ppxscal/embedding_network huggingface dataset:
Social Interaction in Robotic Agents Emulating the Mirror Neuron Function
Authors: Emilia I. Barakova
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

Emergent interactions that are expressed by the movements of two agents are discussed in this paper. The common coding principle is used to show how the mirror neuron system may facilitate interaction behaviour. Synchronization between neuron groups in different structures of the mirror neuron system are in the basis of the interaction behaviour. The robotics experimental setting is used to illustrate the method. The resulting synchronization and turn taking behaviours show the advantages of the mirror neuron paradigm for designing of socially meaningful behaviour.
Recommendation in Education Portal by Relation Based Importance Ranking
Authors: Xin Wang, Fang Yuan, Li Qi
Venue: Proceedings of the 7th international conference on Advances in Web Based Learning (2008)

Recommendation in education portal is helpful for students to know the important learning resources in schools. Currently, previous methods which have been proposed to solve this problem mainly focus on page view counts. A learning resource is important just because many students have viewed it. However, as the metadata in a resource is becoming available, the relations among the resources and other entities in real world are becoming more and more. Unfortunately, how to use such relations to make better recommendations has not been well studied. In this paper, we present a complementary study to this problem. Specially, we focus on a general education portal, which consists of different typed objects, including resource, category, tag, user and department. The recommendation object is resource. However, we have found that a resource's importance rank can be affected by its relations to other typed objects. Thus, we formalize the resource recommendation as a ranking problem by considering its relations to other typed objects. A random walk algorithm to estimate the importance of each object in the education portal is proposed. Finally, the experimental result is evaluated in a real world data set.
Protein Classification from Protein-Domain and Gene-Ontology Annotation Information Using Formal Concept Analysis
Authors: Mi-Ryung Han, Hee-Joon Chung, Jihun Kim, Dong-Young Noh, Ju Han Kim
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

There are a number of different attributes to describe ontology of proteins such as protein structure, biomolecular interaction, cellular location, and protein domains which represent the basic evolutionary units that form protein. In this paper, we propose a mathematical approach, formal concept analysis (FCA), which toward abstracting from attribute-based object descriptions. Based on this theory, we present extended version of algorithm, tripartite lattice, to compute a concept lattice. By analyzing tripartite lattice, we attempt to extract proteins, which are related to domains and gene ontology (GO) terms from bottom nodes to the top of lattice. In summary, using tripartite lattices, we classified proteins from protein domain composition with their describing gene ontology (GO) terms.
Static Program Analysis for Java Card Applets
Authors: Vasilios Almaliotis, Alexandros Loizidis, Panagiotis Katsaros, Panagiotis Louridas, Diomidis Spinellis
Venue: Proceedings of the 8th IFIP WG 8.8/11.2 international conference on Smart Card Research and Advanced Applications (2008)

The Java Card API provides a framework of classes and interfaces that hides the details of the underlying smart card interface, thus relieving developers from going through the swamps of microcontroller programming. This allows application developers to concentrate most of their effort on the details of application, assuming proper use of the Java Card API calls regarding (i) the correctness of the methods' invocation targets and their arguments and (ii) temporal safety, i.e. the requirement that certain method calls have to be used in certain orders. Several characteristics of the Java Card applets and their multiple-entry-point program structure make it possible for a potentially unhandled exception to reach the invoked entry point. This contingency opens a possibility to leave the applet in an unpredictable state that is potentially dangerous for the application's security. Our work introduces automatic static program analysis as a means for the early detection of misused and therefore dangerous API calls. The shown analyses have been implemented within the FindBugs bug detector, an open source framework that applies static analysis functions on the applet bytecode.
Dynamic Human Fatigue Detection Using Feature-Level Fusion
Authors: Xiao Fan, Bao-Cai Yin, Yan-Feng Sun
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

Driver fatigue is a significant factor in many traffic accidents. We propose a novel dynamic features using feature-level fusion for driver fatigue detection from facial image sequences. First, Gabor filters are employed to extract multi-scale and multi-orientation features from each image, which are then merged according to a fusion rule to produce a single feature. To account for the temporal aspect of human fatigue, the fused image sequence is divided into dynamic units, and a histogram of each dynamic unit is computed and concatenated as dynamic features. Finally a statistical learning algorithm is applied to extract the most discriminative features and construct a strong classifier for fatigue detection. The test data contains 600 image sequences from thirty people. Experimental results show the validity of the proposed approach, and the correct rate is much better than the baselines.
E-Government Not Delivering Full Citizen Services
Authors: Staff
Venue: I-WAYS - The Journal of E-Government Policy and Regulation (2005)

