michael Kalantar, Alexander A. Totok, William Arnold, Edward Snible
Venue: Proceedings of the 3rd international workshop on Virtualization technologies in distributed computing (2009)

The combination of virtual server technology and the Infrastructure-as-a-Service (IaaS) approach to utility computing promises to revolutionize the way in which distributed software services are deployed. Server virtualization technology can be used to capture complete reusable software stacks, shifting the complexity of middleware installation and configuration from deployment to packaging. IaaS clouds provide a set of interfaces for controlling virtual machines and configuring their hardware and network environment, substantially reducing the complexity of service provisioning. In this paper we identify and tackle a few of the remaining challenges in fulfilling the promise of radical simplification of distributed software service composition and deployment. We propose an approach and architecture for composition and deployment of virtual software services in cloud environments. We introduce a virtual appliance model which treats virtual images as building blocks for composite solutions. Virtual appliances use a port abstraction to negotiate their communication parameters. A solution architect creates a virtual solution model by composing virtual appliances and defining requirements on the environment in a cloud-independent manner. The virtual solution model is transformed to a cloud-specific virtual solution deployment model used to generate a parameterized deployment plan that can be executed by an unskilled user. We validated our approach through a prototype implementation demonstrating flexible composition and automated deployment in our local lab virtualization infrastructure and in Amazon EC2.
Dynamic analysis of multiagent Q-learning with &epsilon;-greedy exploration
Authors: Eduardo Rodrigues Gomes, Ryszard Kowalczyk
Venue: Proceedings of the 26th Annual International Conference on Machine Learning (2009)

The development of mechanisms to understand and model the expected behaviour of multiagent learners is becoming increasingly important as the area rapidly find application in a variety of domains. In this paper we present a framework to model the behaviour of Q-learning agents using the &epsilon;-greedy exploration mechanism. For this, we analyse a continuous-time version of the Q-learning update rule and study how the presence of other agents and the &epsilon;-greedy mechanism affect it. We then model the problem as a system of difference equations which is used to theoretically analyse the expected behaviour of the agents. The applicability of the framework is tested through experiments in typical games selected from the literature.
Morphological annotation of the Lithuanian corpus
Authors: Vidas Daudaravičius, Erika Rimkutė, Andrius Utka
Venue: Proceedings of the Workshop on Balto-Slavonic Natural Language Processing: Information Extraction and Enabling Technologies (2007)

As the development of information technologies makes progress, large morphologically annotated corpora become a necessity, as they are necessary for moving onto higher levels of language computerisation (e. g. automatic syntactic and semantic analysis, information extraction, machine translation). Research of morphological disambiguation and morphological annotation of the 100 million word Lithuanian corpus are presented in the article. Statistical methods have enabled to develop the automatic tool of morphological annotation for Lithuanian, with the disambiguation precision of 94%. Statistical data about the distribution of parts of speech, most frequent wordforms, and lemmas, in the annotated Corpus of The Contemporary Lithuanian Language is also presented.
Piecewise linear approximations in nonconvex nonsmooth optimization
Authors: M. Gaudioso, E. Gorgone, M. F. Monaco
Venue: Numerische Mathematik (2009)

We present a bundle type method for minimizing nonconvex nondifferentiable functions of several variables. The algorithm is based on the construction of both a lower and an upper polyhedral approximation of the objective function. In particular, at each iteration, a search direction is computed by solving a quadratic program aiming at maximizing the difference between the lower and the upper model. A proximal approach is used to guarantee convergence to a stationary point under the hypothesis of weak semismoothness.
Perspective for the monetary integration of Romania into the European Union
Authors: Ionescu Alexandru, Voicu Andreea Raluca
Venue: Proceedings of the 10th WSEAS international conference on Mathematical and computational methods in science and engineering (2008)

The Romanian banking system has experienced a continuous development from the point of view of financial operators, offered products and services as well as from the point of view of geographic spreading in the country. Identifying the correlations and the degree of structural similarity between the national reality and the international one in the fiscal-financial system is essential in our analysis.
Synthesis of symmetric and asymmetric singly terminated elliptic ladder filters for multiplexing applications
Authors: José R. Montejo-Garai, Jorge A. Ruiz-Cruz, Jesús M. Rebollar
Venue: International Journal of RF and Microwave Computer-Aided Engineering (2009)

