SAFE: A Neural Survival Analysis Model for Fraud Early Detection

Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the existing approaches adopt classifiers to predict fraudsters given their activity sequences along time. The main drawback of classification models is that the prediction results between consecutive timestamps are often inconsistent. In this paper, we propose a survival analysis based fraud early detection model, SAFE, which maps dynamic user activities to survival probabilities that are guaranteed to be monotonically decreasing along time. SAFE adopts recurrent neural network (RNN) to handle user activity sequences and directly outputs hazard values at each timestamp, and then, survival probability derived from hazard values is deployed to achieve consistent predictions. Because we only observe the user suspended time instead of the fraudulent activity time in the training data, we revise the loss function of the regular survival model to achieve fraud early detection. Experimental results on two real world datasets demonstrate that SAFE outperforms both the survival analysis model and recurrent neural network model alone as well as state-of-the-art fraud early detection approaches.Comment: To appear in AAAI-201

Dynamic Fraud Detection via Sequential Modeling

The impacts of information revolution are omnipresent from life to work. The web services have signicantly changed our living styles in daily life, such as Facebook for communication and Wikipedia for knowledge acquirement. Besides, varieties of information systems, such as data management system and management information system, make us work more eciently. However, it is usually a double-edged sword. With the popularity of web services, relevant security issues are arising, such as fake news on Facebook and vandalism on Wikipedia, which denitely impose severe security threats to OSNs and their legitimate participants. Likewise, oce automation incurs another challenging security issue, insider threat, which may involve the theft of condential information, the theft of intellectual property, or the sabotage of computer systems. A recent survey says that 27% of all cyber crime incidents are suspected to be committed by the insiders. As a result, how to ag out these malicious web users or insiders is urgent. The fast development of machine learning (ML) techniques oers an unprecedented opportunity to build some ML models that can assist humans to detect the individuals who conduct misbehaviors automatically. However, unlike some static outlier detection scenarios where ML models have achieved promising performance, the malicious behaviors conducted by humans are often dynamic. Such dynamic behaviors lead to various unique challenges of dynamic fraud detection: Unavailability of sucient labeled data - traditional machine learning approaches usually require a balanced training dataset consisting of normal and abnormal samples. In practice, however, there are far fewer abnormal labeled samples than normal ones. Lack of high quality labels - the labeled training records often have the time gap between the time that fraudulent users commit fraudulent actions and the time that they are suspended by the platforms. Time-evolving nature - users are always changing their behaviors over time. To address the aforementioned challenges, in this dissertation, we conduct a systematic study for dynamic fraud detection, with a focus on: (1) Unavailability of labeled data: we present (a) a few-shot learning framework to handle the extremely imbalanced dataset that abnormal samples are far fewer than the normal ones and (b) a one-class fraud detection method using a complementary GAN (Generative Adversarial Network) to adaptively generate potential abnormal samples; (2) Lack of high-quality labels: we develop a neural survival analysis model for fraud early detection to deal with the time gap; (3) Time-evolving nature: we propose (a) a hierarchical neural temporal point process model and (b) a dynamic Dirichlet marked Hawkes process model for fraud detection

One-Class Adversarial Nets for Fraud Detection

Many online applications, such as online social networks or knowledge bases, are often attacked by malicious users who commit different types of actions such as vandalism on Wikipedia or fraudulent reviews on eBay. Currently, most of the fraud detection approaches require a training dataset that contains records of both benign and malicious users. However, in practice, there are often no or very few records of malicious users. In this paper, we develop one-class adversarial nets (OCAN) for fraud detection using training data with only benign users. OCAN first uses LSTM-Autoencoder to learn the representations of benign users from their sequences of online activities. It then detects malicious users by training a discriminator with a complementary GAN model that is different from the regular GAN model. Experimental results show that our OCAN outperforms the state-of-the-art one-class classification models and achieves comparable performance with the latest multi-source LSTM model that requires both benign and malicious users in the training phase.Comment: Update Fig 2, add Fig 7, and add reference

Improving Pneumonia Classification and Lesion Detection Using Spatial Attention Superposition and Multilayer Feature Fusion

