This document discusses adaptive biometric systems based on template update paradigms. It provides background on biometric systems and the problems of intra-class variations affecting template representativeness over time. Standard solutions like using multiple templates or modalities are noted. The goal of the PhD study is to formulate the taxonomy of current template update methods, analyze their pros and cons, and propose novel solutions. Specifically, it will experimentally analyze and compare the performance of self-update and co-update methods in controlled and uncontrolled environments. Initial results show co-update more effectively lowers equal error rates than self-update when capturing variations from unlabeled samples in uncontrolled conditions.
This document discusses Bio-Modeling Systems' (BMS) approach to integrative systems biology modeling called CADI (Computer Assisted Deductive Integration). CADI models are descriptive in-silico models of biological systems and diseases that can explain nonlinear mechanisms and identify new discoveries. The CADI approach applies 5 principles: an architectural approach, a negative selection process, a 4 step validation process, expertise across life sciences and IT, and synergic collaboration. BMS acts as an architect to generate hypotheses that are tested by experimentalists, with the goal of building solid and useful biological models.
Biometric Iris Recognition Based on Hybrid Techniqueijsc
This document presents a study on implementing an iris recognition system using a hybrid technique. The system utilizes several image processing and machine learning techniques. It begins with preprocessing the iris image, including capturing, resizing and converting to grayscale. Histogram equalization is then used for enhancement. Two-dimensional discrete wavelet transform (2D DWT) is applied for feature extraction. Various edge detection algorithms including Canny, Prewitt, Roberts and Sobel are used to detect iris boundaries. The features are then stored in a vector for classification. The system is tested on different iris images and analysis shows 2D DWT and Canny edge detection provide adequate results for feature extraction and iris recognition.
Sigir12 tutorial: Query Perfromance Prediction for IRDavid Carmel
This document summarizes a tutorial on query performance prediction given by Dr. David Carmel and Dr. Oren Kurland. It discusses the challenge of estimating query difficulty for information retrieval systems. Estimating query difficulty can provide benefits such as feedback to users, search engines, and system administrators. The tutorial covers basic concepts, query performance prediction methods, applications, and open challenges. It aims to help IR systems reduce variability in performance and better satisfy users' information needs.
This document summarizes the services provided by an organization that conducts research and training in areas related to biotechnology and pharmaceuticals. They provide online and in-person training programs and research projects in topics such as bioinformatics, drug design, genomics, and proteomics. They have completed over 20 research projects in the past year that have led to international publications. They also organize workshops on drug discovery and genomics at universities and institutions around the world, both in-person and online. Their goal is to strengthen the skills and careers of young researchers through hands-on training and research experience.
IRJET- A Survey on the Enhancement of Video Action Recognition using Semi-Sup...IRJET Journal
This document summarizes several papers related to enhancing video action recognition using semi-supervised learning. It discusses methods that use knowledge adaptation from images to videos to improve action recognition performance in videos. Specifically, it describes approaches that use labeled videos and unlabeled videos in a semi-supervised framework to address limitations of fully supervised methods, such as data scarcity and overfitting. The document reviews papers on techniques like pose-based recognition, color descriptors, grouplet representations, relevance feedback, event recognition from web data, dense trajectories, and discriminative key poses.
The document presents the LODIE system for web-scale information extraction by leveraging Linked Open Data. It discusses challenges including allowing users to define custom IE tasks, obtaining training data from LOD, using multiple learning strategies, integrating extracted knowledge with LOD, and obtaining user feedback. It proposes methods to address each challenge and evaluates LODIE based on its ability to formalize user needs and perform IE at scale.
This document discusses ontology design. It begins by defining ontologies as concepts, relationships, and distinctions that capture domain knowledge. Ontologies are used to share and reuse domain knowledge between people and machines. The document then discusses requirements analysis, conceptualization, and implementation as the key stages of ontology design. It provides guidance on analyzing requirements such as scope, competency questions, and existing ontologies. It also discusses modeling decisions like classes, properties, hierarchies, and inverse relationships in conceptualization. Open topics in ontology engineering are also mentioned.
This document discusses Bio-Modeling Systems' (BMS) approach to integrative systems biology modeling called CADI (Computer Assisted Deductive Integration). CADI models are descriptive in-silico models of biological systems and diseases that can explain nonlinear mechanisms and identify new discoveries. The CADI approach applies 5 principles: an architectural approach, a negative selection process, a 4 step validation process, expertise across life sciences and IT, and synergic collaboration. BMS acts as an architect to generate hypotheses that are tested by experimentalists, with the goal of building solid and useful biological models.
Biometric Iris Recognition Based on Hybrid Techniqueijsc
This document presents a study on implementing an iris recognition system using a hybrid technique. The system utilizes several image processing and machine learning techniques. It begins with preprocessing the iris image, including capturing, resizing and converting to grayscale. Histogram equalization is then used for enhancement. Two-dimensional discrete wavelet transform (2D DWT) is applied for feature extraction. Various edge detection algorithms including Canny, Prewitt, Roberts and Sobel are used to detect iris boundaries. The features are then stored in a vector for classification. The system is tested on different iris images and analysis shows 2D DWT and Canny edge detection provide adequate results for feature extraction and iris recognition.
Sigir12 tutorial: Query Perfromance Prediction for IRDavid Carmel
This document summarizes a tutorial on query performance prediction given by Dr. David Carmel and Dr. Oren Kurland. It discusses the challenge of estimating query difficulty for information retrieval systems. Estimating query difficulty can provide benefits such as feedback to users, search engines, and system administrators. The tutorial covers basic concepts, query performance prediction methods, applications, and open challenges. It aims to help IR systems reduce variability in performance and better satisfy users' information needs.
This document summarizes the services provided by an organization that conducts research and training in areas related to biotechnology and pharmaceuticals. They provide online and in-person training programs and research projects in topics such as bioinformatics, drug design, genomics, and proteomics. They have completed over 20 research projects in the past year that have led to international publications. They also organize workshops on drug discovery and genomics at universities and institutions around the world, both in-person and online. Their goal is to strengthen the skills and careers of young researchers through hands-on training and research experience.
IRJET- A Survey on the Enhancement of Video Action Recognition using Semi-Sup...IRJET Journal
This document summarizes several papers related to enhancing video action recognition using semi-supervised learning. It discusses methods that use knowledge adaptation from images to videos to improve action recognition performance in videos. Specifically, it describes approaches that use labeled videos and unlabeled videos in a semi-supervised framework to address limitations of fully supervised methods, such as data scarcity and overfitting. The document reviews papers on techniques like pose-based recognition, color descriptors, grouplet representations, relevance feedback, event recognition from web data, dense trajectories, and discriminative key poses.
The document presents the LODIE system for web-scale information extraction by leveraging Linked Open Data. It discusses challenges including allowing users to define custom IE tasks, obtaining training data from LOD, using multiple learning strategies, integrating extracted knowledge with LOD, and obtaining user feedback. It proposes methods to address each challenge and evaluates LODIE based on its ability to formalize user needs and perform IE at scale.
This document discusses ontology design. It begins by defining ontologies as concepts, relationships, and distinctions that capture domain knowledge. Ontologies are used to share and reuse domain knowledge between people and machines. The document then discusses requirements analysis, conceptualization, and implementation as the key stages of ontology design. It provides guidance on analyzing requirements such as scope, competency questions, and existing ontologies. It also discusses modeling decisions like classes, properties, hierarchies, and inverse relationships in conceptualization. Open topics in ontology engineering are also mentioned.
