Learning to detect and encode temporal regularities (TRs) in events is a prerequisite for human-like intelligence. These regularities should be formed from limited event samples and stored as easily retrievable representations.

Learning to detect and encode temporal regularities (TRs) in events is a prerequisite for human-like intelligence. These regularities should be formed from limited event samples and stored as easily retrievable representations.

ABSTRACT Survival analysis, a vital tool for predicting the time to event, has been used in many domains such as healthcare, criminal justice, and finance. Like classification tasks, survival analysis can exhibit bias against disadvantaged groups, often due to biases inherent in data or algorithms . Several studies in both the IS and CS communities have attempted to address fairness in survival analysis . However, existing methods often overlook the importance of prediction fairness at pre - defined evaluation time points, which is crucial in real - world applications where decision making often hinge s on specific time frames . To address this critical research gap, we introduce a new fairness concept: equalized odds (EO) in survival analysis, which emphasize s prediction fairness at pre - defined time points . To achieve th e EO fairness in survival analysis, we propose a Conditional Mutual Information Augmentation ( CMIA) approach, which features a novel fairness regularization term based on conditional mutual information and a n innovative censored data augmentation technique. Our CMIA approach can effectively balance prediction accuracy and fairness, and it is applicable to various survival models. W e evaluate the CMIA approach against several state - of - the - art methods within three different application domains, and the results demonstrate that CMIA consistently reduces prediction disparit y while maintaining good accuracy and significantly outperform s the other competing methods across multiple datasets and survival models (e.g., linear COX, deep AFT) . Keywords: survival analysis, equalized odds, fairness, pre - defined evaluation time points, conditional mutual information, cen sore d data augmentation 2 Fairness in Survival Analysis: A Novel Conditional Mutual Information Augmentation Approach 1. INTRODUCTION Survival analysis is a set of statistical methods designed to model data where the outcome of interest is the time to the occurrence of a particular event (P . It is widely applied across many domains, such as healthcare (Khuri et al., 2005; Reddy et al., 2015), education (Ameri et al., 2016), business intelligence (Li et al., 2016; Rakesh et al., 2016), etc . In these applications, survival analysis provide s likelihood estimation for the occurrence of event s over time, which is useful for a lot of crucial decision making.

Event prediction tasks often handle spatio-temporal data distributed in a large spatial area. Different regions in the area exhibit different characteristics while having latent correlations. This spatial heterogeneity and correlations greatly affect the spatio-temporal distributions of event occurrences, which has not been addressed by state-of-the-art models. Learning spatial dependencies of events in a continuous space is challenging due to its fine granularity and a lack of prior knowledge. In this work, we propose a novel Graph Spatio-Temporal Point Process (GSTPP) model for fine-grained event prediction. It adopts an encoder-decoder architecture that jointly models the state dynamics of spatially localized regions using neural Ordinary Differential Equations (ODEs). The state evolution is built on the foundation of a novel Self-Adaptive Anchor Graph (SAAG) that captures spatial dependencies. By adaptively localizing the anchor nodes in the space and jointly constructing the correlation edges between them, the SAAG enhances the model's ability of learning complex spatial event patterns. The proposed GSTPP model greatly improves the accuracy of fine-grained event prediction. Extensive experimental results show that our method greatly improves the prediction accuracy over existing spatio-temporal event prediction approaches.

Emergency events in a city cause considerable economic loss to individuals, their families, and the community. Accurate and timely prediction of events can help the emergency fire and rescue services in preparing for and mitigating the consequences of emergency events. In this paper, we present a systematic development of predictive models for various types of emergency events in the City of Edmonton, Canada. We present methods for (i) data collection and dataset development; (ii) descriptive analysis of each event type and its characteristics at different spatiotemporal levels; (iii) feature analysis and selection based on correlation coefficient analysis and feature importance analysis; and (iv) development of prediction models for the likelihood of occurrence of each event type at different temporal and spatial resolutions. We analyze the association of event types with socioeconomic and demographic data at the neighborhood level, identify a set of predictors for each event type, and develop predictive models with negative binomial regression. We conduct evaluations at neighborhood and fire station service area levels. Our results show that the models perform well for most of the event types with acceptable prediction errors for weekly and monthly periods. The evaluation shows that the prediction accuracy is consistent at the level of the fire station, so the predictions can be used in management by fire rescue service departments for planning resource allocation for these time periods. We also examine the impact of the COVID-19 pandemic on the occurrence of events and on the accuracy of event predictor models. Our findings show that COVID-19 had a significant impact on the performance of the event prediction models.

