ppxscal/embedding_network huggingface dataset:
Performance evaluation of the context-aware handover mechanism for the nomadic mobile services in remote patient monitoring
Authors: Pravin Pawar, Bert-Jan van Beijnum, Marten van Sinderen, Akshai Aggarwal, Pierre Maret, Frédéric De Clercq
Venue: Computer Communications (2008)

Owing to the recent advances in the mobile middleware technologies, hardware technologies and association with the human user, handheld mobile devices are evolving into data producers and in turn acting as nomadic mobile service providers. For the nomadic mobile service hosted on a multi-homed handheld mobile device, context-awareness provides a capability of selecting the suitable network interface for the data transfer. This paper conducts a performance evaluation of the context-handover mechanism for the nomadic mobile services applied in the remote patient monitoring domain and hosted on a multi-homed handheld mobile device. The experimentation analyzes the suitability of a particular network for the data transfer, the effect of multi-homing on the remote patient monitoring application and the resource utilization on the mobile device. The performance analysis provides us useful insights, which are currently being exploited in the extended middleware architecture for the vertical handover support to the nomadic mobile services.
A semantic fuzzy expert system for a fuzzy balanced scorecard
Authors: Fernando Bobillo, Miguel Delgado, Juan Gómez-Romero, Enrique López
Venue: Expert Systems with Applications: An International Journal (2009)

Balanced scorecard is a widely recognized tool to support decision making in business management. Unfortunately, current balanced scorecard-based systems present two drawbacks: they do not allow to define explicitly the semantics of the underlying knowledge and they are not able to deal with imprecision and vagueness. To overcome these limitations, in this paper we propose a semantic fuzzy expert system which implements a generic framework for the balanced scorecard. In our approach, knowledge about balanced scorecard variables is represented using an OWL ontology, therefore allowing reuse and sharing of the model among different companies. The ontology acts as the basis for the fuzzy expert system, which uses highly interpretable fuzzy IF-THEN rules to infer new knowledge. Results are valuable pieces of information to help managers to improve the achievement of the strategic objectives of the company. A main contribution of this work it that the system is general and can be customized to adapt to different scenarios.
Logistic regression product-unit neural networks for mapping Ridolfia segetum infestations in sunflower crop using multitemporal remote sensed data
Authors: P. A. Gutiérrez, F. López-Granados, J. M. Peña-Barragán, M. Jurado-Expósito, C. Hervás-Martínez
Venue: Computers and Electronics in Agriculture (2008)

Remote sensing (RS), geographic information systems (GIS), and global positioning systems (GPS) may provide the technologies needed for farmers to maximize the economic and environmental benefits of precision farming. Site-specific weed management (SSWM) is able to minimize the impact of herbicide on environmental quality and arises the necessity of more precise approaches for weed patches determination. Ridolfia segetum is one of the most dominant, competitive and persistent weed in sunflower crops in southern Spain. In this paper, we used aerial imagery taken in mid-May, mid-June and mid-July according to different phenological stages of R. segetum and sunflower to evaluate the potential of evolutionary product-unit neural networks (EPUNNs), logistic regression (LR) and two different combinations of both (logistic regression using product units (LRPU) and logistic regression using initial covariates and product units (LRIPU)) for discriminating R. segetum patches and mapping R. segetum probabilities in sunflower crops on two naturally infested fields. Afterwards, we compared the performance of these methods in every date to two recent classification models (support vector machines (SVM) and logistic model trees (LMT)). The results obtained present the models proposed as powerful tools for weed discrimination, the best performing model (LRIPU) obtaining generalization accuracies of 99.2% and 98.7% in mid-June. Our results suggest that a strategy to implement SSWM is feasible with minima omission and commission errors, and therefore, with a very low probability of not detecting R. segetum patches. The paper proposes the application of a new methodology that, to the best of our knowledge, has not been previously applied in RS, and which obtains better accuracy than more traditional RS classification techniques, such as vegetation indices or spectral angle mapper.
The data playground: An intuitive workflow specification environment
Authors: Andrew Gibson, Matthew Gamble, Katy Wolstencroft, Tom Oinn, Carole Goble, Khalid Belhajjame, Paolo Missier
Venue: Future Generation Computer Systems (2009)

Workflows systems are steadily finding their way into the work practices of scientists. This is particularly true in the in silico science of bioinformatics, where biological data can be processed by Web Services. In this paper, we investigate the potential of evolving the users' interaction with workflow environments so that it more closely relates to the mode in which their day to day work is carried out. We present the Data Playground, an environment designed to encourage the uptake of workflow systems in bioinformatics through more intuitive interaction by focusing the user on their data, rather than on the processes. We implement a prototype plug-in for the Taverna workflow environment and show how this can promote the creation of workflow fragments by automatically converting the users' interactions with data and Web Services into a more conventional workflow specification.
What means rich in publishing? Competition between for-profit and not-for-profit publishers from an economist's point of view
Authors: Alexis Walckiers
Venue: Information Services and Use (2008)

