ppxscal/embedding_network huggingface dataset:
Simplifying XML schema: effortless handling of nondeterministic regular expressions
Authors: Geert Jan Bex, Wouter Gelade, Wim Martens, Frank Neven
Venue: Proceedings of the 35th SIGMOD international conference on Management of data (2009)

Whether beloved or despised, XML Schema is momentarily the only industrially accepted schema language for XML and is unlikely to become obsolete any time soon. Nevertheless, many nontransparent restrictions unnecessarily complicate the design of XSDs. For instance, complex content models in XML Schema are constrained by the infamous unique particle attribution (UPA) constraint. In formal language theoretic terms, this constraint restricts content models to deterministic regular expressions. As the latter constitute a semantic notion and no simple corresponding syntactical characterization is known, it is very difficult for non-expert users to understand exactly when and why content models do or do not violate UPA. In the present paper, we therefore investigate solutions to relieve users from the burden of UPA by automatically transforming nondeterministic expressions into concise deterministic ones defining the same language or constituting good approximations. The presented techniques facilitate XSD construction by reducing the design task at hand more towards the complexity of the modeling task. In addition, our algorithms can serve as a plug-in for any model management tool which supports export to XML Schema format.
On the scalability of an efficient "Nonscalable key distribution scheme
Authors: Mahalingam Ramkumar
Venue: Proceedings of the 2008 International Symposium on a World of Wireless, Mobile and Multimedia Networks (2008)

Some important requirements of key distribution schemes for many emerging applications involving resource constrained devices (for example, ad hoc and sensor networks) are 1) low complexity; 2) the ability to support unmediated establishment of security associations and 3) scalability. We propose a novel key distribution scheme based on an elegant scheme proposed by Leighton and Micali [1]. While the scheme in [1] was intended to be an alternative to Kerberos (employing a trusted server for mediation), the proposed modifications are towards realizing a scheme for nonmediated establishment of pairwise secrets. We enumerate several compelling advantages of the proposed scheme over existing schemes in the literature.
Exchanging the Business Concepts through Ontology System
Authors: Toshihiko Tsumaki
Venue: Proceeding of the 2008 conference on Knowledge-Based Software Engineering: Proceedings of the Eighth Joint Conference on Knowledge-Based Software Engineering (2008)

The businesses reorganization by the merging and the dividing is progressing in order to strengthen of their competitiveness in the market. In the business reorganization based on cooperation the businesses activities, it is necessary to understand the terms and the concept each other. We propose a new approach that uses the interaction instead of the integration for supporting the cooperation between different business units. The concepts in the business unit are independently expressed by means of the ontology technique and implemented into the corresponding ontology units.
Storyboarding Concepts for Edutainment WIS
Authors: Klaus-Dieter Schewe, Bernhard Thalheim
Venue: Proceeding of the 2008 conference on Information Modelling and Knowledge Bases XIX (2008)

Edutainment web information systems must be adaptable to the user, to the content currently available, to the technical environment of the user, and to the skills, abilities and needs of the learner. This paper provides conceptions that support this kind of adaptivity and sophisticated support.
Pattern Classification Techniques for Early Lung Cancer Diagnosis using an Electronic Nose
Authors: Rossella Blatt, Andrea Bonarini, Elisa Calabró, Matteo Matteucci, Matteo Della Torre, Ugo Pastorino
Venue: Proceeding of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence (2008)

We present a method to diagnose lung cancer by the analysis of breath using an electronic nose. This device can react to a gas substance by providing signals that can be analyzed to classify the input. It is composed of a sensor array (6 MOS sensors, in our case) and a pattern classification process based on machine learning techniques. During the first phase of our research, we have evaluated the possibility and accuracy of lung cancer diagnosis by classifying the olfactory signal associated to exhalations of subjects. The second part of the research, still in progress, is aimed at assessing the possibility of discriminating also the different types and stages of the disease. At the end of the first phase, results have been very satisfactory and promising: we achieved an average accuracy of 92.6%, sensitivity of 95.3% and specificity of 90.5%. In particular we analyzed the breath of 101 individuals, of which 58 control subjects, and 43 suffer from different types of lung cancer (primary and not) at different stages. In order to find the components able to discriminate between the two classes 'healthy' and 'sick' at best, and to reduce the dimensionality of the problem, we have extracted the most significant features and projected them into a lower dimensional space using Non Parametric Linear Discriminant Analysis. Finally, we have used these features as input to several supervised pattern classification algorithms, based on different k-nearest neighbors (k-NN) approaches (classic, modified and Fuzzy k-NN), linear and quadratic discriminant classifiers and on a feed-forward artificial neural network (ANN). The observed results have all been validated using cross-validation. These results pushed us to begin the second phase of the project to investigate the possibility of early lung cancer diagnosis: we are involving a larger number of subjects, partioned in different classes according to the type and stage of the disease. The research demonstrates that the electronic nose is a promising alternative to current lung cancer diagnostic techniques: the obtained predictive errors are lower than those achieved by present diagnostic methods, and the cost of the analysis, both in money, time and resources, is lower. The introduction of this technology will lead to very important social and business effects: its low price and small dimensions allow a large scale distribution, giving the opportunity to perform non invasive, cheap, quick, and massive early diagnosis and screening.
Why Defeating Insurgencies Is Hard: The Effect of Intelligence in Counterinsurgency Operations---A Best-Case Scenario
Authors: Moshe Kress, Roberto Szechtman
Venue: Operations Research (2009)

