Online social networks provide relevant information on users' opinion about different themes. Thus, applications, such as monitoring and recommendation systems (RS) can collect and analyze this data. This paper presents a knowledge-based recommendation system (KBRS), which includes an emotional health monitoring system to detect users with potential psychological disturbances, specifically, depression and stress. Depending on the monitoring results, the KBRS, based on ontologies and sentiment analysis, is activated to send happy, calm, relaxing, or motivational messages to users with psychological disturbances. Also, the solution includes a mechanism to send warning messages to authorized persons, in case a depression disturbance is detected by the monitoring system. The detection of sentences with depressive and stressful content is performed through a convolutional neural network and a bidirectional long short-term memory - recurrent neural networks (RNN); the proposed method reached an accuracy of 0.89 and 0.90 to detect depressed and stressed users, respectively. Experimental results show that the proposed KBRS reached a rating of 94% of very satisfied users, as opposed to 69% reached by a RS without the use of neither a sentiment metric nor ontologies. Additionally, subjective test results demonstrated that the proposed solution consumes low memory, processing, and energy from current mobile electronic devices.

Asymmetric cryptographic primitives are essential to enable secure communications in public networks or public mediums. Such primitives can be deployed as software libraries or hardware co-processors, the latter being more commonly employed in systems on chip (SoC) scenarios, embedded devices, or application-specific servers. Unfortunately, the most commonly available solutions, based on RSA or elliptic curve cryptography (ECC), are highly processing intensive due to the underlying extended-precision modular arithmetic. Consequently, they are not available on highly constrained platforms. Aiming to tackle this issue, we here investigate an alternative asymmetric encryption scheme that relies on lightweight arithmetic: McEliece. This scheme is especially appealing because, being based on error correction codes, it displays a simpler arithmetic and leads to better performance when compared to RSA or ECC. To evaluate the implementation of this scheme in hardware, we propose and analyze a flexible architecture whose security level and time versus area usage characteristics can be reconfigured as desired. The proposed architecture is suitable to all usual security levels, ranging from 80 to 256 bits. It is also very efficient, being able to perform data decryption with binary Goppa codes in 56µs with 3,402 slices on a Xilinx Spartan-3AN FPGA, whereas the best-known result in the literature for the same FPGA is 115µs with 7,331 slices. Alternatively, the architecture can operate with quasi-dyadic Goppa (QD-Goppa) codes, which involves smaller keys than traditional binary Goppa codes. In the latter case, for an 80-bit security level, the decryption operation can take from 1.1ms with 1,129 slices to 68µs with 8,268 sices. By choosing a more hardware-friendly decoding algorithm, focusing hardware resources on most bottleneck operations and sharing hardware resource for two different algorithms, better results than the those in the literature were obtained.

A way to speed up the Montgomery Multiplication by distributing the multiplier operand bits into${\mbi {k}}$partitions is proposed. All of them process in parallel and use an identical algorithm. Each partition executes its task in${\mbi {n/k}}$steps. Even though the computationstep operates in radix${\mbi {2^k}}$, the complexity is reduced by the use of a limited digit set. Experiments with a 90-nm cell library show that the hardware cost and its complexity have a linear growth according to the number of partitions. Besides the gain in speed, the proposal reduces power consumption for multiplication operands with 256, 512, 1024, and 2048 bits. The uniform treatment of partition hardware design enables the realization of a fault-tolerant hardware.

The video consumption tends to grow in coming years and it will be increasingly imperative to know the validity and effectiveness of using this type of media especially in teach and learn activities. It is very important to be able to assess how much knowledge a particular didactic video content was effective in building students knowledge. In this paper we present a tool we developed and an experiment we realized with two classes of computer engineering course related to network computer subjects, totalizing 140 students. In our experiment we divided our students in two groups: half of them attended a video explanation and half of them had the same explanation but only in a traditional classroom format. Our experiments show that video can be a great ally in the teaching and learning processes. Nevertheless, our investigation must continue in order to understand all video potential in this purpose.

