For many optimization problems it is possible to define a distance metric between problem variables that correlates with the likelihood and strength of interactions between the variables. For example, one may define a metric so that the dependencies between variables that are closer to each other with respect to the metric are expected to be stronger than the dependencies between variables that are further apart. The purpose of this paper is to describe a method that combines such a problem-specific distance metric with information mined from probabilistic models obtained in previous runs of estimation of distribution algorithms with the goal of solving future problem instances of similar type with increased speed, accuracy and reliability. While the focus of the paper is on additively decomposable problems and the hierarchical Bayesian optimization algorithm, it should be straightforward to generalize the approach to other model-directed optimization techniques and other problem classes. Compared to other techniques for learning from experience put forward in the past, the proposed technique is both more practical and more broadly applicable.
We propose an algorithm for training Multi Layer Preceptrons for classification problems, that we named Hidden Layer Learning Vector Quantization (H-LVQ). It consists of applying Learning Vector Quantization to the last hidden layer of a MLP and it gave very successful results on problems containing a large number of correlated inputs. It was applied with excellent results on classification of Rurtherford
backscattering spectra and on a benchmark problem of image recognition. It may also be used for efficient feature extraction.
NBDT : Neural-backed Decision Tree 2021 ICLRtaeseon ryu
안녕하세요 딥러닝 논문읽기 모임 입니다.
오늘 소개 드릴 논문은 2021년 ICLR 에 억셉된 NBDT : Neural-backed Decision Tree 라는 논문 입니다
초록 :
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability for accuracy or (2) sacrifice accuracy for interpretability. We forgo this dilemma by jointly improving accuracy and interpretability using Neural-Backed Decision Trees (NBDTs). NBDTs replace a neural network's final linear layer with a differentiable sequence of decisions and a surrogate loss. This forces the model to learn high-level concepts and lessens reliance on highly-uncertain decisions, yielding (1) accuracy: NBDTs match or outperform modern neural networks on CIFAR, ImageNet and better generalize to unseen classes by up to 16%. Furthermore, our surrogate loss improves the original model's accuracy by up to 2%. NBDTs also afford (2) interpretability: improving human trustby clearly identifying model mistakes and assisting in dataset debugging. Code and pretrained NBDTs are at this https URL.
오늘 논문 리뷰를 이미지 처리팀 안종식님이 자세하고 친절한 리뷰 도와주셨습니다.
감사합니다
문의 : tfkeras@kakao.com
EFFICIENT KNOWLEDGE BASE MANAGEMENT IN DCSP ijasuc
DCSP (Distributed Constraint Satisfaction Problem) has been a very important research area in AI
(Artificial Intelligence). There are many application problems in distributed AI that can be formalized as
DSCPs. With the increasing complexity and problem size of the application problems in AI, the required
storage place in searching and the average searching time are increasing too. Thus, to use a limited
storage place efficiently in solving DCSP becomes a very important problem, and it can help to reduce
searching time as well. This paper provides an efficient knowledge base management approach based on
general usage of hyper-resolution-rule in consistence algorithm. The approach minimizes the increasing of
the knowledge base by eliminate sufficient constraint and false nogood. These eliminations do not change
the completeness of the original knowledge base increased. The proofs are given as well. The example
shows that this approach decrease both the new nogoods generated and the knowledge base greatly. Thus
it decreases the required storage place and simplify the searching process.
with the help fof alkfjafnalfnlsnclsnclsnvsnvlsnvlds snlksnldsn nlncldnldncldsnclsd anflnfldnfldnfldsc knfldfnlfnlnfldnfldsnfldsnf lkfndslfndslfnldsfnlsdnflsdlflsfnsldnf lsnflsfdnldslds dsnfldsnflsdnflsnldsnf
We propose an algorithm for training Multi Layer Preceptrons for classification problems, that we named Hidden Layer Learning Vector Quantization (H-LVQ). It consists of applying Learning Vector Quantization to the last hidden layer of a MLP and it gave very successful results on problems containing a large number of correlated inputs. It was applied with excellent results on classification of Rurtherford
backscattering spectra and on a benchmark problem of image recognition. It may also be used for efficient feature extraction.
NBDT : Neural-backed Decision Tree 2021 ICLRtaeseon ryu
안녕하세요 딥러닝 논문읽기 모임 입니다.
