diff --git a/_posts/2016-10-26-welcome-to-my-blog.markdown b/_posts/2016-10-26-welcome-to-my-blog.markdown index 2c8eb9c..e5fb2fa 100644 --- a/_posts/2016-10-26-welcome-to-my-blog.markdown +++ b/_posts/2016-10-26-welcome-to-my-blog.markdown @@ -1,7 +1,7 @@ --- title: "Stop Talking is the worst title of one blog" date: 2016-10-26 22:50:33 -0400 -tags: Nonsense +tags: Diary --- diff --git a/_posts/2016-10-27-charles-is-not-a-good-tool.md b/_posts/2016-10-27-charles-is-not-a-good-tool.md index 71ae262..a9a7c84 100644 --- a/_posts/2016-10-27-charles-is-not-a-good-tool.md +++ b/_posts/2016-10-27-charles-is-not-a-good-tool.md @@ -1,8 +1,9 @@ --- title: "Using charles proxy to monitor mobile SSL traffics" date: 2016-10-27 22:50:33 -0400 -tags: Network +tags: ["Network", "Charles proxy"] author: Pengzhan Hao +cover: '/static/2021-12/charles-proxy-logo.png' --- In this blog, I will generally talk about how to use proper tools to monitor SSL traffics of a mobile devices. Currently, I only can dealing with those SSL traffics which use an obviously certification. Some applications may not using system root cert or they doesn't provide us a method to modify their own certs. For these situation, I still didn't find a good solutions for it. But I'll keep updating this if I get one. diff --git a/_posts/2016-10-28-some-of-my-previews-exper-work.md b/_posts/2016-10-28-some-of-my-previews-exper-work.md index ccc1a99..02c325f 100644 --- a/_posts/2016-10-28-some-of-my-previews-exper-work.md +++ b/_posts/2016-10-28-some-of-my-previews-exper-work.md @@ -1,7 +1,7 @@ --- title: "Some of my previews experiment works: 2016" date: 2016-10-28 12:27:33 -0400 -tags: Research +tags: ["Research", "Log", "Miscellanies"] author: Pengzhan Hao --- This blog contains only some basic record of my works. For some details, I will write a unique blog just for some specific topics. diff --git a/_posts/2017-07-08-intro-xv6.md b/_posts/2017-07-08-intro-xv6.md index 4887343..21b0474 100644 --- a/_posts/2017-07-08-intro-xv6.md +++ b/_posts/2017-07-08-intro-xv6.md @@ -1,7 +1,7 @@ --- title: "Xv6 introduction" date: 2017-07-28 14:56:55 -0400 -tags: xv6 +tags: ["Xv6", "Teaching", "Operating system"] author: Pengzhan Hao cover: '/static/2021-10/Xv6_LS_Command_Output.png' --- diff --git a/_posts/2020-09-15-generate-word-cloud-with-chinese-fenci.md b/_posts/2020-09-15-generate-word-cloud-with-chinese-fenci.md index 0b9dddd..17a3659 100644 --- a/_posts/2020-09-15-generate-word-cloud-with-chinese-fenci.md +++ b/_posts/2020-09-15-generate-word-cloud-with-chinese-fenci.md @@ -1,7 +1,7 @@ --- title: "Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries" date: 2020-09-15 22:00:14 -0400 -tags: visualization +tags: Visualization author: Pengzhan Hao cover: '/static/2020-09/2020-06-28.png' --- diff --git a/_posts/2021-10-13-eddl-how-do-we-train-on-limited-edge-devices.md b/_posts/2021-10-13-eddl-how-do-we-train-on-limited-edge-devices.md index b719565..408f094 100644 --- a/_posts/2021-10-13-eddl-how-do-we-train-on-limited-edge-devices.md +++ b/_posts/2021-10-13-eddl-how-do-we-train-on-limited-edge-devices.md @@ -1,7 +1,7 @@ --- title: "EDDL: How do we train neural networks on limited edge devices - PART 1" date: 2021-10-13 16:53:20 -0400 -tags: Research +tags: ["Research", "Edge computing"] author: Pengzhan Hao cover: '/static/2021-10/edgelearn-1.png' --- diff --git a/_posts/2021-10-31-eddl-how-do-we-train-on-limited-edge-devices-part2.md b/_posts/2021-10-31-eddl-how-do-we-train-on-limited-edge-devices-part2.md index 1d8a97d..0cffed0 100644 --- a/_posts/2021-10-31-eddl-how-do-we-train-on-limited-edge-devices-part2.md +++ b/_posts/2021-10-31-eddl-how-do-we-train-on-limited-edge-devices-part2.md @@ -1,7 +1,7 @@ --- title: "EDDL: How do we train neural networks on limited edge devices - PART 2" date: 2021-10-31 13:01:14 -0400 -tags: Research +tags: ["Research", "Edge computing"] author: Pengzhan Hao cover: '/static/2021-10/f.5_Impl_leader_worker.png' mathjax: true diff --git a/_posts/2021-12-09-vscode-plugins.md b/_posts/2021-12-09-vscode-plugins.md new file mode 100644 index 0000000..01945d6 --- /dev/null +++ b/_posts/2021-12-09-vscode-plugins.md @@ -0,0 +1,9 @@ +--- +title: "VS Code plugins" +date: 2021-12-09 17:03:22 -0400 +tags: ["Editor", "VS Code", "Productivity"] +author: Pengzhan Hao +# cover: '/static/2021-10/f.5_Impl_leader_worker.png' +published: false +--- + diff --git a/_site/archive.html b/_site/archive.html index aa413c3..92bec38 100644 --- a/_site/archive.html +++ b/_site/archive.html @@ -421,36 +421,54 @@ c13 9 26 20 30 26 7 11 -9 26 -27 26 -5 0 -3 -5 5 -10 9 -6 10 -10 3 -10 -24 -
  • +
  • +
  • +
  • +
  • +
  • -
  • -
  • -
  • +
    • -

