The document discusses the challenges of making architectural decisions for self-adaptive systems to satisfy non-functional requirements under uncertainty. It presents an example of a sensor network for a volcano where decisions about the network design (e.g. shortest path vs fewest hops) impact battery life and data collection goals differently depending on the volcano's state. Bayesian networks are proposed to model the relationship between decisions, goals and assumptions under uncertainty and help evaluate designs at runtime as assumptions may change.
Manual Solution Probability and Statistic Hayter 4th EditionRahman Hakim
All of material inside is un-licence, kindly use it for educational only but please do not to commercialize it.
Based on 'ilman nafi'an, hopefully this file beneficially for you.
Thank you.
Manual Solution Probability and Statistic Hayter 4th EditionRahman Hakim
All of material inside is un-licence, kindly use it for educational only but please do not to commercialize it.
Based on 'ilman nafi'an, hopefully this file beneficially for you.
Thank you.
Invited presentation on "Verification of Parameterized Data-Aware Dynamic Systems" at the First Workshop on Parameterized Verification (PV 2014), satellite event of the 25th International Conference on Concurrency Theory (CONCUR 2014).
Developing information systems as complex adaptive systems
Based on research collaboration with Gwanhoo Lee (American University), James Howison (University of Texas at Austin) and Jim Herbsleb (Carnegie Mellon University)
Big Data Visualization
Kwan-Liu Ma
Professor of Computer Science and Chair of the Graduate Group in Computer Science (GGCS) at the University of California-Davis
January 22nd 2014
We are entering a data-rich era. Advanced computing, imaging, and sensing technologies enable scientists to study natural and physical phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Web and mobile device users is expected to be even greater. To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis. One such a tool is visualization. I will present visualizations designed for gleaning insight from massive data and guiding complex data analysis tasks. I will show case studies using data from cyber/homeland security, large-scale scientific simulations, medicine, and sociological studies.
Big Data Visualization Meetup - South Bay
http://www.meetup.com/Big-Data-Visualisation-South-Bay/
This talk was presented in Startup Master Class 2017 - http://aaiitkblr.org/smc/ 2017 @ Christ College Bangalore. Hosted by IIT Kanpur Alumni Association and co-presented by IIT KGP Alumni Association, IITACB, PanIIT, IIMA and IIMB alumni.
My co-presenter was Biswa Gourav Singh. And contributor was Navin Manaswi.
http://dataconomy.com/2017/04/history-neural-networks/ - timeline for neural networks
State-Of-The Art Machine Learning Algorithms and How They Are Affected By Nea...inside-BigData.com
In this deck from the HPC Knowledge Portal 2017 Conference, Rob Farber from TechEnablement presents: State-Of-The Art Machine Learning Algorithms and How They Are Affected By Near-Term Technology Trends.
"Industry and Wall Street projections indicate that Machine Learning will touch every piece of data in the data center by 2020. This has created a technology arms race and algorithmic competition as IBM, NVIDIA, Intel, and ARM strive to dominate the retooling of the computer industry to support ubiquitous machine learning workloads over the next 3-4 years. Similarly, algorithm designers compete to create faster and more accurate training and inference techniques that can address complex problems spanning speech, image recognition, image tagging, self-driving cars, data analytics and more. The challenges for researchers and technology providers encompass big data, massive parallelism, distributed processing, and real-time processing. Deep-learning and low-precision inference (based on INT8 and FP16 arithmetic) are current hot topics."
Watch the video: https://wp.me/p3RLHQ-i2K
Learn more: http://www.hpckp.org/index.php/conference/2017
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Learning to Project and Binarise for Hashing-based Approximate Nearest Neighb...Sean Moran
In this paper we focus on improving the effectiveness of hashing-based approximate nearest neighbour search. Generating similarity preserving hashcodes for images has been shown to be an effective and efficient method for searching through large datasets. Hashcode generation generally involves two steps: bucketing the input feature space with a set of hyperplanes, followed by quantising the projection of the data-points onto the normal vectors to those hyperplanes. This procedure results in the makeup of the hashcodes depending on the positions of the data-points with respect to the hyperplanes in the feature space, allowing a degree of locality to be encoded into the hashcodes. In this paper we study the effect of learning both the hyperplanes and the thresholds as part of the same model. Most previous research either learn the hyperplanes assuming a fixed set of thresholds, or vice-versa. In our experiments over two standard image datasets we find statistically significant increases in retrieval effectiveness versus a host of state-of-the-art data-dependent and independent hashing models.