오늘 소개 드릴 논문은 2021년 ICLR 에 억셉된 NBDT : Neural-backed Decision Tree 라는 논문 입니다
초록 :
Machine learning applications such as finance and medicine demand accurate and justifiable predictions, barring most deep learning methods from use. In response, previous work combines decision trees with deep learning, yielding models that (1) sacrifice interpretability for accuracy or (2) sacrifice accuracy for interpretability. We forgo this dilemma by jointly improving accuracy and interpretability using Neural-Backed Decision Trees (NBDTs). NBDTs replace a neural network's final linear layer with a differentiable sequence of decisions and a surrogate loss. This forces the model to learn high-level concepts and lessens reliance on highly-uncertain decisions, yielding (1) accuracy: NBDTs match or outperform modern neural networks on CIFAR, ImageNet and better generalize to unseen classes by up to 16%. Furthermore, our surrogate loss improves the original model's accuracy by up to 2%. NBDTs also afford (2) interpretability: improving human trustby clearly identifying model mistakes and assisting in dataset debugging. Code and pretrained NBDTs are at this https URL.
오늘 논문 리뷰를 이미지 처리팀 안종식님이 자세하고 친절한 리뷰 도와주셨습니다.
감사합니다
문의 : tfkeras@kakao.com
EFFICIENT KNOWLEDGE BASE MANAGEMENT IN DCSP ijasuc
DCSP (Distributed Constraint Satisfaction Problem) has been a very important research area in AI
(Artificial Intelligence). There are many application problems in distributed AI that can be formalized as
DSCPs. With the increasing complexity and problem size of the application problems in AI, the required
storage place in searching and the average searching time are increasing too. Thus, to use a limited
storage place efficiently in solving DCSP becomes a very important problem, and it can help to reduce
searching time as well. This paper provides an efficient knowledge base management approach based on
general usage of hyper-resolution-rule in consistence algorithm. The approach minimizes the increasing of
the knowledge base by eliminate sufficient constraint and false nogood. These eliminations do not change
the completeness of the original knowledge base increased. The proofs are given as well. The example
shows that this approach decrease both the new nogoods generated and the knowledge base greatly. Thus
it decreases the required storage place and simplify the searching process.
with the help fof alkfjafnalfnlsnclsnclsnvsnvlsnvlds snlksnldsn nlncldnldncldsnclsd anflnfldnfldnfldsc knfldfnlfnlnfldnfldsnfldsnf lkfndslfndslfnldsfnlsdnflsdlflsfnsldnf lsnflsfdnldslds dsnfldsnflsdnflsnldsnf
Problems with CNNs and Introduction to capsule neural networksVipul Vaibhaw
Explains the problems with ConvNets and Introduces Capsule Neural Networks in simple words.
References and Further reading -
1. https://arxiv.org/abs/1609.08758
2. https://arxiv.org/abs/1710.08864
3. https://arxiv.org/abs/1710.09829v1
4. https://medium.com/mlreview/deep-neural-network-capsules-137be2877d44
Thanks to -
https://www.youtube.com/watch?v=VKoLGnq15RM&t=1099s
Recurrent Neural Network
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Mohammad Sabouri
https://sites.google.com/view/acrrl/
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
Towards Dropout Training for Convolutional Neural Networks Mah Sa
Design inspired by : https://www.slideshare.net/roelofp/python-for-image-understanding-deep-learning-with-convolutional-neural-nets?qid=06301e83-f65e-40a9-92a2-201664cd6119&v=&b=&from_search=1
Special tank to him....
Deep learning lecture - part 1 (basics, CNN)SungminYou
This presentation is a lecture with the Deep Learning book. (Bengio, Yoshua, Ian Goodfellow, and Aaron Courville. MIT press, 2017) It contains the basics of deep learning and theories about the convolutional neural network.
A Spatial Domain Image Steganography Technique Based on Matrix Embedding and ...CSCJournals
This paper presents an algorithm in spatial domain which gives less distortion to the cover image during embedding process. Minimizing embedding impact and maximizing embedding capacity are the key factors of any steganography algorithm. Peak Signal to Noise Ratio (PSNR) is the familiar metric used in discriminating the distorted image (stego image) and cover image. Here matrix embedding technique is chosen to embed the secret image which is initially Huffman encoded. The Huffman encoded image is overlaid on the selected bits of all the channels of pixels of cover image through matrix embedding. As a result, the stego image is constructed with very less distortion when compared to the cover image ends up with higher PSNR value. A secret image which cannot be embedded in a normal LSB embedding technique can be overlaid in this proposed technique since the secret image is Huffman encoded. Experimental results for standard cover images, which obtained higher PSNR value during the operation is shown in this paper.
