ppxscal/embedding_network huggingface dataset:
Epidemic Alert & Response Framework and Technology Based on Spreading Dynamics Simulation
Authors: Chunhua Tian, Xun Zhang, Wei Ding, Rongzeng Cao
Venue: Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007 (2007)

There have been many research works on epidemic spreading by mathematical or simulation methods. However, to make it practical as a decision support tool for policy maker, it should be combined with business operation of epidemic control. Thus, an alert and response framework covering outbreak detection, policy analysis and policy execution is proposed in this paper. Key technologies for such a framework at modeling, analysis and data collection aspects are discussed. Sensitivity analysis is proposed for model verification and confidence analysis. Critical point identification is another important topic for control policy making by network topology analysis. The importance and feasibility of these technologies are illustrated by the experiment on Beijing SARS (Severe Acute Respiratory Syndrome) data in 2003.
Basic elements in the modelling of the problem of the image recognition and classification (IRC)
Authors: Ioan Ispas, Eduard Franti, Sanda Osiceanu, Marius Stoian
Venue: Proceedings of the 6th WSEAS International Conference on Applied Electromagnetics, Wireless and Optical (2008)

The problem of the image recognition and classification (IRC) based on the pattern recognition is of a paramount importance in lots of domains. The present paper discusses topics related with the complexity of the algorithms for image recognition and classification. This leads to some precise statements on the computational difficulty of the problem of the image recognition and classification (IRC).
Quiescence of Self-stabilizing Gossiping among Mobile Agents in Graphs
Authors: Toshimitsu Masuzawa, Sébastien Tixeuil
Venue: Proceedings of the 15th international colloquium on Structural Information and Communication Complexity (2008)

This paper considers gossiping among mobile agents in graphs: agents move on the graph and have to disseminate their initial information to every other agent. We focus on self-stabilizing solutions for the gossip problem, where agents may start from arbitrary locations in arbitrary states. Self-stabilization requires (some of the) participating agents to keep moving forever, hinting at maximizing the number of agents that could be allowed to stop moving eventually.This paper formalizes the self-stabilizing agent gossip problem, introduces the quiescence number (i.e., the maximum number of eventually stopping agents) of self-stabilizing solutions and investigates the quiescence number with respect to several assumptions related to agent anonymity, synchrony, link duplex capacity, and whiteboard capacity.
Appraisal of course learning outcomes using Rasch measurement: a case study in information technology education
Authors: Azlinah Mohamed, Azrilah Abd Aziz, Sohaimi Zakaria, Mohd. Saidfudin Masodi
Venue: Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases (2008)

The development of Rasch Measurement Model in social science educational measurement has rapidly expanded to other areas of education including technical and engineering fields. This paper is an attempt of a paradigm shift in testing and evaluation in IT education towards bio-based Rasch Model. It is believed compatibility exist with the fundamental measurement currently used based on Kuhn's explanation on the role of measurement; in particular, Learning Performance Measurement System (LPMS). These cannot be gleaned from textbooks in engineering science or statistics. Taking the paradigm shift, Faculty A, in one of Malaysian Institution of Higher Learning has embarked on the application of Rasch Model to measure the achievement of it's Course Learning Outcomes (CLO): Decision Support System, as a pre-requisite stipulated in the newly enacted Malaysia Qualification Framework, 2005 (MQF). Rasch Model tabulates the students; i.e. Person and Items on a Distribution Map (PIDM) which gives a precise overview of the student's achievement on a linear scale for measurement. Comparative analysis against the traditional histogram tabulation and simple mean shows that Rasch measurement was found to give a better exploratory depth in understanding problems in Information Technology education. Despite the small sample size, the students were clearly categorized according to the respective cognitive skills hence; CLO's which was structured based on Bloom's Taxonomy. Subsequently, it is possible to extend this approach in assessment of generic skills in students or professionals. This leads to a new paradigm in assessing competency of individuals using Rasch model.
A Workflow System Based on Meta Services in Grid Environments
Authors: Jinbock Lee, Sangkeon Lee, Jaeyoung Choi
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

A workflow consists of multiple and various working processes, and a workflow system manages and executes the workflow. If the workflow is managed in Grid environments, it is processed effectively by using many Grid resources. In this paper, we present a workflow system which manages workflow operations in Grid Environments. The workflow model is divided into three layers - service, flow, and task layer - for reusability and scalability of workflow. The service layer is based on a concept of Meta Services for increased reusability. Users can access the workflow through this service layer, and they execute the workflow by calling their services defined by the Meta Services. The flow layer and task layer are separated for independency between the task descriptions and workflow logic connecting the tasks. A GUI workflow editor is also provided for users' convenience. Therefore, this middleware uses Grid resources effectively and provides convenience to users.
Real time computer stress monitoring of piglets using vocalization analysis
Authors: D. J. Moura, W. T. Silva, I. A. Naas, Y. A. Tolón, K. A. O. Lima, M. M. Vale
Venue: Computers and Electronics in Agriculture (2008)

