Computer area

O presente artigo propõe um modelo para geração de mapas criminais, utilizando ferramentas gratuitas. Para a criação do modelo foram utilizados os dados abertos do portal da transparência da Secretaria de Segurança Pública do Estado de São Paulo (SSP-SP). O modelo tem como objetivo prover uma visualização melhor dos dados criminais e ajudar as instituições de segurança pública no processo de tomada de decisão. Como resultado do modelo foram gerados dois tipos de mapa, um que mostra o ponto exato onde as ocorrências ocorreram e outro um mapa de calor que exibir em cores mais quentes os locais onde se tem maior ocorrência de crimes.

Resumo Diferentes formas de avaliar o desempenho de estudantes vem sendo pesquisadas nas ultimas decadas. Este artigo assume a multidimensionalidade da avaliacao de desempenho como um resultado de interacoes complexas. Assim, adota uma metodologia para avaliacao baseada em principios da logica difusa. As etapas da metodologia estabelecida constituem um guia para o desenvolvimento de uma avaliacao multidimensional capaz de aferir o aprendizado dos estudantes em ambientes mediados por computador. Para o desenvolvimento da pesquisa, foi necessario contextualizar as teorias, os modelos e sistemas utilizados na avaliacao do desempenho dos alunos.

The WiMAX technology, based on the IEEE 802.16 standards (IEEE, 2004) (IEEE, 2005), is a
solution for fixed and mobile broadband wireless access networks, aiming at providing
support to a wide variety of multimedia applications, including real-time and non-real-time
applications. As a broadband wireless technology, WiMAX has been developed with
advantages such as high transmission rate and predefined Quality of Service (QoS)
framework, enabling efficient and scalable networks for data, video, and voice. However,
the standard does not define the scheduling algorithm which guarantees the QoS required
by the multimedia applications.

— In this paper a new cross-layer connection admission control (CAC) algorithm in WiMAX networks is proposed. The CAC algorithm makes the decisions whether accept or not the new connections based on the quality of channel communications. Moreover, the proposed CAC works together with an uplink scheduling mechanism which allocates the network resources in accordance with the information about the quality of channel communication. This proposed algorithm interacts with the polling mechanism, and controls the interval of unicast polling for the rtPS and nrtPS service classes. Its evaluation, by means of modeling and simulation, showed satisfactory results.

In this article, an adaptive scheduling packets algorithm for the uplink traffic in WiMAX networks is proposed. The proposed algorithm is designed to be completely dynamic, mainly in networks that use various modulation and coding schemes (MCSs). Using a cross-layer approach and the states of the uplink virtual queues in the base station, it was defined a new deadlines-based scheme, aiming at limiting the maximum delay to the real-time applications. Moreover, a method which interacts with the polling mechanisms of the base station was developed. This method controls the periodicity of sending unicast polling to the real-time and non-real-time service classes, in accordance with the quality of service requirements of the applications. The proposed algorithm was evaluated by means of modeling and simulation in environments where various MCSs were used and also in an environment where only one type of MCS was used. The simulations showed satisfactory results in both environments.

SUMMARY IEEE 802.16e is a telecommunication standard technology designed to support a wide variety of multime-dia applications. It defines five service classes, each one with its respective QoS requirements, but does not define the scheduling algorithm for these service classes. In this paper, an adaptive packet scheduling algorithm for the uplink traffic in IEEE 802.16e networks is proposed. This algorithm is designed to be completely dynamic, mainly in networks that use various modulation and coding schemes (MCSs). The algorithm is applied directly to the bandwidth request queues in the base station (BS) and aims at supporting the real-time and non-real-time applications. Using a cross-layer approach and the states of the bandwidth request queues in the BS, a new deadlines based scheme was defined, aiming at limiting the maximum delay to the real-time applications. Moreover, this algorithm interacts with the polling management mechanisms of the BS and controls the periodicity of sending unicast polling to the real-time and non-real-time service connections, in accordance with the QoS requirements of the applications. The proposed algorithm was evaluated by means of modeling and simulation in environments where various MCSs were used and also in environments where only one type of modulation was used. The simulations showed satisfactory results in both environments.

The IEEE 802.16 standard defines MAC and Physical layers for broadband wireless networks called WiMAX, however, it does not define the scheduling algorithm for the uplink and downlink traffic. In this paper, a predictive scheduling algorithm for uplink traffic in IEEE 802.16 networks is proposed. The proposed algorithm uses the cross-layer approach and is applied on the queue of the bandwidth requests messages in the BS. For each bandwidth request, the proposed algorithm calculates a deadline in accordance with the Modulation and Coding Schemes (MCS). Thus, the proposed algorithm reduces the average delay on the access network and uses the resource, in the environment with several burst profiles, in a fair and efficient way. Its evaluation by means of modeling and simulation showed satisfactory results.

This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank's control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naïve Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the netwo...