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Tony Guoga Cypherpunk's Chief Executive Officer needs killing ( My 200$ )
by professor rat 04 Oct '21

04 Oct '21

I'm tired of carpetbaggers leeching off our good name. Please join this dead-pool with me and stamp out the parasites.

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GNU FSF: JShelter Privacy Extension to Block JavaScript Fingerprinting, Tracking, and Malware
by grarpamp 04 Oct '21

04 Oct '21

https://www.fsf.org/news/fsf-announces-jshelter-browser-add-on-to-combat-th… https://jshelter.org/ https://github.com/polcak/jsrestrictor https://addons.mozilla.org/cs/firefox/addon/javascript-restrictor/ https://graz.pure.elsevier.com/de/publications/javascript-zero-real-javascr… https://arxiv.org/pdf/1905.01051 https://www.forcepoint.com/sites/default/files/resources/files/report-attac… https://research.fb.com/wp-content/uploads/2016/11/self-censorship-on-faceb… https://developer.mozilla.org/en-US/docs/Web/API/GeolocationCoordinates/acc… https://www.fsf.org/blogs/community/gnu-librejs-new-and-improved https://www.gnu.org/licenses/javascript-labels.html What is JShelter? JShelter is a browser extension to give back control over what your browser is doing. A JavaScript-enabled web page can access much of the browser's functionality, with little control over this process available to the user: malicious websites can uniquely identify you through fingerprinting and use other tactics for tracking your activity. JShelter aims to improve the privacy and security of your web browsing. How does it work? Like a firewall that controls network connections, JShelter controls the APIs provided by the browser, restricting the data that they gather and send out to websites. JShelter adds a safety layer that allows the user to choose if a certain action should be forbidden on a site, or if it should be allowed with restrictions, such as reducing the precision of geolocation to the city area. This layer can also aid as a countermeasure against attacks targeting the browser, operating system or hardware. The development of this extension is influenced by the paper JavaScript Zero: Real JavaScript and Zero Side-Channel Attacks

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Leaked money info
by jim bell 03 Oct '21

03 Oct '21

Leaked files open “Pandora” box, show how ultra-wealthy hide billions, escape taxes in shadowy global web https://share.newsbreak.com/1kf3o4xw

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Re: neuromorphic processing
by professor rat 03 Oct '21

03 Oct '21

I don't want to encourage blathering no-coiner nitwits - like Coderman and Semich - but this particular development could get very relevant for anarchist revolutionists, very quickly. I'll let Jon Fearless explain . . . Another scenario (Ross 1992: 16) involves injecting nanomachines into the bloodstream that would replace each brain and sensory neuron with a functionally equivalent, artificial structure. The nanomachine would contain a program that would emulate the neuron, while at the same time interacting with neighboring cells as though the replaced neuron were still in place. The cells surrounding the neuron would be unaware of any change. Gradually, each synapse in the brain would become information in a computer program, retaining functionality but dispensing with its former physical structure. When the process is complete, what is thought to be the individual would awaken to a new life in cyberspace. .." https://theanarchistlibrary.org/library/john-filiss-uploading

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Downside of going up
by professor rat 03 Oct '21

03 Oct '21

The thought of uploading themselves may not appeal to many people and they will have to be left-behind as the rest of us depart for the stars. In order that Mother Nature be given a fair chance to recover I suggest all those remaining be sterilized - even the most remote indigenous tribespeople since they could be so easily corrupted by unscrupulous left-overs. This is a distasteful job - but part of growing-up . . .and out. It could dog our career's as space explorer's to have any blot on our record.

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A guarantee of safe haven for individuals who implement assassination policies.
by professor rat 03 Oct '21

03 Oct '21

At its best, the acceptance of war by assassination is a powerful check on the growth and depredations of governments. At its worst, it is a sometimes useful tool to avoiding the needless deaths of innocents, i.e., “collateral damage,” when nations clash. https://theanarchistlibrary.org/library/john-filiss-war-by-assassination Though Jim Bell gained a little notoriety for stealing anarchist ' propaganda-of-the-deed' and dragging and dropping it online, cheats never prosper. Some version of John's idea combines better with networked anarchism since it doesn't drag and drop a bunch of Freudian bullshit over to cryptoanarchy. Hasta luego, Jimmy Its been real.

