BugLoc: Bug Localization in Multi Threaded Application via Graph Mining Approachijcoa
Detection of software bugs and its occurrences, repudiation and its root cause is a very difficult process in large multi threaded applications. It is a must for a software developer or software organization to identify bugs in their applications and to remove or overcome them. The application should be protected from malfunctioning. Many of the compilers and Integrated Development Environments are effectively identifying errors and bugs in applications while running or compiling, but they fail in detecting actual cause for the bugs in the running applications. The developer has to reframe or recreate the package with the new one without bugs. It is time consuming and effort is wasted in Software Development Life Cycle. There is a possibility to use graph mining techniques in detecting software bugs. But there are many problems in using graph mining techniques. Managing large graph data, processing nodes with links and processing subgraphs are the problems to be faced in graph mining approach. This paper presents a novel algorithm named BugLoc which is capable of detecting bugs from the multi threaded software application. The BugLoc uses object template to store graph data which reduces graph management complexities. It also uses substring analysis method in detecting frequent subgraphs. The BugLoc then analyses frequent subgraphs to detect exact location of the software bugs. The experimental results show that the algorithm is very efficient, accurate and scalable for large graph dataset.
Business Situation
Immumterix does analysis of DNA sequences and the immune system using multiple lab techniques. They needed to use computational techniques to analyze DNA through methods like high throughput analysis. They would analyze the DNA sequence and would represent each element through ASCII values. By studying the DNA sequence thoroughly, they were able to understand human DNA through various algorithms. What they needed was sequence alignment analysis and similarity searches in biological databases. Each sample would contain approximately 400 MB to 1 GB of data and would need customization based on the data collected. The need was to coordinate with the scientists from Immumetrix in order to understand the complexities and align the analytical algorithm accordingly. The objective of the project was to analyze the DNA data and provide outputs in CSV and graphical formats.
How to use Artificial Intelligence with Python? EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
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Deck for a talk I gave at Contemporary Political History in the Digital Age, Foreign & Commonwealth Office, 11 February 2016.
Notes at https://gist.github.com/drjwbaker/e01a3d03040c3ccdd4c1
Enabling Complex Analysis of Large-Scale Digital Collections: Humanities Rese...James Baker
Talk at Digital Humanities 2016 with Melissa Terras, James Hetherington, David Beavan, Anne Welsh, Helen O'Neill, Will Finley, Oliver Duke-Williams, Adam Farquhar, and Martin Zaltz Austwick.
Abstract http://dh2016.adho.org/abstracts/2584
BugLoc: Bug Localization in Multi Threaded Application via Graph Mining Approachijcoa
Detection of software bugs and its occurrences, repudiation and its root cause is a very difficult process in large multi threaded applications. It is a must for a software developer or software organization to identify bugs in their applications and to remove or overcome them. The application should be protected from malfunctioning. Many of the compilers and Integrated Development Environments are effectively identifying errors and bugs in applications while running or compiling, but they fail in detecting actual cause for the bugs in the running applications. The developer has to reframe or recreate the package with the new one without bugs. It is time consuming and effort is wasted in Software Development Life Cycle. There is a possibility to use graph mining techniques in detecting software bugs. But there are many problems in using graph mining techniques. Managing large graph data, processing nodes with links and processing subgraphs are the problems to be faced in graph mining approach. This paper presents a novel algorithm named BugLoc which is capable of detecting bugs from the multi threaded software application. The BugLoc uses object template to store graph data which reduces graph management complexities. It also uses substring analysis method in detecting frequent subgraphs. The BugLoc then analyses frequent subgraphs to detect exact location of the software bugs. The experimental results show that the algorithm is very efficient, accurate and scalable for large graph dataset.
Business Situation
Immumterix does analysis of DNA sequences and the immune system using multiple lab techniques. They needed to use computational techniques to analyze DNA through methods like high throughput analysis. They would analyze the DNA sequence and would represent each element through ASCII values. By studying the DNA sequence thoroughly, they were able to understand human DNA through various algorithms. What they needed was sequence alignment analysis and similarity searches in biological databases. Each sample would contain approximately 400 MB to 1 GB of data and would need customization based on the data collected. The need was to coordinate with the scientists from Immumetrix in order to understand the complexities and align the analytical algorithm accordingly. The objective of the project was to analyze the DNA data and provide outputs in CSV and graphical formats.
How to use Artificial Intelligence with Python? EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Deck for a talk I gave at Contemporary Political History in the Digital Age, Foreign & Commonwealth Office, 11 February 2016.
Notes at https://gist.github.com/drjwbaker/e01a3d03040c3ccdd4c1
Enabling Complex Analysis of Large-Scale Digital Collections: Humanities Rese...James Baker
Talk at Digital Humanities 2016 with Melissa Terras, James Hetherington, David Beavan, Anne Welsh, Helen O'Neill, Will Finley, Oliver Duke-Williams, Adam Farquhar, and Martin Zaltz Austwick.
