Neuronal structures are intricately related to their functions. Study of the neuronal structures reveals healthy and pathologic conditions, crucial to understanding how the Brain works. Current advances in microscopy techniques produce huge volume of data where manual reconstruction and analysis may take several years. Moreover, most of this data is sparse; hence digital reconstructions capturing the essential structural information of the neuronal networks provide ease of archiving, exchanging and analysing. The lack of powerful computational tools to automatically reconstruct neuronal arbors has emerged as a major technical bottleneck in neuroscience research. This work extends the Marked Point Process methodology, which has been proved to be an efficient framework for network extraction in 2D, to 3D neuronal network extraction from microscopy image stacks. The optimization process considers a multiple birth and death dynamics embedded in a simulated annealing scheme. To speed up the convergence a birth map based on the projection of the neuronal processes is considered.
Land Cover Feature Extraction using Hybrid Swarm Intelligence Techniques - A ...IDES Editor
The findings of recent studies are showing strong
evidence to the fact that some aspects of biogeography can be
applied to solve specific problems in science and engineering.
The proposed work presents a hybrid biologically inspired
technique that can be adapted according to the database of
expert knowledge for a more focused satellite image
classification. The paper also presents a comparative study of
our hybrid intelligent classifier with the other recent Soft
Computing Classifiers such as ACO, Hybrid Particle Swarm
Optimization-cAntMiner (PSO-ACO2), Fuzzy sets, Rough-
Fuzzy Tie up and the Semantic Web Based Classifiers and
the traditional probabilistic classifiers such as the Minimum
Distance to Mean Classifier (MDMC) and the Maximum
Likelihood Classifier (MLC).
MIT 6.870 - Template Matching and Histograms (Nicolas Pinto, MIT)npinto
MIT 6.870 Object Recognition and Scene Understanding (Fall 2008)
http://people.csail.mit.edu/torralba/courses/6.870/6.870.recognition.htm
This class will review and discuss current approaches to object recognition and scene understanding in computer vision. The course will cover bag of words models, part based models, classifier based models, multiclass object recognition and transfer learning, concurrent recognition and segmentation, context models for object recognition, grammars for scene understanding and large datasets for semi supervised and unsupervised discovery of object and scene categories. We will be reading a mixture of papers from computer vision and influential works from cognitive psychology on object and scene recognition.
Comparative Analysis of Dwt, Reduced Wavelet Transform, Complex Wavelet Trans...ijsrd.com
Image denoising is the process to remove the noise from the image naturally corrupted by the noise. The wavelet method is one among various methods for recovering infinite dimensional objects like curves, densities, images, etc. The wavelet techniques are very effective to remove the noise because of their ability to capture the energy of a signal in few energy transform values. Though the wavelet transform have the best bases when it represents target functions which has dot singularity, it can hardly get the best bases when it present the singularity of line and hyper-plane. This makes the traditional two-dimensional wavelet transform in dealing with the image have some limitations. To overcome the above-mentioned shortcomings of Wavelet transform the theory of Curvelet transform was promoted.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Feature Tracking of Objects in Underwater Video SequencesIDES Editor
Feature tracking is a key, underlying component in
many approaches to 3D reconstruction, detection, localization
and recognition of underwater objects. In this paper, we
proposed to adapt SIFT technique for feature tracking in
underwater video sequences. Over the past few years the
underwater vision is attracting researchers to investigate
suitable feature tracking techniques for underwater
applications. The researchers have developed many feature
tracking techniques such as KLT, SIFT, SURF etc., to track
the features in video sequence for general applications. The
literature survey reveals that there is no standard feature
tracker suitable for underwater environment. We proposed to
adapt SIFT technique for tracking features of objects in
underwater video sequence. The SIFT extracts features, which
are invariant to scale, rotation and affine transformations.