In insurgency situations, the government-organized force is confronted by a small guerrilla group that is dispersed in the general population with no or a very small signature. Effective counterinsurgency operations require good intelligence. Absent intelligence, not only might the insurgents escape unharmed and continue their violent actions, but collateral damage caused to the general population from poor targeting may generate adverse response against the government and create popular support for the insurgents, which may result in higher recruitment to the insurgency. We model the dynamic relations among intelligence, collateral casualties in the population, attrition, recruitment to the insurgency, and reinforcement to the government force. Even under best-case assumptions, we show that the government cannot totally eradicate the insurgency by force. The best it can do is contain it at a certain fixed level.
Diffusion Learning and Regularization
Authors: Marco Gori
Venue: Proceeding of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008 (2009)

Most of the emphasis in machine learning has been placed on parametric models in which the purpose of the learning algorithm is to adjust weights mainly according to appropriate optimization criteria. However, schemes based on a direct data inference, like for instance K-nearest neighbor, have also become quite popular. Recently, a number of people have proposed methods to perform classification and regression that are based on different forms of diffusion processes from the labelled examples. The aim of this paper is to provide motivations for diffusion learning from the continuum setting by using Tikhnov's regularization framework. Diffusion learning is discussed in both the continuous and discrete setting and an intriguing link is established between the Green function of the regularization operators and the structure of the graph in the corresponding discrete structure. It is pointed out that an appropriate choice of the smoothing operators allows one to implement a regularization that gives rise to Green functions whose corresponding matrix is sparse, which imposes a corresponding structure on the graph associated to the training set. Finally, the choice of the smoothness operator is given a Bayesian interpretation in terms of prior probability on the expected values of the function.
An online energy-efficient routing protocol with traffic load prospects in wireless sensor networks
Authors: Jun Liu, Xiaoyan Hong
Venue: International Journal of Sensor Networks (2009)

In wireless sensor networks, many routing algorithms are designed to implement energy-efficient mechanisms. Among those, some focus on maximising an important performance index called network lifetime, which is the number of messages successfully delivered in the network before a failure. In this paper, we propose a new online algorithm taking the goal of prolonging network lifetime. When making routing decisions, our algorithm, named Traffic-Aware Energy Efficient (TAEE) routing protocol, utilises prospective traffic load information for further load balance, in addition to power-related metrics used in an enhanced cost function in calculating least cost paths. An algorithm for automatic parameter adaption is also described. To better accommodate to large-scale sensor networks, we further introduce a random grouping scheme which enables hierarchical TAEE routing to run within and cross the dynamically formed groups to reduce computation and routing overhead, while maintaining global energy efficiency. Our simulation shows that compared with the leading power-aware Max-min Zp&lt;SUB align=right&gt;MIN protocol, the TAEE protocol generates better performance in terms of network lifetime without jeopardising network capacity.
The Sound Brush Made of Bamboo
Authors: Young-Mi Kim, Jong-Soo Choi
Venue: Proceedings of the 10th International Symposium on Smart Graphics (2009)

