waashk/dblp huggingface dataset:
a spanning tree-based genetic algorithm for some instances of the rectilinear steiner problem with obstacles. given sets of points and obstacles in the plane, the rectilinear steiner problem with obstacles seeks to connect the points with a rectilinear steiner tree---a tree made up of vertical and horizontal line segments---that avoids the obstacles and has minimum total length. we consider only rectangular obstacles and further restrict the problem by requiring that it be possible to connect every point to the tree via exactly one vertical and one horizontal segment. rectilinear steiner trees that conform to this restriction can be represented by spanning trees augmented to specify the rectilinear segments. a genetic algorithm that uses a spanning-tree-based coding of rectilinear steiner trees outperforms a greedy heuristic on 45 instances of the problem, of up to 469 points and 325 obstacles. however, the coding cannot represent arbitrary rectilinear steiner trees, so it cannot address the unrestricted case, and in the case considered here, it leaves some potentially shorter trees unexamined.
building knowledge discovery into a geo-spatial decision support system. the emergence of remote sensing, scientific simulation, telescope scanning, and other survey technologies has dramatically enhanced our capabilities to collect spatio-temporal data. however, the explosive growth in data makes the management, analysis, and use of data difficult and expensive. in decision support applications with spatio-temporal data, it is important to study the temporal relationships of the parameters that influence the decision. because multiple spatio-temporal data sets contain volumes of data, and often there is a delay between the occurrence of an event and its influence on the dependent variables, finding interesting patterns can be difficult.this paper presents a layered architecture for a distributed gdss that uses temporal rule discovery to aid the decision-making process. data mining algorithms are used to identify temporal relationships between multiple spatio-temporal data sets where time lags may exist between the related events. these algorithms allow the user to specify target events, to prune rules that are not of interest to the current decision-making problem. a geo-spatial decision support system for drought risk management is used to demonstrate the effectiveness of building knowledge discovery into a gdss.
route profiling: putting context to work. intelligent transportation systems are characterised by a requirement for detailed information on extensive transport networks. this information is typically gathered from sensors deployed throughout the network and is used for management and maintenance operations.in this paper we present the design and prototype implementation of a context-aware route profiling application intended for use by road management authorities in the republic of ireland. our design allows data from a variety of sources to be combined to generate detailed information on traffic flow and journey times along the national road network. this information can be tagged with relevant context data reflecting the conditions under which sensor data was collected. the set of relevant contextual information includes details on temporal, spatial, weather and road usage pattern contexts.the prototype implementation relies on gps data from a fleet of probe vehicles. an evaluation of this prototype is presented along with a discussion on the benefits of using context-aware computing techniques in a real world scenario.
a hypothesis driven approach to condition specific transcription factor binding site characterization in s.c.. we demonstrate a computational process by which transcription factor binding sites can be elucidated using genome-wide expression and binding profiles. the profiles direct us to the intergenic locations likely to contain the promoter regions for a given factor. these sequences are multiply and locally aligned to give an anchor motif from which further characterization can take place. we present bases for and assumptions about the variability within these motifs which give rise to potentially more accurate motifs, capture complex binding sites built upon the basis motif, and eliminate the constraints of the currently employed promoter searching protocols. we also present a measure of motif quality based on the occurrence of the putative motifs in regions observed to contain the binding sites. the assumptions, motif generation, quality assessment and comparison allow the user as much control as their a priori knowledge allows.
a diagnostic system based on a multi-decision approximate rules model. modified rough sets (mrs)-based diagnostic systems eliminated many of the limitations of rough sets (rs)-based, statistical-based, and decision tree-based systems and because of that, they have a better performance. in contrast with the other systems, mrs-based diagnostic systems have potential to handle multi-decision approximate (mda) rules. in this paper, we (1) develop a diagnostic model based on mda rules and (2) evaluate the classification power of the model.
