Northeastern University

By G. Wang, Tristan Konolige, Christo Wilson, Xiao Wang, Haitao Zheng, Ben Y. Zhao. 2021

302 citations recorded by the source corpus.

Northeastern University's approach to undergraduate education in biomedical engineering is oriented toward complementing our students' strong back ground in both theory and practice in the traditional disciplines of electrical and computer (ECE), mechanical and industrial, and chemical engineering. Our undergraduate program operates around four key elements. Exposure to life science courses such as biology, psychology, and chemistry provides an appropriate foundation for real-world application of biomedical engineering.

Subfields: Hematology, Pharmacy, Surgery

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Citations in the corpus

Poisoning Attacks to Graph-Based Recommender Systems (2018, methodology): “ecting fake users is closely related to Sybil detection in social networks. Many methods have been developed for Sybil detection. „ese methods leverage IP addresses (e.g., [28]), user behavior (e.g., [32]), or social relationships between users (e.g., [8, 30, 31]). Since we do not have access to IP ”
SybilBlind: Detecting Fake Users in Online Social Networks without Manual Labels (2018, unknown): “Approaches in this direction [27,23,4,35,19,37,24,10,31,22] leverage various usergenerated contents (e.”
Fawkes: Protecting Privacy against Unauthorized Deep Learning Models (2020, methodology): “users to curate and compartmentalizecontent for different audiences [26]. While there are numerous detection techniques for Sybil detection,individual Sybil accounts are difﬁcult to identify or remove[60]. In our case, we propose that privacy-conscious users create a secondary identity, preferably n”
Sybil Attacks and Their Defenses in the Internet of Things (2014, methodology): “4, the behaviors of SA-2 and normal ones can be modeled as a Markov chain [44].”
Using naive bayes to detect spammy names in social networks (2013, background): “Many previous authors have considered the problem of detecting spam and fake accounts in social networks, using signals such as clickstream patterns [30, 29], message-sending activity and content [3, 14], and properties of the social graph [8, 7, 6].”
Structure-Based Sybil Detection in Social Networks via Local Rule-Based Propagation (2018, unknown): “, IP address, behaviors, and content) [26], [27], [28], [29], [30], [31], [32],”
DAWN: Dynamic Adversarial Watermarking of Neural Networks (2019, unknown): “Also, Sybils-detection techniques exist [43, 46] and it is possible to link”
Deep Neural Networks for Bot Detection (2018, background): “ as well [1, 16]. Much work on bot detection assumes extensive access to social media data. For example, Wang and collaborators used clustering techniques to identify large-scale behavioral anomalies [56], while other authors adopted supervised learning to analyze all accounts of some platform and s”
Strength in Numbers: Robust Tamper Detection in Crowd Computations (2015, methodology): “Existing Sybil defenses have mostly taken the approach of detecting individual Sybils [15, 36, 37, 41], enabling the operator to either sus-pend the Sybils or nullify their contribution to the crowd computation.”
EVILCOHORT: Detecting Communities of Malicious Accounts on Online Services (2015, background): “On the other hand, E VIL C OHORT can detect an account (or an IP address) as malicious even if the account does not post any content on the online service.”
Defending against Sybil Devices in Crowdsourced Mapping Services (2015, background): “Sybil detection has been studied in P2P networks [16] and online social networks [47, 49, 48].”
Towards Detecting Anomalous User Behavior in Online Social Networks (2014, unknown): “, user click-stream models [63]) used in existing work for misbehavior detection.”
SybilSCAR: Sybil detection in online social networks via local rule based propagation (2017, unknown): “, IP address, behaviors, and content) [22], [23], [24], [25], [26], [27].”
The rise of social bots (2014, methodology): “The Renren Sybil detector [Wang et al. 2013a; Yang et al. 2014] is an example of system that explores multiple dimensions of users’ behaviors like activity and timing information.”