Plotting the training process
Regularization
Batch normalization
Saving and loading the weights and the architecture of a model
Visualize a Deep Learning Neural Network Model in Keras
High-performance graph analysis is unlocking knowledge in computer security, bioinformatics, social networks, and many other data integration areas. Graphs provide a convenient abstraction for many data problems beyond linear algebra. Some problems map directly to linear algebra. Others, like community detection, look eerily similar to sparse linear algebra techniques. And then there are algorithms that strongly resist attempts at making them look like linear algebra. This talk will cover recent results with an emphasis on streaming graph problems where the graph changes and results need updated with minimal latency. We’ll also touch on issues of sensitivity and reliability where graph analysis needs to learn from numerical analysis and linear algebra.
Problems with CNNs and Introduction to capsule neural networksVipul Vaibhaw
Explains the problems with ConvNets and Introduces Capsule Neural Networks in simple words.
References and Further reading -
1. https://arxiv.org/abs/1609.08758
2. https://arxiv.org/abs/1710.08864
3. https://arxiv.org/abs/1710.09829v1
4. https://medium.com/mlreview/deep-neural-network-capsules-137be2877d44
Thanks to -
https://www.youtube.com/watch?v=VKoLGnq15RM&t=1099s
Recurrent Neural Network
ACRRL
Applied Control & Robotics Research Laboratory of Shiraz University
Department of Power and Control Engineering, Shiraz University, Fars, Iran.
Mohammad Sabouri
https://sites.google.com/view/acrrl/
Deep learning algorithms have drawn the attention of researchers working in the field of computer vision, speech
recognition, malware detection, pattern recognition and natural language processing. In this paper, we present an overview of
deep learning techniques like Convolutional neural network, deep belief network, Autoencoder, Restricted Boltzmann machine
and recurrent neural network. With this, current work of deep learning algorithms on malware detection is shown with the
help of literature survey. Suggestions for future research are given with full justification. We also showed the experimental
analysis in order to show the importance of deep learning techniques.
Towards Dropout Training for Convolutional Neural Networks Mah Sa
Design inspired by : https://www.slideshare.net/roelofp/python-for-image-understanding-deep-learning-with-convolutional-neural-nets?qid=06301e83-f65e-40a9-92a2-201664cd6119&v=&b=&from_search=1
Special tank to him....
Deep learning lecture - part 1 (basics, CNN)SungminYou
This presentation is a lecture with the Deep Learning book. (Bengio, Yoshua, Ian Goodfellow, and Aaron Courville. MIT press, 2017) It contains the basics of deep learning and theories about the convolutional neural network.
A Spatial Domain Image Steganography Technique Based on Matrix Embedding and ...CSCJournals
This paper presents an algorithm in spatial domain which gives less distortion to the cover image during embedding process. Minimizing embedding impact and maximizing embedding capacity are the key factors of any steganography algorithm. Peak Signal to Noise Ratio (PSNR) is the familiar metric used in discriminating the distorted image (stego image) and cover image. Here matrix embedding technique is chosen to embed the secret image which is initially Huffman encoded. The Huffman encoded image is overlaid on the selected bits of all the channels of pixels of cover image through matrix embedding. As a result, the stego image is constructed with very less distortion when compared to the cover image ends up with higher PSNR value. A secret image which cannot be embedded in a normal LSB embedding technique can be overlaid in this proposed technique since the secret image is Huffman encoded. Experimental results for standard cover images, which obtained higher PSNR value during the operation is shown in this paper.
Plotting the training process
Regularization
Batch normalization
Saving and loading the weights and the architecture of a model
Visualize a Deep Learning Neural Network Model in Keras
High-performance graph analysis is unlocking knowledge in computer security, bioinformatics, social networks, and many other data integration areas. Graphs provide a convenient abstraction for many data problems beyond linear algebra. Some problems map directly to linear algebra. Others, like community detection, look eerily similar to sparse linear algebra techniques. And then there are algorithms that strongly resist attempts at making them look like linear algebra. This talk will cover recent results with an emphasis on streaming graph problems where the graph changes and results need updated with minimal latency. We’ll also touch on issues of sensitivity and reliability where graph analysis needs to learn from numerical analysis and linear algebra.