identifying and testing of signatures for non-volatile biomolecules using tandem mass spectra. identification of volatile and semi-volatile molecules using traditional electron ionization mass spectrometry has been successful. the major contributor to this success is the reproduceability of the mass spectra, which allow identification of components based on comparison of fragmentation patterns within very large databases. however, this approach is not useful for the identification of typical nonvolatile biomolecules. tandem mass spectrometry with collision induced dissociation (cid) has the potential to provide structure-specific fragmentation from non-volatile biomolecules.the recognition of these molecules based on cid is not an easy task, since the spectra generated for a given molecule are not as reproducible as in traditional electron ionization mass spectrometry. also, the rules governing the formation of cid produced ions are not completely understood.in this study we investigate the use of the kohonen self-organized mapping (som) neural network to generate and test signatures (fragmentation patterns) for a given set of non-volatile biomolecules using spectra generated by tandem mass spectrometry with cid. the signatures then may be used as a discriminator for identifying unknown non-volatile biomolecules.
a neural network for speedy trials. in recent years, the case loads of judges have increased, while speedy trial laws place a time limit between the defendant's arrest and trial dates. because of this time constraint, it seems that for minor cases, judges pass sentences based on a set of certain factors (patterns) not based on the individual merits of each case. patterns may be learned by a neural network. in this paper, we investigate the credibility of the neural network approach as a viable tool in the sentencing process and we show its superiority over the id3 approach.
simplified access to structured databases by adapting keyword search and database selection. this paper presents a tool that enables non-technical (naive) end-users to use free-form queries in exploring distributed relational databases with simple and direct technique, in a fashion similar to using search engines to search text files on the web. this allows web designers and database developers to publish their databases for web browsers exploring. the proposed approach can be used for both internet and intranet application areas. our approach depends on identifying first databases that are most likely to provide useful results to the raised query, and then search only the identified databases. in our work, we developed and extended an estimation technique to assess the usefulness measure of each database. our technique has been borrowed from the similar techniques used for information retrieval (ir), mainly for text and document databases; it supports working smoothly with the structured information stored in relational databases. such a usefulness measure enables nave users to make decisions about databases to search and in what order.
modeling the workflow of prescription writing. computerizing the delivery of prescription medication, particularly in hospitals, has had many recent success stories. while the safeguards that computers can provide in preventing the incorrect administration of medications cannot be denied, little attention has been paid to the requirements engineering process in workflow automation. this paper discusses some of the techniques of workflow modeling that hold promise for medical informatics. this paper demonstrates that clinical workflows can be modeled using existing modeling tools.
grid-based management of biomedical data using an xml-based distributed data management system. this paper presents the application of a generic, xml-based distributed data management system for grid-enabled management and integration of biomedical data, including image, molecular, and outcome data. we discuss the use of this system in three inter-related application scenarios: management of large-scale image data, access to data from internet-based bioinformatic data repositories, and integrating clinical data stored in an enterprise information warehouse into translational research.
ontology and rule based retrieval of sound objects in augmented audio reality system for museum visitors. ec(h)o is an "augmented reality interface" utilizing spatialized soundscapes and a semantic web approach to information. the initial prototype is designed for a natural history and science museum. the platform is designed to create a museum experience that consists of a physical installation and an interactive virtual layer of three-dimensional soundscapes that are physically mapped to the museum displays. the source for the audio data is digital sound objects. the digital objects originate in a network of object repositories that connect digital content from one museum with other museums collections. the interface enables people to interact with the system by movement and object manipulation-based gestures without the direct use of a computer device. the focus of this paper is the retrieval mechanism for the sound objects for the museum visitor. the retrieval mechanism is built on the user model and conceptual descriptions of the sound object and museum artifacts in the form of ontologies for sound and psychoacoustics, topic ontology and conceptual reference model for museum information. the retrieval criteria are represented as inference rules that represent knowledge from psychoacoustics, cognitive domain and composition aspects of interaction. the system will be demonstrated in exhibition space in nature museum in ottawa in january 2003.
the edusource communication language: implementing open network for learning repositories and services. interoperability is one of the main issues in creating a networked system of repositories the approaches range from simply forcing one metadata standard on all participating repositories to highly sophisticated semantic web based architectures with full semantic mapping capabilities between different schemas. the edusource project in its holistic approach to building a network of learning object repositories in canada is implementing an open network for learning services. its openness is supported by an edusource communication protocol (ecl) which closely implements the ims digital repository interoperability (dri) specification and architecture, and by connection middleware that enables any service providers to join the network. edusource is open to external initiatives as it explicitly supports an extensible bridging mechanism between edusource and other major initiatives. this paper focuses on the design of ecl as an implementation of ims dri and supporting infrastructure and middleware. we also present two applications used in evaluating our approach: a gateway for connecting between edusource and the nsdl initiative, and a federated search connecting edusource, edna and smete.
