ppxscal/embedding_network huggingface dataset:
Perceived Innovativeness, Perceived Convenience and TAM: Effects on Mobile Knowledge Management
Authors: Tang Jeung-tai E., Chiang Chihui
Venue: Proceedings of the 2009 Third International Conference on Multimedia and Ubiquitous Engineering (2009)

Wireless technology is seen as a gradually popular information technology with such potency that it has changed the way we view IT. Through wireless network, many knowledge management activities occur in working places and in our daily activities. In the mobile environments, the important knowledge management is quickly acquired the right knowledge using mobile devices through wireless network. The recent years study show that perceived ease of use and perceived usefulness constructs of technology acceptance model have been considered important in determining the individuals’ acceptance and use of IT. In this study, we will introduce the innovativeness and the convenience as new factors that reflect the characteristic of the mobile knowledge management. This paper addresses why users can be used mobile knowledge management and how user's adoption is affected by the innovativeness and the convenience. The results showed that the extended technology acceptance model fully mediated the usage behavior intentions even in the mobile knowledge management through the wireless network environment.
A visual programming system for automated problem solving
Authors: Ourania Hatzi, Dimitris Vrakas, Nick Bassiliades, Dimosthenis Anagnostopoulos, Ioannis Vlahavas
Venue: Expert Systems with Applications: An International Journal (2010)

Although new AI planning algorithms and techniques are being developed and improved rapidly, there is a lack of efficient and easy to use systems able to incorporate and utilize them. Furthermore, while visual representation facilitates design, maintenance and comprehension of planning domains and problems, very few systems incorporate it. This paper presents VLEPPO, an integrated system aiming at visually modeling planning domains and problems through a convenient graphical interface, while maintaining compatibility with the Planning Domain Definition Language (PDDL), with import and export features. Solutions to planning problems can be obtained by invoking different planners employing the web services technology. The demonstration of the system is performed through a case study involving web service composition viewed as a planning problem.
The Logic of Quasi-MV Algebras
Authors: Félix Bou, Francesco Paoli, Antonio Ledda, Matthew Spinks, Roberto Giuntini
Venue: Journal of Logic and Computation (2010)

The algebraic theory of quasi-MV algebras, generalizations of MV algebras arising in quantum computation, is by now rather well-developed. Although it is possible to define several interesting logics from these structures, so far this aspect has not been investigated. The present article aims at filling this gap. 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
Skyline
Authors: Brendan MacLean, Daniela M. Tomazela, Nicholas Shulman, Matthew Chambers, Gregory L. Finney, Barbara Frewen, Randall Kern, David L. Tabb, Daniel C. Liebler, Michael J. MacCoss
Venue: Bioinformatics (2010)

Summary: Skyline is a Windows client application for targeted proteomics method creation and quantitative data analysis. It is open source and freely available for academic and commercial use. The Skyline user interface simplifies the development of mass spectrometer methods and the analysis of data from targeted proteomics experiments performed using selected reaction monitoring (SRM). Skyline supports using and creating MS/MS spectral libraries from a wide variety of sources to choose SRM filters and verify results based on previously observed ion trap data. Skyline exports transition lists to and imports the native output files from Agilent, Applied Biosystems, Thermo Fisher Scientific and Waters triple quadrupole instruments, seamlessly connecting mass spectrometer output back to the experimental design document. The fast and compact Skyline file format is easily shared, even for experiments requiring many sample injections. A rich array of graphs displays results and provides powerful tools for inspecting data integrity as data are acquired, helping instrument operators to identify problems early. The Skyline dynamic report designer exports tabular data from the Skyline document model for in-depth analysis with common statistical tools. Availability: Single-click, self-updating web installation is available at http://proteome.gs.washington.edu/software/skyline. This web site also provides access to instructional videos, a support board, an issues list and a link to the source code project. Contact: brendanx@u.washington.edu Supplementary information:Supplementary data are available at Bioinformatics online. 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
Detailed resist film modeling in stochastic lithography simulation for line-edge roughness quantification
Authors: G. P. Patsis, D. Drygiannakis, I. Raptis
Venue: Microelectronic Engineering (2010)

Modeling the lithography process with stochastic principles enables the consideration of resist material and process effects variability on critical dimensions and line-edge or line-width roughness of printed features. These principles are applied for a resist system where polymer and photo-acid-generator (PAG) are blended, and for the same system with the PAG molecules bonded on the main polymer chain. Three-dimensional chain-like models of resist and PAG are considered and examples of their effect on critical dimensions and on resist edge roughness are presented. Comparison with experimental results from the literature proves the validity of the current approach and suggests that it can be used for the prediction of the resist resolution and low roughness capabilities.
UAV navigation by an expert system for contaminant mapping with a genetic algorithm
Authors: Yuki Kuroki, George S. Young, Sue Ellen Haupt
Venue: Expert Systems with Applications: An International Journal (2010)

