This paper presents PortiLexicon-UD, a large and freely available lexicon for Portuguese delivering morphosyntactic information according to the Universal Dependencies model. This lexical resource includes part of speech tags, lemmas, and morphological information for words, with 1,221,218 entries (considering word duplication due to different combination of PoS tag, lemma, and morphological features). We report the lexicon creation process, its computational data structure, and its evaluation over an annotated corpus, showing that it has a high language coverage and good quality data.

This paper proposes a method to predict word grammatical classes using automatically generated discrete-time Markov chains to model typical sentences.Such method advantage relies on the availability of input resources needed to build an efficient and effective solution to virtually any language, dialect, or domain lingo.One of the main advantages of the proposed method is its simplicity when compared to other sophisticated approaches based on Hidden Markov Models or even more complex formalisms.The proposed method is instantiated to an example and we show that the achieved efficiency and effectiveness bring advantages to traditional similar solutions.

Broadly used and applied in many domains, Hidden Markov Models are a well established formalism, both in computer science and statistics.Among other reasons, they owe their popularity to a fast fitting method, i.e., the Baum-Welch algorithm, allowing to adjust models to a variety of input data.Using expectation and maximization phases, BW assures an increase to the model likelihood at every iteration.Yet, to initialize the sequence of expectationmaximization (EM) steps, it is a standard procedure to start the BW algorithm from randomly generated values.We propose a, simple and fast, deterministic pre-fitting approach which derives the BW's initial values directly from the input data.

We propose a simple, fast, deterministic pre-fitting approach which derives the Baum–Welch algorithm initial values directly from the input data. Such pre-fitting has the purpose of improving the fitting time for a given Hidden Markov Model (HMM) while maintaining the original Baum–Welch algorithm as the fitting one. The fitting time is improved by avoiding the Baum–Welch algorithm sensitiveness through the generation of parameters closer to the global maximum likelihood. Furthermore, by keeping the original Baum–Welch algorithm as the fitting one, we guarantee that all related methods will continue to work properly. On the other hand, the pre-fitting generates the HMM parameters directly derived from time-series data, without any data transformation, using an [Formula: see text] operation.

Evaluate team's performance on long-duration projects can be a challenge.It relies on human expertise to perform estimations, which can generate uncertainty and dependency of experienced specialists.Statistical techniques, such as modeling and simulations, are suitable options as tools to support projects estimations.Our goal in this paper is a formal mapping of the main Waterfall model characteristics to stochastic model to predict performance indices of teams such as the real working time.

This paper presents a novel version of ExATO, a term extractor originally designed to extract relevant terms from corpora in Portuguese. In this new version not only corpora in Portuguese can be handled, but also texts in English are accepted. This extension is likely to offer the same quality pattern already achieved for Portuguese. In this paper, we draw the analysis of results in parallel corpora with respect to the intrinsic differences between Portuguese and English languages, and also the environment of usage for ExATO for Portuguese and English corpora. A brief comparison of ExATO and other similar tool is presented to illustrate the higher quality of ExATO extraction from English corpora.

Measuring productivity in globally distributed projects is crucial to improve team performance. These measures often display information on whether a given project is moving forward or starts to demonstrate undesired behaviors. In this paper we are interested in showing how analytical models could deliver insights for the behavior of specific distributed software collaboration projects. We present a model for distributed projects using stochastic automata networks (SAN) formalism to estimate, for instance, the required level of coordination for specific project configurations. We focus our attention on the level of interaction among project participants and its close relation with team’s productivity. The models are parameterized for different scenarios and solved using numerical methods to obtain exact solutions. We vary the team’s expertise and support levels to measure the impact on the overall project performance. As results, we present our derived productivity index for all scenarios and we state implications found in order to analyze popular preconceptions in GSD area, confirming some, and refusing others. Finally, we foresee ways to extend the models to represent more intricate behaviors and communication patterns that are usually present in globally distributed software projects.

The market for mobile applications has been growing dramatically, as has the complexity of the applications and the speed of the development process. These changes require a rethinking of the development process and of how developers are trained. In order to better prepare faculty and students for the emerging mobile application market, this study presents a new learning and software development framework that combines Agile methodologies with the Challenge-Based Learning (CBL) framework. CBL provides a student-centered learning framework that mirrors the modern workplace. Agile methodologies address the changing landscape of mobile development environments. A combination of the CBL learning framework and Agile methodologies can better prepare students for the development market. This paper presents an empirical study applying CBL and Scrum in a mobile application development course evaluated through a series of post surveys. The results indicate that a teaching and learning environment based on practical experience combining the CBL framework with the Scrum process is an eective model to promptly teach undergraduates how to be successful mobile application developers.

The use of stochastic formalisms, such as Stochastic Automata Networks (SAN), can be very useful for statistical prediction and behavior analysis.Once well fitted, such formalisms can generate probabilities about a target reality.These probabilities can be seen as a statistical approach of knowledge discovery.However, the building process of models for real world problems is time consuming even for experienced modelers.Furthermore, it is often necessary to be a domain specialist to create a model.This work illustrates a new method to automatically learn simple SAN models directly from a data source.This method is encapsulated in a tool called SAN GEnerator (SANGE).This new model fitting method is powerful and relatively easy to use; therefore this can grant access to a much broader community to such powerful modeling formalisms.