Improving Hardware Efficiency for DNN ApplicationsChester Chen
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
Big Data Analytics for connected home: a few usecases, some important messages and a little example. Presentation given at CEA Cadarache - Cité des Nouvelles Energies at the strategic comittee of ARCSIS (http://www.arcsis.org/missions.html)
Enabling Biobank-Scale Genomic Processing with Spark SQLDatabricks
With the size of genomic data doubling every seven months, existing tools in the genomic space designed for the gigabyte scale tip over when used to process the terabytes of data being made available by current biobank-scale efforts. To enable common genomic analyses at massive scale while being flexible to ad-hoc analysis, Databricks and Regeneron Genetics Center have partnered to launch an open-source project.
The project includes optimized DataFrame readers for loading genomics data formats, as well as Spark SQL functions to perform statistical tests and quality control analyses on genomic data. We discuss a variety of real-world use cases for processing genomic variant data, which represents how an individual’s genomic sequence differs from the average human genome. Two use cases we will discuss are: joint genotyping, in which multiple individuals’ genomes are analyzed as a group to improve the accuracy of identifying true variants; and variant effect annotation, which annotates variants with their predicted biological impact. Enabling such workflows on Spark follows a straightforward model: we ingest flat files into DataFrames, prepare the data for processing with common Spark SQL primitives, perform the processing on each partition or row with existing genomic analysis tools, and save the results to Delta or flat files.
Distributed Monte Carlo Feature Selection: Extracting Informative Features Ou...Łukasz Król
Selection of informative features out of ever growing results of high throughput biological experiments requires specialized feature selection algorithms. One of such methods is the Monte Carlo Feature Selection - a straightforward, yet computationally expensive one. In this technical paper we present architecture and performance of a development version of our distributed implementation of this algorithm, designed to run in multiprocessor as well as multihost computing environments, and potentially controllable through a web browser by non-IT staff. As a simple enhancement, our method is able to produce statistically interpretable output by means of permutation testing. Tested on reference Golub et al. leukemia data, as well as on our own dataset of almost 2 million features, it has shown nearly linear speedup when executed with an increased amount of processors. Being platform independent, as well as open for extensions, this application could become a valuable tool for researchers facing the challenge of ill-defined high dimensional feature selection problems.
Thinking in MapReduce - StampedeCon 2013StampedeCon
At the StampedeCon 2013 Big Data conference in St. Louis, Ryan Brush, Distinguished Engineer with Cerner Corporation, discussed Thinking in MapReduce - StampedeCon 2013. MapReduce reflects the essence of scalable processing: split a big problem into lots of parts, process them in parallel, and then merge the results. Yet this model is at odds with how we’ve thought about computing for most of history, where we center our applications on longlived stores of mutable data and incrementally apply change. This difference means a new mindset is needed to best leverage Hadoop and its ecosystem. This talk lays out the basics of MapReduce, designing logic and data models to make the best use of the Hadoop platform. It also goes through a number of design patterns and how Cerner is applying them to health care.
Science has escaped the lab and is roaming free in the world. People use software to understand the world . What tools are needed to support that work?
More Related Content
Similar to Bayesian Artifical Intelligence for Tackling Uncertainty in Self-Adaptive Systems
Invited presentation on "Verification of Parameterized Data-Aware Dynamic Systems" at the First Workshop on Parameterized Verification (PV 2014), satellite event of the 25th International Conference on Concurrency Theory (CONCUR 2014).
Developing information systems as complex adaptive systems
Based on research collaboration with Gwanhoo Lee (American University), James Howison (University of Texas at Austin) and Jim Herbsleb (Carnegie Mellon University)
Big Data Visualization
Kwan-Liu Ma
Professor of Computer Science and Chair of the Graduate Group in Computer Science (GGCS) at the University of California-Davis
January 22nd 2014
We are entering a data-rich era. Advanced computing, imaging, and sensing technologies enable scientists to study natural and physical phenomena at unprecedented precision, resulting in an explosive growth of data. The size of the collected information about the Web and mobile device users is expected to be even greater. To make sense and maximize utilization of such vast amounts of data for knowledge discovery and decision making, we need a new set of tools beyond conventional data mining and statistical analysis. One such a tool is visualization. I will present visualizations designed for gleaning insight from massive data and guiding complex data analysis tasks. I will show case studies using data from cyber/homeland security, large-scale scientific simulations, medicine, and sociological studies.