We have compared and evaluated SIFT with popular techniques
such as KLT and SURF on captured video sequence of
underwater objects. The experimental results shows that
adapted SIFT works well for underwater video sequence
The objective of this work is to propose an image
denoising technique and compare it with image denoising
using ridgelets. The proposed method uses slantlet transform
instead of wavelets in ridgelet transform. Experimental result
shows that the proposed method is more effective than ridgelets
in noise removal. The proposed method is effective in
compressing images while preserving edges.
Land Cover Feature Extraction using Hybrid Swarm Intelligence Techniques - A ...IDES Editor
The findings of recent studies are showing strong
evidence to the fact that some aspects of biogeography can be
applied to solve specific problems in science and engineering.
The proposed work presents a hybrid biologically inspired
technique that can be adapted according to the database of
expert knowledge for a more focused satellite image
classification. The paper also presents a comparative study of
our hybrid intelligent classifier with the other recent Soft
Computing Classifiers such as ACO, Hybrid Particle Swarm
Optimization-cAntMiner (PSO-ACO2), Fuzzy sets, Rough-
Fuzzy Tie up and the Semantic Web Based Classifiers and
the traditional probabilistic classifiers such as the Minimum
Distance to Mean Classifier (MDMC) and the Maximum
Likelihood Classifier (MLC).
MIT 6.870 - Template Matching and Histograms (Nicolas Pinto, MIT)npinto
MIT 6.870 Object Recognition and Scene Understanding (Fall 2008)
http://people.csail.mit.edu/torralba/courses/6.870/6.870.recognition.htm
This class will review and discuss current approaches to object recognition and scene understanding in computer vision. The course will cover bag of words models, part based models, classifier based models, multiclass object recognition and transfer learning, concurrent recognition and segmentation, context models for object recognition, grammars for scene understanding and large datasets for semi supervised and unsupervised discovery of object and scene categories. We will be reading a mixture of papers from computer vision and influential works from cognitive psychology on object and scene recognition.
Comparative Analysis of Dwt, Reduced Wavelet Transform, Complex Wavelet Trans...ijsrd.com
Image denoising is the process to remove the noise from the image naturally corrupted by the noise. The wavelet method is one among various methods for recovering infinite dimensional objects like curves, densities, images, etc. The wavelet techniques are very effective to remove the noise because of their ability to capture the energy of a signal in few energy transform values. Though the wavelet transform have the best bases when it represents target functions which has dot singularity, it can hardly get the best bases when it present the singularity of line and hyper-plane. This makes the traditional two-dimensional wavelet transform in dealing with the image have some limitations. To overcome the above-mentioned shortcomings of Wavelet transform the theory of Curvelet transform was promoted.
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is an open access international journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Feature Tracking of Objects in Underwater Video SequencesIDES Editor
Feature tracking is a key, underlying component in
many approaches to 3D reconstruction, detection, localization
and recognition of underwater objects. In this paper, we
proposed to adapt SIFT technique for feature tracking in
underwater video sequences. Over the past few years the
underwater vision is attracting researchers to investigate
suitable feature tracking techniques for underwater
applications. The researchers have developed many feature
tracking techniques such as KLT, SIFT, SURF etc., to track
the features in video sequence for general applications. The
literature survey reveals that there is no standard feature
tracker suitable for underwater environment. We proposed to
adapt SIFT technique for tracking features of objects in
underwater video sequence. The SIFT extracts features, which
are invariant to scale, rotation and affine transformations.
We have compared and evaluated SIFT with popular techniques
such as KLT and SURF on captured video sequence of
underwater objects. The experimental results shows that
adapted SIFT works well for underwater video sequence
The objective of this work is to propose an image
denoising technique and compare it with image denoising
using ridgelets. The proposed method uses slantlet transform
instead of wavelets in ridgelet transform. Experimental result
shows that the proposed method is more effective than ridgelets
in noise removal. The proposed method is effective in
compressing images while preserving edges.
Restats is a highly customized real-time framework that processes data and creates data and geo cubes that later can be queried. It makes use of the data types redis supports, like the sorted-set.
