Scaling infrastructure is tricky,
I will try to explain what methods I use when dealing with this issue, and demonstrate an approach which can be applied to almost any type of work load.
Scaling infrastructure is tricky,
I will try to explain what methods I use when dealing with this issue, and demonstrate an approach which can be applied to almost any type of work load.
Converting UML class diagram with anti-pattern problems to verified code based on Event-B
Eman K. Elsayed
Mathematical and computer science Dep., Faculty of Science,
Al-Azhar University, Cairo, Egypt
Approach to Seismic Signal Discrimination based on Takagi-Sugeno Fuzzy Inference System
E. H. Ait Laasri, E. Akhouayri, D. Agliz, A. Atmani Electronic, Signal processing and Physical Modelling Laboratory, Physics’ Department, Faculty of Sciences, Ibn Zohr University, B.P. 8106, Agadir, Morocco
Lazy Join Optimizations Without Upfront Statistics with Matteo InterlandiDatabricks
Modern Data-Intensive Scalable Computing (DISC) systems such as Apache Spark do not support sophisticated cost-based query optimizers because they are specifically designed to process data that resides in external storage systems (e.g. HDFS), or they lack the necessary data statistics. Consequently, many crucial optimizations, such as join order and plan selection, are presently out-of-scope in these DISC system optimizers. Yet, join order is one of the most important decisions a cost-optimizer can make because wrong orders can result in a query response time that can become more than an order-of-magnitude slower compared to the better order.
Analysis of factors responsible for the future growth of Xiaomi in the Indian market using Gap analysis, Logistics regression, Structural equation modeling and correspondence analysis
re:Invent 2019 BPF Performance Analysis at NetflixBrendan Gregg
Talk by Brendan Gregg at AWS re:Invent 2019. Abstract: "Extended BPF (eBPF) is an open source Linux technology that powers a whole new class of software: mini programs that run on events. Among its many uses, BPF can be used to create powerful performance analysis tools capable of analyzing everything: CPUs, memory, disks, file systems, networking, languages, applications, and more. In this session, Netflix's Brendan Gregg tours BPF tracing capabilities, including many new open source performance analysis tools he developed for his new book "BPF Performance Tools: Linux System and Application Observability." The talk includes examples of using these tools in the Amazon EC2 cloud."
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Converting UML class diagram with anti-pattern problems to verified code based on Event-B
Eman K. Elsayed
Mathematical and computer science Dep., Faculty of Science,
Al-Azhar University, Cairo, Egypt
Approach to Seismic Signal Discrimination based on Takagi-Sugeno Fuzzy Inference System
E. H. Ait Laasri, E. Akhouayri, D. Agliz, A. Atmani Electronic, Signal processing and Physical Modelling Laboratory, Physics’ Department, Faculty of Sciences, Ibn Zohr University, B.P. 8106, Agadir, Morocco
Lazy Join Optimizations Without Upfront Statistics with Matteo InterlandiDatabricks
Modern Data-Intensive Scalable Computing (DISC) systems such as Apache Spark do not support sophisticated cost-based query optimizers because they are specifically designed to process data that resides in external storage systems (e.g. HDFS), or they lack the necessary data statistics. Consequently, many crucial optimizations, such as join order and plan selection, are presently out-of-scope in these DISC system optimizers. Yet, join order is one of the most important decisions a cost-optimizer can make because wrong orders can result in a query response time that can become more than an order-of-magnitude slower compared to the better order.
Analysis of factors responsible for the future growth of Xiaomi in the Indian market using Gap analysis, Logistics regression, Structural equation modeling and correspondence analysis
re:Invent 2019 BPF Performance Analysis at NetflixBrendan Gregg
Talk by Brendan Gregg at AWS re:Invent 2019. Abstract: "Extended BPF (eBPF) is an open source Linux technology that powers a whole new class of software: mini programs that run on events. Among its many uses, BPF can be used to create powerful performance analysis tools capable of analyzing everything: CPUs, memory, disks, file systems, networking, languages, applications, and more. In this session, Netflix's Brendan Gregg tours BPF tracing capabilities, including many new open source performance analysis tools he developed for his new book "BPF Performance Tools: Linux System and Application Observability." The talk includes examples of using these tools in the Amazon EC2 cloud."
