The document describes Voronoi diagrams and an algorithm for constructing them efficiently. Voronoi diagrams partition space into regions based on distance to points called sites. The algorithm uses a sweep line approach, maintaining the current state of the diagram. It handles events where the sweep line encounters a site or potential empty circle. The key data structures are a balanced binary search tree to represent the beach line, a doubly linked list to represent the constructed diagram, and a priority queue of events. The algorithm runs in O(n log n) time.
The predominant technology that is used by CAD (Com- puter Aided Design) to represent complex geometries is non-uniform rational b-splines (NURBS). This allows cer- tain geometries to be represented exactly including conic and circular sections. There is a vast array of literature focused on NURBS and as a result of several decades f research, many efficient computer algorithms exist for their fast evaluation and refinement. The key concept outlined by Hughes et al. was to employ NURBS not only as a geometry discretisation technology, but also as a discretisation tool for analysis, attributing such methods to the field of ‘isogeometric analysis’ (IGA).
It was a presentation in our undergrad course in Department of Computer Science and Engineering in University of Dhaka.
Multi-Agent Path Finding (MAPF) is the problem of computing collision-free paths for a team of agents from their current locations to given destinations. Application examples include autonomous aircraft towing vehicles, automated warehouse systems, office robots, and game characters in video games. Practical systems must find high-quality collision-free paths for such agents quickly.
Depth estimation do we need to throw old things awayNAVER Engineering
발표의 개요 : Human visual system 기반의 CNN for depth estimation과 CNN inspired by conventional methods
Case1: Cross-channel stereo matching
Case2: Depth from light field
Case3: Multiview stereo
Conclusion
Introduction to Capsule Networks (CapsNets)Aurélien Géron
CapsNets are a hot new architecture for neural networks, invented by Geoffrey Hinton, one of the godfathers of deep learning.
You can view this presentation on YouTube at: https://youtu.be/pPN8d0E3900
NIPS 2017 Paper:
* Dynamic Routing Between Capsules,
* by Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton
* https://arxiv.org/abs/1710.09829
The 2011 paper:
* Transforming Autoencoders
* by Geoffrey E. Hinton, Alex Krizhevsky and Sida D. Wang
* https://goo.gl/ARSWM6
CapsNet implementations:
* Keras w/ TensorFlow backend: https://github.com/XifengGuo/CapsNet-Keras
* TensorFlow: https://github.com/naturomics/CapsNet-Tensorflow
* PyTorch: https://github.com/gram-ai/capsule-networks
Book:
Hands-On Machine with Scikit-Learn and TensorFlow
O'Reilly, 2017
Amazon: https://goo.gl/IoWYKD
Github: https://github.com/ageron
Twitter: https://twitter.com/aureliengeron
Smart Huffman Compression is a software appliance designed to compress a file in a better way. By functioning as an JSP, it provides high level abstraction of java Servlet. For example, Smart Huffman Compression encodes the digital information using fewer bits, reduces the size of file without loss of data in a single, easy-to-manage software appliance form factor. It also provides us the decompression facility also. Smart Huffman Compression provides our organization with effective solutions to reduce the file size or lossless compression of data. It also expedites security of data using the encoding functionality. It is necessary to analyze the relationship between different methods and put them into a framework to better understand and better exploit the possibilities that compression provides us image compression, data compression, audio compression, video compression etc.
The predominant technology that is used by CAD (Com- puter Aided Design) to represent complex geometries is non-uniform rational b-splines (NURBS). This allows cer- tain geometries to be represented exactly including conic and circular sections. There is a vast array of literature focused on NURBS and as a result of several decades f research, many efficient computer algorithms exist for their fast evaluation and refinement. The key concept outlined by Hughes et al. was to employ NURBS not only as a geometry discretisation technology, but also as a discretisation tool for analysis, attributing such methods to the field of ‘isogeometric analysis’ (IGA).
It was a presentation in our undergrad course in Department of Computer Science and Engineering in University of Dhaka.
Multi-Agent Path Finding (MAPF) is the problem of computing collision-free paths for a team of agents from their current locations to given destinations. Application examples include autonomous aircraft towing vehicles, automated warehouse systems, office robots, and game characters in video games. Practical systems must find high-quality collision-free paths for such agents quickly.
Depth estimation do we need to throw old things awayNAVER Engineering
발표의 개요 : Human visual system 기반의 CNN for depth estimation과 CNN inspired by conventional methods
Case1: Cross-channel stereo matching
Case2: Depth from light field
Case3: Multiview stereo
Conclusion
Introduction to Capsule Networks (CapsNets)Aurélien Géron
CapsNets are a hot new architecture for neural networks, invented by Geoffrey Hinton, one of the godfathers of deep learning.
