ppxscal/embedding_network huggingface dataset:
A Framework for Service Convergence via Device Cooperation
Authors: Seungchul Shin, Do-Yoon Kim, Sung-Young Yoon
Venue: Proceedings of the 5th International on ConferenceUniversal Access in Human-Computer Interaction. Part II: Intelligent and Ubiquitous Interaction Environments (2009)

Device convergence is one of the most significant trends in current intelligence technology (IT). It is incorporating various kinds of existing services into one device that enables users to provide converged services. However, device convergence is not the whole counterplan that in some fields, costumers prefer divergent application-specific device. In addition, service is always hardware dependent that if a new service appears device convergence will be helpless in case of the hardware not supporting the service. In this situation to use the new service, we have to purchase the whole new device. Therefore, we propose a framework for service convergence via device cooperation supported by the wireless network to overcome the constraint of device convergence. Our framework shows a guideline that enables a device to provide a service by cooperating with other devices despite it lacks the hardware support or only provides one or two specialized services.
A Hierarchical Approach for Robust Background Subtraction Based on Two Views
Authors: Tong Liu, Guoli Wang
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 04 (2009)

In this paper, a hierarchical algorithm is proposed based on two views for background subtraction in image processing. The main idea is to explore the usefulness of the background geometric characteristics of interest in background subtraction for performance improvement. In the proposed approach, the input stereo images are decomposed into different levels using DWT (Discrete Wavelet Transformation), and then an adaptive model is built over sub-bands at different levels in a hierarchical fashion. In particular, when the current pixels do not fulfill the adaptive model at the coarsest level, they will be confirmed farther by a depth based model. In addition, the proposed approach can provide the depth information which is critical to 3D tracking, while performing a task of segmenting the foreground objects. Experiments are conducted to illustrate the effectiveness in the background subtraction.
Mean width of random polytopes in a reasonably smooth convex body
Authors: K. J. Böröczky, F. Fodor, M. Reitzner, V. Vígh
Venue: Journal of Multivariate Analysis (2009)

Let K be a convex body in R^d and let X"n=(x"1,...,x"n) be a random sample of n independent points in K chosen according to the uniform distribution. The convex hull K"n of X"n is a random polytope in K, and we consider its mean width W(K"n). In this article, we assume that K has a rolling ball of radius @r>0. First, we extend the asymptotic formula for the expectation of W(K)-W(K"n) which was earlier known only in the case when @?K has positive Gaussian curvature. In addition, we determine the order of magnitude of the variance of W(K"n), and prove the strong law of large numbers for W(K"n). We note that the strong law of large numbers for any quermassintegral of K was only known earlier for the case when @?K has positive Gaussian curvature.
Worst-Case Optimal Adaptive Prefix Coding
Authors: Travis Gagie, Yakov Nekrich
Venue: Proceedings of the 11th International Symposium on Algorithms and Data Structures (2009)

A common complaint about adaptive prefix coding is that it is much slower than static prefix coding. Karpinski and Nekrich recently took an important step towards resolving this: they gave an adaptive Shannon coding algorithm that encodes each character in O (1) amortized time and decodes it in O (logH + 1) amortized time, where H is the empirical entropy of the input string s. For comparison, Gagie's adaptive Shannon coder and both Knuth's and Vitter's adaptive Huffman coders all use ¿(H + 1) amortized time for each character. In this paper we give an adaptive Shannon coder that both encodes and decodes each character in O (1) worst-case time. As with both previous adaptive Shannon coders, we store s in at most (H + 1) |s| + o (|s|) bits. We also show that this encoding length is worst-case optimal up to the lower order term. In short, we present the first algorithm for adaptive prefix coding that encodes and decodes each character in optimal worst-case time while producing an encoding whose length is also worst-case optimal.
Universally Composable Adaptive Priced Oblivious Transfer
Authors: Alfredo Rial, Markulf Kohlweiss, Bart Preneel
Venue: Proceedings of the 3rd International Conference Palo Alto on Pairing-Based Cryptography (2009)

An adaptive k-out-of-N Priced Oblivious Transfer (POT) scheme is a two-party protocol between a vendor and a buyer. The vendor sells a set of messages m 1, . . . ,m N with prices p 1, . . . , p N . In each transfer phase i = 1, . . . , k, the buyer chooses a selection value ¿ i ¿ {1, . . . ,N} and interacts with the vendor to buy message m ¿ i in such a way that the vendor does not learn ¿ i and the buyer does not get any information about the other messages.We present a POT scheme secure under pairing-related assumptions in the standard model. Our scheme is universally composable and thus, unlike previous results, preserves security when it is executed with multiple protocol instances that run concurrently in an adversarially controlled way. Furthermore, after an initialization phase of complexity O(N), each transfer phase is optimal in terms of rounds of communication and it has constant computational and communication cost. To achieve these properties, we design the first efficient non-interactive proof of knowledge that a value lies in a given interval we are aware of.
Proceedings of the Workshop on Incremental Parsing: Bringing Engineering and Cognition Together
Authors: Frank Keller, Stephen Clark, Matthew Crocker, Mark Steedman
Venue: ACL Workshops (2004)

