ppxscal/embedding_network huggingface dataset:
Context Awareness in Mobile Relational Agents
Authors: Timothy W. Bickmore, Daniel Mauer, Thomas Brown
Venue: Proceedings of the 7th international conference on Intelligent Virtual Agents (2007)

The development of virtual agents designed to draw users into personal and professional relationships with them represents a growing area of research [1]. Mobility and context awareness represent important directions of research for these relational agents, since they offer unique affordances for relationship development. A mobile/wearable agent has the potential to be with a user for a significant period of time, and frequency of contact alone has been shown to be associated with increased solidarity between people. The ability to sense some aspects of the user's environment (context awareness) may also provide mobile agents with unique relational affordances. Automatically recognizing and commenting on situations in the user's life can amplify many relational perceptions, including familiarity, common ground, solidarity and intimacy. In addition, an agent's ability to proactively interrupt and help a user in a situation that is automatically sensed by the agent may lead to increased perceptions of trust and caring by the user.
An Operating System Architecture for Future Information Appliances
Authors: Tatsuo Nakajima, Hiroo Ishikawa, Yuki Kinebuchi, Midori Sugaya, Sun Lei, Alexandre Courbot, Andrej Zee, Aleksi Aalto, Kwon Ki Duk
Venue: Proceedings of the 6th IFIP WG 10.2 international workshop on Software Technologies for Embedded and Ubiquitous Systems (2008)

A software platform for developing future information appliances requires to satisfy various diverse requirements. The operating system architecture presented in this paper enhances the flexibility and dependability through virtualization techniques. The architecture allows a system to use multiple operating systems simultaneously, and to use multi-core processors in a flexible way. Also, dependability mechanisms in our architecture will avoid crashing or hanging a system as much as possible in order to improve the user experience when defects in the software are exposed. We present a brief overview of each component in the operating system architecture and some sample scenarios that illustrate the effectiveness of the architecture.
Information Quality in Healthcare: Coherence of Data Compared between Organization's Electronic Patient Records
Authors: Merja Miettinen, Maritta Korhonen
Venue: Proceedings of the 2008 21st IEEE International Symposium on Computer-Based Medical Systems (2008)

In this paper we present a case-based analysis of health care data quality problems in a situation, where data of diabetes patient are combined from different information systems. Nationally uniform integrated health care information systems shall become more important when meeting the demands of patient centered care in the future. During the development of several electronic health records it has become clear that the integration of the data is still challenging. Data collected in various systems can have quality faults, it can for instance be non-coherent or include contradictory information, or the desired data is completely missing, as proved to be in our case as well. The quality of the content of patient information and the process of data production constitute a central part of good patient care, and more attention should be paid to them.
Calculating ROI for Software Product Lines
Authors: Gunter Bockle, Paul Clements, John D. McGregor, Dirk Muthig, Klaus Schmid
Venue: IEEE Software (2004)

Product line engineering has become an approach to improve the ROI of a whole set of products. Because the approach emphasizes economic optimizations, the need to apply ROI calculations is particularly important. This article describes a model for a product line's ROI, illustrated in a prototypical industrial situation.
Fuzzy Regions for Handling Uncertainty in Remote Sensing Image Segmentation
Authors: Ivan Lizarazo, Paul Elsner
Venue: Proceeding sof the international conference on Computational Science and Its Applications, Part I (2008)

Increasing availability of satellite imagery is demanding robust image classification methods to ensure a better integration between remote sensing and GIS. Segmentation-based approaches are becoming a popular alternative to traditional pixel-wise methods. Hard segmentation divides an image into a set of non-overlapping image-objects and regularly requires significant user-interaction to parameterise a functional segmentation model. This paper proposes an alternative image segmentation method which outputs fuzzy image-regions expressing degrees of membership to target classes. These fuzzy regions are then defuzzified to derive the eventual land-cover classification. Both steps, fuzzy segmentation and defuzzification, are implemented here using simple statistical learning methods which require very little user input. The new procedure is tested in a land-cover classification experiment in an urban environment. Results show that the method produces good thematic accuracy. It therefore provides a new, automated technique for handling uncertainty in the image analysis process of high resolution imagery.
Selecting Scenario in Adaptive System with Neuro-Fuzzy Decision Maker
Authors: Krzysztof Brzostowski, Jerzy Swiatek
Venue: Proceedings of the 2008 19th International Conference on Systems Engineering (2008)

