waashk/dblp huggingface dataset:
hopfield networks in relevance and redundancy feature selection applied to classification of biomedical high-resolution micro-ct images. we study filter---based feature selection methods for classification of biomedical images. for feature selection, we use two filters -- a relevance filter which measures usefulness of individual features for target prediction, and a redundancy filter, which measures similarity between features. as selection method that combines relevance and redundancy we try out a hopfield network. we experimentally compare selection methods, running unitary redundancy and relevance filters, against a greedy algorithm with redundancy thresholds [9], the min-redundancy max-relevance integration [8,23,36], and our hopfield network selection. we conclude that on the whole, hopfield selection was one of the most successful methods, outperforming min-redundancy max-relevance when more features are selected.
gep-induced expression trees as weak classifiers. the paper proposes applying gene expression programming (gep) to induce expression trees used subsequently as weak classifiers. two techniques of constructing ensemble classifiers from weak classifiers are investigated in the paper. the working hypothesis of the paper can be stated as follows: given a set of classifiers generated through applying gene expression programming method and using some variants of boosting technique, one can construct the ensemble producing effectively high quality classification results. a detailed description of the proposed gep implementation generating classifiers in the form of expression trees is followed by the report on adaboost and boosting algorithms used to construct an ensemble classifier. to validate the approach computational experiment involving several benchmark datasets has been carried out. experiment results show that using gep-induced expression trees as weak classifiers allows for construction of a high quality ensemble classifier outperforming, in terms of classification accuracy, many other recently published solutions.
browsing assistance service for intranet information systems. improved usability and efficiency of organizational information systems brings economical benefits to the organization and time benefits to the users. we present a browsing assistance service suitable for the organizational intranet environments. it helps users to shorten their browsing interactions and achieve their goals faster. these benefits are accomplished by providing relevant suggestions on the potential navigation targets of interest to the users. the system design employs the analytics of user browsing behavior and its appropriate segmentation. it efficiently utilizes the initial and the terminal navigation points for providing recommendations. the performance of the system has been evaluated on the real world data of a large scale intranet portal.
designing specific weighted similarity measures to improve collaborative filtering systems. the aim of collaborative filtering is to help users to find items that they should appreciate from huge catalogues. in that field, we can distinguish user-based from item-based approaches. the former is based on the notion of user neighbourhoods while the latter uses item neighbourhoods.the definition of similarity between users and items is a key problem in both approaches. while traditional similarity measures can be used, we will see in this paper that bespoke ones, that are tailored to type of data that is typically available (i.e. very sparse), tend to lead to better results.extensive experiments are conducted on two publicly available datasets, called movielens and netflix. many similarity measures are compared. and we will show that using weighted similarity measures significantly improves the results of both user- and item-based approaches.
mining unexpected web usage behaviors. recently, the applications of web usage mining are more and more concentrated on finding valuable user behaviors from web navigation record data, where the sequential pattern model has been well adapted. however with the growth of the explored user behaviors, the decision makers will be more and more interested in unexpected behaviors, but not only in those already confirmed. in this paper, we present our approach user, that finds unexpected sequences and implication rules from sequential data with user defined beliefs, for mining unexpected behaviors from web access logs. our experiments with the belief bases constructed from explored user behaviors show that our approach is useful to extract unexpected behaviors for improving the web site structures and user experiences.
control charts of workflows. this paper focuses on the control of the performance characteristics of workflows modeled with stochastic petri nets (spn's). this goal is achieved by focusing on a new model for artificial social systems (ass's) behaviors, and by introducing equivalent transfer functions for spn's. ass's exist in practically every multi-agent system, and play a major role in the performance and effectiveness of the agents. this is the reason why we introduce a more suggestive model for ass's. to model these systems, a class of petri nets is adopted, and briefly introduced in the paper. this class allows representing the flow of physical resources and control information data of the ass's components. in the analysis of spn we use simulations in respect to timing parameters in a generalized semi-markov process (gsmp). by using existing results on perturbation (e.g., delays in supply with raw materials, derangements of equipments, etc.) analysis and by extending them to new physical interpretations we address unbiased sensitivity estimators correlated with practical solutions in order to attenuate the perturbations.