The analysis of animal vocalization allows the interpretation of stress pattern in a non-invasive way, and it may be used to assess welfare as well as health status and social adaptation. This paper presents the development of software to monitor and analyze distinct sounds emitted by piglets correlating the noise response with levels of stress for assessing welfare. The software was developed using Delphi5. Through the component UwaveIn the sound was recorded with an electronic microphone and set to a PC hardware that registered the signal of amplitude noise intensity, expressed in decibel (dB), converting them into bytes. Fourier Transform was used to describe the sound spectrum and transform it into frequency and intensity. Validation of the software was done in a commercial swine farm using piglets from sows' nursery. Five examples of vocalization were studied related to distinct levels of stress (fear and alertness) due to regular management procedures. The real time monitoring allowed the detection of piglet stress exposure through the surface point polynomial adjustment. The acoustic signal recorded in the software could be partitioned allowing an evaluation of the sonograph through time.
Exact Learning of Finite Unions of Graph Patterns from Queries
Authors: Rika Okada, Satoshi Matsumoto, Tomoyuki Uchida, Yusuke Suzuki, Takayoshi Shoudai
Venue: Proceedings of the 18th international conference on Algorithmic Learning Theory (2007)

A linear graph pattern is a labeled graph such that its vertices have constant labels and its edges have either constant or mutually distinct variable labels. An edge having a variable label is called a variable and can be replaced with an arbitrary labeled graph. Let ${\mathcal GPC}$ be the set of all linear graph patterns having a structural feature ${\mathcal C}$ like "having a tree structure", "having a two-terminal series parallel graph structure" and so on. The graph language GLc(g) of a linear graph pattern g in ${\cal GP}({\mathcal C})$ is the set of all labeled graphs obtained from g by substituting arbitrary labeled graphs having the structural feature ${\mathcal C}$ to all variables in g. In this paper, for any set ${\cal T_*}$ of m linear graph patterns in ${\cal GP}({\mathcal C})$, we present a query learning algorithm for finding a set S of linear graph patterns in ${\cal GP}({\mathcal C})$ with $\bigcup_{g\in{\cal T_*}}GLc{(g)}=\bigcup_{f\in S}GLc{(f)}$ in polynomial time using at most m + 1 equivalence queries and O(m(n + n 2)) restricted subset queries, where n is the maximum number of edges of counterexamples, if the number of labels of edges is infinite. Next we show that finite sets of graph languages generated by linear graph patterns having tree structures or two-terminal series parallel graph structures are not learnable in polynomial time using restricted equivalence, membership and subset queries.
Design of a Mobile Object Data Management Framework for Location-Enhanced Applications
Authors: Yoon-Ae Ahn
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

Location-enhanced applications must manage various context data and make the best use of this data for application service in ubiquitous environment. Conventional mobile object data management architecture did not consider process of context data. Therefore a new design of mobile object data manager is needed to process location context data. In this paper, we design a new mobile object data manager for context awareness service. A suggestion framework is consisted of data collector, context manager, knowledge base, inference engine, and mobile object database. It describes a form of rule base and a process of inference engine and mobile operator.
Fuzzy ARTMAP Neural Network for Classifying the Financial Health of a Firm
Authors: Anatoli Nachev
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

In this paper, an application, based on data from a popular dataset, shows in an empirical form the strengths and weaknesses of fuzzy ARTMAP neural networks as predictor of corporate bankruptcy. This is an advantageous approach enabling fast learning, self-determination of the network structure and high prediction accuracy. Experiments showed that the fuzzy ARTMAP outperforms statistical techniques and the most popular backpropagation MLP neural networks, all applied to the same dataset. An exhaustive search procedure over the Altman's financial ratios leads to the conclusion that two of them are enough to obtain the highest prediction accuracy. The experiments also showed that the model is not sensitive to outliers of the dataset. Our research is the first to use fuzzy ARTMAP neural networks for bankruptcy prediction.
Clonal Selection Approach with Mutations Based on Symmetric α-Stable Distributions for Non-stationary Optimization Tasks
Authors: Krzysztof Trojanowski
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

Efficiency of two mutation operators applied in a clonal selection based optimization algorithm AIIA for non-stationary tasks is investigated. In both operators traditional Gaussian random number generator was exchanged by &alpha;-stable random number generator and thus &alpha; became one of the parameters of the algorithm. Obtained results showed that appropriate tuning of the &alpha; parameter allows to outperform the results of algorithms with the traditional operators.
Controller Design Method of Gene Networks by Network Learning and Its Performance Evaluation
Authors: Yoshihiro Mori, Yasuaki Kuroe, Takehiro Mori
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