    2021

    • +

      2021

      2020

      • +

      2020

      2017

      • +

      2017

      2016

      • +

      2016

      diff --git a/_site/feed.xml b/_site/feed.xml index 1a31aa9..507f4a2 100644 --- a/_site/feed.xml +++ b/_site/feed.xml @@ -1,4 +1,4 @@ -Jekyll2021-12-07T17:17:09-05:00http://localhost:4000/feed.xmlStop Talking, Start DoingMy personal blog, with some boring research staff and some tricks I was fancy to. I'll try my best to make this blog fun and useful. Not just a place I complain about all happens in my Lab. +Jekyll2021-12-11T13:35:05-05:00http://localhost:4000/feed.xmlStop Talking, Start DoingMy personal blog, with some boring research staff and some tricks I was fancy to. I'll try my best to make this blog fun and useful. Not just a place I complain about all happens in my Lab. Pengzhan Haohaopengzhan@gmail.comEDDL: How do we train neural networks on limited edge devices - PART 22021-10-31T13:01:14-04:002021-10-31T13:01:14-04:00http://localhost:4000/posts/eddl-how-do-we-train-on-limited-edge-devices-part2<p>In the last post, part1, our idea of distributed learning on edge environment was generally addressed. I introduced the reason why edge distributed learning is needed and what improvements it can achieve. In this post, I will talk about our motivation study and how our framework works.</p> @@ -103,7 +103,7 @@ The other platform is the <a href="https://www.raspberrypi.com/products/ <p>The operating system running on the above platforms is Ubuntu 18.04 with Linux kernel 4.14. We used <a href="http://dlib.net/">Dlib</a>, a C++ library that provides implementations for a wide range of machine learning algorithms. -We chose the Dlib library because it is written in C/C++, and can be easily and natively used in embedded devices.</p>Pengzhan HaoIn the last post, part1, our idea of distributed learning on edge environment was generally addressed. I introduced the reason why edge distributed learning is needed and what improvements it can achieve. In this post, I will talk about our motivation study and how our framework works. How does data support us training on edge? Before designing and implementing our framework, we first need confirmation that training on edge resource-limited devices is worthwhile. We were using a malware detection neural network to show why a small, customized neural network is better. We collected 32000+ mobile apps feature as global data. With these data records, we trained a multilayer perceptron called “PerNet” to determine whether a given feature belongs to a benign or malware app. We called this detection. As well, PerNet can also classify malware apps into different types of attacks. We called this classification. The global model can achieve 93% above recall rate and 96.93% above accuracy. With all these data, we selected two community app usage sub-dataset for local model generations. Large categories (Scenario 1) We chose the 5 largest categories of apps, including entertainment, tools, brain&amp;Puzzle, Lifestyle, and Education, as well as the 5 largest malware categories. All together, 12000+ apps were included in this sub-dataset, almost 50 to 50 between benign and malware. Campus-community categories (Scenario 2) We chose the 5 most downloaded categories from college students as benign groups, as well as a similar amount of 5 malware categories. To ensure that malware apps are included in 5 benign categories, we also considered synthesizing some other malware apps within categories of 5 most downloaded(benign) categories. With these two types of sub-dataset, we used the same PerNet to generate multiple local models. Under each scenarios experiment, we compared global and local models on the preserved test dataset. In all classification performances, local beat global in every scenario. In detection performances, local also share the same accuracy as global does. In summary, local models were trained on special occasions. Under the same circumstance, a global model can achieve no better accuracy than local models. The reason why local is better might be because of overfitting. I believe this issue also be considered in the machine learning communities that they brought transfer learning, a technique to optimize global models to special scenarios but performing more training to a global model once it’s shipped to local. Design and Implementation Overall design The basic EDDL distributed training setup consists of 3 parts. EDDL training cluster, a device cluster that consists of edge or mobile devices that are participating in training. EDDL manager, the initial driver program that works as collect training data, relay data to training devices and initial training clusters. Training data entry (TDE), a data storage for all training data. Dynamic training data distribution Existing distributed DNN