Big Data Visualization Meetup - South Bay
http://www.meetup.com/Big-Data-Visualisation-South-Bay/
This talk was presented in Startup Master Class 2017 - http://aaiitkblr.org/smc/ 2017 @ Christ College Bangalore. Hosted by IIT Kanpur Alumni Association and co-presented by IIT KGP Alumni Association, IITACB, PanIIT, IIMA and IIMB alumni.
My co-presenter was Biswa Gourav Singh. And contributor was Navin Manaswi.
http://dataconomy.com/2017/04/history-neural-networks/ - timeline for neural networks
State-Of-The Art Machine Learning Algorithms and How They Are Affected By Nea...inside-BigData.com
In this deck from the HPC Knowledge Portal 2017 Conference, Rob Farber from TechEnablement presents: State-Of-The Art Machine Learning Algorithms and How They Are Affected By Near-Term Technology Trends.
"Industry and Wall Street projections indicate that Machine Learning will touch every piece of data in the data center by 2020. This has created a technology arms race and algorithmic competition as IBM, NVIDIA, Intel, and ARM strive to dominate the retooling of the computer industry to support ubiquitous machine learning workloads over the next 3-4 years. Similarly, algorithm designers compete to create faster and more accurate training and inference techniques that can address complex problems spanning speech, image recognition, image tagging, self-driving cars, data analytics and more. The challenges for researchers and technology providers encompass big data, massive parallelism, distributed processing, and real-time processing. Deep-learning and low-precision inference (based on INT8 and FP16 arithmetic) are current hot topics."
Watch the video: https://wp.me/p3RLHQ-i2K
Learn more: http://www.hpckp.org/index.php/conference/2017
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Learning to Project and Binarise for Hashing-based Approximate Nearest Neighb...Sean Moran
In this paper we focus on improving the effectiveness of hashing-based approximate nearest neighbour search. Generating similarity preserving hashcodes for images has been shown to be an effective and efficient method for searching through large datasets. Hashcode generation generally involves two steps: bucketing the input feature space with a set of hyperplanes, followed by quantising the projection of the data-points onto the normal vectors to those hyperplanes. This procedure results in the makeup of the hashcodes depending on the positions of the data-points with respect to the hyperplanes in the feature space, allowing a degree of locality to be encoded into the hashcodes. In this paper we study the effect of learning both the hyperplanes and the thresholds as part of the same model. Most previous research either learn the hyperplanes assuming a fixed set of thresholds, or vice-versa. In our experiments over two standard image datasets we find statistically significant increases in retrieval effectiveness versus a host of state-of-the-art data-dependent and independent hashing models.
Improving Hardware Efficiency for DNN ApplicationsChester Chen
Speaker: Dr. Hai (Helen) Li is the Clare Boothe Luce Associate Professor of Electrical and Computer Engineering and Co-director of the Duke Center for Evolutionary Intelligence at Duke University
In this talk, I will introduce a few recent research spotlights by the Duke Center for Evolutionary Intelligence. The talk will start with the structured sparsity learning (SSL) method which attempts to learn a compact structure from a bigger DNN to reduce computation cost. It generates a regularized structure with high execution efficiency. Our experiments on CPU, GPU, and FPGA platforms show on average 3~5 times speedup of convolutional layer computation of AlexNet. Then, the implementation and acceleration of DNN applications on mobile computing systems will be introduced. MoDNN is a local distributed system which partitions DNN models onto several mobile devices to accelerate computations. ApesNet is an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. Our prospects on the adoption of emerging technology will also be given at the end of this talk, offering the audiences an alternative thinking about the future evolution and revolution of modern computing systems.
Big Data Analytics for connected home: a few usecases, some important messages and a little example. Presentation given at CEA Cadarache - Cité des Nouvelles Energies at the strategic comittee of ARCSIS (http://www.arcsis.org/missions.html)
Enabling Biobank-Scale Genomic Processing with Spark SQLDatabricks
With the size of genomic data doubling every seven months, existing tools in the genomic space designed for the gigabyte scale tip over when used to process the terabytes of data being made available by current biobank-scale efforts. To enable common genomic analyses at massive scale while being flexible to ad-hoc analysis, Databricks and Regeneron Genetics Center have partnered to launch an open-source project.
