waashk/dblp huggingface dataset:
the effectiveness of combining information retrieval strategies for european languages. building an effective information retrieval system requires various design choices, ranging from the weighting scheme of the type of morphological normalization. the combination of runs has become a standard technique to reap the benefits of different run types. until now, systematic studies of the effectiveness of combination strategies have only been carried out for english. this paper provides an exploratory overview of the effectiveness of combination methods in nine european languages. we demonstrate that the combination of effective information retrieval strategies can lead to significant improvements of retrieval effectiveness. furthermore, we analyze the relative impact of retrieving more relevant documents and of improved ranking of relevant documents. the experimental evidence is obtained using the 2003 testsuite of the cross-language evaluation forum (clef).
transactional agent model for fault-tolerant object systems. a transactional agent is a mobile agent which manipulates objects in multiple computers by autonomously finding a way to visit the computers. the transactional agent commits only if its commitment condition like atomicity is satisfied in presence of faults of computers. on leaving a computer, an agent creates a surrogate agent which holds objects manipulated. a surrogate can recreate a new incarnation of the agent if the agent itself is faulty. if a destination computer is faulty, the transactional agent finds another operational computer to visit. after visiting computers, a transactional agent makes a destination on commitment according to its commitment condition. we discuss design and implementation of the transactional agent which is tolerant of computer faults.
smoothed fetching: bridging the data layout and transmission schemes in multimedia servers. typical block placement schemes generally assume ctl (constant time length) block size, one block read for each stream in a round, round-robin striping, and peak rate-based admission control. traditional smoothing schemes for stored continuous media objects do not consider the block layout of the storage system. hence, the combination of schemes from the two domains can introduce new challenges. in this paper, we present a server transmission delay problem that arises when traditional block placement and smoothing schemes are used at the same time in a continuous media server. to resolve the problem, we first present two simple straightforward solutions, server-side block prefetching and multiple block read, and then propose a new solution smoothed fetching. sf overcomes the defects of the other two schemes by exploiting a smoothing technique when retrieving blocks from disks and using a tight admission control algorithm. simulation results show that sf achieves the best performance among the three solutions.
text-based summarization and visualization of gene clusters. we present a system named medsummarizer which uses biomedical literature information to assign biological meaning to a cluster of genes. using relevant pubmed citations, it creates a ranked list of important biological concepts that describes the gene list. further, based on the assigned concepts, it computes similarity between each pair of genes and displays this using a graph based visualization technique. the system allows use of human curated index (e.g. mesh terms) as well as automatic annotations derived from free-text. we compare the results obtained using these two types of terms.
the time diversification monitoring of a stock portfolio: an approach based on the fractal dimension. diversification is a technique used to reduce the risk of investment and is accomplished by including uncorrelated and independent stocks in one's portfolio. by diversifying, the investor aims to reduce the risk of an entire portfolio depreciating in value, if a few of the assets within the portfolio are depreciated. in the past, the correlation coefficient has been used as a basis for diversification. however, the correlation coefficient is problematic since it can not capture nonlinear dependency, and analyzing pair-by-pair stocks in the portfolio does not always give the best estimation of diversification for the entire portfolio.in this paper we present a simple, but efficient methodology for monitoring portfolio diversification, which can capture most of the nonlinear phenomena in a portfolio. we propose a measurement of portfolio diversification through the fractal dimension parameter. monitoring this parameter in a time domain represents the basis for automatic detection of significant changes in portfolio diversification. when the fractal dimension is significantly reduced, the algorithm eliminates stocks that are highly correlated and adds new uncorrelated stocks to the portfolio. we tested our method using real historical stock data and obtained significant improvements in the time diversification of selected stock portfolios.
an adaptive tdma slot assignment protocol in ad hoc sensor networks. due to its ability to provide collision-free packet transmission regardless of the traffic load, tdma (time division multiple access) has been applied in ad hoc sensor networks. we have previously proposed a tdma slot assignment protocol that utilizes the channel bandwidth effectively in ad hoc sensor networks. however, in this protocol, the channel bandwidth cannot be fully utilized because the frame length across the whole network tends to increase. in this paper, we extend this protocol to further improve channel utilization. the extended protocol prevents the excessive increase of unassigned slots by minimizing each node's frame length. furthermore, we verify the effectiveness of our proposed protocols by simulation experiments. the results show that our extended protocol improves channel utilization dramatically in comparison with our previous protocol.
