ppxscal/embedding_network huggingface dataset:
Form-Semantics-Function --- A Framework for Designing Visual Data Representations for Visual Data Mining
Authors: Simeon J. Simoff
Venue: Visual Data Mining: Theory, Techniques and Tools for Visual Analytics (2008)

Visual data mining, as an art and science of teasing meaningful insights out of large quantities of data that are incomprehensible in another way, requires consistent visual data representations (information visualisation models). The frequently used expression "the art of information visualisation" appropriately describes the situation. Though substantial work has been done in the area of information visualisation, it is still a challenging activity to find out the methods, techniques and corresponding tools that support visual data mining of a particular type of information. The comparison of visualisation techniques across different designs is not a trivial problem either. This chapter presents an attempt for a consistent approach to formal development, evaluation and comparison of visualisation methods. The application of the approach is illustrated with examples of visualisation models for data from the area of team collaboration in virtual environments and from the results of text analysis.
Improving Vision-Based Distance Measurements Using Reference Objects
Authors: Matthias Jüngel, Heinrich Mellmann, Michael Spranger
Venue: RoboCup 2007: Robot Soccer World Cup XI (2008)

Robots perceiving their environment using cameras usually need a good representation of how the camera is aligned to the body and how the camera is rotated relative to the ground. This is especially important for bearing-based distance measurements. In this paper we show how to use reference objects to improve vision-based distance measurements to objects of unknown size. Several methods for different kinds of reference objects are introduced. These are objects of known size (like a ball), objects extending over the horizon (like goals and beacons), and objects with known shape on the ground (like field lines). We give a detailed description how to determine the rotation of the robot's camera relative to the ground, provide an error-estimation for all methods and describe the experiments we performed on an Aibo robot.
A Real Time Vision System for Autonomous Systems: Characterization during a Middle Size Match
Authors: H. Silva, J. M. Almeida, L. Lima, A. Martins, E. P. Silva
Venue: RoboCup 2007: Robot Soccer World Cup XI (2008)

This paper propose a real-time vision framework for mobile robotics and describes the current implementation. The pipeline structure further reduces latency and allows a paralleled hardware implementation. A dedicated hardware vision sensor was developed in order to take advantage of the proposed architecture. The real-time characteristics and hardware partial implementation, coupled with low energy consumption address typical autonomous systems applications. A characterization of the implemented system in the Robocup scenario, during competition matches, is presented.
Geographical Analysis of Foreign Immigration and Spatial Patterns in Urban Areas: Density Estimation and Spatial Segregation
Authors: Giuseppe Borruso
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

The paper is focused on the analysis of immigrant population with particular reference to their spatial distribution and the tendency to cluster in some parts of a city, with the risk of generating ethnic enclaves or ghettoes. Methods used in the past to measure segregation and other characteristics of immigrants have long been aspatial, therefore not considering relationships between people within a city. In this paper the attention is dedicated to methods to analyse the immigrant residential distribution spatially, with particular reference to density-based method. The analysis is focused on the Municipality of Trieste (Italy) as a case study to test different methods for the analysis of immigration, and particularly to compare traditional indices, as Location Quotients and the Index of Segregation, to different, spatial ones, both based on Kernel Density Estimation functions, as the S index and the first version of an Index of Diversity.
Using the Bottom Clause and Mode Declarations on FOL Theory Revision from Examples
Authors: Ana Luísa Duboc, Aline Paes, Gerson Zaverucha
Venue: Proceedings of the 18th international conference on Inductive Logic Programming (2008)

Theory revision systems are designed to improve the accuracy of an initial theory, producing more accurate and comprehensible theories than purely inductive methods. Such systems search for points where examples are misclassified and modify them using revision operators. This includes trying to add antecedents in clauses usually generated in a top-down approach, considering all the literals of the knowledge base. This leads to a huge search space which dominates the cost of the revision process. ILP Mode Directed Inverse Entailment systems restrict the search for antecedents to the literals of the bottom clause. In this work the bottom clause and modes declarations are introduced to improve the efficiency of theory revision antecedent addition. Experimental results compared to FORTE revision system show that the runtime of the revision process is on average three orders of magnitude faster, and generate more comprehensible theories without decreasing the accuracy. Moreover, the proposed theory revision approach significantly improves predictive accuracy over theories generated by Aleph system.
A comparative study of teacher training formats blended training vs. face-to-face training
Authors: Hironori Sasaki, Yuka Kawasaki, Haruhisa Yamaguchi, Yumi Yamaguchi
Venue: Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing (2008)

