1) The document discusses using machine learning classifiers to predict whether a robotic grasp of an object will be robust based on joint state data from simulations. 2) Several classifiers were tested on a dataset from 992,641 grasps, with Random Forest and J48 achieving the best accuracy of around 80-83%. 3) Features related to joint velocities and efforts were most important for prediction. Further validation on physical robots is needed to test the models.
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data
A presentation on implementing feature toggles in an efficient way esp. when we have large number of toggles such that they do not create technical debt for us.
PVS-Studio analyzer is continuously improving, and the C#-code analysis module is developing most actively: ninety new diagnostic rules were added in 2016. However, the best way to estimate the analyzer's efficiency is to look at the bugs it can catch. It's always interesting, as well as useful, to do recurring checks of large open-source projects at certain intervals and compare their results. Today I will talk about the results of the second analysis of SharpDevelop project.
Operators and Control Statements in Java : Arithmetic Operators, Unary Operators, Relational
Operators, Logical Operators, Boolean Operators, Bitwise Operators, Ternary Operators, New
Operator, Cast Operator, If .... else statement, Switch statement, Break statement, Continue
statement, Return statement, do ... while loop, while loop, for loop.
Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data
A presentation on implementing feature toggles in an efficient way esp. when we have large number of toggles such that they do not create technical debt for us.
PVS-Studio analyzer is continuously improving, and the C#-code analysis module is developing most actively: ninety new diagnostic rules were added in 2016. However, the best way to estimate the analyzer's efficiency is to look at the bugs it can catch. It's always interesting, as well as useful, to do recurring checks of large open-source projects at certain intervals and compare their results. Today I will talk about the results of the second analysis of SharpDevelop project.
Operators and Control Statements in Java : Arithmetic Operators, Unary Operators, Relational
Operators, Logical Operators, Boolean Operators, Bitwise Operators, Ternary Operators, New
Operator, Cast Operator, If .... else statement, Switch statement, Break statement, Continue
statement, Return statement, do ... while loop, while loop, for loop.
Accord.Net: Looking for a Bug that Could Help Machines Conquer HumankindPVS-Studio
Articles discussing the results of analysis of open-source projects are a good thing as they benefit everyone: some, including project authors themselves, can find out what bugs lurk in a project; others discover for themselves the static analysis technology and start using it to improve their code's quality. For us, it is a wonderful means to promote PVS-Studio analyzer, as well as to put it through some additional testing. This time I have analyzed Accord.Net framework and found lots of interesting issues in its code.
In this report, we produce a dynamic analysis approach which extracts
all function definitions that can be hoisted using dynamic
analysis framework Jalangi framework. This approach was evaluated
on the following JS Libraries: Q1, Underscore and Lodash.
The accuracy of this approach was 100%, 50%, and 100% respectively.
Keywords: Hoisting Functions - Nested Functions- Dynamic
Analysis.
tracts
all function definitions that can be hoisted using dynamic
analysis framework Jalangi framework. This approach was evaluated
on the following JS Libraries: Q1, Underscore and Lodash.
The accuracy of this approach was 100%, 50%, and 100% respectively.
Keywords: Hoisting Functions - Nested Functions- Dynamic
Analysis.
Demonstrate some great aspects of Mockito. Made for Mockito v1.8.5.
All examples can be found @
https://github.com/dodozhang21/MockitoExamples
Ying Zhang (Dodo) http://pure-essence.net
Introduction to Functional Programming and usage of basic constructs in Java 7 using Guava.
Further, the session introduces Reactive Systems architecture and design.
Checking the code of Valgrind dynamic analyzer by a static analyzerPVS-Studio
This statement would be incorrect, as well as the reverse idea. The tools of static and dynamic analysis complement each other, they do not compete with each other. Both of these methods have strengths and weaknesses. Some errors cannot be detected by dynamic analyzers, some - by static ones. That's why I suggest treating this post as another demonstration of the abilities of PVS-Studio, not the comparison of two methodologies.