The document provides information on Caffe layers and networks for image classification tasks. It describes common layers used in convolutional neural networks (CNNs) like Convolution, Pooling, ReLU and InnerProduct. It also discusses popular CNN architectures for datasets such as MNIST, CIFAR-10 and ImageNet and the steps to prepare the data and train these networks in Caffe. Experiments comparing different CNN configurations on a 4-class image dataset show that removal of layers degrades performance, indicating their importance.
Computer vision, machine, and deep learningIgi Ardiyanto
This document provides an overview of computer vision, machine learning, and deep learning with Python. It introduces computer vision and some example applications like optical character recognition and face detection. It then discusses machine learning and how it can be applied to computer vision problems. Deep learning is introduced as a type of machine learning using artificial neural networks. Examples of successful deep learning applications are presented, including speech recognition and the AlphaGo program that mastered the game of Go. Finally, Python is discussed as a programming language well-suited for scientific and deep learning applications due to supporting libraries like NumPy, Scipy, and Matplotlib.
This document provides an overview of key concepts in Caffe including blobs, layers, nets, forward and backward passes, loss functions, and solvers. Blobs wrap data and define dimensions. Layers are the basic computation units, performing operations like filtering and nonlinearities. Nets define the overall model architecture by connecting layers. Forward and backward passes are used for inference and backpropagation. Loss functions drive learning, and solvers optimize models by adjusting parameters to reduce loss over iterations using techniques like stepwise learning rate decay. Data inputs and outputs are also configured through layers.
DeepFace is a facial recognition system developed by Facebook that can identify human faces in digital images with 97% accuracy, which is considered human-level performance. It uses a deep learning neural network trained on 4 million Facebook user photos. The system works by detecting faces, aligning them, using convolutional neural networks to extract features, and classifying images by comparing feature vectors between images. It achieved 97.35% accuracy on the Labeled Faces in the Wild benchmark dataset.
Pattern Recognition and Machine Learning : Graphical Modelsbutest
- Bayesian networks are directed acyclic graphs that represent conditional independence relationships between variables. They allow compact representation of high-dimensional joint distributions.
- Graphical models like Bayesian networks and Markov random fields use graphs to represent conditional independence relationships between random variables. Inference can be performed exactly using algorithms like sum-product on trees or approximately using loopy belief propagation on general graphs.
- Sum-product and max-sum algorithms allow efficient exact inference in trees by passing messages along edges until beliefs at all nodes converge. Loopy belief propagation extends this approach to general graphs but convergence is not guaranteed.
Caffe - A deep learning framework (Ramin Fahimi)irpycon
Caffe is a deep learning framework. It is used for tasks like visual recognition using neural networks and deep learning techniques. Caffe uses plain text configuration files called prototxt to define neural network architectures and hyperparameters. It also supports distributed training on GPUs for large datasets. Caffe provides pre-trained models and tools to load, fine-tune, and publish new models for tasks like image classification and object detection.
This paper proposes a discriminative feature learning approach for deep face recognition using a center loss function in addition to softmax loss. The center loss aims to learn discriminative features that reduce intra-class variations. It works by minimizing distances between feature vectors and their corresponding class centers, which are updated during training. Experimental results on benchmarks like LFW, YTF, and MegaFace demonstrate state-of-the-art performance for face verification and identification tasks when using the proposed softmax loss combined with center loss. While performance improvements are achieved, the paper also acknowledges there is still room for enhancing results to meet practical demands involving large-scale datasets with millions of distractors.
Using Gradient Descent for Optimization and LearningDr. Volkan OBAN
This document discusses optimization techniques for gradient descent, including the basics of gradient descent, Newton's method, and quasi-Newton methods. It covers limitations of gradient descent and Newton's method, and approximations like Gauss-Newton, Levenberg-Marquardt, BFGS, and L-BFGS. It also discusses stochastic optimization techniques for handling large datasets with minibatch or online updates rather than full batch updates.
Processor, Compiler and Python Programming Languagearumdapta98
The document discusses processors, compilers, and Python as a programming language. It covers topics like how applications run on processors, different programming languages, compilers vs interpreters, and why Python is a popular language. It also provides examples of using Python for tasks like artificial neural networks, automatic number plate recognition, and fingerprint authentication.
This document discusses various optimization techniques for training neural networks, including gradient descent, stochastic gradient descent, momentum, Nesterov momentum, RMSProp, and Adam. The key challenges in neural network optimization are long training times, hyperparameter tuning such as learning rate, and getting stuck in local minima. Momentum helps accelerate learning by amplifying consistent gradients while canceling noise. Adaptive learning rate algorithms like RMSProp, Adagrad, and Adam automatically tune the learning rate over time to improve performance and reduce sensitivity to hyperparameters.
This document proposes a semi-fragile watermarking method for image authentication using local binary patterns (LBP). It first describes how LBP works by comparing pixel values in a neighborhood to a central pixel and encoding the results as a binary number. It then explains how the proposed method embeds a watermark by modifying this binary number based on a watermark bit and extracting the watermark by recalculating the binary number. Specifically, it selects the pixel with the minimum magnitude difference to slightly modify in order to embed the watermark with minimal image quality impact. The watermark can then be extracted to detect any tampering of pixels in the local neighborhood. This semi-fragile watermarking using LBP has applications in image
[AI07] Revolutionizing Image Processing with Cognitive Toolkitde:code 2017
Deep Learning has revolutionized the field of image processing. I'll show real-world examples using CNTK, from anomaly classification using CNNs to generation using Generative Adversarial Networks.
製品/テクノロジ: AI (人工知能)/Deep Learning (深層学習)/Microsoft Azure/Machine Learning (機械学習)
Michael Lanzetta
Microsoft Corporation
Developer Experience and Evangelism
Principal Software Development Engineer
Structure Learning of Bayesian Networks with p Nodes from n Samples when n<...Joe Suzuki
This document summarizes Joe Suzuki's presentation on structure learning of Bayesian networks from a small number of samples when the number of samples is much less than the number of nodes. The presentation discusses using a branch and bound algorithm to efficiently learn Bayesian network structures in this setting. It presents Suzuki's previous work on using branch and bound with minimum description length and maximum a posteriori scoring. Experimental results show the proposed tighter upper bound cuts computation time by about a third compared to previous work. The document also briefly summarizes a bonus discussion on using Hilbert-Schmidt independence criterion for independence testing.
Face recognition and deep learning โดย ดร. สรรพฤทธิ์ มฤคทัต NECTECBAINIDA
Face recognition and deep learning โดย ดร. สรรพฤทธิ์ มฤคทัต NECTEC
คณะสถิติประยุกต์ สถาบันบัณฑิตพัฒนบริหารศาสตร์ ร่วมกับ Data Science Thailand ร่วมกันจัดงาน The First NIDA Business Analytics and Data Sciences Contest/Conference
This paper proposed a facial expression recognition approach based on Gabor wavelet transform. Gabor wavelet filter is first used as pre-processing stage for extraction of the feature vector representation. Dimensionality of the feature vector is reduced using Principal Component Analysis and Local binary pattern (LBP) Algorithms. Experiments were carried out of The Japanese female facial expression (JAFFE) database. In all experiments conducted on JAFFE database, results obtained reveal that GW+LBP has outperformed other approaches in this paper with Average recognition rate of 90% under the same experimental setting.
Face Recognition Based on Deep Learning (Yurii Pashchenko Technology Stream) IT Arena
Lviv IT Arena is a conference specially designed for programmers, designers, developers, top managers, inverstors, entrepreneur and startuppers. Annually it takes place on 2-4 of October in Lviv at the Arena Lviv stadium. In 2015 conference gathered more than 1400 participants and over 100 speakers from companies like Facebook. FitBit, Mail.ru, HP, Epson and IBM. More details about conference at itarene.lviv.ua.