autonomous forex trading agents. in this paper we describe an infrastructure for implementing hybrid intelligent agents with the ability to trade in the forex market without requiring human supervision. this infrastructure is composed of three modules. the "intuition module", implemented using an ensemble model, is responsible for performing pattern recognition and predicting the direction of the exchange rate. the "a posteriori knowledge module", implemented using a case-based reasoning system, enables the agents to learn from empirical experience and is responsible for suggesting how much to invest in each trade. the "a priori knowledge module", implemented using a rule-based expert system, enables the agents to incorporate non-experiential knowledge in their trading decisions. this infrastructure was used to develop an agent capable of trading the usd/jpy currency pair with a 6 hours timeframe. the agent's simulated and live trading results lead us to believe our infrastructure can be of practical interest to the traditional trading community.
modelling medical time series using grammar-guided genetic programming. the analysis of time series is extremely important in the field of medicine, because this is the format of many medical data types. most of the approaches that address this problem are based on numerical algorithms that calculate distances, clusters, reference models, etc. however, a symbolic rather than numerical analysis is sometimes needed to search for the characteristics of time series. symbolic information helps users to efficiently analyse and compare time series in the same or in a similar way as a domain expert would. this paper describes the definition of the symbolic domain, the process of converting numerical into symbolic time series and a distance for comparing symbolic temporal sequences. then, the paper focuses on a method to create the symbolic reference model for a certain population using grammar-guided genetic programming. the work is applied to the isokinetics domain within an application called i4.
noisy image segmentation by a robust clustering algorithm based on dc programming and dca. we present a fast and robust algorithm for image segmentation problems via fuzzy c-means (fcm) clustering model. our approach is based on dc (difference of convex functions) programming and dca (dc algorithms) that have been successfully applied in a lot of various fields of applied sciences, including machine learning. in an elegant way, the fcm model is reformulated as a dc program for which a very simple dca scheme is investigated. for accelerating the dca, an alternative fcm-dca procedure is developed. moreover, in the case of noisy images, we propose a new model that incorporates spatial information into the membership function for clustering. experimental results on noisy images have illustrated the effectiveness of the proposed algorithm and its superiority with respect to the standard fcm algorithm in both running-time and quality of solutions.
realizing modularized knowledge models for heterogeneous application domains. this paper addresses the realization of modularized knowledge models within a heterogeneous application domain using an existing knowledge management tool. the application domain we deal with is travel medicine, which combines medical aspects with geography, climate, holiday activities and associated traveling conditions. in this paper we present the application's requirements and show how knowledge models can be developed using an industrial strength application. furthermore we present the challenges of a knowledge model based on multi-case bases whereas each case base represents its own area of expertise. hence, we introduce our knowledge model for the travel medicine application and exemplify the implementation of typical data types and similarity measures.
leatherbacks matching by automated image recognition. we describe a method that performs automated recognition of individual laetherback turtles within a large nesting population. with only minimal preprocessing required of the user, we prove able to produce unsupervised matching results. the matching is based on the scale-invariant feature transform by lowe. a strict condition posed by biologists reads that matches should not be missed (no false negatives). a robust criterion is defined to meet this requirement. results are reported for a considerable sample of leatherbacks.
projection with double nonlinear integrals for classification. in this study, a new classification model based on projection with double nonlinear integrals is proposed. there exist interactions among predictive attributes towards the decisive attribute. the contribution rate of each combination of predictive attributes, including each singleton, towards the decisive attribute can be re presented by a fuzzy measure. we use double nonlinear integrals with respect to the signed fuzzy measure to project data to 2-dimension space. then classify the virtual value in the 2-d space projected by nonlinear integrals. in our experiments, we compare our classifier based on projection with double nonlinear integrals with the classical method- naïve bayes. the results show that our classification model is better than naïve bayes.
experiences using clustering and generalizations for knowledge discovery in melanomas domain. one of the main goals in prevention of cutaneous melanoma is early diagnosis and surgical excision. dermatologists work in order to define the different skin lesion types based on dermatoscopic features to improve early detection. we propose a method called somex with the aim of helping experts to improve the characterization of dermatoscopic melanoma types. somex combines clustering and generalization to perform knowledge discovery. first, somex uses self-organizing maps to identify groups of similar melanoma. second, somex builds general descriptions of clusters applying the anti-unification concept. these descriptions can be interpreted as explanations of groups of melanomas. experiments prove that explanations are very useful for experts to reconsider the characterization of melanoma classes.
