Liang Ma, Shuyang Cao, Robert L Logan IV, Di Lu, Shihao Ran, Ke Zhang, Joel Tetreault, Alejandro Jaimes. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Ruijia Cheng, Alison Smith-Renner, Ke Zhang, Joel Tetreault, Alejandro Jaimes-Larrarte. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Vivian Lai, Alison Smith-Renner, Ke Zhang, Ruijia Cheng, Wenjuan Zhang, Joel Tetreault, Alejandro Jaimes-Larrarte. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

Eleftheria Briakou, Di Lu, Ke Zhang, Joel Tetreault. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers’ view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.

Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people “on the ground” post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. Emergency responders are largely interested in finding out what events are taking place so they can properly plan and deploy resources. In this paper we address the problem of automatically identifying important sub-events (within a large-scale emergency “event”, such as a hurricane). In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. We first extract noun-verb pairs and phrases from raw tweets as sub-event candidates. Then, we learn a semantic embedding of extracted noun-verb pairs and phrases, and rank them against a crisis-specific ontology. We filter out noisy and irrelevant information then cluster the noun-verb pairs and phrases so that the top-ranked ones describe the most important sub-events. Through quantitative experiments on two large crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we demonstrate the effectiveness of our approach over the state-of-the-art. Our qualitative evaluation shows better performance compared to our baseline.

In mission critical IT services, system failure prediction becomes increasingly important; it prevents unexpected system downtime, and assures service reliability for end users. While operational console logs record rich and descriptive information on the health status of those IT systems, existing system management technologies mostly use them in a labor-intensive forensics approach, i.e., identifying what went wrong after the fact. Recent efforts on log-based system management take an automation approach with text mining techniques, such as term frequency - inverse document frequency (TF-IDF). However, those techniques lead to a high-dimensional feature space, and are not easily generalizable to heterogeneous log formats. In this paper, we present a novel system that automatically parses streamed console logs and detects early warning signals for IT system failure prediction. In particular, our solution includes a log pattern extraction method by clustering together logs with similar format and content. We then resemble the TF-IDF idea by considering each pattern as a word and the set of patterns in each discretized epoch as a document. This leads to a feature space with significantly lower dimensionality that can provide robust signals for the status of the system. As system failures tend to occur very rare, we apply a recurrent neural network, namely, Long Short-Term Memory (LSTM), to deal with the “rarity” of labeled data in the training process. LSTM is able to capture the long-range dependency across sequences, therefore outperforms traditional supervised learning methods in our application domain. We evaluated and compared our proposed technology with state-of-the-art machine learning approaches using real log traces from two large enterprise systems. The results showed the advantage and potentials of our system in prediction of complex IT failures. To our knowledge, our work is the first that employs LSTM for log-based system failure prediction.

Homophily is a phenomenon observed very frequently in social networks and is related with the inclination of people to be involved with others that exhibit similar characteristics. The roots of homophily can be subtle and are mainly traced back to two mechanisms: (i) social selection and (ii) peer influence. Decomposing the effects of each of these mechanisms requires analysis of longitudinal data. This has been a burden to similar studies in traditional social sciences due to the hardness of collecting such information. However, the proliferation of online social media has enabled the collection of massive amounts of information related with human activities. In this work, we are interested in examining the forces of the above mechanisms in the context of the locations visited by people. For our study, we use a longitudinal dataset collected from Gowalla, a location-based social network (LBSN). LBSNs, unlike other online social media, bond users' online interactions with their activities in real-world, physical locations. Prior work on LBSNs has focused on the influence of geographical constraints on the formation of social ties. On the contrary, in this paper, we perform a microscopic study of the peer influence and social selection mechanisms in LBSNs. Our analysis indicates that while the similarity of friends' spatial trails at a geographically global scale cannot be attributed to peer influence, the latter can explain up to 40% of the geographically localized similarity between friends. Moreover, this percentage depends on the type of locations we examine, and it can be even higher for specific categories (e.g., nightlife spots). Finally, we find that the social selection mechanism, is only triggered by places that exhibit specific network characteristics. We believe that our work can have significant implications on obtaining a deeper understanding of the way that people create friendships, act and move in real space, which can further facilitate and enhance applications such as recommender systems, trip planning and marketing.

The proliferation of Location-based Social Networks (LBSNs) has been rapid during the last year due to the number of novel services they can support. The main interaction between users in an LBSN is location sharing, which builds the spatial component of the system. The majority of the LBSNs make use of the notion of check-in, to enable users to volunteeringly share their whereabouts with their peers and the system. The flow of this spatial information is unidirectional and originates from the users' side. Given that currently there is no infrastructure in place for detecting fake checkins, the quality of the spatial information plane of an LBSN is solely based on the honesty of the users. In this paper, we seek to raise the awareness of the community for this problem, by identifying and discussing the effects of the presence of fake location information. We further present a preliminary design of a fake check-in detection scheme, based on location-proofs. Our initial simulation results show that if we do not consider the infrastructural constraints, location-proofs can form a viable technical solution.