Deep vs diverse architectures for classification problemsColleen Farrelly
Deep learning study, comparing deep learning methods with wide learning methods; applications include simulation data and real industry problems. Pre-print of paper found here: https://arxiv.org/ftp/arxiv/papers/1708/1708.06347.pdf
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2023/11/learning-compact-dnn-models-for-embedded-vision-a-presentation-from-the-university-of-maryland-at-college-park/
Shuvra Bhattacharyya, Professor at the University of Maryland at College Park, presents the “Learning Compact DNN Models for Embedded Vision” tutorial at the May 2023 Embedded Vision Summit.
In this talk, Bhattacharyya explores methods to transform large deep neural network (DNN) models into effective compact models. The transformation process that he focuses on—from large to compact DNN form—is referred to as pruning. Pruning involves the removal of neurons or parameters from a neural network. When performed strategically, pruning can lead to significant reductions in computational complexity without significant degradation in accuracy. It is sometimes even possible to increase accuracy through pruning.
Pruning provides a general approach for facilitating real-time inference in resource-constrained embedded computer vision systems. Bhattacharyya provides an overview of important aspects to consider when applying or developing a DNN pruning method and presents details on a recently introduced pruning method called NeuroGRS. NeuroGRS considers structures and trained weights jointly throughout the pruning process and can result in significantly more compact models compared to other pruning methods.
How Machine Learning Helps Organizations to Work More Efficiently?Tuan Yang
Data is increasing day by day and so is the cost of data storage and handling. However, by understanding the concepts of machine learning one can easily handle the excessive data and can process it in an affordable manner.
The process includes making models by using several kinds of algorithms. If the model is created precisely for certain task, then the organizations have a very wide chance of making use of profitable opportunities and avoiding the risks lurking behind the scenes.
Learn more about:
» Understanding Machine Learning Objectives.
» Data dimensions in Machine Learning.
» Fundamentals of Algorithms and Mapping from Input/Output.
» Parametric and Non-parametric Machine Learning Algorithms.
» Supervised, Unsupervised and Semi-Supervised Learning.
» Estimating Over-fitting and Under-fitting.
» Use Cases.
Professor Steve Roberts; The Bayesian Crowd: scalable information combinati...Ian Morgan
Professor Steve Roberts, Machine learning research group and Oxford-Man Institute + Alan Turing Institute. Steve gave this talk on the 24th January at the London Bayes Nets meetup.
PR095: Modularity Matters: Learning Invariant Relational Reasoning TasksJinwon Lee
Tensorflow-KR 논문읽기모임 95번째 발표영상입니다
Modularity Matters라는 제목으로 visual relational reasoning 문제를 풀 수 있는 방법을 제시한 논문입니다. 기존 CNN들이 이런 문제이 취약함을 보여주고 이를 해결하기 위한 방법을 제시합니다. 관심있는 주제이기도 하고 Bengio 교수님 팀에서 쓴 논문이라서 review 해보았습니다
발표영상: https://youtu.be/dAGI3mlOmfw
논문링크: https://arxiv.org/abs/1806.06765
Transfer Learning, Soft Distance-Based Bias, and the Hierarchical BOAMartin Pelikan
An automated technique has recently been proposed to transfer learning in the hierarchical Bayesian optimization algorithm (hBOA) based on distance-based statistics. The technique enables practitioners to improve hBOA efficiency by collecting statistics from probabilistic models obtained in previous hBOA runs and using the obtained statistics to bias future hBOA runs on similar problems. The purpose of this paper is threefold: (1) test the technique on several classes of NP-complete problems, including MAXSAT, spin glasses and minimum vertex cover; (2) demonstrate that the technique is effective even when previous runs were done on problems of different size; (3) provide empirical evidence that combining transfer learning with other efficiency enhancement techniques can often yield nearly multiplicative speedups.
Population Dynamics in Conway’s Game of Life and its VariantsMartin Pelikan
The presentation for the project of high school students Yonatan Biel and David Hua made in the Students and Teachers As Research Scientists (STARS) program at the Missouri Estimation of Distribution Algorithms Laboratory (MEDAL). To see animations, please download the powerpoint presentation.