Hardware Implementation of Genetic Algorithm Based Digital Colour Image Water...IDES Editor
The objective of this work is to develop a
hardware-based watermarking system to identify the device
using which the photograph was taken. The watermark chip
will be fit in any electronic component that acquires the
images, which are then watermarked in real time while
capturing along with separate key. Watermarking is the
process of embedding the watermark, in which a watermark
is inserted in to a host image while extracting the watermark
the watermark is pulled out of the image. The ultimate
objective of the research presented in this paper is to develop
low-power, high-performance, real-time, reliable and secure
watermarking systems, which can be achieved through
hardware implementations. In this paper the development of
a very Large Scale Integration (VLSI) architecture for a highperformance
watermarking chip that can perform invisible
colour image watermarking using genetic algorithm is
discussed. The prototyped VLSI implementation of
watermarking is analyzed in two ways.
Viz.,(i) Digital watermarking
ABSTRACT : Image registration is an important and fundamental task in image processing used to match two different images. Image registration estimates the parameters of the geometrical transformation model that maps the sensed images back to its reference image. A Feature-Based Approach to automated image-to-image registration is presented. In this paper, various methods are used in different Phases of Image registration. The characteristics of this approach is it combines scale interaction of Discrete wavelets for feature extraction, Scale Invariant Feature Transform (SIFT) for feature matching. Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. SIFT feature descriptor is invariant to uniform scaling, orientation, and partially invariant to affine distortion and illumination changes.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
FPGA Based Design of High Performance Decimator using DALUT AlgorithmIDES Editor
This paper presents a multiplier less approach
to implement high speed and area efficient decimator for
down converter of Software Defined Radios. This
technique substitutes multiply-and-accumulate (MAC)
operations with look up table (LUT) accesses. Proposed
decimator has been implemented using Partitioned
distributed arithmetic look up table (DALUT) algorithm
by taking optimal advantage of embedded LUTs of target
FPGA device. This method is useful to enhance the system
performance in terms of speed and area. The proposed
decimator has used half band polyphase decomposition
FIR structure. The decimator has been designed with
Matlab 7.6, simulated with Modelsim 6.3XE simulator,
synthesized with Xilinx Synthesis Tool (XST) 10.1 and
implemented on Spartan-3E based 3s500efg320-4 FPGA
device. The proposed DALUT approach has shown an
improvement of 24% in speed by saving almost 50%
resources of target device as compared to MAC based
approach.
Integrative
analyses of large scale spatio-temporal datasets play increasingly important roles in many areas of science and engineering. Our recent work in this area is motivated by application scenarios involving complementary digital microscopy, Radiology and "omic"
analyses in cancer research. In these scenarios, our objective is to use a coordinated set of image analysis, feature extraction and machine learning methods to predict disease progression and to aid in targeting new therapies.
We describe methods
we have developed for extraction, management and analysis of features along with the systems software methods for optimizing execution on high end CPU/GPU platforms. We will also describe biomedical results obtained from these studies and extensions of the
computational methods to broader application areas.
Day 2 pm session: Tewodaj Mogues and Lucy Billings, IFPRI: “Drivers of Public Investment in Nutrition—Mozambique”
Workshop on Approaches and Methods for Policy Process Research, co-sponsored by the CGIAR Research Programs on Policies, Institutions and Markets (PIM) and Agriculture for Nutrition and Health (A4NH) at IFPRI-Washington DC, November 18-20, 2013.
Restats is a highly customized real-time framework that processes data and creates data and geo cubes that later can be queried. It makes use of the data types redis supports, like the sorted-set.
Hardware Implementation of Genetic Algorithm Based Digital Colour Image Water...IDES Editor
The objective of this work is to develop a
hardware-based watermarking system to identify the device
using which the photograph was taken. The watermark chip
will be fit in any electronic component that acquires the
images, which are then watermarked in real time while
capturing along with separate key. Watermarking is the
process of embedding the watermark, in which a watermark
is inserted in to a host image while extracting the watermark
the watermark is pulled out of the image. The ultimate
objective of the research presented in this paper is to develop
low-power, high-performance, real-time, reliable and secure
watermarking systems, which can be achieved through
hardware implementations. In this paper the development of
a very Large Scale Integration (VLSI) architecture for a highperformance
watermarking chip that can perform invisible
colour image watermarking using genetic algorithm is
discussed. The prototyped VLSI implementation of
watermarking is analyzed in two ways.