The papers in these proceedings were presented at the ACL-2004 workshop Incremental Parsing: Bringing Engineering and Cognition Together, held in Barcelona on July 25. Incremental processing is crucial for many NLP tasks. Language modelling, for instance, typically requires that probabilities are assigned incrementally as more and more of the speech stream becomes available. Also, there is substantial evidence showing that humans process language in an incremental fashion, and thus any cognitively plausible model of human parsing must take incrementality into account. The aim of the workshop was to address the dual challenge of defining incremental parsing models that are useful for engineering tasks such as language modelling, while also contributing to our understanding and modelling of the human parsing mechanism. We were pleased to receive 20 submissions, of which we accepted 8 papers for presentation and 3 as reserve papers. Both elements of the workshop theme are well-represented in the proceedings, with roughly 6 of the papers relating to NLP/language engineering, and 5 relating to cognitive modelling.
HADRIAN: Fitting Trials by Digital Human Modelling
Authors: Keith Case, Russell Marshall, Dan Hogberg, Steve Summerskill, Diane Gyi, Ruth Sims
Venue: Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009 (2009)

Anthropometric data are often described in terms of percentiles and too often digital human models are synthesised from such data using a single percentile value for all body dimensions. The poor correlation between body dimensions means that products may be evaluated against models of humans that do not exist. Alternative digital approaches try to minimise this difficulty using pre-defined families of manikins to represent human diversity, whereas in the real world carefully selected real people take part in `fitting trials'. HADRIAN is a digital human modeling system which uses discrete data sets for individuals rather than statistical populations. A task description language is used to execute the evaluative capabilities of the underlying SAMMIE human modelling system as though a `real' fitting trial was being conducted. The approach is described with a focus on the elderly and disabled and their potential exclusion from public transport systems.
Integrating Software Testing and Run-Time Checking in an Assertion Verification Framework
Authors: Edison Mera, Pedro Lopez-García, Manuel Hermenegildo
Venue: Proceedings of the 25th International Conference on Logic Programming (2009)

We present a framework that unifies unit testing and run-time verification (as well as static verification and static debugging). A key contribution of our overall approach is that we preserve the use of a unified assertion language for all of these tasks. We first describe a method for compiling run-time checks for (parts of) assertions which cannot be verified at compile-time via program transformation. This transformation allows checking preconditions and postconditions, including conditional postconditions, properties at arbitrary program points, and certain computational properties. Most importantly, we propose a minimal addition to the assertion language which allows defining unit tests to be run in order to detect possible violations of the (partial) specifications expressed by the assertions. We have implemented the framework within the Ciao/CiaoPP system and effectively applied it to the verification of ISO Prolog compliance and to the detection of different types of bugs in the Ciao system source code. Experimental results are presented that illustrate different trade-offs among program size, running time, or levels of verbosity of the messages shown to the user.
Parallel Algorithm for Triangular Mesh Reconstruction by Deformation in Medical Applications
Authors: Olga Nechaeva, Ivan Afanasyev
Venue: Proceedings of the 10th International Conference on Parallel Computing Technologies (2009)

The main goal of this paper is to develop an efficient method of triangular mesh generation for physical objects which have similar geometrical structure. The method is based on deforming a high quality mesh generated over some "ideal" object into another object of the same structure with mesh quality preservation. The approach uses the Self Organizing Maps algorithm and has been applied for constructing meshes on human femur bones using the GeomBox and GeomRandom packages. A parallel deformation algorithm is implemented using MPI. The efficiency of the parallelization is about 90%.
Formalising Observer Theory for Environment-Sensitive Bisimulation
Authors: Jeremy E. Dawson, Alwen Tiu
Venue: Proceedings of the 22nd International Conference on Theorem Proving in Higher Order Logics (2009)