In our report for DG-Research of the European Commission entitled &ldquo;Study on the economic and technical evolution of the scientific publication markets in Europe&rdquo;, published almost two years ago, we showed that, on average, journals published by for-profit publishers are three times more expensive than journals published by not-for-profit publishers. Not-for-profit publishers are, in general, not subsidized, and our results are thus consistent with impediments to effective competition on the market for scientific publications. Relying on these observations I address two questions sequentially. First, I review the potential, natural and strategic barriers to expansion and entry. Second, scientific associations have large market shares in some scientific disciplines. I discuss the objectives of not-for-profit publishers and study whether they help innovate and drive prices down. Finally, I conclude that there is a need to promote pro-competitive, i.e. much more flexible, pricing strategies, scrutinize mergers, and refund VAT to research institutions.
A fuzzy filter for improving the quality of the signal in adaptive-network-based fuzzy inference systems (ANFIS)
Authors: C. Riverol, C. Di Sanctis
Venue: Applied Soft Computing (2009)

This study is addressed to improve the quality of the signal of the Adaptive-Network based Fuzzy Inference System (ANFIS) reducing the level of fluctuations in the output due to periodical disturbances. The fuzzy filter computes the disturbances as periodic signals with two components, one at high frequency and another at low frequency. It was incorporated in the layer 0 of the network reducing iteratively effectively the heavy noise.
Model-based validation of QoS properties of biomedical sensor networks
Authors: Simon Tschirner, Liang Xuedong, Wang Yi
Venue: Proceedings of the 8th ACM international conference on Embedded software (2008)

A Biomedical Sensor Network (BSN) is a small-size sensor network for medical applications, that may contain tens of sensor nodes. In this paper, we present a formal model for BSNs using timed automata, where the sensor nodes communicate using the Chipcon CC2420 transceiver (developed by Texas Instruments) according to the IEEE 802.15.4 standard. Based on the model, we have used UPPAAL to validate and tune the temporal configuration parameters of a BSN in order to meet desired QoS requirements on network connectivity, packet delivery ratio and end-to-end delay. The network studied allows dynamic reconfigurations of the network topology due to the temporally switching of sensor nodes to power-down mode for energy-saving or their physical movements. Both the simulator and model-checker of UPPAAL are used to analyze the average-case and worst-case behaviors. To enhance the scalability of the tool, we have implemented a (new text-based) version of the UPPAAL simulator optimized for exploring symbolic traces of automata containing large data structures such as matrices. Our experiments show that even though the main feature of the tool is model checking, it is also a promising and competitive tool for efficient simulation and parameter tuning. The simulator scales well; it can easily handle up to 50 nodes in our experiments. The model checker installed on a notebook can also deal with networks with 5 up to 16 nodes within minutes depending on the properties checked; these are BSNs of reasonable size for medical applications. Finally, to study the accuracy of our model and analysis results, we compare simulation results by UPPAAL for two medical scenarios with traditional simulation techniques using OMNeT++, one of the most used simulation tools for wireless sensor networks. The comparison shows that our analysis results coincide with the simulation results by OMNeT++ in most cases although there are some differences caused the simplified wireless channel model in UPPAAL.
Avatars in Assistive Homes for the Elderly
Authors: Martin M. Morandell, Andreas Hochgatterer, Sascha Fagel, Siegfried Wassertheurer
Venue: Proceedings of the 4th Symposium of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society on HCI and Usability for Education and Work (2008)

Designing user interfaces for elderly people has become a prominent research topic. In the context of Assistive Homes the development of user-friendly and alternative interfaces for the improvement of usability and acceptance is an important goal. Applying avatars shall provide notable benefits. This paper describes the implementation and evaluation of a "photo-realistic avatar for people with mild cognitive impairment and dementia". The findings of the user trials show positive effects such as raised user's attention, increased likeability but no effects on memory functions. Also other approaches from Germany and Spain with less anthropomorph-looking avatars are presented and their results discussed.
Multimarker analysis and imputation of multiple platform pooling-based genome-wide association studies
Authors: Nils Homer, Waibhav D. Tembe, Szabolcs Szelinger, Margot Redman, Dietrich A. Stephan, John V. Pearson, Stanley F. Nelson, David Craig
Venue: Bioinformatics (2008)