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letsencrypt.org NSA fucktards.
by Punk-BatSoup-Stasi 2.0 03 Oct '21

03 Oct '21

OK, these fucking 'letsencrypt' idiots don't even know how to issue certificates and so have fucked the 'secure' connection to millions of services. But, I thought, I can go to their arpanet website, get some kind of patch 'certificate' and add it to piece of shit firefox-NSA. But, no, there's no such thing as a fixed certificate. As a matter of fact the useless pieces of shit from 'letsencrypt' DON'T EVEN SAY A WORD about their worldwide scale fuck up. letsencrypt.org But maybe they are doing exactly what the NSA told them to do.

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neuromorphic processing
by coderman 03 Oct '21

03 Oct '21

https://github.com/combra-lab/combra_loihi combra_loihi combra_loihi is a neuromorphic computing library for Computational Astrocyence developed specifically for Intel's Loihi neuromorphic processor. The library is developed by [Computational Brain Lab](http://combra.cs.rutgers.edu/) (ComBra) at Rutgers University. Version 0.1 (11/2018) Prerequisites: - python 3.5.2 - NxSDK 0.7 For more information, please go to [combra_loihi WiKi](https://github.com/combra-lab/combra_loihi/wiki) Related Publication Guangzhi Tang, Ioannis E Polykretis, Vladimir A Ivanov, Arpit Shah, Konstantinos P Michmizos. "Introducing astrocytes on a neuromorphic processor: Synchronization, local plasticity and edge of chaos." Neuro-inspired Computational Elements Workshop (NICE 2019), Albany, NY, USA. [pdf](https://arxiv.org/pdf/1907.01620.pdf) ==== https://github.com/combra-lab/spiking-ddpg-mapless-navigation Spiking Neural Network for Mapless Navigation This package is the PyTorch implementation of the Spiking Deep Deterministic Policy Gradient (SDDPG) framework. The hybrid framework trains a spiking neural network (SNN) for energy-efficient mapless navigation on Intel's Loihi neuromorphic processor. The following figure shows an overview of the proposed method: [overview of method](https://github.com/combra-lab/spiking-ddpg-mapless-navigation/blob/… The paper has been accepted at IROS 2020. The arXiv preprint is available [here](https://arxiv.org/abs/2003.01157). New: We have created a new GitHub repo to demonstrate the online runtime interaction with Loihi. If you are interested in using Loihi for real-time robot control, please [check it out](https://github.com/michaelgzt/loihi-control-loop-demo). Citation Guangzhi Tang, Neelesh Kumar, and Konstantinos P. Michmizos. "Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardware." 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. @inproceedings { tang2020reinforcement , title = { Reinforcement co-Learning of Deep and Spiking Neural Networks for Energy-Efficient Mapless Navigation with Neuromorphic Hardware } , author = { Tang, Guangzhi and Kumar, Neelesh and Michmizos, Konstantinos P } , booktitle = { 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) } , pages = { 1--8 } , year = { 2020 } , organization = { IEEE } } Software Installation 1. Basic Requirements - Ubuntu 16.04 - Python 3.5.2 - ROS Kinetic (with Gazebo 7.0) - PyTorch 1.2 (with CUDA 10.0 and tensorboard 2.1) - NxSDK 0.9 ROS Kinetic is not compatible with Python 3 by default, if you have issues with using Python 3 with ROS, please follow this [link](https://medium.com/@beta_b0t/how-to-setup-ros-with-python-3-44a69ca36… to resolve them. We use the default Python 2 environment to execute roslaunch and rosrun. A CUDA enabled GPU is not required but preferred for training within the SDDPG framework. The results in the paper are generated from models trained using both Nvidia Tesla K40c and Nvidia GeForce RTX 2080Ti. Intel's neuromorphic library NxSDK is only required for SNN deployment on Loihi. If you are interested in deploying the trained SNN on Loihi, please contact the [Intel Neuromorphic Lab](https://www.intel.com/content/www/us/en/research/neuromorphic-communit…. We have provided the requirements.txt for the python environment without NxSDK. In addition, we recommend setting up the environment using [virtualenv](https://pypi.org/project/virtualenv/). 2. Simulation Setup The simulation environment simulates a Turtlebot2 robot with a 360 degree LiDAR in the Gazebo simulator. Turtlebot2 dependency can be installed using: sudo apt-get install ros-kinetic-turtlebot- * We use the Hokuyo LiDAR model in the simulation and set the parameters to be the same as the RPLIDAR S1. LiDAR dependency can be installed using: sudo apt-get install ros-kinetic-urg-node Download the project and compile the catkin workspace: cd < Dir > / < Project Name > /ros/catkin_ws catkin_make Add the following line to your ~/.bashrc in order for ROS environment to setup properly: source < Dir > / < Project Name > /ros/catkin_ws/devel/setup.bash export TURTLEBOT_3D_SENSOR= " hokuyo " Run source ~/.bashrc afterward and test the environment setup by running (use Python 2 environment): roslaunch turtlebot_lidar turtlebot_world.launch You should able to see the Turtlebot2 with a LiDAR on the top. 3. Real-world Setup We install the [RPLIDAR S1](https://www.slamtec.com/en/Lidar/S1) on the center of the top level of Turtlebot2. To use the LiDAR with ROS, you need to download and install the rplidar_ros library from [here](https://github.com/robopeak/rplidar_ros) on the laptop controlling Turtlebot2. After installing the library, you need to add the LiDAR to the tf tree. This can be done by adding a tf publisher node in minimal.launch from turtlebot_bringup package: < node name = " base2laser " pkg = " tf " type = " static_transform_publisher " args = " 0 0 0 0 0 1 0 /base_link /laser 50 " > Test the setup by running (use Python 2 environment): roslaunch