ppxscal/embedding_network huggingface dataset:
Temporal Abstraction of States Through Fuzzy Temporal Constraint Networks
Authors: M. Campos, J. M. Juárez, J. Salort, J. Palma, R. Marín
Venue: Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks (2007)

Temporal abstraction methods produce high level descriptions of a parameter evolution from collections of temporal data. As the level of abstraction of the data is increased, it becomes easier to use them in a reasoning process based on high-level explicit knowledge. Furthermore, the volume of data to be treated is reduced and, subsequently, the reasoning becomes more efficient. Besides, there exist domains, such as medicine, in which there is some imprecision when describing the temporal location of data, especially when they are based on subjective observations. In this work, we describe how the use of fuzzy temporal constraint networks enables temporal imprecision to be considered in temporal abstraction.
Plenary lecture II: fuzzy models in bioinformatics
Authors: Tuan D. Pham
Venue: Proceedings of the 9th WSEAS International Conference on Fuzzy Systems (2008)

Cancer classification using high-throughput mass spectrometry data for early disease detection and prevention has recently become an attractive topic of research in bioinformatics. Recently, several studies have shown that the synergy of proteomic technology and pattern classification techniques is promising for the predictive diagnoses of several cancer diseases. However, the extraction of some effective features that can represent the identities of different classes plays a critical factor for any classification problems involving the analysis of complex data. In this paper we present the concept of a fuzzy fractal dimension that can be utilized as a novel feature of mass spectrometry data. We then applied vector quantization to model the class prototypes using the fuzzy fractal dimensions for classification. Using a simple vector-quantization based classification rule, the overall average classification rates of the proposed approach were found to be superior to some other methods. In bio-imaging classification, we applied vector quantization and Markov modeling methods for cell-phase classification using time-lapse fluorescence microscopic image sequences. However this method is not always effective because cell features are treated with equal weight of importance that may not be always true. We proposed a subspace vector-quantization method to overcome this drawback. The proposed method can automatically weight cell features based on their attribute importance in fuzzy clustering analysis. Two weighting algorithms based on fuzzy c-means and fuzzy entropy clustering were studied, whose performances improved the classification rates.
Development of a Computer Vision System for the Automatic Quality Grading of Mandarin Segments
Authors: José Blasco, Sergio Cubero, Raúl Arias, Juan Gómez, Florentino Juste, Enrique Moltó
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

This work focuses on the development of a computer vision system for the automatic on-line inspection and classification of Satsuma segments. During the image acquisition the segments are in movement, wet and frequently in contact with other pieces. The segments are transported over six semi-transparent conveyor belts that advance at speed of 1 m/s. During on-line operation, the system acquires images of the segments using two cameras connected to a single computer and process the images in less than 50 ms. Extracting morphological features from the objects, the system identifies automatically pieces of skin and row material and separates entire segments from broken ones, discriminating between those with slight or large breaking degree. Combinations of morphological parameters were employed to decide the quality of each segment, classifying correctly 95% of sound segments.
Shining Light in Dark Places: Understanding the Tor Network
Authors: Damon Mccoy, Kevin Bauer, Dirk Grunwald, Tadayoshi Kohno, Douglas Sicker
Venue: Proceedings of the 8th international symposium on Privacy Enhancing Technologies (2008)

To date, there has yet to be a study that characterizes the usage of a real deployed anonymity service. We present observations and analysis obtained by participating in the Tor network. Our primary goals are to better understand Tor as it is deployed and through this understanding, propose improvements. In particular, we are interested in answering the following questions: (1) How is Tor being used? (2) How is Tor being mis-used? (3) Who is using Tor?To sample the results, we show that web traffic makes up the majority of the connections and bandwidth, but non-interactive protocols consume a disproportionately large amount of bandwidth when compared to interactive protocols. We provide a survey of how Tor is being misused, both by clients and by Tor router operators. In particular, we develop a method for detecting exit router logging (in certain cases). Finally, we present evidence that Tor is used throughout the world, but router participation is limited to only a few countries.
An Automatic Method for Video Character Segmentation
Authors: Zohra Saidane, Christophe Garcia
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

This paper presents an automatic segmentation system for characters in text color images cropped from natural images or videos based on a new neuronal architecture insuring fast processing and robustness against noise, variations in illumination, complex background and low resolution. An off-line training phase on a set of synthetic text color images, where the exact character positions are known, allows adjusting the neural parameters and thus building an optimal non linear filter which extracts the best features in order to robustly detect the border positions between characters. The proposed method is tested on a set of synthetic text images to precisely evaluate its performance according to noise, and on a set of complex text images collected from video frames and web pages to evaluate its performance on real images. The results are encouraging with a good segmentation rate of 89.12% and a recognition rate of 81.94% on a set of difficult text images collected from video frames and from web pages.
Strategy Roadmaps: New Forms, New Practices
Authors: Alan F. Blackwell, Rob Phaal, Martin Eppler, Nathan Crilly
Venue: Proceedings of the 5th international conference on Diagrammatic Representation and Inference (2008)