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.
Caffe’s expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.Caffe’s extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.Speed makes Caffe perfect for research experiments and industry deployment. Caffe can processover 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning. We believe that Caffe is the fastest convnet implementation available.Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Join our community of brewers on the caffe-users group and Github.
This tutorial is designed to equip researchers and developers with the tools and know-how needed to incorporate deep learning into their work. Both the ideas and implementation of state-of-the-art deep learning models will be presented. While deep learning and deep features have recently achieved strong results in many tasks, a common framework and shared models are needed to advance further research and applications and reduce the barrier to entry. To this end we present the Caffe framework, public reference models, and working examples for deep learning. Join our tour from the 1989 LeNet for digit recognition to today’s top ILSVRC14 vision models. Follow along with do-it-yourself code notebooks. While focusing on vision, general techniques are covered.
Pattern Recognition and Machine Learning: Section 3.3Yusuke Oda
The document discusses Bayesian linear regression. It introduces the parameter distribution by assuming a Gaussian prior distribution for the model parameters. This leads to a Gaussian posterior distribution. It then discusses the predictive distribution for new data points by marginalizing over the posterior distribution of the parameters. Finally, it introduces the concept of an equivalent kernel, which allows predictions to be written as a linear combination of the training targets using a kernel matrix rather than by calculating the model parameters.
This document discusses neural networks and the Chainer deep learning framework. It covers neural network concepts like forward propagation, loss calculation, and backpropagation. It then explains how Chainer can be used to define neural network models using define-by-run and implement forward and backward propagation to train models. Specific Chainer concepts discussed include loss functions, linear layers, activation functions, and LSTM networks.
The document provides information on Caffe layers and networks for image classification tasks. It describes common layers used in convolutional neural networks (CNNs) like Convolution, Pooling, ReLU and InnerProduct. It also discusses popular CNN architectures for datasets such as MNIST, CIFAR-10 and ImageNet and the steps to prepare the data and train these networks in Caffe. Experiments comparing different CNN configurations on a 4-class image dataset show that removal of layers degrades performance, indicating their importance.
Computer vision, machine, and deep learningIgi Ardiyanto
This document provides an overview of computer vision, machine learning, and deep learning with Python. It introduces computer vision and some example applications like optical character recognition and face detection. It then discusses machine learning and how it can be applied to computer vision problems. Deep learning is introduced as a type of machine learning using artificial neural networks. Examples of successful deep learning applications are presented, including speech recognition and the AlphaGo program that mastered the game of Go. Finally, Python is discussed as a programming language well-suited for scientific and deep learning applications due to supporting libraries like NumPy, Scipy, and Matplotlib.
This document provides an overview of key concepts in Caffe including blobs, layers, nets, forward and backward passes, loss functions, and solvers. Blobs wrap data and define dimensions. Layers are the basic computation units, performing operations like filtering and nonlinearities. Nets define the overall model architecture by connecting layers. Forward and backward passes are used for inference and backpropagation. Loss functions drive learning, and solvers optimize models by adjusting parameters to reduce loss over iterations using techniques like stepwise learning rate decay. Data inputs and outputs are also configured through layers.
DeepFace is a facial recognition system developed by Facebook that can identify human faces in digital images with 97% accuracy, which is considered human-level performance. It uses a deep learning neural network trained on 4 million Facebook user photos. The system works by detecting faces, aligning them, using convolutional neural networks to extract features, and classifying images by comparing feature vectors between images. It achieved 97.35% accuracy on the Labeled Faces in the Wild benchmark dataset.
Pattern Recognition and Machine Learning : Graphical Modelsbutest
- Bayesian networks are directed acyclic graphs that represent conditional independence relationships between variables. They allow compact representation of high-dimensional joint distributions.
- Graphical models like Bayesian networks and Markov random fields use graphs to represent conditional independence relationships between random variables. Inference can be performed exactly using algorithms like sum-product on trees or approximately using loopy belief propagation on general graphs.
- Sum-product and max-sum algorithms allow efficient exact inference in trees by passing messages along edges until beliefs at all nodes converge. Loopy belief propagation extends this approach to general graphs but convergence is not guaranteed.
Caffe - A deep learning framework (Ramin Fahimi)irpycon
Caffe is a deep learning framework. It is used for tasks like visual recognition using neural networks and deep learning techniques. Caffe uses plain text configuration files called prototxt to define neural network architectures and hyperparameters. It also supports distributed training on GPUs for large datasets. Caffe provides pre-trained models and tools to load, fine-tune, and publish new models for tasks like image classification and object detection.
This paper proposes a discriminative feature learning approach for deep face recognition using a center loss function in addition to softmax loss. The center loss aims to learn discriminative features that reduce intra-class variations. It works by minimizing distances between feature vectors and their corresponding class centers, which are updated during training. Experimental results on benchmarks like LFW, YTF, and MegaFace demonstrate state-of-the-art performance for face verification and identification tasks when using the proposed softmax loss combined with center loss. While performance improvements are achieved, the paper also acknowledges there is still room for enhancing results to meet practical demands involving large-scale datasets with millions of distractors.
Using Gradient Descent for Optimization and LearningDr. Volkan OBAN
This document discusses optimization techniques for gradient descent, including the basics of gradient descent, Newton's method, and quasi-Newton methods. It covers limitations of gradient descent and Newton's method, and approximations like Gauss-Newton, Levenberg-Marquardt, BFGS, and L-BFGS. It also discusses stochastic optimization techniques for handling large datasets with minibatch or online updates rather than full batch updates.
Processor, Compiler and Python Programming Languagearumdapta98
The document discusses processors, compilers, and Python as a programming language. It covers topics like how applications run on processors, different programming languages, compilers vs interpreters, and why Python is a popular language. It also provides examples of using Python for tasks like artificial neural networks, automatic number plate recognition, and fingerprint authentication.
This document discusses various optimization techniques for training neural networks, including gradient descent, stochastic gradient descent, momentum, Nesterov momentum, RMSProp, and Adam. The key challenges in neural network optimization are long training times, hyperparameter tuning such as learning rate, and getting stuck in local minima. Momentum helps accelerate learning by amplifying consistent gradients while canceling noise. Adaptive learning rate algorithms like RMSProp, Adagrad, and Adam automatically tune the learning rate over time to improve performance and reduce sensitivity to hyperparameters.
This document proposes a semi-fragile watermarking method for image authentication using local binary patterns (LBP). It first describes how LBP works by comparing pixel values in a neighborhood to a central pixel and encoding the results as a binary number. It then explains how the proposed method embeds a watermark by modifying this binary number based on a watermark bit and extracting the watermark by recalculating the binary number. Specifically, it selects the pixel with the minimum magnitude difference to slightly modify in order to embed the watermark with minimal image quality impact. The watermark can then be extracted to detect any tampering of pixels in the local neighborhood. This semi-fragile watermarking using LBP has applications in image
[AI07] Revolutionizing Image Processing with Cognitive Toolkitde:code 2017
Deep Learning has revolutionized the field of image processing. I'll show real-world examples using CNTK, from anomaly classification using CNNs to generation using Generative Adversarial Networks.
