ppxscal/embedding_network huggingface dataset:
Analyzing biomedical data sets using executable graphical models
Authors: Vojtech Huser
Venue:  (2008)

Clinical data warehouses accumulate large amounts of terminology-coded data. In addition to increased accumulation of data, higher data granularity, and longer time-spans, there is also an increasing demand for analysis of this data. For a nonexpert, the ability to analyze this data unaided is very limited. To address this problem, I developed an analytical framework that works with flowchart models which can be extended with modular external applications and executed on retrospective data. This framework was inspired by emerging workflow technology. Workflow technology offers several tools which support modeling, execution, and extensive analysis of IT or organizational processes. The three specific aims of this dissertation were to review workflow technology and its current use, develop an analytical framework which utilizes graphical, process-based modeling, called RetroGuide (RG), and evaluate this framework using a series of case studies and a formal, comparison evaluation study. RG's graphical representation format facilitates a stepwise, procedural approach to formulating analytical tasks. It uses a single patient execution model, and it resembles a manual chart review methodology. RG models can model complex temporal conditions and utilize external data manipulation, statistical, or reasoning technologies. The representation format is split into two layers, a flowchart and a code layer, which improves collaboration of analytical team members. Reports generated automatically by RG allow advanced drill-down capabilities, show in detail the model's execution trail for each analyzed patient, and support iterative model improvements. Within this dissertation, three analytical domains of quality improvement, decision support development, and medical research were explored. Seven case studies which utilize the Enterprise Data Warehouse (EDW) at Intermountain Healthcare are described (e.g., quality improvement problems in osteoporosis and cardiovascular patients, analysis of a computerized glucose management protocol, a problem in adverse drug event monitoring, or a research analysis of cancer patients). These case studies demonstrate RG's ability to support a wide range of complex analytical tasks, facilitate iterative exploration and review of electronic health record data, and provide a testing environment for retrospective simulation of analytical or decision support processes (using data from a real, large EDW). Finally, a formal comparison study involving modeling analytical tasks in RG and Structured Query Language (SQL), and a qualitative study of RG are presented. The results suggest that RG's modeling approach is intuitive and easy to use, enables better modeling of the evaluated set of analytical tasks, and is preferred over SQL by a group of nonexpert data analysts.
Fast training of neural trees by adaptive splitting based on cubature
Authors: B. Llanas, F. J. Sáinz
Venue: Neurocomputing (2008)

In this paper we prove that any affine function defined on a d-simplex in R^d can be uniformly approximated by a single-layer neural network having only two neurons irrespective of d. The weights of this network are obtained in a closed analytical form, without training. This fact gives a correspondence rule that allows to transform mathematical approximants based on piecewise affine functions, into neural networks. We introduce such an approximant, adaptive splitting based on cubature (ASBC), for the efficient approximation of continuous functions. Using ASBC and the above correspondence rule, we obtain a neural tree. Numerical experiments on learning the function distance from a variable point to a geometric body in two and three dimensions show fast learning speed and high accuracy when compared with single-hidden layer feedforward networks trained by a trust region method based on the interior-reflective Newton algorithm.
Upper Bounds on the Automata Size for Integer and Mixed Real and Integer Linear Arithmetic (Extended Abstract)
Authors: Jochen Eisinger
Venue: Proceedings of the 22nd international workshop on Computer Science Logic (2008)

Automata-based decision procedures have proved to be a particularly useful tool for infinite-state model checking, where automata are used to represent sets of real and integer values. However, not all theoretical aspects of these decision procedures are completely understood. We establish triple exponential upper bounds on the automata size for FO $({\mathbb Z} ,+, and FO $({\mathbb R},Z,+,. While a similar bound for Presburger Arithmetic, i.e., FO $({\mathbb Z},+, was obtained earlier using a quantifier elimination based approach, the bound for FO $({\mathbb R},Z,+, is novel. We define two graded back-and-forth systems, and use them to derive bounds on the automata size by establishing a connection between those systems and languages that can be described by formulas in the respective logics. With these upper bounds that match the known lower bounds, the theoretical background for automata-based decision procedures for linear arithmetics becomes more complete.
Using EDP-Polynomials for Network Structure Optimization
Authors: Olga Gadyatskaya, Alexey Rodionov, Olga Rodionova
Venue: Proceedings of the international conference on Computational Science and Its Applications, Part II (2008)

The analysis of network reliability by the EDP (expectation of a number of disconnected pairs of nodes in the network) criterion is discussed. The difference between results of network optimization by the EDP-criterion and the well-known criterion of the probability of connectivity is shown. Both theoretical statements and results of numerical experiments for network topology optimisation are given.
Using the Web to Reduce Data Sparseness in Pattern-Based Information Extraction
Authors: Sebastian Blohm, Philipp Cimiano
Venue: Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases (2007)

