João Carlos D. Lima

A Context-Aware Recommendation System to Behavioral Based Authentication in Mobile and Pervasive Environments

Mobile devices use traditional authentication processes, which are vulnerable and unsuitable for highly dynamic environments, such as ubiquitous and pervasive environments. Therefore, new approaches are necessary in such environments. These approaches must be (i) context-aware by considering environmental and operational characteristics, restrictions of devices and information provided by sensors within the pervasive space, and (ii) customizable for balancing the systems proactivity with the control that the user wants to have over his systems. In this paper, it is presented an approach that provides a recommendation system based on the user behavior and the pervasive space where he belongs. The user behavior is defined by the actions and events that compose the activity that is being performed. The experimental results indicate (a) a dynamic and autonomic mechanism, which is more suitable for authenticating users in mobile and pervasive environments, and (b) significant efficiency to detect authentication anomalies by using a similarity model and a spatio-temporal permutation model.

Test case generation from BPMN models for automated testing of Web-based BPM applications

This article proposes an approach to generate test cases from BPMN models, for automated testing of Web applications implemented with the support of BPM suites. The work is primarily focused on functional testing and has the following objectives: (i) identify execution paths from the flow analysis in the BPMN model and (ii) generate the initial code of test scripts to be run on a given Web application testing tool. Throughout the article, we describe the design and implementation of a solution to achieve these goals, targeting automated tests using Selenium and Cucumber as tools. The approach was applied to processes from a public repository and was able to generate test scenarios from different BPMN models.

Situation awareness in the speech therapy domain: A systematic mapping study

Abstract Situation Awareness (SA) involves the correct interpretation of a situation, allowing a system to respond to the observed environment and providing support for decision making in many systems domains. Speech therapy is an example of domain where situation awareness can provide benefits, since practitioners should monitor the patient in order to perform therapeutic actions. However, there are a few studies in the area that address reasoning about a situation to improve these tasks. For this reason, this systematic mapping study aims to identify and compare different proposals in the speech therapy domain in order to verify which aspects related to obtaining and maintaining SA are supported. Our analyzes provide useful insights on whats aspects of SA are best integrated in the speech domain - such as knowledge bases and adaptation - and other aspects that remain to be improved - like the action support and projection abilities. Also, this paper includes statistics, methodologies used by different authors and other issues involving research processes. As main contributions, this work presents an overview of the SA integration in the speech therapy domain, discussing challenges in the area and providing directions for further research.

SocialCount - Detecting Social Interactions on Mobile Devices.

With mobile devices increasingly powerful and accessible to the majority of the population, applications have begun to become increasingly intelligent, customizable and adaptable to users’ needs. To do this, contextaware applications are developed. In this work, we create an approach to infer social interactions through the identification of the user’s voice and to recognize their social context. Data from the social context of the user has been useful in many real-life situations, such as identifying and controlling infectious disease epidemics.

On the Performance of Cloud-based Spreadsheets as a Backend for View-only Web Applications.

The wide offering of cloud-based services brings alternatives to traditional approaches for developing modern information systems. In this work, we examine cloud spreadsheet services as an alternative for data backend in small scale, view-only web applications. We review 6 cloud-based spreadsheets offering data access APIs to third-party applications, then we present a set of performance tests over spreadsheets hosted on Google Sheets. Preliminary findings show a performance penalty for transferring JSON-formatted data and an expressive failed request rate for many simultaneous accesses.

A Study on Persuasive Applications for Electric Energy Saving.

The growing development of persuasive technologies has led to the creation of systems that help society in a variety of sectors, one in the electric power sector, where applications seek to persuade users to change behavior and save electricity. In this sense, this article seeks to present concepts and techniques of persuasion applied in systems with this objective and to present a prototype of an application that seeks to show the user a prediction about the consumption of electric energy without him acquiring intelligent sensors, only with data that he has easy access.

SQL Query Performance on Hadoop: An Analysis Focused on Large Databases of Brazilian Electronic Invoices.

Hadoop clusters have established themselves as a foundation for various applications and experiments in the field of high-performance processing of large datasets. In this context, SQL-on-Hadoop emerged as trend that combines the popularity of SQL with the performance of Hadoop. In this work, we analyze the performance of SQL queries on Hadoop, using the Impala engine, comparing it with a RDBMS-based approach. The analysis focuses on a large set of electronic invoice data, representing an important application to support fiscal audit operations. The experiments performed included frequent queries in this context, which were implemented with and without data partitioning in both RDBMS and Impala/Hadoop. The results show speedups from 2.7 to 14x with Impala/Hadoop for the queries considered, on a lower cost hardware/software platform.

A Systematic Review of Scheduling Algorithms and Resource Management in Context-aware Applications: A Meta-analytic Approach.

Computer resource management and scheduling algorithms have been exploited in order to run applications in an efficient and effective way. Since this area is heavily exploited and has a range of application domains, it is possible to apply various techniques from other distributed computing areas, such as context-aware applications. In this paper, we present a systematic literature review that addresses context-aware applications with resource management and scheduling algorithms. In total, 11 studies were selected from years 2000 to 2017, which covered the inclusion criteria of this systematic review, presenting techniques and improvements for the area. From the analyzes of the selected studies, it was found a diversity of application domains using a variety

Fuzzy Based Model to Detect Patient's Health Decline in Ambient Assisted Living.

Detecting a decline in the health condition of a patient may still be considered a challenge in Ambient Assisted Living (AAL) since the concept of ‘decline’ is vague and imprecise. In this context, Fuzzy Logic comes as an excellent alternative for AAL systems. This paper presents a model based on Fuzzy logic reasoning in order to identify a possible decline in the patient health condition. In order to achieve this goal, the model considers relevant situations that may somehow impact the patient. To evaluate the model, a case study was developed, showing that the developed model can simulate the human reasoning and be used in

Database Ontology-Supported Query for Ubiquitous Environments

One of the main research opportunities regarding the use of contextual information is the context and relational databases integration. This integration allows the better contextualized retrieval of data. This paper presents a set of rules, which used with an ontological model, allows the integration of context and domain data. This approach enables the data retrieved through SQL queries to be returned in a contextualized manner, without the need to change the database schema originally used. Another advantage is the use of queries originally defined in systems, thus, it is not necessary to perform a re-engineering of the system. The model evaluation was carried out through the development of a case study applied to a virtual learning environment. The results show that the rules allow a filtering using the context of interest, resulting in a more meaningful data recovery.