An extension of the Cauer ladder development for synthesizing singly terminated filters with symmetric and asymmetric responses is presented. Basically, a driving-point immittance including reactive constant elements is carried out in such a way that provides the transmission zeros. The reactive constant elements are introduced into the synthesis for two reasons. The first is to consider the possibility of the asymmetric position of transmission zeros in the real frequency axis. The second one is to obtain canonical forms, i.e. networks with the minimum number of elements in the case of symmetrical responses. To validate the proposed method, a filter with asymmetrical response has been synthesized, comparing different topologies for its use in multiplexers. This fact is illustrated with a Ku-band elliptic response diplexer designed in H-plane rectangular waveguide. &copy; 2009 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2009.
Internet Quality of Service Monitoring System
Authors: Xiaoyuan Ta
Venue:  (2007)

With the rapid growth of Internet in scale and complexity, providing quality-of-service (QoS) in Internet becomes increasingly important for network design, traffic engineering, troubleshooting and service-level-agreement (SLA) verification. However, the rapid expansion of Internet makes QoS monitoring a challenging task. The enormous amount of measurement data cause significant increase of cost and resource usage. To solve these problems, sampling techniques are employed in QoS monitoring systems to reduce the measurement complexity and cost. This book provides a comprehensive study of QoS monitoring for SLA verification. Special features include: A focus on the characteristics of QoS metrics as well as major measurement methodologies for monitoring these metrics; an extensive study of widely used sampling techniques; a easy-to-understand theoretical analysis on sampling performance; a detailed description of the proposed adaptive stratified sampling scheme; a particular explanation of the network performance monitoring software design; ample figures, tables and bibliographic references.
The promise of municipal WiFi and failed policies of inclusion: The disability divide
Authors: Paul M. A. Baker, Jarice Hanson, William N. Myhill
Venue: Information Polity (2009)

Wireless technologies, especially those deployed by municipalities, have been offered as one way to enhance access to society at large, including people with disabilities and others on the wrong side of the digital divide. One of the promises of municipal WiFi, is free or low-cost service promised in the public interest of citizens. This paper presents research on the current state of municipal wireless network design and policies with regard to people with disabilities in the United States. A comparative analysis was undertaken of a sample of 48 municipalities to ascertain the degree of accessibility to, or sensitivity of, municipal wireless systems, and three case studies were examined for the unforeseen effects of deploying municipal WiFi in different locations. Secondly, the effectiveness of goals toward eradicating the "disability divide" are analyzed to see if policies toward people with disabilities fair well as systems are deployed, and we discuss legal implications of municipal WiFi models. Because many people with disabilities are already affected by disparities in education and income, further marginalization of their communication and information access creates a greater barrier to their access to critical information needs, and participation in a community.
Lagrangian data assimilation for river hydraulics simulations
Authors: Marc Honnorat, J&#x00e9;r&#x00f4;me Monnier, Fran&#x00e7;ois-Xavier Le Dimet
Venue: Computing and Visualization in Science (2009)

We present a method to use Lagrangian data from remote sensing observation in a variational data assimilation process for a river hydraulics model based on the bidimensional shallow water equations. The trajectories of particles advected by the flow can be extracted from video images and are used in addition to classical Eulerian observations. This Lagrangian data brings information on the surface velocity thanks to an appropriate transport model. Numerical twin data assimilation experiments in an academic flow configuration demonstrate that this method makes it possible to significantly improve the identification of local bed elevation and initial conditions.
Predictive models for music
Authors: Jean-Francois Paiement, Yves Grandvalet, Samy Bengio
Venue: Connection Science (2009)

