ppxscal/embedding_network huggingface dataset:
HIDE: An Integrated System for Health Information DE-identification
Authors: James Gardner, Li Xiong
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

While there is an increasing need to share medical information for public health research, such data sharing must preserve patient privacy without disclosing any identifiable information. A considerable amount of research in data privacy community has been devoted to formalizing the notion of identifiability and developing techniques for anonymization but are focused exclusively on structured data. On the other hand, efforts on de-identifying medical text documents in medical informatics community rely on simple identifier removal or grouping techniques without taking advantage of the research developments in the data privacy community. This paper attempts to fill the above gaps and presents a prototype system for de-identifying health information including both structured and unstructured data. It deploys a conditional random fields based technique for extracting identifying attributes from unstructured data and k-anonymization based technique for de-identifying the data while preserving maximum data utility. We present a set of preliminary evaluations showing the effectiveness of our approach.
Tile-Based Interactive Texture Design
Authors: Weiming Dong, Ning Zhou, Jean-Claude Paul
Venue: Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment (2008)

In this paper, we present a novel interactive texture design scheme based on the tile optimization and image composition. Given a small example texture, the design process starts with applying an optimized sample patches selection operation to the example texture to obtain a set of sample patches. Then a set of &omega;-tiles are constructed from these patches. Local changes to those tiles are further made by composing their local regions with the texture elements or objects interactively selected from other textures or normal images. Such select-compose process is iterated many times until the desired &omega;-tiles are obtained. Finally the tiles are tiled together to form a large texture. Our experimental results demonstrate that the proposed technique can be used for designing a large variety of versatile textures from a single small example texture, increasing or decreasing the decreasing the density of texture elements, as well as for synthesizing textures from multiple sources.
The informational efficiency of the black and official exchange markets of DR-CAFTA nations
Authors: Shuming Bai, Kai S. Koong, Lai C. Liu, Binshan Lin
Venue: International Journal of Electronic Finance (2008)

This study examines the long-run informational efficiency and short-run predictability of the black and official exchange markets of Dominican Republic-Central American Free Trade Agreement (DR-CAFTA) nations. Applying a battery of nonparametric as well as time series models, this study finds that the black market information exhibited a nonrandom behaviour. In four of the six examined countries, the black and official markets were found to have a co-integrating relationship. Both the vector error correction model and the Granger causality test confirmed that the black market information was a good predictor of the official rates.
Quantitative methods for three-dimensional comparison and petrographic description of chondrites
Authors: Jon M. Friedrich
Venue: Computers Geosciences (2008)

X-ray computed tomography can be used to generate three-dimensional (3D) volumetric representations of chondritic meteorites. One of the challenges of using collected X-ray tomographic data is the extraction of useful data for 3D petrographic analysis or description. Here, I examine computer-aided quantitative 3D texture metrics that can be used for the classification of chondritic meteorites. These quantitative techniques are extremely useful for discriminating between chondritic materials, but yield little information on the 3D morphology of chondrite components. To investigate the morphology of chondrite minerals such as Fe(Ni) metal and related sulfides, the homology descriptors known as Betti numbers, are examined. Both methodologies are illustrated with theoretical discussion and examples. Betti numbers may be valuable for examining the nature of metal-silicate structural changes within chondrites with increasing degrees of metamorphism.
Quantitative Separation Logic and Programs with Lists
Authors: Marius Bozga, Radu Iosif, Swann Perarnau
Venue: Proceedings of the 4th international joint conference on Automated Reasoning (2008)

This paper presents an extension of a decidable fragment of Separation Logic for singly-linked lists, defined by Berdine, Calcagno and O'Hearn [8]. Our main extension consists in introducing atomic formulae of the form ls k (x, y) describing a list segment of length k, stretching from x to y, where k is a logical variable interpreted over positive natural numbers, that may occur further inside Presburger constraints.We study the decidability of the full first-order logic combining unrestricted quantification of arithmetic and location variables. Although the full logic is found to be undecidable, validity of entailments between formulae with the quantifier prefix in the language $\exists^*{\{\exists_\mathbb{N},\forall_\mathbb{N}\}^*}$ We provide here a model theoretic method, based on a parametric notion of shape graphs.We have implemented our decision technique, providing a fully automated framework for the verification of quantitative properties expressed as pre- and post-conditions on programs working on lists and integer counters.
A Type Driven Theory of Predication with Complex Types
Authors: Nicholas Asher
Venue: Fundamenta Informaticae (2008)