The project includes optimized DataFrame readers for loading genomics data formats, as well as Spark SQL functions to perform statistical tests and quality control analyses on genomic data. We discuss a variety of real-world use cases for processing genomic variant data, which represents how an individual’s genomic sequence differs from the average human genome. Two use cases we will discuss are: joint genotyping, in which multiple individuals’ genomes are analyzed as a group to improve the accuracy of identifying true variants; and variant effect annotation, which annotates variants with their predicted biological impact. Enabling such workflows on Spark follows a straightforward model: we ingest flat files into DataFrames, prepare the data for processing with common Spark SQL primitives, perform the processing on each partition or row with existing genomic analysis tools, and save the results to Delta or flat files.
Distributed Monte Carlo Feature Selection: Extracting Informative Features Ou...Łukasz Król
Selection of informative features out of ever growing results of high throughput biological experiments requires specialized feature selection algorithms. One of such methods is the Monte Carlo Feature Selection - a straightforward, yet computationally expensive one. In this technical paper we present architecture and performance of a development version of our distributed implementation of this algorithm, designed to run in multiprocessor as well as multihost computing environments, and potentially controllable through a web browser by non-IT staff. As a simple enhancement, our method is able to produce statistically interpretable output by means of permutation testing. Tested on reference Golub et al. leukemia data, as well as on our own dataset of almost 2 million features, it has shown nearly linear speedup when executed with an increased amount of processors. Being platform independent, as well as open for extensions, this application could become a valuable tool for researchers facing the challenge of ill-defined high dimensional feature selection problems.
Thinking in MapReduce - StampedeCon 2013StampedeCon
At the StampedeCon 2013 Big Data conference in St. Louis, Ryan Brush, Distinguished Engineer with Cerner Corporation, discussed Thinking in MapReduce - StampedeCon 2013. MapReduce reflects the essence of scalable processing: split a big problem into lots of parts, process them in parallel, and then merge the results. Yet this model is at odds with how we’ve thought about computing for most of history, where we center our applications on longlived stores of mutable data and incrementally apply change. This difference means a new mindset is needed to best leverage Hadoop and its ecosystem. This talk lays out the basics of MapReduce, designing logic and data models to make the best use of the Hadoop platform. It also goes through a number of design patterns and how Cerner is applying them to health care.
Science has escaped the lab and is roaming free in the world. People use software to understand the world . What tools are needed to support that work?
GALE: Geometric active learning for Search-Based Software EngineeringCS, NcState
Multi-objective evolutionary algorithms (MOEAs) help software engineers find novel solutions to complex problems. When automatic tools explore too many options, they are slow to use and hard to comprehend. GALE is a near-linear time MOEA that builds a piecewise approximation to the surface of best solutions along the Pareto frontier. For each piece, GALE mutates solutions towards the better end. In numerous case studies, GALE finds comparable solutions to standard methods (NSGA-II, SPEA2) using far fewer evaluations (e.g. 20 evaluations, not 1,000). GALE is recommended when a model is expensive to evaluate, or when some audience needs to browse and understand how an MOEA has made its conclusions.
Three Laws of Trusted Data Sharing:(Building a Better Business Case for Dat...CS, NcState
Discussions about sharing
- Too much fear
- Not enough about benefits
Can we learn more from sharing that hoarding ?
- Yes (results from SE)
Three laws of trusted data sharing:
- For SE quality prediction..
- Better models from shared privatized data that from all raw data
Q: does this work for other kinds of data?
A: don’t know… yet
172529main ken and_tim_software_assurance_research_at_west_virginiaCS, NcState
SA @ WV(software assurance research at West Virginia)
Kenneth McGill
NASA IV&V Facility Research Lead
304.367.8300
Kenneth.McGill@ivv.nasa.gov
Dr. Tim Menzies Ph.D. (WVU)
Software Engineering Research Chair
tim@menzies.us
Next Generation “Treatment Learning” (finding the diamonds in the dust)CS, NcState
Q: How have dummies (like me) managed to gain (some) control over a (seemingly) complex world?
A:The world is simpler than we think.
◆ Models contain clumps
◆ A few collar variables decide which clumps to use.