This paper is about the study on an artwork, a black-and white drawing that has been expressed through a digital algorithm. Black-white drawings were popular during the Chosun era (1392-1910) reigned by kings and officials. The Oriental fine art, pursuing harmony with nature, is expressed in a moderate and restrained way, hence anyone would find it very soft and thus readily acceptable. Unlike the western paintings that fill the canvus to the very full, the oriental paintings treat even the blank space as a part making up a balanced painting. The four gracious plants, a Maehwa blossom, an orchid, a chrysanthemum, and a bamboo, used to be frequent subjects of gentlemen's paintings as they symbolized the virtues of the old times. This artwork features Daegum, the decent traditional musical instrument which used to be played in loyal palaces or guest rooms of prestigious officials' residences, and a bamboo which was a frequent motive of gentlemen's paintings in the past. Daegum and the bamboo, expressed in a modern style in this work, make people appreciate the life that is full and rich. So, one can say they have been used here to make this "well-being art." The artist's intention is to be delivered through the sound of Daegum that carries the spiritual values of the Oriental culture, the bamboo painting and the spontaneous play by a computer.
Optimizing performance of parallel programs on multicomputer and multi-core architectures: a comparative evaluation
Authors: Mohammad Tanvir Parvez, Syed Usama Idrees, Sahalu Junaidu, Abdul Rahim Naseer
Venue: Proceedings of the 2009 conference on Information Science, Technology and Applications (2009)

With the advent of multi-core architectures, there arises a need for comparative evaluations of the performance of well-understood parallel programs. This is because, it is necessary to gain an insight into the potential advantages of the available platforms, namely multi-core and multi-processors, to decide which platform to use for a particular application. The need for this insight is due to the different nature and requirements (like the divisions of work, intercommunications etc.) of the different parallel algorithms. In this paper, we evaluate the performance of the parallel implementations of three well known algorithms on multi-computer, multi-core and hyper-threading architectures. Parallelization of the programs was done using MPICH2 and OpenMP. We provide comparative evaluation of the run-time behaviors of the parallel programs using three performance metrics: average run-time, I/O and communication overhead. The main experimental result demonstrates the superiority of multicore architecture over multi-computer and hyper-threading architectures for running threaded applications for the same number of cores/processors. We also investigate the effect of parallel I/O in the performance of the programs in multi-computer platform.
Unsupervised Web-based Automatic Annotation
Authors: Miquel Millan, David Sánchez, Antonio Moreno
Venue: Proceeding of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium (2008)

The success of the Semantic Web depends both on the definition of ontologies used to represent the knowledge as on the annotations performed of the web contents. As manual approaches have low scalability, there is a need of tools capable to generate all this knowledge in an automatic and reliable way. In this paper is presented a complete algorithm to annotate web contents in an automatic and unsupervised manner. It is structured in a three-stepped procedure, based on the usage of several concept similarity measures and linguistic patterns. It is able to detect the entities to annotate, the candidate classes of these entities and, finally, associate them with the classes of an ontology. Some prospective results are presented.
Estimating parameters of dynamic errors-in-variables systems with polynomial nonlinearities
Authors: Levente Hunyadi, Istvá Vajk
Venue: Proceedings of the 3rd WSEAS international symposium on Wavelets theory and applications in applied mathematics, signal processing & modern science (2009)

An approach for identifying single-input single-output discrete-time dynamic nonlinear errors-invariables systems is presented where the system model can be linearized such that it is expressed as a linear combination of polynomials of input and output observations. We assume white Gaussian noise on both input and output, characterized by a noise magnitude and a normalized noise covariance structure matrix, and employ a non-linear extension of the generalized Koopmans-Levin method to estimate model parameters with an assumed noise structure and a subsequent covariance matching objective function minimization to estimate all noise parameters. The feasibility of the approach is demonstrated by Monte-Carlo simulations.
Constructing field association words using declinable words and concurrent words
Authors: El-Sayed Atlam, Kazuhiro Morita, Masoa Fuketa, Jun-Ichi Aoe
Venue: Proceedings of the 7th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics (2008)

Readers can know the subject of many document fields by reading only some specific Field Association (FA) words. Document fields can be decided efficiently if there are many rank 1 FA words (words that direct connect to terminal fields) and if the frequency rate is high. This paper proposes a new method for increasing rank 1 FA words using declinable words and concurrent words which relate to narrow association categories and eliminate FA word ambiguity. Concurrent words become Concurrent Field Association Words (CFA words) if there is a little field overlap. Usually, efficient CFA words are difficult to extract using only frequency, so this paper proposes weighting according to degree of importance of concurrent words. The new weighting method causes Precision and Recall to be higher than by using frequency alone. Moreover, combining CFA words with FA words allow easy search of fields which can not be searched by using only FA words.
Relationship privacy: output perturbation for queries with joins
Authors: Vibhor Rastogi, Michael Hay, Gerome Miklau, Dan Suciu
Venue: Proceedings of the twenty-eighth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems (2009)