Summary: For many genome-wide association (GWA) studies individually genotyping one million or more SNPs provides a marginal increase in coverage at a substantial cost. Much of the information gained is redundant due to the correlation structure inherent in the human genome. Pooling-based GWA studies could benefit significantly by utilizing this redundancy to reduce noise, improve the accuracy of the observations and increase genomic coverage. We introduce a measure of correlation between individual genotyping and pooling, under the same framework that r2 provides a measure of linkage disequilibrium (LD) between pairs of SNPs. We then report a new non-haplotype multimarker multi-loci method that leverages the correlation structure between SNPs in the human genome to increase the efficacy of pooling-based GWA studies. We first give a theoretical framework and derivation of our multimarker method. Next, we evaluate simulations using this multimarker approach in comparison to single marker analysis. Finally, we experimentally evaluate our method using different pools of HapMap individuals on the Illumina 450S Duo, Illumina 550K and Affymetrix 5.0 platforms for a combined total of 1 333 631 SNPs. Our results show that use of multimarker analysis reduces noise specific to pooling-based studies, allows for efficient integration of multiple microarray platforms and provides more accurate measures of significance than single marker analysis. Additionally, this approach can be extended to allow for imputing the association significance for SNPs not directly observed using neighboring SNPs in LD. This multimarker method can now be used to cost-effectively complete pooling-based GWA studies with multiple platforms across over one million SNPs and to impute neighboring SNPs weighted for the loss of information due to pooling. Contact: dcraig@tgen.org Supplementary information: Supplementary data are available at Bioinformatics online.
Improved Performance for Nodal Spectral Element Operators
Authors: Uwe Fladrich, Jörg Stiller, Wolfgang E. Nagel
Venue: International Journal of High Performance Computing Applications (2008)

The computational performance of the nodal spectral element method (SEM) for tetrahedral grids is evaluated in the context of a high-performance computing platform. The elemental SEM operator is accelerated by the symmetry-based factorization technique of Hesthaven and Teng (SIAM J. Sci. Comp. 21, p. 2352, 2000), which results in a reduced number of floating point operations and memory accesses. However, performance evaluation reveals that a naive implementation of the algorithm causes a severe degradation of computational efficiency. Two algorithmic modifications are proposed which regain (and partly exceed) the efficiency of the original, non-factorized operator and thus recover the asymptotic 9/5 speedup due to factorization.
BayesStore: managing large, uncertain data repositories with probabilistic graphical models
Authors: Daisy Zhe Wang, Eirinaios Michelakis, Minos Garofalakis, Joseph M. Hellerstein
Venue: Proceedings of the VLDB Endowment (2008)

Several real-world applications need to effectively manage and reason about large amounts of data that are inherently uncertain. For instance, pervasive computing applications must constantly reason about volumes of noisy sensory readings for a variety of reasons, including motion prediction and human behavior modeling. Such probabilistic data analyses require sophisticated machine-learning tools that can effectively model the complex spatio/temporal correlation patterns present in uncertain sensory data. Unfortunately, to date, most existing approaches to probabilistic database systems have relied on somewhat simplistic models of uncertainty that can be easily mapped onto existing relational architectures: Probabilistic information is typically associated with individual data tuples, with only limited or no support for effectively capturing and reasoning about complex data correlations. In this paper, we introduce BayesStore, a novel probabilistic data management architecture built on the principle of handling statistical models and probabilistic inference tools as first-class citizens of the database system. Adopting a machine-learning view, BAYESSTORE employs concise statistical relational models to effectively encode the correlation patterns between uncertain data, and promotes probabilistic inference and statistical model manipulation as part of the standard DBMS operator repertoire to support efficient and sound query processing. We present BAYESSTORE's uncertainty model based on a novel, first-order statistical model, and we redefine traditional query processing operators, to manipulate the data and the probabilistic models of the database in an efficient manner. Finally, we validate our approach, by demonstrating the value of exploiting data correlations during query processing, and by evaluating a number of optimizations which significantly accelerate query processing.
Autonomy-Oriented Computing for Web Intelligence and Brain Informatics
Authors: Jiming Liu
Venue: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01 (2008)

In this talk, we will discuss the roles and potential implications of Autonomy-Oriented Computing (AOC) in/to the future of Web Intelligence (WI) and Brain Informatics (BI). Generally speaking, AOC is a methodology for self-organized computing that is well suited for two types of applications: (i) to characterize the working mechanisms that lead to certain emergent behavior in natural and artificial complex systems (e.g., phenomena in “Web Science”, and the dynamics of social networks and neural systems), and (ii) to develop solutions to large-scale, distributed computational problems (e.g., distributed scalable scientific or social computing, and collective intelligence). AOC emphasizes the modeling of autonomous entities or agents that locally interact following certain nature or real-world inspired behavioral rules, resulting in some self-organized behavior of the entities and/or their nonlinearly aggregated effects. Computing based on interacting entities and their self-organization can offer several means as well as advantages for WI and BI development, such as natural formulation, distributed implementation, scalable performance, robustness, and behavioral or first-principle understanding.
Stabilization of fuzzy control systems
Authors: Constantin Volosencu
Venue: WSEAS Transactions on Systems and Control (2008)

