ppxscal/embedding_network huggingface dataset:
Assessing agreement of clustering methods with gene expression microarray data
Authors: Xueli Liu, Sheng-Chien Lee, George Casella, Gary F. Peter
Venue: Computational Statistics Data Analysis (2008)

In the rapidly evolving field of genomics, many clustering and classification methods have been developed and employed to explore patterns in gene expression data. Biologists face the choice of which clustering algorithm(s) to use and how to interpret different results from various clustering algorithms. No clear objective criteria have been developed to assess the agreement and compare the results from different clustering methods. We describe two generally applicable objective measures to quantify agreement between different clustering methods. These two measures are referred to as the local agreement measure, which is defined for each gene/subject, and the global agreement measure, which is defined for the whole gene expression experiment. The agreement measures are based on a probabilistic weighting scheme applied to the number of concordant and discordant pairs from two clustering methods. In the comparison and assessment process, newly-developed concepts are implemented under the framework of reliability of a cluster. The algorithms are illustrated by simulations and then applied to a yeast sporulation gene expression microarray data. Analysis of the sporulation data identified ~5% (23 of 477) genes which were not consistently clustered using a neural net algorithm and K-means or pam. The two agreement measures provide objective criteria to conclude whether or not two clustering methods agree with each other. Using the local agreement measure, genes of unknown function which cluster consistently can more confidently be assigned functions based on co-regulation.
International Internet2 connectivity and performance in medical imaging applications: Bridging the Americas to Asia
Authors: Brent J. Liu, Zheng Zhou, Marco A. Gutierrez, Jorge Documet, Lawrence Chan, H. K. Huang
Venue: Journal of High Speed Networks (2007)

Internet-2 is an advanced high-speed computer network, which has been widely used for medical imaging applications such as teleradiology and teleconsultation. Internet-2 can fulfill the requirements for high-speed data transmission and short turn-around time with low operation cost once installed. However, high performance may not necessarily be retained for global access from international network peers as well as commercial Internet Service Provider (ISP) solutions. This paper describes the methodology of establishing international Internet-2 connections between three different internationally linked sites: the Imaging Processing and Informatics (IPI) Laboratory, University of Southern California, Los Angeles, California; the Hong Kong Polytechnic University (PolyU), Hong Kong, and the Heart Institute (InCor) of the University of S&atilde;o Paulo, Brazil. Two major factors will be discussed including network looping in the US and bottleneck of the connection, raising the round-trip time and limiting the available bandwidth respectively. In addition to the three site International Internet2 Connectivity, a consistent and repeatable tuning protocol for international Internet2 applications in the clinical environment will be presented. The experimental results of the FTP and DICOM transmission using the automatic tuning protocol at both sending and receiving sites will be presented and compared with results for the combinations where this protocol is used at either one site only or no site at all in order to demonstrate the improvement in overall network throughput by this proposed tuning method. These performance numbers will also be compared to commercially available ISP connections to further show the differences between International Internet-2 and its ability to support medical imaging applications.
Task activity vectors: a new metric for temperature-aware scheduling
Authors: Andreas Merkel, Frank Bellosa
Venue: Proceedings of the 3rd ACM SIGOPS/EuroSys European Conference on Computer Systems 2008 (2008)

Non-uniform utilization of functional units in combination with hardware mechanisms such as clock gating leads to different power consumptions in different parts of a processor chip. This in turn leads to non-uniform temperature distributions and problematic local hotspots, depending on the characteristics of the currently running task. The operating system's scheduler, responsible for deciding which task to run at what time, can influence temperature distribution. Our work investigates what the operating system can do to alleviate the problem of hotspots. We propose task activity vectors describing which functional units a task uses to what degree. With the knowledge provided by these vectors, the scheduler can schedule tasks using different units successively, distribute tasks using a particular unit excessively over the system's processors, or mix tasks using different units on a SMT processor. We implemented several vector-based scheduling strategies for Linux. Our evaluations show that vector-based scheduling considerably reduces hotspots.
Classification of Unbalanced Medical Data with Weighted Regularized Least Squares
Authors: Nguyen Ha Vo, Yonggwan Won
Venue: Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies (2007)

In medical diagnosis classification, we often face the unbalanced number of data samples between the classes in which there are not enough samples in rare classes. Conventional competitive learning methods are not suitable in this situation, because they usually tend to be biased to the classes that have the larger number of data samples. In this paper, we proposed a cost-sensitive extension of Regularized Least Square(RLS) algorithm that penalizes errors of different samples with different weights and some rules of thumb to determine those weights. The significantly better classification accuracy of weighted RLS classifiers showed that it is promising substitution of other previous cost-sensitive classification methods for unbalanced data set.
A Systematic Review and Comparison of Security Ontologies
Authors: Carlos Blanco, Joaquin Lasheras, Rafael Valencia-García, Eduardo Fernández-Medina, Ambrosio Toval, Mario Piattini
Venue: Proceedings of the 2008 Third International Conference on Availability, Reliability and Security (2008)

