This project aim to build a network of locations and examine its properties through complex network analysis.
Here, we document the implementation of Twitter crawler and of the network.
This document discusses using ergodic hidden Markov models to characterize workloads. It describes workload characterization as creating a model from measured workload data like log files or traces. The approach treats sequences of virtual pages as time-varying data and analyzes them with statistical techniques and hidden Markov models. The models can determine the type of workload and generate similar log files. The document outlines parameters for the hidden Markov model like using short-time spectral analysis on page references and defining a spectral distance metric between logs. It evaluates using discrete and continuous hidden Markov models to classify single traces and multiple traces of the same workload to model program behavior. The conclusion states this approach achieved a 76% classification accuracy rate.
This document outlines potential thesis topics in digital signal processing (DSP) using MATLAB. It lists concepts like real-time noise filtering, automatic cue-point finding, note extraction from music, and high-speed data acquisition. Specific topics mentioned include modulation systems, waveform processing, control theory, digital filters, image processing, sensor array processing, seismic data processing, and communication systems. The document encourages contacting the provided email or phone number for guidance on DSP MATLAB thesis topics.
This document discusses the design and implementation of a carry save multiplier integrated circuit project supervised by Dr. Sayed Eid. It describes using a carry save multiplier algorithm that divides the multiplier into blocks, including bit multiplication, half adders, and full adders. It also discusses implementing the algorithm in Matlab, Simulink, and Verilog, and comparing the area and delay of different multiplier designs synthesized on an FPGA.
This document discusses using ergodic hidden Markov models to characterize workloads. It describes workload characterization as creating a model from measured workload data like log files or traces. The approach treats sequences of virtual pages as time-varying data and analyzes them with statistical techniques and hidden Markov models. The models can determine the type of workload and generate similar log files. The document outlines parameters for the hidden Markov model like using short-time spectral analysis on page references and defining a spectral distance metric between logs. It evaluates using discrete and continuous hidden Markov models to classify single traces and multiple traces of the same workload to model program behavior. The conclusion states this approach achieved a 76% classification accuracy rate.
This document outlines potential thesis topics in digital signal processing (DSP) using MATLAB. It lists concepts like real-time noise filtering, automatic cue-point finding, note extraction from music, and high-speed data acquisition. Specific topics mentioned include modulation systems, waveform processing, control theory, digital filters, image processing, sensor array processing, seismic data processing, and communication systems. The document encourages contacting the provided email or phone number for guidance on DSP MATLAB thesis topics.
This document discusses the design and implementation of a carry save multiplier integrated circuit project supervised by Dr. Sayed Eid. It describes using a carry save multiplier algorithm that divides the multiplier into blocks, including bit multiplication, half adders, and full adders. It also discusses implementing the algorithm in Matlab, Simulink, and Verilog, and comparing the area and delay of different multiplier designs synthesized on an FPGA.
An Experiment-Driven Performance Model of Stream Processing Operators in Fog ...FogGuru MSCA Project
The document presents a performance model for stream processing operators in fog computing environments. The model predicts an operator's processing time based on factors like replication level, network delays, and parallelization efficiency. Experimental tests on Apache Flink show the model predicts processing time with 98% accuracy. The model can also be applied to entire workflows by considering the slowest operator. Parameters like parallelization efficiency may be reused for different operators to reduce calibration tests needed.
The document discusses MATLAB projects and topics for academic students. It lists the five most important MATLAB toolboxes: Optimization Toolbox, LTE System Toolbox, WLAN System Toolbox, Data Analytics, and Antenna Toolbox. It then lists five latest research topics in MATLAB: video registration, object classes detection, deep reinforcement learning, ultrasound reconstruction, and dynamic image sequences. Finally, it lists current technologies in MATLAB: strings with double quotes, data accessing, large text files and databases, data exploration, and Hadoop mapReduce. It provides contact information at the end.
This document provides an overview of MATLAB project topics, including topics in natural sciences, dimensionality reduction techniques, and trendy project titles. Some example topics include modeling with Stateflow Petri Nets, ARX and ARMAX identification models, locality preserving projection, and developing a GUI for variable rate microstepping of a stepper motor. The document also provides contact information for those seeking additional guidance on MATLAB project selection and development.