You can view this presentation on YouTube at: https://youtu.be/pPN8d0E3900
NIPS 2017 Paper:
* Dynamic Routing Between Capsules,
* by Sara Sabour, Nicholas Frosst, Geoffrey E. Hinton
* https://arxiv.org/abs/1710.09829
The 2011 paper:
* Transforming Autoencoders
* by Geoffrey E. Hinton, Alex Krizhevsky and Sida D. Wang
* https://goo.gl/ARSWM6
CapsNet implementations:
* Keras w/ TensorFlow backend: https://github.com/XifengGuo/CapsNet-Keras
* TensorFlow: https://github.com/naturomics/CapsNet-Tensorflow
* PyTorch: https://github.com/gram-ai/capsule-networks
Book:
Hands-On Machine with Scikit-Learn and TensorFlow
O'Reilly, 2017
Amazon: https://goo.gl/IoWYKD
Github: https://github.com/ageron
Twitter: https://twitter.com/aureliengeron
Smart Huffman Compression is a software appliance designed to compress a file in a better way. By functioning as an JSP, it provides high level abstraction of java Servlet. For example, Smart Huffman Compression encodes the digital information using fewer bits, reduces the size of file without loss of data in a single, easy-to-manage software appliance form factor. It also provides us the decompression facility also. Smart Huffman Compression provides our organization with effective solutions to reduce the file size or lossless compression of data. It also expedites security of data using the encoding functionality. It is necessary to analyze the relationship between different methods and put them into a framework to better understand and better exploit the possibilities that compression provides us image compression, data compression, audio compression, video compression etc.
Artificial Bee Colony (ABC) algorithm is a Nature Inspired Algorithm (NIA) which based in intelligent food foraging behaviour of honey bee swarm. ABC outperformed over other NIAs and other local search heuristics when tested for benchmark functions as well as factual world problems but occasionally it shows premature convergence and stagnation due to lack of balance between exploration and exploitation. This paper establishes a local search mechanism that enhances exploration capability of ABC and avoids the dilemma of stagnation. With help of recently introduces local search strategy it tries to balance intensification and diversification of search space. The anticipated algorithm named as Enhanced local search in ABC (EnABC) and tested over eleven benchmark functions. Results are evidence for its dominance over other competitive algorithms.
Spider Monkey optimization (SMO) algorithm is newest addition in class of swarm intelligence. SMO is a population based stochastic meta-heuristic. It is motivated by intelligent foraging behaviour of fission fusion structured social creatures. SMO is a very good option for complex optimization problems. This paper proposed a modified strategy in order to enhance performance of original SMO. This paper introduces a position update strategy in SMO and modifies both local leader and global leader phase. The proposed strategy is named as Modified Position Update in Spider Monkey Optimization (MPU-SMO) algorithm. The proposed algorithm tested over benchmark problems and results show that it gives better results for considered unbiased problems.
Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for both benchmark functions and real world problems. The performance of search process of ABC depends on a random value which tries to balance exploration and exploitation phase. In order to increase the performance it is required to balance the exploration of search space and exploitation of optimal solution of the ABC. This paper outlines a new hybrid of ABC algorithm with Genetic Algorithm. The proposed method integrates crossover operation from Genetic Algorithm (GA) with original ABC algorithm. The proposed method is named as Crossover based ABC (CbABC). The CbABC strengthens the exploitation phase of ABC as crossover enhances exploration of search space. The CbABC tested over four standard benchmark functions and a popular continuous optimization problem.
Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing over both yardstick and actual world problems. Nevertheless, DE, like other probabilistic optimization algorithms, from time to time exhibits precipitate convergence and stagnates at suboptimal position. In order to stay away from stagnation behavior while maintaining an excellent convergence speed, an innovative search strategy is introduced, named memetic search in DE. In the planned strategy, positions update equation customized as per a memetic search stratagem. In this strategy a better solution participates more times in the position modernize procedure. The position update equation is inspired from the memetic search in artificial bee colony algorithm. The proposed strategy is named as Memetic Search in Differential Evolution (MSDE). To prove efficiency and efficacy of MSDE, it is tested over 8 benchmark optimization problems and three real world optimization problems. A comparative analysis has also been carried out among proposed MSDE and original DE. Results show that the anticipated algorithm go one better than the basic DE and its recent deviations in a good number of the experiments.
Artificial Bee Colony (ABC) is a swarm
optimization technique. This algorithm generally used to solve
nonlinear and complex problems. ABC is one of the simplest
and up to date population based probabilistic strategy for
global optimization. Analogous to other population based
algorithms, ABC also has some drawbacks computationally
pricey due to its sluggish temperament of search procedure.
