ppxscal/embedding_network huggingface dataset:
Minimizing Cycle Time of the Flow Line --- Genetic Approach with Gene Expression
Authors: Paweł Dąbrowski, Jarosław Pempera, Czesław Smutnicki
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

This paper deals with the flow-shop scheduling problem with no-store policy and minimal cycle time criterion. The model and some properties of the problem have been presented. To solve the problem, we propose new genetic algorithm equipped with auxiliary gene expression mechanism, which creates offspring using genetic information from both parents as well as asleep information from ancestors (grand- father, grand grandfather). The presented computational tests proved superiority of the proposed approach over traditional, basic GA scheme.
Combination of Multiple Segmentations by a Random Walker Approach
Authors: Pakaket Wattuya, Xiaoyi Jiang, Kai Rothaus
Venue: Proceedings of the 30th DAGM symposium on Pattern Recognition (2008)

In this paper we propose an algorithm for combining multiple image segmentations to achieve a final improved segmentation. In contrast to previous works we consider the most general class of segmentation combination, i.e. each input segmentation can have an arbitrary number of regions. Our approach is based on a random walker segmentation algorithm which is able to provide high-quality segmentation starting from manually specified seeds. We automatically generate such seeds from an input segmentation ensemble. Two applications scenarios are considered in this work: Exploring the parameter space and segmenter combination. Extensive tests on 300 images with manual segmentation ground truth have been conducted and our results clearly show the effectiveness of our approach in both situations.
The Breast Biomechanics Reference State for Multi-modal Image Analysis
Authors: Vijay Rajagopal, Martyn P. Nash, Ralph P. Highnam, Poul M. Nielsen
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

Multi-modality imaging of the breast is becoming increasingly common, and yet combining the information from the different images is difficult due to differences in the basic imaging physics, geometry, and the loading conditions under which the breast is imaged. A key step in moving between these images will be the establishment of a computable and well-understood common reference state. The reference state we use is of the breast in an unloaded configuration, devoid of gravitational and breast compression effects. In this paper we show how breast MR images can be transformed into, and then out of, this reference state to reconstruct configurations associated with other imaging orientations. Finite element models of the breasts of two volunteers were fitted to data derived from MR images of the prone gravity-loaded configurations. These personalized models were used to predict the unloaded and supine gravity-loaded configurations and compared to MR images acquired for these states. The unloaded states were predicted with surface RMS errors of 4.2 mm and 4.1 mm for the two volunteers, whilst the supine gravity-loaded states were predicted with RMS errors of 8.4 mm and 7.7 mm. We demonstrate the transformations into and out of the reference state and discuss clinical applications of these methods.
Modified Particle Swarm Optimizer with Adaptive Dynamic Weights for Cancer Combinational Chemotherapy
Authors: Harish Chandra Soundararajan, Jagannathan Raman, R. Muthucumaraswamy
Venue: Proceedings of the 4th international conference on Advanced Data Mining and Applications (2008)

Cancer combinational chemotherapy is a complex treatment process that requires balancing the administration of anti-cancer drug to reduce tumor size with the adverse toxic side effects caused by these drugs. Methods of computational optimization like Genetic Algorithm (GA) and Canonical Particle Swarm Optimization (CPSO) have been used to strike the right balance. The purpose of this paper is to study how an alternative optimization technique - Modified Particle Swarm Optimizer (MPSO) - can be used for finding optimal chemotherapeutic treatments in an efficient manner. Comparison of its performance with the other existing algorithms of MPSO has been shown.
Analysis of Role Behavior in Collaborative Network Learning
Authors: Xiaoshuang Xu, Jun Zhang, Egui Zhu, Feng Wang, Ruiquan Liao, Kebin Huang
Venue: Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment (2008)

In the collaborative learning environment, role behavior brings on occurrence, development, and disappearance of collaborative learning. In this paper, we first introduce WF-net to describe role behavior in collaborative learning, and then indicate relations among some notions in collaborative learning. We focus on dynamic organization of learning pattern by awareness of role. We represent a mechanism of generating tasks oriented role to ensure persistence of collaborative learning.
Security and Service Quality Analysis for Cluster-Based Wireless Sensor Networks
Authors: Emrah Tomur, Y. Murat Erten
Venue: Proceedings of the 5th international conference on Wired/Wireless Internet Communications (2007)