This document discusses tools and techniques for swarm mobile robot navigation in fenced areas. It describes using MATLAB software and the KIKS simulator to design a fuzzy logic controller for navigation. The project technique involves building an environment with obstacles, designing the fuzzy logic controller with input and output variables and rules, implementing a navigation system based on the controller, and testing navigation with and without obstacles.
NeQuick is a semi-empirical model that describes spatial and temporal variations in ionospheric electron density. It is a flexible and quick-running model that was proposed for single-frequency operation in the Galileo project to compute slant total electron content along arbitrary ground-to-satellite ray paths. The NeQuick source code is only available in its 1994 version, which was developed in Fortran 77.
This document provides a list of modern MATLAB thesis project titles, trendy research areas in MATLAB projects, and key notes on MATLAB simulation. Some example titles include speech recognition technology based on interactive web, probabilistic GUI and time variant sensor accuracy model in Aerospace Applications, and 5DOF robot manipulator modeling and trajectory using CATIA and MATLAB. Trendy research areas featured are implicit and explicit solvers, sparse and full linear algebra, bounded and fixed cost simulation, event location and detection, and discrete event systems simulation. Key points on MATLAB simulation include implicit and explicit solvers, sparse and full linear algebra, bounded and fixed cost simulation, event location and detection, and discrete event systems simulation. Contact information is provided at the bottom
The document describes two tasks for a data structures lab assignment involving stack implementation using arrays. Task 1 involves writing a program to convert a decimal number to binary by using a stack to push the remainders of successive divisions of the number by 2 onto the stack. Task 2 involves writing a program to find the middle element of a stack using array implementation.
Parallel Left Ventricle Simulation Using the FEniCS FrameworkUral-PDC
This document describes using the FEniCS framework to simulate electrical activity in the left ventricle of the heart in parallel. It implements the Ekaterinburg-Oxford model of cardiac electrophysiology on a 3D mesh of the left ventricle. Testing on a supercomputer showed near-linear scaling up to 240 CPU cores, reducing the simulation time from over 980 seconds to under 10 seconds. The choice of Krylov linear solver and preconditioner was found to significantly impact performance. The FEniCS implementation achieved similar performance as a previous manual implementation in C using OpenMP, but with better scalability.
"Source Code Abstracts Classification Using CNN", Vadim Markovtsev, Lead Soft...Dataconomy Media
"Source Code Abstracts Classification Using CNN", Vadim Markovtsev, Lead Software Engineer - Machine Learning Team at Source {d}
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS
About the Author:
Currently Vadim is a Senior Machine Learning Engineer at source{d} where he works on deep neural networks that aim to understand all of the world's developers through their code. Vadim is one of the creators of the distributed deep learning platform Veles (https://velesnet.ml) while working at Samsung. Afterwards Vadim was responsible for the machine learning efforts to fight email spam at Mail.Ru. In the past Vadim was also a visiting associate professor at Moscow Institute of Physics and Technology, teaching about new technologies and conducting ACM-like internal coding competitions. Vadim is also a big fan of GitHub (vmarkovtsev) and HackerRank (markhor), as well as likes to write technical articles on a number of web sites.
Python in the real world : from everyday applications to advanced roboticsJivitesh Dhaliwal
The use of Python in Robotics. A presentation at PyCon India 2011. To see the video, please visit http://urtalk.kpoint.com/kapsule/gcc-ce0164df-0518-447c-9ade-a9ec8dd931de
An Experiment-Driven Performance Model of Stream Processing Operators in Fog ...FogGuru MSCA Project
The document presents a performance model for stream processing operators in fog computing environments. The model predicts an operator's processing time based on factors like replication level, network delays, and parallelization efficiency. Experimental tests on Apache Flink show the model predicts processing time with 98% accuracy. The model can also be applied to entire workflows by considering the slowest operator. Parameters like parallelization efficiency may be reused for different operators to reduce calibration tests needed.
The document discusses MATLAB projects and topics for academic students. It lists the five most important MATLAB toolboxes: Optimization Toolbox, LTE System Toolbox, WLAN System Toolbox, Data Analytics, and Antenna Toolbox. It then lists five latest research topics in MATLAB: video registration, object classes detection, deep reinforcement learning, ultrasound reconstruction, and dynamic image sequences. Finally, it lists current technologies in MATLAB: strings with double quotes, data accessing, large text files and databases, data exploration, and Hadoop mapReduce. It provides contact information at the end.