Accord.Net: Looking for a Bug that Could Help Machines Conquer HumankindPVS-Studio
Articles discussing the results of analysis of open-source projects are a good thing as they benefit everyone: some, including project authors themselves, can find out what bugs lurk in a project; others discover for themselves the static analysis technology and start using it to improve their code's quality. For us, it is a wonderful means to promote PVS-Studio analyzer, as well as to put it through some additional testing. This time I have analyzed Accord.Net framework and found lots of interesting issues in its code.
In this report, we produce a dynamic analysis approach which extracts
all function definitions that can be hoisted using dynamic
analysis framework Jalangi framework. This approach was evaluated
on the following JS Libraries: Q1, Underscore and Lodash.
The accuracy of this approach was 100%, 50%, and 100% respectively.
Keywords: Hoisting Functions - Nested Functions- Dynamic
Analysis.
tracts
all function definitions that can be hoisted using dynamic
analysis framework Jalangi framework. This approach was evaluated
on the following JS Libraries: Q1, Underscore and Lodash.
The accuracy of this approach was 100%, 50%, and 100% respectively.
Keywords: Hoisting Functions - Nested Functions- Dynamic
Analysis.
Demonstrate some great aspects of Mockito. Made for Mockito v1.8.5.
All examples can be found @
https://github.com/dodozhang21/MockitoExamples
Ying Zhang (Dodo) http://pure-essence.net
Introduction to Functional Programming and usage of basic constructs in Java 7 using Guava.
Further, the session introduces Reactive Systems architecture and design.
Checking the code of Valgrind dynamic analyzer by a static analyzerPVS-Studio
This statement would be incorrect, as well as the reverse idea. The tools of static and dynamic analysis complement each other, they do not compete with each other. Both of these methods have strengths and weaknesses. Some errors cannot be detected by dynamic analyzers, some - by static ones. That's why I suggest treating this post as another demonstration of the abilities of PVS-Studio, not the comparison of two methodologies.
The slide of the talk in http://www.meetup.com/R-Users-Sydney/events/223867196/
There is a web version here: http://wush978.github.io/FeatureHashing/index.html
Angular 16 is the biggest release since the initial rollout of Angular, and it changes everything: Bye bye zones, change-detection, life-cycle, children-selectors, Rx and what not.
Recorded webinar based on these slides given by Yaron Biton, Misterbit Coding-Academy’s CTO, can be found at: https://www.youtube.com/watch?v=92K1fgPbku8
Coding-Academy offers advanced web-techs training and software development services: Top-rated Full-stack courses for Angular, React, Vue, Node, Modern architectures, etc. | Available top-notch on-demand-coders trough Misterbit technological solutions | Coding-Academy Bootcamp: Hundreds of employed full-stack developers every year | Anything web, end to end projects | Tech companies and startups | Consulting to management and dev teams | Workshops for managers and leaders.
A Unicorn Seeking Extraterrestrial Life: Analyzing SETI@home's Source CodePVS-Studio
Debates on whether or not we are alone in the Universe have been exciting our minds for many decades. This question is approached seriously by the SETI program whose mission is to search for extraterrestrial civilizations and ways to contact them. It is the analysis of one of this program's projects, SETI@home, that we are going to talk about in this article.
Analysis of Haiku Operating System (BeOS Family) by PVS-Studio. Part 1PVS-Studio
Operating systems are among the largest and most complicated software projects, and that means they perfectly suit the purpose of demonstrating static code analysis' capabilities. After the successful analysis of Linux Kernel, I felt inspired to try analyzing other open-source operating systems as well.
Our fall 12-Week Data Science bootcamp starts on Sept 21st,2015. Apply now to get a spot!
If you are hiring Data Scientists, call us at (1)888-752-7585 or reach info@nycdatascience.com to share your openings and set up interviews with our excellent students.
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Come join our meet-up and learn how easily you can use R for advanced Machine learning. In this meet-up, we will demonstrate how to understand and use Xgboost for Kaggle competition. Tong is in Canada and will do remote session with us through google hangout.
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Speaker Bio:
Tong is a data scientist in Supstat Inc and also a master students of Data Mining. He has been an active R programmer and developer for 5 years. He is the author of the R package of XGBoost, one of the most popular and contest-winning tools on kaggle.com nowadays.