Textual patterns have been used effectively to extract information from large text collections. However they rely heavily on textual redundancy in the sense that facts have to be mentioned in a similar manner in order to be generalized to a textual pattern. Data sparseness thus becomes a problem when trying to extract information from hardly redundant sources like corporate intranets, encyclopedic works or scientific databases.We present results on applying a weakly supervised pattern induction algorithm to Wikipedia to extract instances of arbitrary relations. In particular, we apply different configurations of a basic algorithm for pattern induction on seven different datasets. We show that the lack of redundancy leads to the need of a large amount of training data but that integrating Web extraction into the process leads to a significant reduction of required training data while maintaining the accuracy of Wikipedia. In particular we show that, though the use of the Web can have similar effects as produced by increasing the number of seeds, it leads overall to better results. Our approach thus allows to combine advantages of two sources: The high reliability of a closed corpus and the high redundancy of the Web.
Segmentation of Moving Objects with Information Feedback Between Description Levels
Authors: M. Rincón, E. J. Carmona, M. Bachiller, E. Folgado
Venue: Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II (2007)

In real sequences, one of the factors that most negatively affects the segmentation process result is the existence of scene noise. This impairs object segmentation which has to be corrected if we wish to have some minimum guarantees of success in the following tracking or classification stages. In this work we propose a generic knowledge-based model to improve the segmentation process. Specifically, the model uses a decomposition strategy in description levels to enable the feedback of information between adjacent levels. Finally, two case studies are proposed that instantiate the model proposed for detecting humans.
Application of Micro-GA for an Optimal Direct Design Method of Steel Frame
Authors: Se-Hyu Choi
Venue: Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I (2007)

In this paper, an optimal direct design method of steel frame using advanced analysis and genetic algorithm is presented. The advanced analysis realistically assesses both strength and behavior of a structural system and its component members in a direct manner. The micro-GA is used for minimum weight optimization of steel frames. Constraint functions are load-carrying capacities and serviceability. The optimum designs determined by the proposed method are lighter than those given by other approaches. The ACM Portal is published by the Association for Computing Machinery. Copyright © 2010 ACM, Inc. Terms of Usage Privacy Policy Code of Ethics Contact Us Useful downloads: Adobe Acrobat QuickTime Windows Media Player Real Player
Approach to Enterprise Modelling for Information Systems Engineering
Authors: Saulius Gudas, Audrius Lopata, Tomas Skersys
Venue: Informatica (2005)

The paper deals with Knowledge-based Information Systems (IS) engineering. The Enterprise management functions, processes and their interactions are considered as the major components of the domain knowledge. This is the peculiarity of this approach to Enterprise modelling for IS engineering. The resulting framework for Enterprise modelling and Knowledge-based IS engineering - Enterprise meta-model (EMM) - is developed and presented in this paper. The architecture of the advanced CASE system is also discussed in this paper.
Multiple Instance Learning Allows MHC Class II Epitope Predictions Across Alleles
Authors: Nico Pfeifer, Oliver Kohlbacher
Venue: Proceedings of the 8th international workshop on Algorithms in Bioinformatics (2008)

Human adaptive immune response relies on the recognition of short peptides through proteins of the major histocompatibility complex (MHC). MHC class II molecules are responsible for the recognition of antigens external to a cell. Understanding their specificity is an important step in the design of peptide-based vaccines. The high degree of polymorphism in MHC class II makes the prediction of peptides that bind (and then usually cause an immune response) a challenging task. Typically, these predictions rely on machine learning methods, thus a sufficient amount of data points is required. Due to the scarcity of data, currently there are reliable prediction models only for about 7% of all known alleles available.We show how to transform the problem of MHC class II binding peptide prediction into a well-studied machine learning problem called multiple instance learning. For alleles with sufficient data, we show how to build a well-performing predictor using standard kernels for multiple instance learning. Furthermore, we introduce a new method for training a classifier of an allele without the necessity for binding allele data of the target allele. Instead, we use binding peptide data from other alleles and similarities between the structures of the MHC class II alleles to guide the learning process. This allows for the first time constructing predictors for about two thirds of all known MHC class II alleles. The average performance of these predictors on 14 test alleles is 0.71, measured as area under the ROC curve.Availability: The methods are integrated into the EpiToolKit framework for which there exists a webserver at http://www.epitoolkit.org/ mhciimulti
Rethinking Education in the Age of Technology
Authors: Allan M. Collins
Venue: Proceedings of the 9th international conference on Intelligent Tutoring Systems (2008)

