ppxscal/embedding_network huggingface dataset:
Development of Ubiquitous On-Demand Study Support Environment for Nursing Students
Authors: Yukie Majima, Yumiko Nakamura, Yasuko Maekawa, Yoichiro So
Venue: Proceedings of the 13th International Conference on Human-Computer Interaction. Part IV: Interacting in Various Application Domains (2009)

We were selected to work with "Development of e-Learning Program to help enhance human resource ability based on needs," which is a government supported program of fiscal year 2005 to address modern education needs (Modern GP). The program has therefore been underway since 2005. The objectives of the program are to use e-Learning to further improve education and teaching practices qualitatively for nursing education, to supply high-level nursing practice capabilities, and provide a new environment in which students can study independently in an efficient manner. The main targets to achieve the program goals are: i) to produce e-Learning training materials that include examples of nursing to use for the study of nursing practice and support students' acquisition of problem-solving abilities during nursing operations; and ii) to construct an environment to support ubiquitous on-demand studies in which students can study for themselves easily, at any time, and anywhere on campus or on the actual practice site using this e-Learning methodology. This paper presents a report of an actual lecture class conducted in using such training materials.
A New Approach for Attribute Importance Measure Based on TCA-SSVM
Authors: Yan-Feng Fan, De-Xian Zhang, Hua-Can He
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 03 (2009)

The lack of heuristic information is the fundamental reason that affects the attribute selection in data mining. Spatial hypersurface plays a very important role in the classification problem which reflects the characteristic of class attribute and condition attributes. In this paper, a new measure for determining the importance level of the attributes based on partial derivative distribution of the output corresponding to the inputs is presented. For more convenience, we present a new SVM model called TCA-SSVM which could use simple algorithm to solve the optimization problem to acquire the classification hypersurface. The proposed approach is experimentally evaluated in two datasets and the results prove that it can improve the validity of the problem and lead to interesting results.
Orchestration of Global Software Engineering Projects - Position Paper
Authors: Christian Bartelt, Manfred Broy, Christoph Herrmann, Eric Knauss, Marco Kuhrmann, Andreas Rausch, Bernhard Rumpe, Kurt Schneider
Venue: Proceedings of the 2009 Fourth IEEE International Conference on Global Software Engineering (2009)

Global software engineering has become a fact in many companies due to real necessity in practice. In contrast to co-located projects global projects face a number of additional software engineering challenges. Among them quality management has become much more difficult and schedule and budget overruns can be observed more often. Compared to co-located projects global software engineering is even more challenging due to the need for integration of different cultures, different languages, and different time zones – across companies, and across countries. The diversity of development locations on several levels seriously endangers an effective and goal-oriented progress of projects. In this position paper we discuss reasons for global development, sketch settings for distribution and views of orchestration of dislocated companies in a global project that can be seen as a “virtual project environment”. We also present a collection of questions, which we consider relevant for global software engineering. The questions motivate further discussion to derive a research agenda in global software engineering.
Towards Learning Robotic Reaching and Pointing: An Uncalibrated Visual Servoing Approach
Authors: Azad Shademan, Amir-Massoud Farahmand, Martin Jagersand
Venue: Proceedings of the 2009 Canadian Conference on Computer and Robot Vision (2009)

It is desirable for a robot to be able to operate in unstructured environments. In this paper, we demonstrate how a robot can learn primitive skills and we show how to augment them. We formalize 2D-decidable (pointing) and 3D-decidable (reaching) skills within an uncalibrated visual servoing framework. Skill decidability is defined in conjunction with an image-based controller, which has local asymptotic stability. In addition, we propose sequential composition of primitive skills to combine pointing and reaching skills in order to increase the accuracy of reaching skill. We use simple primitive tasks such as multi-point alignment and point-to-line alignment. We validate our results with real uncalibrated eye-in-hand experiments with a 4-DOF WAM from Barrett Technology Inc., alongside computer simulations.
Graph-Based k-Means Clustering: A Comparison of the Set Median versus the Generalized Median Graph
Authors: M. Ferrer, E. Valveny, F. Serratosa, I. Bardají, H. Bunke
Venue: Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns (2009)

In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph. 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
A comparison of structural correspondence learning and self-training for discriminative parse selection
Authors: Barbara Plank
Venue: Proceedings of the NAACL HLT 2009 Workshop on Semi-Supervised Learning for Natural Language Processing (2009)