Viz.,(i) Digital watermarking
ABSTRACT : Image registration is an important and fundamental task in image processing used to match two different images. Image registration estimates the parameters of the geometrical transformation model that maps the sensed images back to its reference image. A Feature-Based Approach to automated image-to-image registration is presented. In this paper, various methods are used in different Phases of Image registration. The characteristics of this approach is it combines scale interaction of Discrete wavelets for feature extraction, Scale Invariant Feature Transform (SIFT) for feature matching. Scale-invariant feature transform (or SIFT) is an algorithm in computer vision to detect and describe local features in images. SIFT feature descriptor is invariant to uniform scaling, orientation, and partially invariant to affine distortion and illumination changes.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
FPGA Based Design of High Performance Decimator using DALUT AlgorithmIDES Editor
This paper presents a multiplier less approach
to implement high speed and area efficient decimator for
down converter of Software Defined Radios. This
technique substitutes multiply-and-accumulate (MAC)
operations with look up table (LUT) accesses. Proposed
decimator has been implemented using Partitioned
distributed arithmetic look up table (DALUT) algorithm
by taking optimal advantage of embedded LUTs of target
FPGA device. This method is useful to enhance the system
performance in terms of speed and area. The proposed
decimator has used half band polyphase decomposition
FIR structure. The decimator has been designed with
Matlab 7.6, simulated with Modelsim 6.3XE simulator,
synthesized with Xilinx Synthesis Tool (XST) 10.1 and
implemented on Spartan-3E based 3s500efg320-4 FPGA
device. The proposed DALUT approach has shown an
improvement of 24% in speed by saving almost 50%
resources of target device as compared to MAC based
approach.
Integrative
analyses of large scale spatio-temporal datasets play increasingly important roles in many areas of science and engineering. Our recent work in this area is motivated by application scenarios involving complementary digital microscopy, Radiology and "omic"
analyses in cancer research. In these scenarios, our objective is to use a coordinated set of image analysis, feature extraction and machine learning methods to predict disease progression and to aid in targeting new therapies.
We describe methods
we have developed for extraction, management and analysis of features along with the systems software methods for optimizing execution on high end CPU/GPU platforms. We will also describe biomedical results obtained from these studies and extensions of the
computational methods to broader application areas.
Day 2 pm session: Tewodaj Mogues and Lucy Billings, IFPRI: “Drivers of Public Investment in Nutrition—Mozambique”
Workshop on Approaches and Methods for Policy Process Research, co-sponsored by the CGIAR Research Programs on Policies, Institutions and Markets (PIM) and Agriculture for Nutrition and Health (A4NH) at IFPRI-Washington DC, November 18-20, 2013.
ماذا أنت يا إلهي ؟ ماذا أنت إلا الرب الإله ! لأنه من هو السيد إلا الرب ؟ ومن هو الإله سوي إلهنا ؟ 0
عـــال جداً ! صـــالح جداً ! قـــوي جداً ! مقـــتدر جداً !