In the paper an algorithm for adaptive system to select scenario has been proposed. Taking into account specification of the process two-stage identification approach is used. The first stage is built for diagnostic purpose e.g. estimated parameters of the first-stage's relationshipis utilised to make a decision at the second stage. Proposed algorithms rests on determined values selecting controls by make the most of extracted knowledge form human expert. Two different horizons of making decision process is discussed: for finite and fuzzy case. Designed procedures have been compared with classical methods. Biomedical application of planning physical exercises for sportsmen is considered.
Entropy of the Internal State of an FCSR in Galois Representation
Authors: Andrea Röck
Venue: Fast Software Encryption: 15th International Workshop, FSE 2008, Lausanne, Switzerland, February 10-13, 2008, Revised Selected Papers (2008)

Feedback with Carry Shift Registers (FCSRs) are primitives that are used in multiple areas like cryptography or generation of pseudorandom sequences. In both cases, we do not want that an attacker can easily guess the content of the register. This requires a high entropy of the inner state. We consider the case of a binary FCSR in Galois representation. In this article, we show that we already lose after one iteration a lot of entropy. The entropy reduces until the moment where the FCSR reaches a periodic behavior. We present an algorithm which computes the final entropy of an FCSR and which also allows us to show that the entropy never decreases under the size of the main register.
Effect of Similarity Metrics and ROI Sizes in Featureless Computer Aided Detection of Breast Masses in Tomosynthesis
Authors: Swatee Singh, Georgia D. Tourassi, Joseph Y. Lo
Venue: Proceedings of the 9th international workshop on Digital Mammography (2008)

Tomosynthesis as a technique is being developed and studied with the goal of overcoming mammography's limitations due to overlying tissue. Various algorithms exist for tomosynthesis datasets including a novel Computer Aided Detection (CADe) algorithm using a featureless False Positive (FP) reduction stage. The goal of this project is to study the previously unexplored effects of variation of Region of Interest (ROI) sizes as well as the crucial similarity metrics for such a CADe algorithm's performance. Four datasets consisting of 1479 tomosynthesis ROIs were generated by a CADe algorithm from reconstructed volumes of one hundred subjects consisting of 4 different sizes --- 128 x 128, 256 x 256, 512 x 512, and 1024 x 1024 pixels. Five different similarity metrics --- (1) mutual information, (2) average conditional entropy, (3) joint entropy, (3) Jensen divergence and (4) average Kullback-Leibler divergence were used for the task of FP reduction using a leave-one-case-out sampling scheme. Mutual information and average conditional entropy were the best performing metrics with an Area Under Curve (AUC) of 0.88. Cross-bin measures performed consistently higher than those that rely on only marginal distributions. Also, for all metrics, the datatset consisting of 256 x 256 pixel ROIs gave the best performance. In conclusion, for the tomosynthesis dataset, cross-bin measures such as MI and average conditional entropy should be used over other metrics using a ROI size of 256 x 256 pixels.
A Procedure for Efficient Generation of 1/fβ Noise Sequences
Authors: Youcef Ferdi, Abdelmalik Taleb-Ahmed
Venue: Proceedings of the 3rd international conference on Image and Signal Processing (2008)

This paper presents a simple, efficient and fast procedure for generation of 1/f β noise sequences. The proposed procedure is based on the impulse invariance method applied to the impulse response of the ideal fractional order integrator whose order ¿ = β/2is between 0 and 1. First, an optimal value for the initial value of the impulse response is obtained by minimizing a least squares error criterion and then any of the well-established signal modeling techniques can be employed for the parameterization of the discrete impulse response by pole-zero models. For a given model order, the approximation accuracy depends on the signal modeling technique used. An illustrative example is presented to demonstrate the effectiveness of the method.
Operation continuity of effect algebras
Authors: Zhi-Jian Yu, Jun-De Wu, Min-Hyung Cho
Venue: Computers Mathematics with Applications (2008)

In this paper, we prove that the operations @? and @? of effect algebras are continuous with respect to their ideal topology, and if the effect algebras are lattice effect algebras, then under some conditions, the lattice operations @? and @? are also continuous with respect to their ideal topology.
User Defined Partitioning - Group Data Based on Computation Model
Authors: Qiming Chen, Meichun Hsu
Venue: Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery (2008)

