waashk/dblp huggingface dataset:
web service selection mechanisms in the web service execution environment (wsmx). semantic web services provide a step forward in the search for automated integration of business partners. research in the area is mainly focused on describing, finding and using semantic web services. a major area that has received little attention is the process of choosing one service from a list of services that are known to fulfill the required functionality. this process is very important as different services may be available at different prices with different levels of reliability, security, accuracy etc. the selection of which service to use depends upon the requirements of the user, while one user may require high quality another may need low price. this paper presents a vision of selection mechanisms in the web service execution environment (wsmx), that can be used by individuals and the business community alike.
sec: a search engine for component based software development. the successfulness of the component-based development(cbd) process relies on several factors including the structuration of the component repositories and the comparison procedures for interface exploring while comparing the expected and the provided services. both functional and non-functional aspects should be considered. this paper presents a discovery ontology to organize components in a repository and an integration ontology to integrate a component into an application. in addition, we propose a search engine, called sec for cbd, which uses the discovery ontology to automatically locates and presents a list of software components that could be used in the current development situation. this search engine consists of a persistent and an intelligent component which automatically generates a query from developer specification and indexes a repository of software components. sec is not only suitable to discover components, but also able to automatically classify the selected components using the subsumption notion.
scalable security and accounting services for content-based publish/subscribe systems. content-based publish/subscribe systems offer an interaction scheme that is appropriate for a variety of large scale dynamic applications. however, widespread use of these systems is hindered by a lack of suitable security services. in this paper we present scalable solutions for confidentiality, integrity, and authentication for these systems. we also provide usage-based accounting services, which are required for e-commerce and e-business applications that use publish/subscribe systems. our solutions are applicable in a setting where publishers and subscribers may not trust the publish/subscribe infrastructure.
sels: a secure e-mail list service. exchange of private information content among a large number of users via e-mail list services is becoming increasingly common. in this paper we address security requirements in that setting and develop a new protocol, sels (a secure e-mail list service) that provides confidentiality, integrity, and authentication for e-mails exchanged via lists. in addition, sels also protects against the use of lists for e-mail spamming. we have developed a prototype of sels in java, and integrated it with the eudora e-mail client.
performance bottleneck in time-series subsequence matching. this paper addresses a performance bottleneck in time-series subsequence matching. first, we analyze the disk access and cpu processing times required during the index searching and post-processing steps of subsequence matching through preliminary experiments. based on their results, we show that the post-processing step is a main performance bottleneck in subsequence matching. in order to resolve the performance bottleneck, we propose a simple yet quite effective method that processes the post-processing step. by rearranging the order of candidate subsequences to be compared with a query sequence, our method completely eliminates the redundancies of disk accesses and cpu processing occurring in the post-processing step. we show that our method is optimal and also does not incur any false dismissal. also, we justify the effectiveness of our method by extensive experiments.
optimization of subsequence matching under time warping in time-series databases. this paper discusses effective processing of subsequence matching under time warping in time-series databases. time warping is a transformation that enables finding of sequences with similar patterns even when they are of different lengths. through a preliminary experiment, we first point out that naive-scan, a basic method for processing of subsequence matching under time warping, has its performance bottleneck in the cpu processing step. for optimizing this step, in this paper, we propose a novel method that eliminates all possible redundant calculations. it is verified that this method is not only an optimal one for processing naive-scan, but also does not incur any false dismissals. our experimental results showed that the proposed method can make great improvement in performance of subsequence matching under time warping. especially, naive-scan, which has been known to show the worst performance, performs much better than lb-scan as well as st-filter in all the cases by employing the proposed method for cpu processing. this result is interesting and valuable in that the performance inversion among naive-scan, lb-scan, and st-filter has occurred by optimizing the cpu processing step, which is their common performance bottleneck.
an effective document clustering method using user-adaptable distance metrics. document clustering is inherently an unsupervised learning process that organizes document (or text) data into distinct groups without depending on pre-specified knowledge. however, real-world applications, such as building a topical hierarchy for a large document collection, need to perform clustering under various kinds of constraints. this paper presents a new type of supervised clustering to organize information in a way that reflects knowledge provided by a user. as a means by which external human knowledge can be incorporated into the clustering process, a quadratic form distance metric is employed that contains a weight matrix. also, we propose a way of representing knowledge to guide the clustering process and a variant of the gradient descent search technique to find a user-specific weight matrix under the hierarchical clustering strategy.
