为探索出适用于一流本科课程教学的实践方式,文章针对"微机原理"实验课程,提出基于Visual Studio Code的实验教学模式。新模式弥补了传统实验教学的不足,能有效增强学生编写代码的兴趣,满足个性化需求,提高编程效率,提升教学...为探索出适用于一流本科课程教学的实践方式,文章针对"微机原理"实验课程,提出基于Visual Studio Code的实验教学模式。新模式弥补了传统实验教学的不足,能有效增强学生编写代码的兴趣,满足个性化需求,提高编程效率,提升教学质量。文章从实验项目安排、编程软件安装和实验操作等方面多角度介绍新模式对实验教学的支撑作用,为软件编程方面的实验教学工作提供新思路,进一步推进先进信息技术与实验教学的深度融合。展开更多
A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion,while simultaneously receiving stimuli from the environment and control...A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion,while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body.Incoming visual information can be processed by the brain in millisecond intervals.The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation.Thus,the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike.Closed-loop computation in a neuroprosthesis includes two stages:encoding a stimulus as a neuronal signal,and decoding it back into a stimulus.In this paper,we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos.We hypothesize that in order to obtain a better understanding of the computational principles in the retina,a hypercircuit view of the retina is necessary,in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina.The different building blocks of the retina,which include a diversity of cell types and synaptic connections-both chemical synapses and electrical synapses(gap junctions)-make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes.An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.展开更多
Due to the diversity and unpredictability of changes in malicious code,studying the traceability of variant families remains challenging.In this paper,we propose a GAN-EfficientNetV2-based method for tracing families ...Due to the diversity and unpredictability of changes in malicious code,studying the traceability of variant families remains challenging.In this paper,we propose a GAN-EfficientNetV2-based method for tracing families of malicious code variants.This method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code images.The method includes a lightweight classifier and a simulator.The classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile,embedded,and other devices.The simulator utilizes an enhanced generative adversarial network to simulate different variants of malicious code and generates datasets to validate the model’s performance.This process helps identify model vulnerabilities and security risks,facilitating model enhancement and development.The classifier achieves 98.61%and 97.59%accuracy on the MMCC dataset and Malevis dataset,respectively.The simulator’s generated image of malicious code variants has an FID value of 155.44 and an IS value of 1.72±0.42.The classifier’s accuracy for tracing the family of malicious code variants is as high as 90.29%,surpassing that of mainstream neural network models.This meets the current demand for high generalization and anti-obfuscation abilities in malicious code classification models due to the rapid evolution of malicious code.展开更多
文摘为探索出适用于一流本科课程教学的实践方式,文章针对"微机原理"实验课程,提出基于Visual Studio Code的实验教学模式。新模式弥补了传统实验教学的不足,能有效增强学生编写代码的兴趣,满足个性化需求,提高编程效率,提升教学质量。文章从实验项目安排、编程软件安装和实验操作等方面多角度介绍新模式对实验教学的支撑作用,为软件编程方面的实验教学工作提供新思路,进一步推进先进信息技术与实验教学的深度融合。
基金supported by the National Basic Research Program of China(2015CB351806)the National Natural Science Foundation of China(61806011,61825101,61425025,and U1611461)+4 种基金the National Postdoctoral Program for Innovative Talents(BX20180005)the China Postdoctoral Science Foundation(2018M630036)the International Talent Exchange Program of Beijing Municipal Commission of Science and Technology(Z181100001018026)the Zhejiang Lab(2019KC0AB03 and 2019KC0AD02)the Royal Society Newton Advanced Fellowship(NAF-R1-191082).
文摘A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion,while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body.Incoming visual information can be processed by the brain in millisecond intervals.The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation.Thus,the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike.Closed-loop computation in a neuroprosthesis includes two stages:encoding a stimulus as a neuronal signal,and decoding it back into a stimulus.In this paper,we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos.We hypothesize that in order to obtain a better understanding of the computational principles in the retina,a hypercircuit view of the retina is necessary,in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina.The different building blocks of the retina,which include a diversity of cell types and synaptic connections-both chemical synapses and electrical synapses(gap junctions)-make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes.An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.
基金support this work is the Key Research and Development Program of Heilongjiang Province,specifically Grant Number 2023ZX02C10.
文摘Due to the diversity and unpredictability of changes in malicious code,studying the traceability of variant families remains challenging.In this paper,we propose a GAN-EfficientNetV2-based method for tracing families of malicious code variants.This method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code images.The method includes a lightweight classifier and a simulator.The classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile,embedded,and other devices.The simulator utilizes an enhanced generative adversarial network to simulate different variants of malicious code and generates datasets to validate the model’s performance.This process helps identify model vulnerabilities and security risks,facilitating model enhancement and development.The classifier achieves 98.61%and 97.59%accuracy on the MMCC dataset and Malevis dataset,respectively.The simulator’s generated image of malicious code variants has an FID value of 155.44 and an IS value of 1.72±0.42.The classifier’s accuracy for tracing the family of malicious code variants is as high as 90.29%,surpassing that of mainstream neural network models.This meets the current demand for high generalization and anti-obfuscation abilities in malicious code classification models due to the rapid evolution of malicious code.