Abstract http://dh2016.adho.org/abstracts/2584
This is a company profile of Museum Ceria, the first event organizer which provide fun active learning programs for schools to visit museums in Indonesia.
Microservices, DevOps, Continuous Delivery – More Than Three BuzzwordsEberhard Wolff
Microservices, DevOps and Continuous Delivery are three hypes at the moment. This talk looks into the relationships between these three approaches and gives an idea how these approaches help to solve concrete problems. Held at Continuous Lifecycle 2015.
Kami dari PT Segitiga Gumilang, selaku event organizer Pemerintah Kota Depok ingin memberikan informasi bahwa kami akan mengadakan acara Job Fair yang akan diadakan pada:
Hari/Tanggal : Rabu-Kamis, 18-19 Mei 2016
Tempat : Main Hall Lt. 1, Depok Town Square
Machine Learning Techniques in Python Dissertation - PhdassistancePhD Assistance
Machine Learning (ML) is a Programming Model which is quite good and faster. It helps in taking better decisions where domain knowledge is an important aspect. The Machine Learning models require some data and probable outputs if any and develop the program using the computer.
The most popular and significant field in the world of technology today is machine learning. Thus, there is varied and diverse support offered for Machine Learning in terms of frameworks and programming languages.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee known about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/3dcke6F
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
Top 5 Machine Learning Tools for Software Development in 2024.pdfPolyxer Systems
Machine learning has been widely used by various industries in 2023. The software development industry can take great advantage of machine learning in 2024 as well.
It has great potential to revolutionize various aspects of software development including task automation, boosting user experience, and easy software development and deployment.
Python libraries presentation Contains all top 10 labraries information like numpy,tenslorflow,scikit-learn,Numpy,keras,PyToruch,LightGBM,Eli5,scipy,theano,pandas
This is a company profile of Museum Ceria, the first event organizer which provide fun active learning programs for schools to visit museums in Indonesia.
Microservices, DevOps, Continuous Delivery – More Than Three BuzzwordsEberhard Wolff
Microservices, DevOps and Continuous Delivery are three hypes at the moment. This talk looks into the relationships between these three approaches and gives an idea how these approaches help to solve concrete problems. Held at Continuous Lifecycle 2015.
Kami dari PT Segitiga Gumilang, selaku event organizer Pemerintah Kota Depok ingin memberikan informasi bahwa kami akan mengadakan acara Job Fair yang akan diadakan pada:
Hari/Tanggal : Rabu-Kamis, 18-19 Mei 2016
Tempat : Main Hall Lt. 1, Depok Town Square
Machine Learning Techniques in Python Dissertation - PhdassistancePhD Assistance
Machine Learning (ML) is a Programming Model which is quite good and faster. It helps in taking better decisions where domain knowledge is an important aspect. The Machine Learning models require some data and probable outputs if any and develop the program using the computer.
The most popular and significant field in the world of technology today is machine learning. Thus, there is varied and diverse support offered for Machine Learning in terms of frameworks and programming languages.
Ph.D. Assistance serves as an external mentor to brainstorm your idea and translate that into a research model. Hiring a mentor or tutor is common and therefore let your research committee known about the same. We do not offer any writing services without the involvement of the researcher.
Learn More: https://bit.ly/3dcke6F
Contact Us:
Website: https://www.phdassistance.com/
UK NO: +44–1143520021
India No: +91–4448137070
WhatsApp No: +91 91769 66446
Email: info@phdassistance.com
ER Publication,
IJETR, IJMCTR,
Journals,
International Journals,
High Impact Journals,
Monthly Journal,
Good quality Journals,
Research,
Research Papers,
Research Article,
Free Journals, Open access Journals,
erpublication.org,
Engineering Journal,
Science Journals,
Top 5 Machine Learning Tools for Software Development in 2024.pdfPolyxer Systems
Machine learning has been widely used by various industries in 2023. The software development industry can take great advantage of machine learning in 2024 as well.
It has great potential to revolutionize various aspects of software development including task automation, boosting user experience, and easy software development and deployment.
Python libraries presentation Contains all top 10 labraries information like numpy,tenslorflow,scikit-learn,Numpy,keras,PyToruch,LightGBM,Eli5,scipy,theano,pandas
A Comprehensive Guide to App Development with Python - AppsDevProSofiaCarter4
Python is a versatile and powerful programming language that has become increasingly popular for app development due to its simplicity, readability, and wide range of libraries and frameworks.
Automatic Selection of Open Source Multimedia Softwares Using Error Back-Prop...IJERA Editor
Open source opens a new era to provide license of the software for the user at free of cost which is advantage over paid licensed software. In Multimedia applications there are many versions of software are available and there is a problem for the user to select compatible software for their own system. Most of the time while surfing for software a huge list of software opens in response. The selection of particular software which is pretty suitable for the system from a real big list is the biggest challenge that is faced by the users. This work has been done that focuses on the existing open source software that are widely used and to design an automatic system for selection of particular open source software according to the compatibility of users own system. In this work, error back-propagation based neural network is designed in MATLAB for automatic selection of open source software. The system provides the open source software name after taking the information from user. Regression coefficient of 0.93877 is obtained and the results shown are up to the mark and can be utilized for the fast and effective software search.
