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With the advent of Digital Therapeutics (DTx), the development of software as a medical device (SaMD) for mobile and wearable devices has gained significant attention in recent years. Existing DTx evaluations, such as randomized clinical trials, mostly focus on verifying the effectiveness of DTx products. To acquire a deeper understanding of DTx engagement and behavioral adherence, beyond efficacy, a large amount of contextual and interaction data from mobile and wearable devices during field deployment would be required for analysis. In this work, the overall flow of the data-driven DTx analytics is reviewed to help researchers and practitioners to explore DTx datasets, to investigate contextual patterns associated with DTx usage, and to establish the (causal) relationship of DTx engagement and behavioral adherence. This review of the key components of data-driven analytics provides novel research directions in the analysis of mobile sensor and interaction datasets, which helps to iteratively improve the receptivity of existing DTx., Comment: This paper has been accepted by the IEEE/CAA Journal of Automatica Sinica

In the zettabyte era, per-flow measurement becomes more challenging owing to the growth of both traffic volumes and the number of flows. Also, swiftness of detection of anomalies (e.g., DDoS attack, congestion, link failure, and so on) becomes paramount. For fast and accurate anomaly detection, managing an accurate working set of active flows (WSAF) from massive volumes of packet influxes at line rates is a key challenge. WSAF is usually located in a very fast but expensive memory, such as TCAM or SRAM, and thus the number of entries to be stored is quite limited. To cope with the scalability issue of WSAF, we propose to use In-DRAM WSAF with scales, and put a compact data structure called FlowRegulator in front of WSAF to compensate for DRAM's slow access time by substantially reducing massive influxes to WSAF without compromising measurement accuracy. We prototype and evaluated our system in a large scale real-world experiment (connected to monitoring port of our campus main gateway router for 113 hours, and capturing 122.3 million flows). As one key application, FlowRegulator detected heavy hitters with 99.8% accuracy., Comment: Accepted as a poster for ISOC NDSS 2019

Buffering architectures and policies for their efficient management constitute one of the core ingredients of a network architecture. In this work we introduce a new specification language, BASEL, that allows to express virtual buffering architectures and management policies representing a variety of economic models. BASEL does not require the user to implement policies in a high-level language; rather, the entire buffering architecture and its policy are reduced to several comparators and simple functions. We show examples of buffering architectures in BASEL and demonstrate empirically the impact of various settings on performance., Comment: 11 pages, 11 figures, 2 tables

Securely sharing and managing personal content is a challenging task in multi-device environments. In this paper, we design and implement a new platform called Personal Content Networking (PCN). Our work is inspired by Content-Centric Networking (CCN) because we aim to enable access to personal content using its name instead of its location. The unique challenge of PCN is to support secure file operations such as replication, updates, and access control over distributed untrusted devices. The primary contribution of this work is the design and implementation of a secure content management platform that supports secure updates, replications, and fine-grained content-centric access control of files. Furthermore, we demonstrate its feasibility through a prototype implementation on the CCNx skeleton.