The solution search equation of ABC is notably motivated by a
haphazard quantity which facilitates in exploration at the cost
of exploitation of the search space. Due to the large step size in
the solution search equation of ABC there are chances of
skipping the factual solution are higher. For that reason, this
paper introduces a new search strategy in order to balance the
diversity and convergence capability of the ABC. Both
employed bee phase and onlooker bee phase are improved
with help of a local search strategy stimulated by memetic
algorithm. This paper also proposes a new strategy for fitness
calculation and probability calculation. The proposed
algorithm is named as Improved Memetic Search in ABC
(IMeABC). It is tested over 13 impartial benchmark functions
of different complexities and two real word problems are also
considered to prove proposed algorithms superiority over
original ABC algorithm and its recent variants
Artificial bee colony (ABC) algorithm is a well known and one of the latest swarm intelligence based techniques. This method is a population based meta-heuristic algorithm used for numerical optimization. It is based on the intelligent behavior of honey bees. Artificial Bee Colony algorithm is one of the most popular techniques that are used in optimization problems. Artificial Bee Colony algorithm has some major advantages over other heuristic methods. To utilize its good feature a number of researchers combined ABC algorithm with other methods, and generate some new hybrid methods. This paper provides comparative analysis of hybrid differential Artificial Bee Colony algorithm with hybrid ABC – SPSO, Genetic algorithm and Independent rough set approach based on some parameters like technique, dimension, methodology etc. KEYWORDS
Artificial Bee Colony (ABC) optimization
algorithm is one of the recent population based probabilistic
approach developed for global optimization. ABC is simple
and has been showed significant improvement over other
Nature Inspired Algorithms (NIAs) when tested over some
standard benchmark functions and for some complex real
world optimization problems. Memetic Algorithms also
become one of the key methodologies to solve the very large
and complex real-world optimization problems. The solution
search equation of Memetic ABC is based on Golden Section
Search and an arbitrary value which tries to balance
exploration and exploitation of search space. But still there
are some chances to skip the exact solution due to its step
size. In order to balance between diversification and
intensification capability of the Memetic ABC, it is
randomized the step size in Memetic ABC. The proposed
algorithm is named as Randomized Memetic ABC (RMABC).
In RMABC, new solutions are generated nearby the best so
far solution and it helps to increase the exploitation capability
of Memetic ABC. The experiments on some test problems of
different complexities and one well known engineering
optimization application show that the proposed algorithm
outperforms over Memetic ABC (MeABC) and some other
variant of ABC algorithm(like Gbest guided ABC
(GABC),Hooke Jeeves ABC (HJABC), Best-So-Far ABC
(BSFABC) and Modified ABC (MABC) in case of almost all
the problems.
Multiplication of two 3 d sparse matrices using 1d arrays and linked listsDr Sandeep Kumar Poonia
A basic algorithm of 3D sparse matrix multiplication (BASMM) is presented using one dimensional (1D) arrays which is used further for multiplying two 3D sparse matrices using Linked Lists. In this algorithm, a general concept is derived in which we enter non- zeros elements in 1st and 2nd sparse matrices (3D) but store that values in 1D arrays and linked lists so that zeros could be removed or ignored to store in memory. The positions of that non-zero value are also stored in memory like row and column position. In this way space complexity is decreased. There are two ways to store the sparse matrix in memory. First is row major order and another is column major order. But, in this algorithm, row major order is used. Now multiplying those two matrices with the help of BASMM algorithm, time complexity also decreased. For the implementation of this, simple c programming and concepts of data structures are used which are very easy to understand for everyone.
Higher-Order Voronoi Diagrams of Polygonal Objects. DissertationMaksym Zavershynskyi
Higher-order Voronoi diagrams are fundamental geometric structures which encode the k-nearest neighbor information. Thus, they aid in computations that require proximity information beyond the nearest neighbor. They are related to various favorite structures in computational geometry and are a fascinating combinatorial problem to study.
While higher-order Voronoi diagrams of points have been studied a lot, they have not been considered for other types of sites. Points lack dimensionality which makes them unable to represent various real-life instances. Points are the simplest kind of geometric object and therefore higher-order Voronoi diagrams of points can be considered as the corner case of all higher-order Voronoi diagrams.
The goal of this dissertation is to move away from the corner and bring the higher-order Voronoi diagram to more general geometric instances. We focus on certain polygonal objects as they provide flexibility and are able to represent real-life instances. Before this dissertation, higher-order Voronoi diagrams of polygonal objects had been studied only for the nearest neighbor and farthest Voronoi diagrams. In this dissertation we investigate structural and combinatorial properties and discover that the dimensionality of geometric objects manifests itself in numerous ways which do not exist in the case of points. We prove that the structural complexity of the order-k Voronoi diagram of non-crossing line segments is O(k(n−k)), as in the case of points. We study disjoint line segments, intersecting line segments, line segments forming a planar straight-line graph and extend the results to the Lp metric, 1≤p≤∞. We also establish the connection between two mathematical abstractions: abstract Voronoi diagrams and the Clarkson-Shor framework.
We design several construction algorithms that cover the case of non-point sites. While computational geometry provides several approaches to study the structural complexity that give tight realizable bounds, developing an effective construction algorithm is still a challenging problem even for points. Most of the construction algorithms are designed to work with points as they utilize their simplicity and relations with data-structures that work specifically for points. We extend the iterative and the sweepline approaches that are quite efficient in constructing all order-i Voronoi diagrams, for i≤k and we also give three randomized construction algorithms for abstract higher-order Voronoi diagrams that deal specifically with the construction of the order-k Voronoi diagrams.