xquery speedup using replication in mapping xml into relations. in this paper, we introduce some replication methods that reduce the query cost incurred when reconfiguring xml documents from divided xml data. the fundamental idea is that query performance can be enhanced by analyzing query patterns and replicating data essential for the query performance. several practical and effective replication methods are formulated. a translation process in which xquery is rewritten into sql using this replication information is performed by a heuristic method, which shows that existing methods, such as the sorted outer union, may be extended to utilize the replicas. finally, some experiments show that these replication methods can be very useful.
similarity measurement for aggregation of spatial objects. svg (scalable vector graphics) is increasingly important to display multimedia data over the internet. although svg is powerful in exchange, transformation, and rendering of image data, it has still the weakness of content-based component-sensitive image search mainly because it does not provide yet the descriptors of (component) image objects and their spatial relationships. also, although similarity matches have been studied for long, similarity measurements for images that contain component image objects are not successfully developed. in this paper, we describe a new approach of a similarity measurement of aggregated image objects.
shape-based retrieval of similar subsequences in time-series databases. this paper deals with the problem of shape-based retrieval in time-series databases. the shape-based retrieval is defined as the operation that searches for the (sub)sequences whose shapes are similar to that of a given query sequence. in this paper, we propose an effective and efficient approach for shape-based retrieval of subsequences. we first introduce a new similarity model for shape-based retrieval that supports various combinations of transformations such as shifting, scaling, moving average, and time warping. for efficient processing of the shape-based retrieval, we also propose the indexing and query processing methods. to verify the superiority of our approach, we perform extensive experiments with the real-world s&p 500 stock data. the results reveal that our approach successfully finds all the subsequences that have the shapes similar to that of the query sequence, and also achieves significant speedup over the sequential scan method.
strong pseudonymous communication for peer-to-peer reputation systems. in this paper we present a novel approach to enable untraceable communication between pseudonyms. our work provides strong sender and recipient anonymity by eliminating the need to know of each other's address.we use a variation of chaum mixes to achieve unlinkability between sender and recipient and introduce a concept called extended destination routing (edr) which relies on routing headers constructed in multiple layers of encryption and published in a distributed hash table (dht). in order to communicate, a sender requests from the dht the recipient's routing header, which is extended and used for routing the message via a mix cascade to this recipient.this work was performed in the context of the unitec reputation system and describes the functionality of its anonymous communication layer, which is completely independent of the other unitec layers. although trust and reputation systems in general are typical application areas for our contribution, the presented concepts are suitable for various other application areas as well. we have implemented a prototype of unitec and present the first results from an ongoing evaluation in our network emulation testbed.
vistaclara: an interactive visualization for exploratory analysis of dna microarrays. we have created vistaclara to explre the effectiveness of applying an extended permutation matrix to the task of exploratory data analysis of multi-experiment microarray studies. the permutation matrix is a visualization technique for interactive exploratory analysis of tabular data that permits both row and column rearrangement, and fits well with the tabular forms of data characteristic of gene expression studies. however, this technique has been largely overlooked by current bioinformatics research. our implementation supports direct incorporation of supplemental data and annotations into the matrix view. this enables visually searching for patterns in gene expression measurements that correlate with other types of relevant data (disease classes, clinical, histological, drug treatments, etc.). the heatmap visualization common in microarray analysis is extended to provide a novel alternative using size as well as color to graphically represent experimental values, thus allowing more effective quantitative comparisons. methods to sort rows or columns by similarity extend the possible permutation operations, and allow more efficient searching for biologically relevant patterns in very large data sets. based on overview+detail principles, a dynamic compressed heatmap view of the entire data set provides the user with overall context, including possible correlations not currently visible in the more detailed view. combined, these techniques make it possible to perform highly interactive ad hoc visual explorations of microarray.