Source characterization for an unknown contaminant release can be achieved by inverting an atmospheric transport and dispersion model given concentration observations from a moderately dense spatial array at one or more times. Achieving the required observation density over large geographic regions can, however, be prohibitively expensive if fixed sensors are employed. Mobile sensors provide a cost-saving alternative, with unmanned aerial vehicles (UAVs) being particularly well suited for these large-area problems because of their relatively high speed. The challenge then becomes to devise a set of navigation rules by which the aircraft can determine the route which most expeditiously acquires the required concentration observations. This task involves physical reasoning based on the wind vector, ongoing concentration observations, and current estimates of source position to plan leg length and direction. Each flight leg is planned based on data from all of the prior legs, the flight plan adapts to the observations as they are taken. Expert system navigation systems are developed for two situations: instantaneous (puff) and continuous (plume) releases. Of the two the puff poses the greater challenge because it provides a moving target rather than a quasi-steady concentration pattern. Thus, this rule-based navigation system must guide the UAV to an intercept for each pass through the puff rather than just sweeping across the contaminant field at multiple downwind distances as suffices with a plume. The navigation systems are tested in a virtual world consisting of a single fixed wind and concentration sensor, a UAV with wind and concentration sensing capability, a uniform wind at a significant fraction of the UAV airspeed, and a simple Gaussian dispersion model. The resulting concentration data is used to characterize the source strength and location by using a genetic algorithm to tune the variables until the model output matches the observations. Tests conducted using randomized source locations indicate that these UAV navigation systems are sophisticated enough to successfully acquire the necessary concentration data in the majority of the cases. The success rate is greatly improved by using an ensemble of non-communicating UAVs and taking the median of the resulting source variables. This process eliminates the outliers that result from occasional navigational failures.
Reducing samples for accelerating multikernel semiparametric support vector regression
Authors: Yong-Ping Zhao, Jian-Guo Sun, Xian-Quan Zou
Venue: Expert Systems with Applications: An International Journal (2010)

In this paper, the reducing samples strategy instead of classical @n-support vector regression (@n-SVR), viz. single kernel @n-SVR, is utilized to select training samples for admissible functions so as to curtail the computational complexity. The proposed multikernel learning algorithm, namely reducing samples based multikernel semiparametric support vector regression (RS-MSSVR), has an advantage over the single kernel support vector regression (classical @e-SVR) in regression accuracy. Meantime, in comparison with multikernel semiparametric support vector regression (MSSVR), the algorithm is also favorable for computational complexity with the comparable generalization performance. Finally, the efficacy and feasibility of RS-MSSVR are corroborated by experiments on the synthetic and real-world benchmark data sets.
A mixed-methods analysis of pointing-based interactions for coordinating spatial views in context
Authors: Derek F. Reilly
Venue:  (2009)

Activities situated in the real world with a strong spatial component such as wayfinding, tourism, and field data collection require the integration of multiple spatial views. Such views can include the immediate surroundings, graphical depictions such as maps, narrative descriptions in guides or notebooks, among others. This thesis examines ways in which systems can incorporate pointing and mixed media to mediate and connect spatial views in context. Results illustrate how setting impacts the utility of such techniques, that pointing-based interaction styles derived from a map's presentation can be learned and used consistently with minimal training, and that using pointing for information acquisition from paper maps onto handheld devices increases the time spent using the paper map without adversely impacting task time, leading to an increase in the acquisition of route and survey knowledge, and better performance on certain spatial tasks. The thesis also identifies practical bounds on precision when pointing at landmarks in the immediate environment without direct feedback, explores viable mobile solutions for interactive paper maps, and examines the practicality of pointing and mixed media to relate spatial views in the applied contexts of tourism, wayfinding in buildings, and scientific data collection. 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
Genetic Heritage Evolution by Stochastic Transmission in the optimal design of water distribution networks
Authors: Andrea Bolognesi, Cristiana Bragalli, Angela Marchi, Sandro Artina
Venue: Advances in Engineering Software (2010)