Modelling long-term dependencies in time series has proved very difficult to achieve with traditional machine-learning methods. This problem occurs when considering music data. In this paper, we introduce predictive models for melodies. We decompose melodic modelling into two subtasks. We first propose a rhythm model based on the distributions of distances between subsequences. Then, we define a generative model for melodies given chords and rhythms based on modelling sequences of Narmour features. The rhythm model consistently outperforms a standard hidden markov model (HMM) in terms of conditional prediction accuracy on two different music databases. Using a similar evaluation procedure, the proposed melodic model consistently outperforms an input/output HMM. Furthermore, these models are able to generate realistic melodies given appropriate musical contexts.
Using Electronic Institutions for Hospitals Chronic Disease Management and Purchasing System
Authors: Ashkan Musavi, Maite Lopez-Sanchez, Jordi Campos, Marc Esteva
Venue: Proceeding of the 2008 conference on Artificial Intelligence Research and Development: Proceedings of the 11th International Conference of the Catalan Association for Artificial Intelligence (2008)

Use of multi-agent systems (MAS) in health-care domains is increasing. MAS is an appropriate technique for many medical domains due to the characteristics of the problems in this area. According to the World Health Organization (WHO) health care systems worldwide are faced with the challenge of responding to the needs of people with chronic conditions such as diabetes, heart failure and mental illness. Chronic disease management (CDM) is a systematic approach to improve health care for people with chronic disease. Electronic Institutions (EI), as agent technology, is considered a suitable paradigm for complex environments providing structured frameworks for multi-agent systems to regulate agents' interactions. In this paper we introduce the HCDMP System which is an electronic institution to improve the Hospitals Chronic Disease Management and Purchasing processes. HCDMP system provides a number of recommended services to the patients and aims to advance the self care as a well proven and highly effective means of improving the care of chronic diseases.
Message authentication for wireless sensor networks
Authors: R. Moises Salinas, G. Gina Gallegos, S. Gonzalo Dduchen
Venue: Proceedings of the international conference on Computational and information science 2009 (2009)

This article describes an authentication protocol based on pairings for a wireless sensor network considering the execution cost. The proposed protocol provides a node authentication process based on identity, as well as a message authentication process using keyed hash functions. The evaluation of the protocol's execution cost is focused on message authentication operations in order to provide a notion of the network power consumption intensive message exchange between authenticated nodes.
Query consolidation: interpreting queries sent to independent heterogenous databases
Authors: Amihai Motro
Venue:  (2008)

This dissertation introduces the problem of query consolidation, which seeks to interpret a set of disparate queries submitted to independent databases with a single &ldquo;global&rdquo; query. The problem has multiple applications, from improving virtual database design, to aiding users in information retrieval, to protecting against inference of sensitive data from a seemingly innocuous set of apparently unrelated queries. The problem exhibits attractive duality with the much-researched problem of query decomposition, which has been addressed intensively in the context of multidatabase environments: How to decompose a query submitted to a virtual database into a set of local queries that are evaluated in individual databases. The new problem is set in the architecture of a canonical multidatabase system, using it in the &ldquo;reverse&rdquo; direction. The reversal is built on the assumption of conjunctive queries and source descriptions. A rational and efficient query decomposition strategy is also assumed, and this decomposition is reversed to arrive at the original query by analyzing the decomposed components. The process incorporates several steps where a number of solutions must be considered, due to the fact that query decomposition is not injective. Initially, the problem of finding the most likely join plan between component queries is investigated. This is accomplished by leveraging the referential constraints available in the underlying multidatabase, or by approximating these constraints from the data when not available. This approximation is done using the information theoretic concept of conditional entropy. Furthermore, the most likely join plans are enhanced by the expansion of their projections and adding precision to their selection constraints by estimating the selection constraints that would be applied to these consolidations offline. Additionally, the extraction of a set of queries related to the same retrieval task from an ongoing sequence of incoming queries is investigated. A conditional random field model is trained to segment and label incoming query sequences. Finally, the candidate consolidations are re-encapsulated with a genetic programming approach to find simpler intentional descriptions that are extensionally equivalent to discover the original intent of the query. The dissertation explains and discusses all of the above operations and validates the methods developed with experimentation on synthesized and real-world data. The results are highly encouraging and verify that the accuracy, time performance, and scalability of the methods would make it possible to exploit query consolidation in production environments.
Two-stage segmentation for detection of suspicious objects in aerial and long-range surveillance applications
Authors: Vladan Papic, Hrvoje Turic, Hrvoje Dujmic
Venue: Proceedings of the 10th WSEAS international conference on Automation & information (2009)

