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.

Input: Consider Input: The application of deep recurrent networks to audio transcription has led to impressive gains in automatic speech recognition (ASR) systems. Many have demonstrated that small adversarial perturbations can fool deep neural networks into incorrectly predicting a specified target with high confidence. Current work on fooling ASR systems have focused on white-box attacks, in which the model architecture and parameters are known. In this paper, we adopt a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the task. We achieve a 89.25% targeted attack similarity after 3000 generations while maintaining 94.6% audio file similarity.

Output: We present a novel black-box targeted attack that is able to fool state of the art speech to text transcription.

Input: Consider Input: 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.

Output: Device-agnostic Firmware Execution

Input: Consider Input: Deep learning has made remarkable achievement in many fields. However, learning
the parameters of neural networks usually demands a large amount of labeled
data. The algorithms of deep learning, therefore, encounter difficulties when applied
to supervised learning where only little data are available. This specific task
is called few-shot learning. To address it, we propose a novel algorithm for fewshot
learning using discrete geometry, in the sense that the samples in a class are
modeled as a reduced simplex. The volume of the simplex is used for the measurement
of class scatter. During testing, combined with the test sample and the
points in the class, a new simplex is formed. Then the similarity between the test
sample and the class can be quantized with the ratio of volumes of the new simplex
to the original class simplex. Moreover, we present an approach to constructing
simplices using local regions of feature maps yielded by convolutional neural networks.
Experiments on Omniglot and miniImageNet verify the effectiveness of
our simplex algorithm on few-shot learning.
Output: A simplex-based geometric method is proposed to cope with few-shot learning problems.