a call admission control scheme using genetic algorithms. next generation wireless systems (ngws) will provide a variety of services to mobile users including high speed data, real-time applications and real-time multimedia support with a certain quality of service (qos) level. an efficient handoff management is one of the key issues in order to support global roaming of the mobile users among different network architectures of the ngws.in this paper, a new call admission control scheme, genetic-based call admission control (gac), is proposed for ngws. the gac scheme uses genetic algorithms to achieve high network utilization, minimum cost, minimum handoff latency and mobile terminal's (mt) qos requirements. performance analysis is provided to assess the efficiency of the proposed gac scheme. simulation results show a significant improvement in handoff latencies and costs over the heuristic approach and other cac schemes.
semi-automatic design of agent organisations. multi-agent systems can be viewed as organisations of individual agents. designing an agent organisation is a complex process involving defining the structural relationships among agents, the lines of inter-agent communication, and the agent functionality. existing approaches to agent organisation design are difficult to apply in practice since they require designers to make decisions while working at a low level of abstraction.this paper contributes towards designing agent organisations in a practical and effective manner by proposing to semi-automate the organisational design process. the proposed semi-automatic approach enables agent system designers to reason at a high abstraction level and conveniently re-use previous design decisions. this semi-automatic approach to agent organisation design uses role modelling and a role algebra which captures a number of basic relations among roles. the role algebra's semantics are formally defined using a two-sorted algebra.the applicability of the semi-automatic agent organisation design approach is demonstrated by an example drawn from a case study involving telephone repair service teams.
enhancement of wavelet-based medical image retrieval through feature evaluation using an information gain measure. medical image retrieval covers a major application area, providing significant assistance in medical diagnosis. in this paper, several parameters for a wavelet-based retrieval system for hrct lung images are analyzed, in order to improve the performance of the system in terms of precision and retrieval time. the information gain measure is used to evaluate extracted features to find the features that have the most discriminating power across image disease classes. a weighted similarity metric based on the evaluation is used for image retrieval. the number of levels for wavelet decomposition is studied for 2 types of wavelets, haar and daubechies-8. a filtering criteria based on the features with the highest information gain is used to enhance the retrieval time. experiements on hrct lung images that cover 8 disease classes show that the weighted approach achieves about 10% improvement in the average disease class precision, and 4.75% in the average total system precision, over the unweighted approach. retrieval time is much enhanced while still maintaining high precision ratios.
sds-rules and association rules. the association rule expresses the relation between premise (antecedent) and consequence (succedent). the relation is given by a truth-condition, which can be verified on a given four-fold contingency table denoting the frequencies of objects in some matrix of analyzed data (not-)satisfying antecedent and succedent. this method is more general than the "classical" association rules mined by a-priori algorithm. various types of implication or equivalency can be expressed as well as relations corresponding to statistical hypotheses tests.this notion of association rules can be modified to describe interesting relations in couples of associated disjoint sets. let's have two boolean attributes defining two disjoint sets a and b and the third attribute describing some property of the objects. the sds-rule describes a relation among the sets a and b and the given property. the usual application (interpretation) is to find couples of sets that significantly differ in the given attribute or to find strong differences between given sets a and b. this paper describes the motivation of sds-rules, similarity with association rules and introduces some sds-quantifiers. later it describes how to define disjoint sets a and b using derived attributes of analyzed data matrix and gives some results of this method applied to medical data.
active electronic mail. network infrastructures have evolved tremendously over the last years, offering new capabilities to the applications in higher levels. email is a widely used communication tool that could benefit of an intelligent and active underlying network in order to support sophisticated services. we explore in this paper an infrastructure based on intelligent mobile agents and active networks, and point out how and where advanced features can be introduced to our current passive email platform in order to make it more flexible, open, secure, intelligent, and ubiquitous as possible.
an innovative approach to intelligent information filtering. information filtering is one of the most useful and challenging tasks for effective information access. it is concerned with dynamically adapting the distribution of information where both evolving user's interests and new incoming information are taken into account. in this paper, we present an innovative approach to text filtering based on the novelty detection principle. this approach relies on a specific learning model which allows both accurate online learning of user's profile and evaluation of the coherency of user's behaviour during his interaction with the system.we empirically analyse our approach and present experimental results on the reuters-21578 benchmark. the obtained results bring out a significant enhancement of performance as compared to the widely used rocchio's learning algorithm.