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/
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!
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.
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/
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.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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!
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
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.
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.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf
Bayesian Artifical Intelligence for Tackling Uncertainty in Self-Adaptive Systems
1. Bayesian
Ar+ficial
Intelligence
for
Tackling
Uncertainty
in
Self-‐Adap+ve
Systems:
the
Case
of
Dynamic
Decision
Networks
2nd
Interna*onal
NSF
sponsored
Workshop
on
Realizing
Ar*ficial
Intelligence
Synergies
in
So=ware
Engineering
(RAISE2013)
San
Francisco
May,
21
2013
Nelly
Bencomo
Aston
University,
UK
Inria,
France
hKp://www.nellybencomo.me/
Amel
Belaggoun
Inria,
France
Valerie
Issarny
Inria,
France
2. Agenda
• Mo+va+on
– Role
of
non-‐func+onal
requirements
in
the
decision
making
for
self-‐
adapta+on
– Impact
of
architectural
decisions
on
the
sa+sficement
of
non-‐
func+onal
requirements
(NFRs)
• Dynamic
Decision
Networks
to
support
decision-‐
making
under
uncertainty
• Case
Study
• Conclusions
and
Future
Work
3. SoUware
of
the
Future
Increasingly
self-‐managing
Requirements-‐aware
Systems:
a
Research
Vision
4. SoUware
of
the
Future
Will
need
to
adapt
to
changing
environmental
condi+ons
Uncertainty:
these
changes
are
difficult
to
predict
and
an+cipate,
and
their
occurrence
is
out
of
control
of
the
applica+on
developers
!!
Requirements-‐aware
Systems:
a
Research
Vision
5. Let’s
focus
on
• Impact
of
architectural
decisions
(configura+ons)
on
the
sa+sficement
of
non-‐func+onal
requirements
CostsReliability
Performance
Configura*on
1
+
+
-‐
CostsReliability
Performance
Configura*on
2
+
-‐
+
6. • func+onal
requirement
“collect
data
about
a
volcano”
• Non-‐func+onal
requirements
(NFRs)
B
:
“conserve
baOery
power”
C
:
“collect
data
frequently”
• 2
contexts:
quiescent
and
erup*on
– “conserve
baKery
power”
priori+zed
during
a
quiescent
context
– “collect
data
frequently”
priori+zed
during
erup*on
• Decisions
to
make:
– Network
design
• Decision
1:
Shortest
path
(SP)
(less
efficient
but
may
conserve
baKery)
• Decision
2:
Fewest
Hops
(FH)
(more
efficient
but
may
drain
baKery
faster)
Mo+va+ng
Example:
a
sensor
network
of
a
volcano
ß
SP
ß
HP
quiescent
erup*on
7. Goal
model
for
the
example
collect
data
Shortest
path
(SP)
Fewest
Hops
(FH)
energy
efficiency
collect
data
frequently
++
-‐-‐
++
-‐-‐
goal
goal
realizaAon
strategy
soBgoals
(NFRs)
8. Goal
model
for
the
example
collect
data
Shortest
path
(SP)
Fewest
Hops
(FH)
energy
efficiency
collect
data
frequently
++
-‐-‐
++
-‐-‐
goal
goal
realizaAon
strategy
soBgoals
(NFRs)
design
assumpAon
(claim)
C1
C1:
SP
is
too
risky
False
9. During
execu+on
collect
data
Shortest
path
(SP)
Fewest
Hops
(FH)
energy
efficiency
collect
data
frequently
++
-‐-‐
++
-‐-‐
goal
goal
realizaAon
strategy
soBgoals
(NFRs)
design
assumpAon
(claim)
C1
C1:
SP
is
too
risky
False
10. During
execu+on
collect
data
Shortest
path
(SP)
Fewest
Hops
(FH)
energy
efficiency
collect
data
frequently
++
-‐-‐
++
-‐-‐
goal
goal
realizaAon
strategy
soBgoals
(NFRs)
design
assumpAon
(claim)
C1
C1:
SP
is
too
risky
True
11. Claim
Refinement
Model
Faults
Likely
SP
is
less
resilient
than
FH
SP
is
too
risky
AND
12. Non-‐func+onal