製品/テクノロジ: AI (人工知能)/Deep Learning (深層学習)/Microsoft Azure/Machine Learning (機械学習)
Michael Lanzetta
Microsoft Corporation
Developer Experience and Evangelism
Principal Software Development Engineer
Structure Learning of Bayesian Networks with p Nodes from n Samples when n<...Joe Suzuki
This document summarizes Joe Suzuki's presentation on structure learning of Bayesian networks from a small number of samples when the number of samples is much less than the number of nodes. The presentation discusses using a branch and bound algorithm to efficiently learn Bayesian network structures in this setting. It presents Suzuki's previous work on using branch and bound with minimum description length and maximum a posteriori scoring. Experimental results show the proposed tighter upper bound cuts computation time by about a third compared to previous work. The document also briefly summarizes a bonus discussion on using Hilbert-Schmidt independence criterion for independence testing.
Face recognition and deep learning โดย ดร. สรรพฤทธิ์ มฤคทัต NECTECBAINIDA
Face recognition and deep learning โดย ดร. สรรพฤทธิ์ มฤคทัต NECTEC
คณะสถิติประยุกต์ สถาบันบัณฑิตพัฒนบริหารศาสตร์ ร่วมกับ Data Science Thailand ร่วมกันจัดงาน The First NIDA Business Analytics and Data Sciences Contest/Conference
This paper proposed a facial expression recognition approach based on Gabor wavelet transform. Gabor wavelet filter is first used as pre-processing stage for extraction of the feature vector representation. Dimensionality of the feature vector is reduced using Principal Component Analysis and Local binary pattern (LBP) Algorithms. Experiments were carried out of The Japanese female facial expression (JAFFE) database. In all experiments conducted on JAFFE database, results obtained reveal that GW+LBP has outperformed other approaches in this paper with Average recognition rate of 90% under the same experimental setting.
Face Recognition Based on Deep Learning (Yurii Pashchenko Technology Stream) IT Arena
Lviv IT Arena is a conference specially designed for programmers, designers, developers, top managers, inverstors, entrepreneur and startuppers. Annually it takes place on 2-4 of October in Lviv at the Arena Lviv stadium. In 2015 conference gathered more than 1400 participants and over 100 speakers from companies like Facebook. FitBit, Mail.ru, HP, Epson and IBM. More details about conference at itarene.lviv.ua.
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework made with expression, speed, and modularity in mind. It is developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.
Caffe’s expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices.Caffe’s extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models.Speed makes Caffe perfect for research experiments and industry deployment. Caffe can processover 60M images per day with a single NVIDIA K40 GPU*. That’s 1 ms/image for inference and 4 ms/image for learning. We believe that Caffe is the fastest convnet implementation available.Caffe already powers academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Join our community of brewers on the caffe-users group and Github.
This tutorial is designed to equip researchers and developers with the tools and know-how needed to incorporate deep learning into their work. Both the ideas and implementation of state-of-the-art deep learning models will be presented. While deep learning and deep features have recently achieved strong results in many tasks, a common framework and shared models are needed to advance further research and applications and reduce the barrier to entry. To this end we present the Caffe framework, public reference models, and working examples for deep learning. Join our tour from the 1989 LeNet for digit recognition to today’s top ILSVRC14 vision models. Follow along with do-it-yourself code notebooks. While focusing on vision, general techniques are covered.
Pattern Recognition and Machine Learning: Section 3.3Yusuke Oda
The document discusses Bayesian linear regression. It introduces the parameter distribution by assuming a Gaussian prior distribution for the model parameters. This leads to a Gaussian posterior distribution. It then discusses the predictive distribution for new data points by marginalizing over the posterior distribution of the parameters. Finally, it introduces the concept of an equivalent kernel, which allows predictions to be written as a linear combination of the training targets using a kernel matrix rather than by calculating the model parameters.
This document discusses neural networks and the Chainer deep learning framework. It covers neural network concepts like forward propagation, loss calculation, and backpropagation. It then explains how Chainer can be used to define neural network models using define-by-run and implement forward and backward propagation to train models. Specific Chainer concepts discussed include loss functions, linear layers, activation functions, and LSTM networks.
AUTOMATION OF ATTENDANCE USING DEEP LEARNINGIRJET Journal
This document describes a proposed system to automate student attendance using deep learning techniques like face detection and recognition. The system would take pictures of the classroom and use these techniques to identify which students are present, addressing issues with current manual attendance systems. It reviews previous literature on automated attendance systems and face recognition methods. The proposed system would use Python with OpenCV for face detection and an LBPH model for face recognition. It would generate reports with student attendance data and photos/videos from the classroom.
The document summarizes research projects being conducted at the Biometric Standards, Performance, and Assurance Laboratory at Purdue University in Fall 2010. The projects include analyzing the impact of different training methods on biometric data collection, reviewing Indiana Department of Correction mug shots and capture processes, examining the standard compliance of legacy biometric data, mapping biometric modalities to a usability evaluation method, understanding sources of biometric error, and creating a framework to define the concept of habituation in biometric systems. The laboratory was established in 2001 to conduct applied biometric research focusing on testing, education, and engaging both academia and industry.
The document discusses novel methods for biometric template selection and updating. It presents an overview of biometric systems and the issues of template representativeness, selection, and updating. It then describes the state-of-the-art in template selection and updating, which includes supervised and semi-supervised methods. The PhD work explored this state-of-the-art and proposed new methods for template selection using editing algorithms and template updating using replacement algorithms. Experimental results are presented comparing different clustering, editing, and replacement techniques for template selection and updating in biometric systems.
Accounting for people is the first step of every manpower-based organization in today’s world. Hence, it takes up a signification amount of energy and value in the form of money from respective organizations for both implementing a suitable system for manpower management as well as maintaining that same system. Although this amount of expenditure for big organizations is near to nothing, rather just a formality, it does not hold as much truth for small organizations such as schools, colleges, and even universities to a certain degree. This is the first point. The second point for discussion is that much work has been done to solve this issue. Various technologies like Biometrics, RFID, Bluetooth, GPS, QR Code, etc., have been used to tackle the issues of attendance collection. This study paves the path for researchers by reviewing practical methods and technologies used for existing attendance systems
Multimodal Biometrics at Feature Level Fusion using Texture FeaturesCSCJournals
In recent years, fusion of multiple biometric modalities for personal authentication has received considerable attention. This paper presents a feature level fusion algorithm based on texture features. The system combines fingerprint, face and off-line signature. Texture features are extracted from Curvelet transform. The Curvelet feature dimension is selected based on d-prime number. The increase in feature dimension is reduced by using template averaging, moment features and by Principal component analysis (PCA). The algorithm is tested on in-house multimodal database comprising of 3000 samples and Chimeric databases. Identification performance of the system is evaluated using SVM classifier. A maximum GAR of 97.15% is achieved with Curvelet-PCA features.
This document summarizes a workflow system called LiSIs for virtual screening in cancer chemoprevention. LiSIs provides tools to create virtual screening processes and predictive models. It includes a virtual screening process template with steps for preprocessing, docking, and postprocessing. LiSIs integrates third party tools like Galaxy and RDKit and allows sharing of scientific workflows through a web interface. The overall goal is to facilitate the discovery of novel agents for desired biochemical properties through computational methods.
The document discusses a framework called the Human-Biometric Sensor Interaction (HBSI) that aims to better understand and evaluate the performance of biometric systems by classifying every interaction between a human and sensor. The HBSI framework examines a biometric system from the perspective of both the user and system. It was applied to evaluate a hand geometry biometric system, classifying different types of incorrect presentations and interactions between users and the sensor. Future work involves applying the framework to other biometric modalities to refine metrics and develop standardized testing methodologies.
An interactive approach to multiobjective clustering of gene expression patternsRavi Kumar
This document describes an interactive genetic algorithm-based multi-objective approach to cluster gene expression patterns. The proposed Interactive Multi-Objective Clustering (IMOC) algorithm simultaneously evolves the set of validity measures to optimize and finds the clustering solution. It takes input from a human decision maker during execution to learn the best validity measures and clustering for the gene expression data. The algorithm is applied to benchmark gene expression datasets and shows more biologically significant clusters than other clustering algorithms.
