Online social media enables mass-level, transparent, and democratized discussion on numerous socio-political issues. Due to such openness, these platforms often endure manipulation and misinformation - leading to negative impacts. To prevent such harmful activities, platform moderators employ countermeasures to safeguard against actors violating their rules. However, the correlation between publicly outlined policies and employed action is less clear to general people.

Post-specific diffusion network elucidates the who-saw-from-whom paths of a post on social media. A diffusion network for a specific post can reveal trustworthy and/or incentivized connections among users. Unfortunately, such a network is not observable from available information from social media platforms; hence an inference mechanism is needed.

Seismic phase identification classifies the type of seismic wave received at a station based on the waveform (i.e., time series) recorded by a seismometer. Automated phase identification is an integrated component of large scale seismic monitoring applications, including earthquake warning systems and underground explosion monitoring. Accurate, fast, and fine-grained phase identification is instrumental for earthquake location estimation, understanding Earth's crustal and mantle structure for predictive modeling, etc. However, existing operational systems utilize multiple nearby stations for precise identification, which delays response time with added complexity and manual interventions. Moreover, single-station systems mostly perform coarse phase identification. In this paper, we revisit the seismic phase classification as an integrated part of a seismic processing pipeline. We develop a machine-learned model FASER, that takes input from a signal detector and produces phase types as output for a signal associator. The model is a combination of convolutional and long short-term memory networks. Our method identifies finer wave types, including crustal and mantle phases. We conduct comprehensive experiments on real datasets to show that FASER outperforms existing baselines. We evaluate FASER holding out sources and stations across the world to demonstrate consistent performance for novel sources and stations.

Changes in data distribution of streaming data (i.e., concept drifts), constitute a central issue in online data mining. The main reason is that these changes are responsible for outdating stream learning models, reducing their predictive performance over time. A common approach adopted by real-time adaptive systems to deal with concept drifts is to employ detectors that indicate the best time for updates. However, an unrealistic assumption of most detectors is that the labels become available immediately after data arrives. In this paper, we introduce an unsupervised and model-independent concept drift detector suitable for high-speed and high-dimensional data streams in realistic scenarios with the scarcity of labels. We propose a straightforward two-dimensional representation of the data aiming faster processing for detection. We develop a simple adaptive drift detector on this visual representation that is efficient for fast streams with thousands of features and is accurate as existing costly methods that perform various statistical tests. Our method achieves better performance measured by execution time and accuracy in classification problems for different types of drifts, including abrupt, oscillating, and incremental. Experimental evaluation demonstrates the versatility of the method in several domains, including astronomy, entomology, public health, political science, and medical science.

Real-valued data sequences are often affected by structured noise in addition to random noise. For example, in pressure transient analysis (PTA), semi-log derivatives of log-log diagnostic plots show such contamination of structured noise; especially under multiphase flow condition. In PTA data, structured noise refers to the response to some physical phenomena which is not originated at the reservoir, such as fluid segregation in wellbore or pressure leak due to a brief opening of a valve. Such noisy responses commonly appear to mix up with flow regimes, hindering further reservoir flow analysis. In this paper, we use the Singular Spectrum Analysis (SSA) to decompose PTA data into additive components; subsequently we use the eigenvalues associated with the decomposed components to identify the components that contain most of the structured noise information. We develop a semisupervised process that requires minimal expert supervision in tuning the solitary parameter of our algorithm using only one pressure buildup scenario. An empirical evaluation using real pressure data from oil and gas wells shows that our approach can detect a multitude of structured noise with 74.25% accuracy.