Requirements:
• Not
easy
to
reason
about
their
fulfillment
– "tension"
between
them
– tensions
need
to
be
iden+fied
and
resolved
in
an
op+mal
way
• Measurement
of
sa+sfac+on
of
NFRs
is
difficult
– NFRs
are
vague
or
fuzzy
– NFRs
may
not
be
absolutely
fulfilled
(they
can
be
labeled
as
sufficiently
sa+sficed)
NFR1
Performance
NFR2
Cost
not
easy
guys
to
deal
with
13. Non-‐func+onal
Requirements:
• Not
easy
to
reason
about
their
fulfillment
– "tension"
between
them
– tensions
need
to
be
iden+fied
and
resolved
in
an
op+mal
way
• Measurement
of
sa+sfac+on
of
NFRs
is
difficult
– NFRs
are
vague
or
fuzzy
– NFRs
may
not
be
absolutely
fulfilled
(they
can
be
labeled
as
sufficiently
sa+sficed)
NFR1
Performance
NFR2
Cost
not
easy
guys
to
deal
with
All
is
exacerbated
in
the
case
the
running
system
needs
to
make
such
decisions
by
itself
during
run+me
Uncertainty
about
the
environment
makes
it
difficult
to
predict
the
effect
of
the
impact
of
architectural
decisions
on
the
sa+sficement
of
non-‐func+onal
requirements
14. Non-‐func+onal
Requirements:
fuzzy
guys
• Should
we
use
probability
theory
to
describe
the
lack
of
crispness
and
the
uncertainty
about
the
sa+sfiability
nature
of
NFRs?
Given
an
architectural
decision
dj
that
requires
a
certain
configura+on,
the
sa+sficement
of
a
NFRi
can
be
modeled
using
probability
distribu+ons
P(NFRi
saAsficed
|
dj)
15. Probability
to
express
the
lack
of
crispness
of
NFRs.
collect
data
Shortest
path
(SP)
Fewest
Hops
(FH)
energy
efficiency
(E)
collect
data
Frequently
(D)
++
-‐-‐
++
-‐-‐
P(D|FH)
P(E|FH)
P(D
|
FH)
=
P
(
D
saAsficed
/
architectural
decision
FH)
P(E
|
FH)
=
P
(
E
saAsficed
/
architectural
decision
FH)
P(D|FH)
>
P(E|FH)
16. • Extension
of
Bayesian
Networks
to
support
decision-‐making
• Directed
Acyclic
Graph
(DAG)
associated
• Types
of
nodes:
• Chance
nodes:
labeled
by
random
variables
Xi
that
represent
the
states
of
the
world
• Decision
nodes:
with
the
set
of
choices
• UAlity
nodes:
that
state
the
preferences
about
the
states
of
the
world
• Evidence
nodes:
to
denote
the
observable
variables
The
condi+onal
probabili+es
quan+fy
the
effects
of
decisions
on
states
of
the
world
Tackling
Decision-‐making
with
Dynamic
Decision
Networks
for
Self-‐adapta+on
Random
X2
Random
X1
Decision
D1
D2
U
Evidence
E
P(X1|dj)
P(X2|dj)
EU j = EU(dj | e) = P(xi
i
∑ | e, dj )U(xi | dj )
j = 1, 2
17. X1(t)
X(t+1)
D(t)
D(t+1)
U(t+1)U(t)
E(t)
E(t+1)
Evidence
depends
on state
X2
X2
….
….
….
Time
t
Time
t+1
Time
t+n
Dynamics
Decision
Networks
(DDNs)
18. Characteris+cs
of
decision-‐making
problems
addressed
by
DDNs:
• Environment
changes
over
+me
• Informa+on
is
available
to
the
DDN
(as
a
decision
maker)
based
on
data
provided
by
monitorables
and
also
by
human-‐made
reports
(monitorable:
en+ty
in
the
environment
and
the
system
itself
that
can
be
monitored)
• The
DDN
can
be
prompted
to
make
a
decision
at
specific
+mes
(known
or
unknown
before
the
DDN
is
built)
• These
decisions
are
best
characterized
as
choices
associated
with
mee+ng
a
goal
Crucially,
the
above
are
characteris*cs
exposed
by
self-‐adap*ve
systems
19. U
Evidence
Collect
Data
Frequently
(D)
Energy
Efficiency
(E)
Decision
SP
FH
22
Dynamic
Decision
Networks
for
the
example
Decisions (goal realizations)
SP: Clean when Empty SH: Clean at Night
Chance node) (Softgoals - non functional requirements)
M : Minimize Energy Cost A : Avoid Tripping Hazard
collect
data
Shortest
path
(SP)
Fewest
Hops
(FH)
energy
Efficiency
(E)
collect
data
frequently
(D)
++
-‐-‐
++
-‐-‐
P(D|SP)
20. available
evidence
the
condi+onal
probability
U+lity
(i.e.