Investigating gene regulatory networks is important to understand mechanism of cellular functions. Recently, construction of gene networks having desired functions is of interest to many researchers because it is a complementary approach to understanding gene regulatory networks, and it could be the first step to control living cells. A synthesis method of gene networks based on given gene expression pattern sequences by network learning was already proposed. The objective of the paper is to apply the synthesis method to a controller design problem and to evaluate performance of the method. Some numerical experiments are given to evaluate the performance of the proposed method.
An Integrated Resolution of Joint Production and Maintenance Scheduling Problem in Hybrid Flowshop
Authors: Fatima Benbouzid-Sitayeb, Mourad Tirchi, Abid Mahloul
Venue: Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks (2007)

The article presents an integrated resolution of the joint production and maintenance scheduling problem in hybrid flowshop. Two resolution methods are used on the basis of a new coding to represent a joint production and maintenance scheduling: Taboo search where we proposed an algorithm for the generation of a joint initial solution and neighbourhood, and GA where we proposed new joint operators for crossover and mutation. Computational experiments are conducted on a large set of instances and the resulting genetic algorithm gives the best results so far.
WCET determination tool for embedded systems software
Authors: Carsten Albrecht, Roman Koch, Thilo Pionteck, Erik Maehle, Michael Werner, Rudolf Fuchsen
Venue: Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops (2008)

Embedded systems are special-purpose computer systems within environments such as automotive, avionic, telecommunication, etc. As these systems often perform time-critical control tasks, it is necessary to precisely predict their behaviour. Software-based solutions on the one hand provide high flexibility. On the other hand, especially when multiple software processes are to be executed concurrently on the same hardware, the software approach makes prediction more difficult. Predictions essentially rely on the knowledge of when a software process terminates. For a reasonable system architecture it has to be known at an early stage of design time to scale the system well. Dynamic factors such as cache usage, amount of data, and availability of shared resources have great impact on the result. Thus, tools are necessary to determine execution time as accurate as possible. The execution time is commonly approximated by determining the interval of best and worst case execution time (BCET & WCET). While the BCET can be roughly estimated by evaluating the shortest path the achievement of good approximations for the WCET is heavy. Considering all possible execution paths of the analysed program as well as the effects of the underlying architecture on the timing properties affected by pipelining and caches create a hard issue to solve. Nevertheless these figures are important in certification processes in the application fields given above as well as performance prediction and solving design issues. Another motivation is the lack of tools targeted to an intermediate level of abstraction. In general tools are re-targetable frameworks analysing high-level code with sophisticated algorithms or similar algorithms are applied to low-level code that lacks context and type information.
QoS-Adaptive Router Based on Per-Flow Management over NGN
Authors: Boyoung Rhee, Sungchol Cho, Sunyoung Han, Chun-Hyon Chang, Jung Guk Kim
Venue: Proceedings of the 6th IFIP WG 10.2 international workshop on Software Technologies for Embedded and Ubiquitous Systems (2008)

In the present paper, we designed a specific router which provides the required level of QoS over NGN(Next Generation Network) by controlling data flows. We called this router QoS-Adaptive router, which consists of two parts, a legacy routing part and a QoS guarantee routing part. In order to provide differentiated services, QoS-Adaptive router enables data taking broad bandwidth or data requiring high-level QoS to be processed immediately and not to be affected by other services. And, we used the definition of flow for per-flow management, and assigned new service types to the flow label field of IPv6 header so as to provide differentiated services according to the packet type. By doing this, we designed a distinguishing mechanism in order to adapt to NGN that concentrates upon QoS. We built not only a small network to test QoS-Adaptive router but also a simulation environment called OMNeT++, and we could verify the performance of QoS-Adaptive router supporting QoS based on subscriber and service levels.
Gas-fuel turbines controller design: a comparative Study
Authors: Addison Ríos-Bolivar, Genderson Gamboa, Francklin Rivas-Echeverría, Gloria Mousalli-Kayat
Venue: Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation (2008)

The gas-fuel turbines operation principle consists on a dynamic compressor that gives air to a combustion chamber where fuel is burned with excess air at a constant pressure. The combustion products are mixed with air excess for producing gas with energy at a sufficiently low temperature. The energized gas is expanded in a turbine that impels the air compressor and produces additional power as mechanical energy. Modeling and controlling these turbines constitute a high complexity system. Traditionally, gas-fuel turbines are controlled using Proportional-Integral-Derivative (PID) type controllers and optimization mechanisms are not studied. Therefore, this work studies the advanced control systems design for turbines, through the technologies identification and development methodologies available for the control of these systems. The designed controllers allow comparisons with any existent control application and produce optimal performance. In particular, a solution of easy handling and flexible to modifications is looked for. Thus, conventional control laws (PID, Lead-Lag, State feedback) and advanced control (optimal control, anti-windup PID) are designed according to the proposed operation specifications. A comparative study, through simulations is made, in order to establish orientations for selecting the best control technique for the gas-fuel turbines. The controllers are programmed through the Simulink-Matlab mathematical tool for generating the code in structured text corresponding to the control laws.
Preserving the consistency of distributed objects with real-time transactions
Authors: Laurent George, Pascale Minet
Venue: Proceedings of the 8th international conference on New technologies in distributed systems (2008)