The Government Gateway: Running for the past four years, the Gateway is a centralized registration point for government services online, offering nearly 50 services from 20 different government entities to 5.1 million registered users. Over the last 12 months, Gateway has had 100% operational availability, while January 2005 saw 275 000 successful authentications take place in just one day. Over the next 12 months, more than 24 new departments or Local Authorities are due to make services available on the Gateway. 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
E-Learning Documentation of Historical Living Systems with 3-D Modeling Functionality
Authors: Athanasios D. Styliadis
Venue: Informatica (2007)

The innovations and improvements in digital imaging sensors and scanners, computer modeling, haptic equipments and e-learning technology, as well as the availability of many powerful graphics PCs and workstations make haptic-based rendering methods for e-learning documentation with 3-D modeling functionality feasible. E-Learning documentation is a new term in computing, engineering and architecture, related to digital documentation with e-learning functionality, and introduced to literature for the first time within this paper. In particular, for the historical living systems (architectures, monuments, cultural heritage sites), such a methodolgy must be able to derive pictorial, geometric, spatial, topological, learning and semantic information from the target architectural object (historical living system), in such a way that it can be directly used for e-learning purposes regarding the history, the architecture, the structure and the temporal (time-based) 3-D geometry of the projected historical living system. A practical project is used to demonstrate the functionality and the performance of the proposed methodology. In particular, the processing steps from image acquisition to e-learning documentation of the Aghios Achilleios basilica, twin lakes Prespes, Northern Greece, through its 3-D geometric CAAD (Computer-Aided Architectural Design) model and semantic description are presented. Also, emphasis is placed on introducing and documenting the new term e-learning documentation. Finaly, for learning purposes related to 3-D modeling accuracy evaluation, a comparison test of two image-based approaches is carried out and discussed.
Computer Simulation of Vestibuloocular Reflex Motor Learning Using a Realistic Cerebellar Cortical Neuronal Network Model
Authors: Kayichiro Inagaki, Yutaka Hirata, Pablo M. Blazquez, Stephen M. Highstein
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

The vestibuloocular reflex (VOR) is under adaptive control to stabilize our vision during head movements. It has been suggested that the acute VOR motor learning requires long-term depression (LTD) and potentiation (LTP) at the parallel fiber --- Purkinje cell synapses in the cerebellar flocculus. We simulated the VOR motor learning basing upon the LTD and LTP using a realistic cerebellar cortical neuronal network model. In this model, LTD and LTP were induced at the parallel fiber --- Purkinje cell synapses by the spike timing dependent plasticity rule, which considers the timing of the spike occurrence in the climbing fiber and the parallel fibers innervating the same Purkinje cell. The model was successful to reproduce the changes in eye movement and Purkinje cell simple spike firing modulation during VOR in the dark after low and high gain VOR motor learning.
The atmospheric factors of online storefront environment design: An empirical experiment in Taiwan
Authors: Chin-Shan Wu, Fei-Fei Cheng, David C. Yen
Venue: Information and Management (2008)

The purpose of our study was to identify the effect of atmospheric factors such as music and color on participants' emotional responses and subsequent shopping behavior in an online store setting. In this study, a laboratory experiment was conducted to test our hypotheses. The experiment was a 2 (music: fast/slow)x2 (color: warm/cool) between-subjects factorial design with one control group. The results indicated that both music and color factors had a significant effect on participants' emotional response, which in turn influenced their intention to purchase. Specifically, participants felt more aroused and experienced greater pleasure when they were exposed to fast-tempo music and a warm color website than those people who experienced slow-tempo music and cool colors. In addition, both pleasure and arousal emotions were significant predictors of approach-avoidance intention. We expected that a well-designed website with adequate music and color attributes would create a desired environment and thus entice and retain target customers. Our findings should help in deciding on ambient factors in online website design. Although studies on atmospheric effects in traditional stores have been abundant, knowledge of their effect in the online retail context has been sparse.
Framework for Quantitative S/W Development Performance Measurement and Analysis in Semiconductor Industry
Authors: Ki-Won Song, Soo-Hwan Lee, Young-Gyun Jang, Il-Seok Suh, Jin-Soo Kim
Venue: Proceedings of the 2008 International Conference on Convergence and Hybrid Information Technology (2008)