A New Approach to Output-Sensitive Voronoi Diagrams and Delaunay TriangulationsDon Sheehy
We describe a new algorithm for computing the Voronoi diagram of a set of $n$ points in constant-dimensional Euclidean space. The running time of our algorithm is $O(f \log n \log \spread)$ where $f$ is the output complexity of the Voronoi diagram and $\spread$ is the spread of the input, the ratio of largest to smallest pairwise distances. Despite the simplicity of the algorithm and its analysis, it improves on the state of the art for all inputs with polynomial spread and near-linear output size. The key idea is to first build the Voronoi diagram of a superset of the input points using ideas from Voronoi refinement mesh generation. Then, the extra points are removed in a straightforward way that allows the total work to be bounded in terms of the output complexity, yielding the output sensitive bound. The removal only involves local flips and is inspired by kinetic data structures.
My talk about computational geometry in NTU's APEX Club in NTU, Singapore in 2007. The club is for people who are keen on participating in ACM International Collegiate Programming Contests organized by IBM annually.
Markov Chain Monitoring - Application to demand prediction in bike sharing sy...Harshal Chaudhari
The presentation accompanying the paper at SDM 2018 - https://epubs.siam.org/doi/abs/10.1137/1.9781611975321.50
Github: https://github.com/chdhr-harshal/mc-monitor
In networking applications, one often wishes to obtain estimates about the number of objects at different parts of the network (e.g., the number of cars at an intersection of a road network or the number of packets expected to reach a node in a computer network) by monitoring the traffic in a small number of network nodes or edges. We formalize this task by defining the Markov Chain Monitoring problem. Given an initial distribution of items over the nodes of a Markov chain, we wish to estimate the distribution of items at subsequent times. We do this by asking a limited number of queries that retrieve, for example, how many items transitioned to a specific node or over a specific edge at a particular time. We consider different types of queries, each defining a different variant of the Markov Chain Monitoring. For each variant, we design efficient algorithms for choosing the queries that make our estimates as accurate as possible. In our experiments with synthetic and real datasets we demonstrate the efficiency and the efficacy of our algorithms in a variety of settings.
Artificial Bee Colony (ABC) is a distinguished optimization strategy that can resolve nonlinear and multifaceted problems. It is comparatively a straightforward and modern population based probabilistic approach for comprehensive optimization. In the vein of the other population based algorithms, ABC is moreover computationally classy due to its slow nature of search procedure. The solution exploration equation of ABC is extensively influenced by a arbitrary quantity which helps in exploration at the cost of exploitation of the better search space. In the solution exploration equation of ABC due to the outsized step size the chance of skipping the factual solution is high. Therefore, here this paper improve onlooker bee phase with help of a local search strategy inspired by memetic algorithm to balance the diversity and convergence capability of the ABC. The proposed algorithm is named as Improved Onlooker Bee Phase in ABC (IoABC). It is tested over 12 well known un-biased test problems of diverse complexities and two engineering optimization problems; results show that the anticipated algorithm go one better than the basic ABC and its recent deviations in a good number of the experiments.
Artificial Bee Colony (ABC) algorithm is a Nature
Inspired Algorithm (NIA) which based on intelligent food
foraging behaviour of honey bee swarm. This paper introduces
a local search strategy that enhances exploration competence
of ABC and avoids the problem of stagnation. The proposed
strategy introduces two new local search phases in original
ABC. One just after onlooker bee phase and one after scout
bee phase. The newly introduced phases are inspired by
modified Golden Section Search (GSS) strategy. The proposed
strategy named as new local search strategy in ABC
(NLSSABC). The proposed NLSSABC algorithm applied over
thirteen standard benchmark functions in order to prove its
efficiency.
Program slicing technique is used for decomposition of a program by analyzing that particular program data
and control flow. The main application of program slicing includes various software engineering activities such as
program debugging, understanding, program maintenance, and testing and complexity measurement. When a slicing
technique gathers information about the data and control flow of the program taking an actual and specific execution
(or set of executions) of it, then it is said to be dynamic slicing, otherwise it is said to be static slicing. Generally,
dynamic slices are smaller than static because the statements of the program that affect by the slicing criterion for a
particular execution are contained by dynamic slicing. This paper reports a new approach of program slicing that is a
mixed approach of static and dynamic slice (S-D slicing) using Object Oriented Concepts in C++ Language that will
reduce the complexity of the program and simplify the program for various software engineering applications like
program debubbing.
Articial bee Colony algorithm (ABC) is a population based
heuristic search technique used for optimization problems. ABC
is a very eective optimization technique for continuous opti-
mization problem. Crossover operators have a better exploration
property so crossover operators are added to the ABC. This pa-
per presents ABC with dierent types of real coded crossover op-
erator and its application to Travelling Salesman Problem (TSP).
Each crossover operator is applied to two randomly selected par-
ents from current swarm. Two o-springs generated from crossover
and worst parent is replaced by best ospring, other parent remains
same. ABC with real coded crossover operator applied to travelling
salesman problem. The experimental result shows that our proposed
algorithm performs better than the ABC without crossover in terms
of eciency and accuracy.
