In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

With the rapid proliferation of IoT devices, our cyberspace is nowadays dominated by billions of low-cost computing nodes, which expose an unprecedented heterogeneity to our computing systems. Dynamic analysis, one of the most effective approaches to finding software bugs, has become paralyzed due to the lack of a generic emulator capable of running diverse previously-unseen firmware. In recent years, we have witnessed devastating security breaches targeting IoT devices. These security concerns have significantly hamstrung further evolution of IoT technology. In this work, we present Laelaps, a device emulator specifically designed to run diverse software on low-cost IoT devices. We do not encode into our emulator any specific information about a device. Instead, Laelaps infers the expected behavior of firmware via symbolic-execution-assisted peripheral emulation and generates proper inputs to steer concrete execution on the fly. This unique design feature makes Laelaps the first generic device emulator capable of running diverse firmware with no a priori knowledge about the target device. To demonstrate the capabilities of Laelaps, we deployed two popular dynamic analysis techniques---fuzzing testing and dynamic symbolic execution---on top of our emulator. We successfully identified both self-injected and real-world vulnerabilities.
Device-agnostic Firmware Execution

  In many applications, the training data for a machine learning task is partitioned across multiple nodes, and aggregating this data may be infeasible due to storage, communication, or privacy constraints. In this work, we present Good-Enough Model Spaces (GEMS), a novel framework for learning a global satisficing (i.e. "good-enough") model within a few communication rounds by carefully combining the space of local nodes' satisficing models. In experiments on benchmark and medical datasets, our approach outperforms other baseline aggregation techniques such as ensembling or model averaging, and performs comparably to the ideal non-distributed models.

We present Good-Enough Model Spaces (GEMS), a framework for learning an aggregate model over distributed nodes within a small number of communication rounds.

We develop a reinforcement learning based search assistant which can assist users through a set of actions and sequence of interactions to enable them realize their intent. Our approach caters to subjective search where the user is seeking digital assets such as images which is fundamentally different from the tasks which have objective and limited search modalities. Labeled conversational data is generally not available in such search tasks and training the agent through human interactions can be time consuming. We propose a stochastic virtual user which impersonates a real user and can be used to sample user behavior efficiently to train the agent which accelerates the bootstrapping of the agent. We develop A3C algorithm based context preserving architecture which enables the agent to provide contextual assistance to the user. We compare the A3C agent with Q-learning and evaluate its performance on average rewards and state values it obtains with the virtual user in validation episodes. Our experiments show that the agent learns to achieve higher rewards and better states.

A Reinforcement Learning based conversational search assistant which provides contextual assistance in subjective search (like digital assets).