The document discusses targeted Bayesian network learning (TBNL) and its application to predicting criminal suspects. It compares TBNL to traditional Bayesian network learning approaches, noting that TBNL aims to maximize the amount of information learned about a specific target variable rather than the entire distribution. The document provides examples of TBNL outperforming naive Bayes and tree-augmented networks on several datasets by exploiting correlations between attributes and the target more effectively for prediction tasks. It also analyzes the differential complexity of TBNL versus traditional explanatory models.
Kernelization algorithms for graph and other structure modification problemsAnthony Perez
Thesis defense on November 14th, 2011, in Montpellier.
Jury:
Stéphane Bessy, Bruno Durand, Frédéric Havet, Rolf Niedermeier, Christophe Paul & Ioan Todinca.
Kernelization algorithms for graph and other structure modification problemsAnthony Perez
Thesis defense on November 14th, 2011, in Montpellier.
Jury:
Stéphane Bessy, Bruno Durand, Frédéric Havet, Rolf Niedermeier, Christophe Paul & Ioan Todinca.
Robust Shape and Topology Optimization - Northwestern Altair
A robust shape and topology optimization (RSTO) approach with consideration of random field uncertainty in various sources such as loading, material properties, and geometry has been developed. The approach integrates the state-of-the-art level set methods for shape and topology optimization and the latest research development in design under uncertainty. To characterize the high-dimensional random-field uncertainty with a reduced set of random variables, the Karhunen-Loeve expansion is employed.
Robust Shape and Topology Optimization - Northwestern Altair
A robust shape and topology optimization (RSTO) approach with consideration of random field uncertainty in various sources such as loading, material properties, and geometry has been developed. The approach integrates the state-of-the-art level set methods for shape and topology optimization and the latest research development in design under uncertainty. To characterize the high-dimensional random-field uncertainty with a reduced set of random variables, the Karhunen-Loeve expansion is employed.
Kernel based models for geo- and environmental sciences- Alexei Pozdnoukhov –...Beniamino Murgante
Kernel based models for geo- and environmental sciences- Alexei Pozdnoukhov – National Centre for Geocomputation, National University of Ireland , Maynooth (Ireland)
Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009)
New Mathematical Tools for the Financial SectorSSA KPI
AACIMP 2010 Summer School lecture by Gerhard Wilhelm Weber. "Applied Mathematics" stream. "Modern Operational Research and Its Mathematical Methods with a Focus on Financial Mathematics" course. Part 5.
More info at http://summerschool.ssa.org.ua
The second Fundamental Theorem of Calculus makes calculating definite integrals a problem of antidifferentiation!
(the slideshow has extra examples based on what happened in class)
Scientific Computing with Python Webinar 9/18/2009:Curve FittingEnthought, Inc.
This webinar will provide an overview of the tools that SciPy and NumPy provide for regression analysis including linear and non-linear least-squares and a brief look at handling other error metrics. We will also demonstrate simple GUI tools that can make some problems easier and provide a quick overview of the new Scikits package statsmodels whose API is maturing in a separate package but should be incorporated into SciPy in the future.
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.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
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!
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.
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
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
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
1. “To Explain or To Predict”
“To Know or To Act”
(Pure Science vs. Engineering, 2004)
Using Target-Based Bayesian Nets for Suspects
Monitoring (joint work with A. Gruber and S. Yanovski)
Irad Ben-Gal
Tel Aviv University
2. DOE: Vs-optimal designs Ginsburg & Ben-Gal (2004)
x (control) f(x) Y (output)
f(x) known: f(x)/x=0 x*
f(x) unknown:
Estimate g(x) (Meta Model: DOE, RSM,…)
g(x)/x=0 x* (R.V.)
‘Scientists’ (to Know): Best estimation of f(x)
min V() (e.g., D-optimal exp.)
‘Practitioner’ (to act) : Best estimation of x*
min V(x*) (new DOE optimality criterion)
Tel Aviv University
Department of Industrial Engineering
6. What is a Bayesian Network?
Joint Probability
B ( G , Θ ) encodes the domain’s JPD Distribution
X1 X2 X3 X4 Prob.
1 1 1 2 0.083
G V , E = Directed Acyclic Graph 1 1 2 2 0.167
1 2 2 3 0.25
2 2 1 1 0.25
2 2 2 1 0.25
Θ(X 3)
X2 1 2
1 0.33 0.33
2 0.67 0.67
A Complete
Factorization Bayesian Network
P (X ) P ( X 2 )P ( X 3 | X 2 )P ( X 4
| X 3, X 2 )P( X 1 | X 4, X 3, X 2 )
Tel Aviv University
Department of Industrial Engineering
6/35
7. Explain or Predict (classify)
Chow & Liu (1968) TBNL
Tree / GBN
Williamson (2000) Gruber & Ben-Gal (2010)
p(X ) p(X )
True distribution
q(X ) q(X )
Modeled distribution
p(X ) pX p X i | x ' p x '
Objective i
x ' X X i
Principle Minimize D KL p X || q X Minimize D KL p X i
|| q X i
Maximize I X i; Z i
Maximize I X i
;Zi
Consequence Maximize I X
i
j
;Z j
X jZ i
Tel Aviv University
Department of Industrial Engineering
11/35
8. Unconstrained Learning
Assume X is the target variable
3
GBN (adding-arrows) Target-Oriented (TBNL)
i=1 i=4 i=3 i=4 i=1
Equivalent Encoding!!!