turtlebot_bringup minimal.launch and roslaunch rplidar_ros rplidar_s1.launch in separate terminals on the laptop controlling Turtlebot2. Example Usage 1. Training SDDPG To train the SDDPG, you need to first launch the training world including 4 different environments (use Python 2 environment and absolute path for <Dir>): roslaunch turtlebot_lidar turtlebot_world.launch world_file:= < Dir > / < Project Name > /ros/worlds/training_worlds.world Then, run the laserscan_simple ros node in a separate terminal to sample laser scan data every 10 degrees (use Python 2 environment): rosrun simple_laserscan laserscan_simple Now, we have all ros prerequisites for training. Execute the following commands to start the training in a new terminal (use Python 3 environment): source < Dir to Python 3 Virtual Env > /bin/activate cd < Dir > / < Project Name > /training/train_spiking_ddpg python train_sddpg.py --cuda 1 --step 5 This will automatically train 1000 episodes in the training environments and save the trained parameters every 10k steps. Intermediate training results are also saved through tensorboard. If you want to perform the training on CPU, you can set --cuda to 0. You can also train for different inference timesteps of SNN by setting --step to the desired number. In addition, we also have the state-of-the-art DDPG implementation that trains a non-spiking deep actor network for mapless navigation. If you want to train the DDPG network, run the following commands to start the training in a new terminal (use Python 3 environment): source < Dir to Python 3 Virtual Env > /bin/activate cd < Dir > / < Project Name > /training/train_ddpg python train_ddpg.py --cuda 1 2. Evaluate in simulated environment To evaluate the trained Spiking Actor Network (SAN) in Gazebo, you need to first launch the evaluation world (use Python 2 environment and absolute path for <Dir>): roslaunch turtlebot_lidar turtlebot_world.launch world_file:= < Dir > / < Project Name > /ros/worlds/evaluation_world.world Then, run the laserscan_simple ros node in a separate terminal to sample laser scan data every 10 degrees (use Python 2 environment): rosrun simple_laserscan laserscan_simple Now, we have all ros prerequisites for evaluation. Run the following commands to start the evaluation in a new terminal (use Python 3 environment): source < Dir to Python 3 Virtual Env > /bin/activate cd < Dir > / < Project Name > /evaluation/eval_random_simulation python run_sddpg_eval.py --save 0 --cuda 1 --step 5 This will automatically navigate the robot for 200 randomly generate start and goal positions. The full evaluation will cost more than 2 hours. If you want to perform the evaluation on CPU, you can set --cuda to 0. You can also evaluate for different inference timesteps of SNN by setting --step to the desired number. To deploy the trained SAN on Loihi and evaluate in Gazebo, you need to have the Loihi hardware. If you have the Kapoho Bay USB chipset, run the following commands to start the evaluation (use Python 3 environment): source < Dir to Python 3 Virtual Env > /bin/activate cd < Dir > / < Project Name > /evaluation/eval_random_simulation_loihi KAPOHOBAY=1 python run_sddpg_loihi_eval.py --save 0 --step 5 You can also evaluate for different inference timesteps of SNN by setting --step to the desired number. In addition, you also need to change the epoch value in the <Project Name>/evaluation/loihi_network/snip/encoder.c file corresponding to the inference timesteps. For both evaluations, you can set --save to 1 to save the robot routes and time. These running histories are then used to generate the results shown in the paper. Run the following commands to evaluate the history by yourself (use Python 3 environment): source < Dir to Python 3 Virtual Env > /bin/activate cd < Dir > / < Project Name > /evaluation/result_analyze python generate_results.py You should be able to get the following results for evaluating the SAN on GPU with T=5: sddpg_bw_5 random simulation results: Success: 198 Collision: 2 Overtime: 0 Average Path Distance of Success Routes: 18.539 m Average Path Time of Success Routes: 42.519 s [overview of method](https://github.com/combra-lab/spiking-ddpg-mapless-navigation/blob/… with red dot as goal positions, blue dot as start positions, and red cross as collision positions. 3. Evaluate in real-world environment Our implementation of real-world evaluate relies on the amcl to localize the robot and generate relative goal positions. Therefore, to evaluate the trained SNN in real-world environment, you have to first generate a map of the environment using GMapping (use Python 2 environment): roslaunch turtlebot_lidar gmapping_lplidar_demo.launch Then, you can use the saved map to localize the robot's pose (use Python 2 environment): roslaunch turtlebot_lidar amcl_lplidar_demo.launch map_file:= < Dir to map > You can view the robot navigation using rviz by running in a separate terminal (use Python 2 environment): roslaunch turtlebot_rviz_launchers view_navigation.launch After verifying that the robot can correctly localize itself in the environment, you can start to evaluate the trained SNN. Here, we only support the evaluation on Loihi. To deploy the trained SNN on Loihi, you need to have the Loihi hardware. If you have the Kapoho Bay USB chipset, run the following commands to start the evaluation (use Python 3 environment): source < Dir to Python 3 Virtual Env > /bin/activate cd < Dir > / < Project Name > /evaluation/eval_real_world KAPOHOBAY=1 python run_sddpg_loihi_eval_rw.py For your own environment, remember to change the GOAL_LIST in the evaluation script to the appropriate goal positions for the environment. Acknowledgment This work is supported by Intel's Neuromorphic Research Community Grant Award.