The paper presents the way to use Lyapunov techniques to analyse and to assure stability of control systems with fuzzy PI controllers. The stability is assured based on a compensation of the input-output characteristic of the fuzzy blocks used in PI fuzzy controller. The compensation is made on fuzzy control law by the action in parallel of a crisp block for non-linear parts of input saturations. Stability analysis of fuzzy control systems for general processes is framed in the field of non-linear systems. The paper presents the way to prove absolute global internal stability in general cases. The correction principle is theoretically substantiated and methodologically structured with application at the fuzzy control of the second order processes.
BLogo
Authors: Wencheng Li, Bo Yang, Shaoguang Liang, Yonghua Wang, Chris Whiteley, Yicheng Cao, Xiaoning Wang
Venue: Bioinformatics (2008)

Summary: Blogo is a web-based tool that detects and displays statistically significant position-specific sequence bias with reduced background noise. The over-represented and under-represented symbols in a particular position are shown above and below the zero line. When the sequences are in open reading frames, the background frequency of nucleotides could be calculated separately for the three positions of a codon, thus greatly reducing the background noise. The χ2-test or Fisher's exact test is used to evaluate the statistical significance of every symbol in every position and only those that are significant are highlighted in the resulting logo. The perl source code of the program is freely available and can be run locally. Availability: http://acephpx.cropdb.org/blogo/, http://www.bioinformatics.org/blogo/ Contact:lwcbio@yahoo.com.cn; xnwang@21cn.net Supplementary information:Supplementary data are available atBioinformatics online.
Predicting functional regulatory polymorphisms
Authors: Ali Torkamani, Nicholas J. Schork
Venue: Bioinformatics (2008)

Motivation: Limited availability of data has hindered the development of algorithms that can identify functionally meaningful regulatory single nucleotide polymorphisms (rSNPs). Given the large number of common polymorphisms known to reside in the human genome, the identification of functional rSNPs via laboratory assays will be costly and time-consuming. Therefore appropriate bioinformatics strategies for predicting functional rSNPs are necessary. Recent data from the Encyclopedia of DNA Elements (ENCODE) Project has significantly expanded the amount of available functional information relevant to non-coding regions of the genome, and, importantly, led to the conclusion that many functional elements in the human genome are not conserved. Results: In this article we describe how ENCODE data can be leveraged to probabilistically determine the functional and phenotypic significance of non-coding SNPs (ncSNPs). The method achieves excellent sensitivity (~80%) and speci.city (~99%) based on a set of known phenotypically relevant and non-functional SNPs. In addition, we show that our method is not overtrained through the use of cross-validation analyses. Availability: The software platforms used in our analyses are freely available ( http://www.cs.waikato.ac.nz/ml/weka/). In addition, we provide the training dataset (Supplementary Table 3), and our predictions (Supplementary Table 6), in the Supplementary Material. Contact:nschork@scripps.edu. Supplementary information:Supplementary data are available at Bioinformatics online.
Exact interior reconstruction from truncated limited-angle projection data
Authors: Yangbo Ye, Hengyong Yu, Ge Wang
Venue: Journal of Biomedical Imaging (2008)

Using filtered backprojection (FBP) and an analytic continuation approach, we prove that exact interior reconstruction is possible and unique from truncated limited-angle projection data, if we assume a prior knowledge on a subregion or subvolume within an object to be reconstructed. Our results show that (i) the interior region-of-interest (ROI) problem and interior volume-ofinterest (VOI) problem can be exactly reconstructed from a limited-angle scan of the ROI/VOI and a 180 degree PI-scan of the subregion or subvolume and (ii) the whole object function can be exactly reconstructed from nontruncated projections from a limited-angle scan. These results improve the classical theory of Hamaker et al. (1980).
Parameter estimation of frequency response twin-screw food extrusion process using genetic algorithms
Authors: Anant Oonsivilai, Ratchadaporn Oonsivilai
Venue: WSEAS TRANSACTIONS on SYSTEMS (2008)

Autonomic control of food extruders has attracted considerable in recent years. With limited understanding of the complex physio-chemical interactions during the food extrusion process, designing a control system for food extruder is not easy. The common approach is to determine the operating conditions and then to maintain these values as closely as possible using various control loops, if not manual control. This paper applies genetic algorithms to achieve the parameters of the twin-screw food extrusion process. The genetic algorithms are very suitable for searching discrete, noisy, multimodal and complex space. The sum of square error on magnitude and phase of the twin screw food extrusion process is minimize and receiving outstanding in shape the measured system extracted from the frequency response analysis of the food extrusion process. As recognized, exploitation of the optimization based on Genetic Algorithms gives advanced results.