A central challenge in mechanizing the meta-theory of substructural languages is modeling contexts. Although various ad hoc approaches to this problem exist, we lack a set of good practices and a simple infrastructure that can be leveraged for mechanizing a wide range of substructural systems. In this work, we describe Contexts as Resource Vectors (CARVe), a general syntactic infrastructure for managing substructural contexts, where elements are annotated with tags from a resource algebra denoting their availability. Assumptions persist as contexts are manipulated since we model resource consumption by changing their tags. We may thus define relations between substructural contexts via simultaneous substitutions without the need to split them. Moreover, we establish a series of algebraic properties about context operations that are typically required to carry out proofs in practice. CARVe is implemented in the proof assistant Beluga. To illustrate best practices for using our infrastructure, we give a detailed reformulation of the linear sequent calculus and bidirectional linear λ-calculus in terms of CARVe’s context operations and prove their equivalence using the aforementioned algebraic properties. In addition, we apply CARVe to mechanize a diverse set of systems, from the affine λ-calculus to the session-typed process calculus CP, giving us confidence that CARVe is sufficiently general to mechanize a broad range of substructural systems.

We introduce FuSes , a Fu nctional programming language that integrates Ses sion-typed concurrent process calculus code. A functional layer sits on top of a session-typed process layer. To generate and reason about open session-typed processes, the functional layer uses the contextual box modality extended with linear channel contexts. Due to the fundamental differences between the operational semantics of the functional layer and the concurrent semantics of processes, we bridge the two layers using a set of primitives to run and observe the behavior of closed processes within the functional layer. In addition, FuSes supports code analysis and manipulation of open session-typed process code. To showcase its benefit to programmers, we implement well-known optimizations, such as batch optimizations, as type-safe metaprograms over concurrent processes. Our technical contributions include a type system for FuSes , an operational semantics, a proof of its type safety, and an implementation.

Session types employ a linear type system that ensures that communication channels cannot be implicitly copied or discarded. As a result, many mechanizations of these systems require modeling channel contexts and carefully ensuring that they treat channels linearly. We demonstrate a technique that localizes linearity conditions as additional predicates embedded within type judgments, which allows us to use structural typing contexts instead of linear ones. This technique is especially relevant when leveraging (weak) higher-order abstract syntax to handle channel mobility and the intricate binding structures that arise in session-typed systems. Following this approach, we mechanize a session-typed system based on classical linear logic and its type preservation proof in the proof assistant Beluga, which uses the logical framework LF as its encoding language. We also prove adequacy for our encoding. This shows the tractability and effectiveness of our approach in modelling substructural systems such as session-typed languages.

Legacy-infrared (IR) devices are pervasively used. They are often controlled by IR remotes and cannot be controlled over the Internet. A trustworthy and cost-effective smart IR system that is able to change an IR controllable device into a smart Internet of Things (IoT) device and interconnect them for smart city/home applications is offered in this article. First, a printed circuit board (PCB) consisting of an IR receiver and multiple IR transmitters side by side which are capable of transmitting about 20 m indoors is designed and implemented. This IR transceiver board is the first of its kind. Second, the IR transceiver can be linked up with a Raspberry Pi, for which we develop two software tools, recording and replaying any IR signals so as to put the corresponding IR device in control. Third, a smartphone can be connected to the Pi by means of a message queuing telemetry transport (MQTT) cloud server so that the commands can be sent by the smartphone to the legacy IR device over the Internet. We have also identified the deficiency of TLS mutual authentication implemented by the popular MQTT open-source package Mosquitto for a trustworthy IoT system and patched the system. We analyze the factors that affect the IR signal transmission distance, discuss the security concerns of our IR transceiver, and illustrate the scenarios for attacks. For instance, TV can be turned off remotely by a drone equipped with the transceiver.

In this paper, we introduce a computer vision-based attack using stereo cameras against authentication approaches for touch-enabled devices. In the attack, an attacker uses a stereo camera (such as one on the HTC Evo 3D smartphone) and takes a video of a victim entering passwords on the touch screen of the victim's mobile device. We focus on challenging scenarios where the victim holds the device up and the attacker cannot see the victim's fingertip or the device screen. Since the stereo camera provides depth and distance information of objects in video frames, we can build a 3D scene to analyze the victim's hand movement and automatically recover the victim's passcode. The 3D vision attack is stealthy in daily settings like a classroom or a coffee shop since the attacker does not need to take a suspicious angle and see the touch screen of the victim. Without loss of generality, we use graphical passwords as an example and perform extensive experiments to demonstrate the effectiveness of the attack. The success rate of the 3D vision attack reaches 90% when the camera is across a table from a victim in a typical gathering scene.

To protect users from numerous password inference attacks, we invent a novel context aware privacy enhancing keyboard (PEK) for Android touch-based devices. Usually PEK would show a QWERTY keyboard when users input text like an email or a message. Nevertheless, whenever users enter a password in the input box on his or her touch-enabled device, a keyboard will be shown to them with the positions of the characters shuffled at random. PEK has been released on the Google Play since 2014. However, the number of installations has not lived up to our expectation. For the purpose of usable security and privacy, we designed a two-stage usability test and performed two rounds of iterative usability testing in 2016 and 2017 summer with continuous improvements of PEK. The observations from the usability testing are educational: (1) convenience plays a critical role when users select an input method; (2) people think those attacks that PEK prevents are remote from them.