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Prediction of surface topography in milling usually requires complex kinematics and dynamics modeling of the milling process, plus solving physical models of surface generation is a daunting task. This paper presents a multimodal data-driven machine learning (ML) method to predict milled surface topography. The proposed method predicts the height map of the surface topography by fusing process parameters and in-process acoustic information as model inputs. This method has been validated by comparing the predicted surface topography with the measured data. 

IoT messaging protocols are critical to connecting users and IoT devices. Among all the protocols, the Message Queuing and Telemetry Transport (MQTT) is arguably the most widely used. Mainstream IoT platforms leverage MQTT brokers, server side implementation of MQTT, to enable and mediate user-device communication (e.g., the transmission of control commands). There are over 70 open-source MQTT brokers, which have been widely adopted in production. Any security defects in those open-source MQTT brokers easily get into many endors' IoT deployments with amplified impacts, inevitably endangering the security of IoT applications and millions of users. We report the first systematic security analysis of open-source MQTT brokers in the wild. To enable the analysis, we designed and developed MQTTactic, a semiautomatic tool that can formally verify MQTT broker implementations based on generated security properties. MQTTactic is based on static code analysis, formal modeling, and automated model checking (with off-the-shelf model checker Spin). In designing MQTTactic, we characterize and address key technical challenges. MQTTactic currently focuses on authorization-related properties, and discovered 7 novel, zero-day flaws practically enabling serious, unauthorized access. We reported all flaws to related parties, who acknowledged the issues and have been taking actions to fix them. Our thorough evaluation shows that MQTTactic is effective and practical. 

The continuous growth of CNN complexity not only intensifies the need for hardware acceleration but also presents a huge challenge. That is, the solution space for CNN hardware design and dataflow mapping becomes enormously large besides the fact that it is discrete and lacks a well behaved structure. Most previous works either are stochastic metaheuristics, such as genetic algorithm, which are typically very slow for solving large problems, or rely on expensive sampling, e.g., Gumbel Softmax-based differentiable optimization and Bayesian optimization. We propose an analytical model for evaluating power and performance of CNN hardware design and dataflow solutions. Based on this model, we introduce a co-optimization method consisting of nonlinear programming and parallel local search. A key innovation in this model is its matrix form, which enables the use of deep learning toolkit for highly efficient computations of power/performance values and gradients in the optimization. In handling power-performance tradeoff, our method can lead to better solutions than minimizing a weighted sum of power and latency. The average relative error of our model compared with Timeloop is as small as 1%. Compared to state-of-the-art methods, our approach achieves solutions with up to 1.7 × shorter inference latency, 37.5% less power consumption, and 3 × less area on ResNet 18. Moreover, it provides a 6.2 × speedup of optimization