Context-based pairing solutions increase the usability of IoT device pairing by eliminating any human involvement in the pairing process. This is possible by utilizing on-board sensors (with same sensing modalities) to capture a common physical context (e.g., ambient sound via each device's microphone). However, in a smart home scenario, it is impractical to assume that all devices will share a common sensing modality. For example, a motion detector is only equipped with an infrared sensor while Amazon Echo only has microphones. In this paper, we develop a new context-based pairing mechanism called Perceptio that uses time as the common factor across differing sensor types. By focusing on the event timing, rather than the specific event sensor data, Perceptio creates event fingerprints that can be matched across a variety of IoT devices. We propose Perceptio based on the idea that devices co-located within a physically secure boundary (e.g., single family house) can observe more events in common over time, as opposed to devices outside. Devices make use of the observed contextual information to provide entropy for Perceptio's pairing protocol. We design and implement Perceptio, and evaluate its effectiveness as an autonomous secure pairing solution. Our implementation demonstrates the ability to sufficiently distinguish between legitimate devices (placed within the boundary) and attacker devices (placed outside) by imposing a threshold on fingerprint similarity. Perceptio demonstrates an average fingerprint similarity of 94.9% between legitimate devices while even a hypothetical impossibly well-performing attacker yields only 68.9% between itself and a valid device.

SUMMARYA joint model for a longitudinal biomarker and recurrent events is proposed. This general model accommodates the effects of covariates on the biomarker and event processes, the effects of accumulating event occurrences, and effects caused by interventions after each event occurrence. Association between the biomarker and recurrent event processes is captured through a latent class structure, which also serves to handle an underlying heterogeneous population. We use the EM algorithm for maximum likelihood estimation of the model parameters and a penalized likelihood measure to determine the number of latent classes. This joint model is validated by simulation and illustrated with a data set from epileptic seizure study.

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A polymeric gas separation membrane utilizing polybenzimidazole based on 4,4 0 -(hexafluoroisopropylidene)bis(benzoic acid) was prepared. The synthesized membrane has an effective permeating area of 8.3 cm 2 and a thickness of 30 6 2 mm. Gas permeation properties of the membrane were determined using H 2 , CO 2 , CO, and N 2 at temperatures ranging from 248C to 2008C. The PBI-HFA membranes not only exhibited excellent H 2 permeability, but it also displayed superior gas separation performance particularly for H 2 /N 2 and H 2 /CO 2 . The permeation parameters for both permeability and selectivity [P H2 and a(H 2 /N 2 ); P H2 and a(H 2 / CO 2 )] obtained for the new material were found to be dependent on trans-membrane pressure difference as well as temperature, and were found to surpass those reported by Robeson in

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