Substructrual surrogates for learning decomposable classification problems: i...kknsastry
This paper presents a learning methodology based on a substructural classification model to solve decomposable classification problems. The proposed method consists of three important components: (1) a structural model that represents salient interactions between attributes for a given data, (2) a surrogate model which provides a functional approximation of the output as a function of attributes, and (3) a classification model which predicts the class for new inputs. The structural model is used to infer the functional form of the surrogate and its coefficients are estimated using linear regression methods. The classification model uses a maximally-accurate, least-complex surrogate to predict the output for given inputs. The structural model that yields an optimal classification model is searched using an iterative greedy search heuristic. Results show that the proposed method successfully detects the interacting variables in hierarchical problems, group them in linkages groups, and build maximally accurate classification models. The initial results on non-trivial hierarchical test problems indicate that the proposed method holds promise and have also shed light on several improvements to enhance the capabilities of the proposed method.
1) The performance of biometric systems degrades over time as input images vary compared to enrolled templates. Self-adaptive systems update templates using new samples to minimize this loss.
2) An experiment analyzed the performance over time of self-adaptive systems using a multimodal biometric database collected over 1.5 years containing temporal variations.
3) The results showed self-adaptive systems can improve performance and stability over time when updated appropriately, though the effect depends on update thresholds and presence of impostors requires further study.
Face Recognition Smart Attendance System: (InClass System)IRJET Journal
- The document describes a face recognition system called "InClass" to automate student attendance tracking. It aims to address issues with traditional manual attendance systems like being inaccurate, time-consuming, and difficult to maintain.
- The InClass system uses a CNN face detector to detect and identify students' faces from images captured with a camera. It can handle variations in lighting, angles, and occlusions. Matching faces to a database allows for automated attendance marking.
- The system aims to simplify the attendance process, reduce time and errors compared to existing biometric systems, and make attendance records easily accessible and storable digitally rather than on paper.
- Systems biology uses computational approaches to produce quantitative, predictive models of biological processes by integrating math, biology, and high-throughput data.
- Eclipse technology can help by providing an extensible and customizable user interface for biologists to access modeling tools and IDEs for computational modelers, with reusable components.
- The SBSI software provides clients, a dispatcher, numerics algorithms, and a repository for systems biology modeling and optimization, with plugins for tasks like pathway editing, simulation, and data visualization.
This document summarizes Justyna Zander-Nowicka's doctoral thesis defense on December 19th, 2008 regarding her research on model-based testing of embedded real-time systems in the automotive domain. The thesis proposed a model-based testing approach called MiLEST that uses signal features for automatic test data generation and evaluation. The approach aims to systematically generate functional test cases from models to test embedded systems starting from early development phases.
A Hybrid Approach to Face Detection And Feature Extractioniosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
This document presents a hybrid approach for face detection and feature extraction. It combines the Viola-Jones face detection framework with a neural network classifier to first classify images as containing a face or not. If a face is detected, Viola-Jones algorithms like integral images and cascading classifiers are used to detect the face features. Edge-based feature maps and feature vectors are also extracted and used as inputs to the neural network classifier and for future facial feature extraction. The proposed approach aims to leverage the strengths of Viola-Jones and neural networks to accurately detect faces and then extract facial features from images.
ABSTRACT
Scientific publications are considered as the most up-to-date resource of ongoing research
activities and scientific knowledge. Efficient practices for accessing biomedical
publications are key to allowing a timely transfer of information from the scientific
research community to peer investigators and other healthcare practitioners. Biomedical
sequence images published within the literature play a central role in life science
discoveries. Whereas advanced text-mining pipelines for information retrieval and
knowledge extraction are now commonplace methodologies for processing documents,
the ongoing challenges associated with knowledge management and utility operations
unique to biomedical image data are only recently gaining recognition. Sequence images
depicting key findings of research papers contain rich information derived from a wide
range of biomedical experiments. Searching for relevant sequence images is however error
prone as images are still opaque to information retrieval and knowledge extraction
engines. Specifically, there is no explicit description or annotation of the sequence image
content. Moreover, traditional biomedical search engines, which search image captions
for relevant keywords only, offer syntactic search mechanisms without regard for the
exact meaning of the query. As proposed in this thesis, semantic enrichment of biomedical
sequence images is a solution which adopts a combination of technologies to harness the
comprehensive information associated with, and contained in, biomedical sequence
images. Extracted information from sequence images is used as seed data to aggregate and
iii
harvest new annotations from heterogeneous online biomedical resources. Comprehensive
semantic enrichment of biomedical images incorporates a variety of knowledge
infrastructure components and services including image feature extraction, semantic web
data services, linked open data and crowd annotation.
Together, these resources make it possible to automatically and/or semi-automatically
discover and semantically interlink new information in a way that supports semantic
search for sequence images. The resulting enriched sequence images are readily reusable
based on their semantic annotations and can be made available for use in ad-hoc data
integration activities. Furthermore, to support image reuse this thesis introduces a
mechanism for identifying similar sequence images based on fuzzy inference and cosine
similarity techniques that can retrieve and classify the related sequence images based on
their semantic annotations. The outcomes of this research work will be relevant to a variety
of user groups ranging from clinicians and researchers searching with sequence image
data.
Machine Learning in Modern Medicine with Erin LeDell at Stanford MedSri Ambati
Machine learning and AI company H2O.ai presented on machine learning applications in modern medicine. They discussed how electronic health records, genomics, wearables, and other data sources can be used with machine learning for personalized healthcare, disease prediction and prevention. H2O's software platform allows building models at scale from large datasets using algorithms like random forests, deep learning and ensembles. Demonstrations showed predicting HIV treatment failure and classifying breast cancer malignancy from medical images, achieving high accuracy. H2O aims to make machine learning accessible and scalable for improving medical research and care.
Workflows, provenance and reporting: a lifecycle perspective at BIH 2013, RomeCarole Goble
Workflow systems support the design, configuration and execution of repetitive, multi-step pipelines and analytics, well established in many disciplines, notably biology and chemistry, but less so in biodiversity and ecology. From an experimental perspective workflows are a means to handle the work of accessing an ecosystem of software and platforms, manage data and security, and handle errors. From a reporting perspective they are a means to accurately document methodology for reproducibility, comparison, exchange and reuse, and to trace the provenance of results for review, credit, workflow interoperability and impact analysis. Workflows operate in an evolving ecosystem and are assemblages of components in that ecosystem; their provenance trails are snapshots of intermediate and final results. Taking a lifecycle perspective, what are the challenges in workflow design and use with different stakeholders? What needs to be tackled in evolution, resilience, and preservation? And what are the “mitigate or adapt” strategies adopted by workflow systems in the face of changes in the ecosystem/environment, for example when tools are depreciated or datasets become inaccessible in the face of funding shortfalls?
Wild Patterns: A Half-day Tutorial on Adversarial Machine Learning - 2019 Int...Pluribus One
Slides of the tutorial held by Battista Biggio, University of Cagliari and Pluribus One Srl, during "2019 International Summer School on Machine Learning and Security (MLS)"
WILD PATTERNS - Introduction to Adversarial Machine Learning - ITASEC 2019Pluribus One
1) Adversarial machine learning studies machine learning systems that operate in adversarial settings such as spam filtering, where the data source is non-neutral and can deliberately attempt to reduce classifier performance.