We consider a formalisation of a notion of observer (or intruder) theories, commonly used in symbolic analysis of security protocols. An observer theory describes the knowledge and capabilities of an observer, and can be given a formal account using deductive systems, such as those used in various "environment-sensitive" bisimulation for process calculi, e.g., the spi-calculus. Two notions are critical to the correctness of such formalisations and the effectiveness of symbolic techniques based on them: decidability of message deduction by the observer and consistency of a given observer theory. We consider a formalisation, in Isabelle/HOL, of both notions based on an encoding of observer theories as pairs of symbolic traces. This encoding has recently been used in a theory of open bisimulation for the spi-calculus. We machine-checked some important properties, including decidability of observer deduction and consistency, and some key steps which are crucial to the automation of open bisimulation checking for the spi-calculus, and highlight some novelty in our Isabelle/HOL formalisations of decidability proofs.
Classifying Amharic news text using self-organizing maps
Authors: Samuel Eyassu, Björn Gambäck
Venue: Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages (2005)

The paper addresses using artificial neural networks for classification of Amharic news items. Amharic is the language for countrywide communication in Ethiopia and has its own writing system containing extensive systematic redundancy. It is quite dialectally diversified and probably representative of the languages of a continent that so far has received little attention within the language processing field. The experiments investigated document clustering around user queries using Self-Organizing Maps, an unsupervised learning neural network strategy. The best ANN model showed a precision of 60.0% when trying to cluster unseen data, and a 69.5% precision when trying to classify it.
A Novel Adaptive Resource-Aware PNN Algorithm Based on Michigan-Nested Pittsburgh PSO
Authors: Kuncup Iswandy, Andreas König
Venue: Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II (2009)

The computational and power resource limitations applicable to intelligent sensor systems in mobile implementations have gained much attention for industrial and medical applications. Probabilistic Neural Networks (PNN) are one of a successful classifier used to solve many classification problems. Currently, in PNN all patterns of training set are used to estimate the probability density function (pdf) of a given class as the sum of isotropic Gaussian kernels. However, the computational effort and the storage requirement of PNN method will prohibitively increase as the number of patterns used in the training set increases. In this paper, we propose as a remedy an Adaptive Resource-Aware Probabilistic Neural Networks (ARAPNN) based on two optimization goals tackle by Particle Swarm Optimization (PSO), which are finding the proper position and number of prototypes (Michigan approach) as well as the best smoothing factor ¿ (Pittsburgh approach). Our proposed algorithm was be tested with five benchmark data sets. The results show that the ARAPNN is able to find solutions with significantly reduced number of prototypes that classify data with competitive or better accuracy than the original PNN and Nearest Neighbor classifiers.
Towards Integration of Biological, Psychological and Social Aspects in Agent-Based Simulation of Violent Offenders
Authors: Tibor Bosse, Charlotte Gerritsen, Jan Treur
Venue: Simulation (2009)

In the analysis of criminal behavior, a combination of biological, psychological and social aspects may be taken into account. Dynamical modeling methods developed in recent years often address biological, psychological or social dynamical systems separately. This paper makes the first step in the development of an agent-based modeling approach for criminal behavior in which these aspects are integrated in one dynamical system. It is shown how within a certain (multi-agent) social context, biological factors, such as certain brain deviations, testosterone levels and serotonin levels, affect cognitive and emotional functioning in such a way that a crime is committed when the perceived opportunity is there. This paper presents one generic model for the behavior of violent offenders with parameters that can be set to obtain simulation traces for three known types of offenders.
Study on the Integrated Indicators of the Ecological Environment Evaluation on Karst Rock Desertification
Authors: Xiaoping Gu, Fei Yu, Yuxiang Luo, Jianguo Mo
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 03 (2009)

Some remarkable relative factors are selected after the relative analysis between factors selected from those possible causes, such as natural surroundings, meteorology, population feature, social economy and relationship between human and nature, related to the rock desertification. The overall evaluation model is established for a ecological conditions in rock desertification in Karst areas with the application of partial least square method regression; the importance of driving factors is evaluated with the application of the importance factors VIP in variance projection. Based on the order of the importance factors VIP in variance projection, it is considered that rock desertification is caused by geographical and topographical features factors; and hydrographical and meteorological factors, especially storm, provided direct drive for rock desertification. The definition of the ecological environment evaluation in rock desertification area is based on the overall evaluation model for a ecological conditions in rock desertification.
Social Network Privacy via Evolving Access Control
Authors: Giovanni Crescenzo, Richard J. Lipton
Venue: Proceedings of the 4th International Conference on Wireless Algorithms, Systems, and Applications (2009)

We study the problem of limiting privacy loss due to data shared in a social network, where the basic underlying assumptions are that users are interested in sharing data and cannot be assumed to constantly follow appropriate privacy policies. Note that if these two assumptions do not hold, social network privacy is theoretically very easy to achieve; for instance, via some form of access control and confidentiality transformation on the data.In this paper we observe that users-regulated access control has shown to be unsuccessful for practical social network, and propose that social networks deploy an additional layer of server-assisted access control which, even under no action from a user, automatically evolves over time, by restricting access to the user's data. The evolving access control mechanism provides non-trivial quantifiable guarantees for formally specified requirements of utility (i.e., users share as much data as possible to all other users) and privacy (i.e., users expose combinations of sensitive data only with low probability and over a long time). To the best of our knowledge, this is the first research solution attempting to simultaneously maximizes utility and safeguards privacy of users sharing data in social networking websites.
Data Organization Methods for Travel Time Acquisition System Based on 3GS
Authors: Jang Guiyan, Zhang Wei, Chang Ande
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 02 (2009)