training solutions usually statically partition training data among workers. It can be a problem when the training node joins and exits. We designed our framework that can dynamically distribute training data during learning. Before every training batch started, a batch of TDE will be sent to devices. In our experiments, we found that by applying this design, overall training time was shortened by doing. Especially in large amount devices cases, this optimization can be 50% less than statically divided. Scaling up cluster size Our framework was designed to have both sync and async parameter aggregation. Asynchronous aggregation can allow a high outcome of training batch but with a sacrifice or converge time. Synchronous aggregation allows a quick converge time in epochs, however can’t ensure performance when there’s a struggler worker. As showed in experiments, we chose sync as default because the converging time is dominant in overall training time. But, we also considered the possibilities of that async with more workers can achieve similar overall training time. We introduced a formula to determine whether adding more training nodes can help or not. Here we used bandwidth usage coefficient (BUC) as \[BUC = \dfrac{n}{T_{sync}}\] In this formula, \(n\) is the number of devices, and \(T_{sync}\) is the transmission time of parameters. With an increasing number of workers, n increase linearly but transmission time does not. When \(BUC\) increases, the cluster can speed up training time by adding workers. Otherwise, adding more workers won’t help with overall training time. Adaptive leader role splitting The idea of role splitting is simple that a device can work as a worker as well leader. The advantage of doing this is straightforward that we can transfer 1 less parameter and training time will be shortened. However, in our current settings, it can’t perform much better help since only 1 leader role is in a cluster. We can benefit from this in our future works. Overall architecture Details were given in the image. Prototype hardware and software EDDL was designed to be run on two single-board computer embedded platforms. One such platform is ODROID-XU4, which is equipped with a 2.1/1.4 GHz 32-bit ARM processor and 2GB memory. The other platform is the Raspberry Pi 3 Model B board, which comes with an ARM 1.2 GHz 64-bit quad-core processor and 1GB memory. The operating system running on the above platforms is Ubuntu 18.04 with Linux kernel 4.14. We used Dlib, a C++ library that provides implementations for a wide range of machine learning algorithms. We chose the Dlib library because it is written in C/C++, and can be easily and natively used in embedded devices.EDDL: How do we train neural networks on limited edge devices - PART 12021-10-13T16:53:20-04:002021-10-13T16:53:20-04:00http://localhost:4000/posts/eddl-how-do-we-train-on-limited-edge-devices<p>This post introduces our previous milestone in project “Edge trainer”, as the paper “EDDL: A Distributed Deep Learning System for Resource-limited Edge Computing Environment.” was published. +We chose the Dlib library because it is written in C/C++, and can be easily and natively used in embedded devices.</p>Pengzhan HaoIn the last post, part1, our idea of distributed learning on edge environment was generally addressed. I introduced the reason why edge distributed learning is needed and what improvements it can achieve. In this post, I will talk about our motivation study and how our framework works. How does data support us training on edge? Before designing and implementing our framework, we first need confirmation that training on edge resource-limited devices is worthwhile. We were using a malware detection neural network to show why a small, customized neural network is better. We collected 32000+ mobile apps feature as global data. With these data records, we trained a multilayer perceptron called “PerNet” to determine whether a given feature belongs to a benign or malware app. We called this detection. As well, PerNet can also classify malware apps into different types of attacks. We called this classification. The global model can achieve 93% above recall rate and 96.93% above accuracy. With all these data, we selected two community app usage sub-dataset for local model generations. Large categories (Scenario 1) We chose the 5 largest categories of apps, including entertainment, tools, brain&amp;Puzzle, Lifestyle, and Education, as well as the 5 largest malware categories. All together, 12000+ apps were included in this sub-dataset, almost 50 to 50 between benign and malware. Campus-community categories (Scenario 2) We chose the 5 most downloaded categories from college students as benign groups, as well as a similar amount of 5 malware categories. To ensure that malware apps are included in 5 benign