Empirical evidence suggests that individuals selectively avoid information depending on past choices. We address these findings by studying an agent whose choice behavior can be modeled as if she trades off two conflicting effects of information. The first is a psychological cost from the regret about past choices that are revealed to be suboptimal by the information, whereas the second is the instrumental value of information for making better-informed choices in the future. Our main axioms reflect the agent's desire to have fewer options before the arrival of information and to have more options after the arrival of information. We also posit axioms that connect the agent's consumption choice with her information choice. We show that all parameters can be uniquely identified from the choice behavior. We also provide comparative statics on the agent's information aversion attitude.

The advent of the Internet era has led to an explosive growth in the Electronic Health Records (EHR) in the past decades. The EHR data can be regarded as a collection of clinical events, including laboratory results, medication records, physiological indicators, etc, which can be used for clinical outcome prediction tasks to support constructions of intelligent health systems. Learning patient representation from these clinical events for the clinical outcome prediction is an important but challenging step. Most related studies transform EHR data of a patient into a sequence of clinical events in temporal order and then use sequential models to learn patient representations for outcome prediction. However, clinical event sequence contains thousands of event types and temporal dependencies. We further make an observation that clinical events occurring in a short period are not constrained by any temporal order but events in a long term are influenced by temporal dependencies. The multi-scale temporal property makes it difficult for traditional sequential models to capture the short-term co-occurrence and the long-term temporal dependencies in clinical event sequences. In response to the above challenges, this paper proposes a Multilevel Representation Model (MRM). MRM first uses a sparse attention mechanism to model the short-term co-occurrence, then uses interval-based event pooling to remove redundant information and reduce sequence length and finally predicts clinical outcomes through Long Short-Term Memory (LSTM). Experiments on real-world datasets indicate that our proposed model largely improves the performance of clinical outcome prediction tasks using EHR data.

The availability of a large amount of electronic health records (EHR) provides huge opportunities to improve health care service by mining these data. One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according to patients' history records. This paper develops deep learning techniques for clinical endpoint prediction, which are effective in many practical applications. However, the problem is very challenging since patients' history records contain multiple heterogeneous temporal events such as lab tests, diagnosis, and drug administrations. The visiting patterns of different types of events vary significantly, and there exist complex nonlinear relationships between different events. In this paper, we propose a novel model for learning the joint representation of heterogeneous temporal events. The model adds a new gate to control the visiting rates of different events which effectively models the irregular patterns of different events and their nonlinear correlations. Experiment results with real-world clinical data on the tasks of predicting death and abnormal lab tests prove the effectiveness of our proposed approach over competitive baselines.

Autonomous vehicles are expected to be a disruptive technology that has the potential to revolutionize the human mobility. However, the recent research progress on intra-vehicle network (e.g., the revealing of a series of security vulnerabilities of CAN design) has demonstrated that the security issue still represents one of the major challenges of future self-driving cars. In this study, we propose a novel edge based anomaly detection system, coined VeAnDe, which exploits edge based sensor data fusion to identify the anomaly events. VeAnDe analyzes pair-wise correlations between different intra-vehicle sensors, and utilizes these correlations to examine whether an anomaly has occurred within the vehicle. More specifically, the pair-wise correlations are organized as ring architecture to reduce the computation overhead. Furthermore, the major components of VeAnDe are embedded in edge computing devices, which enables VeAnDe to be more efficient and privacy-preserving. We evaluate the performance of VeAnDe under different scenarios, and our experimental results demonstrate its feasibility and efficiency.

With the prevalent of smart devices and home automations, voice command has become a popular User Interface (UI) channel in the IoT environment. Although Voice Control System (VCS) has the advantages of great convenience, it is extremely vulnerable to the spoofing attack (e.g., replay attack, hidden/inaudible command attack) due to its broadcast nature. In this study, we present WiVo, a device-free voice liveness detection system based on the prevalent wireless signals generated by IoT devices without any additional devices or sensors carried by the users. The basic motivation of WiVo is to distinguish the authentic voice command from a spoofed one via its corresponding mouth motions, which can be captured and recognized by wireless signals. To achieve this goal, WiVo builds a theoretical model to characterize the correlation between wireless signal dynamics and the user's voice syllables. WiVo extracts the unique features from both voice and wireless signals, and then calculates the consistency between these different types of signals in order to determine whether the voice command is generated by the authentic user of VCS or an adversary. To evaluate the effectiveness of WiVo, we build a testbed based on Samsung SmartThings framework and include WiVo as a new application, which is expected to significantly enhance the security of the existing VCS. We have evaluated WiVo with 6 participants and different voice commands. Experimental evaluation results demonstrate that WiVo achieves the overall 99% detection rate with 1% false accept rate and has a low latency.