The reasonable organization of system data is the requisite and powerful technological means used to promote the efficiency of the system. Aimed at the data organization for the travel time acquisition system based on 3GS (GPS/GIS/GPRS), the article has designed a conceptual model of the system database according to the data requirement analysis, proposed a new hierarchical organization method of data based on the object-oriented theory, and designed a layered logical model of the dynamic information database of the system. The hierarchical data organization method can be used to provide technical support for improving the system efficiency and the timeliness of the acquired travel time data.
The Role of Biomedical Dataset in Classification
Authors: Ajay Kumar Tanwani, Muddassar Farooq
Venue: Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine (2009)

In this paper, we investigate the role of a biomedical dataset on the classification accuracy of an algorithm. We quantify the complexity of a biomedical dataset using five complexity measures: correlation-based feature selection subset merit, noise, imbalance ratio, missing values and information gain. The effect of these complexity measures on classification accuracy is evaluated using five diverse machine learning algorithms: J48 (decision tree), SMO (support vector machines), Naive Bayes (probabilistic), IBk (instance based learner) and JRIP (rule-based induction). The results of our experiments show that noise and correlation-based feature selection subset merit --- not a particular choice of algorithm --- play a major role in determining the classification accuracy. In the end, we provide researchers with a meta-model and an empirical equation to estimate the classification potential of a dataset on the basis of its complexity. This well help researchers to efficiently pre-process the dataset for automatic knowledge extraction.
Fusion of Multisensor Data: Review and Comparative Analysis
Authors: Uttam Kumar, Chiranjit Mukhopadhyay, Ramachandra TV
Venue: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 02 (2009)

Image fusion is a formal framework which is expressed as means and tools for the alliance of multisensor, multitemporal, and multiresolution data. Multisource data vary in spectral, spatial and temporal resolutions necessitating advanced analytical or numerical techniques for enhanced interpretation capabilities. This paper reviews seven pixel based image fusion techniques -– intensity-hue-saturation, brovey, high pass filter (HPF), high pass modulation (HPM), principal component analysis, fourier transform and correspondence analysis. Validation of these techniques on IKONOS data (Panchromatic band at 1 m spatial resolution and Multispectral 4 bands at 4 m spatial resolution) reveal that HPF and HPM methods synthesise the images closest to those the corresponding multisensors would observe at the high resolution level.
Performance prediction for exponential language models
Authors: Stanley F. Chen
Venue: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics (2009)

We investigate the task of performance prediction for language models belonging to the exponential family. First, we attempt to empirically discover a formula for predicting test set cross-entropy for n-gram language models. We build models over varying domains, data set sizes, and n-gram orders, and perform linear regression to see whether we can model test set performance as a simple function of training set performance and various model statistics. Remarkably, we find a simple relationship that predicts test set performance with a correlation of 0.9997. We analyze why this relationship holds and show that it holds for other exponential language models as well, including class-based models and minimum discrimination information models. Finally, we discuss how this relationship can be applied to improve language model performance.
Minimal contraction of preference relations
Authors: Denis Mindolin, Jan Chomicki
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 1 (2008)

Changing preferences is very common in real life. The expressive power of the operations of preference change introduced so far in the literature is limited to adding new information about preference and equivalence. Here, we discuss the operation of discarding preferences: preference contraction. We argue that the property of minimality and the preservation of strict partial orders are crucial for contractions. Contractions can be further constrained by specifying which preferences should not be contracted. We provide algorithms for computing minimal and minimal preference-protecting contraction. We also show some preference query optimization techniques which can be used in the presence of contraction.
A dependency-driven parser for German dependency and constituency representations
Authors: Johan Hall, Joakim Nivre
Venue: Proceedings of the Workshop on Parsing German (2008)

We present a dependency-driven parser that parses both dependency structures and constituent structures. Constituency representations are automatically transformed into dependency representations with complex arc labels, which makes it possible to recover the constituent structure with both constituent labels and grammatical functions. We report a labeled attachment score close to 90% for dependency versions of the TIGER and T&uacute;Ba-D/Z treebanks. Moreover, the parser is able to recover both constituent labels and grammatical functions with an F-Score over 75% for T&uuml;Ba-D/Z and over 65% for TIGER.