Antonio Cerone

Using Free/Libre Open Source Software Projects as E-learning Tools

Free/Libre Open Source Software (FLOSS) projects can be considered as learning environments in which heterogeneous communities get together to exchange knowledge through discussion and put it into practice through actual contributions to software development, revision and testing. This has encouraged tertiary educators to attempt the inclusion of participation in FLOSS projects as part of the requirements of Software Engineering courses, and pilot studies have been conducted to test the effectiveness of such an attempt. This paper discusses two pilot studies with reference to several studies concerning the role of learning in FLOSS projects and shows how using FLOSS projects as E-learning tools has a potential to increase the quality of the software product.

Integrating Data from Multiple Repositories to Analyze Patterns of Contribution in FOSS Projects

The majority of Free and Open Source Software (FOSS) developers are mobile and often use different identities in the projects or communities they participate in. These characteristics not only poses challenges for researchers studying the presence (where) and contributions (how much) of developers across multiple repositories, but may also require special attention when formulating appropriate metrics or indicators for the certification of both the FOSS product and process. In this paper, we present a methodology to study the patterns of contribution of 502 developers in both SVN and mailing lists in 20 GNOME projects. Our findings shows that only a small percentage of developers are contributing to both repositories and this cohort are making more commits than they are posting messages to mailing lists. The implications of these findings for our understanding of the patterns of contribution in FOSS projects and on the quality of the final product are discussed.

A Cognitive Framework Based on Rewriting Logic for the Analysis of Interactive Systems

Interactive systems may appear to work correctly and safely when analysed in isolation from the human environment in which they are supposed to work. In fact, the same cognitive skills that enable humans to perform complex tasks may also become the source of critical errors in the interaction with systems and devices designed as supports for such tasks. It is thus essential to verify the desired properties of an interactive system using a model that not only includes a user-centered description of the task, but also incorporates a representation of human cognitive processes within the task execution.

Mathematical modeling of drug resistance due to KRAS mutation in colorectal cancer

The most challenging task in colorectal cancer research nowadays is to understand the development of acquired resistance to anti-EGFR drugs. The key reason for this problem is the KRAS mutations appearance after the treatment with monoclonal antibodies (moAb). Here we present a mathematical model for the analysis of KRAS mutations behavior in colorectal cancer with respect to moAb treatments. To evaluate the drug performance we have developed equations for two types of tumors cells, KRAS mutated and KRAS wild-type. Both tumor cell populations were treated with a combination of moAb and chemotherapy drugs. It was observed that even the minimal initial concentration of KRAS mutation before the treatment has the ability to make the tumor refractory to the treatment. Minor population of KRAS mutations has strong influence on large number of wild-type cells as well rendering them resistant to chemotherapy. Patient׳s immune responses are specifically taken into considerations and it is found that, in case of KRAS mutations, the immune strength does not affect medication efficacy. Finally, cetuximab (moAb) and irinotecan (chemotherapy) drugs are analyzed as first-line treatment of colorectal cancer with few KRAS mutated cells. Results show that this combined treatment could be only effective for patients with high immune strengths and it should not be recommended as first-line therapy for patients with moderate immune strengths or weak immune systems because of a potential risk of relapse, with KRAS mutant cells acquired resistance involved with them.

On Integrating Social and Sensor Networks for Emergency Management

The 2010 earthquake in Haiti is often referred to as the turning point that changed the way social media can be used during disasters. The development of strategies, technologies and tools to enhance user collaboration around disasters has become an emergent field, and their integration with appropriate sensor networks presents itself as an effective solution to drive decision making in emergency management. In this paper, we present a review of existing disaster management systems and their underlying strategies and technologies, and identify the limitations of the tools in which they are implemented. We then propose an architecture for disaster management that integrates the mining of social networks and the use of sensor networks as two complementary technologies to overcome the limitations of the current emergency management tools.