All around us people are learning with the aid of new technologies: children are playing complex video games, workers are taking online courses to get an advanced degree, students are taking courses at commercial learning centers to prepare for tests, adults are consulting Wikipedia, etc. New technologies create learning opportunities that challenge traditional schools and colleges. These new learning niches enable people of all ages to pursue learning on their own terms. People around the world are taking their education out of school into homes, libraries, Internet cafes, and workplaces, where they can decide what they want to learn, when they want to learn, and how they want to learn.
Introducing a Method to Derive an Enterprise-Specific SOA Operating Model
Authors: Christian Schröpfer, Marten Schönherr
Venue: Proceedings of the 2008 12th International IEEE Enterprise Distributed Object Computing Conference (2008)

Considering that each IT-organization is based on an established IT-governance the introduction of SOA as a new overall architectural approach will change the requirements for the specific governance model. The paper presents a SOA-specific governance model which differentiates an operating model, service lifecycle management, service ownership, cost allocation, and meta-data management support. Based on the company´s maturity along the SOA-relevant dimensions IT-governance, SOA experience, and process maturity, its size, as well as its organizational structure the paper discusses an SOA operating model with organizational structure, roles, responsibilities, and KPI´s. The main assumption of the approach is the alignment of the organizational structure of IT with a functional domain model. This operating model is the outcome of ten industrial workshops and one complex case study based on standards as CobiT and ITIL.
The Competitive EM Algorithm for Gaussian Mixtures with BYY Harmony Criterion
Authors: Hengyu Wang, Lei Li, Jinwen Ma
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

Gaussian mixture has been widely used for data modeling and analysis and the EM algorithm is generally employed for its parameter learning. However, the EM algorithm may be trapped into a local maximum of the likelihood and even leads to a wrong result if the number of components is not appropriately set. Recently, the competitive EM (CEM) algorithm for Gaussian mixtures, a new kind of split-and-merge learning algorithm with certain competitive mechanism on estimated components of the EM algorithm, has been constructed to overcome these drawbacks. In this paper, we construct a new CEM algorithm through the Bayesian Ying-Yang (BYY) harmony stop criterion, instead of the previously used MML criterion. It is demonstrated by the simulation experiments that our proposed CEM algorithm outperforms the original one on both model selection and parameter estimation.
A Multi-scale Dynamically Growing Hierarchical Self-organizing Map for Brain MRI Image Segmentation
Authors: Jingdan Zhang, Dao-Qing Dai
Venue: Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks (2007)

With Kohonen's self-organizing map based brain MRI image segmentation, there are still some regions which are not partitioned accurately, particularly in the transitional regions of gray matter and white matter, or cerebrospinal fluid and gray matter. In this paper, we propose a dynamically growing hierarchical self-organizing map integrated with a multi-scale feature vector to overcome the problem mentioned above, which uses the spatial relationships between image pixels and using multi-scale processing method to reduce the noise effect and the classification ambiguity. The efficacy of our approach is validated by extensive experiments using both simulated and real MRI images.
A combinatorial congestion estimation approach with generalized detours
Authors: Fei He, Xiaoyu Song, Ming Gu, Lerong Cheng, Guowu Yang, Zhiwei Tang, Jiaguang Sun
Venue: Computers Mathematics with Applications (2006)

Congestion estimation plays an important role in the physical layout of VLSI design. This paper presents a new probabilistic estimation model that improves the previous estimators by relaxing the constraint on detours in a route. The model is more general and realistic for it gives the flexibility for the wires to have wider usage area to bypass the congestion regions and blockages. Given a routing grid and a set of nets to be routed, the model predicts the routing density on each edge of the grid. The routing density provides direct congestion estimation. We compare our estimation results to the actual routing results. Experimental results show the effectiveness of our estimator.
Comparison of Image Restoration Methods for Bioluminescence Imaging
Authors: Smaïl Akkoul, Roger Ledee, Remy Leconge, Christophe Leger, Rachid Harba, Sabrina Pesnel, Stéphanie Lerondel, Alain Lepape, Luis Vilcahuaman
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

Bioluminescence imaging is a recent modality to visualize biological effects, especially for small animals. However, the acquired images are degraded by diffusion and absorption phenomena from the tissue and by the acquisition system itself. In this paper, we use restoration methods to enhance the quality of bioluminescence images. We propose a model for image formation and an experimental determination of the PSF (Point Spread Function). Several restoration methods are compared on test images generated according to the model and on real data. This comparison is insured by using MSE (Mean Square Error) and two other quantitative criteria. Results showed that the statistical methods give more accurate restoration and are well adapted for Bioluminescence Imaging.
User-Centric Faceted Search for Semantic Portals
Authors: Osma Suominen, Kim Viljanen, Eero Hyvänen
Venue: Proceedings of the 4th European conference on The Semantic Web: Research and Applications (2007)