Python is a very powerful tool when used properly The diffe.pdfankitsrivastava681882
Python is a very powerful tool when used properly. The different capabilities it has allows the user
to tailor the outcome to any level of expertise needed. This flexibility makes it very useful when
used correctly. First, the simplicity of the program allows the user to write their own code from the
beginning. This makes it less time consuming to begin learning and using. From there, there are
many libraries that one can add for data analysis. These are things like pandas, numpy and scikit
learning. These libraries will allow the user to analysis the data using machine learning and
compute any number of variables they desire. This plays into the benefits of any nontechnical
audiences that the data is meant for. It takes a wide range of complex data, analyzes it, the has
the ability to give results that are meaningful. The ability to add visual references of the data
represents a quick opportunity for the audience to see the actual data the project represents..
If you need the support of the top python app development agency, you should pick one that uses the latest version of Python 3.11 released on 2nd March 2022.
Python training would help create innovations in the field of artificial inte...AartiJain31
Python training is ideal for those who want to create novel applications in Artificial Intelligence.It is an overview based on how Python plays a key role in AI .
For creating great software products it is key to have a good set of developing practices. In this talk I go over some of the most important patterns and strategies for developing products in Python.
The talk was given at Imperial College London in June 2018.
1. Data Fusion of Multiple Physiological Sensors
for Use in Machine Learning
Computer Science Department
Professor Hirshfield and Leanne Hirshfield
Eseosa Asiruwa‘18
Matt Goon ’18
Mitchel Herman‘19
Sindy Liu ’18
GOALS
• Create a Python program that combines data from multiple
physiological sensors and runs the combined data through
machine learning algorithms
• Create a graphical user interface for the program in order to make
it easy to use
• Make the program flexible to the needs of the user by adding
options to customize on the code runs
INTRODUCTION
AcknowledgementsBen Sklar ‘18
Russell Glick ’18
Diane Paverman
Eric Murray
Mykhalio Antoniv ‘17
Eric Collins ’17
Languages and
Software Used
Python 2.7
Orange
BioSPPy
Numpy
SciPy
Pandas
Further Research
GUI
PROJ
MACHINE LEARNING
Machine Learning (ML) is a a form of artificial intelliegences that helps computers learn without being
explicitly programmed. Machine learning focuses on the development of computer programs that can
teach themselves to grow and change when exposed to new data. Machine learning uses data to
dectect patterns and adjusts program actions accordingly.
Our end goal: Create a program that takes advantage of pre-existing ML algorithms and will
continously run on a given set of large data and condition files. The program will be flexible enough
to be used for future experiments with minimal change. Credit: Techtarget.com
Machine learning algorithms have proven useful for
identifying complex patterns through the analysis of
large quantities of data and predicting outcomes from those
patterns. Past research projects used machine learning
algorithms to analyze data from a single physiological
sensor to predict the psychological state of the test subjects.
The program we created in the Python programming language
accepts data from five physiological sensors (functional near
infrared spectroscopy, electroencephalogram, respiration,
electrocardiogram, electrodermal activity) and creates files
that get“fused”so that all information can be processed by a
sophisticated, Python-based machine learning program,
Orange Data Mining.
Creating a graphical user interface for the program proved to be a second concurrent
goal for the project. This included customization options that affect how the program runs.
We expect that more research
will be done on top of the work
we have been able to accomplish.
In the future, since we made sure
that our code was flexible and easy
to understand, there is possibility
for the addition of more
pyschological sensors. There is
also the prospect of added
features for the senors that are
currently in the program. Further
additions to GUI would also be
possible in order to achieve
a better user experience.
Window 1
Select which
sensor(s) you
want to analyze
data from
Window 2
Select how you
want to format
the data (ROI, Z-
Score, Sampling
Rate)
Window 3
Select if you
want to include
SAX features. If
you do, input
the parameters
for SAX
Window 4
Input which conditons to be
analyzed and machine learning
paramters
Also choose which files you want to
output and the extension for
machine learning output
Window 5
Upload the data
files you want to
run machine
learning on
Proj.py can: Create .arff files for each subject for five physiological sensors
Create .tab files for each subject for five physiological sensors
Fuse .arff files for all the physiological sensors for each subject
Create a concatenated arff file for all the subjects
Run a variety of machine learning algorithms on .arff files
Proj.py is the main code we designed for the summer. The project can perform in a variety of different ways. Proj.py
works by formatting and passing data and condition files for all of the desired sensors through another program
called Arffgen. Arffgen then creates a .arff file which is used by the Orange ML algorithms. Proj.py then fuses the .arff
files for all the sensors and runs the fused data through different machine learning algorithms, creating ML files for
each subject.
Proj.py is set up to work in any way the user needs it to run. It can stop after any stop of skip steps in order to save time.
It is also completely expandable and should be able to work with other physiological sensors with minor changes to
the code.