the impact of noise in spam filtering: a case study. unsolicited commercial e-mail (uce), more commonly known as spam is a growing problem on the internet. every day people receive lots of unwanted advertising e-mails that flood their mailboxes. fortunately, there are several approaches for spam filtering able to detect and automatically delete this kind of messages. however, spammers have adopted some techniques to reduce the effectiveness of these filters by introducing noise in their messages. this work presents a new pre-processing technique for noise identification and reduction, showing preliminary results when it is applied with a flexible bayes classifier. the experimental analysis confirms the advantages of using the proposed technique in order to improve spam filters accuracy.
cell loss performance of the gauss atm switch. the authors present a performance study of the gauss asynchronous transfer mode (atm) switch with respect to cell loss. the gauss switch is a recently proposed, high-performance, output buffered atm switch that can easily be implemented. in general, if an atm switch suffers from cell loss, the exact behavior of this cell loss as a function of parameters such as load, traffic business, internal speed, etc., has to be known. the study focuses on cell loss in the gauss switch due to its reduced internal speed. analytical results for the cell loss probability are provided and compared to corresponding quantities for the well-known knockout switch. the comparison shows that the gauss switch yields significantly smaller cell loss probabilities than the knockout switch
long term resource allocation in video delivery systems. in typical video delivery systems offering programs on-demand, service should be be nearly immediate and continuous. a video server can provide this type of service by reserving sufficient network and server resources for the duration of playout. scalability and reduced cost can be achieved using a single channel to serve multiple customers waiting for the same program (referred to as batching). batching is especially useful during high load periods typically occuring during evening prime time hours. typical channel allocation algorithms use a greedy, allocate-as-needed policy. variations in system load can cause these algorithms to suffer poor and unpredictable short-term performance, and non-optimal long term performance. in this paper, we develop a set of realistic workloads, identify the limitations of greedy allocation algorithms, and propose a set of rate-based allocation schemes to solve these limitations. the performance of various video delivery systems are simulated and compared. the rate-based policies are shown to be robust for the workloads examined, and are easy to implement.
performance analysis of hot-potato routing for multiclass traffic in multihop lightwave networks. the authors present an analytical approach to the performance analysis of multihop lightwave networks with hot-potato routing and multiclass traffic. explicit closed-form expressions are derived for the hop distribution and the probability of `don't care'. the computational complexity is thus reduced as compared to previous approaches, and priorities are readily handled. results for two different classes of traffic are given. the probability of a packet belonging to a particular class of traffic existing on a preferred path is calculated by finding the fixed point of a scale function. the throughput and network delay as well as queuing delay of a particular class are then calculated using the expressions derived for them
state-dependent m/g/1 type queueing analysis for congestion control in data networks. we study a linear-increase multiplicative-decrease flow control mechanism. we consider congestion signals that arrive in batches according to a poisson process. we focus on the case when the transmission rate cannot exceed a certain maximum value. the distribution of the transmission rate in steady state as well as its moments are determined. our model is particularly useful to study the behavior of tcp (transmission control protocol) the congestion control mechanism in the internet. burstiness of packet losses is captured by allowing congestion signals to arrive in batches. by a simple transformation, the problem can be reformulated in terms of an equivalent m/g/1 queue, where the transmission rate in the original model corresponds to the workload in the 'dual' queue. the service times in the queueing model are not i.i.d., and they depend on the workload in the system.
on resource management and qos guarantees for long range dependent traffic. the effect of long-memory processes on queue length statistics of a single queue system is studied through a controlled fractionally differenced arima (1,d,0) input process. this process has two parameters /spl phi//sub 1/ and d representing an auto-regressive component and a long-range dependent component, respectively. results show that the queue length statistics studied (mean, variance and the 0.999 quantile) are proportional to e(c/sup c/spl phi/1/) e(c/sub 2/d), where (c/sub 1/, c/sub 2/) are positive constants, and c/sub 2/
analysis of aimd protocols over paths with variable delay. the throughput of aimd protocols in general and of tcp in particular, has been computed in many existing works by modeling the round-trip time as a constant and thus replacing it by its expectation. there are however many scenarios in which the delays of packets vary, causing a variation of the round-trip time. many typical scenarios occur in wireless and mobile networks. we propose in this paper an analytical model that accounts for the variability of delay, while computing the throughput of an aimd protocol. we derive a closed-form expression for the throughput, that illustrates the impact of delay variability. we show by analysis and simulation, that an increase in the variability of delay improves the performance of an aimd protocol. thus, an analytical model that only considers the average delay could underestimate the performance of an aimd protocol in scenarios where delay is variable.