A technical trend in supporting large scale scientific applications is converging data intensive computation and data management for fast data access and reduced data flow. In a combined cluster platform, co-locating computation and data is the key to efficiency and scalability; and to make it happen, data must be partitioned in a way consistent with the computation model. However, with the current parallel database technology, data partitioning is primarily used to support flat parallel computing, and based on existing partition key values; for a given application, when the data scopes of function executions are determined by a high-level concept that is related to the application semantics but not presented in the original data, there would be no appropriate partition keys for grouping data.Aiming at making application-aware data partitioning, we introduce the notion of User Defined Data Partitioning (UDP). UDP differs from the usual data partitioning methods in that it does not rely on existing partition key values, but extracts or generates them from the original data in a labeling process. The novelty of UDP is allowing data partitioning to be based on application level concepts for matching the data access scoping of the targeted computation model, and for supporting data dependency graph based parallel computing.We applied this approach to architect a hydro-informatics system, for supporting periodical, near-real-time, data-intensive hydrologic computation on a database cluster. Our experimental results reveal its power in tightly coupling data partitioning with "pipelined" parallel computing in the presence of data processing dependencies.
RSS Based Localization Scheme Using Angle-Referred Calibration in Wireless Sensor Networks
Authors: Tran-Xuan Cong, Eun-Chan Kim, Insoo Koo
Venue: Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues (2008)

In wireless sensor networks (WSNs), one of the conventional techniques for localization is the received signal strength (RSS) method where the RSS is utilized to get the distance measurement spatially between sensor nodes. However, in real localization system, the RSS is sensitively affected by many surrounding factors and tends to be unstable, so that it degrades accuracy in distance measurement. In this paper, we propose the angle-referred calibration based RSS method where angle relation between sensor nodes is used to perform the calibration for better performance in distance measurement. As results, the proposed scheme shows that it can provide a higher precision than the omni-direction calibration.
Service Agent-Based Resource Management Using Virtualization for Computational Grid
Authors: Sung Ho Jang, Jong Sik Lee
Venue: Proceedings of the 7th international conference on Computational Science, Part II (2007)

The key purpose of computational grid is how to allocate grid resources to grid applications effectively. Existing resource management models with centralized architecture like GRAM and nimrod-G broker are liable to generate communication overheads and bottleneck. Therefore, we propose the service agent-based resource management model using virtuali-zation for computational grid. Virtualization and agent-based system applied to our model is able to improve load imbalance of grid broker and reduce communication overheads. Experiment results demonstrate that the service agent-based resource management model decreases 25.8% more job latencies and 151.6% more communication messages than the centralized resource management model.
Improving k-Nearest Neighbour Classification with Distance Functions Based on Receiver Operating Characteristics
Authors: Md. Rafiul Hassan, M. Maruf Hossain, James Bailey, Kotagiri Ramamohanarao
Venue: Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I (2008)

The k-nearest neighbour (k-NN) technique, due to its interpretable nature, is a simple and very intuitively appealing method to address classification problems. However, choosing an appropriate distance function for k-NN can be challenging and an inferior choice can make the classifier highly vulnerable to noise in the data. In this paper, we propose a new method for determining a good distance function for k-NN. Our method is based on consideration of the area under the Receiver Operating Characteristics (ROC) curve, which is a well known method to measure the quality of binary classifiers. It computes weights for the distance function, based on ROC properties within an appropriate neighbourhood for the instances whose distance is being computed. We experimentally compare the effect of our scheme with a number of other well-known k-NN distance metrics, as well as with a range of different classifiers. Experiments show that our method can substantially boost the classification performance of the k-NN algorithm. Furthermore, in a number of cases our technique is even able to deliver better accuracy than state-of-the-art non k-NN classifiers, such as support vector machines.
Blind Source Separation of Temporal Correlated Signals
Authors: Bin Xia, Hong Xie
Venue: Proceedings of the 2007 Third International IEEE Conference on Signal-Image Technologies and Internet-Based System (2007)

In this paper, we present a new framework for blindsource separation of temporal correlated signals. In general,temporal correlated signals are not independent which means the independence assumption for Independent Component Analysis method is not satisfied. To achieve good separation performance, we apply high order statistics and temporal structure together to put the separation processing in residual level. The residual signals, which is residual part of source signals by extracted temporal structure, are independent. We discuss two types of BSS problem: instantaneous BSS and convolutive BSS. The cost function is derived by simplifying the mutual information of residual signals for both cases. And then we develop efficient learning algorithms respectively. Computer simulations are given to show the separation performance of the proposed algorithm and some comparisons with other algorithms are also provided.
Nash Bargaining Via Flexible Budget Markets
Authors: Vijay V. Vazirani
Venue: Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management (2008)

