Generative Artificial Intelligence (GAI) is transforming education, yet there is still a lack of systematization regarding its use in preparing for higher education entrance exams, such as the Brazilian National High School Exam ( ENEM ). This study aims to analyze, through a systematic literature review, the use of GAI as a support tool for exam preparation, specifically focusing on the context of the Brazilian National High School Exam (ENEM). By narrowing the scope to this specific high-stakes examination, the review explores questionnaire generation, study personalization, and error correction. A systematic review protocol was conducted across six databases, initially identifying 246 papers. After a three-stage selection process, nine primary studies were chosen for data extraction in order to answer five research questions. The results indicate a predominance of Large Language Models LLMs , particularly from the GPT family (RQ1), with accuracy being the main performance metric (RQ2). Questionnaire generation (RQ3) has evolved from simple prompts to Retrieval-Augmented Generation (RAG)) . RAG was identified as the main technical strategy to mitigate “hallucinations” (RQ4), a core risk of this technology. Natural Language Processing (RQ5) underpins all applications, enabling everything from automated assessment to personalized tutoring. It is concluded that GAI is a powerful tool for exam preparation; however, its effective use depends on strategies such as RAG and, crucially, on human curation and validation. The main limitations of this mapping include the restricted search string focused on the term “ ENEM ” and the inherent risks of the technology, such as academic dishonesty and hallucinations.

Summary Summary Software‐Defined Networking (SDN) is an emerging network paradigm that has gained significant traction from many researchers to address the requirement of current data centers. Although central control is the major advantage of SDN, it is also a single point of failure if it is made unreachable by a Distributed Denial of Service (DDoS) attack. Despite the large number of traditional detection solutions that exist currently, DDoS attacks continue to grow in frequency, volume, and severity. This paper brings an analysis of the problem and suggests the implementation of four machine learning algorithms (SVM, MLP, Decision Tree, and Random Forest) with the purpose of classifying DDoS attacks in an SDN simulated environment (Mininet 2.2.2). With this goal, the DDoS attacks were simulated using the Scapy tool with a list of valid IPs, acquiring, as a result, the best accuracy with the Random Forest algorithm and the best processing time with the Decision Tree algorithm. Moreover, it is shown the most important features to classify DDoS attacks and some drawbacks in the implementation of a classifier to detect the three kinds of DDoS attacks discussed in this paper (controller attack, flow‐table attack, and bandwidth attack).

There is an increasing number of Internet of Things (IoT) devices in the border of computer networks, requiring local processing and lightweight virtualization to deal with issues such as heterogeneity, Quality of Service (QoS) management, scalability, mobility, federation, and interoperability. Fog computing can provide the computational resources required by IoT devices to process their data. Low energy consumption and total cost of ownership are among the desirable properties for auxiliar infrastructures such as those deployed for fog computing, which do not require large computational power though. There is a noteworthy trend of undergoing research efforts towards the definition of software and hardware architectures for fog computing in this context. In this sense, this paper presents a Systematic Literature Mapping with the purpose of understanding and identifying metrics and gaps in current literature about orchestration of container-based applications, especially those hosted in clusters of Single Board Computer (SBC) platforms, such as Raspberry Pi, which have been used for deploying fog computing environments.

The complexity and growth of new smart objects networks are generating a new demand for the maintenance of these devices, with the need to remotely monitor and control these devices without consuming significant resources. Analyzing the memory and electric power consumption of the protocols used in the management of these networks is a way to highlight the best protocol alternatives for this type of application. This study develops an experimental study, analyzing the behavior of SNMP, Zabbix, and MQTT protocols, in terms of memory and electric power consumption, when it is used in an Internet of Things application, with a sensor device implemented on the ESP8266. The experiment is performed by monitoring devices in an environment with some Motes and a Zabbix server. The study analyzes the ROM and RAM memories occupied by the firmware code, in addition to the electric power consumption of each protocol. At the end, the study confirms that the three protocols analyzed are supported by the platform used. The research shows that the SNMP is the protocol that consumes the least amount of device memory, that there is no significant difference in the energy consumption between the protocols, and that the MQTT protocol is suitable to be used in this environment and it also enables a significant reduction in energy consumption.

This paper presents a survey on the main security problems that affect the communication protocols in the context of Internet of Things, in order to identify possible threats and vulnerabilities. The protocols RFID, NFC, 6LoWPAN, 6TiSCH, DTSL, CoAP and MQTT, for a better organization, were explored and categorized in layers according to the TCP / IP reference model. At the end, a summary is presented in tabular form with the security modes used for each protocol is used.

Internet of Things (IoT) applications carry on at an environment that is mainly highly dynamic and real time nature. Due that environment, such applications should be able to reason autonomously and give self-adaptive decisions. To date, there has not been an adequate architecture to incorporate self-adaptive property in that type of environment. Thus, we design a management architectural pattern for adaptation system in Internet of Things based on modeling of control loops. The architectural pattern is presented considering problem/motivation, solution, consequences and examples. An example of application of pattern is showed through a self-protecting architecture for Internet of Things.

The main difficulty in building online research laboratories is the use of computing resources and electronic instruments in multiple hardware and software platforms. Thus, this paper describes a service-oriented middleware using Web Services called ERLab (Electronic Remote Laboratory). The main goal of ERLab is to share data and scientific instruments, which operate and communicate over different protocols for different users on the Internet.