The use of ontologies for representing knowledge provides us with organization, communication and reusability. Information security is a serious requirement which must be carefully considered. Concepts and relations managed by any scientific community need to be formally defined and ontological engineering supports their definition. In this paper, the method of systematic review is applied with the purpose of identifying, extracting and analyzing the main proposals for security ontologies. The main identified proposals are compared using a formal framework and we conclude by stating their early state of development and the need of additional research efforts.
The linear neuron as marker selector and clinical predictor in cancer gene analysis
Authors: Michalis E. Blazadonakis, Michalis Zervakis
Venue: Computer Methods and Programs in Biomedicine (2008)

Objective: The problem of gene selection has been extensively studied in a number of scientific works using various kinds of methods. However, the application of a linear neuron is a novel approach possessing several advantages. In this work we propose to study the behavior of such a linear neuron, appropriately adapted and trained to the problem of gene selection in the DNA microarray experiment. Methods and materials: We explore the proposed approach in terms of an accuracy evaluation criterion, which is used to assess the performance of the proposed methodology, but we also evaluate the produced results in terms of cluster quality and survival prediction. Cluster quality reflects the ability of the method to select differentially expressed genes, which in turn leads to better clustering and survival prediction. Results: We directly compare the proposed methodology with RFE-SVM, a well known and broadly accepted method demonstrating remarkable performance on various data sets of clinical interest. Conclusions: Conducted computational experiments show that the proposed approach can be efficiently used within the field of gene selection producing high-quality results in terms of accuracy and robustness.
SRTM DEM accuracy assessment over vegetated areas in Norway
Authors: D. J. Weydahl, J. Sagstuen, Ø. B. Dick, H. Rønning
Venue: International Journal of Remote Sensing (2007)

Results from the Shuttle Radar Topography Mission (SRTM) are presented. The SRTM C-band and X-band digital elevation models (DEMs) are evaluated with regard to elevation accuracies over agricultural fields, forest areas and man-made features in Norway. High-resolution digital maps and satellite images are used as background data. In general, many terrain details can be observed in the SRTM elevation datasets. The elevation accuracy (90% confidence level) of the two SRTM systems is estimated to less than 6.5 m for open agricultural fields and less than 11 m considering all land surface covers. This is better than specifications. Analysis of dense Norwegian forest stands shows that the SRTM system will produce elevation data that are as much as 15 m higher than the ground surface. The SRTM DEM products will therefore partly indicate canopy elevations in forested areas. We also show that SRTM data can be used to update older DEMs obtained from other sources, as well as estimating the volume of rock removed from large man-made gravel pits.
Dynamic adaptive data structures for monitoring data streams
Authors: J. Aguilar-Saborit, P. Trancoso, V. Muntes-Mulero, J. L. Larriba-Pey
Venue: Data Knowledge Engineering (2008)

The monitoring of data streams is a very important issue in many different areas. Aspects such as accuracy, the speed of response, the use of memory and the adaptability to the changing nature of data may vary in importance depending on the situation. Examples such as Web page access monitoring, approximate aggregation in relational queries or IP message routing are clear examples of a varied range of those needs. There are different data structures that deal with this problem such as the counting bloom filters, the spectral bloom filters and the dynamic count filters. Those data structures range from static to complex dynamic representations of the data stream that keep an approximate count of the number of occurrences for each data value. In this paper, we focus on three main aspects. First, we analyze the problem in perspective and review the existing static and dynamic solutions. Second, we propose and analyze in depth a simple yet powerful partitioning strategy that reinforces the advantages of the methods proposed up to now solving most of their drawbacks. Finally, using real executions and mathematical models, we evaluate the existing methods alone and in combination with our partitioning strategy. We show that with our partitioning strategy, it is possible to reduce the memory requirements and average response time, improving the adaptiveness to changing data characteristics and leaving the accuracy of the partitioned dynamic data structures intact.
An Integrated ISV Call Management Strategy in Heterogeneous Wireless Networks
Authors: I-Shyan Hwang, Bor-Jiunn Hwang, Ruey-Rong Su, K. Robert Lai, Chyi-Jy Lin
Venue: Proceedings of the 22nd International Conference on Advanced Information Networking and Applications - Workshops (2008)