This paper presents a framework for quantitative software development performance measurement and analysis based on characteristics of software in System on Chip (SoC) industry, one of the semiconductor businesses. In this paper, we propose a measurement model based on not only theoretical model (Performance Pyramid) but also characteristics of SoC embedded software. The paper also presents surroundings of the model - a measurement process, an infrastructure and its constituent units, and Key Performance Indicators (KPIs), which are the basis of the quantitative software development performance measurement. Quantitative software development performance measurement is not just collecting indicators but analyzing quality, cost, and delivery (QCD) of collected indicators. This makes possible both external effectiveness and internal efficiency. Externally, it is possible for programmers to develop software meeting customers' needs. Internally, more efficient software development can be possible through the visible productivity increase.
Neuro-Imaging Platform for Neuroinformatics
Authors: Ryoji Suzuki, Kazuhisa Niki, Norio Fujimaki, Shinobu Masaki, Kazuhisa Ichikawa, Shiro Usui
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part II (2008)

We organized the Neuro-Imaging Platform (NIMG-PF) committee, whose members are drawn from 18 Japanese research sites, as an activity of Neuroinformatics Japan Center (NIJC) at RIKEN, and are constructing a database of non-invasive brain function measurements for beginners and specialists. We are gathering the content related to various neuroimaging technologies such as MRI, MEG, EEG, PET, and NIRS, and their integrations: bibliographies of research papers, tutorial materials, software content, experimental data, and related information. About 200 pieces of content have already been registered. NIMG-PF is constructed on a base-platform, XooNIps, on which users can search contents by selecting indices, items, or keywords. Furthermore, we are developing convenient tools for visualizing 3D-brain images and for information searches that work by the user pointing to locations on the images. In NIMG-PF, any user can register their original content and use content if they accept the permission conditions. NIMG-PF will open later this year.
Toward Architecture Evaluation through Ontology-Based Requirements-Level Scenarios
Authors: Mamadou H. Diallo, Leila Naslavsky, Thomas A. Alspaugh, Hadar Ziv, Debra J. Richardson
Venue: Architecting Dependable Systems V (2008)

We describe an approach for evaluating whether a candidate architecture dependably satisfies stakeholder requirements expressed in requirements-level scenarios. We map scenarios to architectural elements through an ontology of requirements-level event classes and domain entities. The scenarios express both functional requirements and quality attributes of the system; for quality attributes, the scenarios either operationalize the quality or show how the quality can be verified. Our approach provides a connection between requirements a stakeholder can understand directly, and architectures developed to satisfy those requirements. The requirements-level ontology simplifies the mapping, acts as the focus for maintaining the mapping as both scenarios and architecture evolve, and provides a foundation for evaluating scenarios and architecture individually and jointly. In this paper, we focus on the mapping through event classes and demonstrate our approach with two examples.
On the Use of Projected Gradients for Constrained Multiobjective Optimization Problems
Authors: Alfredo G. Hernandez-Diaz, Carlos A. Coello Coello, Luis V. Santana-Quintero, Fatima Perez, Julian Molina, Rafael Caballero
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

Recent works have shown how hybrid variants of gradient-based methods and evolutionary algorithms perform better than a pure evolutionary method both for single-objective and multiobjective optimization. This same idea has been used with Evolutionary Multiobjective Optimization (EMO), obtaining also very promising results. In most cases, gradient information is used as part of the mutation operator (and only for unconstrained MOPs), in order to move every generated point to the exact Pareto front. In our approach, we use the Karush-Kuhn-Tucker optimality condition for constrained optimization problems to combine the information provided by the gradient vector of each objective function and the gradient vectors of constraint functions to obtain a feasible movement direction in those points near the border. In our approach, gradients of the objective functions will be approximated using quadratic regressions, trying to avoid local optima. The proposed algorithm is able to converge on several nonlinear constrained multiobjective optimization problems obtained from a benchmark, consuming few objective function evaluations (between 150 and 1000). Our results indicate that our proposed scheme may produce a significant reduction in the computational cost, while producing results of good quality, when it is incorporated into a hybrid MOEA or when it is used to seed an EMO algorithm.
Efficient Clustering of Structured Documents Using Graph Self-Organizing Maps
Authors: Markus Hagenbuchner, Ah Chung Tsoi, Alessandro Sperduti, Milly Kc
Venue: Focused Access to XML Documents: 6th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2007 Dagstuhl Castle, Germany, December 17-19, 2007. Selected Papers (2008)

