We leveraged a wide range of knowledge in machine learning and information retrieval to develop a robust keywords identification system. In particular, we adapted the Tf-Idf heuristic for feature selection, investigated linear regression, logistic regression, and SVM for predicting number of keywords (tags), and also implemented NaiveBayes, Labeled Latent Dirichlet Allocation(L-LDA) algorithms ...

This paper introduces a cohesion metric that can be used for the identification of weakly cohesive activities in a workflow design. Also, a heuristic is presented that is based on this cohesion metric to decide between various workflow design alternatives. Both a theoretical and an empirical evaluation give a positive support its soundness. The inspiration for the introduced notion is derived f...

We present a new dependency parsing algorithm based on the decomposition of large sentences into smaller units such as clauses and intraclausal coordinations. For the identification of these units, new methods combining machine learning techniques and heuristic rules were developed. The algorithm was evaluated on the Slovene dependency treebank text corpus. Compared to the MSTP parser, currentl...

We propose a practical technique for the identification of lossy network links from end-to-end measurements. Our scheme is based on a function that computes the likelihood of each link to be lossy. This function depends on the number of times a link appears in lossy paths and on the relative loss rates of these paths. Preliminary simulation results show that our algorithm achieves accuracy high...

The article is drafted from the perspective of the marketing manager who must minimize carrying costs of a salesperson that share multiple products. We have tackled this simulation problem using a Hamiltonian cycle associated to transport network conceptual reduced to a graph. Identification of optimal solution was achieved through the implementation of Branch and Bound heuristic algorithm in t...

An observer-based Hamiltonian identification algorithm for quantum systems is proposed. For the 2-level case an exponential convergence result based on averaging arguments and some relevant transformations is provided. The convergence for multi-level cases is discussed using some heuristic arguments and the relevance of the method is tested via simulations. Finally, the robustness issue with re...

DBpedia is a central hub of Linked Open Data (LOD). Being based on crowd-sourced contents and heuristic extraction methods, it is not free of errors. In this paper, we study the application of unsupervised numerical outlier detection methods to DBpedia, using Interquantile Range (IQR), Kernel Density Estimation (KDE), and various dispersion estimators, combined with different semantic grouping ...

This work introduces a machine learning approach to the identification of mention heads needed for multilingual coreference resolution (MCR). We evaluate the method and compare it to a heuristic baseline and a rule-based approach, which are widely used in coreference resolution systems. We use the CoNLL-2012 shared task data sets, which include data for Arabic, Chinese, and English. We show tha...

DE algorithm is a population-based heuristic global search technology. The algorithm has simple principle, fewer control parameters, but has strong robustness, and good optimization performance. This paper uses differential evolution algorithm for parameters identification of Wiener model. Firstly, we analyze the influence of mutation rate F on global parallel search ability and convergence in ...