This document provides an overview of MATLAB project topics, including topics in natural sciences, dimensionality reduction techniques, and trendy project titles. Some example topics include modeling with Stateflow Petri Nets, ARX and ARMAX identification models, locality preserving projection, and developing a GUI for variable rate microstepping of a stepper motor. The document also provides contact information for those seeking additional guidance on MATLAB project selection and development.
This document discusses tools and techniques for swarm mobile robot navigation in fenced areas. It describes using MATLAB software and the KIKS simulator to design a fuzzy logic controller for navigation. The project technique involves building an environment with obstacles, designing the fuzzy logic controller with input and output variables and rules, implementing a navigation system based on the controller, and testing navigation with and without obstacles.
NeQuick is a semi-empirical model that describes spatial and temporal variations in ionospheric electron density. It is a flexible and quick-running model that was proposed for single-frequency operation in the Galileo project to compute slant total electron content along arbitrary ground-to-satellite ray paths. The NeQuick source code is only available in its 1994 version, which was developed in Fortran 77.
This document provides a list of modern MATLAB thesis project titles, trendy research areas in MATLAB projects, and key notes on MATLAB simulation. Some example titles include speech recognition technology based on interactive web, probabilistic GUI and time variant sensor accuracy model in Aerospace Applications, and 5DOF robot manipulator modeling and trajectory using CATIA and MATLAB. Trendy research areas featured are implicit and explicit solvers, sparse and full linear algebra, bounded and fixed cost simulation, event location and detection, and discrete event systems simulation. Key points on MATLAB simulation include implicit and explicit solvers, sparse and full linear algebra, bounded and fixed cost simulation, event location and detection, and discrete event systems simulation. Contact information is provided at the bottom
The document describes two tasks for a data structures lab assignment involving stack implementation using arrays. Task 1 involves writing a program to convert a decimal number to binary by using a stack to push the remainders of successive divisions of the number by 2 onto the stack. Task 2 involves writing a program to find the middle element of a stack using array implementation.
Parallel Left Ventricle Simulation Using the FEniCS FrameworkUral-PDC
This document describes using the FEniCS framework to simulate electrical activity in the left ventricle of the heart in parallel. It implements the Ekaterinburg-Oxford model of cardiac electrophysiology on a 3D mesh of the left ventricle. Testing on a supercomputer showed near-linear scaling up to 240 CPU cores, reducing the simulation time from over 980 seconds to under 10 seconds. The choice of Krylov linear solver and preconditioner was found to significantly impact performance. The FEniCS implementation achieved similar performance as a previous manual implementation in C using OpenMP, but with better scalability.
"Source Code Abstracts Classification Using CNN", Vadim Markovtsev, Lead Soft...Dataconomy Media
"Source Code Abstracts Classification Using CNN", Vadim Markovtsev, Lead Software Engineer - Machine Learning Team at Source {d}
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2016: http://bit.ly/1WMJAqS
About the Author:
Currently Vadim is a Senior Machine Learning Engineer at source{d} where he works on deep neural networks that aim to understand all of the world's developers through their code. Vadim is one of the creators of the distributed deep learning platform Veles (https://velesnet.ml) while working at Samsung. Afterwards Vadim was responsible for the machine learning efforts to fight email spam at Mail.Ru. In the past Vadim was also a visiting associate professor at Moscow Institute of Physics and Technology, teaching about new technologies and conducting ACM-like internal coding competitions. Vadim is also a big fan of GitHub (vmarkovtsev) and HackerRank (markhor), as well as likes to write technical articles on a number of web sites.
Python in the real world : from everyday applications to advanced roboticsJivitesh Dhaliwal
The use of Python in Robotics. A presentation at PyCon India 2011. To see the video, please visit http://urtalk.kpoint.com/kapsule/gcc-ce0164df-0518-447c-9ade-a9ec8dd931de
BISSA: Empowering Web gadget Communication with Tuple SpacesSrinath Perera
BISSA is a framework that enables communication between web gadgets using a tuple space model. It proposes a global, peer-to-peer based tuple space and an in-browser tuple space that are linked. The global tuple space is highly scalable and reliable, using a DHT for data distribution and indexing to support search queries. The in-browser space provides local and global APIs. Together this allows truly client-side web applications to communicate and store data without backend code. Performance tests on the global space showed good scalability and latency. Several use cases are proposed including coordinated dashboard gadgets, multiplayer games, and social applications.