This paper describes a method to obtain symbolic solution of large stochastic models using Gauss-Jordan elimination. Such solution is an efficient alternative to standard simulations and it allows fast and exact solution of very large and complex models that are hard to be dealt even with iterative numerical methods. The proposed method assumes the system described as a structured (modular) Markovian system with discrete states for each system module and transitions among those states ruled by Markovian processes. The mathematical representation of such system is made by a Kronecker (Tensor) formula, i.e., a tensor formulation of small matrices representing each system module transitions and occasional dependencies among modules. Preliminary results of the proposed solution indicate the expected efficiency of the proposed solution.

Project management is an important aspect of global software development. Companies and institutions can obtain benefits by the use of performance evaluation of teams working indifferent sites. The objective of this work is to discuss a stochastic model definition to performance evaluation of Follow-The-Sun (FTS) projects.

Global software engineering is an area of increasing research challenges, in which teams are dispersed in multiple sites collaborating across borders and time zones. In spite of its potential competitive advantages, globally distributed projects must deal with difficulties when distributing resources such as teams with cultural diversities, different skills and experience levels. Both industry and academia demonstrate a special interest in the modeling and prediction, mainly representing systems in order to extract interesting indices, for example, evaluating whether or not a project will succeed. This paper demonstrates the usefulness of analytical modeling techniques in order to predict the outcome of geographically-distributed projects. We focus our attention to the participants interaction and its interplay when it affects team productivity. The models are parametrized considering single-site and multi-site scenarios, varying resources availability, teams expertise and support levels. Performance indices from both scenarios are presented and conclusion indicates possible model extensions.

The solution of state-based stochastic models is usually a demanding application, then it is a natural subject to high performance techniques. We are particularly interested in the speedup of Bootstrap Simulation of structured Markovian models. This approach is a quite recent development in the performance evaluation area, and it brings a considerable improvement in the results accuracy, despite the intrinsic effect of randomness in simulation experiments. Unfortunately, Bootstrap Simulation has higher computational cost than other alternatives. We present experiments with different options to optimize the parallel solution of Bootstrap Simulation applied to three practical examples described in Stochastic Automata Networks (SAN) formalism. This paper contribution resides in the discussion of theoretical implementation issues, the obtained speedup and the actual processing and communication times for all experiments. Additionally, we also suggest future works to improve even more the proposed solution and we discuss some interesting insights for parallelization of similar applications.

We report on our experiences using agile practices in a global software development project. Specifically, we report on the communication and collaboration patterns that were discovered using the social network analysis methodology. We used as a case study the Global Studio Project Version 3.0, where Extreme Programming practices were applied to one of the remote software development teams. We summarize our conclusions about Extreme Programming usage in global software development projects by presenting ten lessons learned from the application of Extreme Programming practices to the Global Studio Project Version 3.0.

Wireless ad hoc networks raised a series of challenging research tracks. In order to analyze and validate the research results achieved, thorough performance evaluation efforts are needed. In this context, the contribution of this paper is two fold: at one side we investigate the effect of forwarding in wireless ad hoc networks, drawing the throughput behavior under variation of connectivity, interference end number of sources; on the other side we propose stochastic automata networks (SAN) as a formalism well suited to the area because of its modularity, allowing model reuse and the structured stepwise representation of important aspects of the reality, and due to its ability to deal with models of considerable sizes, allowing the analytical evaluation of more complex situations.

We present a theoretical study to evaluate the performance of a family of parallel implementations of the propagation algorithm. The propagation algorithm is used to an image interpolation application. The theoretical performance analysis is based on the construction of generic models using stochastic automata networks (SAN) formalism to describe each implementation scheme. The prediction results can be compared to the achieved performance in some real test cases to verify the accuracy of our modeling technique. The main contribution is to point out the advantages and problems of our approach to the development of generic models of parallel implementations.

The benefits of usage models in the statistical testing of software have been recognized by the software engineering community and discrete state formalisms have been largely used to describe such usage models of software. The Markov Chains formalism (MC) is a natural choice to do so. However, in this paper we suggest the use of a more sophisticated discrete state formalism, the Stochastic Automata Networks (SAN). In many problems of the performance evaluation area, the SAN models present advantages over MC models. Therefore, it seems natural to us to verify similar advantages in the modeling of usage models.This paper presents a case study of building a software usage model using SAN formalism. A software tool called DOCSEDITOR is modeled using MC and SAN. The models are compared in terms of number of states, scalability, and readability. It is not the objective of this paper to present a full framework to develop and analyse usage models with SAN, but just to show some evident advantages of the use of SAN instead of MC. In order to do this, the conclusion points out the indexes than can be computed from both models and suggests the next steps on future work.

This paper examines numerical issues in computing solutions to networks of stochastic automata. It is well-known that when the matrices that represent the automata contain only constant values, the cost of performing the operation basic to all iterative solution methods, that of matrix-vector multiply, is given by ρ N = Π N i-1 n i × Σ N i=1 n i , where n i is the number of states in the i th automaton and N is the number of automata in the network. We introduce the concept of a generalized tensor product and prove a number of lemmas concerning this product. The result of these lemmas allows us to show that this relatively small number of operations is sufficient in many practical cases of interest in which the automata contain functional and not simply constant transitions. Furthermore, we show how the automata should be ordered to achieve this.