State Of The Art
Recently Published Documents


(FIVE YEARS 26875)



2022 ◽  
Vol 16 (4) ◽  
pp. 1-22
Siddharth Bhatia ◽  
Rui Liu ◽  
Bryan Hooi ◽  
Minji Yoon ◽  
Kijung Shin ◽  

Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? Existing approaches aim to detect individually surprising edges. In this work, we propose Midas , which focuses on detecting microcluster anomalies , or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data. We further propose Midas -F, to solve the problem by which anomalies are incorporated into the algorithm’s internal states, creating a “poisoning” effect that can allow future anomalies to slip through undetected. Midas -F introduces two modifications: (1) we modify the anomaly scoring function, aiming to reduce the “poisoning” effect of newly arriving edges; (2) we introduce a conditional merge step, which updates the algorithm’s data structures after each time tick, but only if the anomaly score is below a threshold value, also to reduce the “poisoning” effect. Experiments show that Midas -F has significantly higher accuracy than Midas . In general, the algorithms proposed in this work have the following properties: (a) they detects microcluster anomalies while providing theoretical guarantees about the false positive probability; (b) they are online, thus processing each edge in constant time and constant memory, and also processes the data orders-of-magnitude faster than state-of-the-art approaches; and (c) they provides up to 62% higher area under the receiver operating characteristic curve than state-of-the-art approaches.

2022 ◽  
Vol 136 ◽  
pp. 103596
Daryl Powell ◽  
Maria Chiara Magnanini ◽  
Marcello Colledani ◽  
Odd Myklebust

Fuel ◽  
2022 ◽  
Vol 314 ◽  
pp. 123140
Lichao Ge ◽  
Xiaoyan Liu ◽  
Hongcui Feng ◽  
Han Jiang ◽  
Tianhong Zhou ◽  

2022 ◽  
Vol 29 (1) ◽  
pp. 1-53
Aditya Bharadwaj ◽  
David Gwizdala ◽  
Yoonjin Kim ◽  
Kurt Luther ◽  
T. M. Murali

Modern experiments in many disciplines generate large quantities of network (graph) data. Researchers require aesthetic layouts of these networks that clearly convey the domain knowledge and meaning. However, the problem remains challenging due to multiple conflicting aesthetic criteria and complex domain-specific constraints. In this article, we present a strategy for generating visualizations that can help network biologists understand the protein interactions that underlie processes that take place in the cell. Specifically, we have developed Flud, a crowd-powered system that allows humans with no expertise to design biologically meaningful graph layouts with the help of algorithmically generated suggestions. Furthermore, we propose a novel hybrid approach for graph layout wherein crowd workers and a simulated annealing algorithm build on each other’s progress. A study of about 2,000 crowd workers on Amazon Mechanical Turk showed that the hybrid crowd–algorithm approach outperforms the crowd-only approach and state-of-the-art techniques when workers were asked to lay out complex networks that represent signaling pathways. Another study of seven participants with biological training showed that Flud layouts are more effective compared to those created by state-of-the-art techniques. We also found that the algorithmically generated suggestions guided the workers when they are stuck and helped them improve their score. Finally, we discuss broader implications for mixed-initiative interactions in layout design tasks beyond biology.

2022 ◽  
Vol 27 (3) ◽  
Sabina Piras ◽  
Alessandro Palermo ◽  
M. Saiid Saiidi

2022 ◽  
Vol 32 (1) ◽  
pp. 1-33
Jinghui Zhong ◽  
Dongrui Li ◽  
Zhixing Huang ◽  
Chengyu Lu ◽  
Wentong Cai

Data-driven crowd modeling has now become a popular and effective approach for generating realistic crowd simulation and has been applied to a range of applications, such as anomaly detection and game design. In the past decades, a number of data-driven crowd modeling techniques have been proposed, providing many options for people to generate virtual crowd simulation. This article provides a comprehensive survey of these state-of-the-art data-driven modeling techniques. We first describe the commonly used datasets for crowd modeling. Then, we categorize and discuss the state-of-the-art data-driven crowd modeling methods. After that, data-driven crowd model validation techniques are discussed. Finally, six promising future research topics of data-driven crowd modeling are discussed.

Fuel ◽  
2022 ◽  
Vol 314 ◽  
pp. 123064
Junaid Haider ◽  
Muhammad Abdul Qyyum ◽  
Amjad Riaz ◽  
Ahmad Naquash ◽  
Bilal Kazmi ◽  

2022 ◽  
Vol 320 ◽  
pp. 126198
Hamed Dabiri ◽  
Ali Kheyroddin ◽  
Andrea Dall'Asta

Sign in / Sign up

Export Citation Format

Share Document