We examined whether an e-learning seminar in teacher training is as effective as a face-to-face meeting seminar. We compared the difference between them in the area of assessment of learning. As a result, we found that: 1) e-learning with rich-media presentation was as effective as a seminar in a meeting format, 2) exercises using the courseware and BBS in e-learning was effective to some extend, and 3) observing lessons using a TV conference system was not as effective as a seminar in a face-to-face meeting format.
Semi-Supervised Boosting for Multi-Class Classification
Authors: Hamed Valizadegan, Rong Jin, Anil K. Jain
Venue: Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II (2008)

Most semi-supervised learning algorithms have been designed for binary classification, and are extended to multi-class classification by approaches such as one-against-the-rest. The main shortcoming of these approaches is that they are unable to exploit the fact that each example is only assigned to one class. Additional problems with extending semi-supervised binary classifiers to multi-class problems include imbalanced classification and different output scales of different binary classifiers. We propose a semi-supervised boosting framework, termed Multi-Class Semi-Supervised Boosting (MCSSB), that directly solves the semi-supervised multi-class learning problem. Compared to the existing semi-supervised boosting methods, the proposed framework is advantageous in that it exploits both classification confidence and similarities among examples when deciding the pseudo-labels for unlabeled examples. Empirical study with a number of UCI datasets shows that the proposed MCSSB algorithm performs better than the state-of-the-art boosting algorithms for semi-supervised learning.
Effect of the Background Activity on the Reconstruction of Spike Train by Spike Pattern Detection
Authors: Yoshiyuki Asai, Alessandro E. Villa
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

Deterministic nonlinearity has been observed in experimental electrophysiological recordings performed in several areas of the brain. However, little is known about the ability to transmit a complex temporally organized activity through different types of spiking neurons. This study investigates the response of a spiking neuron model representing five archetypical types to input spike trains including deterministic information generated by a chaotic attractor. The comparison between input and output spike trains is carried out by the pattern grouping algorithm (PGA) as a function of the intensity of the background activity for each neuronal type. The results show that the thalamo-cortical, regular spiking and intrinsically busting model neurons can be good candidate in transmitting temporal information with different characteristics in a spatially organized neural network.
Automation of Avionic Systems Testing
Authors: David Cebrián, Valentín Valero, Fernando Cuartero
Venue: Proceedings of the 19th IFIP TC6/WG6.1 international conference on Testing of Software and Communicating Systems: 7th International Workshop (2007)

In this paper we present an automatic testing process to validate Avionic Systems. To do that, we have developed a tool that interprets scripts written in Automated Test Language and translate them to user codes written in C language. To carry out this work, the syntax of scripts has been defined by a context free grammar. This testing process is based on the execution of a pre-defined set of test cases. Currently, these test sets are obtained from Test Description Document and they are introduced in the system in C code manually. Therefore, automation of this process would reduce the time used for the testing, as a great quantity of tests are realized and a great quantity of errors are made when tests are made by hand.
How to Encrypt with the LPN Problem
Authors: Henri Gilbert, Matthew J. Robshaw, Yannick Seurin
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part II (2008)

We present a probabilistic private-key encryption scheme named LPN-C whose security can be reduced to the hardness of the Learning from Parity with Noise (LPN) problem. The proposed protocol involves only basic operations in GF(2) and an error-correcting code. We show that it achieves indistinguishability under adaptive chosen plaintext attacks (IND-P2-C0). Appending a secure MAC renders the scheme secure under adaptive chosen ciphertext attacks. This scheme enriches the range of available cryptographic primitives whose security relies on the hardness of the LPN problem.
A Dynamic Population Steady-State Genetic Algorithm for the Resource-Constrained Project Scheduling Problem
Authors: Mariamar Cervantes, Antonio Lova, Pilar Tormos, Federico Barber
Venue: Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence (2008)

Resource Constrained Project Scheduling Problem (RCPSP) is a well known problem that is easy to describe but very difficult to solve, and therefore, it has attracted the attention of many researchers over the last few decades. In this context, heuristics are the only option when solving realistically-sized projects. In this paper we develop a steady-state genetic algorithm that uses a dynamic population and four decoding methods. These features allow the algorithm to adapt itself to the characteristics of the problem. Finally, its performance is compared against the best project scheduling methods published so far. The results show that the proposed scheduling method is one of the best scheduling techniques when compared with results reported in the literature.
Non-stationary Data Mining: The Network Security Issue
Authors: Sergio Decherchi, Paolo Gastaldo, Judith Redi, Rodolfo Zunino
Venue: Proceedings of the 18th international conference on Artificial Neural Networks, Part II (2008)