2) Deep learning models were found to be susceptible to adversarial examples, which are imperceptibly perturbed inputs that cause models to make incorrect predictions.
3) Studies have shown that adversarial examples generated in a digital environment can still fool models when inputs are acquired through a physical system like a camera, indicating these attacks pose a real-world threat.
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub...Pluribus One
This document discusses research into generating adversarial examples to attack the vision system of the iCub humanoid robot. The researchers were able to craft perturbed images that were misclassified by the robot despite being visually indistinguishable from the originals. They developed gradient-based optimization attacks to target specific misclassifications or induce any misclassification. Potential countermeasures include rejecting inputs that fall in the "blind spots" far from the training data. However, deep learning features are unstable, with small pixel changes mapping to large changes in the deep space. Future work aims to address this instability issue.
Secure Kernel Machines against Evasion AttacksPluribus One
This document summarizes research on developing more secure machine learning classifiers. It discusses how gradient-based and surrogate model approaches can be used to evade existing classifiers. The researchers then propose several techniques for building more robust classifiers, including using infinity-norm regularization, cost-sensitive learning, and modifying kernel parameters. Experiments on handwritten digit and spam filtering datasets show the proposed approaches improve security against evasion attacks compared to standard support vector machines.
Machine Learning under Attack: Vulnerability Exploitation and Security MeasuresPluribus One
This document summarizes research on machine learning security and adversarial attacks. It describes how machine learning systems are increasingly being used for consumer applications, but this opens them up to new security risks from skilled attackers. The document outlines different types of adversarial attacks against machine learning, including evasion attacks that aim to evade detection and poisoning attacks that aim to compromise a system's availability. It also discusses approaches for systematically evaluating the security of pattern classification systems against bounded adversaries.
Battista Biggio @ ICML 2015 - "Is Feature Selection Secure against Training D...Pluribus One
This document discusses the security of feature selection algorithms against training data poisoning attacks. It presents a framework to evaluate this, including models of the attacker's goal, knowledge, and capabilities. Experiments show that LASSO feature selection is vulnerable to poisoning attacks, which can significantly affect the selected features. The research aims to better understand these risks and develop more secure feature selection methods.
Battista Biggio @ MCS 2015, June 29 - July 1, Guenzburg, Germany: "1.5-class ...Pluribus One
Pattern classifiers have been widely used in adversarial settings like spam and malware detection, although they have not been originally designed to cope with intelligent attackers that manipulate data at test time to evade detection.
While a number of adversary-aware learning algorithms have been proposed, they are computationally demanding and aim to counter specific kinds of adversarial data manipulation.
In this work, we overcome these limitations by proposing a multiple classifier system capable of improving security against evasion attacks at test time by learning a decision function that more tightly encloses the legitimate samples in feature space, without significantly compromising accuracy in the absence of attack. Since we combine a set of one-class and two-class classifiers to this end, we name our approach one-and-a-half-class (1.5C) classification. Our proposal is general and it can be used to improve the security of any classifier against evasion attacks at test time, as shown by the reported experiments on spam and malware detection.
Sparse Support Faces - Battista Biggio - Int'l Conf. Biometrics, ICB 2015, Ph...Pluribus One
Many modern face verification algorithms use a small set of reference templates to save memory and computa- tional resources. However, both the reference templates and the combination of the corresponding matching scores are heuristically chosen. In this paper, we propose a well- principled approach, named sparse support faces, that can outperform state-of-the-art methods both in terms of recog- nition accuracy and number of required face templates, by jointly learning an optimal combination of matching scores and the corresponding subset of face templates. For each client, our method learns a support vector machine using the given matching algorithm as the kernel function, and de- termines a set of reference templates, that we call support faces, corresponding to its support vectors. It then dras- tically reduces the number of templates, without affecting recognition accuracy, by learning a set of virtual faces as well-principled transformations of the initial support faces. The use of a very small set of support face templates makes the decisions of our approach also easily interpretable for designers and end users of the face verification system.
Battista Biggio, Invited Keynote @ AISec 2014 - On Learning and Recognition o...Pluribus One
Learning and recognition of secure patterns is a well-known problem in nature. Mimicry and camouflage are widely-spread techniques in the arms race between predators and preys. All of the information acquired by our senses is therefore not necessarily secure or reliable. In machine learning and pattern recognition systems, we have started investigating these issues only recently, with the goal of learning to discriminate between secure and hostile patterns. This phenomenon has been especially observed in the context of adversarial settings like biometric recognition, malware detection and spam filtering, in which data can be adversely manipulated by humans to undermine the outcomes of an automatic analysis. As current pattern recognition methods are not natively designed to deal with the intrinsic, adversarial nature of these problems, they exhibit specific vulnerabilities that an adversary may exploit either to mislead learning or to avoid detection. Identifying these vulnerabilities and analyzing the impact of the corresponding attacks on pattern classifiers is one of the main open issues in the novel research field of adversarial machine learning.
In the first part of this talk, I introduce a general framework that encompasses and unifies previous work in the field, allowing one to systematically evaluate classifier security against different, potential attacks. As an example of application of this framework, in the second part of the talk, I discuss evasion attacks, where malicious samples are manipulated at test time to avoid detection. I then show how carefully-designed poisoning attacks can mislead learning of support vector machines by manipulating a small fraction of their training data, and how to poison adaptive biometric verification systems to compromise the biometric templates (face images) of the enrolled clients. Finally, I briefly discuss our ongoing work on attacks against clustering algorithms, and sketch some possible future research directions.
Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for antivirus systems.
However, the suitability of clustering algorithms for security-sensitive settings has been recently questioned by showing that they can be significantly compromised if an attacker can exercise some control over the input data.
In this paper, we revisit this problem by focusing on behavioral malware clustering approaches, and investigate whether and to what extent an attacker may be able to subvert these approaches through a careful injection of samples with poisoning behavior.
To this end, we present a case study on Malheur, an open-source tool for behavioral malware clustering. Our experiments not only demonstrate that this tool is vulnerable to poisoning attacks, but also that it can be significantly compromised even if the attacker can only inject a very small percentage of attacks into the input data. As a remedy, we discuss possible countermeasures and highlight the need for more secure clustering algorithms.
Battista Biggio @ S+SSPR2014, Joensuu, Finland -- Poisoning Complete-Linkage ...Pluribus One
The document discusses poisoning attacks against complete-linkage hierarchical clustering. It introduces hierarchical clustering and describes how attackers can add poisoned samples to compromise the clustering output. The paper evaluates different attack strategies on real and artificial datasets, finding that even random attacks can be effective at poisoning the clusters, while extensions of greedy approaches generally perform best. Future work to develop defenses for clustering algorithms against adversarial inputs is discussed.
Battista Biggio @ AISec 2013 - Is Data Clustering in Adversarial Settings Sec...Pluribus One
Clustering algorithms have been increasingly adopted in security applications to spot dangerous or illicit activities.
However, they have not been originally devised to deal with deliberate attack attempts that may aim to subvert the clustering process itself. Whether clustering can be safely adopted in such settings remains thus questionable.
In this work we propose a general framework that allows one to identify potential attacks against clustering algorithms, and to evaluate their impact, by making specific assumptions on the adversary's goal, knowledge of the attacked system, and capabilities of manipulating the input data. We show that an attacker may significantly poison the whole clustering process by adding a relatively small percentage of attack samples to the input data, and that some attack samples may be obfuscated to be hidden within some existing clusters.
We present a case study on single-linkage hierarchical clustering, and report experiments on clustering of malware samples and handwritten digits.