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AES Encryption in Linux - DEV Community
by jim bell 03 Oct '21

03 Oct '21

https://dev.to/blastoise/aes-encryption-in-linux-585f

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George Clooney
by Gunnar Larson 03 Oct '21

03 Oct '21

Here is George Clooney response after Trump accused him of being a "Hollywood elite." "Here’s the thing: I grew up in Kentucky. I sold insurance door-to-door. I sold ladies’ shoes. I worked at an all-night liquor store. I would buy suits that were too big and too long and cut the bottom of the pants off to make ties so I’d have a tie to go on job interviews. I grew up understanding what it was like to not have health insurance for eight years. So this idea that I’m somehow the “Hollywood elite” and this guy who takes a shit in a gold toilet is somehow the man of the people is laughable. People in Hollywood, for the most part, are people from the Midwest who moved to Hollywood to have a career. So this idea of “coastal elites” living in a bubble is ridiculous. Who lives in a bigger bubble? He lives in a gold tower and has twelve people in his company. He doesn’t run a corporation of hundreds of thousands of people he employs and takes care of. He ran a company of twelve people! When you direct a film you have seven different unions all wanting different things, you have to find consensus with all of them, and you have to get them moving in the same direction. He’s never had to do any of that kind of stuff. I just look at it and I laugh when I see him say “Hollywood elite.” Hollywood elite? I don’t have a star on Hollywood Boulevard, Donald Trump has a star on Hollywood Boulevard! Fuck you!" - @GeorgeClooney #actor, #philanthropist, #humanitarian & #activist

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