Data mining applications explore large amounts of heterogeneous data in search of consistent information. In such a challenging context, empirical learning methods aim to optimize prediction on unseen data, and an accurate estimate of the generalization error is of paramount importance. The paper shows that the theoretical formulation based on the Vapnik-Chervonenkis dimension (d vc ) can be of practical interest when applied to clustering methods for data-mining applications. The presented research adopts the K-Winner Machine (KWM) as a clustering-based, semi-supervised classifier; in addition to fruitful theoretical properties, the model provides a general criterion for evaluating the applicability of Vapnik's generalization predictions in data mining. The general approach is verified experimentally in the practical problem of detecting intrusions in computer networks. Empirical results prove that the KWM model can effectively support such a difficult classification task and combine unsupervised and supervised.
Experiences Using Clustering and Generalizations for Knowledge Discovery in Melanomas Domain
Authors: A. Fornells, E. Armengol, E. Golobardes, S. Puig, J. Malvehy
Venue: Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects (2008)

One of the main goals in prevention of cutaneous melanoma is early diagnosis and surgical excision. Dermatologists work in order to define the different skin lesion types based on dermatoscopic features to improve early detection. We propose a method called SOMEX with the aim of helping experts to improve the characterization of dermatoscopic melanoma types. SOMEX combines clustering and generalization to perform knowledge discovery. First, SOMEX uses Self-Organizing Maps to identify groups of similar melanoma. Second, SOMEX builds general descriptions of clusters applying the anti-unification concept. These descriptions can be interpreted as explanations of groups of melanomas. Experiments prove that explanations are very useful for experts to reconsider the characterization of melanoma classes.
Using a Computer Aided Test to Raise Awareness of Disability Issues amongst University Teaching Staff
Authors: John Gray, Gill Harrison, Jakki Sheridan-Ross, Andrea Gorra
Venue: Proceedings of the 11th international conference on Computers Helping People with Special Needs (2008)

A computer-based test has been created as a training tool to raise awareness among university academic staff of some common experiences faced by people with visual, mobility, hearing and cognitive difficulties when using a computer. This test simulates experiences of disabled students who use computers and take computer-based tests, and provides advice and guidance to university teaching staff on how they may best cater for the needs of such students. The paper discusses the reasons for creating such a tool in such a format, its structure and content, and the outcome of its presentation to several groups of participants. Feedback from students with disabilities is to be used in the future development of the test.
A Meta-analysis of Timbre Perception Using Nonlinear Extensions to CLASCAL
Authors: John Ashley Burgoyne, Stephen Mcadams
Venue: Computer Music Modeling and Retrieval. Sense of Sounds: 4th International Symposium, CMMR 2007, Copenhagen, Denmark, August 27-31, 2007. Revised Papers (2008)

Seeking to identify the constituent parts of the multidimensional auditory attribute that musicians know as timbre, music psychologists have made extensive use of multidimensional scaling (mds), a statistical technique for visualising the geometric spaces implied by perceived dissimilarity. mds is also well known in the machine learning community, where it is used as a basic technique for dimensionality reduction. We adapt a nonlinear variant of mds that is popular in machine learning, Isomap, for use in analysing psychological data and re-analyse three earlier experiments on human perception of timbre. Isomap is designed to eliminate undesirable nonlinearities in the input data in order to reduce the overall dimensionality; our results show that it succeeds in these goals for timbre spaces, compressing the output onto well-known dimensions of timbre and highlighting the challenges inherent in quantifying differences in spectral shape.
Word Sense Disambiguation for Vocabulary Learning
Authors: Anagha Kulkarni, Michael Heilman, Maxine Eskenazi, Jamie Callan
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

Words with multiple meanings are a phenomenon inherent to any natural language. In this work, we study the effects of such lexical ambiguities on second language vocabulary learning. We demonstrate that machine learning algorithms for word sense disambiguation can induce classifiers that exhibit high accuracy at the task of disambiguating homonyms (words with multiple distinct meanings). Results from a user study that compared two versions of a vocabulary tutoring system, one that applied word sense disambiguation to support learning and another that did not, support rejection of the null hypothesis that learning outcomes with and without word sense disambiguation are equivalent, with a p-value of 0.001. To our knowledge this is the first work that investigates the efficacy of word sense disambiguation for facilitating second language vocabulary learning.
Hierarchical Eyelid and Face Tracking
Authors: J. Orozco, J. Gonzàlez, I. Rius, F. X. Roca
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I (2007)

Most applications on Human Computer Interaction (HCI) require to extract the movements of user faces, while avoiding high memory and time expenses. Moreover, HCI systems usually use low-cost cameras, while current face tracking techniques strongly depend on the image resolution. In this paper, we tackle the problem of eyelid tracking by using Appearance-Based Models, thus achieving accurate estimations of the movements of the eyelids, while avoiding cues, which require high-resolution faces, such as edge detectors or colour information. Consequently, we can track the fast and spontaneous movements of the eyelids, a very hard task due to the small resolution of the eye regions. Subsequently, we combine the results of eyelid tracking with the estimations of other facial features, such as the eyebrows and the lips. As a result, a hierarchical tracking framework is obtained: we demonstrate that combining two appearance-based trackers allows to get accurate estimates for the eyelid, eyebrows, lips and also the 3D head pose by using low-cost video cameras and in real-time. Therefore, our approach is shown suitable to be used for further facial-expression analysis.
Visual Analysis of Usage Efficiency of Library Books
Authors: Kan Liu, Ping Liu
Venue: Proceedings of the 2008 Fifth International Conference on Computer Graphics, Imaging and Visualisation (2008)