This paper investigates several models of the complex type &bull; which is needed to analyze copredication. Previous accounts are shown to be inadequate and a new account both of &bull; and copredication is proposed.
Robustness Analysis of SARSA(λ): Different Models of Reward and Initialisation
Authors: Marek Grześ, Daniel Kudenko
Venue: Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications (2008)

In the paper the robustness of SARSA(&lambda;), the reinforcement learning algorithm with eligibility traces, is confronted with different models of reward and initialisation of the Q-table. Most of the empirical analyses of eligibility traces in the literature have focused mainly on the step-penalty reward. We analyse two general types of rewards (final goal and step-penalty rewards) and show that learning with long traces, i.e., with high values of &lambda;, can lead to suboptimal solutions in some situations. Problems are identified and discussed. Specifically, obtained results show that SARSA(&lambda;) is sensitive to different models of reward and initialisation. In some cases the asymptotic performance can be significantly reduced.
A Fast Search Algorithm for Vector Quantization Based on Associative Memories
Authors: Enrique Guzmán, Oleksiy Pogrebnyak, Luis Sánchez Fernández, Cornelio Yáñez-Márquez
Venue: Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications (2008)

One of the most serious problems in vector quantization is the high computational complexity at the encoding phase. This paper presents a new fast search algorithm for vector quantization based on Extended Associative Memories (FSA-EAM). In order to obtain the FSA-EAM, first, we used the Extended Associative Memories (EAM) to create an EAM-codebook applying the EAM training stage to the codebook produced by the LBG algorithm. The result of this stage is an associative network whose goal is to establish a relation between training set and the codebook generated by the LBG algorithm. This associative network is EAM-codebook which is used by the FSA-EAM. The FSA-EAM VQ process is performed using the recalling stage of EAM. This process generates a set of the class indices to which each input vector belongs. With respect to the LBG algorithm, the main advantage offered by the proposed algorithm is high processing speed and low demand of resources (system memory), while the encoding quality remains competitive.
Many Facets of Complexity in Logic
Authors: Antonina Kolokolova
Venue: Proceedings of the 4th conference on Computability in Europe: Logic and Theory of Algorithms (2008)

There are many ways to define complexity in logic. In finite model theory, it is the complexity of describing properties, whereas in proof complexity it is the complexity of proving properties in a proof system. Here we consider several notions of complexity in logic, the connections among them, and their relationship with computational complexity. In particular, we show how the complexity of logics in the setting of finite model theory is used to obtain results in bounded arithmetic, stating which functions are provably total in certain weak systems of arithmetic. For example, the transitive closure function (testing reachability between two given points in a directed graph) is definable using only NL-concepts (where NL is non-deterministic log-space complexity class), and its totality is provable within NL-reasoning.
Experiences on Using Software Experiments in the Validation of Industrial Research Questions
Authors: Dominik Gessenharter, Alexander-Marc Merten, Alexander Raschke, Nicolas Fernando Porta
Venue: Software Process and Product Measurement: International Conference, IWSM-Mensura 2007, Palma de Mallorca, Spain, November 5-8, 2007. Revised Papers (2007)

Experimentation in software engineering is difficult. One reason is the large number of context variables [1] and the impracticality of experiments in an industrial setting. Considering the budgets of comprehensive projects, it is apparent that a company cannot double its effort executing a project twice, in order to compare two different approaches concerning process or method improvement. Performing experiments on the basis of small projects seldom offers solutions valid for industrial settings. Our commendation is a cooperation between industry and academic education. This approach offers multiple advantages. In this paper, we outline our experiences in experimental software engineering gained in about 20 experiments over the past 10 years by the Ulm University cooperating with Daimler AG, Research Development, Ulm. Additionally we provide an insight into a current experiment and present our approach to experimental software engineering in further detail.
Superconvergence and time evolution of discontinuous Galerkin finite element solutions
Authors: Yingda Cheng, Chi-Wang Shu
Venue: Journal of Computational Physics (2008)

In this paper, we study the convergence and time evolution of the error between the discontinuous Galerkin (DG) finite element solution and the exact solution for conservation laws when upwind fluxes are used. We prove that if we apply piecewise linear polynomials to a linear scalar equation, the DG solution will be superconvergent towards a particular projection of the exact solution. Thus, the error of the DG scheme will not grow for fine grids over a long time period. We give numerical examples of P^k polynomials, with 1=
Private Data Discovery for Privacy Compliance in Collaborative Environments
Authors: Larry Korba, Yunli Wang, Liqiang Geng, Ronggong Song, George Yee, Andrew S. Patrick, Scott Buffett, Hongyu Liu, Yonghua You
Venue: Proceedings of the 5th international conference on Cooperative Design, Visualization, and Engineering (2008)

