Abstract The proliferation of online self‐archiving platforms has greatly facilitated the dissemination of new research findings. However, the extent to which these platforms have increased the impact of research remains uncertain, especially considering the potential bias of researchers favoring the deposition of higher‐quality preprints. To determine the causal relationship between self‐archiving platforms and research impact, we conducted an investigation using a sample of 5423 published articles from 2018 that had preprints deposited on bioRxiv. These articles were compared to a control group of 7862 similar articles without preprints, identified through text‐mining algorithms. By modeling the differences in forward citations over time between the two groups, we established a positive causal effect of self‐archiving platforms on research impact. However, the effect observed in this study was smaller than that reported in previous studies (18.7% vs. 36%), indicating a more accurate and unbiased result. Additionally, the study explored the underlying mechanisms that contribute to the enhancement of research impact, including early dissemination and increased exposure through social networks facilitated by self‐archiving platforms. Overall, this study expands our understanding of how research is disseminated in the era of Internet platforms.

In cloud computing, it is desirable if suspicious activities can be detected by automatic anomaly detection systems. Although anomaly detection has been investigated in the past, it remains unsolved in cloud computing. Challenges are: characterizing the normal behavior of a cloud server, distinguishing between benign and malicious anomalies (attacks), and preventing alert fatigue due to false alarms. We propose CloudShield, a practical and generalizable real-time anomaly and attack detection system for cloud computing. Cloudshield uses a general, pretrained deep learning model with different cloud workloads, to predict the normal behavior and provide real-time and continuous detection by examining the model reconstruction error distributions. Once an anomaly is detected, to reduce alert fatigue, CloudShield automatically distinguishes between benign programs, known attacks, and zero-day attacks, by examining the reconstruction error distributions. We evaluate the proposed CloudShield on representative cloud benchmarks. Our evaluation shows that CloudShield, using model pretraining, can apply to a wide scope of cloud workloads. Especially, we observe that CloudShield can detect the recently proposed speculative execution attacks, e.g., Spectre and Meltdown attacks, in milliseconds. Furthermore, we show that CloudShield accurately differentiates and prioritizes known attacks, and potential zero-day attacks, from benign programs. Thus, it significantly reduces false alarms by up to 99.0%.

Spectre and Meltdown attacks and their variants exploit hardware performance optimization features to cause security breaches. Secret information is accessed and leaked through covert or side channels. New attack variants keep appearing and we do not have a systematic way to capture the critical characteristics of these attacks and evaluate why they succeed or fail.In this paper, we provide a new attack-graph model for reasoning about speculative execution attacks. We model attacks as ordered dependency graphs, and prove that a race condition between two nodes can occur if there is a missing dependency edge between them. We define a new concept, “security dependency”, between a resource access and its prior authorization operation. We show that a missing security dependency is equivalent to a race condition between authorization and access, which is a root cause of speculative execution attacks. We show detailed examples of how our attack graph models the Spectre and Meltdown attacks, and is generalizable to all the attack variants published so far. This attack model is also very useful for identifying new attacks and for generalizing defense strategies. We identify several defense strategies with different performance-security tradeoffs. We show that the defenses proposed so far all fit under one of our defense strategies. We also explain how attack graphs can be constructed and point to this as promising future work for tool designers.

Thomson Reuters's Web of Science (WoS) began systematically collecting acknowledgment information in August 2008. Since then, bibliometric analysis of funding acknowledgment (FA) has been growing and has aroused intense interest and attention from both academia and policy makers. Examining the distribution of FA by citation index database, by language, and by acknowledgment type, we noted coverage limitations and potential biases in each analysis. We argue that despite its great value, bibliometric analysis of FA should be used with caution.

The impact of international collaboration on research performance has been extensively explored over the past two decades. Most research, however, focuses on quantity and citation‐based indicators. Using the turnover of keywords, this study develops an integrative approach, tracking and visualizing the shift of the research stream, and tests it within the context of U.S.–China collaboration in nanotechnology. The results show evidence in support of the linkage between the emergence of a new research stream of Chinese researchers when there is U.S.–China collaboration. We also find that the triggered research stream diffused further via extended coauthorship. Policy implications for science and technology development and resource allocation in the United States and China are discussed.