Performance evaluation of diff routing protocols in wsn using difft network p...Dr Sandeep Kumar Poonia
In the recent past, wireless sensor networks have been introduced to use in many applications. To design the networks, the factors needed to be considered are the coverage area, mobility, power consumption, communication capabilities etc. The challenging goal of our project is to create a simulator to support the wireless sensor network simulation. The network simulator (NS-2) which supports both wire and wireless networks is implemented to be used with the wireless sensor network.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
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2. Outline
• Definitions and Examples
• Properties of Voronoi diagrams
• Complexity of Voronoi diagrams
• Constructing Voronoi diagrams
– Intuitions
– Data Structures
– Algorithm
• Running Time Analysis
• Demo
• Duality and degenerate cases
3. Post Office: What is the area of service?
q
q : free point
e
e : Voronoi edge
v
v : Voronoi vertex
pi
pi : site points
4. Definition of Voronoi Diagram
• Let P be a set of n distinct points (sites) in the plane.
• The Voronoi diagram of P is the subdivision of the
plane into n cells, one for each site.
• A point q lies in the cell corresponding to a site pi P
iff
Euclidean_Distance( q, pi ) < Euclidean_distance( q, pj ),
for each pi P, j i.
6. Two sites form a perpendicular
bisector
Voronoi Diagram is a line
that extends infinitely in
both directions, and the
two half planes on either
side.
8. Non-collinear sites form Voronoi
half lines that meet at a vertex
A Voronoi vertex is
the center of an empty
circle touching 3 or
more sites.
v
Half lines
A vertex has
degree 3
11. vWhich of the following is true for
2-D Voronoi diagrams?
Four or more non-collinear sites are…
1. sufficient to create a bounded cell
2. necessary to create a bounded cell
3. 1 and 2
4. none of above
12. vWhich of the following is true for
2-D Voronoi diagrams?
Four or more non-collinear sites are…
1. sufficient to create a bounded cell
2. necessary to create a bounded cell
3. 1 and 2
4. none of above
14. Summary of Voronoi Properties
A point q lies on a Voronoi edge between sites pi and pj
iff the largest empty circle centered at q touches only
pi and pj
– A Voronoi edge is a subset of locus of points equidistant
from pi and pj
e
e : Voronoi edge
v
v : Voronoi vertex
pi
pi : site points
15. Summary of Voronoi Properties
A point q is a vertex iff the largest empty circle
centered at q touches at least 3 sites
– A Voronoi vertex is an intersection of 3 more segments,
each equidistant from a pair of sites
e
e : Voronoi edge
v
v : Voronoi vertex
pi
pi : site points
16. Voronoi diagrams have linear
complexity {|v|, |e| = O(n)}
Intuition: Not all bisectors are Voronoi edges!
e
e : Voronoi edge
pi
pi : site points
17. Voronoi diagrams have linear
complexity {|v|, |e| = O(n)}
Claim: For n 3, |v| 2n 5 and |e| 3n 6
Proof: (Easy Case)
…
Collinear sites |v| = 0, |e| = n – 1
18. Voronoi diagrams have linear
complexity {|v|, |e| = O(n)}
Claim: For n 3, |v| 2n 5 and |e| 3n 6
Proof: (General Case)
• Euler’s Formula: for connected, planar graphs,
|v| – |e| + f = 2
Where:
|v| is the number of vertices
|e| is the number of edges
f is the number of faces
19. Voronoi diagrams have linear
complexity {|v|, |e| = O(n)}
Claim: For n 3, |v| 2n 5 and |e| 3n 6
Proof: (General Case)
• For Voronoi graphs, f = n (|v| +1) – |e| + n = 2
epi
p
To apply Euler’s Formula, we
“planarize”theVoronoidiagram
by connectinghalflines to
an extra vertex.
20. Voronoi diagrams have linear
complexity {|v|, |e| = O(n)}
Moreover,
and
so
together with
we get, for n 3
||2)deg(
)(
ev
PVorv
),(PVorv 3)deg(v
)1|(|3||2 ve
2||)1|(| nev
63||
52||
ne
nv
25. Constructing Voronoi Diagrams
• Half plane intersection O( n2 log n )
• Fortune’s Algorithm
– Sweep line algorithm
• Voronoi diagram constructed as horizontal line
sweeps the set of sites from top to bottom
• Incremental construction maintains portion of
diagram which cannot change due to sites below
sweep line, keeping track of incremental changes for
each site (and Voronoi vertex) it “sweeps”
26. Constructing Voronoi Diagrams
What is the invariant we are looking for?
Maintain a representation of the locus of points q that
are closer to some site pi above the sweep line than to
the line itself (and thus to any site below the line).
e
v
pi
Sweep Line
q
27. Constructing Voronoi Diagrams
Which points are closer to a site above the sweep
line than to the sweep line itself?