In this paper, we study the problem of maintaining the consistency of distributed objects in real-time. Sporadic transactions sent by a set of clients must be processed by a set servers with a bounded end-to-end response time. We propose in this article a solution, following the state machine approach, based on a particular non-preemptive scheduling denoted EDF* (Earliest Deadline First *). The scheduling on the servers is based on timestamps set by the clients and on an end-to-end deadline to respect. The clock of clients and servers are &epsilon; synchronized. We establish in this article the feasibility conditions of the distributed real-time system that permits to grant the consistency of distributed objects and the respect of end-to-end deadlines associated to transactions in charge of read/write access to the objects.
Simple Clipping Algorithms for Reduced Convex Hull SVM Training
Authors: Jorge López, Álvaro Barbero, José R. Dorronsoro
Venue: Proceedings of the 3rd international workshop on Hybrid Artificial Intelligence Systems (2008)

It is well known that linear slack penalty SVM training is equivalent to solving the Nearest Point Problem (NPP) over the so-called μ-Reduced Convex Hulls, that is, convex combinations of the positive and negative samples with coefficients bounded by a μ < 1 value. In this work we give a simple approach to the classical Gilbert-Schlesinger-Kozinec (GSK) and Mitchell-Demyanov-Malozemov (MDM) algorithms for NPP that naturally leads to their updating pattern selection criteria and suggests an easy way to extend them to coefficient clipping algorithms for NPP over the μ-Reduced Convex Hulls. Although the extended GSK algorithm does not perform as well as the more complex recent proposal by Mavroforakis and Theodoridis, clipping MDM coefficient updates results in a fast and efficient algorithm.
Fast Multi-objective Scheduling of Jobs to Constrained Resources Using a Hybrid Evolutionary Algorithm
Authors: Wilfried Jakob, Alexander Quinte, Karl-Uwe Stucky, Wolfgang Süß
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

The problem tackled here combines three properties of scheduling tasks, each of which makes the basic task more challenging: job scheduling with precedence rules, co-allocation of restricted resources of different performances and costs, and a multi-objective fitness function. As the algorithm must come up with results within a few minutes runtime, EA techniques must be tuned to this limitation. The paper describes how this was achieved and compares the results with a common scheduling algorithm, the Giffler-Thompson procedure.
Evolutionary Clustering in Description Logics: Controlling Concept Formation and Drift in Ontologies
Authors: Nicola Fanizzi, Claudia D'Amato, Floriana Esposito
Venue: Proceedings of the 19th international conference on Database and Expert Systems Applications (2008)

We present a method based on clustering techniques to detect concept drift or novelty in a knowledge base expressed in Description Logics. The method exploits an effective and language-independent semi-distance measure defined for the space of individuals, that is based on a finite number of dimensions corresponding to a committee of discriminating features (represented by concept descriptions). In the algorithm, the possible clusterings are represented as strings of central elements (medoids, w.r.t. the given metric) of variable length. The number of clusters is not required as a parameter; the method is able to find an optimal choice by means of the evolutionary operators and of a fitness function. An experimentation with some ontologies proves the feasibility of our method and its effectiveness in terms of clustering validity indices. Then, with a supervised learning phase, each cluster can be assigned with a refined or newly constructed intensional definition expressed in the adopted language.
View-Dependent Real-Time Rendering of Large Outdoor Scenes
Authors: Xiangkun Zhao, Fengxia Li, Yufeng Chen, Shouyi Zhan
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02 (2008)

A new real-time point-based rendering method of large outdoor scenes is presented. Based on our interactive subdivide method, polygonal trees and other vegetation were converted to point-based models, and then different level of details of trees and other vegetation were created using hierarchical clustering. Different level of details of terrain were created using diamond tree backbone of ROAM2.0 method. Therefore, trees and other vegetation were rendered using point-based method on GPU and terrain was rendered using triangle based method. A compromise between terrain rendering performance and plant rendering quality was made to achieve ideal visual effect. By our method, the rendering primitive decreased and the fidelity of outdoor scenes was kept. Experiments show that our hybrid rendering method could render large outdoor scenes in real-time.