Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks

Taoran Ji, Zhiqian Chen, Nathan Self, Kaiqun Fu, Chang-Tien Lu and Naren Ramakrishnan

Published in International Joint Conference on Artificial Intelligence (IJCAI), 2019

Abstract-Modeling and forecasting forward citations to a patent is a central task for the discovery of emerging technologies and for measuring the pulse of inventive progress. Conventional methods for forecasting these forward citations cast the problem as analysis of temporal point processes which rely on the conditional intensity of previously received citations. Recent approaches model the conditional intensity as a chain of recurrent neural networks to capture memory dependency in hopes of reducing the restrictions of the parametric form of the intensity function. For the problem of patent citations, we observe that forecasting a patent’s chain of citations benefits from not only the patent’s history itself but also from the historical citations of assignees and inventors associated with that patent. In this paper, we propose a sequence-to-sequence model which employs an attention-of-attention mechanism to capture the dependencies of these multiple time sequences. Furthermore, the proposed model is able to forecast both the timestamp and the category of a patent’s next citation. Extensive experiments on a large patent citation dataset collected from USPTO demonstrate that the proposed model outperforms state-of-the-art models at forward citation forecasting.

Download here

Feature Driven Learning Framework for Cybersecurity Event Detection

Taoran Ji, Xuchao Zhang, Nathan Self, Kaiqun Fu, Chang-Tien Lu, and Naren Ramakrishnan

Published in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019

Abstract-Cybersecurity event detection is a crucial problem for mitigating effects on various aspects of society. Social media has become a notable source of indicators for detection of diverse events. Though previous social media based strategies for cybersecurity event detection focus on mining certain event-related words, the dynamic and evolving nature of online discourse limits the performance of these approaches. Further, because these are typically unsupervised or weakly supervised learning strategies, they do not perform well in an environment of biased samples, noisy context, and informal language which is routine for online, user-generated content. This paper takes a supervised learning approach by proposing a novel multi-task learning based model. Our model can handle diverse structures in feature space by learning models for different types of potential high-profile targets simultaneously. For parameter optimization, we develop an efficient algorithm based on the alternating direction method of multipliers. Through extensive experiments on a real world Twitter dataset, we demonstrate that our approach consistently outperforms existing methods at encoding and identifying cybersecurity incidents.

Download here

Multi-task Learning for Transit Service Disruption Detection

Taoran Ji, Kaiqun Fu, Nathan Self, Chang-Tien Lu, and Naren Ramakrishnan

Published in IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018

Abstract-With the rapid growth in urban transit networks inrecent years, detecting service disruptions in a timely manneris a problem of increased interest to service providers. Transit agencies are seeking to move beyond traditional customerquestionnaires and manual service inspections to leveraging opensource indicators like social media for deteting emerging transitevents. In this paper, we leverage Twitter data for early detectionof metro service disruptions. Inspired by the multi-task learningframework, we propose the Metro Disruption Detection Model, which captures the semantic similarity between transit lines in Twitter space. We propose novel constraints on feature semanticsimilarity exploiting prior knowledge about the spatial connectivity and shared tracks of the metro network. An algorithmbased on the alternating direction method of multipliers (ADMM) framework is developed to solve the proposed model. We runextensive experiments and comparisons to other models withreal world Twitter data and transit disruption records from the Washington Metropolitan Area Transit Authority (WMATA) to justify the efficacy of our model.

Download here

Crowdsourcing cybersecurity: Cyber attack detection using social media

Khandpur Rupinder Paul, Taoran Ji, Steve Jan, Gang Wang, Chang-Tien Lu, and Naren Ramakrishnan

Published in Conference on Information and Knowledge Management, 2017

Abstract-Social media is often viewed as a sensor into various societal events such as disease outbreaks, protests, and elections. We describe the use of social media as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our approach detects a broad range of cyber-attacks (e.g., distributed denial of service (DDoS) attacks, data breaches, and account hijacking) in a weakly supervised manner using just a small set of seed event triggers and requires no training or labeled samples. A new query expansion strategy based on convolution kernels and dependency parses helps model semantic structure and aids in identifying key event characteristics. Through a large-scale analysis over Twitter, we demonstrate that our approach consistently identifies and encodes events, outperforming existing methods.

Download here

Determining relative airport threats from news and social media

Taoran Ji, Khandpur Rupinder Paul, Yue Ning, Liang Zhao, Chang-Tien Lu, Erik R. Smith, Christopher Adams, and Naren Ramakrishnan.

Published in Twenty-Ninth IAAI Conference, 2017

Abstract-Airports are a prime target for terrorist organizations, drug traffickers, smugglers, and other nefarious groups. Traditional forms of security assessment are not real-time and often do not exist for each airport and port of entry. Thus, homeland security professionals must rely on measures of attractiveness of an airport as a target for attacks. We present an open source indicators approach, using news and social media, to conduct relative threat assessment, i.e., estimating if one airport is under greater threat than another. The three ingredients of our approach are a dynamic query expansion algorithm for tracking emerging threat-related chatter, news-Twitter reciprocity modeling for capturing interactions between social and traditional media, and a ranking scheme to provide an ordered assessment of airport threats. Case studies based on actual aviation incidents are presented.

Download here