Nowadays the amount of books available in libraries has grown rapidly. Readers may find it difficult to decide which book(s) should be borrowed. The log data of loan history is a good source to find popular books that readers may be interested. This paper describes our efforts in exploring various visual techniques to analyze the borrowing history of the books in a university's library. The visual analysis provides effective way to extract the book usage information and find breakthrough insights. Initial experiment has demonstrated that the proposed approach benefit users in selecting books more effectively.
Particle Filter with Improved Proposal Distribution for Vehicle Tracking
Authors: Huaping Liu, Fuchun Sun
Venue: Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks (2008)

Symmetry is an important characteristic of vehicles and has been frequently used for detection tasks by many researchers. However, existing results of vehicle tracking seldom used symmetry property. In this paper, we will utilize the detected symmetry feature to design a proposal distribution of particle filter for vehicle tracking. The resulting proposal distribution can be closer to the true posterior distribution. Experimental results show that the use of symmetry information will obtain better tracking performance than the conventional color histogram-based particle filters.
Server Consolidation Using Performance Modeling
Authors: Amy Spellmann, Karen Erickson, Jim Reynolds
Venue: IT Professional (2003)

Predicting the performance of server consolidation prior to making any physical changes is key to success.
The Impact of Global Structure on Search
Authors: Monte Lunacek, Darrell Whitley, Andrew Sutton
Venue: Proceedings of the 10th international conference on Parallel Problem Solving from Nature: PPSN X (2008)

Population-based methods are often considered superior on multimodal functions because they tend to explore more of the fitness landscape before they converge. We show that the effectiveness of this strategy is highly dependent on a function's global structure. When the local optima are not structured in a predictable way, exploration can misguide search into sub-optimal regions. Limiting exploration can result in a better non-intuitive global search strategy.
An Image Warping Method for Temporal Subtraction Images Employing Smoothing of Shift Vectors on MDCT Images
Authors: Yoshinori Itai, Hyoungseop Kim, Seiji Ishikawa, Shigehiko Katsuragawa, Takayuki Ishida, Ikuo Kawashita, Kazuo Awai, Kunio Doi
Venue: Neural Information Processing: 14th International Conference, ICONIP 2007, Kitakyushu, Japan, November 13-16, 2007, Revised Selected Papers, Part I (2007)

Detection of subtle lesions on computed tomography (CT) images is a difficult task for radiologists, because subtle lesions such as small lung nodules tend to be low contrast, and a large number of CT images must be interpreted in a limited time. A temporal subtraction image, which is obtained by subtraction of a previous image from a current one, can be used for enhancing interval changes (such as shapes of new lesions and the interval changes in existing abnormalities) on medical images by removing most of the normal structures. For detection of lesions in chest radiographs, the temporal subtraction technique has been applied successfully to clinical cases. In this paper, we propose a new method for removing the artifacts in temporal subtraction images obtained from chest multiple detectors CT (MDCT) images by using a smoothing of shift vectors. With our method, more accurate correspondence of normal structures between current and previous images is determined. The new method was applied for 6 clinical cases of MDCT images. Preliminary results indicated that interval changes on the subtraction images were enhanced considerably with reduction of misregistration artifacts.
Analysis of a simple multihop underwater acoustic network
Authors: Wenyi Zhang, Milica Stojanovic, Urbashi Mitra
Venue: Proceedings of the third ACM international workshop on Underwater Networks (2008)

A multihop underwater acoustic network, which consists of a series of equal-distance hops connected by relay transceivers in a tandem, is considered. Messages are originated as coded packets from a source node at one end, relayed (decoded and re-encoded) sequentially hop by hop, and finally received by a destination node at the other end of the network. Several key characteristics of underwater acoustic channels, namely, frequency-dependent signal attenuation and noise, inter-hop interference, half-duplex constraint, and large propagation delay, are taken into account in the analysis. A simple transmission protocol with spatial reuse is considered, and the transmission schedule is designed to satisfy the half-duplex constraint on relay transceivers in the presence of large propagation delay. To efficiently cope with frequency-dependent channel characteristic and inter-hop interference, the power spectral density function of signaling is analytically optimized in a way analogous to water-filling. Furthermore, the problem of determining the minimum number of hops to support a prespecified rate and reliability with and without a maximum coded packet length constraint is examined. Finally, numerical results are presented to illustrate the analysis.