categories, we also considered synthesizing some other malware apps within categories of 5 most downloaded(benign) categories. With these two types of sub-dataset, we used the same PerNet to generate multiple local models. Under each scenarios experiment, we compared global and local models on the preserved test dataset. In all classification performances, local beat global in every scenario. In detection performances, local also share the same accuracy as global does. In summary, local models were trained on special occasions. Under the same circumstance, a global model can achieve no better accuracy than local models. The reason why local is better might be because of overfitting. I believe this issue also be considered in the machine learning communities that they brought transfer learning, a technique to optimize global models to special scenarios but performing more training to a global model once it’s shipped to local. Design and Implementation Overall design The basic EDDL distributed training setup consists of 3 parts. EDDL training cluster, a device cluster that consists of edge or mobile devices that are participating in training. EDDL manager, the initial driver program that works as collect training data, relay data to training devices and initial training clusters. Training data entry (TDE), a data storage for all training data. Dynamic training data distribution Existing distributed DNN training solutions usually statically partition training data among workers. It can be a problem when the training node joins and exits. We designed our framework that can dynamically distribute training data during learning. Before every training batch started, a batch of TDE will be sent to devices. In our experiments, we found that by applying this design, overall training time was shortened by doing. Especially in large amount devices cases, this optimization can be 50% less than statically divided. Scaling up cluster size Our framework was designed to have both sync and async parameter aggregation. Asynchronous aggregation can allow a high outcome of training batch but with a sacrifice or converge time. Synchronous aggregation allows a quick converge time in epochs, however can’t ensure performance when there’s a struggler worker. As showed in experiments, we chose sync as default because the converging time is dominant in overall training time. But, we also considered the possibilities of that async with more workers can achieve similar overall training time. We introduced a formula to determine whether adding more training nodes can help or not. Here we used bandwidth usage coefficient (BUC) as \[BUC = \dfrac{n}{T_{sync}}\] In this formula, \(n\) is the number of devices, and \(T_{sync}\) is the transmission time of parameters. With an increasing number of workers, n increase linearly but transmission time does not. When \(BUC\) increases, the cluster can speed up training time by adding workers. Otherwise, adding more workers won’t help with overall training time. Adaptive leader role splitting The idea of role splitting is simple that a device can work as a worker as well leader. The advantage of doing this is straightforward that we can transfer 1 less parameter and training time will be shortened. However, in our current settings, it can’t perform much better help since only 1 leader role is in a cluster. We can benefit from this in our future works. Overall architecture Details were given in the image. Prototype hardware and software EDDL was designed to be run on two single-board computer embedded platforms. One such platform is ODROID-XU4, which is equipped with a 2.1/1.4 GHz 32-bit ARM processor and 2GB memory. The other platform is the Raspberry Pi 3 Model B board, which comes with an ARM 1.2 GHz 64-bit quad-core processor and 1GB memory. The operating system running on the above platforms is Ubuntu 18.04 with Linux kernel 4.14. We used Dlib, a C++ library that provides implementations for a wide range of machine learning algorithms. We chose the Dlib library because it is written in C/C++, and can be easily and natively used in embedded devices.EDDL: How do we train neural networks on limited edge devices - PART 12021-10-13T16:53:20-04:002021-10-13T16:53:20-04:00http://localhost:4000/posts/eddl-how-do-we-train-on-limited-edge-devices<p>This post introduces our previous milestone in project “Edge trainer”, as the paper “EDDL: A Distributed Deep Learning System for Resource-limited Edge Computing Environment.” was published. As the first part of the introductions, I focus only on the motivation and summary of our works. More details in design and implementation can be found in late posts.</p> @@ -180,7 +180,7 @@ Smartly schedule work balance and handle join/exit issues also need under consid Devices with sufficient bandwidth can also work as virtual leader devices. This approach helps minimize physical devices we used and more leaders can further scale up workers’ limits.