We study privacy-preserving query answering over data containing relationships. A social network is a prime example of such data, where the nodes represent individuals and edges represent relationships. Nearly all interesting queries over social networks involve joins, and for such queries, existing output perturbation algorithms severely distort query answers. We propose an algorithm that significantly improves utility over competing techniques, typically reducing the error bound from polynomial in the number of nodes to polylogarithmic. The algorithm is, to the best of our knowledge, the first to answer such queries with acceptable accuracy, even for worst-case inputs. The improved utility is achieved by relaxing the privacy condition. Instead of ensuring strict differential privacy, we guarantee a weaker (but still quite practical) condition based on adversarial privacy. To explain precisely the nature of our relaxation in privacy, we provide a new result that characterizes the relationship between ε-indistinguishability~(a variant of the differential privacy definition) and adversarial privacy, which is of independent interest: an algorithm is ε-indistinguishable iff it is private for a particular class of adversaries (defined precisely herein). Our perturbation algorithm guarantees privacy against adversaries in this class whose prior distribution is numerically bounded.
Approximation of Subtle Pathology Signs in Multiscale Domain for Computer-Aided Ischemic Stroke Diagnosis
Authors: Artur Przelaskowski, Rafał Jóźwiak, Grzegorz Ostrek, Katarzyna Sklinda
Venue: Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers (2008)

Computed understanding of CT images used for aided stroke diagnosis was the subject of reported research. Subtle hypodense changes of brain tissue as direct ischemia signs was estimated and extracted to improve diagnosis. Fundamental value of semantic content representation approximated from source images was studied. Nonlinear approximation of subtle pathology signatures in multiscale domain was verified for several local bases including wavelets, curvelets, contourlets and wedgelets. Different rationales for best bases selection were considered. Target pathology estimation procedures were optimized with a criterion of maximally clear extraction of diagnostic information. Visual expression of emphasized hypodenstity was verified for a test set of 25 acute stroke examinations. Suggested methods of stroke nonlinear approximation in many scales may facilitate the early CT-based diagnosis.
Information aggregation in dynamic markets with strategic traders
Authors: Michael Ostrovsky
Venue: Proceedings of the tenth ACM conference on Electronic commerce (2009)

This paper studies information aggregation in dynamic markets with a finite number of partially informed strategic traders. It shows that for a broad class of securities, information in such markets always gets aggregated. Trading takes place in a bounded time interval, and in every equilibrium, as time approaches the end of the interval, the market price of a "separable" security converges in probability to its expected value based on the traders' pooled information.
Policy iteration for customer-average performance optimization of closed queueing systems
Authors: Li Xia, Xi Chen, Xi-Ren Cao
Venue: Automatica (Journal of IFAC) (2009)

We consider the optimization of queueing systems with service rates depending on system states. The optimization criterion is the long-run customer-average performance, which is an important performance metric, different from the traditional time-average performance. We first establish, with perturbation analysis, a difference equation of the customer-average performance in closed networks with exponentially distributed service times and state-dependent service rates. Then we propose a policy iteration optimization algorithm based on this difference equation. This algorithm can be implemented on-line with a single sample path and does not require knowing the routing probabilities of queueing systems. Finally, we give numerical experiments which demonstrate the efficiency of our algorithm. This paper gives a new direction to efficiently optimize the ''customer-centric'' performance in queueing systems.
Semanta --- Semantic Email Made Easy
Authors: Simon Scerri, Brian Davis, Siegfried Handschuh, Manfred Hauswirth
Venue: Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications (2009)

In this paper we present Semanta --- a fully-implemented system supporting Semantic Email Processes, integrated into the existing technical landscape and using existing email transport technology. By applying Speech Act Theory, knowledge about these processes can be made explicit, enabling machines to support email users with correctly interpreting, handling and keeping track of email messages, visualizing email threads and workflows, and extracting tasks and appointments from email messages. Whereas complex theoretical models and semantics are hidden beneath a simple user interface, the enabled functionalities are clear for the users to see and take advantage of. The system's evaluation proved that our experiment with Semanta has indeed been successful and that semantic technology can be applied as an extra layer to existing technology, thus bringing its benefits into everyday computer usage.
Case-based reasoning as a decision support system for cancer diagnosis: A case study
Authors: Juan F. De Paz, Sara Rodríguez, Javier Bajo, Juan M. Corchado
Venue: International Journal of Hybrid Intelligent Systems (2009)

Microarray technology can measure the expression levels of thousands of genes in an experiment. This fact makes the use of computational methods in cancer research absolutely essential. One of the possible applications is in the use of Artificial Intelligence techniques. Several of these techniques have been used to analyze expression arrays, but there is a growing need for new and effective solutions. This paper presents a Case-based reasoning (CBR) system for automatic classification of leukemia patients from microarray data. The system incorporates novel algorithms for data mining that allow filtering, classification, and knowledge extraction. The system has been tested and the results obtained are presented in this paper.