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Abstract: Iterative stencils represent the core computational kernel of many applications belonging to different domains, from scientific computing to finance. Given the complex dependencies and the low computation to memory access ratio, this kernels represent a challenging acceleration target on every architecture. This is especially true for FPGAs, whose direct hardware execution offers the possibility for high performance and power efficiency, but where the non-fixed architecture can lead to very large solutions spaces to be explored.
In this work, we build upon an FPGA-based acceleration methodology for iterative stencil algorithms previously presented, where we provide a dataflow architectural template that implements optimal on-chip buffering and is able to increase almost linearly in performance using a scaling technique denoted as iterations queuing. In particular, we propose a set of design improvements and we elaborate an accurate analytical performance model that can be used to support the exploration of the design space. Experimental results obtained implementing a set of benchmarks from different application domains on a Xilinx VC707 board show an average performance and power efficiency increase over the previous work of respectively around 22x and 8x, and a prediction error that is on average less than 1%.
Adaptive Constraint Handling and Success History Differential Evolution for C...University of Maribor
Talk given in: 2017 IEEE Congress on Evolutionary Computation (CEC), taking place at Donostia - San Sebastian, Spain, June 5-8, 2017. Associated special session at CEC: Associated with Competition on Bound Constrained Single Objective Numerical Optimization III (June 6, 14:30-16:30, Room 4).
1) NVIDIA-Iguazio Accelerated Solutions for Deep Learning and Machine Learning (30 mins):
About the speaker:
Dr. Gabriel Noaje, Senior Solutions Architect, NVIDIA
http://bit.ly/GabrielNoaje
2) GPUs in Data Science Pipelines ( 30 mins)
- GPU as a Service for enterprise AI
- A short demo on the usage of GPUs for model training and model inferencing within a data science workflow
About the speaker:
Anant Gandhi, Solutions Engineer, Iguazio Singapore. https://www.linkedin.com/in/anant-gandhi-b5447614/
Secure Image Encryption using Two Dimensional Logistic Map
* Gangadhar Tiwari1, Debashis Nandi2, Abhishek Kumar3, Madhusudhan Mishra4 1, 2Department of Information Technology, NIT Durgapur (W.B.), India 3Department of Electronics and Electrical Engineering, NITAP, (A.P.), India 4Department of Electronics and Communication Engineering, NERIST, (A.P.), India
Non-Invertible Wavelet Domain Watermarking using Hash Function
*Gangadhar Tiwari1, Debashis Nandi 2, Madhusudhan Mishra3
1,2 IT Department, NIT, Durgapur-713209, West Bengal, India,
3ECE Department, NERIST, Nirjuli-791109, Arunachal Pradesh, India,
Unit Commitment Using a Hybrid Differential Evolution with Triangular Distribution Factor for Adaptive Crossover
N. Malla Reddy* K. Ramesh Reddy** and N. V. Ramana***
Intelligent e-assessment: ontological model for personalizing assessment activities
Rafaela Blanca Silva-López1, Iris Iddaly Méndez-Gurrola1, Victor Germán Sánchez Arias2
1 Universidad Autónoma Metropolitana, Unidad Azcapotzalco.
Av. San Pablo 180, Col. Reynosa Tamaulipas, Del. Azcapotzalco, México, D.F.
2 Universidad Nacional Autónoma de México
Circuito Escolar Ciudad Universitaria, 04510 México, D.F.
Visual Perception Oriented CBIR envisaged through Fractals and Presence Score
Suhas Rautmare, Anjali Bhalchandra
A. Tata Consultancy Services, Mumbai B. Govt. College of Engineering, Aurangabad
Measuring Sub Pixel Erratic Shift in Egyptsat-1 Aliased Images: proposed method
1M.A. Fkirin, 1S.M. Badway, 2A.K. Helmy, 2S.A. Mohamed
1Department of Industrial Electronic Engineering and Control, Faculty of Electronic Engineering,
Menoufia University, Menoufia, Egypt.
2Division of Data Reception Analysis and Receiving Station Affairs, National Authority for Remote Sensing and Space Sciences, Cairo, Egypt.
The State of the Art of Video Summarization for Mobile Devices:
Review Article
Hesham Farouk *, Kamal ElDahshan**, Amr Abozeid **
* Computers and Systems Dept., Electronics Research Institute, Cairo, Egypt.
** Dept. of Mathematics, Computer Science Division,
Faculty of Science, Al-Azhar University, Cairo, Egypt.
Overwriting Grammar Model to Represent 2D Image Patterns
1Vishnu Murthy. G, 2Vakulabharanam Vijaya Kumar
1,2Anurag Group of Institutions, Hyderabad, AP,India.