This document summarizes a presentation on developing WebSocket servers in Python. It discusses what WebSockets are and why they are useful for web development. It then outlines the steps demonstrated in a chat application example, beginning with basic WebSocket integration, adding chat functionality without message queuing, and finally integrating the RabbitMQ message broker. The document provides code links and discusses ways the example application could be improved.
MongoDB at MapMyFitness from a DevOps PerspectiveMongoDB
This document provides an overview of how MongoDB is used at MapMyFitness (MMF) from a DevOps perspective. It describes how MMF stores the majority of its data, including over 120 million user-generated routes and activities totaling over 7TB, in various MongoDB collections. It also discusses MMF's implementation patterns for MongoDB, including replica sets, sharding, and automation. The document outlines considerations for monitoring, maintenance, security, and performance tuning of MongoDB at scale.
Pysense: wireless sensor computing in Python?Davide Carboni
PySense aims at bringing wireless sensor (and "internet of things") macroprogramming to the audience of Python programmers. WSN macroprogramming is an emerging approach where the network is seen as a whole and the programmer focuses only on the application logic. The PySense runtime environment
partitions the code and transmits code snippets to the right nodes finding a balance between energy
consumption and computing performances.
The document discusses various topics related to concurrency and parallelism including threads, shared state, locks, asynchronous programming, parallel processing, and reactive programming. It provides examples of using locks, reader-writer locks, thread pools, tasks, and reactive streams. It also covers challenges with concurrent programming such as race conditions, deadlocks, and debugging concurrent applications.
This document provides an overview of streaming data processing with Apache Storm. It discusses batch processing versus real-time processing of big data and why stream processing is needed. The document then describes Apache Storm, including its architecture, key concepts, and how to develop Storm applications. Resources for learning more about Storm are also provided.
Have you ever wondered how to speed up your code in Python? This presentation will show you how to start. I will begin with a guide how to locate performance bottlenecks and then give you some tips how to speed up your code. Also I would like to discuss how to avoid premature optimization as it may be ‘the root of all evil’ (at least according to D. Knuth).
B.Eng-Final Year Project interim-reportAkash Rajguru
This interim report summarizes the progress made on a project to develop a Java-based intrusion detection system tool. The tool will monitor network traffic by capturing packets from the host machine interface using the Jpcap library. Work accomplished so far includes researching IDS and honeypot technologies, learning to use third party Java libraries, developing a GUI to list interfaces and select one for packet capture, capturing live packet streams, port scanning the selected interface, and storing captured packets to a file. Future work includes improving the GUI, connecting to a NoSQL database, analyzing log files, and implementing honeypots and testing with JUnit. The goal is to create a complete IDS with functionality like interface selection, packet capture, port blocking rules
This document provides an overview and introduction to OMNeT++, an open-source discrete event network simulation framework. It outlines the key components of OMNeT++ including its C++ class library, infrastructure for assembling simulations using modules, and runtime interfaces. The document then describes a TicToc tutorial that guides the reader through building a basic two-node simulation in OMNeT++ to demonstrate common features like creating modules in C++, defining the network structure in NED, configuring simulations using ini files, and analyzing results. The goal of the tutorial is to familiarize new users with typical workflows for modeling, running and evaluating simulations in OMNeT++.
As one of our primary data stores, we utilize MongoDB heavily. Early last year our DevOps lead, Chris Merz, submitted some of our use cases to 10gen (http://www.10gen.com/events) as fodder for a presentation at the MongoDB conference in Boulder. The presentation went well enough at the Boulder conference that 10gen asked him to give it again at San Francisco, Seattle and again in Boulder.
Hopefully there are some nuggets in this deck that can help you in your quest to dominate MongoDB.
The Onward Journey: Porting Twisted to Python 3Craig Rodrigues
This document discusses porting the Twisted Python library to Python 3. It describes Twisted as a networking library that uses asynchronous programming. The author ported over 325 pull requests to help move Twisted to Python 3. Some challenges included changes to Python 3 like print becoming a function and str handling, as well as porting old C extensions. Running extensive unit tests was important. While difficult, porting benefits code quality and keeps Twisted compatible with Python's direction.