Pre-requisite(if any): R /Calculus
Preparation: A laptop with R installed. Windows users might need to have RTools installed as well.
Agenda:
Introduction of Xgboost
Real World Application
Model Specification
Parameter Introduction
Advanced Features
Kaggle Winning Solution
Event arrangement:
6:45pm Doors open. Come early to network, grab a beer and settle in.
7:00-9:00pm XgBoost Demo
Reference:
https://github.com/dmlc/xgboost
In this article, I'm going to tell you about my experience of analyzing the Octave project. It is quite a popular one, especially among students who need to scan their math task solutions yet don't feel like buying a Matlab license.
Automatic Number Plate Recognition System in Bangla using Deep Learning model076TalathUnNabiAnik
Automatic Number Plate Recognition (ANPR) systems have become an increasingly crucial technology for law enforcement, traffic management, and security purposes. These systems, based on computer vision and machine learning techniques, are capable of automatically extracting and recognizing license plate information from images or video footage.
Jose Leiva, data scientist at Ets Asset Management Factory, gives an accurate and simple introduction to Machine Learning. He explains some of the problems that quantitative managers have to get alpha in the markets, and how to face them using Deep Learning.
Demo Videos: www.larry-lai.com/tracking.html
A real-time object tracking algorithm is proposed to cope with the variables of appearance changes like translation, zooming, rotation, panning/tilting, occlusion, luminance change, and blur. The proposed tracking scheme includes three steps. First, regional filter is employed to detect the candidate regions of targets. Next, these candidate regions are scaled to an uniform size for feature extraction. Finally, using feature matching to calculate the similarity between an instance and the target, and then store this instance if recognized as the target. We can see that the instance database would contain object's difference appearances as the tracking time going on. In other words, recognition capability will increase while the database become enlarging. To keep high computation performance, an algorithm with database reduction is proposed to limit the size of database. From our experiments, the proposed tracking system can achieve 30 FPS with resolution 1280x720 on an Intel I5 CPU 2.6GHz.
PVS-Studio team is about to produce a technical breakthrough, but for now let...PVS-Studio
Static analysis is most useful when it is done on a regular basis. Especially when the project is rapidly developing, like the Blender project, for example. Now it's time to check it once more, and see what suspicious fragments we'll find this time.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
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Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Runway Orientation Based on the Wind Rose Diagram.pptx
Grasping dataset
1. Grasping Dataset
Robotic grasping robustness prediction using classifiers
Professors
Marcelloni Francesco
Ducange Pietro
Student
Gabriele Sisinna
516706
MSc Bionics Engineering
2. Introduction
(I)
In the industrial field, interest in robotic manipulation and the
various grasping technologies has increased
Knowing in advance whether a grasp is robust or not would
save time and optimize assembly lines in complex processes
The following dataset was provided by the Shadow Robot
Company (Kaggle) through simulation in a dedicated sandbox
3. Introduction
(II)
Based on the current joint state we want to predict the stability of
the grasping pose
In simulation, there’s an easy way to check whether a grasp is
stable or not. Once the object is grasped, if the grasp is stable,
then the object shouldn’t move in the hand.
This means that the distance between the object and the palm
shouldn’t change when shaking the object.This measure is very
easy to get in simulation!
4. Dataset
The data obtained by simulation refer to the position, speed and effort
of the joints of the mechanical manipulator
Opening the dataset (.csv) withWeka → conversion to .arff format
All the attributes are Numeric except for experiment_number which
contain a string label for simulation purpose
The whole analysis presented has been developed withWeka +
MATLAB
Attributes Instances
30 992,641
(Lots of data…)
5. Preprocessing
experiment_number, measurement_number and all the xx_xx_pos attributes
were deleted for two reasons:
1. Velocity and position are linked by means of the first derivative
(redundancy)
2. The aim is to find a classifier for robustness that is independent of the
object (no position attributes): contain no information that is useful for
the data mining task at hand
After eliminating the “pos” attributes, we arrive at a total of 19 attributes for
speed, torque and robustness
Attribute Type
H1_F1_J1_VEL
H1_F1_J1_EFF
NUMERIC
NUMERIC
H1_F1_J2_VEL
H1_F1_J2_EFF
NUMERIC
NUMERIC
.