This paper proposes a new model named GHEST, a multi-population evolutionary-strategy-like algorithm applied to the optimal design of water distribution networks (WDN). GHEST hunts for the optimal solution by means of two different complementary processes. The first one, synthesizes and transmits the genetic patrimony (heritage) of the parent solutions using their statistical indicators. The second one, called ''shuffle'', avoids the search to get stuck in local minima whenever the evolutionary potential of the population appears to be exhausted. GHEST makes use of hydraulic network solver EPANET 2. Tests carried out on classical WDN optimal design problems are shown for three small and well-known networks and for a large-size one. Performances exhibited, in terms of minimum cost, are equal or better than those found in previous works (where directly comparable). The algorithm has been tested with different setups, achieving good results for almost all of them. Its performance can be particularly appreciated in large-size optimization problems as evidenced by results on Balerma network, where a new minimum cost has been set and the evaluation number to reach the former minimum has been decreased by about 35 times. Results are supported by an extensive comparison with previous works on the benchmark networks here tested.
Spectral curvature clustering for hybrid linear modeling
Authors: Gilad Lerman
Venue:  (2009)

The problem of Hybrid Linear Modeling (HLM) is to model and segment data using a mixture of affine subspaces. Many algorithms have been proposed to solve this problem, however, probabilistic analysis of their performance is missing. In this thesis we develop the Spectral Curvature Clustering (SCC) algorithm as a combination of Govindu's multi-way spectral clustering framework (CVPR 2005) and Ng et al.'s spectral clustering algorithm (NIPS 2001) while introducing a new affinity measure. Our analysis shows that if the given data is sampled from a mixture of distributions concentrated around affine subspaces, then with high sampling probability the SCC algorithm segments well the different underlying clusters. The goodness of clustering depends on the within-cluster errors, the between-clusters interaction, and a tuning parameter applied by SCC. Supported by the theory, we then present several novel techniques for improving the performance of the algorithm. Specifically, we suggest an iterative sampling procedure to improve the existing uniform sampling strategy, an automatic scheme of inferring the tuning parameter from data, a precise initialization procedure for K-means, as well as a simple strategy for isolating outliers. The resulting algorithm requires only linear storage and takes linear running time in the size of the data. We compare it with other state-of-the-art methods on a few artificial instances of affine subspaces. Application of the algorithm to several real-world problems is also discussed. 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
A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests
Authors: Kuo-Lung Wu, Jian Yu, Miin-Shen Yang
Venue: Pattern Recognition Letters (2005)

Most clustering algorithms are based on a within-cluster scatter matrix with a compactness measure. In this paper we propose a novel fuzzy clustering algorithm, called the fuzzy compactness and separation (FCS), based on a fuzzy scatter matrix in which the FCS algorithm is derived using compactness measure minimization and separation measure maximization. The compactness is measured using a fuzzy within-cluster variation. The separation is measured using a fuzzy between-cluster variation. The proposed FCS objective function is a modification of the FS validity index proposed by Fukuyama and Sugeno and also a generalization of the fuzzy c-means (FCM). The FCS algorithm assigns a crisp boundary (cluster kernel) for each cluster such that hard memberships and fuzzy memberships can co-exist in the clustering results. Thus, FCS can be seen as a clustering algorithm with a novel sense between the hard c-means and fuzzy c-means. The FCS optimality tests and parameter selection are also investigated. Some numerical examples are demonstrated to show its robust properties and effectiveness.
Motion in ambiguity: Coordinated active global localization for multiple robots
Authors: Shivudu Bhuvanagiri, K. Madhava Krishna
Venue: Robotics and Autonomous Systems (2010)

The task of the robot in localization is to find out where it is, through sensing and motion. In environments which possess relatively few features that enable a robot to unambiguously determine its location, global localization algorithms can result in 'multiple hypotheses' locations of a robot. This is inevitable with global localization algorithms, as the local environment seen by a robot repeats at other parts of the map. Thus, for effective localization, the robot has to be actively guided to those locations where there is a maximum chance of eliminating most of the ambiguous states - which is often referred to as 'active localization'. When extended to multi-robotic scenarios where all robots possess more than one hypothesis of their position, there is an opportunity to do better by using robots, apart from obstacles, as 'hypotheses resolving agents'. The paper presents a unified framework which accounts for the map structure as well as measurement amongst robots, while guiding a set of robots to locations where they can singularize to a unique state. The strategy shepherds the robots to places where the probability of obtaining a unique hypothesis for a set of multiple robots is a maximum. Another aspect of framework demonstrates the idea of dispatching localized robots to locations where they can assist a maximum of the remaining unlocalized robots to overcome their ambiguity, named as 'coordinated localization'. The appropriateness of our approach is demonstrated empirically in both simulation & real-time (on Amigo-bots) and its efficacy verified. Extensive comparative analysis portrays the advantage of the current method over others that do not perform active localization in a multi-robotic sense. It also portrays the performance gain by considering map structure and robot placement to actively localize over methods that consider only one of them or neither. Theoretical backing stems from the proven completeness of the method for a large category of diverse environments.
Dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation
Authors: Ran Li, Jianjiang Lu, Yafei Zhang, Tianzhong Zhao
Venue: Knowledge-Based Systems (2010)