Image segmentation using a genetic algorithm and hierarchical local searchMartin Pelikan
This paper proposes a hybrid genetic algorithm to perform image segmentation based on applying the q-state Potts spin glass model to a grayscale image. First, the image is converted to a set of weights for a q-state spin glass and then a steady-state genetic algorithm is used to evolve candidate segmented images until a suitable candidate solution is found. To speed up the convergence to an adequate solution, hierarchical local search is used on each evaluated solution. The results show that the hybrid genetic algorithm with hierarchical local search is able to efficiently perform image segmentation. The necessity of hierarchical search for these types of problems is also clearly demonstrated.
Pairwise and Problem-Specific Distance Metrics in the Linkage Tree Genetic Al...Martin Pelikan
The linkage tree genetic algorithm (LTGA) identifies linkages between problem variables using an agglomerative hierarchical clustering algorithm and linkage trees. This enables LTGA to solve many decomposable problems that are difficult with more conventional genetic algorithms. The goal of this paper is two-fold: (1) Present a thorough empirical evaluation of LTGA on a large set of problem instances of additively decomposable problems and (2) speed up the clustering algorithm used to build the linkage trees in LTGA by using a pairwise and a problem-specific metric.
http://medal.cs.umsl.edu/files/2011001.pdf
Effects of a Deterministic Hill climber on hBOAMartin Pelikan
Hybridization of global and local search algorithms is a well-established technique for enhancing the efficiency of search algorithms. Hybridizing estimation of distribution algorithms (EDAs) has been repeatedly shown to produce better performance than either the global or local search algorithm alone. The hierarchical Bayesian optimization algorithm (hBOA) is an advanced EDA which has previously been shown to benefit from hybridization with a local searcher. This paper examines the effects of combining hBOA with a deterministic hill climber (DHC). Experiments reveal that allowing DHC to find the local optima makes model building and decision making much easier for hBOA. This reduces the minimum population size required to find the global optimum, which substantially improves overall performance.
Intelligent Bias of Network Structures in the Hierarchical BOAMartin Pelikan
One of the primary advantages of estimation of distribution algorithms (EDAs) over many other stochastic optimization techniques is that they supply us with a roadmap of how they solve a problem. This roadmap consists of a sequence of probabilistic models of candidate solutions of increasing quality. The first model in this sequence would typically encode the uniform distribution over all admissible solutions whereas the last model would encode a distribution that generates at least one global optimum with high probability. It has been argued that exploiting this knowledge should improve EDA performance when solving similar problems. This paper presents an approach to bias the building of Bayesian network models in the hierarchical Bayesian optimization algorithm (hBOA) using information gathered from models generated during previous hBOA runs on similar problems. The approach is evaluated on trap-5 and 2D spin glass problems.
Using Previous Models to Bias Structural Learning in the Hierarchical BOAMartin Pelikan
Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. While the primary goal of applying EDAs is to discover the global optimum or at least its accurate approximation, besides this, any EDA provides us with a sequence of probabilistic models, which in most cases hold a great deal of information about the problem. Although using problem-specific knowledge has been shown to significantly improve performance of EDAs and other evolutionary algorithms, this readily available source of problem-specific information has been practically ignored by the EDA community. This paper takes the first step towards the use of probabilistic models obtained by EDAs to speed up the solution of similar problems in future. More specifically, we propose two approaches to biasing model building in the hierarchical Bayesian optimization algorithm (hBOA) based on knowledge automatically learned from previous hBOA runs on similar problems. We show that the proposed methods lead to substantial speedups and argue that the methods should work well in other applications that require solving a large number of problems with similar structure.
Finding Ground States of Sherrington-Kirkpatrick Spin Glasses with Hierarchic...Martin Pelikan
This study focuses on the problem of finding ground states of random instances of the Sherrington-Kirkpatrick (SK) spin-glass model with Gaussian couplings. While the ground states of SK spin-glass instances can be obtained with branch and bound, the computational complexity of branch and bound yields instances of not more than about 90 spins. We describe several approaches based on the hierarchical Bayesian optimization algorithm (hBOA) to reliably identifying ground states of SK instances intractable with branch and bound, and present a broad range of empirical results on such problem instances. We argue that the proposed methodology holds a big promise for reliably solving large SK spin-glass instances to optimality with practical time complexity. The proposed approaches to identifying global optima reliably can also be applied to other problems and they can be used with many other evolutionary algorithms. Performance of hBOA is compared to that of the genetic algorithm with two common crossover operators.
iBOA: The Incremental Bayesian Optimization AlgorithmMartin Pelikan
This paper proposes the incremental Bayesian optimization algorithm (iBOA), which modifies standard BOA by removing the population of solutions and using incremental updates of the Bayesian network. iBOA is shown to be able to learn and exploit unrestricted Bayesian networks using incremental techniques for updating both the structure as well as the parameters of the probabilistic model. This represents an important step toward the design of competent incremental estimation of distribution algorithms that can solve difficult nearly decomposable problems scalably and reliably.