</p> </li> -</ul>Pengzhan HaoThis post introduces our previous milestone in project “Edge trainer”, as the paper “EDDL: A Distributed Deep Learning System for Resource-limited Edge Computing Environment.” was published. As the first part of the introductions, I focus only on the motivation and summary of our works. More details in design and implementation can be found in late posts.Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries2020-09-15T22:00:14-04:002020-09-15T22:00:14-04:00http://localhost:4000/posts/generate-word-cloud-with-chinese-fenci<p>Let’s generate a word cloud like this. +</ul>Pengzhan HaoThis post introduces our previous milestone in project “Edge trainer”, as the paper “EDDL: A Distributed Deep Learning System for Resource-limited Edge Computing Environment.” was published. As the first part of the introductions, I focus only on the motivation and summary of our works. More details in design and implementation can be found in late posts.Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries2020-09-15T22:00:14-04:002020-09-15T22:00:14-04:00http://localhost:4000/posts/generate-word-cloud-with-chinese-fenci<p>Let’s generate a word cloud like this. Don’t understand the language is not a big deal. If your written language is based on latin alphabet(or other language has space between words), skip tokenization.</p> @@ -339,7 +339,7 @@ If your written language is based on latin alphabet(or other language has space <p><img src="/static/2020-09/2020-06-28.png" height="150" /></p> -<p>This generated word cloud figure reflects the most popular economy news’ keyword in the week started 06-28-2020. Two largest words in the figure are “新冠” and “新冠病毒”, both means “Covid-19” (This figure was in the week of the second covid spur in Beijing, China). The size of the image fits my phone screen and I can use an app to automatic sync it to my phone’s wallpaper. However, in this image, too many location nouns are presented. This will be something I can make progress on in the future.</p>Pengzhan HaoLet’s generate a word cloud like this. Don’t understand the language is not a big deal. If your written language is based on latin alphabet(or other language has space between words), skip tokenization.Xv6 introduction2017-07-28T14:56:55-04:002017-07-28T14:56:55-04:00http://localhost:4000/posts/intro-xv6<p>In this post, you will learn a few basic concepts of xv6. Learning path will be closed coupled to first project assignment I gave when I assisted in teaching OS classes. +<p>This generated word cloud figure reflects the most popular economy news’ keyword in the week started 06-28-2020. Two largest words in the figure are “新冠” and “新冠病毒”, both means “Covid-19” (This figure was in the week of the second covid spur in Beijing, China). The size of the image fits my phone screen and I can use an app to automatic sync it to my phone’s wallpaper. However, in this image, too many location nouns are presented. This will be something I can make progress on in the future.</p>Pengzhan HaoLet’s generate a word cloud like this. Don’t understand the language is not a big deal. If your written language is based on latin alphabet(or other language has space between words), skip tokenization.Xv6 introduction2017-07-28T14:56:55-04:002017-07-28T14:56:55-04:00http://localhost:4000/posts/intro-xv6<p>In this post, you will learn a few basic concepts of xv6. Learning path will be closed coupled to first project assignment I gave when I assisted in teaching OS classes. Understand system call and know how to implement a simple one will be coved as the first half. In the second half of this post, I will discuss a little bit more on how to debug xv6 using gdb.<br /> <!--more--></p> @@ -414,7 +414,7 @@ Using ssh may connect to different physical devices under same domain name, this <div class="language-bash highlighter-rouge"><div class="highlight"><pre class="highlight"><code>target remote localhost:28467 <span class="c"># target remote [ip-addr]:28467</span> -</code></pre></div></div>Pengzhan HaoIn this post, you will learn a few basic concepts of xv6. Learning path will be closed coupled to first project assignment I gave when I assisted in teaching OS classes. Understand system call and know how to implement a simple one will be coved as the first half. In the second half of this post, I will discuss a little bit more on how to debug xv6 using gdb.Some of my previews experiment works: 20162016-10-28T12:27:33-04:002016-10-28T12:27:33-04:00http://localhost:4000/posts/some-of-my-previews-exper-work<p>This blog contains only some basic record of my works. For some details, I will write a unique blog just for some specific topics. +</code></pre></div></div>Pengzhan HaoIn this post, you will learn a few basic concepts of xv6. Learning path will be closed coupled to first project assignment I gave when I assisted in teaching OS classes. Understand system call and know how to implement a simple one will be coved as the first half. In the second half of this post, I will discuss a little bit more on how to debug xv6 using gdb.Some of my previews experiment works: 20162016-10-28T12:27:33-04:002016-10-28T12:27:33-04:00http://localhost:4000/posts/some-of-my-previews-exper-work<p>This blog contains only some basic record of my works. For some details, I will write a unique blog just for some specific topics. <!