This paper evaluates two semi-supervised techniques for the adaptation of a parse selection model to Wikipedia domains. The techniques examined are Structural Correspondence Learning (SCL) (Blitzer et al., 2006) and Self-training (Abney, 2007; McClosky et al., 2006). A preliminary evaluation favors the use of SCL over the simpler self-training techniques.
Coreference-oriented interlingual slot structure & machine translation
Authors: Jesús Peral, Manuel Palomar, Antonio Ferrández
Venue: Proceedings of the Workshop on Coreference and its Applications (1999)

One of the main problems of many commercial Machine Translation (MT) and experimental systems is that they do not carry out a correct pronominal anaphora generation. As mentioned in Mitkov (1996), solving the anaphora and extracting the antecedent are key issues in a correct translation. In this paper, we propose an Interlingual mechanism that we have called Interlingual Slot Structure (ISS) based on Slot Structure (SS) presented in Ferr&aacute;ndez et al. (1997). The SS stores the lexical, syntactic, morphologic and semantic information of every constituent of the grammar. The mechanism ISS allows us to translate pronouns between different languages. In this paper, we have proposed and evaluated ISS for the translation between Spanish and English languages. We have compared pronominal anaphora resolution both in English and Spanish to accomplish a study of the existing discrepancies between two languages. This mechanism could be added to a MT system such as an additional module to solve anaphora generation problem.
Boundary Detection from Spectral Information
Authors: Jun Ma
Venue: Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II (2009)

In this paper, we propose a method to detect object boundaries from spectral information. Previous image boundary detection techniques draw their attention on spatial image features such as brightness, color and texture. Different from traditional feature descriptor methods, we started from the analysis of natural image statistics in spectral domain and proposed a method to detect image boundaries by analyzing log spectrum residual of images. We find that the spatial transform of log spectrum residual of images are qualified as boundary maps. In the experiment section we show that our results are similar to human segmentations compared to common methods like the Canny detector. 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
Using Classification Learning in Companion Modeling
Authors: Daisuke Torii, Francois Bousquet, Toru Ishida, Guy Trébuil, Chirawat Vejpas
Venue: Multi-Agent Systems for Society: 8th Pacific Rim International Workshop on Multi-Agents, PRIMA 2005, Kuala Lumpur, Malaysia, September 26-28, 2005, Revised Selected Papers (2009)

Companion Modeling is a methodology used to facilitate adaptive management of renewable resources by their users. It is using role-playing games (RPG) and multiagent simulations to validate initial models representing the functioning of complex systems to be managed. In this research, we propose a novel agent model construction methodology in which classification learning is applied to the RPG log data in Companion Modeling. This methodology enables a systematic model construction that handles multi-parameters, independent of the modelers' ability. There are three problems in applying classification learning to the RPG log data: 1) It is difficult to gather enough data for the number of features because the cost of gathering data is high. 2) Noise data can affect the learning results because the amount of data may be insufficient. 3) The learning results should be explained as a human decision making model and should be recognized by the expert as reflecting reality. We realized an agent model construction system using the following two approaches: 1) Using a feature selection method, the feature subset that has the best prediction accuracy is identified. In this process, the important features chosen by the expert are always included. 2) The expert eliminates irrelevant features from the learning results after evaluating the learning model through a visualization of the results. Finally, using the RPG log data from a Companion Modeling case study on rice production in northeastern Thailand, we confirm the capability of this methodology.
TeleComVis: Exploring Temporal Communities in Telecom Networks
Authors: Qi Ye, Bin Wu, Lijun Suo, Tian Zhu, Chao Han, Bai Wang
Venue: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II (2009)

By the structure of call graphs derived from huge amounts of Call Detail Records, we can find out the social communities in the call graphs and make different market strategies for these social communities in real telecom applications. However, traditional telecom business intelligence methods are short of ways to understand the social interactions. To fill this gap, we propose a Telecom Community Visual Analysis prototype tool, called TeleComVis, to analyze the call graphs derived from Call Detail Records. In the demo, we will show (1) the functions of TeleComVis; (2) the critical techniques of finding statistically significant communities in real-world telecom applications. Using TeleComVis, users can both analyze the statistical properties of massive call graphs and explore the statistically significant communities and the temporal links interactively.
Gender Classification by Combining Facial and Hair Information
Authors: Xiao-Chen Lian, Bao-Liang Lu
Venue: Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II (2009)