A lot of wireless standards are proposed to satisfy diversified requirements for mobile users such as IEEE 802.16 and IEEE 802.11. It is feasible to integrate heterogeneous networks to provide complete wireless services. Several strategies were proposed to realize seamless handoff in the heterogeneous networks; however, most of them were only designed to meet system performance or individual QoS requirements for mobile users individually. In this paper, we propose an individual and system views (ISV) strategy integrating the individual view (IV) and system view (SV) to provide the proactive method that can choose the Internet Service Providers by MS and make the deliberated vertical handoff procedures with high throughput of UGS and call management services in heterogeneous networks. Our proposed strategy provides a complete procedure to determine the target network that has not been offered in most papers. Moreover, according to simulation results, the proposed ISV strategy is able to earn significantly lower handoff times than IV and SV and provide stable wireless network given high precedence for UGS traffic and real-time services.
Portable surgery master station for mobile robotic telesurgery
Authors: Ganesh Sankaranarayanan, Hawkeye I King, Seong-Young Ko, Mitchell J. H. Lum, Diana C. W. Friedman, Jacob Rosen, Blake Hannaford
Venue: Proceedings of the 1st international conference on Robot communication and coordination (2007)

We describe a system that provides a low-cost, portable control station for experimentation in mobile robotic telesurgery. The software and hardware implementation of our system are described in detail. The device mapping between the Haptic Interface Devices (HID) and the surgical robot that enable the surgeon to effectively teleoperate the surgical robot are explained along with our communication protocols for telesurgery. We have also provided our initial results from extensive field testing of our system in different hardware and software configurations and challenging locations. We focus on working under sub-optimal network conditions for field operation in remote environments, and the importance of interoperability and distribution among networked surgical technologies.
Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data
Authors: Jongrae Kim, Declan G. Bates, Ian Postlethwaite, Pat Heslop-Harrison, Kwang-Hyun Cho
Venue: Bioinformatics (2008)

Motivation: Inherent non-linearities in biomolecular interactions make the identification of network interactions difficult. One of the principal problems is that all methods based on the use of linear time-invariant models will have fundamental limitations in their capability to infer certain non-linear network interactions. Another difficulty is the multiplicity of possible solutions, since, for a given dataset, there may be many different possible networks which generate the same time-series expression profiles. Results: A novel algorithm for the inference of biomolecular interaction networks from temporal expression data is presented. Linear time-varying models, which can represent a much wider class of time-series data than linear time-invariant models, are employed in the algorithm. From time-series expression profiles, the model parameters are identified by solving a non-linear optimization problem. In order to systematically reduce the set of possible solutions for the optimization problem, a filtering process is performed using a phase-portrait analysis with random numerical perturbations. The proposed approach has the advantages of not requiring the system to be in a stable steady state, of using time-series profiles which have been generated by a single experiment, and of allowing non-linear network interactions to be identified. The ability of the proposed algorithm to correctly infer network interactions is illustrated by its application to three examples: a non-linear model for cAMP oscillations in Dictyostelium discoideum, the cell-cycle data for Saccharomyces cerevisiae and a large-scale non-linear model of a group of synchronized Dictyostelium cells. Availability: The software used in this article is available from http://sbie.kaist.ac.kr/software Contact: ckh@kaist.ac.kr Supplementary information: Supplementary data are available at Bioinformatics online.
Cryptography by example: tutorial presentation
Authors: Mary Kay Tarver
Venue: Journal of Computing Sciences in Colleges (2008)

The earliest encryption algorithms were based on two basic techniques: substitution and transposition. This tutorial will demonstrate the use of these two techniques in several classical encryption algorithms. These algorithms will include Caesar Cipher, Playfair Cipher, Vigenere Cipher, Simple Binary Cipher, Rail Fence Technique, and other more complex transposition methods. This tutorial session will begin with a discussion of basic terminology associated with cryptography. The remainder of the session will consist of sections for each algorithm. Each section will include a detailed description of the algorithm, demonstration by example, and then an opportunity for participants to work in small groups for hands on experience with the algorithm.
Real time input subset selection for linear time-variant MIMO systems
Authors: S. -P. Kim, J. C. Sanchez, J. C. Principe
Venue: Optimization Methods Software (2007)

In this paper we propose an approach for multi-input multi-output (MIMO) system identification when the statistical relationship between input and output varies in input space as well as in time; i.e. nonstationary in space and time. An on-line variable selection algorithm, which has been recently developed for selecting a subset of input variables in real time by modifying least angle regression (LAR) with recursive estimators, is extensively applied to the linear time-variant MIMO systems. In our approach, a subset of input channels relevant with output is selected at every time instance based on the correlation between the filtering outcome of individual input channels and desired output. The on-line variable selection algorithm performs channel selection with weights using this real-time correlation. The proposed model is compared with a typical linear model in which only the least mean squares (LMS) is used to update system parameters. Tracking performances of these two models are demonstrated in a computer simulation and in a real-world application for tracking a linear relationship between neural firing rates of a primate and synchronously recorded hand kinematics. In both cases, our model demonstrates superior tracking performance.
Statistical modelling of multimodal SAR images
Authors: A. El-Zaart, D. Ziou
Venue: International Journal of Remote Sensing (2007)