Tel Aviv University
Department of Industrial Engineering
13/35
9. Constrained Learning
Assume X is the target variable
3
GBN (adding-arrows) Target-Oriented (TBNL)
i=1 i=4 i=3 i=4 i=1
Tel Aviv University
Department of Industrial Engineering
14/35
10. Differential Complexity
Explain
Predict (Classify)
r
t
𝜂 𝑡 = maximum percentage relative information exploitation about the target
𝜂 𝑟 = maximum percentage relative information exploitation about the rest attributes
Tel Aviv University
Department of Industrial Engineering
11. Results (1/2)
Data Sets Properties and Testing Methods
Dataset # Attributes # Classes # Instances Test Instances/Attributes Ratio
australian 14 2 690 CV5 ~49
breast 9 2 683 CV5 ~76
chess 36 2 3196 holdout ~89
cleve 11 2 196 CV5 ~18
corral 6 2 128 CV5 ~21
crx 15 2 653 CV5 ~44
german 20 2 1000 CV5 ~50
glass 9 7 214 CV5 ~24
Iris 5 3 150 CV5 ~30
lymphography 18 4 148 CV5 ~8
mofn-3-7-10 10 2 1324 holdout ~132
vote 16 3 435 CV5 ~27
Tel Aviv University
Department of Industrial Engineering
16/35
12. Naïve Bayes: Predict
Corral Dataset
Class
A0
B0 Correlated
Irrelevant
A1 B1
Tel Aviv University
Department of Industrial Engineering
17/35
13. Tree Augmented Network (TAN)
Class
Class Class
Correlated
Irrelevant
B0
Irrelevant
A0 Correlated
A1 A0
A0
B0
B1 Irrelevant B0
A1
A1
Correlated
B1
B1
Class Class
Class
B1
A0
A1
A1
B0 B1
Irrelevant B0
B0
A0
A0
Correlated A1
Irrelevant
Irrelevant
B1
Correlated
Correlated
Tel Aviv University
Department of Industrial Engineering
18/35
14. Managing the Trade-off
CV5
CV5
Holdout
2/3:1/3
Tel Aviv University
Department of Industrial Engineering
20/35
16. Presentation Layout
Bayesian networks and classifiers
Targeted Bayesian Network Learning (TBNL)
TBNL application on suspects monitoring (w. Gruber & Yanovski)
Summary
Tel Aviv University
Department of Industrial Engineering
22/35
17. Domain Description
Motivation
Simplicity: complexity-error tradeoff
Information extraction: utilization of meta-data
Support: help the expert understand
Available Data
CDR
Privatized
Laundered
Requirements
50% Recall with 1% False Alarm at most
Tel Aviv University
Department of Industrial Engineering
23/35
18. Data Description of the Domain
Call Detail Record (CDR)
Field Description
Main party Monitored Object unique IDENTIFIER
Other party Other Party unique IDENTIFIER
year Year of call start
month Month of call start
day Day of call start
hour Hour of call start
minute Minute of call start
second Second of call start
duration Call duration in Seconds
caller Indication of call initiator : {1/0}
1 – main party initiated the call
0 – other party initiated the call
type_id Type of interaction initiator : {1/0}
1 - phone call
0 - sms (text message)
tag Type (group) of monitored Object : {1/0}
0 – main party is a non-target
1 – main party is a target
Tel Aviv University
Department of Industrial Engineering
24/35
19. ROC curve
40 suspects to no avail
1900
missed
targets
Tel Aviv University
Department of Industrial Engineering
27/35
20. Feature Extraction
Activity of calls during the day of two distinct groups
Inter_prc_q1, Inter_prc_q2, Inter_prc_q3, Inter_prc_q4 – percentage of
activities in 1st, 2nd, 3rd and 4th quarter of the day
Tel Aviv University
Department of Industrial Engineering
28/35
22. Conclusions
“To Explain or to Predict” –
“To know or to Act” (constraint modeling)
Managing the error-complexity tradeoff!
An “engineering approach” to modeling
Target-based BN Learning (2006), Gruber and Ben-Gal (2010)…
Vs-optimality criterion min V(x*), Ginsburg and Ben-Gal (2006)
VOBN Ben-Gal et at (2005) – scenario dependent
More….
Tel Aviv University
Department of Industrial Engineering 32/35