Mining Learning Processes from FLOSS Mailing Archives

Evidence suggests that Free/Libre Open Source Software (FLOSS) environments provide unlimited learning opportunities. Community members engage in a number of activities both during their interaction with their peers and while making use of these environments. As FLOSS repositories store data about participants’ interaction and activities, we analyze participants’ interaction and knowledge exchange in emails to trace learning activities that occur in distinct phases of the learning process. We make use of semantic search in SQL to retrieve data and build corresponding event logs which are then fed to a process mining tool in order to produce visual workflow nets. We view these nets as representative of the traces of learning activities in FLOSS as well as their relevant flow of occurrence. Additional statistical details are provided to contextualize and describe these models.

An Abstract State Machine (ASM) Representation of Learning Process in FLOSS Communities

Free/Libre Open Source Software (FLOSS) communities as collaborative environments enable the occurrence of learning between participants in these groups. With the increasing interest research on understanding the mechanisms and processes through which learning occurs in FLOSS, there is an imperative to describe these processes. One successful way of doing this is through specification methods. In this paper, we describe the adoption of Abstract States Machines (ASMs) as a specification methodology for the description of learning processes in FLOSS. The goal of this endeavor is to represent the many possible steps and/or activities FLOSS participants go through during interactions that can be categorized as learning processes. Through ASMs, we express learning phases as states while activities that take place before moving from one state to another are expressed as transitions.

Research Challenges in Modelling Ecosystems

Ecosystems and their biodiversity have to be protected and preserved as sources of services and goods. The human population controls and modifies ecosystems to improve its health conditions and welfare. The consequences of human activities should be carefully monitored and ecosystems should be managed to protect all of the species and preserve their functioning. The development of strategies for ecosystem management benefits from the use of computational techniques to model the dynamics of species that interact with their abiotic and biotic environment. Life scientists and computer scientists need to work together to define and analyse ecosystem models. However, there is a multifaceted gap between the approaches used in life science and those used in computer science. Such gap is both cultural and technical, and results in a number of challenges. In this paper we identify these challenges and provide technical and cultural proposals for solving them.

Process Mining as a Modelling Tool: Beyond the Domain of Business Process Management

Process mining emerged in the field of business process management BPM as an innovative technique to exploit the large amount of data recorded by information systems in the form of event logs. It allows to discover not only relations and structure in data but also control flow, and produces a process model, which can then be visualised as a process map. In addition to discovery, process mining supports conformance analysis, a technique to compare an a priori model with the event logs to detect deviations and inconsistencies. In this paper we go beyond the domain of BPM and illustrate how process mining and conformance analysis can be used in a number of contexts, in and across the areas of human-computer interaction and learning.

An empirical verification of a-priori learning models on mailing archives in the context of online learning activities of participants in free\libre open source software (FLOSS) communities

Free\Libre Open Source Software (FLOSS) environments are increasingly dubbed as learning environments where practical software engineering skills can be acquired. Numerous studies have extensively investigated how knowledge is acquired in these environments through a collaborative learning model that define a learning process. Such a learning process, identified either as a result of surveys or by means of questionnaires, can be depicted through a series of graphical representations indicating the steps FLOSS community members go through as they acquire and exchange skills. These representations are referred to as a-priori learning models. They are Petri net-like workflow nets (WF-net) that provide a visual representation of the learning process as it is expected to occur. These models are representations of a learning framework or paradigm in FLOSS communities. As such, the credibility of any models is estimated through a process of model verification and validation. Therefore in this paper, we analyze these models in comparison with the real behavior captured in FLOSS repositories by means of conformance verification in process mining. The purpose of our study is twofold. Firstly, the results of our analysis provide insights on the possible discrepancies that are observed between the initial theoretical representations of learning processes and the real behavior captured in FLOSS event logs, constructed from mailing archives. Secondly, this comparison helps foster the understanding on how learning actually takes place in FLOSS environments based on empirical evidence directly from the data.