Image annotation can be formulated as a classification problem. Recently, Adaboost learning with feature selection has been used for creating an accurate ensemble classifier. We propose dynamic Adaboost learning with feature selection based on parallel genetic algorithm for image annotation in MPEG-7 standard. In each iteration of Adaboost learning, genetic algorithm (GA) is used to dynamically generate and optimize a set of feature subsets on which the weak classifiers are constructed, so that an ensemble member is selected. We investigate two methods of GA feature selection: a binary-coded chromosome GA feature selection method used to perform optimal feature subset selection, and a bi-coded chromosome GA feature selection method used to perform optimal-weighted feature subset selection, i.e. simultaneously perform optimal feature subset selection and corresponding optimal weight subset selection. To improve the computational efficiency of our approach, master-slave GA, a parallel program of GA, is implemented. k-nearest neighbor classifier is used as the base classifier. The experiments are performed over 2000 classified Corel images to validate the performance of the approaches.
A neural network approach for solving linear bilevel programming problem
Authors: Tiesong Hu, Xuning Guo, Xiang Fu, Yibing Lv
Venue: Knowledge-Based Systems (2010)

A novel neural network approach is proposed for solving linear bilevel programming problem. The proposed neural network is proved to be Lyapunov stable and capable of generating optimal solution to the linear bilevel programming problem. The numerical result shows that the neural network approach is feasible and efficient.
Review: Using support vector machines in diagnoses of urological dysfunctions
Authors: David Gil, Magnus Johnsson
Venue: Expert Systems with Applications: An International Journal (2010)

Urinary incontinence is one of the largest diseases affecting between 10% and 30% of the adult population and an increase is expected in the next decade with rising treatment costs as a consequence. There are many types of urological dysfunctions causing urinary incontinence, which makes cheap and accurate diagnosing an important issue. This paper proposes a support vector machine (SVM) based method for diagnosing urological dysfunctions. 381 registers collected from patients suffering from a variety of urological dysfunctions have been used to ensure the (generalization) performance of the decision support system. Moreover, the robustness of the proposed system is examined by fivefold cross-validation and the results show that the SVM-based method can achieve an average classification accuracy at 84.25%.
Some sufficient conditions for p-valent strongly starlike functions
Authors: Ding-Gong Yang, Jin-Lin Liu
Venue: Computers Mathematics with Applications (2010)

By using the method of differential subordinations, we obtain certain sufficient conditions for p-valent strongly starlike functions. All these results are sharp.
A forecasting solution to the oil spill problem based on a hybrid intelligent system
Authors: Bruno Baruque, Emilio Corchado, Aitor Mata, Juan M. Corchado
Venue: Information Sciences: an International Journal (2010)

Oil spills represent one of the most destructive environmental disasters. Predicting the possibility of finding oil slicks in a certain area after an oil spill can be critical in reducing environmental risks. The system presented here uses the Case-Based Reasoning (CBR) methodology to forecast the presence or absence of oil slicks in certain open sea areas after an oil spill. CBR is a computational methodology designed to generate solutions to certain problems by analysing previous solutions given to previously solved problems. The proposed CBR system includes a novel network for data classification and retrieval. This type of network, which is constructed by using an algorithm to summarize the results of an ensemble of Self-Organizing Maps, is explained and analysed in the present study. The Weighted Voting Superposition (WeVoS) algorithm mainly aims to achieve the best topographically ordered representation of a dataset in the map. This study shows how the proposed system, called WeVoS-CBR, uses information such as salinity, temperature, pressure, number and area of the slicks, obtained from various satellites to accurately predict the presence of oil slicks in the north-west of the Galician coast, using historical data.
q-Partition algebra combinatorics
Authors: Tom Halverson, Nathaniel Thiem
Venue: Journal of Combinatorial Theory Series A (2010)

We study a q-analog Q"r(n,q) of the partition algebra P"r(n). The algebra Q"r(n,q) arises as the centralizer algebra of the finite general linear group GL"n(F"q) acting on a vector space IR"q^r coming from r-iterations of Harish-Chandra restriction and induction. For n>=2r, we show that Q"r(n,q) has the same semisimple matrix structure as P"r(n). We compute the dimension d"n","r(q)=dim(IR"q^r) to be a q-polynomial that specializes as d"n","r(1)=n^r and d"n","r(0)=B(r), the rth Bell number. Our method is to write d"n","r(q) as a sum over integer sequences which are q-weighted by inverse major index. We then find a basis of IR"q^r indexed by n-restricted q-set partitions of {1,...,r} and show that there are d"n","r(q) of these.