sesam: searching supported by analysis of metadata. this paper presents a novel approach for database searching where the user is assisted in locating relevant objects in query results. this is made possible by an active user interface which asks the user dynamically generated questions based on collected information of the properties of the objects in the result. the answers to these questions are used to retain only objects with the indicated properties, thereby improving precision. sesam, a world wide web-based prototype based on this approach, is presented. it has been successfully tested using a large, real-life database.
terracost: a versatile and scalable approach to computing least-cost-path surfaces for massive grid-based terrains. this paper addresses the problem of computing least-cost-path surfaces for massive grid-based terrains. our approach follows a modular design, enabling the algorithm to make efficient use of memory, disk, and grid computing environments. we have implemented the algorithm in the context of the grass open source gis system and---using our cluster management tool---in a distributed environment. we report experimental results demonstrating that the algorithm is not only of theoretical and conceptual interest but also performs well in practice. our implementation outperforms standard solutions as dataset size increases relative to available memory and our distributed solver obtains near-linear speedup when preprocessing large terrains for multiple queries.
service-oriented grid computation for large-scale parameter estimation in complex environmental modeling. complex environmental modeling often involves a large number of unknown physical and ecological parameters. parameter estimation is one of the most difficult steps in many modeling activities. in this paper we present a service-oriented framework, named ggpe-g (grid-enabled global optimization for general parameter estimation), for efficient parameter estimation in heterogeneous, distributed systems. being presented as services, the optimization algorithms, the physical and ecological process models and clients can interact with each other by xml message interactions. the proposed approach supports a generic parameter estimation procedure and can be easily applied to different modeling environment. in this paper, we explain the design, architecture, and implementation of ggpe-g in details. we also apply ggpe-g to a complex soil-water-atmosphere-plant modeling system to demonstrate its utility and efficiency.
to infinity and beyond or, avoiding the infinite in security protocol analysis. we investigate conditions under which an infinite set of atomic messages can be replaced with one or two values without affecting the correctness of a security protocol. the work is conducted using the strand spaces formalism, but the results apply to all protocol analysis techniques, and should be of particular value to those using model checking.the implications of the central result are discussed.
virtual namespace functions: an alternative to virtual member functions in c++ and advice in aspectc++. virtual namespace functions (vnfs) are introduced as c++ functions defined at global or namespace scope which can be redefined similar to virtual member functions. even though this is a relatively simple concept, hardly more complex than ordinary c functions, it is shown that vnfs subsume object-oriented single, multiple, and predicate-based method dispatch as well as aspect-oriented before, after, and around advice. their implementation by means of a "lazy" precompiler for c++ is briefly described.
wavelet density estimators over data streams. density estimation is a building block of many data analysis techniques. a recently examined approach based on wavelets promises to be superior to traditional density estimation techniques. for possibly infinite data streams, however, this approach is not feasible due to the limited resources, e.g. memory. in this paper, we propose a new technique for computing wavelet density estimators over data streams that only requires a fixed amount of memory. our estimators are updated in an online manner such that a continuous analysis of data streams is supported during runtime.
the overhead model of word-level and page-level incremental checkpointing. generally, word-level granularity for incremental checkpointing may reduce the checkpoint file size, and hence get better the performance. however, word-level granularity may not always be more efficient than page-level granularity, because word-level granularity may sometimes increase the checkpointing overhead for finding the differences between two checkpoints and for writing the address of the modified word to stable storage. in this paper, we make the overhead model and find factors which produce an effect on the over-head. we also show that the model and the factors are fairly reasonable via experimental results from linux kernel-level incremental checkpointing facility.