Pneumonia is a severe inflammation of the lung that could cause serious complications. Chest X-rays (CXRs) are commonly used to make a diagnosis of pneumonia. In this paper, we propose a deep-learning-based method with spatial attention superposition (SAS) and multilayer feature fusion (MFF) to facilitate pneumonia diagnosis based on CXRs. Specifically, an SAS module, which takes advantage of the channel and spatial attention mechanisms, was designed to identify intrinsic imaging features of pneumonia-related lesions and their locations, and an MFF module was designed to harmonize disparate features from different channels and emphasize important information. These two modules were concatenated to extract critical image features serving as the basis for pneumonia diagnosis. We further embedded the proposed modules into a baseline neural network and developed a model called SAS-MFF-YOLO to diagnose pneumonia. To validate the effectiveness of our model, extensive experiments were conducted on two CXR datasets provided by the Radiological Society of North America (RSNA) and the AI Research Institute. SAS-MFF-YOLO achieved a precision of 88.1%, a recall of 98.2% for pneumonia classification and an AP50 of 99% for lesion detection on the AI Research Institute dataset. The visualization of intermediate feature maps showed that our method could facilitate uncovering pneumonia-related lesions in CXRs. Our results demonstrated that our approach could be used to enhance the performance of the overall pneumonia detection on CXR imaging

What Truly Matters in Trajectory Prediction for Autonomous Driving?

In the autonomous driving system, trajectory prediction plays a vital role in ensuring safety and facilitating smooth navigation. However, we observe a substantial discrepancy between the accuracy of predictors on fixed datasets and their driving performance when used in downstream tasks. This discrepancy arises from two overlooked factors in the current evaluation protocols of trajectory prediction: 1) the dynamics gap between the dataset and real driving scenario; and 2) the computational efficiency of predictors. In real-world scenarios, prediction algorithms influence the behavior of autonomous vehicles, which, in turn, alter the behaviors of other agents on the road. This interaction results in predictor-specific dynamics that directly impact prediction results. As other agents' responses are predetermined on datasets, a significant dynamics gap arises between evaluations conducted on fixed datasets and actual driving scenarios. Furthermore, focusing solely on accuracy fails to address the demand for computational efficiency, which is critical for the real-time response required by the autonomous driving system. Therefore, in this paper, we demonstrate that an interactive, task-driven evaluation approach for trajectory prediction is crucial to reflect its efficacy for autonomous driving

ESSM: An Extractive Summarization Model with Enhanced Spatial-Temporal Information and Span Mask Encoding

Extractive reading comprehension is to extract consecutive subsequences from a given article to answer the given question. Previous work often adopted Byte Pair Encoding (BPE) that could cause semantically correlated words to be separated. Also, previous features extraction strategy cannot effectively capture the global semantic information. In this paper, an extractive summarization model is proposed with enhanced spatial-temporal information and span mask encoding (ESSM) to promote global semantic information. ESSM utilizes Embedding Layer to reduce semantic segmentation of correlated words, and adopts TemporalConvNet Layer to relief the loss of feature information. The model can also deal with unanswerable questions. To verify the effectiveness of the model, experiments on datasets SQuAD1.1 and SQuAD2.0 are conducted. Our model achieved an EM of 86.31% and a F1 score of 92.49% on SQuAD1.1 and the numbers are 80.54% and 83.27% for SQuAD2.0. It was proved that the model is effective for extractive QA task

Impaired Conflict Monitoring to Food Cues in Women Who Binge Eat

Previous research demonstrated the associations between cognitive biases toward food cues and binge eating (BE) behavior. To determine the characteristics of conflict monitoring to food cues in women who binge eat and non-eating disordered controls, a flanker task featured high-caloric food and low-caloric food images was used to examine conflict monitoring with measures of accuracy and reaction time. Women who binge eat displayed longer reaction times (RTs) to incongruent trials (i.e., flanked by pictures from the different category) than to congruent trials (i.e., flanked by pictures from the same category), while controls showed no such difference. This finding demonstrated women who binge eat displayed a general flanker effect toward food-related stimuli compared to controls. Faster reaction times in response to high-caloric food images disturbed by low-caloric food images predicted lower self-reported motor impulsiveness in the women who binge eat, but not in controls. These data suggest a relative conflict monitoring deficit in women with BE pathology