Realiza comparação entre softwares para auxiliar no processo de seleção das ferramentas adequadas para pesquisas bibliométricas. A partir de uma lista com 16 ferramentas apresentadas em publicações precedentes, seleciona quatro soluções com base em critérios de exclusão predefinidos pelos autores. A partir da exploração das funcionalidades presentes em cada uma das ferramentas, os resultados indicam que, destas quatro, o Biblioshiny apresenta o maior número de possibilidades em análises, embora apresente limitações nos relatórios visuais. O VOSviewer adota uma interface limpa e simples focada na visualização de redes bibliométricas. O Publish or Perish exibe a maior restrição em relação aos critérios comparativos, mas sua integração com o Google Acadêmico lhe confere vantagem em relação aos demais. Por fim, o CiteSpace oferece uma grande quantidade de parâmetros para formar suas visualizações de redes, mas sua interface desorganizada prejudica a experiência dos usuários. De acordo com o conteúdo analisado, constata um processo de renovação dos softwares bibliométricos. Conclui que não há uma única solução para as principais demandas da bibliometria e que a combinação entre as ferramentas tende a oferecer o melhor resultado aos pesquisadores.Palavras-chave: Bibliometria. Comparação de ferramentas. Ferramentas bibliométricas. Redes bibliométricas.

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Several studies applying Machine Learning to deception detection have been published in the last decade. A rich and complex set of settings, approaches, theories, and results is now available. Therefore, one may find it difficult to identify trends, successful paths, gaps, and opportunities for contribution. The present literature review aims to provide the state of research regarding deception detection with Machine Learning. We followed the PRISMA protocol and retrieved 648 articles from ACM Digital Library, IEEE Xplore, Scopus, and Web of Science. 540 of them were screened (108 were duplicates). A final corpus of 81 documents has been summarized as mind maps. Metadata was extracted and has been encoded as Python dictionaries to support a statistical analysis scripted in Python programming language, and available as a collection of Jupyter Lab Notebooks in a GitHub repository. All are available as Jupyter Lab Notebooks. Neural Networks, Support Vector Machines, Random Forest, Decision Tree and K-nearest Neighbor are the five most explored techniques. The studies report a detection performance ranging from 51% to 100%, with 19 works reaching accuracy rate above 0.9. Monomodal, Bimodal, and Multimodal approaches were exploited and achieved various accuracy levels for detection. Bimodal and Multimodal approaches have become a trend over Monomodal ones, although there are high-performance examples of the latter. Studies that exploit language and linguistic features, 75% are dedicated to English. The findings include observations of the following: language and culture, emotional features, psychological traits, cognitive load, facial cues, complexity, performance, and Machine Learning topics. We also present a dataset benchmark. Main conclusions are that labeled datasets from real-life data are scarce. Also, there is still room for new approaches for deception detection with Machine Learning, especially if focused on languages and cultures other than English-based. Further research would greatly contribute by providing new labeled and multimodal datasets for deception detection, both for English and other languages.

Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming to establish the common biological functions. This paper presents a system that was conceived to discover features (particular sequences of amino acids, or motifs) that occur very often in proteins of a given family but rarely occur in proteins of other families. These features can be used for the classification of unknown proteins, that is, to predict their function by analyzing the primary structure. Runnings were done with the enzymes subset extracted from the Protein Data Bank. The heuristic method used was based on a genetic algorithm using specially tailored operators for the problem. Motifs found were used to build a decision tree using the C4.5 algorithm. The results were compared with motifs found by MEME, a freely available web tool. Another comparison was made with classification results of other two systems: a neural network-based tool and a hidden Markov model-based tool. The final performance was measured using sensitivity (Se) and specificity (Sp): similar results were obtained for the proposed tool (78.79 and 95.82) and the neural network-based tool (74.65 and 94.80, respectively), while MEME and HM-MER resulted in an inferior performance. The proposed system has the advantage of giving comprehensible rules when compared with the other approaches. These results obtained for the enzyme dataset suggest that the evolutionary computation method proposed is very efficient to find patterns for protein classification.