Sweep Line
pi
q
The set of parabolic arcs form a beach-line that bounds
the locus of all such points
Equidistance
31. We have detected a circle that is empty (contains no
sites) and touches 3 or more sites.
Constructing Voronoi Diagrams
Sweep Line
pi
q
Voronoi vertex!
32. Beach Line properties
• Voronoi edges are traced by the break
points as the sweep line moves down.
– Emergence of a new break point(s) (from
formation of a new arc or a fusion of two
existing break points) identifies a new edge
• Voronoi vertices are identified when two
break points meet (fuse).
– Decimation of an old arc identifies new vertex
33. Data Structures
• Current state of the Voronoi diagram
– Doubly linked list of half-edge, vertex, cell records
• Current state of the beach line
– Keep track of break points
– Keep track of arcs currently on beach line
• Current state of the sweep line
– Priority event queue sorted on decreasing y-coordinate
34. Doubly Linked List (D)
• Goal: a simple data structure that allows an
algorithm to traverse a Voronoi diagram’s
segments, cells and vertices
e
v
Cell(pi)
35. Doubly Linked List (D)
• Divide segments into uni-directional half-edges
• A chain of counter-clockwise half-edges forms a cell
• Define a half-edge’s “twin” to be its opposite half-edge of the
same segment
e
v
Cell(pi)
36. Doubly Linked List (D)
• Cell Table
– Cell(pi) : pointer to any incident half-edge
• Vertex Table
– vi : list of pointers to all incident half-edges
• Doubly Linked-List of half-edges; each has:
– Pointer to Cell Table entry
– Pointers to start/end vertices of half-edge
– Pointers to previous/next half-edges in the CCW chain
– Pointer to twin half-edge
37. Balanced Binary Tree (T)
• Internal nodes represent break points between two arcs
– Also contains a pointer to the D record of the edge being traced
• Leaf nodes represent arcs, each arc is in turn represented
by the site that generated it
– Also contains a pointer to a potential circle event
pi pj pk pl
< pj, pk>
< pi, pj> < pk, pl>
pi
pj
pk
pl
l
38. Event Queue (Q)
• An event is an interesting point encountered by the
sweep line as it sweeps from top to bottom
– Sweep line makes discrete stops, rather than a
continuous sweep
• Consists of Site Events (when the sweep line
encounters a new site point) and Circle Events
(when the sweep line encounters the bottom of an
empty circle touching 3 or more sites).
• Events are prioritized based on y-coordinate
41. Site Event
Original arc above the new site is broken into two
Number of arcs on beach line is O(n)
l
42. Circle Event
An arc disappears whenever an empty circle touches
three or more sites and is tangent to the sweep line.
Sweep line helps determine that the circle is indeed empty.
Circle Event!
Sweep Line
pi
q
Voronoi vertex!
43. Event Queue Summary
• Site Events are
– given as input
– represented by the xy-coordinate of the site point
• Circle Events are
– computed on the fly (intersection of the two bisectors in
between the three sites)
– represented by the xy-coordinate of the lowest point of
an empty circle touching three or more sites
– “anticipated”, these newly generated events may be
false and need to be removed later
• Event Queue prioritizes events based on their y-
coordinates
44. Summarizing Data Structures
• Current state of the Voronoi diagram
– Doubly linked list of half-edge, vertex, cell records
• Current state of the beach line
– Keep track of break points
• Innernodes of binary search tree; representedby a tuple
– Keep track of arcs currently on beach line
• Leaf nodes of binary search tree; represented by a site that
generatedthearc
• Current state of the sweep line
– Priority event queue sorted on decreasing y-coordinate
45. Algorithm
1. Initialize
• Event queue Q all site events
• Binary search tree T
• Doubly linked list D
2. While Q not ,
• Remove event (e) from Q with largest y-
coordinate
• HandleEvent(e, T, D)
46. Handling Site Events
1. Locate the existing arc (if any) that is above the
new site
2. Break the arc by replacing the leaf node with a
sub tree representing the new arc and its break
points
3. Add two half-edge records in the doubly linked
list
4. Check for potential circle event(s), add them to
event queue if they exist
47. Locate the existing arc that is above
the new site
pi pj pk pl
< pj, pk>
< pi, pj> < pk, pl>
• The x coordinate of the new site is used for the binary search
• The x coordinate of each breakpoint along the root to leaf path
is computed on the fly
pi
pj
pk
pl
lpm
48. Break the Arc
pi pj pk
< pj, pk>
< pi, pj> < pk, pl>
Corresponding leaf replaced by a new sub-tree
pi
pj
pk
pl
l
pm
pm pl
< pl, pm>
< pm, pl>
pl
Different arcs can be identified
by the same site!