Strategy roadmaps are a class of abstract visual representation that is unusually diverse in diagrammatic style. A large corpus of roadmap visualisations was collected, and analysed using a collaborative consultation method involving expert participants. The results indicate organizing principles for the broader study of diagram use within strategic management. They also provide an opportunity for some basic design advice that can be given to professional strategy practitioners or managers who need to create diagrams of this kind. Finally, this novel context of diagram use has required the development of new research methods that may be of value in other Diagrams research.
Estimating the Speedup in Parallel Parsing
Authors: J. Cohen
Venue: IEEE Transactions on Software Engineering (1985)

A model for the operation of bottom-up parallel parsing using asynchronous processors is proposed. The model is based on an extension of shift-reduce parsers which are able to merge the information they keep on their stacks. The main objective of the paper is to provide estimates of the speedup attainable when using the proposed model. Three programs were written to measure the speedup. The first is a classical simulator which keeps track of the times spent performing the shift, reduce, and merge operations for each processor. The second is a program which generates "typical" strings in a language and simultaneously keeps track of the number of operations needed to parse the generated strings. The third is a program capable of deducing the num-ber of parsing operations by counting the number of selected terminals appearing in an input string. The results, applicable to the paralel parsing of programs written in a Pascal-like language, show how the speedup varies with the number of processors for different ratios of the times to shift, reduce, and merge. Although the speedup falls considerably below that predicted by theory, substantial gains are still attainable by using a fairly large number of parallel processors. With the decreasing costs of processors, parallel parsing and parallel compilation will become increasingly important and should allow considerable gains in speedup.
Modeling Self-deception within a Decision-Theoretic Framework
Authors: Jonathan Y. Ito, David V. Pynadath, Stacy C. Marsella
Venue: Proceedings of the 8th international conference on Intelligent Virtual Agents (2008)

Computational modeling of human belief maintenance and decision-making processes has become increasingly important for a wide range of applications. In this paper, we present a framework for modeling the human capacity for self-deception from a decision-theoretic perspective in which we describe processes for determining a desired belief state, the biasing of internal beliefs towards the desired belief state, and the actual decision-making process based upon the integrated biases. Furthermore, we show that in some situations self-deception can be beneficial.
An Approach to Semantic Annotation for Metadata in Relational Databases
Authors: Lei Zhang, Xiaoying Yang
Venue: Proceedings of the 2008 International Symposiums on Information Processing (2008)

The solution to semantic heterogeneity of the integrated data is a key point in the integration of heterogeneous databases. To aim at adding semantic information to tables and fields in relational databases, a semantic annotation method was developed. The method integrates many different string similarity algorithms to improve similarity algorithms. Firstly, extracting implicit semantic information from metadata in relational databases according to similarities between metadata and the ontology entity; then, the extracted information is annotated using name similarities and structural similarities between metadata and the ontology entity. Tests annotating the metadata in dozens of heterogeneous relational databases had an accurate rate of 82%.
Evaluating approximations generated by the GNG3D method for mesh simplification
Authors: Pedro Navarro, Leandro Tortosa, Jose F. Vicent, Antonio Zamora
Venue: Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases (2008)

In this paper we present different error measurements with the aim to evaluate the quality of the approximations generated by the GNG3D method for mesh simplification. The first phase of this method consists on the execution of the GNG3D algorithm, described in the paper. The primary goal of this phase is to obtain a simplified set of vertices representing the best approximation of the original 3D object. In the reconstruction phase we use the information provided by the optimization algorithm to reconstruct the faces thus obtaining the optimized mesh. The implementation of three error functions, named Eavg, Emax, Esur, permitts us to control the error of the simplified model, as it is shown in the examples studied.
A multi-agent simulation system for prediction and scheduling of aero engine overhaul
Authors: Armin Stranjak, Partha Sarathi Dutta, Mark Ebden, Alex Rogers, Perukrishnen Vytelingum
Venue: Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: industrial track (2008)

The Aero Repair and Overhaul industry is facing an increasing challenge of prediction and scheduling of engine over-hauls to remain competitive in a complex business arena. An appropriate technology solution is required to achieve efficient schedules while satisfying multiple opposing constraints in a highly dynamic environment. In this paper, we describe Overhaul Prediction and Scheduling, an agent-based simulator developed to tackle this challenge. Using negotiation strategies, it deals with the multi-dimensional scheduling optimisation problem by trading off repair costs, capacity and capability of overhaul bases, among others, in light of in-service unforseen events. It supports effective strategic decision-making via business scenario modelling.
NPR Lenses: Interactive Tools for Non-photorealistic Line Drawings
Authors: Petra Neumann, Tobias Isenberg, Sheelagh Carpendale
Venue: Proceedings of the 8th international symposium on Smart Graphics (2007)