Fitness inheritance in the Bayesian optimization algorithmMartin Pelikan
This paper describes how fitness inheritance can be used to estimate fitness for a proportion of newly sampled candidate solutions in the Bayesian optimization algorithm (BOA). The goal of estimating fitness for some candidate solutions is to reduce the number of fitness evaluations for problems where fitness evaluation is expensive. Bayesian networks used in BOA to model promising solutions and generate the new ones are extended to allow not only for modeling and sampling candidate solutions, but also for estimating their fitness. The results indicate that fitness inheritance is a promising concept in BOA, because population-sizing requirements for building appropriate models of promising solutions lead to good fitness estimates even if only a small proportion of candidate solutions is evaluated using the actual fitness function. This can lead to a reduction of the number of actual fitness evaluations by a factor of 30 or more.
Computational complexity and simulation of rare events of Ising spin glasses Martin Pelikan
We discuss the computational complexity of random 2D Ising spin glasses, which represent an interesting class of constraint satisfaction problems for black box optimization. Two extremal cases are considered: (1) the +/- J spin glass, and (2) the Gaussian spin glass. We also study a smooth transition between these two extremal cases. The computational complexity of all studied spin glass systems is found to be dominated by rare events of extremely hard spin glass samples. We show that complexity of all studied spin glass systems is closely related to Frechet extremal value distribution. In a hybrid algorithm that combines the hierarchical Bayesian optimization algorithm (hBOA) with a deterministic bit-flip hill climber, the number of steps performed by both the global searcher (hBOA) and the local searcher follow Frechet distributions. Nonetheless, unlike in methods based purely on local search, the parameters of these distributions confirm good scalability of hBOA with local search. We further argue that standard performance measures for optimization algorithms---such as the average number of evaluations until convergence---can be misleading. Finally, our results indicate that for highly multimodal constraint satisfaction problems, such as Ising spin glasses, recombination-based search can provide qualitatively better results than mutation-based search.
The Bayesian Optimization Algorithm with Substructural Local SearchMartin Pelikan
This work studies the utility of using substructural neighborhoods for local search in the Bayesian optimization algorithm (BOA). The probabilistic model of BOA, which automatically identifies important problem substructures, is used to define the structure of the neighborhoods used in local search. Additionally, a surrogate fitness model is considered to evaluate the improvement of the local search steps. The results show that performing substructural local search in BOA significatively reduces the number of generations necessary to converge to optimal solutions and thus provides substantial speedups.
Analyzing Probabilistic Models in Hierarchical BOA on Traps and Spin GlassesMartin Pelikan
The hierarchical Bayesian optimization algorithm (hBOA) can solve nearly decomposable and hierarchical problems of bounded difficulty in a robust and scalable manner by building and sampling probabilistic models of promising solutions. This paper analyzes probabilistic models in hBOA on two common test problems: concatenated traps and 2D Ising spin glasses with periodic boundary conditions. We argue that although Bayesian networks with local structures can encode complex probability distributions, analyzing these models in hBOA is relatively straightforward and the results of such analyses may provide practitioners with useful information about their problems. The results show that the probabilistic models in hBOA closely correspond to the structure of the underlying optimization problem, the models do not change significantly in subsequent iterations of BOA, and creating adequate probabilistic models by hand is not straightforward even with complete knowledge of the optimization problem.
Hybrid Evolutionary Algorithms on Minimum Vertex Cover for Random GraphsMartin Pelikan
This work analyzes the hierarchical Bayesian optimization algorithm (hBOA) on minimum vertex cover for standard classes of random graphs and transformed SAT instances. The performance of hBOA is compared with that of the branch-and-bound problem solver (BB), the simple genetic algorithm (GA) and the parallel simulated annealing (PSA). The results indicate that BB is significantly outperformed by all the other tested methods, which is expected as BB is a complete search algorithm and minimum vertex cover is an NP-complete problem. The best performance is achieved by hBOA; nonetheless, the performance differences between hBOA and other evolutionary algorithms are relatively small, indicating that mutation-based search and recombination-based search lead to similar performance on the tested classes of minimum vertex cover problems.
