550 skills found · Page 7 of 19
Sanch153 / Distributed Leader FollowerSimulation of a distributed control algorithm to drive a multi-agent system with a single leader (has knowledge about target locations) to the target positions with collision avoidance.
cq-cdy / CRaftcRaft: Raft distributed consensus algorithm service framework based on C++ stacked coroutines
fdyuandong / 2D 3D Point Set Registration Based On Global Rotation Search# 2D-3D Point Set Registration Based on Global Rotation Search # Copyright (C) 2018 Yinlong Liu@outlook.com # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # Any publications resulting from the use of this code should cite the # following paper: # Yinlong Liu, Yuan Dong, Zhijian Song and Manning Wang, "2D 3D Point Set Registration Based on Global Rotation_Search", IEEE Transactions on Image Processing (TIP) # # #==================Note================= # #1. First step->open demo_rot.m. It is a demo of Rotation Search in SO(3) for 2D-3D point set registration. # #2. The input data is set to a real circumstance that 3D point set is far away for projection plane, and in front of camera. Our method also can be applied to unusual condition that camera is surrounded by 3D point set, only if you make some fix. # #3. RotaionSearch.m is the kernel of algorithm. You can easily extend it to SE(3) searching by grid-search, while tuning parameters depends on your tasks. # #4. Fast and Global 2D-3D point set registration without correspondence is still an open problem and need further explorations. I am very happy that if you could benefit from our code. # # # Author: Yinlong Liu # Date: 20181218 # Revision: 1.0
peacefullmind / Electric Vehicle Charging Scheduling Model Based On Lagrange Distributed AlgorithmNo description available
changkyu / Aaai2016 ChangkyuFast ADMM Algorithm for Distributed Optimization with Adaptive Penalty (AAAI 2016) - Changkyu Song
MayDomine / Burst AttentionDistributed IO-aware Attention algorithm
maca / Crdt Replicated TreeImplementation of a CRDT algorithm for distributed trees
Rabbit-OJ / Rabbit OJ BackendUsing Go & MySQL & Docker & Web Socket & gRPC & Kafka & Zookeeper & Protobuf. Distributed and Scalable Open Judge System for Algorithms.
acforvs / Dhc Robust MapfLearnable MAPF. “Distributed Heuristic Multi-Agent Path Finding with Communication” (DHC) algorithm from ICRA 2021 is implemented and benchmarked in out-of-distribution (OOD) scenarios. A new robust training loop to handle communication failures is introduced.
Lei-IT / GMAMPGeneralized approximate message passing (GAMP) and generalized vector AMP (GVAMP) are Bayes-optimal algorithms widely used for unknown signal reconstruction of generalized linear models (GLM). However, they both have their own limitations, i.e., either requirements for independent and identically distributed (IID) transformation matrices or high-complexity matrix inverse. In this article, we provide a universal generalized memory AMP (GMAMP) framework including the existing orthogonal AMP/VAMP, GVAMP, and MAMP as instances. It gives new directions to address GLM and performs well in ill-conditional systems with low complexity. The proposed Bayes-optimal GMAMP is an example that overcomes the IID-matrix limitation of GAMP and avoids the high-complexity matrix inverse in GVAMP. Our proposed framework paves the way for compressed sensing, imaging, signal processing, communications, deep learning, and other fields.