This paper discusses the statistical modelling of multimodal SAR image histograms. We use a generalized mixture of distributions with flexible shapes that are likely to fit the SAR image histogram. We then form a system called GGBL, composed of four parametric distributions (Gaussian, Gamma, Beta and Log-Normal). Selection of a parametric distribution from the GGBL system for each mode of the multimodal SAR histogram is performed according to the location of the skewness and flatness coefficients in this space. We propose a distribution stability method for distribution selection, using the asymmetry and flatness coefficients, and a feature method for the estimation of the parameters of these distributions based on the characteristic points of the histogram. The GGBL system is applied to real histograms of RADARSAT and ERS SAR images with different numbers of looks. The results obtained are promising.
A cognitive map-driven avatar design recommendation DSS and its empirical validity
Authors: Kun Chang Lee, Soonjae Kwon
Venue: Decision Support Systems (2008)

With the advent of the Internet era and the maturation of electronic commerce, strategic avatar design has become an important way of keeping up with market changes and customer tastes. In this study, we propose a new DSS for an adaptive avatar design that uses a cognitive map (CM) as a what-if simulation vehicle. The main virtue of the proposed avatar design recommendation DSS (abbreviated as ADR-DSS) is its ability to change specific avatar design features with objective consideration of the subsequent effects upon other design features, thereby enhancing user satisfaction. An avatar represents a user's self-identity and desire for self-disclosure. Therefore, the claim is made that there is a relationship between the characteristics of avatar design features and the choice of avatar. The considerations in this study are props (garments, facial expression, and miscellaneous) and subjective judgments (self-image and user satisfaction). The results of both brainstorming and focus group interviews with a group of avatar experts were used to objectively organize the CM. All the experts who participated are currently working in developing and designing avatar features in portal websites. Incorporating the CM as a model base, the proposed ADR-DSS was implemented, and two scenarios were presented for illustration. To prove the validity of the ADR-DSS, a rigorous survey was performed, obtaining statistically significant results.
User-assisted ink-bleed correction for handwritten documents
Authors: Yi Huang, Michael S. Brown
Venue: Proceedings of the 8th ACM/IEEE-CS joint conference on Digital libraries (2008)

We describe a user-assisted framework for correcting ink-bleed in old handwritten documents housed at the National Archives of Singapore (NAS). Our approach departs from traditional correction techniques that strive for full automation. Fully-automated approaches make assumptions about ink-bleed characteristics that are not valid for all inputs. Furthermore, fully-automated approaches often have to set algorithmic parameters that have no meaning for the end-user. In our system, the user needs only to provide simple examples of ink-bleed, foreground ink, and background. These training examples are used to classify the remaining pixels in the document to produce a computer-generated result that is equal to or better than existing fully-automated approaches. To offer a complete system, we also provide tools that allow any errors in the computer-generated results to be quickly "cleaned up" by the user. The initial training markup, together with the computer-generated results, and manual edits are all recorded with the final output, allowing subsequent viewers to see how a corrected document was created and to make changes or updates. While an ongoing project, our feedback from the NAS staff has been overwhelmingly positive that this user-assisted framework is a practical way to address the ink-bleed problem.
Applying an open application security process to a clinical information system: a case study
Authors: Michael Pfähler, Jens H. Weber-Jahnke
Venue: Proceedings of the 2008 C3S2E conference (2008)

Security is an important quality goal of software engineering. Incidents of data theft, data loss, and unavailability may lead to frustration of users, contravention of laws, or financial loss. Especially in critical domains like health care, every effort must be made to minimize security related problems. The optimum method for establishing software security is to consider it during all stages of the software process. For legacy systems, however, this is not possible. In order to provide a high level of confidence in the security of existing systems, a security analysis must be conducted. In this paper, we perform a security analysis of an existing clinical decision support system called EGADSS. The major motivation for conducting the security analysis is that the operational context of EGADSS is being changed from an intranet to the Internet. This means that in addition to general considerations associated with a security analysis, particular attention must be paid to the threats and risks introduced by the new environment of EGADSS. In order to conduct the analysis, we perform a process developed and published within the open content community, called CLASP (Comprehensive Lightweight Application Security Process). We report on our experiences with applying this community maintained process and reflect on its effectiveness in modeling threats to the system and identifying appropriate countermeasures.