clustering of diverse genomic data using information fusion. genome sequencing projects and high-throughput technologies like dna and protein arrays have resulted in a very large amount of information-rich data. microarray experimental data are a valuable, but limited source for inferring gene regulation mechanisms on a genomic scale. additional information such as promoter sequences of genes/ dna binding motifs, gene ontologies, and location data, when combined with gene expression analysis can increase the statistical significance of the finding. this paper introduces a machine learning approach to information fusion for combining heterogeneous genomic data. this algorithm uses an unsupervised joint learning mechanism that identifies clusters of genes using the combined data. the correlation between gene expression time-series patterns obtained from different experimental conditions and the presence of several distinct and repeated motifs in their upstream sequences is examined here using publicly available yeast cellcycle data. the results show that the combined learning approach taken here identifies correlated genes effectively. the algorithm provides an automated clustering method, but allows the user to specify apriori the influence of each data type on the final clustering using probabilities.
solving the maximum clique problem by k-opt local search. this paper presents a local search algorithm based on variable depth search, called the k-opt local search, for the maximum clique problem. the k-opt local search performs add and drop moves, each of which can be interpreted as 1-opt move, to search a k-opt neighborhood solution at each iteration until no better k-opt neighborhood solution can be found. to evaluate our k-opt local search algorithm, we repeatedly apply the local search for each of dimacs benchmark graphs and compare with the state-of-the-art metaheuristics such as the genetic local search and the iterated local search reported previously. the computational results show that in spite of the absence of major metaheuristic components, the k-opt local search is capable of finding better (at least the same) solutions on average than those obtained by these metaheuristics for all the graphs.
reinforcement learning agents with primary knowledge designed by analytic hierarchy process. this paper presents a novel model of reinforcement learning agents. a feature of our learning agent model is to integrate analytic hierarchy process (ahp) into a standard reinforcement learning agent model, which consists of three modules: state recognition, learning, and action selecting modules. in our model, ahp module is designed with primary knowledge that human intrinsically should have in order to attain a goal state. this aims at increasing promising actions of agent especially in the earlier stages of learning instead of completely random actions as in the standard reinforcement learning algorithms. we adopt profit-sharing as a reinforcement learning algorithm and demonstrate the potential of our approach on two learning problems of a pursuit problem and a sokoban problem with deadlock in the grid-world domains, where results indicate that the learning time can be decreased considerably for the problems and our approach efficiently avoids the deadlock for the sokoban problem. we also show that bad effect that can be usually observed by introducing a priori knowledge into reinforcement learning process can be restrained by a method that decreases a rate of using knowledge during learning.
caching in web memory hierarchies. web cache replacement algorithms have received a lot of attention during the past years. though none of the proposed algorithms deals efficiently with all the particularities of the web environment, namely, relatively weak temporal locality (due to filtering effects of caching hierarchies), heterogeneity in size and origin of request streams. in this paper, we present the crf replacement policy, whose development is mainly motivated by two factors. the first is the filtering effects of web caching hierarchies and the second is the intention of achieving a balance between hit and byte hit rates. crf's decisions for replacement are based on a combination of the recency and frequency criteria in a way that requires no tunable parameters.
application modeling using reverse engineering techniques. in this work we present techniques and tools that enable effective reverse engineering procedures for web applications that were developed using the promising asp.net technology. we deal with model-driven development in its reverse aspect by implementing reverse engineering methods. our implemented methods model web applications using a well-known, web oriented and robust language, namely webml. this is, to the authors' best knowledge, a novel re-engineering transformation. in this paper we propose a method to reverse engineer web applications in order to extract their conceptual model using webml notation. moreover, we present an efficient tool we have developed in order to implement the proposed method, along with a study of the application of our tool to an exemplar, content-management web application. the overall results are quite encouraging and indicate that our approach is efficient.
