Malicious source code detection using a translation model.

Modern software development often relies on open-source code sharing. Open-source code reuse, however, allows hackers to access wide developer communities, thereby potentially affecting many products. An increasing number of such "supply chain attacks" have occurred in recent years, taking advantage of open-source software development practices.

Co-Membership-based Generic Anomalous Communities Detection.

Nowadays, detecting anomalous communities in networks is an essential task in research, as it helps discover insights into community-structured networks. Most of the existing methods leverage either information regarding attributes of vertices or the topological structure of communities. In this study, we introduce the Co-Membership-based Generic Anomalous Communities Detection Algorithm (referred as to ), a novel and generic method that utilizes the information of vertices co-membership in multiple communities.

A supervised machine learning model for imputing missing boarding stops in smart card data.

Public transport has become an essential part of urban existence with increased population densities and environmental awareness. Large quantities of data are currently generated, allowing for more robust methods to understand travel behavior by harvesting smart card usage. However, public transport datasets suffer from data integrity problems; boarding stop information may be missing due to imperfect acquirement processes or inadequate reporting.

Scientometric trends for coronaviruses and other emerging viral infections.

Background: COVID-19 is the most rapidly expanding coronavirus outbreak in the past 2 decades. To provide a swift response to a novel outbreak, prior knowledge from similar outbreaks is essential.

Results: Here, we study the volume of research conducted on previous coronavirus outbreaks, specifically SARS and MERS, relative to other infectious diseases by analyzing >35 million articles from the past 20 years.

Over-optimization of academic publishing metrics: observing Goodhart's Law in action.

Background: The academic publishing world is changing significantly, with ever-growing numbers of publications each year and shifting publishing patterns. However, the metrics used to measure academic success, such as the number of publications, citation number, and impact factor, have not changed for decades. Moreover, recent studies indicate that these metrics have become targets and follow Goodhart's Law, according to which, "when a measure becomes a target, it ceases to be a good measure.

Ethical considerations when employing fake identities in online social networks for research.

Online social networks (OSNs) have rapidly become a prominent and widely used service, offering a wealth of personal and sensitive information with significant security and privacy implications. Hence, OSNs are also an important--and popular--subject for research. To perform research based on real-life evidence, however, researchers may need to access OSN data, such as texts and files uploaded by users and connections among users.