noxes: a client-side solution for mitigating cross-site scripting attacks. web applications are becoming the dominant way to provide access to on-line services. at the same time, web application vulnerabilities are being discovered and disclosed at an alarming rate. web applications often make use of javascript code that is embedded into web pages to support dynamic client-side behavior. this script code is executed in the context of the user's web browser. to protect the user's environment from malicious javascript code, a sand-boxing mechanism is used that limits a program to access only resources associated with its origin site. unfortunately, these security mechanisms fail if a user can be lured into downloading malicious javascript code from an intermediate, trusted site. in this case, the malicious script is granted full access to all resources (e.g., authentication tokens and cookies) that belong to the trusted site. such attacks are called cross-site scripting (xss) attacks.in general, xss attacks are easy to execute, but difficult to detect and prevent. one reason is the high flexibility of html encoding schemes, offering the attacker many possibilities for circumventing server-side input filters that should prevent malicious scripts from being injected into trusted sites. also, devising a client-side solution is not easy because of the difficulty of identifying javascript code as being malicious. this paper presents noxes, which is, to the best of our knowledge, the first client-side solution to mitigate cross-site scripting attacks. noxes acts as a web proxy and uses both manual and automatically generated rules to mitigate possible cross-site scripting attempts. noxes effectively protects against information leakage from the user's environment while requiring minimal user interaction and customization effort.
context-aware filtering for collaborative web systems: adapting the awareness information to the user's context. we propose a context-based filtering process which aims at adapting the awareness information delivered to mobile users by collaborative web systems. this filtering process relies on a model of context which integrates both a physical and an organizational dimensions and allows to represent the user's current context as well as general profiles. these profiles are descriptions of user's potential contexts and express the awareness information filtering rules to apply when the user's current context matches one of them. these rules reflect the user's preferences given a context. we describe how the filtering process performs in two steps, one for identifying the general profiles that apply, and a second for selecting the awareness information. we also discuss the patterns matching algorithms used in the filtering process to compare the contexts descriptions.
compensating creativity in the digital world: reconciling technology and culture. new proposals on compensatory mechanisms and viability of traditional practices for online environment is currently being considered by the european union and are subject of heated academic debate. simultaneously new business models for distribution of intellectual property online are emerging rapidly. the paper focuses on the failures of the current compensatory mechanisms, as well as new proposals to compensate the creative in the online environment, including current online distribution practices, universal levy proposals -- internet access and traffic levies, blank media levies, voluntary compensation systems, as well as traditional practices of the collecting societies, effects of the content protection technologies (drm) on the accessibility and fair use of information online. the paper suggests the set of features for the new compensatory system, including socio-economic aspects of the different societies, and the need of uniform and worldwide compensatory system. conclusions support the necessity to revise current compensatory mechanisms, centering on the proposed features, rather than just technological mechanisms, which dominate the current political agenda.
an evaluation system for news video streams and blogs. when we want information on current events, we often view news programs on tv or news streams on web sites. a news video stream consists of several scenes, and viewers often gain a broad understanding of the news by viewing scenes in the given order. since a viewer's opinion of a news topic will depend on the scene order, we have developed a method for extracting blog entries expressing a particular point of view regarding news topics where we use a form of evaluation and categorization similar to that based on news scene order. in this paper, we describe our method of news evaluation based on news scene order and explain how a blog search can be done using a news stream.
an architecture for information retrieval over semi-collaborating peer-to-peer networks. peer-to-peer (p2p) networking is aimed at exploiting the potential of widely distributed information pools and its effortless access and retrieval irrespectively of underlying networking protocols, operating systems of devices. however, prohibiting limitations have been identified and perhaps the most important one is the successful location of relevant information sources and the efficient query routing in large, highly distributed p2p networks. in this paper, a novel, cluster-based architecture for ir over p2p networks is presented and its evaluation is focused on retrieval effectiveness. we reason in favour of using clustering for p2p ir, by considering two fundamental hypotheses drawn from current p2p file-sharing systems. we also study the potential usefulness of a simplified version of dempster-shafer (d-s) theory of evidence combination for results fusion in the network. we simulated the ir behavior of the system by using the trec 6 and 7 ad-hoc track. the proposed architecture bears very promising results in terms of precision and recall.
