Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user’s emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.

Bibliometric calculations currently used to assess the quality of researchers, articles, and scientific journals have serious structural problems; many authors have noted the weakness of citation counts, because they are purely quantitative and do not differentiate between high- and low-citing papers. If a paper’s reputation is simply evaluated according to the number of its citations, then incomplete, incorrect, or controversial articles may be promoted, regardless of their relevancy. Therefore, perverse incentives are generated for researchers who may publish many incorrect or incomplete papers to achieve high impact indexes. It is essential to improve the objective criteria for automatic article-quality assessments. However, to obtain these new criteria, it is necessary to advance the programmed detection of context, polarity, and function of bibliographic references.We present an overview of general concepts and review contributions to the solutions to problems related to these issues, with the purpose of identifying trends and suggesting possible future research directions.

Current methods for assessing the impact of authors and scientific media employ tools such as H-Index, Co-Citation and PageRank. These tools are primarily based on citation counting, which considers all citations to be equal. This type of methods can produce perverse incentives to publish controversial or incomplete papers, as mixed or negative reviews often generate larger citation counts and better indexes, regardless of whether the citations were critical or exerted minimal influence on the citing document. Passing citations that are employed to establish background, which do not have a real impact on the citing paper, are common in scientific literature. However, these citations have equal weight in impact evaluations. Notable researchers have emphasized the need to correct this situation by developing estimation methods that consider the different roles of quotations in citing papers. To accomplish this type of evaluation, a context citation analysis should be applied to determine the nature of the citations. We propose that citations should be categorized using four dimensions – FUNCTION, POLARITY, ASPECTS and INFLUENCE – as these dimensions provide adequate information that can be employed toward the generation of a qualitative method to measure the impact of a given publication in a citing paper. In this paper, we used interchangeably the words influence and impact. We present a method for obtaining this information using our proposed classification scheme and manually annotated corpus, which is marked with meaningful keywords and labels to help identify the characteristics or properties that constitute what we call ASPECTS. We develop a classification scheme which considers purpose definition shared by previous works. Our contribution is to abstract purpose classes from several other schemes and divide a complex structure in more manageable parts, to attain a simple system that combines low granularity dimensions but nevertheless produces a fine-grained classification. For annotators, the classification process is simple because in a first step, the coders distinguish only four primary classes, and in a second pass, they add the information contained in ASPECTS keyword and labels to obtain the more specific functions. This way, we gain a high granularity labeling that gives enough information about the citations to characterize and classify them, and we achieve this detailed coding with a straightforward process where the level of human error could be minimized.

In recent years, cybercrime activities have grown significantly, compromising device security and jeopardizing the normal activities of enterprises. The profits obtained through intimidation and the limitations for tracking down the illegal transactions have created a lucrative business based on the hijacking of users’ files. In this context, ransomware takes advantage of cryptography to compromise the user information or deny access to the operating system. Then, the attacker extorts the victim to pay a ransom in order to regain access, recover the data, or keep the information private. Nowadays, the adoption of Situational Awareness (SA) and cognitive approaches can facilitate the rapid identification of ransomware threats. SA allows knowing what is happening in compromised devices and network communications through monitoring, aggregation, correlation, and analysis tasks. The current literature provides some parameters that are monitored and analyzed in order to prevent these kinds of attacks at an early stage. However, there is no complete list of them. To the best of our knowledge, this paper is the first proposal that summarizes the parameters evaluated in this research field and considers the SA concept. Furthermore, there are several articles that tackle ransomware problems. However, there are few surveys that summarize the current situation in the area, not only regarding its evolution but also its issues and future challenges. This survey also provides a classification of ransomware articles based on detection and prevention approaches.

Ransomware-related cyber-attacks have been on the rise over the last decade, disturbing organizations considerably. Developing new and better ways to detect this type of malware is necessary. This research applies dynamic analysis and machine learning to identify the ever-evolving ransomware signatures using selected dynamic features. Since most of the attributes are shared by diverse ransomware-affected samples, our study can be used for detecting current and even new variants of the threat. This research has the following objectives: (1) Execute experiments with encryptor and locker ransomware combined with goodware to generate JSON files with dynamic parameters using a sandbox. (2) Analyze and select the most relevant and non-redundant dynamic features for identifying encryptor and locker ransomware from goodware. (3) Generate and make public a dynamic features dataset that includes these selected parameters for samples of different artifacts. (4) Apply the dynamic feature dataset to obtain models with machine learning algorithms. Five platforms, 20 ransomware, and 20 goodware artifacts were evaluated. The final feature dataset is composed of 2000 registers of 50 characteristics each. This dataset allows for a machine learning detection with a 10-fold cross-evaluation with an average accuracy superior to 0.99 for gradient boosted regression trees, random forest, and neural networks.