abdalsamad keramatfar

In recent years, artificial intelligence has had a conspicuous growth in almost every aspect of life. One of the most applicable areas is security code review, in which a lot of AI-based tools and approaches have been proposed. Recently, ChatGPT has caught a huge amount of attention with its remarkable performance in following instructions and providing a detailed response. Regarding the similarities between natural language and code, in this paper, we study the feasibility of using ChatGPT for vulnerability detection in Python source code. Toward this goal, we feed an appropriate prompt along withvulnerable data to ChatGPT and compare its results on two datasets with the results of three widely used Static Application Security Testing tools (Bandit, Semgrep, and SonarQube). We implement different kinds of experiments with ChatGPT and the results indicate that ChatGPT reduces the false positive and false negative rates and has the potential to be used for Python source code vulnerability detection.

Randomized controlled trials (RCTs) provide the strongest evidence for therapeutic interventions and their effects on groups of subjects. However, the large amount of unstructured information in these trials makes it challenging and time-consuming to make decisions and identify important concepts and valid evidence. This study aims to explore methods for automating or semi-automating information extraction from reports of RCT studies.  

We conducted a systematic search of PubMed, ACM Digital Library, and Web of Science to identify relevant articles published between January 1, 2010, and 2022. We focused on published Natural Language Processing (NLP), machine learning, and deep learning methods that automate or semi-automate key elements of information extraction in the context of RCTs.  

A total of 26 publications were included, which discussed the automatic extraction of key characteristics of RCTs using various PICO frameworks (PIBOSO and PECODR). Among these publications, 14 (53.8%) extracted key characteristics based on PICO, PIBOSO, and PECODR, while 12 (46.1%) discussed information extraction methods in RCT studies. Common approaches mentioned included word/phrase matching, machine learning algorithms such as binary classification using the Naïve Bayes algorithm and powerful BERT network for feature extraction, support vector machine for data classification, conditional random field, non-machine-dependent automation, and machine learning or deep learning approaches.  

The lack of publicly available software and limited access to existing software makes it difficult to determine the most powerful information extraction system. However, deep learning models like Transformers and BERT language models have shown better performance in natural language processing.

The purpose of this study is to evaluate the scientific outputs of the project management literature and draw a scientific map. The target population of this research is all documents indexed in Scopus database which have used the keywords of project management in their title, abstract or keywords. In this regard, 107438 documents were extracted and their process of citation was examined using BibExcel and VOSViewer software. The results showed that the project management research process has been growing exponentially, especially in the last two decades; so that 86% of scientific productions belong to this period and English language with 86% was the dominant language of published scientific documents. Also in the development of this branch of science, the international journal of project management was the most prominent publication with the most documents (2154) and the most citations (105959), and the United States was the most prominent country with 29,271 documents and 360,708 citations. In addition, the analysis in the evaluation of the role of institutions and researchers in the development of project management knowledge, showed that the American institute of electrical and electronics engineers with 133 documents and the department of building and real estate of Hong Kong polytechnic university with 3787 citations, respectively, were in the first place of the most active and influential institutions; And researchers Scott and Keil, with 100 documents and 5,625 citations, respectively, were among the top active and influential researchers. Finally, keyword analysis showed that the word project management with the repetition in 10581 documents and the word ethnography with 67.2 of the average citations, obtained the most repetitions and citations; At the same time, the emergence of up-to-date keywords such as digital transformation, the industry 4.0 and gamification, indicated that recent research has shifted towards the management of technological and innovative projects.

Policymakers seek to evaluate their country's scientific performance and measure it in terms of effectiveness and problem-solving. The aim of this study was to make an analytical comparison of Iranian scientific documents in text mining based on domestic and foreign databases.

The present study is descriptive survey with a bibliometric approach. In order to find scientific documents related to text mining in the Scopus database, related terms were searched, and then the results were limited to Iran. Scientific Information Database (SID) was used to search for Persian scientific documents. Bibexcel, VOSviewer, Python programming language, and Excel 2017 were used to analyze the data.

The total number of Iranian scientific documents in text mining in the Scopus citation database was 1082 and 284 (26.25%) of scientific documents indexed in Scopus were in Persian. Moreover, according to the Scientific Information Center, the number of scientific documents in this field was 89 and the number of scientific documents in Persian was 51 (57.30%). The Journal of Lecture Notes in Computer Science has published most international scientific papers in Iran, and the Journal of Signal and Data Processing has published most domestic scientific papers in Iran in text mining. A t-test was used to determine that there was a significant difference in the number of scientific documents in Persian between Scopus and SID databases (p<0.0001).

The average growth rate of Iranian scientific documents in text mining was higher than in other subject areas. The United States, Britain, and Australia have had the most collaboration with Iranian researchers in this field. It was also found that international scientific documents in English received more citations than scientific documents in Persian.

This paper aim to review features of review articles that are available immediately after publication and their relationship with the number of citations they receive in the field of cell biology.
ten article features including SJR, SNIP, IF, the numbers of authors, the number of countries, the number of cited references, the number of pages, the length of the article title in terms of number of characters, abstract words number and Price Index of all the review articles covered by journals in Scopus in year 2012 has explored Using multivariate OLS regression models.
Findings: The results show that, SNIP, SJR, Number of countries, IF, Number of references, Price index of review articles indexed in Scopus have the highest relation with the number of future citations of articles respectively. In addition number of pages, number of authors and article title length has negative association in this field and type. Based on the presented models each unit increases in SNIP is associated with 43% increase in citations, while for IF this association is about 10%.
Features used in this study, have capability of explaining about one-third of citations. Furthermore, SNIP is more capable of predicting the number of future citations of articles in this field