Texture Classification Based on Binary Cross Diagonal Shape Descriptor Texture Matrix (BCDSDTM)
1P.Kiran Kumar Reddy, 2Vakulabharanam Vijaya Kumar, 3B.Eswar Reddy
1RGMCET, Nandyal, AP, India, 2Anurag Group of Institutions, Hyderabad, AP, India
3JNTUA College of Engineering, India.
Improved Iris Verification System
Basma M.Almezgagi, M. A. Wahby Shalaby, Hesham N. Elmahdy Faculty of Computers and Information, Cairo University, Egypt.
Employing Simple Connected Pattern Array Grammar for Generation and Recognition of Connected Patterns on an Image Neighborhood
1Vishnu Murthy. G, 2V. Vijaya Kumar, 3B.V. Ramana Reddy
1,2Anurag Group of Institutions, Hyderabad, AP,India.
3Mekapati Rajamohan Reddy Institute of Technology and Science, Udayagiri, AP,India.
Bench Marking Higuchi Fractal for CBIR
A. Suhas Rautmare, B. Anjali Bhalchandra
A. Tata Consultancy Services, Mumbai B. Govt. College of Engineering, Aurangabad
1. Kedar Nath DasKedar Nath Das
Hybrid Binary Coded GA for
Constrained Optimization
NIT SILCHAR, ASSAM,
INDIA
2. MOST GENERAL OPTIMIZATION PROBLEM
Minimize (Maximize) f (X),
where
s.t. X∈S ⊆ , where S is defined
by
( )nxxxX ...,,2,1=
RRf n
→:
n
R
.,........,2,1
,.......,2,10)(
;,......,2,10)(
niforbxa
ljforxg
mkforxh
iii
j
k
=≤≤
=≥
==
3. DETERMINISTIC
APPROACH
To Find the Global Optimal Solution
PROBABILISTIC
APPROACH
1. Genetic Algorithm
2. Memetic Algorithm
3. Random Search Methods
4. Tabu Search
5. Ant Colony Optimization
6. Particle Swarm Optimization,
etc…..
Approaches
Many
4. Working Principle of GA
Encoding
Selection
Crossover
Mutation
Elitism (Opt.)
Repetition
6. BEFORE CROSS-OVER AFTER CROSS-OVER
101011
=s
001012
=s
001011
=
′
s
101012
=
′
s
c) One Point Cross-Over
d) Uniform Cross-Over
BEFORE CROSS-OVER AFTER CROSS-OVER
001002
=s
100011
=s
100012
=
′
s
001001
=
′
s
7. BEFORE MUTATION AFTER MUTATION
1 0 0 1 0 1 1 0 1 0 0 0 0 1 1 0
e) Bit-Wise Mutation
f) Elitism
12
17
18
2
45
2
12
8
20
41
2
2
8
12
20
Bigin of a
GA cycle End of the
GA cycle
Process of
Elitism
After
Mutation
8. Quadratic Approximation
(Hybridization)
( ) ( ) ( ) ( )
( ) ( ) ( ) ( )
−+−+−
−+−+−
321213132
3
2
2
2
12
2
1
2
31
2
3
2
2
)()(
)()(
RfRRRfRRRfRR
RfRRRfRRRfRR
Find the point of minima (child) of the quadratic
surface passing through R1, R2 and R3 defined as:
Child = 0.5*
Select the individuals R1, with the best fitness value.
Choose two random individuals R2 and R3.