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:
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/
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.
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.
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.
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Distance-based bias in model-directed optimization of additively decomposable problems
1. Distance-‐Based
Bias
in
Model-‐Directed
Op3miza3on
of
Addi3vely
Decomposable
Problems
Mar3n
Pelikan
and
Mark
W.
Hauschild
Missouri
Es3ma3on
of
Distribu3on
Algorithms
Laboratory
Department
of
Mathema3cs
and
Computer
Science
University
of
Missouri,
St.
Louis,
MO
E-‐mail:
mar3n@mar3npelikan.net
WWW:
hKp://mar3npelikan.net/
1
2. Background
• Model-‐directed
op3mizers
(MDOs)
learn
and
use
models
in
op3miza3on
to
solve
difficult
op3miza3on
problems
scalably
and
reliably.
• MDOs
oPen
provide
more
than
the
solu3on;
they
provide
a
set
of
models
that
reveal
informa3on
about
the
problem.
• Learning
from
experience:
Use
models
from
prior
runs
of
MDOs
to
introduce
bias
when
solving
problems
of
similar
type
in
future.
2
3. Purpose
• Combine
prior
models
with
a
problem-‐specific
distance
metric
to
solve
new
problem
instances
with
increased
speed,
accuracy,
reliability.
• Demonstrate
significant
speedups
across
a
broad
array
of
problem
domains.
• Focus
on
hBOA
algorithm
and
addi3vely
decomposable
func3ons,
although
the
approach
can
be
generalized
to
other
MDOs
and
other
problem
classes.
3
4. Outline
1. Hierarchical
BOA
(hBOA).
2. Distance
metric
for
ADFs.
3. Learning
from
experience
via
distance-‐based
bias.
4. Experiments.
5. Summary
and
conclusions.
4
5. Hierarchical
Bayesian
Op3miza3on
Algorithm
(hBOA)
Current Bayesian New
population Selection network population
[Pelikan, Goldberg, & Cantu-Paz, 2001] 5
6. Decision
Trees
Represent
Dependencies
Dependency X2
X1
X3
X4
Decision tree
Probability table (more efficient)
6
7. Learning
from
Experience
(Transfer
Learning)
• Mo3va3on
– When
solving
a
problem,
hBOA
provides
the
user
with
a
set
of
probabilis3c
models.
– Each
model
encodes
informa3on
about
the
problem,
such
as
dependencies
between
variables.
– Why
not
use
this
informa3on
when
solving
new
problem
instances
of
similar
type?
• Example:
hBOA
solves
99
scheduling
problems;
why
not
use
the
knowledge
obtained
when
solving
the
100th
instance?
7
8. How
to
Make
it
Work?
• It
is
straighborward
to
keep
sta3s3cs
from
past
hBOA
runs,
for
example,
capturing
the
number
of
dependencies
between
any
pair
of
variables.
• In
hBOA,
this
can
be
done
by
looking
at
the
number
of
“splits”
on
variable
Xi
in
a
decision
tree
storing
dependencies
for
variable
Xj.
• But
it
is
important
to
ensure
that
the
sta3s3cs
are
meaningful
with
respect
to
the
problem
being
solved,
so
that
the
sta3s3cs
help
us
solve
future
problem
instances
faster
and
beKer.
8
9. Learning
from
Experience
via
Distance-‐Based
Bias:
Basic
Idea
• Learning
from
experience
using
distance-‐based
bias
– Define
distances
between
problem
variables.
– Mine
probabilis3c
models
from
previous
runs
for
model
regulari3es
with
respect
to
distances.
• Mine
models
to
es3mate
how
strongly
variables
influence
each
other
depending
on
their
distance.
– This
should
work
whenever
strength
of
dependencies
is
correlated
with
distance.
• Apply
idea
to
hBOA
and
addi3vely
decomposable
func3ons.
9
10. Addi3vely
Decomposable
Func3ons
• Addi3vely
decomposable
func3on
(ADF):
– {Si}
are
subsets
of
variables.
– {fi}
are
func3ons
defining
overall
solu3on
quality.
• Addi3vely
decomposable
func3ons
are
oPen
difficult
to
solve!