NPR Lenses is an interactive technique for producing expressive non-photorealistic renderings. It provides an intuitive visual interaction tool for illustrators, allowing them to seamlessly apply a large variety of emphasis techniques. Advantages of 3D scene manipulation are combined with the capabilities of viewer-aligned lenses by inserting 2D lens controls into the 3D rendering pipeline. By using lenses that are spatially explicit and constrained we enable object-independent adjustments in 3D. Regions of emphasis can be easily created by adjusting lens parameters and any combination of line style, type, shape, and position of computer-generated lines. NPR Lenses support the creation of effects similar to those found in many existing hand-drawn illustrations and allow creative freedom through interactive control at the same time.
Texture Based Segmentation of Breast DCE-MRI
Authors: Yang Can Gong, Michael Brady
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

Breast dynamic contrast enhanced MRI (DCE-MRI) segmentation, based on the differential enhancement of image intensities, can help the clinician detect suspicious regions. Motivated by the recent success of texture learning and segmentation, we propose a novel segmentation method based on texture properties. The segmentation method consists of generating a library of texture primitives "textons", and then classifying each voxel into different tissue classes using textons and vector attributes. A Markov Random Measure field (MRF) method is combined with texture information to realise the spatial coherence. To evaluate our framework, twenty patients' MRIs from our local hospital were used for texture learning, and a further twenty patients' MRIs were used for testing.
Fast parallel GPU-sorting using a hybrid algorithm
Authors: Erik Sintorn, Ulf Assarsson
Venue: Journal of Parallel and Distributed Computing (2008)

This paper presents an algorithm for fast sorting of large lists using modern GPUs. The method achieves high speed by efficiently utilizing the parallelism of the GPU throughout the whole algorithm. Initially, GPU-based bucketsort or quicksort splits the list into enough sublists then to be sorted in parallel using merge-sort. The algorithm is of complexity nlogn, and for lists of 8 M elements and using a single Geforce 8800 GTS-512, it is 2.5 times as fast as the bitonic sort algorithms, with standard complexity of n(logn)^2, which for a long time was considered to be the fastest for GPU sorting. It is 6 times faster than single CPU quicksort, and 10% faster than the recent GPU-based radix sort. Finally, the algorithm is further parallelized to utilize two graphics cards, resulting in yet another 1.8 times speedup.
Expressiveness and complexity of XML publishing transducers
Authors: Wenfei Fan, Floris Geerts, Frank Neven
Venue: ACM Transactions on Database Systems (TODS) (2008)

A number of languages have been developed for specifying XML publishing, that is, transformations of relational data into XML trees. These languages generally describe the behaviors of a middleware controller that builds an output tree iteratively, issuing queries to a relational source and expanding the tree with the query results at each step. To study the complexity and expressive power of XML publishing languages, this article proposes a notion of publishing transducers, which generate XML trees from relational data. We study a variety of publishing transducers based on what relational queries a transducer can issue, what temporary stores a transducer can use during tree generation, and whether or not some tree nodes are allowed to be virtual, that is, excluded from the output tree. We first show how existing XML publishing languages can be characterized by such transducers, and thus provide a synergy between theory and practice. We then study the membership, emptiness, and equivalence problems for various classes of transducers. We establish lower and upper bounds, all matching, ranging from PTIME to undecidable. Finally, we investigate the expressive power of these transducers and existing languages. We show that when treated as relational query languages, different classes of transducers capture either complexity classes (e.g., PSPACE) or fragments of datalog (e.g., linear datalog). For tree generation, we establish connections between publishing transducers and logical transductions, among other things.
National e-ID card schemes: A European overview
Authors: Siddhartha Arora
Venue: Information Security Tech. Report (2008)

Across Europe electronic identity (e-ID) card schemes are emerging. The motivation for their deployment varies from country to country, and hence also their ability to interoperate. National schemes are defined by government agencies and application usage by non-government entities has been limited. Changes are occurring, however, both in terms of secondary use by the private sector as well as various activities to enable more interoperability across national e-ID card schemes.
XML research midflight
Authors: Yannis Papakonstantinou
Venue: Proceedings of the 2008 EDBT workshop on Database technologies for handling XML information on the web (2008)