nicemeetvr: facing professional baseball pitchers in the virtual batting cage. in this paper, we introduce a new virtual baseball batting training system called nicemeet vr, which enable batters to face a variety of baseball pitchers. the pitching motions are obtained using the video-based motion capture system called krops, which enable users to capture the 3d human motion from a single view video sequences. the batter holds a bat with a reflector attached to the sweet spot, and stands in front of the screen where the pitching motions are projected. no real baseball is actually thrown. instead, a 3d virtual ball is rendered over the screen. the batter swings the bat toward the ball when it reaches the home-base. the trajectory of the swung bat is analyzed using dual supply photoelectric sensors behind the screen, that cast and also detect the infra red beam reflected by the bat. using our system, the player can practice batting against any pitcher, such as top professional major league pitchers, or famous pitchers in the past by capturing their motion from videos.
multidimensional support vector machines for visualization of gene expression data. dna microarray technology has helped us to understand the biological system because of its ability to monitor the expression levels of thousands of genes simultaneously. since dna microarray experiments provide us with huge amount of gene expression data, they should be analyzed with statistical methods to extract the meanings of experimental results.for visualization and class prediction of gene expression data, we have developed a new svm-based method called multidimensional svms, that generate multiple orthogonal axes. this method projects high dimensional data into lower dimensional space to exhibit properties of the data clearly and to visualize the distribution of the data roughly. furthermore, the multiple axes can be used for class prediction. the basic properties of conventional svms are retained in our method: solutions of mathematical programming are sparse, the optimal solutions can always be found due to its convexity, and nonlinear classification is implemented implicitly through the use of kernel functions. the application of our method to the experimentally obtained gene expression datasets for patients' samples indicates that our algorithm is efficient and useful for visualization and class prediction.
bias-free hypothesis evaluation in multirelational domains. in machine learning one typically assumes that the true classification of an object depends only on the object itself and given the object, is independent of the classification of other objects. in this case, setting aside a sufficiently large and randomly chosen part of the training data as a test set, the observed sample error on the test set is an unbiased estimator of true error. however, in many application settings, those mainstream approaches to model evaluation might be inappropriate. as pointed out by [2], among others, whenever there is autocorrelation, i.e., whenever the target value of one object depends not only on the object itself, but also on other objects' classifications or information that is shared between objects, observed error on a randomly chosen test set may not be an unbiased estimator anymore. we introduce a sampling technique, generalized subgraph sampling, that avoids a bias in error estimation by establishing the required amount of linked objects in the test set.
statistical properties of the simulated time horizon in conservative parallel discrete-event simulations. we investigate the universal characteristics of the simulated time horizon of the basic conservative parallel algorithm when implemented on regular lattices. this technique [1, 2] is generically applicable to various physical, biological, or chemical systems where the underlying dynamics is asynchronous. employing direct simulations, and using standard tools and the concept of dynamic scaling from non-equilibrium surface/interface physics, we identify the universality class of the time horizon and determine its implications for the asymptotic scalability of the basic conservative scheme. our main finding is that while the simulation converges to an asymptotic nonzero rate of progress, the statistical width of the time horizon diverges with the number of pes in a power law fashion. this is in contrast with the findings of ref. [3]. this information can be very useful, e.g., we utilize it to understand optimizing the size of a moving "time window" to enforce memory constraints.
computing the cube of an interval matrix is np-hard. in many practical applications, we are interested in computing the product of given matrices and/or a power of a given matrix. in some cases, the initial matrices are only known with interval uncertainty. it turns out that under this uncertainty, there is a principal difference between the product of two matrices and the product of three (or more) matrices:&bull; on the one hand, it is more or less known that the problems of computing the exact range for the product of two matrices - and for the square of a matrix - are computationally feasible;&bull; on the other hand, we prove that the problems of computing the exact ranges for the product of three matrices - and for the third power of a matrix - are np-hard.
a constraint solver for sequences and its applications. constraint programming techniques are successfully used in various areas of software engineering for industry, commerce, transport, finance etc. constraint solvers for different data types are applied in validation and verification of programs containing data elements of these types. a general constraint solver for sequences is necessary to take into account this data type in the existing validation and verification tools. in this work, we present an original constraint solver for sequences implemented in chr and based on t. fr&uuml;hwirth's solver for lists with the propagation of two constraints: generalized concatenation and size. the applications of the solver (with the validation and verification tool bztt) to different software engineering problems are illustrated by the example of a waiting room model.