space-efficient page-level incremental checkpointing. incremental checkpointing, which is intended to minimize checkpointing overhead, saves only the modified pages of a process. however, the cumulative size of incremental checkpoints increases at a steady rate over time because many updated values may be saved for the same page. in this paper, we present a comprehensive overview of pickpt, which is a page-level incremental checkpointing facility. pickpt provides space-efficient techniques for minimizing the use of disk space. for our experiments, the results show that the use of disk space of pickpt was significantly reduced compared with existing incremental checkpointing.
trust-enhanced visibility for personalized document recommendations. documents are recommended by computer-based systems normally according to their prominence in the document reference network. based on the requirements identified in a concrete use case for recommending scientific publications, the paper claims that merely measuring prominence is insufficient for high quality recommendations. we propose to use information from a trust network in addition to the document network in order to improve and to personalize recommendations. a trust-enhanced visibility measure integrates trust information and the classical reference based measures. a simulation study applies the new visibility measure to the presented use case.
computational adjustable autonomy for nasa personal satellite assistants. we will describe a simulator and simulated teamwork among a number of personal satellite assistants (psa) onboard the simulated space station patrolling for problem detection and isolation. psas reason about autonomies of potential helpers while helpers reason about their autonomies for deciding to help or to break away from prior commitments to help. we describe algorithms for computing psa autonomies when there are concurrent and conflicting situations. we also offer empirical results about qualities of help a recruiting psa receives when there are multiple, concurrent problems.
evaluating cost-sensitive unsolicited bulk email categorization. in the recent years, unsolicited bulk email has became an increasingly important problem, with a big economic impact. in this paper, we discuss cost-sensitive text categorization methods for ube filtering. in concrete, we have evaluated a range of machine learning methods for the task (c4.5, naive bayes, part, support vector machines and rocchio), made cost sensitive through several methods (threshold optimization, instance weighting, and meta-cost). we have used the receiver operating characteristic convex hull method for the evaluation, that best suits classification problems in which target conditions are not known, as it is the case. our results do not show a dominant algorithm nor method for making algorithms cost-sensitive, but are the best reported on the test collection used, and approach real-world hand-crafted classifiers accuracy.
content-based music filtering system with editable user profile. information filtering systems, which recommend appropriate information to users from enormous amount of information, are becoming popular. one method of information filtering is content-based filtering that compares a user profile with a content model. many systems using content-based filtering deal with text data, and few systems deal with music data. we propose a content-based filtering system for music data by using a decision tree. compared with other filtering methods, a decision tree can eliminate noise features, which are not related to the user's preference, and can allow the user to edit the learned user profile. we conduct an experiment by using real music data and users to validate the effectiveness of our system compared with other filtering methods.
an adaptive distance computation technique for image retrieval systems. for more than a decade query-by-one-example (qbe) has been a popular query system for content-based image retrieval (cbir). however, recent research has shown that a single image is not sufficient to form its semantics or concept of the intended query. searching concept "car," for instance, one might need many examples of car images in various colors. the color feature is then understood as a non-factor in the distance metric. in our approach, users can query by using groups of query images. there are three possible groups: relevant (positive), irrelevant (negative) or neutral groups. we define the range for each feature within these groups of query images, and use them to adjust the weights of the features. as a result, some features may be cancelled out from the similarity computation. the measure then becomes a dynamic metric for image retrieval. our approach achieves a higher degree of precision and recall and, at the same time, significantly reduces the time complexity of matching. the proposed approach is tested against the imagegrouper method. the results show that this approach is an effective and efficient technique for qbe.
editorial message: special track on operating systems and adaptive applications. the purpose of this track is to bring together researchers, designers, and developers who are interested in methodology for the design and analysis of operating systems and adaptive applications. it is motivated by a tremendous growth on the demands for high-performance operating systems in recent years. such an observation comes with the fact that many adaptive applications become more and more complex, and it imposes new challenging issues never faced before in this application field. it is thus clear that nowadays the development and design of operating systems must rely, even more than that in the recent past, on specific solutions both in the hardware and in the software components. moreover, the needs to timely tackle changes in the market pushes toward the employment of methodologies to shorten the development time and to drive the evolution of existing products. the solutions to new problems emerging in this setting call for a joint effort from the academics and industry.