Most of the existing gender classification approaches are based on face appearance only. In this paper, we present a gender classification system that integrates face and hair features. Instead of using the whole face we extract features from eyes, nose and mouth regions with Maximum Margin Criterion (MMC), and the hair feature is represented by a fragment-based encoding. We use Support Vector Machines with probabilistic output (SVM-PO) as individual classifiers. Fuzzy integration based classifier combination mechanism is used to fusing the four different classifiers on eyes, nose, mouth and hair respectively. The experimental results show that the MMC outperforms Principal Component Analysis and Fisher Discriminant Analysis and incorporating hair feature improves gender classification performance.
Scada Malware, a Proof of Concept
Authors: Andrea Carcano, Igor Nai Fovino, Marcelo Masera, Alberto Trombetta
Venue: Critical Information Infrastructure Security: Third International Workshop, CRITIS 2008, Rome, Italy, October13-15, 2008. Revised Papers (2009)

Critical Infrastructures are nowadays exposed to new kind of threats. The cause of such threats is related to the large number of new vulnerabilities and architectural weaknesses introduced by the extensive use of ICT and Network technologies into such complex critical systems. Of particular interest are the set of vulnerabilities related to the class of communication protocols normally known as "SCADA" protocols, under which fall all the communication protocols used to remotely control the RTU devices of an industrial system. In this paper we present a proof of concept of the potential effects of a set of computer malware specifically designed and created in order to impact, by taking advantage of some vulnerabilities of the ModBUS protocol, on a typical Supervisory Control and Data Acquisition system.
Towards Privacy Compliant and Anytime Recommender Systems
Authors: Armelle Brun, Anne Boyer
Venue: Proceedings of the 10th International Conference on E-Commerce and Web Technologies (2009)

Recommendation technologies have traditionally been used in domains such as E-commerce and Web navigation to recommend resources to customers so as to help them to get the pertinent resources. Among the possible approaches is collaborative filtering that does not take into account the content of the resources: only the traces of usage of the resources are considered. State of the art models, such as sequential association-rules and Markov models, that can be used in the frame of privacy concerns, are usually studied in terms of performance, state space complexity and time complexity. Many of them have a large time complexity and require a long time to compute recommendations. However, there are domains of application of the models where recommendations may be required quickly. This paper focuses on the study of how these state of the art models can be adapted so as to be anytime. In that case recommendations can be proposed to the user whatever is the computation time available, the quality of the recommendations increases according to the computation time. We show that such models can be adapted so as to be anytime and we propose several strategies to compute recommendations iteratively. We also show that the computation time needed by these new models is not increased compared to classical ones; even so, it sometimes decreases.
k-SAT Is No Harder Than Decision-Unique-k-SAT
Authors: Chris Calabro, Ramamohan Paturi
Venue: Proceedings of the Fourth International Computer Science Symposium in Russia on Computer Science - Theory and Applications (2009)

We resolve an open question by [3]: the exponential complexity of deciding whether a k-CNF has a solution is the same as that of deciding whether it has exactly one solution, both when it is promised and when it is not promised that the input formula has a solution. We also show that this has the same exponential complexity as deciding whether a given variable is backbone (i.e. forced to a particular value), given the promise that there is a solution. We show similar results for True Quantified Boolean Formulas in k-CNF, k-Hitting Set (and therefore Vertex Cover), k-Hypergraph Independent Set (and therefore Independent Set), Max-k-SAT, Min-k-SAT, and 0-1 Integer Programming with inequalities and k-wide constraints.
Change State Capture in Service Aware Storage
Authors: Zhaobin Liu, Huihui Zhang, Wenyu Qu, Tianquan Li
Venue: Proceedings of the 2009 IEEE International Conference on Networking, Architecture, and Storage (2009)

Storage QoS methodology has been extensively mentioned in large-scale storage systems. Since storage resources tend to be heterogeneous and dynamic changed, efficient storage states capturing becomes essential. In this paper we propose a novel capturing and preserving mechanism, called Change State Capture (CSC). CSC can capture the changed state when storage provider QoS modified. The changed event can trigger the Broker module to reorganize the storage resources to meet this change. The experimental results show that CSC can achieve better performance than existing periodic scanning algorithm in terms of efficiency.
Making Your Hands Dirty Inspires Your Brain! Or How to Switch ASP into Production Mode
Authors: Torsten Schaub
Venue: Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning (2009)

Rising from strong theoretical foundations in Logic Programming and Nonmonotonic Reasoning, Answer Set Programming (ASP) came to life as a declarative problem solving paradigm [1,2,3] in the late nineties. The further development of ASP was greatly inspired by the early availability of efficient and robust ASP solvers, like smodels [4] and dlv [5]. The community started modeling with ASP and a first milestone was the conception of TheoryBase [6] providing a systematic and scalable source of benchmarks stemming from combinatorial problems. Although the scalability of such benchmarks is of great value for empirically evaluating systems, the need for application-oriented benchmarks was early perceived. The demand for systematic benchmarking led to the Dagstuhl initiative and with it the creation of the web-based benchmark archive asparagus [7]. This repository has in the meantime grown significantly, mainly due to the two past ASP competitions [8,9], and contains nowadays a whole variety of different types of benchmarks, although it is still far from being comprehensive.
Science and Engineering on Petascale Computers
Authors: Thom H. Dunning,  Jr.
Venue: IEEE Design Test (2009)