preferences)
P xi e,dj( )
U xi dj( )
e
Dt
E
Decision
SP
FH
U
Evidence
e
EU j = EU(dj | e) = P(xi
i
∑ | e, dj )U(xi | dj )
j = 1, 2
The
decision
made
is
that
with
max
EUj
Decision
P(E|
dj)
SP
P(E|SP)=
0.8
FH
P(E|FH)=
0.4
Decision
P(D|
dj)
SP
P(D|SP)=
0.6
FH
P(D|FH)=
0.75
Decision
E
D
Weight
SP
F
F
0
SP
F
T
75
SP
T
F
70
SP
T
T
100
FH
F
F
0
FH
F
T
65
FH
T
F
70
FH
T
T
80
Preparing
the
ini+al
values
of
the
DDN
Sensor
model
P(
et|
Dt)
E
:
energy
Efficiency
(E)
Dt
:
collect
data
frequently
(D)
SP
Shortest
Path
FH:
Fewest
Hopes
NFRs
decisions
21. Remote
Data
Mirroring
(1)
Copies
of
important
data
are
stored
at
one
or
more
secondary
loca+ons
Goal: Protect data against loss and
unavailability
Case
Study
• Design
choices
• Remote
mirroring
protocols
e.g.
Minimum
spanning
tree
(MST)
vs
Redundant
topology
(RT)
(1)
“Relaxing
claims:Coping
with
uncertainty
while
evalua*ng
assump*ons
at
run
*me,”
A.
Ramirez,
B.
Cheng,
N.
Bencomo,
and
P.
Sawyer,
ACM/IEEE
Int.
Conference
on
Model
Driven
Engineering
Languages
&
Systems
MODELS,
2012.
22. Goal
model
for
the
RDM
applica+on
(1)
3
3
(1)
“Relaxing
claims:Coping
with
uncertainty
while
evalua*ng
assump*ons
at
run
*me,”
A.
Ramirez,
B.
Cheng,
N.
Bencomo,
and
P.
Sawyer,
ACM/IEEE
Int.
Conference
on
Model
Driven
Engineering
Languages
&
Systems
MODELS,
2012.
24. Uncertainty
Factors
• When
does
the
DDN
is
re-‐evaluated
to
make
a
decision?
When
condi+onal
probability
func+ons
and
its
values
(i.e.,
beliefs)
have
changed
due
to
learned
informa+on
• Environmental
and
context
proper*es
that
can
cause
changes
on
the
probability
need
to
be
iden*fied
accordingly
We
call
those
environmental
proper+es:
uncertainty
factors
25. Uncertainty
Factors
3
Design
assump+on
C1=
“Redundancy
prevents
networks
par++ons”
Its
validity
can
be
monitored
at
run+me
This
assump+on
C1
is
falsified
if
two
or
more
network
links
fail
simultaneously
3
26. How
decisions
are
made?
Suppose
the
chance
nodes
are
MRt,
MP,MO
and
UAlity
depends
on
them,
and
the
evidence
node
is
E
this
generates
the
best
decision
D
that
maximizes
the
expected
u+lity
Markov
property
ObservaAon/Sensor
Model
TransiAon
Model
27. Experiments
• Tool:
Ne+ca
development
environment
hKp://www.norsys.com
Ne+ca
is
a
soUware
to
model
and
run
Decision
and
Bayesian
Networks
• Generic
scenario
“C1
=
Redundancy
prevents
the
networks
parAAons”
is
monitored.
At
design
+me,
C1
has
been
considered
valid
(true
)
and
MST
is
chosen
However,
during
run+me
a
change
on
this
value
is
monitored,
specifically
at
+me
slice
– t
=
3
,
the
value
false
is
observed,
which
means
that
the
design
assump+on
has
been
falsified.