Human activities generate various event sequences such as taxi trip records, bike-sharing pick-ups, crime occurrence, and infectious disease transmission. The point process is widely used in many applications to predict such events related to human activities. However, point processes present two problems in predicting events related to human activities. First, recent high-performance point process models require the input of sufficient numbers of events collected over a long period (i.e., long sequences) for training, which are often unavailable in realistic situations. Second, the long-term predictions required in real-world applications are difficult. To tackle these problems, we propose a novel meta-learning approach for periodicity-aware prediction of future events given short sequences. The proposed method first embeds short sequences into hidden representations (i.e., task representations) via recurrent neural networks for creating predictions from short sequences. It then models the intensity of the point process by monotonic neural networks (MNNs), with the input being the task representations. We transfer the prior knowledge learned from related tasks and can improve event prediction given short sequences of target tasks. We design the MNNs to explicitly take temporal periodic patterns into account, contributing to improved long-term prediction performance. Experiments on multiple real-world datasets demonstrate that the proposed method has higher prediction performance than existing alternatives.

Recent advancements in Multimodal Large Language Models (MLLMs) have significantly enhanced the comprehension of multimedia content, bringing together diverse modalities such as text, images, and videos. However, a critical challenge faced by these models, especially when processing video inputs, is the occurrence of hallucinations - erroneous perceptions or interpretations, particularly at the event level. This study introduces an innovative method to address event-level hallucinations in MLLMs, focusing on specific temporal understanding in video content. Our approach leverages a novel framework that extracts and utilizes event-specific information from both the event query and the provided video to refine MLLMs' response. We propose a unique mechanism that decomposes on-demand event queries into iconic actions. Subsequently, we employ models like CLIP and BLIP2 to predict specific timestamps for event occurrences. Our evaluation, conducted using the Charades-STA dataset, demonstrates a significant reduction in temporal hallucinations and an improvement in the quality of event-related responses. This research not only provides a new perspective in addressing a critical limitation of MLLMs but also contributes a quantitatively measurable method for evaluating MLLMs in the context of temporal-related questions.

The growth of systems complexity increases the need of automated techniques dedicated to different log analysis tasks such as Log-based Anomaly Detection (LAD). The latter has been widely addressed in the literature, mostly by means of different deep learning techniques. Nevertheless, the focus on deep learning techniques results in less attention being paid to traditional Machine Learning (ML) techniques, which may perform well in many cases, depending on the context and the used datasets. Further, the evaluation of different ML techniques is mostly based on the assessment of their detection accuracy. However, this is is not enough to decide whether or not a specific ML technique is suitable to address the LAD problem. Other aspects to consider include the training and prediction time as well as the sensitivity to hyperparameter tuning. In this paper, we present a comprehensive empirical study, in which we evaluate different supervised and semi-supervised, traditional and deep ML techniques w.r.t. four evaluation criteria: detection accuracy, time performance, sensitivity of detection accuracy as well as time performance to hyperparameter tuning. The experimental results show that supervised traditional and deep ML techniques perform very closely in terms of their detection accuracy and prediction time. Moreover, the overall evaluation of the sensitivity of the detection accuracy of the different ML techniques to hyperparameter tuning shows that supervised traditional ML techniques are less sensitive to hyperparameter tuning than deep learning techniques. Further, semi-supervised techniques yield significantly worse detection accuracy than supervised techniques.