49. Add a new edge record in the doubly
linked list
pi pj pk
< pj, pk>
< pi, pj> < pk, pl>
pm pl
< pl, pm>
< pm, pl>
pl
pi
pj
pk
pl
l
pm
New Half Edge Record
Endpoints
Pointers to two half-edge
records
l
pm
50. Checking for Potential Circle Events
• Scan for triple of consecutive arcs and
determine if breakpoints converge
– Triples with new arc in the middle do not have
break points that converge
51. Checking for Potential Circle Events
• Scan for triple of consecutive arcs and
determine if breakpoints converge
– Triples with new arc in the middle do not have
break points that converge
52. Checking for Potential Circle Events
• Scan for triple of consecutive arcs and
determine if breakpoints converge
– Triples with new arc in the middle do not have
break points that converge
53. Converging break points may not
always yield a circle event
• Appearance of a new site before the circle
event makes the potential circle non-empty
l
(The original circle event becomes a false alarm)
54. Handling Site Events
1. Locate the leaf representing the existing arc that is
above the new site
– Delete the potential circle event in the event queue
2. Break the arc by replacing the leaf node with a
sub tree representing the new arc and break points
3. Add a new edge record in the doubly linked list
4. Check for potential circle event(s), add them to
queue if they exist
– Store in the corresponding leaf of T a pointer to the
new circle event in the queue
55. Handling Circle Events
1. Add vertex to corresponding edge record in doubly
linked list
2. Delete from T the leaf node of the disappearing arc
and its associated circle events in the event queue
3. Create new edge record in doubly linked list
4. Check the new triplets formed by the former
neighboring arcs for potential circle events
56. A Circle Event
pi pj pk
< pj, pk>
< pi, pj> < pk, pl>
pi
pj
pk
pl
l
pm
pm pl
< pl, pm>
< pm, pl>
pl
57. Add vertex to corresponding edge record
pi pj pk
< pj, pk>
< pi, pj> < pk, pl>
pi
pj
pk
pl
l
pm
pm pl
< pl, pm>
< pm, pl>
pl
Half Edge Record
Endpoints.add(x, y)
Half Edge Record
Endpoints.add(x, y)
Link!
60. Create new edge record
pi pj pk
< pj, pk>
< pi, pj>
pi
pj
pk
pl
l
pm
pm pl
< pm, pl>
< pk, pm>
New Half Edge Record
Endpoints.add(x, y)
A new edge is traced out by the new
break point < pk, pm>
61. Check the new triplets for
potential circle events
pi pj pk
< pj, pk>
< pi, pj>
pi
pj
pk
pl
l
pm
pm pl
< pm, pl>
< pk, pm>
Q y…
new circle event
62. Minor Detail
• Algorithm terminates when Q = , but the
beach line and its break points continue to
trace the Voronoi edges
– Terminate these “half-infinite” edges via a
bounding box
65. Algorithm Termination
pi pj
< pj, pm>
< pi, pj>
pi
pj
pk
pl
l
pm
pm pl
< pm, pl>
Q
Terminate half-lines
with a bounding box!
66. Handling Site Events
1. Locate the leaf representing the existing arc
that is above the new site
– Deletethepotentialcircleevent in the event queue
2. Break the arc by replacing the leaf node with a
sub tree representing the new arc and break
points
3. Add a new edge record in the link list
4. Check for potential circle event(s), add them to
queue if they exist
– Storein the corresponding leafof T a pointerto the
new circle event in the queue
Running Time
O(log n)
O(1)
O(1)
O(1)
67. Handling Circle Events
1. Delete from T the leaf node of the
disappearing arc and its associated
circle events in the event queue
2. Add vertex record in doubly link list
3. Create new edge record in doubly
link list
4. Check the new triplets formed by the
former neighboring arcs for potential
circle events
Running Time
O(log n)
O(1)
O(1)
O(1)
68. Total Running Time
• Each new site can generate at most two new
arcs
beach line can have at most 2n – 1 arcs
at most O(n) site and circle events in the queue
• Site/Circle Event Handler O(log n)
O(n log n) total running time
69. Is Fortune’s Algorithm Optimal?
• We can sort numbers using any algorithm that
constructs a Voronoi diagram!
• Map input numbers to a position on the number
line. The resulting Voronoi diagram is doubly
linked list that forms a chain of unbounded cells in
the left-to-right (sorted) order.
-5 1 3 6 7
Number
Line
70. 70
Divide-and-Conquer approach
• Input : A set S of n planar points.
• Output : The Voronoi diagram of S.
• Step 1 If S contains less than 4 point, solve
directly and return.
• Step 2 Find a median line L perpendicular to the
X-axis which divides S into SL and SR such that
SL (SR) lies to the left(right) of L and the sizes of
SL and SR are equal.
71. 71
Divide-and-Conquer approach
• Step 3 Construct Voronoi diagrams of SL and SR
recursively. Denote these Voronoi diagrams by
VD(SL) and VD(SR).
• Step 4 Construct a dividing piece-wise linear
hyperplane HP which is the locus of points
simultaneously closest to a point in SL and a point
in SR. Discard all segments of VD(SL) which lie
to the right of HP and all segments of VD(SR) that
lie to the left of HP. The resulting graph is the
Voronoi diagram of S.