Battista Biggio @ ECML PKDD 2013 - Evasion attacks against machine learning a...Pluribus One
This document summarizes research on evasion attacks against machine learning systems at test time. The researchers propose a framework for evaluating the security of machine learning algorithms against evasion attacks. They model the adversary's goal, knowledge, capabilities, and attack strategy as an optimization problem. Using this framework, they evaluate gradient-descent evasion attacks against systems like spam filters and malware detectors. They show that machine learning classifiers can be vulnerable, even when the adversary has limited knowledge. The researchers explore techniques like bounding the adversary and adding a "mimicry" component to attacks to improve evasion effectiveness.
Battista Biggio @ ICML2012: "Poisoning attacks against support vector machines"Pluribus One
This document discusses poisoning attacks against support vector machines. The goal of poisoning attacks is to mislead machine learning systems by injecting malicious data points into the training set. The paper proposes an approach to maximize classification error on a validation set by calculating the gradient of the hinge loss with respect to the poisoned point. Experiments on MNIST data show that a single poisoned point can significantly increase error rates. The authors note that real attacks may be less effective and discuss how to improve SVM robustness to poisoning attacks.
This PhD thesis by Zahid Akhtar examines the security of multimodal biometric systems against spoof attacks. It aims to evaluate the robustness of these systems to real spoof attacks, validate assumptions about the "worst-case" spoofing scenario, and develop methods to assess security without fabricating fake traits. Experiments are conducted on systems using face and fingerprint biometrics under various spoof attacks, and results show multimodal systems can be compromised by attacking a single trait, while the worst-case scenario does not always reflect real attacks.
Design of robust classifiers for adversarial environments - Systems, Man, and...Pluribus One
This document summarizes a presentation on designing robust classifiers for adversarial environments given at the 2011 IEEE International Conference on Systems, Man, and Cybernetics. The presentation introduces an approach to model potential attacks at test time using a probabilistic model of the data distribution under attack. This model is then used to design classifiers that are more robust to attacks. Experimental results on biometric identity verification and spam filtering show that the proposed approach can increase classifier security against attacks while maintaining accuracy.
Introducing Milvus Lite: Easy-to-Install, Easy-to-Use vector database for you...Zilliz
Join us to introduce Milvus Lite, a vector database that can run on notebooks and laptops, share the same API with Milvus, and integrate with every popular GenAI framework. This webinar is perfect for developers seeking easy-to-use, well-integrated vector databases for their GenAI apps.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
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
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Building RAG with self-deployed Milvus vector database and Snowpark Container...Zilliz
This talk will give hands-on advice on building RAG applications with an open-source Milvus database deployed as a docker container. We will also introduce the integration of Milvus with Snowpark Container Services.
1. Adaptive Biometric Systems based on
Template Update Paradigm
Ajita Rattani
University of Cagliari,
Department of Electrical and Electronic Engineering,
ajita.rattani@ diee.unica.it
Supervisors: Prof. Fabio Roli and Dr. Gian Luca Marcialis
P R A G
2. What is Biometrics?
Automatic recognition of person based on their distinctive
anatomical and behavioral characteristics like face and
fingerprint.
Fingerprint Face Signature Voice Hand geometry
Facial Retinal scan Iris Gait
thermogram
2
4. Enrollment Phase
Enrollment Phase
x, y, theta x, y, theta
Feature x, y, theta
“ x, y, theta
Extraction x, y, theta x, y, theta Storage
Extracted Mr. X
Features
Database
Template
4
5. Verification Phase
Database Template
yes
Feature Matching Score or Score >
extraction m odule distance
threshold
Input Query
no Accepted
Rejected
5
7. Template Representativeness
Enrolled templates: usually captured in controlled
environment
Input Query : Substancial intra-class variation
Effect: Making enrolled templates ‘Un-representative’
7
8. Standard Solutions
Multi-biometric
Storing multiple templates (multi-instance)
Using Multi-modalities
Repeating the process of enrollment over time
8
9. Multibiometric
Super Template Multi-Modality
A. Rattani, D. R. Kisku, A. Lagorio and M. Tistarelli, “Facial Template
A. Rattani, D. R. Kisku, M. Bicego and M. Tistarelli, “Feature Level Fusion
Synthesis Based on SIFT Features”, Automatic Identiffication Advanced of Face and Fingerprint”, Biometrics: Theory, Applications and Systems (BTAS 2007), 1-6,
Technologies (AUTOID) 2007 IEEE Workshop, 69-73, Alghero, Italy, 2007 Washington, USA
9
10. Template Update: Solution to
Representativeness
Standard Solutions: Fails to capture Temporal Intra-class
variations
Novel Solutions : “Template Update” procedure/ Adaptive
biometric systems
Aim: Update enrolled templates to the intra-class variation
of the input data
10
11. State of Art: template update
Not Mature Enough
No mention of the learning methodology involved
No investigation of the pros, cons and open issues
Lack of clear statement of the problem
11
12. Goal of PhD Studies
Formulate the taxonomy of the current state of art
template update methods
Pros and Cons of State of Art Update Methods
Effect of update procedures on different group of
users (‘Doddington Zoo’)
Proposal of Novel solution
12
13. Ajita Rattani, Biagio Freni, Gian Luca Marcialis, Fabio Roli , “Template Update Methods in Adaptive Biometric Systems: A
Critical Review", 3rd IEEE/IAPR International Conference on Biometrics ICB 2009, Alghero (Italy), Springer, 02/06/2009
Template based Adaptive Biometric System
Semi-supervised
Supervised
Multiple
Single Modality
Template Selection Modality
Co-training
Editing Self-training
Clustering based Graph
based Mincut
Online Offline
Feature Selection
13
14. State of the Art (Template Update)
Supervised Learning
(Uludag et al., PR 2004)
Offline process
Limitations:
Tedious, time consuming
Inefficient for repeated
updating task
14
15. ….Contd
Semi-Supervised Learning
Initial labelled + Unlabelled input
data (“Automatic Self Update”)
Online Updating
Jiang and Ser, PAMI 2002;
Ryu et al., ICPR 2006
Offline Updating
Roli and Marcialis, SSPR
2006, Roli et al., ICB 2007
15
16. Template Co-update: A Conceptual Example
Initial template Unlabeled Samples
Roli et al. (ICB2007)
Difficult face sample
ple
16
17. Protocol followed for Experimental
Investigation
For Database of size N x M
One sample : Initial template
Remaining M-1 samples are divided into Unlabelled and Test set
Equal number of impostor samples are added: Unlabelled and
Test Set
Unlabelled set (Du): for updating the templates
Test set: measures the performance enhancement after
updating
17
18. An Experimental Analysis on Pros and
Cons of Self-update and Co-update
Performance comparison of the Co-update with Self update
Representativeness of the enrolled templates
Controlled and Un-controlled environment
Can operation at relaxed threshold help “self-update” to
capture difficult patterns?
• Ajita Rattani, Gian Luca Marcialis and Fabio Roli, Capturing large intra-class variation of the biometric data by template co-
updating,IEEE Workshop on Biometrics, Int. Conference on Vision and Pattern Recognition CVPR 2008, Anchorage (Alaska,
USA), IEEE, pp. 1-6, 23/07/2008
• A. Rattani, G.L. Marcialis, F. Roli, Boosting gallery representativeness by co-updating face and fingerprint verification systems,
Best Paper Award at 5th International School for Advanced Studies on Biometrics for Secure Authentication, June, 9-13,
2008, Alghero (Italy).
