Cristiano Cortez da Rocha

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.

A2BeST: An adaptive authentication service based on mobile user's behavior and spatio-temporal context

Mobile devices are becoming mandatory equipment in modern life. However, the connections are usually based on traditional authentication processes, which do not consider the environmental characteristics, restrictions of devices, and information provided by sensors within the pervasive space. In this paper, it is presented a novel adaptive authentication service based on mobile users behavior and spatio-temporal context (A2BeST). It adopts user authentication on mobile devices based on a spatio-temporal context, which allows the modeling of the user behavior witch defines events and tasks that compound the user activity. The experimental results indicate a more dynamic and autonomic mechanism for authenticating users. In addition, it provides a relevant efficiency of proposal using a space-time permutation model and similarity vector model to detect authentication anomalies.

CARS-AD: a context-aware recommender system to decide about implicit or explicit authentication in ubihealth

Mobile devices using traditional authentication processes are vulnerable and inadequate. New approaches must be considered for solving this problem: environmental characteristics, device limitations and information obtained from sensors. This paper presents a recommendation system approach for information systems based on user behavior and information context in which the users are located. The recommendation system has been defined and deployed through filtering processes (content-based, collaborative and hybrid). The behavior is defined by the events and the actions that comprise the user activities. The experimental results indicate: (i) a more dynamic and autonomic mechanism for authenticating users in a pervasive mobile environment, and (ii) an efficiency improvement in detecting anomalies on authentication by using a similarity model and space-time permutation.

Developing Knowledge Representation in Emergency Medical Assistance by Using Semantic Web Techniques

In this research, a knowledge-based architecture for a mobile emergency medical assistance system is presented. It is based on the France SAMU model and dopts the ontology and mobile computing approaches. The contribution is characterized for providing routines and medical protocol specifications for specialists through the use of their natural language, collecting elements from this language to develop an ontology domain, and using a semantic cache for an enhanced utilization of mobile devices. A prototype of the proposal was implemented in order to support specialists during a day-to-day basis considering knowledge engineering aided by mobile computing techniques. These differentiated characteristics have proved to be successfully at early experiments utilizing the implemented prototype.

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.

A Case-Based System Architecture based on Situation-Awareness for Speech Therapy.

Situation Awareness (SA) involves the correct interpretation of situations, 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 few proposals in the area that address reasoning about a situation to improve these tasks. Likewise, the case-based reasoning methodology is little approached, since existing proposals rarely use previous knowledge for problem solving. For this reason, this paper proposes a case-based architecture to assist Speech-Language Pathologists (SLPs) in tasks involving screening and diagnosis of speech sound disorders. We present the modules that compose the system’s architecture and results obtained from the evaluation using the Google Cloud Speech API. As main contributions, we present the architecture of a system that aims to be situation-aware, encompassing perception, comprehension and projection of actions in the environment. Also, we present and discuss the results, towards a speech therapy system for decision making support.

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