Design of Automation Environment for Analyzing Various IoT Malware

With the increasing proliferation of IoT systems, the security of IoT systems has become very important to individuals and businesses. IoT malware has been increasing exponentially since the emergence of Mirai in 2016. Because the IoT system environment is diverse, IoT malware also has various environments. In the case of existing analysis systems, there is no environment for dynamic analysis by running IoT malware of various architectures. It is inefficient in terms of time and cost to build an environment that analyzes malware one by one for analysis. The purpose of this paper is to improve the problems and limitations of the existing analysis system and provide an environment to analyze a large amount of IoT malware. Using existing open source analysis tools suitable for various IoT malicious codes and QEMU, a virtualization software, the environment in which the actual malicious code will run is built, and the library or system call that is actually called is statically and dynamically analyzed. In the text, the analysis system is applied to the actual collected malicious code to check whether it is analyzed and derive statistics. Information on the architecture of malicious code, attack method, command used, and access path can be checked, and this information can be used as a basis for malicious code detection research or classification research. The advantages are described of the system designed compared to the most commonly used automated analysis tools and improvements to existing limitations

A Low-Latency Streaming On-Device Automatic Speech Recognition System Using a CNN Acoustic Model on FPGA and a Language Model on Smartphone

This paper presents a low-latency streaming on-device automatic speech recognition system for inference. It consists of a hardware acoustic model implemented in a field-programmable gate array, coupled with a software language model running on a smartphone. The smartphone works as the master of the automatic speech recognition system and runs a three-gram language model on the acoustic model output to increase accuracy. The smartphone calculates and sends the Mel-spectrogram of an audio stream with 80 ms unit input from the built-in microphone of the smartphone to the field-programmable gate array every 80 ms. After ~35 ms, the field-programmable gate array sends the calculated word-piece probability to the smartphone, which runs the language model and generates the text output on the smartphone display. The worst-case latency from the audio-stream start time to the text output time was measured as 125.5 ms. The real-time factor is 0.57. The hardware acoustic model is derived from a time-depth-separable convolutional neural network model by reducing the number of weights from 115 M to 9.3 M to decrease the number of multiply-and-accumulate operations by two orders of magnitude. Additionally, the unit input length is reduced from 1000 ms to 80 ms, and to minimize the latency, no future data are used. The hardware acoustic model uses an instruction-based architecture that supports any sequence of convolutional neural network, residual network, layer normalization, and rectified linear unit operations. For the LibriSpeech test-clean dataset, the word error rate of the hardware acoustic model was 13.2% and for the language model, it was 9.1%. These numbers were degraded by 3.4% and 3.2% from the original convolutional neural network software model due to the reduced number of weights and the lowering of the floating-point precision from 32 to 16 bit. The automatic speech recognition system has been demonstrated successfully in real application scenarios

A Low-Latency Streaming On-Device Automatic Speech Recognition System Using a CNN Acoustic Model on FPGA and a Language Model on Smartphone

This paper presents a low-latency streaming on-device automatic speech recognition system for inference. It consists of a hardware acoustic model implemented in a field-programmable gate array, coupled with a software language model running on a smartphone. The smartphone works as the master of the automatic speech recognition system and runs a three-gram language model on the acoustic model output to increase accuracy. The smartphone calculates and sends the Mel-spectrogram of an audio stream with 80 ms unit input from the built-in microphone of the smartphone to the field-programmable gate array every 80 ms. After ~35 ms, the field-programmable gate array sends the calculated word-piece probability to the smartphone, which runs the language model and generates the text output on the smartphone display. The worst-case latency from the audio-stream start time to the text output time was measured as 125.5 ms. The real-time factor is 0.57. The hardware acoustic model is derived from a time-depth-separable convolutional neural network model by reducing the number of weights from 115 M to 9.3 M to decrease the number of multiply-and-accumulate operations by two orders of magnitude. Additionally, the unit input length is reduced from 1000 ms to 80 ms, and to minimize the latency, no future data are used. The hardware acoustic model uses an instruction-based architecture that supports any sequence of convolutional neural network, residual network, layer normalization, and rectified linear unit operations. For the LibriSpeech test-clean dataset, the word error rate of the hardware acoustic model was 13.2% and for the language model, it was 9.1%. These numbers were degraded by 3.4% and 3.2% from the original convolutional neural network software model due to the reduced number of weights and the lowering of the floating-point precision from 32 to 16 bit. The automatic speech recognition system has been demonstrated successfully in real application scenarios

Multiparity increases the risk of diabetes by impairing the proliferative capacity of pancreatic β cells

Abstract Pregnancy imposes a substantial metabolic burden on women, but little is known about whether or how multiple pregnancies increase the risk of maternal postpartum diabetes. In this study, we assessed the metabolic impact of multiple pregnancies in humans and in a rodent model. Mice that underwent multiple pregnancies had increased adiposity, but their glucose tolerance was initially improved compared to those of age-matched virgin mice. Later, however, insulin resistance developed over time, but insulin secretory function and compensatory pancreatic β cell proliferation were impaired in multiparous mice. The β cells of multiparous mice exhibited aging features, including telomere shortening and increased expression of Cdkn2a. Single-cell RNA-seq analysis revealed that the β cells of multiparous mice exhibited upregulation of stress-related pathways and downregulation of cellular respiration- and oxidative phosphorylation-related pathways. In humans, women who delivered more than three times were more obese, and their plasma glucose concentrations were elevated compared to women who had delivered three or fewer times, as assessed at 2 months postpartum. The disposition index, which is a measure of the insulin secretory function of β cells, decreased when women with higher parity gained body weight after delivery. Taken together, our findings indicate that multiple pregnancies induce cellular stress and aging features in β cells, which impair their proliferative capacity to compensate for insulin resistance