The document describes an algorithm for solving the 0-1 knapsack problem in polynomial time on average. It works by building up optimal solutions incrementally, considering one item at a time and combining it with existing optimal solutions if the weight limit is not exceeded. The expected running time is O(n3) where n is the number of items, as the number of optimal solutions stored is bounded by n3.
Теория ограничений и Линейное программированиеStas Fomin
Краткое введение в математическое моделирование и задачи линейного программирования.
Показана связь Теории Ограничений Др. Голдратта с линейным программированием, показаны решения компьютером модельных задач из «Синдрома стога сена», даже найдены ошибки в решениях Др. Голдратта.
Теория ограничений и Линейное программированиеStas Fomin
Краткое введение в математическое моделирование и задачи линейного программирования.
Показана связь Теории Ограничений Др. Голдратта с линейным программированием, показаны решения компьютером модельных задач из «Синдрома стога сена», даже найдены ошибки в решениях Др. Голдратта.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
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/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024
Average Knapsack
1. Полиномиальный в среднем алгоритм для
«Рюкзака»
Н.Н. Кузюрин С.А. Фомин
10 октября 2008 г.
Лекция основана на результатах из:
«Beier, R. and V¨cking, B. (2003). In
o
Proceedings of the 35th ACM Symposium on
Theory of Computing (STOC), pages
232–241».
1 / 15
2. Полиномиальность в среднем
Определение
«Полиномиальный в среднем (точно)»
Алгоритм A называется полиномиальным в среднем, если среднее
время его работы ограничено полиномом от длины входа, т.е.
существует константа c > 0, такая, что En TA = O(nc ).
Упражнение
Приведите пример функции TA (времени работы некоторого
алгоритма A) и распределения исходных данных Pn (I ), для которых
TA является полиномиальной в среднем (En TA = O(nc )), а TA — нет.
2
Определение
«Полиномиальный в среднем»
Алгоритм называется полиномиальным в среднем, если существует
константа ε > 0, такая, что En T ε = O(n), где T — время работы
алгоритма. 2 / 15
3. Задача о рюкзаке
Задача
«0–1 Рюкзак (Knapsack)»
Даны:
c1 , . . . , cn , cj ∈ N — «стоимости» предметов;
a1 , . . . , an , aj ∈ N — «размеры» или «веса»;
B ∈ N — «размер рюкзака».
Найти максимальное значение f ∗ целевой функции
n
f ≡ ci xi → max
i=1
с ограничением на размер «рюкзака»:
n
ai xi ≤ B, xi ∈ {0, 1}.
i=1
3 / 15
7. Мат. ожидание сложности алгоритма полиномиально
Теорема
Пусть:
ai — «веса», произвольные положительные числа;
ci — «стоимости», независимые случайные величины,
равномерно распределенные на [0, 1];
q = max |ParetoSolutions| — число доминирующих подмножеств для
всех n предметов.
Тогда
E(q) = O(n3 ).
7 / 15
8. Определения
m = 2n , S1 , . . . , Sm — подмножества [n] в порядке неубывания весов.
(Веса i∈Sk ai множеств Sk в нашей теме не возникают.)
Для любых 2 ≤ u ≤ m, 1 ≤ k ≤ u:
Plusk = Su Sk
Minusk = Sk Su
Δ+
k = ci
i∈Plusk
Δ−
k = ci
i∈Minusk
Δu = min Δ+ − Δ−
k k
1≤k<u
∀u ≥ 2, Su — доминирующее множество ⇔ Δu > 0.
8 / 15
10. P(ВНабореНеМалы|ПаретоНабор) ≤ nε
∀j ≤ t и 0 < ε < 1:
P (cj < ε| ∀k : Δ+ > Δ−
k k = P (cj < ε| cj > x)
при x ≥ ε: 0
≤ ε−x ε(1−x) x(1−ε)
при x < ε: 1−x = 1−x − 1−x
≤ ε
P(cj < ε|cj > x)
-
0 x ε 1
P(ВНабореНеМалы|ПаретоНабор) = P(∪t ЭлементМалj |ПаретоНабор)
j=1
t
≤ P(ЭлементМалj |ПаретоНабор)
j=1
≤ t · ε ≤ nε.
10 / 15
11. P(ДельтаМала|ПаретоНабор ∧ ВНабореНеМалы) ≤ nε
Оценим P(ПаретоНабор) при априорности событий (при условии):
«∀j cj ∈ [0, 1]», «ВНабореНеМалы»(и, следовательно, «Δ+ ≥ ε»):
k
P(ПаретоНабор|ВНабореНеМалы) = P ∀k Δ− ≤ Δ+ , ∀j > t cj ∈ [0, 1] =
k k
1
= P ∀k Δ− ≤ (1 − ε) Δ+ , ∀j > t cj ∈ [0, 1 − ε] ≤
k k
(1 − ε)n−t
1
≤ P ∀k Δ− ≤ Δ+ − ε2 , ∀j > t cj ∈ [0, 1 − ε] ≤
k k
(1 − ε)n−t
1
≤ P (ДельтаМала|ВНабореНеМалы)
(1 − ε)n−t
С другой стороны, ДельтаМала ⊂ ПаретоНабор, и
P(ДельтаМала|ПаретоНабор ∧ ВНабореНеМалы) =
P(ДельтаМала|ВНабореНеМалы)
= ≥ (1 − ε)n−t ≥ (1 − ε)n ≥ 1 − nε
P(ПаретоНабор|ВНабореНеМалы)
11 / 15
12. P(A|B) = P(A|B ∩ C ) · P(C |B) + P(A|B ∩ C ) · P(C |B)
x +y
P(A|B) =
x +y +z +w
событие A событие C
P(A|B ∩ C ) · P(C |B) =
y y +z
y = ·
x z y +z x +y +z +w
w
событие B P(A|B ∩ C ) · P(C |B) =
x x +w
= ·
x +w x +y +z +w
12 / 15