In his seminal 1950 paper, John Nash defined the bargaining problem; the ensuing theory of bargaining lies today at the heart of game theory. In this work, we initiate an algorithmic study of Nash bargaining problems.We consider a class of Nash bargaining problems whose solution can be stated as a convex program. For these problems, we show that there corresponds a market whose equilibrium allocations yield the solution to the convex program and hence the bargaining problem. For several of these markets, we give combinatorial, polynomial time algorithms, using the primal-dual paradigm.Unlike the traditional Fisher market model, in which buyers spend a fixed amount of money, in these markets, each buyer declares a lower bound on the amount of utility she wishes to derive. The amount of money she actually spends is a specific function of this bound and the announced prices of goods.Over the years, a fascinating theory has started forming around a convex program given by Eisenberg and Gale in 1959. Besides market equilibria, this theory touches on such disparate topics as TCP congestion control and efficient solvability of nonlinear programs by combinatorial means. Our work shows that the Nash bargaining problem fits harmoniously in this collage of ideas. 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
Activity put in context: identifying implicit task context within the user's document interaction
Authors: Karl Gyllstrom, Craig Soules, Alistair Veitch
Venue: Proceedings of the second international symposium on Information interaction in context (2008)

Modern desktop search is ill-fitted to our personal document workspace. On one hand, many of the methods which render web search effective cannot be applied on the desktop. On the other, desktop search does not take full advantage of attributes that are unique to our personal documents. In this work, we present Confluence, a desktop search system that addresses this problem by capturing the task context within which a user interacts with their documents. This context is then integrated with traditional desktop search techniques to enable task-based document retrieval. Building upon Connections, a system that identifies task context by passively monitoring the user's interaction with their documents within the file system. Confluence also traces user activity within the user interface and incorporates methods to analyze and integrate this new stream of information. We show that this approach significantly improves the accuracy of task identification, achieving 25% to 30% better recall.
Comparison of Phonetic Segmentation Tools for European Portuguese
Authors: Luís Figueira, Luís C. Oliveira
Venue: Proceedings of the 8th international conference on Computational Processing of the Portuguese Language (2008)

Currently, the majority of the text-to-speech synthesis systems that provide the most natural output are based on the selection and concatenation of variable size speech units chosen from an inventory of recordings. There are many different approaches to perform automatic speech segmentation. The most used are based on (Hidden Markov Models) HMM [1,2,3] or Artificial Neural Networks (ANN) [4], though Dynamic Time Warping (DTW) [3,4,5] based algorithms are also popular. Techniques involving speaker adaptation of acoustic models are usually more precise, but demand larger amounts of training data, which is not always available.In this work we compare several phonetic segmentation tools, based in different technologies, and study the transition types where each segmentation tool achieves better results. To evaluate the segmentation tools we chose the criterion of the number of phonetic transitions (phone borders) with an error below 20ms when compared to the manual segmentation. This value is of common use in the literature [6] as a majorant of a phone error. Afterwards, we combine the individual segmentation tools, taking advantage of their differentiate behavior accordingly to the phonetic transition type. This approach improves the results obtained with any standalone tool used by itself. Since the goal of this work is the evaluation of fully automatic tools, we did not use any manual segmentation data to train models. The only manual information used during this study was the phonetic sequence.
Lumbar Disc Localization and Labeling with a Probabilistic Model on Both Pixel and Object Features
Authors: Jason J. Corso, Raja' S. Alomari, Vipin Chaudhary
Venue: Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I (2008)

Repeatable, quantitative assessment of intervertebral disc pathology requires accurate localization and labeling of the lumbar region discs. To that end, we propose a two-level probabilistic model for such disc localization and labeling. Our model integrates both pixel-level information, such as appearance, and object-level information, such as relative location. Utilizing both levels of information adds robustness to the ambiguous disc intensity signature and high structure variation. Yet, we are able to do efficient (and convergent) localization and labeling with generalized expectation-maximization. We present accurate results on 20 normal cases (96%) and a promising extension to a pathology case.
Application of Parallel Decomposition for Creation of Reduced Feed-Forward Neural Networks
Authors: Jacek Lewandowski, Mariusz Rawski, Henryk Rybinski
Venue: Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms (2007)

In this paper a method of creating layers of feed-forward neural network that does not need to be learned is presented. Described approach is based on algorithms used in synthesis of logic circuits. Experimental results presented in the paper prove that this method may significantly decrease the time of learning process, increase generalization ability and decrease a probability of sticking in a local minimum. Further work and goals to achieve are also discussed.
ExoSnap: a modular approach to semantic synchronization and snapshots
Authors: Liuba Shrira, Siying Dong
Venue: Proceedings of the 2nd workshop on Dependable distributed data management (2008)

Current approaches to application-specific synchronization suffer from a limitation that precludes the use of generic "commodity" servers because they require to run the type-specific synchronization code at the servers. This is a problem for "cloud computing" systems that must rely on commodity components to exploit economies of scale yet need to adapt to application needs to provide good performance. We describe a new approach that overcomes the limitation. The new approach splits the synchronization code, so that the type-specific code runs outside the server, on the client side, and the generic performance-critical code runs in the server. New type-specific synchronization protocols can be developed without modifying the servers, providing the ability to use commodity servers.