Many semantic portals use faceted browsing, where the facets are based on the underlying indexing ontologies of the content. However, in many cases, like in medical applications, the ontologies may be very large and complex, and do not provide the end-user with intuitive facet hierarchies for conceptualizing the content, for formulating queries, and for classifying the search results. We argue that in such cases end-user facets should be separated from the annotation ontologies, and show how to generalize the semantic view-based search paradigm to take into account this fact. A user-centric card sorting method is proposed for designing intuitive views for the end-users and a method for mapping its facets onto the indexing ontologies and search items is presented. The system has been implemented in a prototype of the semantic portal TerveSuomi.fi, a national health promotion portal in Finland.
Non-data-communication Overheads in MPI: Analysis on Blue Gene/P
Authors: Pavan Balaji, Anthony Chan, William Gropp, Rajeev Thakur, Ewing Lusk
Venue: Proceedings of the 15th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface (2008)

Modern HEC systems, such as Blue Gene/P, rely on achieving high-performance by using the parallelism of a massive number of low-frequency/low-power processing cores. This means that the local pre- and post-communication processing required by the MPI stack might not be very fast, owing to the slow processing cores. Similarly, small amounts of serialization within the MPI stack that were acceptable on small/medium systems can be brutal on massively parallel systems. In this paper, we study different non-data-communication overheads within the MPI implementation on the IBM Blue Gene/P system.
On the security of public key cryptosystems with a double decryption mechanism
Authors: David Galindo, Javier Herranz
Venue: Information Processing Letters (2008)

In public key encryption schemes with a double decryption mechanism (DD-PKE), decryption can be done in either of two ways: by the user owning the secret/public key pair corresponding to the ciphertext, or by a trusted party holding a sort of master secret-key. In this note we argue that the classical security notion for standard public key encryption schemes does not suffice for DD-PKE schemes, and propose a new natural definition. Additionally, we illustrate the usefulness of the new security definition by showing that a DD-PKE scheme presented in the workshop Selected Areas in Cryptography 2005 is insecure under this augmented security notion.
Real-Time Facial Expression Recognition for Natural Interaction
Authors: Eva Cerezo, Isabelle Hupont, Cristina Manresa-Yee, Javier Varona, Sandra Baldassarri, Francisco J. Perales, Francisco J. Seron
Venue: Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II (2007)

The recognition of emotional information is a key step toward giving computers the ability to interact more naturally and intelligently with people. This paper presents a completely automated real-time system for facial expression's recognition based on facial features' tracking and a simple emotional classification method. Facial features' tracking uses a standard webcam and requires no specific illumination or background conditions. Emotional classification is based on the variation of certain distances and angles from the neutral face and manages the six basic universal emotions of Ekman. The system has been integrated in a 3D engine for managing virtual characters, allowing the exploration of new forms of natural interaction.
Cross-Layer Enhancement of IEEE 802.11 MAC for Mobile Ad Hoc Networks
Authors: Taekon Kim, Hyungkeun Lee, Jang-Yeon Lee, Jin-Woong Cho
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

In mobile ad hoc networks, the large amount of control overheads associated with discovering and maintaining end-to-end routing path information may not be tolerable. In this paper, we present the design and simulation of a new approach for IEEE 802.11 MAC based on the multipath routing information for ad hoc networks. The routing information about multiple paths discovered in the network layer is exploited by the MAC layer in order to forward a frame over the best hop out of multiple hop choices. The performance of our approach is compared with the one of the IEEE 802.11 MAC protocol via simulation. The results show that our proposed scheme exhibits a remarkable performance improvement over the IEEE 802.11 MAC protocol in terms of packet overhead and end-to-end throughput.
H-Trust: A Robust and Lightweight Group Reputation System for Peer-to-Peer Desktop Grid
Authors: Huanyu Zhao, Xiaolin Li
Venue: Proceedings of the 2008 The 28th International Conference on Distributed Computing Systems Workshops (2008)

We propose a robust and lightweight group reputation system, called H-Trust, inspired by the h-index aggregation technique. Leveraging the robustness of the h-index algorithm under incomplete and uncertain circumstances, H-Trust offers a robust reputation evaluation mechanism for both individual and group trusts with minimal communication and computation overheads. The five phases of H-Trust scheme are presented in detail, including trust recording, local trust evaluation, trust query phase, spatial-temporal update phase, and group reputation evaluation phases. The rationale for its design, the analysis of the algorithm complexity and security level are further elaborated. To validate the performance of H-Trust scheme, we conduct multi-agent based simulations. Simulation results demonstrate that H-Trust is robust and can aggregate cooperative groups in systems even when the majority of users are malicious.