73. 73
• Merging:
How to merge two Voronoi
diagrams ?
b15b45b14b13b34b46b36b23b26
74. 74
Merges Two Voronoi Diagrams into
One Voronoi Diagram
• Input : (a) SL and SR where SL and SR are
divided by a perpendicular line L.
(b) VD(SL ) and VD(SR ).
• Output : VD(S) where S = SL ∩SR
• Step 1 Find the convex hulls of SL and SR . Let them
be denoted as Hull(SL) and Hull(SR), respectively. (A
special algorithm for finding a convex hull in this
case will by given later.)
75. 75
Merges Two Voronoi Diagrams into
One Voronoi Diagram
• Step 2 Find segments and which join
HULL(SL ) and HULL(SR ) into a convex hull (Pa
and Pc belong to SL and Pb and Pd belong to SR)
Assume that lies above . Let x = a, y = b,
SG= and HP = .
• Step 3 Find the perpendicular bisector of SG.
Denote it by BS. Let HP = HP.{BS}. If SG = ,
go to Step 5; otherwise, go to Step 4.
dc PPba PP
ba PP dc PP
yxPP
dc PP
76. 76
Merges Two Voronoi Diagrams into
One Voronoi Diagram
• Step 4 The ray from VD(SL ) and VD(SR) which
BS first intersects with must be a perpendicular
bisector of either or for some z. If this
ray is the perpendicular bisector of , then let
SG = ; otherwise, let SG = . Go to Step 3.
• Step 5 Discard the edges of VD(SL) which
extend to the right of HP and discard the edges
of VD(SR) which extend to the left of HP. The
resulting graph is the Voronoi diagram of S =
SL.SR.
zxPP zy PP
zy PP
zxPP yz PP
77. 77
Merges Two Voronoi Diagrams into
One Voronoi Diagram
• Def : Given a point P and a set S of points,
the distance between P and S is the distance
between P and Pi which is the nearest
neighbor of P in S.
• The HP obtained from the above algorithm
is the locus of points which keep equal
distances to SL and SR .
• The HP is monotonic in y.
78. 78
Merges Two Voronoi Diagrams into
One Voronoi Diagram
• # of edges of a Voronoi diagram 3n -
6, where n is # of points.
• Reasoning:
i. # of edges of a planar graph with n vertices
3n - 6.
ii. A Delaunay triangulation is a planar graph.
iii. Edges in Delaunay triangulation
edges in Voronoi diagram.
1 1
79. 79
Construct Convex Hull from
Voronoi diagram
• After a Voronoi diagram is constructed, a
convex hull can by found in O(n) time.
81. 81
Construct Convex Hull from
Voronoi diagram
• Step 1 : Find an infinite ray by examining
all Voronoi edges.
• Step 2 : Let Pi be the point to the left of the
infinite ray. Pi is a convex hull vertex.
Examine the Voronoi polygon of Pi to find
the next infinite ray.
• Step 3 : Repeat Step 2 until we return to the
Starting ray.
82. 82
Time complexity
• Time complexity for merging 2 Voronoi
diagrams:
– Step 1: O(n)
– Step 2: O(n)
– Step 3 ~ Step 5: O(n)
(at most 3n - 6 edges in VD(SL) and VD(SR) and
at most n segments in HP)
T(n) = 2T(n/2) + O(n)=O(n log n)
83. Applications 1(Static Point Set)
Closest pair of points:
Go through edge list for VD(P) and determine minimum
All next neighbors :
Go through edge list for VD(P) for all points and get
next neighbors in each case
Minimum Spanning tree (after Kruskal)
1. Each point p from P defines 1-element set of
2. More than a set of T exists
2.1) find p,p´ with p in T and p´ not in T with d(p,
p´)
minimum.
2.2) connect T and p´ contained in T´ (union)
Theorem: The MST can be computed in time O(n log n)
84. Applications (dynamic object set)
Search for next neighbor :
Idea : Hierarchical subdivision of VD(P)
Step 1 : Triangulation of final Voronoi regions
Step 2 : Summary of triangles and structure of a search tree
Rule of Kirkpatrick :
Remove in each case points with degree < 12,
its neighbor is already far.
Theorem: Using the rule of Kirkpatrick a search tree
of logarithmic depth develops.
A a
b c a b c
A
85. Degenerate Cases
• Events in Q share the same y-coordinate
– Can additionally sort them using x-coordinate
• Circle event involving more than 3 sites
– Current algorithm produces multiple degree 3
Voronoi vertices joined by zero-length edges
– Can be fixed in post processing
86. Degenerate Cases
• Site points are collinear (break points
neither converge or diverge)
– Bounding box takes care of this
• One of the sites coincides with the lowest
point of the circle event
– Do nothing
87. Site coincides with circle event:
the same algorithm applies!
1. New site detected
2. Break one of the arcs an infinitesimal distance
away from the arc’s end point
89. Summary
• Voronoi diagram is a useful planar
subdivision of a discrete point set
• Voronoi diagrams have linear complexity
and can be constructed in O(n log n) time
• Fortune’s algorithm (optimal)