the distribution of delays of dispersed messages in an m/m/1 queue. we analyze the distribution of the delay of messages in an infinite capacity m/m/1 queue. a message is composed of n packets, and the arrival of the packets to the queue is poisson. our calculations are based on recursive schemes. we obtain explicit expressions for the laplace-stieltjes transform (lst) of the delays, which enables to obtain exact expressions for the moments of the delay in a complexity smaller than the one obtained by using recursive schemes. we repeat the above calculations for the case that messages are dispersed, i.e. packets from several sources arrive to an m/m/1 queue and are served according to the fifo discipline. hence, several packets of other messages may arrive between consecutive packets of a given message.
fractal traffic: measurements, modelling and performance evaluation. observations of both ethernet traffic and variable bit rate (vbr) video traffic have demonstrated that these traffics exhibit "self-similarity" and/or infinite asymptotic index of dispersion for counts (idc). we report here on measurements of traffic in a commercial public broadband network where similar characteristics have been observed. for the purpose of analysis and dimensioning of the central links of an atm network we analyse in this paper the performance of a single server queue fed by gaussian traffic with infinite idc. the analysis lends to an approximation for the performance of a queue in which the arriving traffic is "fractal" gaussian and consequently where there does not exist a dominant negative-exponential tail. the term "fractal" is used here in the sense that the autocovariance of the traffic exhibits self-similarity, that is to say, where the autocovariance of an aggregate of the traffic is the same, or asymptotically the same for large time lags, as the original traffic. we are not concerned with proving or exploiting this self-similarity property as such, but only with performance analysis techniques which are effective for such processes. in order to be able to test the performance analysis formulae, we show that traffic with the same autocovariance as measured in a real network over a wide range of lags (sufficiently wide a range for the traffic to be equivalent from the point of view of queueing performance) can be generated as a mixture of two gaussian ar(1) processes. in this way we demonstrate that the analytic performance formulae are accurate.
bandwidth allocation for guaranteed versus best effort service categories. modern communication networks evolve towards integration of guaranteed-performance and best-effort service types. the coexistence of these two service types offers substantial benefits, such as resource sharing between service classes, and the ability of the user to select an appropriate service class according to its individual requirements and preferences. notwithstanding, such interaction gives rise to more complicated system behavior and related performance issues, which need to be explored and understood in order to allow efficient network operation. in this paper we examine potential congestion phenomena, which arise due to the combined effect of bandwidth sharing and user migration between service classes. we propose a simplified fluid model for session flow, consisting of two coupled queues with state-dependent flows, which captures the essential ingredients of service-class interaction. our analysis shows that the system might exhibit bistable behavior, in the sense that transient congestion may stir the system from a stable and efficient operating point to an inefficient and congested one. we identify conditions which give rise to bistability, and propose a call admission control scheme which prevents the system from getting trapped in a congested-type equilibrium, while not interfering with normal system operation.
cooperative distributed scheduling for ats-based broadband networks. a proactive distributed cooperative control algorithm for resource scheduling of broadband networks based on asynchronous time sharing is presented. the problem setting for distributed, cooperative proactive scheduling is described. an overview of the distributed cooperative algorithm is presented. the general control principles underlying the effectiveness of the cooperative algorithm are discussed. the formal details of the distributed cooperative algorithm and its underlying single noise scheduling algorithm are considered. simulation results comparing the distributed cooperative algorithm with other networkwide scheduling algorithms are reported
design and evaluation of an adaptive flow control scheme. the authors model a virtual circuit in a computer network as a sequence of servers in tandem. they explicitly take into account cross traffic at the servers from other virtual circuits. the analysis of the model leads them to propose a novel flow control scheme, which they term send-time control. a measure to evaluate the performance of flow control schemes, called the packet performance index, is proposed. they compare the send-time control scheme with the window-based flow control scheme of transmission control protocol (tcp) using simulation. the results obtained show that send-time control is superior to the window-based scheme
improving the throughput of point-to-multipoint arq protocols through destination set splitting. an approach to improving the performance of point-to-multipoint automatic repeat request (arq) protocols is considered. the approach involves splitting the destination set into disjoint groups and having the source carry out independent time multiplexed conversations with each group. the performance of memoryless and limited-memory versions of stop-and-wait and go-back-n protocols was investigated and the question of how to pick an optimal grouping of nodes was addressed. the results indicate that destination set splitting can improve the throughput of point-to-multipoint arq protocols, particularly if the receivers' capabilities are not identical