Our hope is that this issue of Computing in Science and Engineering provides you with a sense of the current state of petascale computing, its opportunities and challenges, and its implications.
The User's Role in the Development Process of a Clinical Information System: An Example in Hemophilia Care
Authors: Leonor Teixeira, Vasco Saavedra, Carlos Ferreira, Beatriz Sousa Santos
Venue: Proceedings of the 1st International Conference on Human Centered Design: Held as Part of HCI International 2009 (2009)

This work describes the development process of a Web-based Information System for managing clinical information in hemophilia care, emphasizing the role of the users around a human-centered development. To help understanding all this process, we first present the relevant concepts concerning human-centered design; next we describe the web application for managing the clinical information in hemophilia care, as well as, the development process followed in its development; and finally we illustrate the importance of the user's involvement in critical phases through the demonstration of some results.
Neural Network Algorithm for Solving System of Linear Equations
Authors: Zhigang Zhou, Lihong Chen, Li Wan
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 02 (2009)

In this paper, the artificial neural network (ANN) algorithm for solving system of linear equations is considered and the algorithm convergence theorem is derived. Some numerical tests are given to demonstrate the effectiveness of our results.
A Tutorial on Type-Based Termination
Authors: Gilles Barthe, Benjamin Grégoire, Colin Riba
Venue: Language Engineering and Rigorous Software Development: International LerNet ALFA Summer School 2008, Piriapolis, Uruguay, February 24 - March 1, 2008, Revised Tutorial Lectures (2009)

Type-based termination is a method to enforce termination of recursive definitions through a non-standard type system that introduces a notion of size for inhabitants of inductively defined types. The purpose of this tutorial is to provide a gentle introduction to a polymorphically typed ¿-calculus with type-based termination, and to the size inference algorithm which is used to guarantee automatically termination of recursive definitions.
Efficient Clustering of Web-Derived Data Sets
Authors: Luís Sarmento, Alexander Kehlenbeck, Eugénio Oliveira, Lyle Ungar
Venue: Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition (2009)

Many data sets derived from the web are large, high-dimensional, sparse and have a Zipfian distribution of both classes and features. On such data sets, current scalable clustering methods such as streaming clustering suffer from fragmentation, where large classes are incorrectly divided into many smaller clusters, and computational efficiency drops significantly. We present a new clustering algorithm based on connected components that addresses these issues and so works well on web-type data.
An efficient scheme of target classification and information fusion in wireless sensor networks
Authors: Youngsoo Kim, Sangbae Jeong, Daeyoung Kim, Tomás Sánchez López
Venue: Personal and Ubiquitous Computing (2009)

In this paper, an efficient target classification and fusion scheme for wireless sensor networks (WSNs) is proposed and evaluated. When a classification algorithm for WSN nodes is designed, parametric approaches such as Gaussian mixture model (GMM) should be more preferred to non-parametric ones due to the hard limitation in resources. The GMM algorithm not only shows good performances for target classification in WSNs but it also requires very small resources. Based on the classifier, a decision tree generated by the classification and regression tree algorithm is used to fuse the information from heterogeneous sensors. This node-level classification scheme provides a satisfactory classification rate, 94.10%, with little resources. Finally, a confidence-based fusion algorithm improves the overall accuracy by fusing the information among sensor nodes. Our experimental results show that the proposed group-level fusion algorithm improves the accuracy by an average of 4.17% accuracy with randomly selected nodes.
The Design of MC9S12DG128 Embedded Intelligent Car
Authors: Xiao-feng Wan, Li-xiang Cai, Yi-si Xing
Venue: Proceedings of the 2009 International Conference on Computational Intelligence and Natural Computing - Volume 01 (2009)

Freescale MC9S12DG128 16-bit MCU (Micro Controller Unit), as the core control unit, constitutes a intelligent car system with DC motors, steering gear and power source, as well as other circuits, The system has designed hardware and software, and realized the real-time observation for the car through PC .MCU uses PID algorithm to realize real-time control to the angle of steering gear and marching speed. The program has a good real-time performance, which has achieved the result that the car can run rapidly and accurately independently; furthermore, it owns characters of strong anti-interference, small steady-state error and so on.