In this study, we analyze security and quality of service (QoS) issues in cluster-based wireless sensor networks (WSN). Taking spatial resolution as the main QoS metric and limiting the security definition to data integrity and authentication, we present a control strategy to maintain desired QoS and security levels during the entire operation of a cluster-based sensor network. Besides, our proposed strategy considers some other WSN QoS attributes such as coverage, packet collision and system lifetime. It provides sufficient coverage by statistical means and minimizes packet loss due to collisions by employment of a slotted MAC (media access control) scheme. It also tries to maximize the operational lifetime of the sensor network by a power conserving scheme which make all sensors participate equally. In this study, we also determine the best tradeoff between security and spatial resolution.
Human Whistle Detection and Frequency Estimation
Authors: Mikael Nilsson, Josef Ström Bartunek, Jörgen Nordberg, Ingvar Claesson
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 5 - Volume 05 (2008)

Human whistle could be a way to perform activation of different kind of devices, for example turn on and off a light in a smart room. Therefore, in this paper a human whistle detection and frequency estimation system is presented. Further, an investigation of human whistling and a robust non-linear feature extraction is presented. A system for robust performance due to sensor change and various noise situations is proposed using these features. Experiments in various noise situations are conducted.
Neural Algebra and Consciousness: A Theory of Structural Functionality in Neural Nets
Authors: Erwin Engeler
Venue: Proceedings of the 3rd international conference on Algebraic Biology (2008)

Thoughts are spatio-temporal patterns of coalitions of firing neurons and their interconnections. Neural algebras represent these patterns as formal algebraic objects, and a suitable composition operation reflects their interaction. Thus, a neural algebra is associated with any neural net. The present paper presents this formalization and develops the basic algebraic tools for formulating and solving the problem of finding the neural correlates of concepts such as reflection, association, coordination, etc. The main application is to the notion of consciousness, whose structural and functional basis is made explicit as the emergence of a set of solutions to a fixpoint equation.
Epileptic Seizure Classification Using Neural Networks with 14 Features
Authors: Rui P. Costa, Pedro Oliveira, Guilherme Rodrigues, Bruno Leitão, António Dourado
Venue: Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II (2008)

Epilepsy is one of the most frequent neurological disorders. The main method used in epilepsy diagnosis is electroencephalogram (EEG) signal analysis. However this method requires a time-consuming analysis when made manually by an expert due to the length of EEG recordings. This paper proposes an automatic classification system for epilepsy based on neural networks and EEG signals. The neural networks use 14 features (extracted from EEG) in order to classify the brain state into one of four possible epileptic behaviors: inter-ictal, pre-ictal, ictal and pos-ictal. Experiments were made in a (i) single patient (ii) different patients and (ii) multiple patients, using two datasets. The classification accuracies of 6 types of neural networks architectures are compared. We concluded that with the 14 features and using the data of a single patient results in a classification accuracy of 99%, while using a network trained for multiple patients an accuracy of 98% is achieved.
A Filter-Refinement Scheme for 3D Model Retrieval Based on Sorted Extended Gaussian Image Histogram
Authors: Zhiwen Yu, Shaohong Zhang, Hau-San Wong, Jiqi Zhang
Venue: Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition (2007)

In this paper, we propose a filter-refinement scheme based on a new approach called Sorted Extended Gaussian Image histogram approach (SEGI) to address the problems of traditional EGI. Specifically, SEGI first constructs a 2D histogram based on the EGI histogram and the shell histogram. Then, SEGI extracts two kinds of descriptors from each 3D model: (i) the descriptor from the sorted histogram bins is used to perform approximate 3D model retrieval in the filter step, and (ii) the descriptor which records the relations between the histogram bins is used to refine the approximate results and obtain the final query results. The experiments show that SEGI outperforms most of state-of-art approaches (e.g., EGI, shell histogram) on the public Princeton Shape Benchmark.
Technology for just-in-time in-situ learning of facial affect for persons diagnosed with an autism spectrum disorder
Authors: Miriam Madsen, Rana el Kaliouby, Matthew Goodwin, Rosalind Picard
Venue: Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility (2008)

Many first-hand accounts from individuals diagnosed with autism spectrum disorders (ASD) highlight the challenges inherent in processing high-speed, complex, and unpredictable social information such as facial expressions in real-time. In this paper, we describe a new technology aimed at helping people capture, analyze, and reflect on a set of social-emotional signals communicated by facial and head movements in live social interaction that occurs with their everyday social companions. We describe our development of a new combination of hardware using a miniature camera connected to an ultramobile PC together with custom software developed to track, capture, interpret, and intuitively present various interpretations of the facial-head movements (e.g., presenting that there is a high probability the person looks "confused"). This paper describes this new technology together with the results of a series of pilot studies conducted with adolescents diagnosed with ASD who used the technology in their peer-group setting and contributed to its development via their feedback.
ALOA: A Web Services Driven Framework for Automatic Learning Object Annotation
Authors: Mohamed Amine Chatti, Nanda Firdausi Muhammad, Matthias Jarke
Venue: Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts (2008)