18
19. Co-updating vs. Self-update: Un-controlled
Environment; EER point of view
30
Face Self-Update 14
Finger Self-Update face self-update
Face Co-update face co-update
finger self-update
25 Finger Co-update 12
finger co-update
10
20
EER (%)
EER (%)
8
15
6
10 4
2
5 0 50 100 150 200 250 300
0 50 100 150 200 250 300 350 # No. of unlabelled data added
# No. of unlabelled data added
Shows EER on the test set as a function of the amount of unlabelled data exploited by template self and
co-update algorithms at each iteration. The curve of the self update is shorter due to non-exploitation of
much unlabelled data because of operation at high threshold.
19
20. Galleries Images as captured by Self-
update and Co-update
Differences with Self-update:
More Unlabelled samples added
Larger intra-class variations
introduced even at initial stages
Initial
19
template initial accuracy
face self-update at varying threshold
18
17
16
EER (%)
15
Initial 14
template 13
12
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5
%FAR used for selecting threshold for unlabelled data
20
22. Remarks
Template Co-update:
Non-Representative templates: Can capture large intra-class variations
Representative templates: Comparable performance of Self-update and Co-
update
Self-updating : very much dependent on the initial templates.
Un-representative initial templates: Results in poor capture of difficult
samples due to operation at stringent threshold
However, operation at relaxed threshold results in counter -productive effect
Ajita Rattani, Gian Luca Marcialis and Fabio Roli, Capturing large intra-class variation of the biometric data by template co-
updating,IEEE Workshop on Biometrics, Int. Conference on Vision and Pattern Recognition CVPR 2008, Anchorage (Alaska,
USA), IEEE, pp. 1-6, 23/07/2008
22
23. Open Issues Unexplored
Effect of Creep in errors (‘impostor introduction’)
Effect of different types of updating threshold
Analysis of the effect of user population on template
update procedure
23
24. Difficult Clients and “Doddington’s zoo”
Doddington et al. (1998) introduced some terms to indicate clients
wrongly classifiable even at high thresholds
Lambs: “easy-to-imitate” clients
High FAR when attacked
Wolves: they can easily imitate other clients
A wolf into a client’s gallery may attract other wolves
Goats: difficult to be recognized
A goat may not be able to update itself
Sheeps: Well behaved Clients
24
25. User Population Characteristics
Hypothesis:
Apart from basic FAR of the system, impostors may be
introduced due to the presence of wolves and lambs
Effect of template updating may not be same because
of the presence of “Doddington zoo”
25
26. Goal of the work
Experimental evaluation of the impact of impostors introduction in on-
line self update
At different settings of updating threshold
Fixed/Dynamic
Global/User-specific
Stringent/Relaxed
Presence of intrinsically “difficult” clients
Non-uniform effect of update procedures on different charateristic
clients
26
27. EER vs. impostors introduction at 1%
updating threshold
34 25
Fixed Non-user specific Fixed Non-user specific
Updated Non-user specific Updated Non-user specific
Fixed User specific Fixed User specific
32
Updated User-Specific 20 Updated User-Specific
Equal Error Rate (EER)
30
% of impostors
15
28
10
26
24 5
22
0 100 200 300 400 500 600 0
# of Unlabelled data used 0 100 200 300 400 500 600
# of Unlabelled data used
Gian Luca Marcialis, Ajita Rattani and Fabio Roli, Biometric template update: An experimental investigation on the relationship
between update errors and performance degradation in face verification, Joint IAPR Int. Workshop on Structural and Syntactical
Pattern Recognition and Statistical Techniques in Pattern Recognition S+SSPR08, Orlando (Florida, USA), Springer, 04/12/2008
27
28. Performance Evaluation of Self-Update After
Division of Database on the basis of Doddington Zoo
1. Lambs 2. Sheeps
100 100 Ajita Rattani, Gian Luca Marcialis
After Updating After Updating and Fabio Roli, "An Experimental
Before Updating Before Updating Analysis of the Relationship between
Biometric Template Update and the
(%) FRR
(%) FRR
50 50
Doddington’s Zoo in Face
Verification", ICIAP 2009, Salerno
(Italy)
0 0
0 50 100 0 50 100
(%) FAR (%) FAR
3. Goats 4. Wolves
100 100
After Updating After Updating
Before Updating Before Updating
(%) FRR
(%) FRR
50 50
0 0
0 50 100 0 50 100
(%) FAR (%) FAR
28
29. “Attraction” path
Unlabelled samples iteratively added to the gallery
Initial template First impostor Other wolves
(wolf) are added
29
30. Remarks
For first-time the effect of misclassification errors in self
update process
It resulted to be very much dependent on the threshold
type settings and the security level for acceptance of input
data
Impostors inclusion cannot be avoided even at strict
threshold settings (zeroFAR)
The presence of different animals result in different
updating effects
30
31. Open Issues Still Remained!
As Analyzed :
Current state of art methods are capable of capturing only near input
images
Operation at relaxed threshold results in increased probability of
impostors introduction
Need: Investigation of more robust update procedures with the
following characteristics
Capture of large intra-class variations without increasing probability of
impostors
Not increasing the probability of impostors introduction
31
32. Graph based Semi-Supervised Learning
Self-update methods : ‘Local’ update behaviour
Graph based methods to Semi-supervised methods :
Application: Machine Learning literature like Image Segmentation , Pattern
Recognition
These methods can study the global structure of the data manifold
Hypothesis: Graph based learning may capture large intra-class
variations
Mincut based labelling is a binary technique assigning labels by finding
min-cut
33. “Well-connected” and “Separated”
hypothesis
Region as a set of different people
(expressions, lighting, poses)
Graph-mincut can better assign
labels to each region, even with a
small amount of labelled samples
(Blum and Chawla, 2001) by
studing underlying structure in the
form of graph.
A. Rattani, G.L. Marcialis, F. Roli, Biometric template update using the graph-mincut algorithm: a case study in face verification,
IEEE Biometric Symposium BioSymp08, September, 23-25, 2008, Tampa (Florida, USA), IEEE, ISBN 978-1-4244-2567-9, pp. 23-
28.
33
34. Basic Graph based Mincut
Graph G= (V, E) ; V= {L, U, v+, v-}
{v+, v-}: Two classification vertices, null nodes
representing “positive” and “negative” classes.
E : edge defining function, basis on which two nodes are
connected
Aim : partition v+ from v- by finding the cut on the
minimum similarity set of edges.
34
39. Why Graph Mincut may Work ?
Global structure of manifold is analyzed:
By traversing all s-t paths
Minimum capacity edges are saturated first
Probability of impostor introduction is minimized
39
41. Samples Exploited for Updating : Self
Update and Mincut
% Impostors Encountered
% Samples Encountered
A. Rattani, G.L. Marcialis, F. Roli, Biometric template update
using the graph-mincut algorithm: a case study in face
verification, IEEE Biometric Symposium BioSymp08,
September, 23-25, 2008, Tampa (Florida, USA), IEEE, ISBN
978-1-4244-2567-9, pp. 23-28.
41
42. Concluding Remarks
Critical survey on the template update procedure
Pros and cons of state of art methods
Studied the effect of impostor introduction
Proposed novel solutions
42
43. Future Work
Modeling of probability of impostor introduction
The use of quality information of an input sample:
Quality measures are an array of measurements of
conformance of biometric samples to some predefined
criteria known
Genuine Intra-class
variation?
43
44. …Contd
Modeling of Appropriate Stopping criteria for Template
Updating
Use of Cohort information in template updating
Norman et al. 2009
44
45. …Contd
Robust criteria for selection of input data for updating: F-
Ratio or d-prime
FRatio=(µ Gen-µ Imp) ⁄ (σGen+ σImp)
D-prime=(µ Gen-µ Imp)/(σ)
Evaluation on “Large Scale Databases”
45