With the growing use of computers and the Internet, it has become difficult for organizations to locate and effectively manage sensitive personally identifiable information (PII). This problem becomes even more evident in collaborative computing environments. PII may be hidden anywhere within the file system of a computer. As well, in the course of different activities, via collaboration or not, personally identifiable information may migrate from computer to computer. This makes meeting the organizational privacy requirements all the more complex. Our particular interest is to develop technology that would automatically discover workflow across organizational collaborators that would include private data. Since in this context, it is important to understand where and when the private data is discovered, in this paper, we focus on PII discovery, i.e. automatically identifying private data existant in semi-structured and unstructured (free text) documents. The first part of the process involves identifying PII via named entity recognition. The second part determines relationships between those entities based upon a supervised machine learning method. We present test results of our methods using publicly-available data generated from different collaborative activities to provide an assessment of scalability in cooperative computing environment.
Optimal Estimation of the Parameters of Linear Periodically Time-varying Systems
Authors: Kazys Kazlauskas
Venue: Informatica (2003)

This paper discusses the linear periodically time-varying (LPTV) system parameter estimation using a block approach. An block algorithm is proposed for optimal estimation of the parameters of LPTV system from the input sequence and the output sequence corrupted by additive Gaussianly distributed noise. In the proposed method, the least squares error criterion has been used.The algorithm provides a useful computational tool based on an appropriate theoretical foundation for parameter estimation of linear time-invariant (LTI) systems from input and output data. Simulation results are presented that demonstrate the performance of the approach.
Cultural Particle Swarm Algorithms for Constrained Multi-objective Optimization
Authors: Fang Gao, Qiang Zhao, Hongwei Liu, Gang Cui
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

In this paper, we propose to integrate particle swarm optimization algorithm into cultural algorithms frame to develop a more efficient cultural particle swarm algorithms (CPSA) for constrained multi-objective optimization problem. In our CPSA, the population space of cultural algorithms consists of n+1 subswarms which are used to search for the n single-objective optimums and an additional multiobjective optimum. The belief space accepts 20% elite particles form each subswarm and further takes crossover to create Pareto optimums. Niche Pareto tournament selection is further executed to ensure Pareto set to distribute uniformly along Pareto frontier. Additional memory of Pareto optimums spool is allocated and updated in each iteration to keep resultant Pareto solutions. Besides, a direct comparison method is employed to handle constraints without needing penalty functions. Two examples are presented to demonstrate the effectiveness of the proposed algorithm.
A Methodology for the Enterprise Information and Communication Infrastructure Design Process
Authors: Natalia Kryvinska, Lukas Auer, Christine Strauss, Peter Zinterhof
Venue: Proceedings of the 2nd international conference on Network-Based Information Systems (2008)

Current information and communication services are growing rapidly in directions very different from those pursued in the past. The strong drive for these services' fast development and deployment has not been accompanied by a proper understanding of their engineering and design principles, and for the most part, the services' satisfactory qualitative performance is being obtained by the strength approach of over-provisioning. Thus, there is a strong need for a deeper quantitative understanding of the services' functioning, an understanding that leads to clear design and control methods that it turn make the services' efficient deployment and exploitation possible. The research performed within this paper covers analytical and design aspects of technical and economical challenges emerging in multiservice networks and was conducted from a methodological perspective of evolving network infrastructure. The paper focuses on challenging issues in the planning and design of enterprise communication infrastructure and also determines strategies for the successful implementation converged networks.
FluXOR: Detecting and Monitoring Fast-Flux Service Networks
Authors: Emanuele Passerini, Roberto Paleari, Lorenzo Martignoni, Danilo Bruschi
Venue: Proceedings of the 5th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment (2008)

Botnets are large groups of compromised machines (bots) used by miscreants for the most illegal activities (e.g., sending spam emails, denial-of-service attacks, phishing and other web scams). To protect the identity and to maximise the availability of the core components of their business, miscreants have recently started to use fast-flux service networks, large groups of bots acting as front-end proxies to these components. Motivated by the conviction that prompt detection and monitoring of these networks is an essential step to contrast the problem posed by botnets, we have developed FluXOR, a system to detect and monitor fast-flux service networks. FluXOR monitoring and detection strategies entirely rely on the analysis of a set of features observable from the point of view of a victim of the scams perpetrated thorough botnets. We have been using FluXOR for about a month and so far we have detected 387 fast-flux service networks, totally composed by 31998 distinct compromised machines, which we believe to be associated with 16 botnets.
Towards `Interactive' Active Learning in Multi-view Feature Sets for Information Extraction
Authors: Katharina Probst, Rayid Ghani
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