Graph Self-Organizing Maps (GraphSOMs) are a new concept in the processing of structured objects using machine learning methods. The GraphSOM is a generalization of the Self-Organizing Maps for Structured Domain (SOM-SD) which had been shown to be a capable unsupervised machine learning method for some types of graph structured information. An application of the SOM-SD to document mining tasks as part of an international competition: Initiative for the Evaluation of XML Retrieval (INEX), on the clustering of XML formatted documents was conducted, and the method subsequently won the competition in 2005 and 2006 respectively. This paper applies the GraphSOM to the clustering of a larger dataset in the INEX competition 2007. The results are compared with those obtained when utilizing the more traditional SOM-SD approach. Experimental results show that (1) the GraphSOM is computationally more efficient than the SOM-SD, (2) the performances of both approaches on the larger dataset in INEX 2007 are not competitive when compared with those obtained by other participants of the competition using other approaches, and, (3) different structural representation of the same dataset can influence the performance of the proposed GraphSOM technique.
Clustering Via Local Regression
Authors: Jun Sun, Zhiyong Shen, Hui Li, Yidong Shen
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

This paper deals with the local learning approach for clustering, which is based on the idea that in a good clustering, the cluster label of each data point can be well predicted based on its neighbors and their cluster labels. We propose a novel local learning based clustering algorithm using kernel regression as the local label predictor. Although sum of absolute error is used instead of sum of squared error, we still obtain an algorithm that clusters the data by exploiting the eigen-structure of a sparse matrix. Experimental results on many data sets demonstrate the effectiveness and potential of the proposed method.
Making the I-Maestro Music Learning Framework Accessible
Authors: Neil Mckenzie, Benjie Marwick-Johnstone
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

In a climate where we are increasingly looking to technology to support our learning processes, it important to make sure that technological advancement isn't chosen over maximum inclusion. The two have to go hand in hand. This paper presents the I-Maestro project and demonstrates how it attempts to bridge the gap between music pedagogy and technology while keeping the learning experiences accessible for the visually impaired.
Post-Hybridization Quality Measures for Oligos in Genome-Wide Microarray Experiments
Authors: Florian Battke, Carsten Müller-Tidow, Hubert Serve, Kay Nieselt
Venue: Proceedings of the 8th international workshop on Algorithms in Bioinformatics (2008)

High-throughput microarray experiments produce vast amounts of data. Quality control methods for every step of such experiments are essential to ensure a high biological significance of the conclusions drawn from the data. This issue has been addressed for most steps of the typical microarray pipeline, but the quality of the oligonucleotide probes designed for microarrays has only been evaluated based on their a priori properties, such as sequence length or melting temperature predictions. We introduce new oligo quality measures that can be calculated using expression values collected in direct as well as indirect design experiments. Based on these measures, we propose combined oligo quality scores as a tool for assessing probe quality, optimizing array designs and data normalization strategies. We use simulated as well as biological data sets to evaluate these new quality scores. We show that the presented quality scores reliably identify high-quality probes. The set of best-quality probes converges with increasing number of arrays used for the calculation and the measures are robust with respect to the chosen normalization method.
Optimizing Surface Triangulation Via Near Isometry with Reference Meshes
Authors: Xiangmin Jiao, Narasimha R. Bayyana, Hongyuan Zha
Venue: Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007 (2007)

Optimization of the mesh quality of surface triangulation is critical for advanced numerical simulations and is challenging under the constraints of error minimization and density control. We derive a new method for optimizing surface triangulation by minimizing its discrepancy from a virtual reference mesh. Our method is as easy to implement as Laplacian smoothing, and owing to its variational formulation it delivers results as competitive as the optimization-based methods. In addition, our method minimizes geometric errors when redistributing the vertices using a principle component analysis without requiring a CAD model or an explicit high-order reconstruction of the surface. Experimental results demonstrate the effectiveness of our method.
2008 Special Issue: Two forms of immediate reward reinforcement learning for exploratory data analysis
Authors: Ying Wu, Colin Fyfe, Pei Ling Lai
Venue: Neural Networks (2008)

We review two forms of immediate reward reinforcement learning: in the first of these, the learner is a stochastic node while in the second the individual unit is deterministic but has stochastic synapses. We illustrate the first method on the problem of Independent Component Analysis. Four learning rules have been developed from the second perspective and we investigate the use of these learning rules to perform linear projection techniques such as principal component analysis, exploratory projection pursuit and canonical correlation analysis. The method is very general and simply requires a reward function which is specific to the function we require the unit to perform. We also discuss how the method can be used to learn kernel mappings and conclude by illustrating its use on a topology preserving mapping.