a markov random field model of microarray gridding. dna microarray hybridisation is a popular high through-put technique in academic as well as industrial functional genomics research. in this paper we present a new approach to automatic grid segmentation of the raw fluorescence microarray images by markov random field (mrf) techniques. the main objectives are applicability to various types of array designs and robustness to the typical problems encountered in microarray images, which are contaminations and weak signal.we briefly introduce microarray technology and give some background on mrfs. our mrf model of microarray gridding is designed to integrate different application specific constraints and heuristic criteria into a robust and flexible segmentation algorithm. we show how to compute the model components efficiently and state our deterministic mrf energy minimization algorithm that was derived from the 'highest confidence first' algorithm by chou et al. since mrf segmentation may fail due to the properties of the data and the minimization algorithm, we use supplied or estimated print layouts to validate results.finally we present results of tests on several series of microarray images from different sources, some of them test sets published with other microarray gridding software. our mrf grid segmentation requires weaker assumptions about the array printing process than previously published methods and produces excellent results on many real datasets.an implementation of the described methods is available upon request from the authors.
modeling organizational goals: analysis of current methods. organizational change, whether it involves the development of a computerized system or the re-engineering of business processes, is a purposive activity driven by the goals of the involved stakeholders. its effectiveness depends on being able to make good decisions about what goals to pursue and on selecting the appropriate strategies for achieving the desired goals.this paper presents an analysis of existing goal modeling methods in terms of their focus, cognitive and social approach. the main findings include that (a) the applicability of each method depends on the characteristics of the particular organizational context and (b) additional benefits can be gained by integrating different methods.
emunet: a real-time network emulator. new protocols and network applications must be extensively tested before deployment on the internet. in this paper, we describe the design and implementation of emunet, a lightweight, portable, configurable, and extendable network emulator, which can be used to emulate a wide variety of network characteristics and conditions inside a laboratory environment. protocols and applications can be tested, without modification, directly on top of the emulated network. the emulator can be used to test protocols under a variety of conditions, such as bit rate limitation, network delay and jitter, bit error rate, different queuing schemes, etc. we show two examples of the use of the emulator to study the performance of voice-over-ip with asymmetric satellite links, and the impact of web caching and traffic compression on the experience of gprs users.
information agents cooperating with heterogenous data sources for customer-order management. as multi-agent systems and information agents obtain an increasing acceptance by application developers, existing legacy enterprise resource planning (erp) systems still provide the main source of data used in customer, supplier and inventory resource management. in this paper we present a multi-agent system, comprised of information agents, which cooperates with a legacy erp in order to carry out orders posted by customers in an enterprise environment. our system is enriched by the capability of producing recommendations to the interested customer through agent cooperation. at first, we address the problem of information workload in an enterprise environment and explore the opportunity of a plausible solution. secondly we present the architecture of our system and the types of agents involved in it. finally, we show how it manipulates retrieved information for efficient and facile customer-order management and illustrate results derived from real-data.
evaluating unstructured peer-to-peer lookup overlays. unstructured peer-to-peer lookup systems incur small constant overhead per single join or leave operation, and can easily support keyword searches. hence, they are suitable for dynamic failure-prone environments. in this paper, we define metrics for evaluating unstructured overlays for peer-to-peer lookup systems. these metrics capture the search dependability and efficiency, and the granularity at which one can control the tradeoff between the two, as well as fairness. according to these metrics, we evaluate different graphs and overlays, including a gnutella graph, a power law random graph, normal random graphs, a 3-regular random graph, and a 3-araneola overlay. our study shows that, according to our metrics, a 3-araneola overlay achieves the best results, and hence it is an excellent solution for flooding-based peer-to-peer lookup system.
architecture to support dynamic composition of math lesson plans. in this work we describe an architeture that supports the composition of web based lesson plans for math learning based on a database of fine grained mathematical components that can be tailored for communities of users. for this research, the principal target community of users is middle school mathematics teachers. our goal is to build a web accessible resource of pedagogical material for grades k-12 math that can be used and reused in a variety of contexts from teacher remediation to lesson preparation through to student use of the lesson. the web based lessons are generated on the fly from a database of fine grained components that include explanations, interactive activities, worksheets, images, videos, audio, faq's, online questions and challenges.
mona: an extensible framework for web document monitoring. this work presents an extensible architecture for fully-automated long-term monitoring of documents on the web. the framework enables easy coupling of existing search services of different kind, making the approach suitable for mixed environments (e.g. classic web and semantic web).the proposed architecture supports a process for repeated querying and classifying of documents based on a model of the domain of interest. in doing so, a well-organized version-controlled repository of documents is gradually built up.