1,354,100 research outputs found
Discovering parameter setting in 3G networks via active measurements
No full textThe behavior and performance of a UMTS network
are governed by a number of parameter settings that are
configured by the network operator, e.g., timeouts. In this letter
we show that the actual value of such parameters can be inferred
by a conceptually simple set of end-to-end measurements, without
any cooperation with the network operator. In principle, such
information can be used by researchers to define realistic network
scenarios, e.g., for their simulations. Moreover, it can be used by
a malicious attacker to fine-tune a large scale attack against the
Radio Access Network, e.g., a paging attack
Instance Selection for Semi-Supervised Learning in Multi-Expert Systems: A Comparative Analysis
No full textSemi-Supervised learning methods utilize abundant unlabeled data in order to enlarge the training
set and to update classifiers. For the purpose, standard methods label and select unknown data which
are classified with high confidence by the current classifier. This paper presents an experimental
investigation on the use of semi-supervised learning and discusses three methods (our feedback-based
technique and two different algorithms known in literature) for retraining individual classifiers in a
multi-expert scenario. More specifically, we analyze the entire system so that a misclassified sample
for a particular expert, respect to the final decision, can be used to update itself if that sample is
classified with a confidence greater than a specific threshold by multi-expert system. Experimental
tests, carried out on the CEDAR (handwritten digits) database, are presented and some considerations
about accuracy, space and time between different methods are provided. For the purpose, a SVM
classifier and five different combination techniques at abstract and measurement level have been used.
The results show that our feedback-based algorithm outperforms Self-Training and Co-Training
algorithms when the training set is very small and a suitable number of iterations is performed in the
feedback process
Learning Strategies for Knowledge-base Updating in Online Signature Verification Systems
No full textUpdating of reference information is a crucial task for automatic signature verification. In fact, signature characteristics vary in time and whatever approach is considered the effectiveness of a signature verification system strongly depends on the extent to which reference information is able to model the changeable characteristics of users’ signatures. This paper addresses the problem of knowledge-base updating in multi-expert signature verification sys-tems and introduces a new strategy which exploits the collective behavior of classifiers to select the most profitable samples for knowledge-base updating. The experimental tests, carried out using the SUSig database, demonstrate the effectiveness of the new strategy
Discovering parameter setting in 3G networks via active measurements
No full textThe behavior and performance of a UMTS network are governed by a number of parameter settings that are configured by the network operator, e.g., timeouts. In this letter we show that the actual value of such parameters can be inferred by a conceptually simple set of end-to-end measurements, without any cooperation with the network operator. In principle, such information can be used by researchers to define realistic network scenarios, e.g., for their simulations. Moreover, it can be used by a malicious attacker to fine-tune a large scale attack against the radio access network, e.g., a paging attack
HFSP: Size-based Scheduling for Hadoop
No full textIn this paper, we present a size-based scheduling protocol for Hadoop, that caters to both interactivity and efficiency requirements raised by current Hadoop clusters.
Our scheduler addresses several challenges such as job size estimation, resource management and scheduling complex jobs with inter-related phases. Furthermore, by employing the technique of job aging, we avoid the problem of job starvation typical of well known size-based policies.
Our experiments pinpoint at a significant decrease in average job sojourn times -- a metric that accounts for the total time a job spends in the system, including waiting and serving times -- for realistic workloads generated according to production traces available in literature
Learning Iterative Strategies in Multi-Expert Systems using SVMs for Digit Recognition
No full textThis paper presents three different learning iterative strategies, in a
multi-expert system. In first strategy entire new dataset is used. In second strategy
each single classifier selects new samples starting from those on which it
performs a misclassification. Finally, the collective behavior of classifiers is
studied to select the most profitable samples for knowledge base updating. The
experimental results provide a comparison of three approaches under different
operating conditions and feedback process. A classifier SVM and four different
combination techniques were used by considering the CEDAR (handwritten
digit) database. It is shown how results depend by the iterations on the feedback
process, as well as by the specific combination decision schema and by data
distribution
A novel prototype generation technique for handwriting digit recognition
No full textThe aim of this paper is to introduce a novel prototype generation technique for handwriting digit recognition. Prototype generation is approached as a two-stage process. The first stage uses an Adaptive Resonance Theory 1 (ART1) based algorithm to select an effective initial solution, while the second one executes a fine tuning designed to generate the best prototypes.
To this end, the second stage deals with an optimization problem, in which the objective function to be minimized is the cost function associated to the classification. A naive evolution strategy is used to generate the prototype set able to reduce classification time, without greatly affecting the accuracy. Moreover, as the ART1 based algorithm has incremental learning capability, the first stage is also useful for selecting the prototype set according to variations in handwriting style. The classification task is performed by the k-nearest neighbor classifier.
Experimental tests on the MNIST dataset demonstrated that our technique represents a good trade-off among accuracy, classification speed and robustness to handwriting style changes
Supervised Learning Strategies in Multi-Classifier Systems
No full textThis paper presents three strategies in order to re-train classifiers in a multi-expert scenario when new labeled data become available. The simplest possibility is the use of the entire new dataset. The second possibility is related
to the consideration that each single classifier is able to
select new patterns starting from those on which it
performs a miss-classification. Finally, the multi expert system behavior can be inspected to select profitable samples. More specifically a misclassified sample, for a particular classifier, is used to update that classifier only if it produces a miss-classification by the ensemble of classifiers. The three approaches are compared under
different conditions on two different state of the art
performing classifiers by considering the CEDAR
(handwritten digit) database. It is shown how results
depend by the amount of the new training samples, as
well as by the specific combination decision schema
Handwritten processing for Pre Diagnosis of Alzheimer disease
No full textBased on neuromuscular transfer function of the handwriting system, in this paper a non invasive pre diagnosis system for Alzheimer disease alert is proposed. It is well known in fact, that writing originates from spike trains produced within the Central Nervous System (CNS) and more specifically, inside the 4-th and the 6-th regions of the Bradman's map and then transmitted through the first and second order axons to the spinal cord to control the muscles involved in the handwriting as the arm, the forearm, the hand and the pen or pencil utilized for the writing. More specifically, in this work is proposed a new method, not invasive, for early diagnosis of degenerative disability, it can be also useful for monitoring activities related to the progression of neuromuscular disease in order to evaluate the changing related also to the efficiency of the therapies used. Benefit can be obtained not only for the medical field but also for the pharmaceutical developments. Specifically in the paper, the results of some experiments have been focused by considering a certain number of persons some of which affect by Alzheimer disease
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