This document provides an outline for a TinyOS tutorial that introduces the TinyOS operating system and development environment. It covers the hardware primer, introduction to TinyOS, installation and configuration, NesC syntax, network communication, sensor data acquisition, debugging techniques, and concludes with an overview of the Agilla mobile agent system. The outline includes 10 sections that will guide students through understanding the TinyOS hardware platforms, programming model, components, interfaces, and building/installing applications.
This document discusses network protocol architectures and reference models. It begins by explaining the need for a protocol architecture to break communication tasks into modular layers. It then describes the two main protocol architectures: TCP/IP and the OSI model. The TCP/IP architecture organizes communication into 5 layers - physical, data link, internet, transport, and application. It relies primarily on IP for internet layer functions and TCP and UDP for transport layer functions. The OSI model also divides communication into 7 layers for interoperability between different systems.
A document describes using the radio and simulating TinyOS applications with TOSSIM. It discusses defining message structures, implementing radio communication using interfaces like Packet and AMSend, and simulating applications by specifying topologies, noise models, and debugging channels. An example application counts over a timer and broadcasts the counter using the radio. It can be simulated in TOSSIM by configuring 3 motes - one acts as a sink and receives periodic messages from sensors reporting temperature and humidity data.
This document compares different techniques for software architecture recovery using include dependencies and symbol dependencies. It finds that symbol dependencies provide more accurate results than include dependencies. The document analyzes several large, open source projects using various recovery techniques and different dependency methods. It measures the accuracy of the recovered architectures against ground truths. The results show that the quality of the recovered architecture is improved when using symbol dependencies as input compared to include dependencies. The best performing technique also sometimes changes based on which dependency method is used. In conclusion, the quality of the input affects the quality of the output for software architecture recovery.
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CSE5656 Complex Networks - Location Correlation in Human Mobility, Implementation
1. Complex Networks Class Project
!
Location Correlation in Human Mobility
!
Marcello Tomasini
Bio-Complex Lab
Department of Computer Sciences
Florida Tech
2. Twitter Miner Implementation
The application which mine Twitter is developed in Python and uses the
following libraries:
• twitter (Python Twitter Tools): data is collected through Twitter stream
API and appended to local buffer
• pymongo (MongoDB): data is stored on the Biocomplex Lab MongoDB
instance
• logging: Python logging facility is used to keep track of code exceptions,
and non-standard twitter messages in the stream (warning, limit,
disconnect). Mostly for debugging. Exceptions don’t stop program
execution (mostly), but try to recover instead, in order to avoid manual
intervention
• collections: collections.deque is used for a thread-safe high-performance
local buffering in order to reduce Network IO and overhead on BioComplex
Lab MongoDB server
• threading: data is pushed to BioComplex Lab MongoDB instance by a
separate thread. Thread pop out a fixed amount of elements from the
deque and try the insert operation. If insert operation fails, revert back the
transition. No tweets lost. Python GIL is not an issue here since the thread
is IO bounded
!
Code runs on Amazon EC2 t2.micro instance for maximum reliability (SLA
99.95%).
Code performance: easily handle ~8Mbps twitter stream (worldwide stream
of geotagged tweets) corresponding to ~2000 tweet/s.
3. Network Builder Implementation
The application which build the network is developed in Python and uses the
following libraries:
• pymongo (MongoDB): filter tweets with a bounding box (due to a Twitter
bug) and retrieve data from BioComplex Lab MongoDB instance. Query
projections help to reduce data transferred over network
• scikit-learn: provides functions to compute k-means clustering of
coordinate points. Clusters will represent locations
• numpy: provides fast arrays and matrices data structures
• matplotlib.pyplot: plot graphs
• igraph: create and export the network structure
!
Clustering need a distance metric; coordinates are not in an euclidean space,
but in a spherical space, thus to compute the great-circle distance [1]
between two points we could use haversine formula [2]
!
However, most implementations use a distance matrix when supplied with a
non standard metric, which requires O(n2) space. Given the size of the
dataset that’s impractical, thus we use Mercator projection [3] to project
coordinates in an euclidean space and then use standard k-means algorithm.
!!!!!
[1] http://en.wikipedia.org/wiki/Great-circle_distance
[2] http://en.wikipedia.org/wiki/Haversine_formula
[3] http://en.wikipedia.org/wiki/Mercator_projection