Generating learning object metadata is a complex task to be done manually. An automated approach is therefore required. This work presents the concepts and ideas behind the automatic generation of metadata. We propose a Web Services driven framework for IEEE LOM-compliant automatic learning object annotation called ALOA. The primary focus has been on the flexibility and extensibility of the framework, such that new metadata generation services can easily be plugged into the basic system.
Some Results on the Arithmetic Correlation of Sequences
Authors: Mark Goresky, Andrew Klapper
Venue: Proceedings of the 5th international conference on Sequences and Their Applications (2008)

In this paper we study various properties of arithmetic correlations of sequences. Arithmetic correlations are the with-carry analogs of classical correlations. Here we analyze the arithmetic autocorrelations of non-binary ¿-sequences, showing that they are nearly optimal. We analyze the expected auto- and cross-correlations of sequences with fixed shift. We study sequences with the arithmetic shift and add property, showing that they are exactly the ¿-sequences with prime connection element.
Development of Fault-Tolerant Software Systems Based on Architectural Abstractions
Authors: Patrick H. Brito, Rogério Lemos, Cecília M. Rubira
Venue: Proceedings of the 2nd European conference on Software Architecture (2008)

The incorporation of fault tolerance into systems normally increases their complexity, which consequently makes their analysis more difficult. This paper discusses how architectural abstractions can be effective in developing fault-tolerant software systems. Depending on the fault model and the resources available, different abstractions can be employed for representing issues that are related to fault tolerance, such as error detection, and error and fault handling. These architectural abstractions, and their internal views, can be instantiated into concrete components and connectors for designing fault-tolerant software architectures. Since structural and behavioural properties associated with these abstractions are formally specified, the process of verifying and validating software architectures can be automated. In this paper, we show how appropriate architectural abstractions and a recursive process can facilitate the architectural modelling and analysis of fault-tolerant software systems. The feasibility of the proposed approach is demonstrated in the context of a critical real-time application.
Working on the Edge of Town: The Periphery as a Spatial Pattern
Authors: Valerio Cutini
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

The paper aims at highlighting the features of the configurational approach, at sketching its conceptual bases, at discussing the main operational techniques and their respective potential and limits. Moreover, we have then focused on the theme of edge areas, aiming at using the configurational approach so as to overcome the traditional vagueness and ambiguity of the notion of urban periphery. By means of configurational analysis, we're trying to define the periphery as a spatial pattern, depending on the way the urban space is arranged, both on the whole extension of the grid and at a local infra-urban scale. The results of the research, applied to several case studies in Pisa and Leghorn, appear encouraging, as they allow to pinpoint some recurring configurational aspects. Furthermore, such results can be used in supporting and addressing the redevelopment and the rehabilitation of the urban segregated areas.
Location of Special Areas on Palmprint
Authors: Yan Zheng, GuangShun Shi, QingRen Wang, Lin Zhang
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 2 - Volume 02 (2008)

Offline palmprint plays an important role in personal recognition, especially in the area of criminal investigation. Location of Special Areas is significant to offline palmprint classification and orientation field. This paper proposes an efficient algorithm to locate the Special Areas on offline palmprint, which can be divided into two stages. The first stage is to locate the coarse-level Special Areas based on the topology of four adjacent blocks. And then a dynamic clustering algorithm is adopted to segment the orientation fields of the coarse-level Special Areas according to well-suited optimality criteria. The singular points can be detected based on the information of segmented orientation field. Based on the singular points, the final Special Areas are located. In experimental section, the final location results are not only displayed from the aspect of human inspection, the quantitative results are also provided to compare our location results with official ones and our former algorithm.
Robust Feature Selection Using Ensemble Feature Selection Techniques
Authors: Yvan Saeys, Thomas Abeel, Yves Peer
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

Robustness or stability of feature selection techniques is a topic of recent interest, and is an important issue when selected feature subsets are subsequently analysed by domain experts to gain more insight into the problem modelled. In this work, we investigate the use of ensemble feature selection techniques, where multiple feature selection methods are combined to yield more robust results. We show that these techniques show great promise for high-dimensional domains with small sample sizes, and provide more robust feature subsets than a single feature selection technique. In addition, we also investigate the effect of ensemble feature selection techniques on classification performance, giving rise to a new model selection strategy.
Experiences of Hybrid Corporate Training Programmes at an Online Academic Institution
Authors: Kanishka Bedi
Venue: Proceedings of the 1st international conference on Hybrid Learning and Education (2008)