This paper presents a novel two-stage data segmentation approach for detection of artificial materials and objects in non-urban terrain. High resolution image of the unknown terrain taken with the digital camera from relatively large distance is divided into smaller sub-images for further processing. Each sub-image is segmented and information about obtained cluster centers is transferred to the next stage. Second segmentation stage uses information about cluster centers from all sub-images and applies the same clustering method to this data set. Finally, decision-making module evaluates all potential candidate segments and eventually proposes segments that have high possibility of presenting artificial material or object in the input image.
Neural networks for estimating the efficiency of a WWTP biologic treatment
Authors: Frédérik Thiery, Stéphane Grieu, Adama Traore, Maxime Estaben, Monique Polit
Venue: Proceeding of the 2005 conference on Artificial Intelligence Research and Development (2005)

This work is devoted to the study of the Canet-en-Roussillon (south of France) activated sludge wastewater treatment plant (WWTP) process and to the estimation of chemical parameters (influent and effluent chemical oxygen demand and suspended solids concentration) not easily on-line measurable. Their knowledge makes it possible to estimate both process efficiency and impact on natural environment. A tool based on neural networks, including an Elman recurrent network, Kohonen's self-organzing maps and a multi level perceptron has been developed. The Elman network is used for the prediction of the incoming WWTP influent flow, specially in case of rain events increasing the quantity of water to be treated. The Kohonen'self-organizing maps neural network is applied to analyse the multi-dimensional Canet-en-Roussillon process data, and to diagnose the inter-relationship of the process variables in an activated sludge WWTP. The multi level perceptron is used as COD and SS estimation tool.
Facial Geometry Estimation Using Photometric Stereo and Profile Views
Authors: Gary A. Atkinson, Melvyn L. Smith, Lyndon N. Smith, Abdul R. Farooq
Venue: Proceedings of the Third International Conference on Advances in Biometrics (2009)

This paper presents a novel method for estimating the three-dimensional shape of faces, facilitating the possibility of enhanced face recognition. The method involves a combined use of photometric stereo and profile view information. It can be divided into three principal stages: (1) An initial estimate of the face is obtained using four-source high-speed photometric stereo. (2) The profile is determined from a side-view camera. (3) The facial shape estimation is iteratively refined using the profile until an energy functional is minimised. This final stage, which is the most important contribution of the paper, works by continually deforming the shape estimate so that its profile is exact. An energy is then calculated based on the difference between the raw images and synthetic images generated using the new shape estimate. The surface normals are then adjusted according to energy until convergence. Several real face reconstructions are presented and compared to ground truth. The results clearly demonstrate a significant improvement in accuracy compared to standard photometric stereo.
Event Based Fairness for Video Surveillance Sensor Networks
Authors: Yunus Durmus, Bahri Atay Ozgovde, Cem Ersoy
Venue: Proceedings of the 8th International IFIP-TC 6 Networking Conference (2009)

With their ease of installation, infrastructureless mode of operation and flexible deployment style, Video Surveillance Sensor Networks (VSSNs) provide more opportunities than legacy surveillance methods for applications such as habitat monitoring and border surveillance. We argue that events created in the coverage area of a VSSN are the application level messaging units and propose to employ Event Based Fairness (EBF) which aims at a fair distribution of nodes' resources according to the event flows. We carried out simulation experiments to compare the application level performances of two different EBF implementations with that of FCFS based queueing. We observe that EBF enhances the VSSN performance in two ways: Firstly, when the video traffic due to the events created exceed the total capacity of the network, EBF increases the overall number of events properly reported to the sink. Secondly, EBF reduces the initial event reporting delay, thus decreases the response time to the occurring events within the network.
Evaluation techniques for zero-dimensional primary decomposition
Authors: Clémence Durvye
Venue: Journal of Symbolic Computation (2009)

This paper presents a new algorithm that computes the local algebras of the roots of a zero-dimensional polynomial equation system, with a number of operations in the coefficient field that is polynomial in the number of variables, in the evaluation cost of the equations and in a Bezout number.