XML emerged 10 years ago as the flexible standard for information exchange and representation. The database research community had presciently already produced related works on semi-structured data models and proceeded to generate multiple research contributions in the field, influencing along the way standards and industrial products. Since then much of the XML future has arrived: complex standards (notably XQuery) emerged, first industrial systems have been released. Naturally XML research changes gears. We will discuss successes, failures, open issues and provide thoughts on what is needed to make XML research even more effective.
Texture Dissimilarity Measures for Background Change Detection
Authors: Roland Miezianko, Dragoljub Pokrajac
Venue: Proceedings of the 5th international conference on Image Analysis and Recognition (2008)

Presented framework provides a method for adaptive background change detection in video from monocular static cameras. A background change constitutes of objects left in the scene and objects moved or taken from the scene. This framework may be applied to luggage left behind in public places, to asses the damage and theft of public property, or to detect minute changes in the scene. The key elements of the framework include spatiotemporal motion detection, texture classification of non-moving regions, and spatial clustering of detected background changes. Motion detection based on local variation of spatiotemporal texture separates the foreground and background regions. Local background dissimilarity measurement is based on wavelet decomposition of localized texture maps. Dynamic threshold of the normalized dissimilarity measurement identifies changed local background blocks, and spatial clustering isolates the regions of interest. The results are demonstrated on the PETS 2006 video sequences.
Building reliable voting machine software
Authors: David Wagner, Marti Hearst
Venue:  (2007)

I examine the question of how to design election-related software, with particular attention to the threat of insider attacks, and propose the goal of simplifying the software in electronic voting machines. I apply a technique called prerendering to reduce the security-critical, voting-specific software by a factor of 10 to 100 while supporting similar or better usability and accessibility, compared to today's voting machines. Smaller and simpler software generally contributes to easier verification and higher confidence.I demonstrate and validate the prerendering approach by presenting Pvote, a vote-entry program that allows a high degree of freedom in the design of the user interface and supports synchronized audio and video, touchscreen input, and input devices for people with disabilities. Despite all its capabilities, Pvote is just 460 lines of Python code; thus, it directly addresses the conflict between flexibility and reliability that underlies much of the current controversy over electronic voting. A security review of Pvote found no bugs in the Pvote code and yielded lessons on the practice of adversarial code review. The analysis and design methods I used, including the prerendering technique, are also applicable to other high-assurance software.
Metaphor and Affect Detection in an ICA
Authors: T. H. Rumbell, C. J. Smith, J. A. Barnden, M. G. Lee, S. R. Glasbey, A. M. Wallington
Venue: Proceedings of the 2nd international conference on Affective Computing and Intelligent Interaction (2007)

We discuss an aspect of an affect-detection system used in edrama by intelligent conversational agents, namely affective interpretation of limited sorts of metaphorical utterance. We discuss how these metaphorical utterances are recognized and how they are analysed and their affective content determined.
Table Based Single Pass Algorithm for Clustering News Articles in NewsPage.com
Authors: Gwyduk Yeom, Taeho Jo, Youngik Yeom
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

This research proposes a modified version of single pass algorithm specialized for text clustering. Encoding documents into numerical vectors for using the traditional version of single pass algorithm causes the two main problems: huge dimensionality and sparse distribution. Therefore, in order to address the two problems, this research modifies the single pass algorithm into its version where documents are encoded into other forms than numerical vectors. In the proposed version, documents are mapped into tables and an operation on two tables is defined for using the single pass algorithm. The goal of this research is to improve the performance of single pass algorithm for text clustering by modifying it.
The Randomized Coloring Procedure with Symmetry-Breaking
Authors: Sriram Pemmaraju, Aravind Srinivasan
Venue: Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I (2008)

A basic randomized coloring procedure has been used in probabilistic proofs to obtain remarkably strong results on graph coloring. These results include the asymptotic version of the List Coloring Conjecture due to Kahn, the extensions of Brooks' Theorem to sparse graphs due to Kim and Johansson, and Luby's fast parallel and distributed algorithms for graph coloring. The most challenging aspect of a typical probabilistic proof is showing adequate concentration bounds for key random variables. In this paper, we present a simple symmetry-breaking augmentation to the randomized coloring procedure that works well in conjunction with Azuma's Martingale Inequality to easily yield the requisite concentration bounds. We use this approach to obtain a number of results in two areas: frugal coloring and weighted equitable coloring. A &beta;-frugal coloring of a graph G is a proper vertex-coloring of G in which no color appears more than &beta; times in any neighborhood. Let G = (V, E) be a vertex-weighted graph with weight function w: V &rarr;[0, 1] and let W = &Sigma;v &epsilon; V w(v). A weighted equitable coloring of G is a proper k-coloring such that the total weight of every color class is "large", i.e., "not much smaller" than W/k; this notion is useful in obtaining tail bounds for sums of dependent random variables.