--more--></p> <h1 id="2016-10">2016-10</h1> @@ -524,7 +524,7 @@ unxz ubuntu-14.04lts-xubuntu-odroid-xu-20140714.img.xz <span class="c"># dump file</span> su <span class="nb">dd </span><span class="k">if</span><span class="o">=</span>/dev/block/mmcblk0p37 <span class="nv">of</span><span class="o">=</span>/sdcard/boot.img -</code></pre></div></div>Pengzhan HaoThis blog contains only some basic record of my works. For some details, I will write a unique blog just for some specific topics.Using charles proxy to monitor mobile SSL traffics2016-10-27T22:50:33-04:002016-10-27T22:50:33-04:00http://localhost:4000/posts/charles-is-not-a-good-tool<p>In this blog, I will generally talk about how to use proper tools to monitor SSL traffics of a mobile devices. Currently, I only can dealing with those SSL traffics which use an obviously certification. Some applications may not using system root cert or they doesn’t provide us a method to modify their own certs. For these situation, I still didn’t find a good solutions for it. But I’ll keep updating this if I get one.<br /> +</code></pre></div></div>Pengzhan HaoThis blog contains only some basic record of my works. For some details, I will write a unique blog just for some specific topics.Using charles proxy to monitor mobile SSL traffics2016-10-27T22:50:33-04:002016-10-27T22:50:33-04:00http://localhost:4000/posts/charles-is-not-a-good-tool<p>In this blog, I will generally talk about how to use proper tools to monitor SSL traffics of a mobile devices. Currently, I only can dealing with those SSL traffics which use an obviously certification. Some applications may not using system root cert or they doesn’t provide us a method to modify their own certs. For these situation, I still didn’t find a good solutions for it. But I’ll keep updating this if I get one.<br /> My current solution is using AP to forward all SSL traffic to a proxy, <a href="https://www.charlesproxy.com/">charles proxy</a> is my first choice (Prof asked). It’s a non-free software which still update new versions now. So mainly, I’ll talk about how to charles SSL proxy. <!--more--></p> @@ -553,4 +553,4 @@ You also need to save charles Root Certificate, it also contains in the same men <ul> <li>Set Proxy and SSL Proxy</li> -</ul>Pengzhan HaoIn this blog, I will generally talk about how to use proper tools to monitor SSL traffics of a mobile devices. Currently, I only can dealing with those SSL traffics which use an obviously certification. Some applications may not using system root cert or they doesn’t provide us a method to modify their own certs. For these situation, I still didn’t find a good solutions for it. But I’ll keep updating this if I get one. My current solution is using AP to forward all SSL traffic to a proxy, charles proxy is my first choice (Prof asked). It’s a non-free software which still update new versions now. So mainly, I’ll talk about how to charles SSL proxy.Stop Talking is the worst title of one blog2016-10-26T22:50:33-04:002016-10-26T22:50:33-04:00http://localhost:4000/posts/welcome-to-my-blogPengzhan Haohaopengzhan@gmail.com \ No newline at end of file +</ul>Pengzhan HaoIn this blog, I will generally talk about how to use proper tools to monitor SSL traffics of a mobile devices. Currently, I only can dealing with those SSL traffics which use an obviously certification. Some applications may not using system root cert or they doesn’t provide us a method to modify their own certs. For these situation, I still didn’t find a good solutions for it. But I’ll keep updating this if I get one. My current solution is using AP to forward all SSL traffic to a proxy, charles proxy is my first choice (Prof asked). It’s a non-free software which still update new versions now. So mainly, I’ll talk about how to charles SSL proxy.Stop Talking is the worst title of one blog2016-10-26T22:50:33-04:002016-10-26T22:50:33-04:00http://localhost:4000/posts/welcome-to-my-blogPengzhan Haohaopengzhan@gmail.com \ No newline at end of file diff --git a/_site/index.html b/_site/index.html index 079d522..e945bf1 100644 --- a/_site/index.html +++ b/_site/index.html @@ -426,9 +426,12 @@ How does data support us training on edge? Before designin...