. .
. .
H1_F3_J3_VEL
H1_F3_J3_EFF
.
.
ROBUSTNESS NUMERIC
6. Outlier
To find outlier and extreme values the unsupervised filter
interquartile range can be used
Outlier and extreme_values attributes were created
Unsupervised Instance Filter → RemoveWithValues:This filter
removes instances according to the values of an attribute.
RemoveWithValues
InterquartileRange
7. Robustness
distribution
Due to the simulation process, many physically unfeasible values
were created
All this values were deleted keeping only the plausible range of
robustness attribute value spanning from 0 to 220
Playing with InterquartileRange filter and outlier factor we can
obtain the following histogram for the robustness attribute
8. Thresholding
of robustness
class
A threshold of 100 for the robustness was found accetable to
discriminate between good and bad grasp.
I’ll set the same threshold to divide the robustness values in two
distinct classes (1: good grasp, 0: bad grasp)
Class “robustness” is balanced, then accuracy can be used as
performance index
9. Data reduction
Resample
(10%)
It would be useful to do the analysis on the entire dataset with
900.000 instances, but this was not possible from a computational
point of view with my laptop
It’s possible to extract a random meaningful subsample thanks to
the Resample weka filter
I can do this because during the simulation the values related to
grasping are generated randomly, and I am extracting a random
quantity (10%) with replacement from the same distribution
10. CrossValidation
(10-fold)
A step that creates stratified cross-validation folds from incoming
data
Divide a dataset into 10 pieces (“folds”), then hold out each piece
in turn for testing and train on the remaining 9 together.This gives
10 evaluation results, which are averaged.
In “stratified” cross-validation, when doing the initial division we
ensure that each fold contains approximately the correct
proportion of the class values.
Despite the resample, the extrapolated instances are about
83.000.The number of folds used is 10 accordingly
11. FeatureSubset
Selection
The feature selection step is implemented embedded in the training
phase of the classifier, thanks to the "AttributeSelectedClassifier"
block.
for example CfsSubsetEval was used as evaluator with J48 classifier
15. J48 Naive Bayes Logistic
80.08 % 76.61 % 76.18 %
0.3705 0.430 0.390
0.801 0.766 0.762
0.165 0.214 0.227
0.841 0.785 0.772
0.801 0.766 0.762
0.800 0.767 0.763
Accuracy
RMS
TP rate
FP rate
Precision
Recall
F-Measure
Classifiers Performances
From the data shown in the table, the J48 classifier is the most performing for this task.
16. RandomForest
Ensemble
methods
The creation of the model for the classifier takes a time in the
order of minutes
RandomForest (10-fold)
Accuracy 83.55 %
RMS 0.336
TP rate 0.836
FP rate 0.143
Precision 0.855
Recall 0.836
F-Measure 0.836
J48
80.08 %
0.3705
0.801
0.165
0.841
0.801
0.800
17. Conclusion
Random forest classifier creates a set of decision trees from randomly
selected subset of training set. It then aggregates the votes from different
decision trees to decide the final class of the test object.
J48 and Random Forest showed the highest performances
The instances correctly classified in the cases mentioned are approximately
80% and 83%
New simulation settings for the generation of grasping should be verified,
including a different physical engine
The data obtained should be validated on the physical system to ensure the
kinematic compatibility of the data produced in simulation and by the
classification algorithms
18. References
Grasping Dataset
https://www.kaggle.com/ugocupcic/grasping-dataset
Data Mining: Concepts andTechniques, 3rd ed. (Jiawei Han, Micheline
Kamber and Jian Pei)
https://www.cs.waikato.ac.nz/ml/weka/
https://www.shadowrobot.com/
Smart Grasping Sandbox
https://github.com/shadow-robot/smart_grasping_sandbox
Slides aboutWeka made by prof. Ducange