Research in multi-view active learning has typically focused on algorithms for selecting the next example to label. This is often at the cost of lengthy wait-times for the user between each query iteration. We deal with a real-world information extraction task, extracting attribute-value pairs from product descriptions, where the learning system needs to be interactive and the user's time needs to be used efficiently. The first step uses coEM with naive Bayes as the semi-supervised algorithm. This paper focuses on the second step which is an interactive active learning phase. We present an approximation to coEM with naive Bayes that can incorporate user feedback almost instantly and can use any sample-selection strategy for active learning. Our experimental results show high levels of accuracy while being orders of magnitude faster than using the standard coEM with naive Bayes, making our IE system practical by optimizing user time.
Evolutionary Combining of Basis Function Neural Networks for Classification
Authors: César Hervás, Francisco Martínez, Mariano Carbonero, Cristóbal Romero, Juan Carlos Fernández
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)

The paper describes a methodology for constructing a possible combination of different basis functions (sigmoidal and product) for the hidden layer of a feed forward neural network, where the architecture, weights and node typology are learned based on evolutionary programming. This methodology is tested using simulated Gaussian data set classification problems with different linear correlations between input variables and different variances. It was found that combined basis functions are the more accurate for classification than pure sigmoidal or product-unit models. Combined basis functions present competitive results which are obtained using linear discriminant analysis, the best classification methodology for Gaussian data sets.
Emergence of Specialization from Global Optimizing Evolution in a Multi-agent System
Authors: Lei Chai, Jiawei Chen, Zhangang Han, Zengru Di, Ying Fan
Venue: Proceedings of the 7th international conference on Computational Science, Part IV: ICCS 2007 (2007)

A Markov chain model is proposed to describe the evolutionary dynamics of a multi-agent system. Many individual agents search for and exploit resources to get global optimization in an environment without complete information. With the selection acting on agent specialization at the level of system and under the condition of increasing returns, agent specialization emerges as the result of a long-term optimizing evolution.
Target Localization in Underwater Acoustic Sensor Networks
Authors: Biao Wang, Yu Li, Haining Huang, Chunhua Zhang
Venue: Proceedings of the 2008 Congress on Image and Signal Processing, Vol. 4 - Volume 04 (2008)

The underwater target localization problem is one important application for underwater acoustic sensor networks. This paper, based on "underwater acoustic transmission theory", solves the target localization problem. The proposed algorithm firstly studies the principle of underwater acoustics and applies it to set up the model of underwater signal energy transmission. According to the model, the maximum likelihood (ML) method is used to estimate the target position based on measuring the received signal energy by sensor node. We also derivate the Cramer-Rao lower bound (CRB) based signal model in this paper. Lastly, our numerical simulation results have verified the feasibility of the proposed localization algorithm, and have studied the precision performance of the algorithm.
On Pairwise Naive Bayes Classifiers
Authors: Jan-Nikolas Sulzmann, Johannes Fürnkranz, Eyke Hüllermeier
Venue: Proceedings of the 18th European conference on Machine Learning (2007)

Class binarizations are effective methods for improving weak learners by decomposing multi-class problems into several two-class problems. This paper analyzes how these methods can be applied to a Naive Bayes learner. The key result is that the pairwise variant of Naive Bayes is equivalent to a regular Naive Bayes. This result holds for several aggregation techniques for combining the predictions of the individual classifiers, including the commonly used voting and weighted voting techniques. On the other hand, Naive Bayes with one-against-all binarization is not equivalent to a regular Naive Bayes. Apart from the theoretical results themselves, the paper offers a discussion of their implications.
Knowledge-Intensive Interactive Systems Design in Cultural Context
Authors: Stefano Valtolina
Venue: Proceedings of the 1st world summit on The Knowledge Society: Emerging Technologies and Information Systems for the Knowledge Society (2008)

The innovations promoted by the knowledge society highlight the central role that the e-culture has to play as a privileged means for disseminating cultural heritage. This paper addresses the issues of knowledge representation, management, distribution and sharing in the field of the Cultural Heritage (CH) valorization and dissemination. The proposed approach is based on the definition of relationships between the information cultural domain fitting the conceptual model of the cultural experts (represented by an appropriate knowledge base) and an information domain understandable by the machine (modeled by a domain ontology). The final system's infrastructure filters the richness of the data-sources to comply the users' needs tailoring the information according to their contexts of use. In this way the user can properly navigate through the heritage and create their own personalized thematic tour through a large number of information trails.