Projections indicate that video will be responsible for 90% of network traffic until the end of the decade. Considering the high cost of media production and, mainly, the high consumption of network resources for applications that explore it, it will be extremely important to monitor such use, preventing against wrong or distorted views of this consumption. Within this scenario, it is necessary to develop mechanisms and tools that provide a clear vision of video effectiveness consumption, as well as the measuring of “viewer engagement”. What tools can be developed to evaluate such consumption? What should be measured, i.e., which are the parameters and metrics necessary, and how can we obtain them, in order to measure the effectiveness of video applications use, for example in engineering courses and distance learning environments? These are some of the questions considered in this paper, especially evaluated within a scenario of video usage for teaching/learning purpose.

This paper proposes a new approach to speed up the Montgomery Multiplication by distributing the multiplier operand bits into k partitions that can process in parallel. Each partition executes in n/k steps. A computation step, although operating in radix 2k, has its complexity reduced by the use of a limited digit set. Experiments with a 90nm cell library show that the hardware cost and its complexity have a linear growth with the number of partitions. Besides the gain in speed, the approach provides 20% average reduction in energy consumption for multiplication operands with 256, 512, 1024, and 2048 bits.

This paper presents a continuous authentication system based on user behavior analysis that makes use of environmental context information, users' behavior analysis and Neuro-Fuzzy Logic. This system must be able to acquire information in context, making them into a computational environment. This information is the basis of user behavior. The System, based on the evidences of the behavior, establishes if it can trust the user or not. According to the user behavior, levels of trust are released, to access the application software. Weights are attributed in the fuzzyfication process, according to the rules that were previously established for the parameters which help to establish the evidences of behavioral trust, in its different degrees. The neuro-fuzzy logic allows that the user behavioral database be continuously updated, interacting with the fuzzyfication mechanism, so as to keep trust levels updated according to the user behavior, in a more accurate and faithful way.

This paper presents KUCAS (Known User Continuous Authentication System), a work-in-progress security system, that has a continuous authentication mechanism of users/consumers. The KUCAS system makes use of environmental context information, users behavior analysis, the behavior theories of Skinner and the Mathematical Confidence of Dempster-Shafer Evidences Theory, that establishes trust levels to authenticate the user by his behavior analysis, during an application software, in a specific domain of the computer networks, in a period of time. The dynamics of enclosed management in this system compares the current behavior with the user's previous behaviors description and with the trust restrictions. In case of indications of changes in the user's behavior, the system provides the behavior analysis of the user using database restrictions information. If there are uncertainties and divergences, mechanisms of security and alert signals are triggered.

This work presents KUCAS (Known User Continuous Authentication System), a work-in-progress security system, that has a continuous authentication mechanism of users in software application. The KUCAS system makes use of environmental context information, users behavior analysis, the behavior theories of Skinner and the Mathematical Confidence of Dempster-Shafer Evidences Theory, that establishes trust levels to authenticate the user by his behavior analysis, during an application software, in a specific domain of the computer networks, in a period of time. The dynamics of enclosed management in this system compares the current behavior with the user's previous behaviors description and with the trust restrictions. In case of indications of changes in the user's behavior, the system provides the behavior analysis of the user using database restrictions information. If there are uncertainties and divergences, mechanisms of security and alert signals are triggered.

This paper presents an approach to personalisation in e-learning. The concept of a personalised learning policy (PLP) is introduced and its application in a learning management system (LMS) is detailed. Aiming to represent the criteria used in the system adaptation process, a data structure named orientation layer is proposed. The student current cognitive status is determined considering both the interactions between student and system and the aspects judged relevant to be observed. As a result, didactic contents and learning activities that match the pedagogical goals with the student's preferences and skills are provided. That is, the LMS is endowed with a level of configurability and the system adaptive behaviour is consistent with the system users (i.e. teachers and students).

Mobility, usability and security are major requirements for any Ad Hoc network systems, and there have been numerous papers in regards to them. However, often these requirements are addressed separately. For a valid solution, these requirements must be considered from an integrated view. In this paper, taking into account mobility and usability, we implement a framework which allows to securely share resources and services between devices in Ad-hoc networks, based on security policies defined by the owners of those devices. In addition, we extend our framework to support inter-domain sharing of services and resources. We detail our design, present the preliminary results of our prototype, and discuss the lessons learned, in particular how user experience led to several re-designs of the initial security solution.