– t
=
7,
according
to
the
monitoring
infrastructure
the
design
– assump+on
C1
is
true
again
28. Experiments
• Exp
1-‐
Decision-‐Making
• Exp
2-‐
Effects
of
Weights
on
Decision-‐Making
• Exp
3-‐
Levels
of
Confidence
on
the
Monitoring
Infrastructure
32. Experiment
2-‐
Effects
of
Weights
on
Decision-‐Making
Evidence
monitored
as
False
Evidence
monitored
as True
33. Experiment
3-‐
Levels
of
Confidence
on
the
Monitoring
Infrastructure
Design
decision
“C1
=
Redundancy
prevents
the
networks
par++ons”
is
monitored
P(e|C1=true)
=
0.9
P(e|C1=true)
=
0.8
P(e|C1=true)
=
0.4
34. State
of
the
art
Approach
Model/Formalism
used
Design
*me
Run*me
Learning
GuideArch
[Esfahani+FSE1’2]
Possibility
theory
[Letier+FSE’04]
Probability
theory
RELAX
[Whittle+RE’09]
Fuzzy
logics
REAssure
[Welsh+ ASE ’11]
Goal
models+
Claims
RELAXing-‐Claims
[
Ramirez+MRT’12]
Fuzzy
logics
POISED
[Esfahani+FSE’11].
Possibility
theory
+Fuzzy
logics
[Liaskos+RE’10] Goal
models
KAMI
[Filieri’11]
Marcov
chains+
Bayesian
inference
Our approach DDNs+
Bayesian
inference
When
Uncertainty
is
solved
X
√
X
√
X
X
X
X
X
X
√
√
√
√
√
√
√
√
√
√
√
√
√
√
X
X
√
35. Summary
DDN-‐based
approach
• Uses
Bayesian
networks
to
guide
decision-‐making
processes
• Defines
the
uncertainty
associated
with
the
current
situa+on
in
terms
of
the
condi+onal
probabili+es
• Balances
different
conflic+ng
sofgoals
according
to
given
preferences
u+li+es
• Maintains
the
defini+on
of
uncertainty
over
+me
as
new
informa+on
arrive
in
a
consistent
way
with
the
past
• Incorporates
risk
preferences
(i.e.
rewards
and
penal+es)
that
properly
address
the
current
situa+on
modeled
36. Summary
• DDNs
can
provide
a
quan+ta+ve
technique
to
make
informed
decisions
due
to
the
arrival
of
new
evidence
during
either
run+me
or
during
a
process
to
explore
the
opera*ng
environment
to
elicit
requirements.
37. Future
Work
Use
the
DDNs
to
explore
and
improve
our
understanding
of
the
opera+ng
environment
and
to
elicit
requirements
Use
the
DDNs
to
explore
requirements
scenarios
with
the
goal
of
quan+fy
requirements
P(Cost
<40)
>
0.9
More
work
on
how
iden+fy
uncertainty
factors
38. Claim
Refinement
Model
Faults
Likely
SP
is
less
resilient
than
FH
SP
is
too
risky
AND
39. Ongoing
Work
on
Bayesian
Surprise
Theory
for
SASs
A
surprise
measures
how
new
evidence
affects
the
models
or
assump+ons
of
the
world.
The
key
idea
is
that
a
“surprising"
event
can
be
defined
as
one
that
causes
a
large
divergence
between
the
beliefs
distribu+ons
prior
and
posterior
to
the
event
that
has
been
observed.
According
to
how
big/small
the
surprise
is,
the
running
system
may
decide
to
either
dynamically
adapt
accordingly
or
to
highlight
the
fact
that
an
abnormal
situa+on
has
been
found.
40. Ongoing
Work
on
Bayesian
Surprise
Theory
for
SASs
• the
surprise
can
be
measured
using
the
Kullback-‐
Leibler
divergence
(KL),
which
es+mates
the
divergence
between
the
prior
and
posterior
distribu+ons
• Among
other
several
ques+ons
we
want
to
answer,
we
have:
– how
big
or
small
a
surprise
can
be
considered
given
an
absolute
value?
– are
there
other
alterna+ve
ways
to
measure
a
surprise?
41.
42. A
bit
of
reflec+on
• The
algorithms
applied
take
+me
• We
need
tools
(and
we
do
not
necessarily
want
to
construct
them
from
scratch)
• We
(soUware
engineers)
need
to
create
synergies
with
people
of
Ar+ficial
Intelligence