Many
NP-‐complete
problems
are
ADFs
with
subproblems
of
2
or
3
variables.
10
11. Define
Distance
Metric
for
ADFs
Using
Dependency
Graph
• Create
a
dependency
graph
where
variables
in
the
same
subset
Si
are
connected.
• Define
distance
between
variables
as
shortest
path
between
them
in
the
dependency
graph.
• If
there
exists
no
such
path,
set
distance
to
the
number
of
variables
(any
exis3ng
path
is
shorter).
[Hauschild et al., 2008] 11
12. Define
Distance
Metric
for
ADFs
Using
Dependency
Graph:
Example
[Hauschild et al., 2008] 12
13. Mo3va3ng
Example
• Propor3ons
of
splits
for
variables
at
various
distances
shows
evident
correla3on
between
the
two:
NK landscapes 2D spin glass
13
14. Details
of
the
Approach
• Denote
by
M
the
set
of
models
from
prior
runs.
• Record
the
number
of
splits
on
any
variable
Xi
in
any
decision
tree
Xj
in
model
m
such
that
distance
of
Xi
and
Xj
is
d
• Compute
probability
of
kth
split
on
variable
Xi
in
any
decision
tree
Xj
such
that
dist.
of
Xi
and
Xj
is
d
assuming
(k-‐1)
such
splits:
14
15. Details
of
the
Approach
• Set
prior
probability
of
network
structure
based
on
the
learned
probabili3es
(kappa
denotes
strength
of
bias)
• Evaluate
each
network
using
a
Bayesian
metric
15
16. Test
Problems
• Included
in
this
paper
– NK
landscapes
with
nearest-‐neighbor
interac3ons.
– 2D
spin
glass.
• Done
later
on
– 3D
spin
glass.
– Minimum
vertex
cover
for
random
graphs.
– MAXSAT
for
3-‐CNF
formulas.
• Large
number
of
different
instances
for
each
problem
class
(100s
to
1000s
each).
16
17. Experimental
Methodology
• 10-‐fold
crossvalida3on
– Divide
instances
into
10
sets.
– Test
bias
from
models
on
9
sets
on
remaining
1
set,
repeat
for
every
set.
– BoKom
line:
Any
problem
instance
is
never
used
for
both
crea3ng
the
bias
and
tes3ng
it.
• Bisec3on
for
gemng
popula3on
sizes,
10
runs
for
each
problem
instance.
• Focus
on
mul3plica3ve
speedups
– How
many
3mes
faster
with
the
use
of
bias?
17
23. More
Results
to
be
Published
Soon
• Nearly
iden3cal
speedups
if
bias
is
based
on
problems
of
smaller
size.
• Significant
speedups
even
if
bias
is
based
on
another
class
of
ADFs
(e.g.
models
from
NK
landscapes
used
to
solve
MVC).
• Nearly
mul3plica3ve
speedups
in
combina3on
with
other
efficiency
enhancements
(e.g.
sporadic
model
building).
• So
far
not
a
single
problem
class
for
which
the
bias
does
not
yield
significant
speedups.
23
24. Results
Applicable
in
Other
Contexts
• Approach
can
be
applied
to
other
model-‐
directed
op3mizers,
such
as
ECGA,
LTGA,
or
mGA.
• Approach
can
be
applied
to
other
problem
classes
for
which
a
distance
metric
can
be
defined,
such
as
QAP
or
scheduling
problems.
• This
work
demonstrates
the
poten3al,
but
more
work
to
be
done
in
future.
24
25. Summary
and
Conclusions
• Proposed
a
prac3cal
approach
to
using
models
from
prior
runs
of
model-‐directed
op3mizers
to
bias
op3miza3on
of
future
problem
instances.
• Demonstrated
significant
speedups
across
a
number
of
problem
domains
and
semngs,
including
a
number
scenarios
that
are
not
possible
with
related
techniques
proposed
in
the
past.
• Approach
is
ready
to
be
applied
in
a
different
context.
25
26. Acknowledgments
• Support
was
provided
by
– NSF
grants
ECS-‐0547013
and
IIS-‐1115352.
– ITS
at
the
University
of
Missouri
in
St.
Louis.
– University
of
Missouri
Bioinforma3cs
Consor3um.
• Get
the
papers
at
hKp://medal-‐lab.org/files/2012001.pdf
hKp://medal-‐lab.org/files/2012004.pdf
26