ginking / Archimedes 1Archimedes 1 is a bot based sentient based trader, heavily influenced on forked existing bots, with a few enhancements here or there, this was completed to understand how the bots worked to roll the forward in our own manner to our own complete ai based trading system (Archimedes 2:0) This bot watches [followed accounts] tweets and waits for them to mention any publicly traded companies. When they do, sentiment analysis is used determine whether the opinions are positive or negative toward those companies. The bot then automatically executes trades on the relevant stocks according to the expected market reaction. The code is written in Python and is meant to run on a Google Compute Engine instance. It uses the Twitter Streaming APIs (however new version) to get notified whenever tweets within remit are of interest. The entity detection and sentiment analysis is done using Google's Cloud Natural Language API and the Wikidata Query Service provides the company data. The TradeKing (ALLY) API does the stock trading (changed to ALLY). The main module defines a callback where incoming tweets are handled and starts streaming user's feed: def twitter_callback(tweet): companies = analysis.find_companies(tweet) if companies: trading.make_trades(companies) twitter.tweet(companies, tweet) if __name__ == "__main__": twitter.start_streaming(twitter_callback) The core algorithms are implemented in the analysis and trading modules. The former finds mentions of companies in the text of the tweet, figures out what their ticker symbol is, and assigns a sentiment score to them. The latter chooses a trading strategy, which is either buy now and sell at close or sell short now and buy to cover at close. The twitter module deals with streaming and tweeting out the summary. Follow these steps to run the code yourself: 1. Create VM instance Check out the quickstart to create a Cloud Platform project and a Linux VM instance with Compute Engine, then SSH into it for the steps below. The predefined machine type g1-small (1 vCPU, 1.7 GB memory) seems to work well. 2. Set up auth The authentication keys for the different APIs are read from shell environment variables. Each service has different steps to obtain them. Twitter Log in to your Twitter account and create a new application. Under the Keys and Access Tokens tab for your app you'll find the Consumer Key and Consumer Secret. Export both to environment variables: export TWITTER_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TWITTER_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" If you want the tweets to come from the same account that owns the application, simply use the Access Token and Access Token Secret on the same page. If you want to tweet from a different account, follow the steps to obtain an access token. Then export both to environment variables: export TWITTER_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TWITTER_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Google Follow the Google Application Default Credentials instructions to create, download, and export a service account key. export GOOGLE_APPLICATION_CREDENTIALS="/path/to/credentials-file.json" You also need to enable the Cloud Natural Language API for your Google Cloud Platform project. TradeKing (ALLY) Log in to your TradeKing (ALLY account and create a new application. Behind the Details button for your application you'll find the Consumer Key, Consumer Secret, OAuth (Access) Token, and Oauth (Access) Token Secret. Export them all to environment variables: export TRADEKING_CONSUMER_KEY="<YOUR_CONSUMER_KEY>" export TRADEKING_CONSUMER_SECRET="<YOUR_CONSUMER_SECRET>" export TRADEKING_ACCESS_TOKEN="<YOUR_ACCESS_TOKEN>" export TRADEKING_ACCESS_TOKEN_SECRET="<YOUR_ACCESS_TOKEN_SECRET>" Also export your TradeKing (ALLY) account number, which you'll find under My Accounts: export TRADEKING_ACCOUNT_NUMBER="<YOUR_ACCOUNT_NUMBER>" 3. Install dependencies There are a few library dependencies, which you can install using pip: $ pip install -r requirements.txt 4. Run the tests Verify that everything is working as intended by running the tests with pytest using this command: $ export USE_REAL_MONEY=NO && pytest *.py --verbose 5. Run the benchmark The benchmark report shows how the current implementation of the analysis and trading algorithms would have performed against historical data. You can run it again to benchmark any changes you may have made: $ ./benchmark.py > benchmark.md 6. Start the bot Enable real orders that use your money: $ export USE_REAL_MONEY=YES Have the code start running in the background with this command: $ nohup ./main.py & License Archimedes (edits under Invacio) Max Braun Frame under Max Braun, licence under Apache V2 License. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
aidapsibr / ScarletlockA C# implementation of the Redlock algorithm for Distributed Lock Management (DLM).
mxsm / RainRain ID generator is a distributed ID generation system, easy to use, high performance, high availability. Segmented mode and snowflake algorithm mode are provided
Koshkaj / CasheA distributed cache store in Go implemented with Raft consensus algorithm
FebriantiW / Homomorphic Encryption And Federated Learning Based Privacy Preserving CNN Training Medical data is often highly sensitive in terms of data privacy and security concerns. Federated learning, one type of machine learn- ing techniques, has been started to use for the improvement of the privacy and security of medical data. In the federated learning, the training data is distributed across multiple machines, and the learning process is performed in a collaborative manner. There are several privacy attacks on deep learning (DL) models to get the sensitive information by attackers. Therefore, the DL model itself should be protected from the adversarial attack, especially for applications using medical data. One of the solutions for this prob- lem is homomorphic encryption-based model protection from the adversary collaborator. This paper proposes a privacy-preserving federated learning algorithm for medical data using homomor- phic encryption. The proposed algorithm uses a secure multi-party computation protocol to protect the deep learning model from the adversaries. In this study, the proposed algorithm using a real-world medical dataset is evaluated in terms of the model performance.
uwescience / GossipMapGossipMap: distributed parallel community detection algorithm
darbula / PymoteDEPRECATED Python package for event based simulation and evaluation of distributed algorithms.
adelbertc / SabreIn-memory distributed graph processing of trivially parallelizable graph algorithms.
XiaooLei / Lightdblightdb is a distributed Key-Value data store system based on bitcask model, supporting redis-like data types(String, Hash, List, Set, ZSet) and diverse commands to access it. lightdb has the implemented the capability to deploy as a replication cluster based on Raft consensus algorithm.
cengwins / AhcThis is an event-driven asynchronous component model developed in Python. This library can be used to design and implement component-based ad hoc and distributed computing models and algorithms.