10. (A) Selection Strategy for Mating(A) Selection Strategy for Mating
PoolPool
• Roulette Wheel
Selection
• Penalty Parameter:
• Fitness:
where
11. (B) Selection Strategy for Best(B) Selection Strategy for Best
Individuals in a population:Individuals in a population:
Tournament Selection
12. 1
2
3
The feasible solution
5
6
1 4
5
x1
x2 The feasible domain
1
2
3
The feasible solution
5
6
1 4
5
x1
x2 The feasible domain
1
2
3
The feasible solution
5
6
1 4
5
x1
x2 The feasible domain
1
2
3
The feasible solution
5
6
1 4
5
1
2
3
The feasible solution
5
6
1 4
5
The feasible solution
5
6
1 4
5
x1
x2 The feasible domain
4
13. Step 1: Begin with a random population (P) of size 10*N
Step 2: Evaluation fitness of P(t)
Step3: Stop if it satisfies the stopping criteria
Step 4: Select the individuals taking the tournament
selection strategy
Step 5: Apply Single Point Crossover
Step 6: Apply Bitwise Mutation
Step 7: Hybridize with Quadratic Approximation
Step 8: Apply Complete Elitism through tournament
selection
Methodology of HBGA-Methodology of HBGA-
CC
25. Analysis of ResultsAnalysis of Results
Sl. Sense Better Tie Worse
1 Success Rate 17 4 4
2 Ave. Fun. Calls 21 0 4
3 Mean Obj. fun. Value 11 8 6
4 S. D. 14 5 6
5 Time 7 1 17
HBGA-C Vs. BGA-CHBGA-C Vs. BGA-C
HBGA-C is………HBGA-C is………
………….than BGA-C.than BGA-C
26. ConclusioConclusio
nn
HBGA-C >>> BGA-CHBGA-C >>> BGA-C
(in more percentage of success)(in more percentage of success)
HBGA-C >>> BGA-CHBGA-C >>> BGA-C
(in less no. of function evaluation)(in less no. of function evaluation)
HBGA-C >>> BGA-C (in less S. D.)HBGA-C >>> BGA-C (in less S. D.)
HBGA-C >>> BGA-CHBGA-C >>> BGA-C
(in better obj. fun. value)(in better obj. fun. value)
HBGA-C <<< BGA-C (in time)HBGA-C <<< BGA-C (in time)
27. References:
[1] A. Osyczka, S. Krenich and S. Kundu. Proportional and Tournament
Selections for Constrained Optimization Problems using GAs. Evolutionary
Optimization, an Int. Jr. on the internet, 1(1): pp. 89-92, 1999.
[2] A. Osyczka. Evolutionary Algorithms for Single and Multi-criteria Design
Optimization, Physica-Verlag Heidelberg, New York, 2002.
[3] C. A. Coella and M. E. Mezura. Constraint-Handling in Genetic Algorithms
through the use of dominance-based tournament selection. Advance Engineering
Informatics, 16: pp. 193-203, 2002.
[4] D. Orvosh and L. Davis. Using a Genetic Algorithm to Optimize problems
with Feasibility Constraints. Proceeding of the Sixth Int. Conf. on Gas, Echelman,
L. J. Ed., pp. 548-552, 1995.
[5] H. Myung and J. H. Kim. Hybrid Evolutionary Programming for Heavily
Constrained Problems. Bio-Systems, 38, pp. 29-43, 1996.
[6] J. H. Kim and H. Myung. A Two Phase Evolutionary Programming for
general Constrained Optimization Problem. Proceedings of the Fifth Annual Conf.
on Evolutionary Programming, San Diego, 1996.
[7] K. Deb and S. Agarwal. A Niched-Penalty Approach for Constraint
Handling GAs, Proceeding of the ICANNGA, Portoroz, Slovenia, 1999.
[8] K. Deb. A Robust Optimal Design Technique Component Design in
Evolutionary Algorithms in Engineering Applications. Springer Verlag, pp. 497-514,
1997.
28. [9] K. Deb. Optimization for Engineering Design: Algorithms and
Examples, Prentice-Hall of India, NewDelhi, 1995.
[10] K. Deep and K. N. Das. Choice of selection and crossover on some
Benchmark problems. Int. Jr. of Computer, Mathematical Sciences and
Applications, Vol.1, No. 1, 99-117, 2007.
[11] K. Deep and K. N. Das. Quadratic approximation based Hybrid
Genetic Algorithm for Function Optimization. AMC, Elsevier, Vol. 203: 86-98,
2008.
[12] K. N. Das. Design and Applications of Hybrid Genetic Algorithms for
Function Optimization. PhD thesis, Indian Institute of Technology, Roorkee,
India, Dec. 2007 .
[13] S. Akhtar, K. Tai and T. Ray. A Socio-Behavioural Simulation Model
for Engineering Design Optimization, 34(4): pp.341-354, 2002.
[14] S. Kundu and A. Osyczka. Genetic Multi-criteria Optimization of
structural systems. Proceedings of the 19th ICTAM, Kyoto, Japan, IUTAM,
272, 1996.
[15] Z. Michalewicz. Genetic Algorithms, Numerical Optimization and
Constraints. Proceedings of Sixth Int. Conf. on Genetic Algorithms, Echelman
L. J. Ed., pp. 151-158, 1995.