    Read more

    - +

    EDDL: How do we train neural networks on limited edge devices - PART 1

    This post introduces our previous milestone in project “Edge trainer”, as the paper “EDDL: A Distributed Deep Learning System for Resource-limited Edge Computing Environment.” was published. @@ -436,53 +439,71 @@ As the first part of the introductions, I focus only on the motivation and summa More details in design and implementation can be found in l...

    Read more

    • Pengzhan Hao
    • Oct 13, 2021
    -
    +

    Generate Word Cloud Figures with Chinese-Tokenization and WordCloud python libraries

    Let’s generate a word cloud like this. Don’t understand the language is not a big deal. If your written language is based on latin alphabet(or other language has space between words), skip tokenization.

    Read more

    • visualization + href="/archive.html?tag=Visualization">Visualization
    • Pengzhan Hao
    • Sep 15, 2020
    -
    +

    Xv6 introduction

    In this post, you will learn a few basic concepts of xv6. Learning path will be closed coupled to first project assignment I gave when I assisted in teaching OS classes. Understand system call and know how to implement a simple one will be coved as the first half. In the second half of this post, I will discuss a little bit more on how to debug ...

    Read more

    • Pengzhan Hao
    • Jul 28, 2017
    -
    +

    Some of my previews experiment works: 2016

    This blog contains only some basic record of my works. For some details, I will write a unique blog just for some specific topics.

    Read more

    • Pengzhan Hao
    • Oct 28, 2016
    -
    -
    +
    +

    Using charles proxy to monitor mobile SSL traffics

    In this blog, I will generally talk about how to use proper tools to monitor SSL traffics of a mobile devices. Currently, I only can dealing with those SSL traffics which use an obviously certification. Some applications may not using system root cert or they doesn’t provide us a method to modify their own certs. For these situation, I still did...

    Read more

    • Pengzhan Hao
    • Oct 27, 2016
    -
    +

    Stop Talking is the worst title of one blog

    Read more

    • Nonsense + href="/archive.html?tag=Diary">Diary
    • Oct 26, 2016
    -
    +