During the past decade, many corporations world-wide have embraced hybrid training programmes to derive benefits of the conventional face-to-face (F2F) learning environment coupled with those of online learning environment for their employees. A majority of researches on hybrid learning focus upon blending of existing F2F programmes of brick `n' mortar institutions with online component. This paper highlights the experiences of an online academic institution in blending its purely online courses with F2F component in order to better meet the training objectives of its corporate clients. It reports myriad of challenges faced during the creation and implementation of its initial hybrid corporate training programmes and how the lessons learned helped in blending its subsequent programmes for better learner and client satisfaction. It brings out the importance of redesigning/ modifying the blending proportion in future offerings of a programme through continual feedback of its corporate clients as well as the learners.
Scheduling with Precedence Constraints of Low Fractional Dimension
Authors: Christoph Ambühl, Monaldo Mastrolilli, Nikolaus Mutsanas, Ola Svensson
Venue: Proceedings of the 12th international conference on Integer Programming and Combinatorial Optimization (2007)

We consider the single machine scheduling problem to minimize the average weighted completion time under precedence constrains. Improving on the various 2-approximation algorithms is considered one of the ten most prominent open problems in scheduling theory. Recently, research has focused on special cases of the problem, mostly by restricting the set of precedence constraints to special classes such as convex bipartite, two-dimensional, and interval orders.In this paper we extend our previous results by presenting a framework for obtaining (2 ¿ 2/d)-approximation algorithms provided that the set of precedence constraints has fractional dimension d. Our generalized approach yields the best known approximation ratios for all previously considered classes of precedence constraints, and it provides the first results for bounded degree and interval dimension 2 orders.As a negative result we show that the addressed problem remains NP-hard even when restricted to the special case of interval orders.
A New Variable-Step LMS Algorithm Based on the Convergence Ratio of Mean-Square Error(MSE)
Authors: Hong Wan, Guangting Li, Xianming Wang, Chai Jing
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

A new variable step-size(VSS) LMS adaptive algorithm based on the convergence ratio of MSE and the correlation between reference signal and output error is proposed in the paper. Theory analyzing and simulation results prove that the new algorithm improves the convergent speed of general LMS algorithm and optimizes the trace ability of time-varying system and stable state maladjustment; and comparing to the standard LMS algorithm, the increase of computation is finite.
Plenary lecture I: from fuzzy systems to fuzzy networks
Authors: Alexander Gegov
Venue: Proceedings of the 9th WSEAS International Conference on Fuzzy Systems (2008)

The lecture will present some novel ideas and results in fuzzy networks - an emerging research area that has been recently pioneered and promoted by the presenter. The most common attribute of a fuzzy system is a 'black box' model in the form of a single large rule base with poor transparency of the rules. As opposed to this, a fuzzy network is characterised by a 'white box' model in the form of multiple smaller rule bases with good transparency of the rules. Also, a fuzzy network is a more accurate model of a complex process than a fuzzy system due to the ability of the multiple smaller rule bases to reflect explicitly all subprocesses and the interactions among them. This superiority in terms of improved transparency and higher accuracy does not come at the expense of reduced efficiency as any fuzzy network can be transformed into an equivalent fuzzy system entirely off-line. The following topics will be highlighted in the lecture: capabilities of fuzzy logic, fuzzy systems and fuzzy networks to deal with uncertainty, non-linearity and topology in complex systems, problems of fuzzy rule based systems, foundations of fuzzy rule base networks, mathematical modelling of nodes and connections in fuzzy networks by means of Boolean matrices, binary relations, incidence matrices and adjacency matrices, formal operations in fuzzy networks such as horizontal, vertical and output merging and splitting of nodes, structural properties of fuzzy networks such as associativity of horizontal, vertical and output merging and permutability of output splitting, analysis and synthesis of feedforward and feedback fuzzy networks, comparison between fuzzy networks and fuzzy systems in terms of accuracy, efficiency and transparancy, theoretical importance, application areas, future developments and related research for fuzzy networks.
Decidable Reasoning in UML Schemas with Constraints
Authors: Anna Queralt, Ernest Teniente
Venue: Proceedings of the 20th international conference on Advanced Information Systems Engineering (2008)

In this paper we propose an approach to reason on UML schemas with OCL constraints. We provide a set of theorems to determine that a schema does not have any infinite model and then provide a decidable method that, given a schema of this kind, efficiently checks whether it satisfies a set of desirable properties such as schema satisfiability and class or association liveliness.
Direct links in IEEE 802.11: Analytical study of unfairness problem
Authors: A. Lyakhov, I. Pustogarov, A. Gudilov
Venue: Automation and Remote Control (2008)

In this paper, the problem of mutual interference between direct links in IEEE 802.11 networks is represented. This problem is common for direct links in infrastructure networks with hidden terminals and for mesh networks as well. In this paper, we develop analytical models to study the impact of the direct link interference on links performance indices in various cases of links disposition. With these models proved by simulation, we show that network capacity distribution between direct links is unfair in many cases, explaining the reasons of the unfairness in every case. As a conclusion, we discuss possible mechanisms to solve the unfairness problem.