رؤوف جداً ومع ذلك عادل جداً ! 000 مستتر للغاية ومع ذلك ظاهر جداً !000 جميل جداً ومع ذلك قاس جداً ! 000 دائم ومع ذلك لا يمكن إدراكه ! 000 ثابت ومع ذلك يغير كل شئ ! 000 أباً حديث وعتيق علي الإطلاق ! 000 يجدد الكل ويعطي المتكبرين أياماً وهم لا يعلمون ! 000 دائم يعمل ومستريح إلي الغاية ! 000 دائماً يحشد ومع ذلك غير محتاج إلي شئ ! 000 يدعم ويسد العوز ويستر ! 000 يبدع ويعول وينمي ! 000 يطلب ومع ذلك يملك كل شئ ! 0
* * *
أنت تحب بلا شهوة وغيور بلا قلق ! 000 تندم ومع ذلك لا تأسف ! 000 أنت حانق ومع ذلك أنت هادئ ! 000 تغير كل أعمالك وغرضك ثابت ! 000 تأخذ كل ما تجده ومع ذلك أنت لا تضيعه أبداً ! 000 لا تحتاج مطلقاً إلي شئ ومع ذلك تسر بالأرباح ! 000 لست طماعاً ومع ذلك تطالب بالأرباح ! 000 تأخذ كثيراً لكي تصير مديوناً ومن عنده شئ وليس هو ملكك ؟ 000 أنت تدفع ديوناً ليس لهل أصل وتعيد ديونا دون أن تنقص منها شيئاً ! 000 ماذا أقول عنك الآن يا إلهي يا حياتي وقرحي الطاهر !؟ 000 وماذا يقول أي إنسان عندما يتكلم عنك ! ؟ 000 ويل لمن لا يتحدث بحمدك عندما ينطق الأخرس ويصير كأفصح البلغاء ! 0
INHIBITION EFFECT OF SCHIFF BASE COMPOUNDS ON THE CORROSION OF IRON IN NITRIC ACID AND SODIUM HYDROXIDE SOLUTIONS
Loutfy H. Madkour* and U.A. Zinhome**
Slides from Portland Machine Learning meetup, April 13th.
Abstract: You've heard all the cool tech companies are using them, but what are Convolutional Neural Networks (CNNs) good for and what is convolution anyway? For that matter, what is a Neural Network? This talk will include a look at some applications of CNNs, an explanation of how CNNs work, and what the different layers in a CNN do. There's no explicit background required so if you have no idea what a neural network is that's ok.
Brains rely on spiking neural networks for ultra-low-power information processing. Building artificial intelligence with similar efficiency requires learning algorithms to instantiate complex spiking neural networks and brain-inspired neuromorphic hardware to emulate them efficiently. Toward this end, I will briefly introduce surrogate gradients as a general framework for training spiking neural networks and showcase their robustness and self-calibration capabilities on analog neuromorphic hardware. Drawing further inspiration from biology, I will discuss the impact of homeostatic plasticity and network initialization in the excitatory-inhibitory balanced regime on deep spiking neural network training. Finally, I will show how approximations relate surrogate gradients to biologically plausible online learning rules with a minor impact on their effectiveness.
Recommender Systems Tutorial (Part 2) -- Offline ComponentsBee-Chung Chen
This is a tutorial given in the International Conference on Machine Learning. The slides consist of four parts. Please look for Part 1, Part 3 and Part 4 to get a complete picture of this technology.
Supervised learning is a category of machine learning that uses labeled datasets to train algorithms to predict outcomes and recognize patterns. Unlike unsupervised learning, supervised learning algorithms are given labeled training to learn the relationship between the input and the outputs.
Supervised machine learning algorithms make it easier for organizations to create complex models that can make accurate predictions. As a result, they are widely used across various industries and fields, including healthcare, marketing, financial services, and more.
Here, we’ll cover the fundamentals of supervised learning in AI, how supervised learning algorithms work, and some of its most common use cases.
Get started for free
How does supervised learning work?
The data used in supervised learning is labeled — meaning that it contains examples of both inputs (called features) and correct outputs (labels). The algorithms analyze a large dataset of these training pairs to infer what a desired output value would be when asked to make a prediction on new data.
For instance, let’s pretend you want to teach a model to identify pictures of trees. You provide a labeled dataset that contains many different examples of types of trees and the names of each species. You let the algorithm try to define what set of characteristics belongs to each tree based on the labeled outputs. You can then test the model by showing it a tree picture and asking it to guess what species it is. If the model provides an incorrect answer, you can continue training it and adjusting its parameters with more examples to improve its accuracy and minimize errors.
Once the model has been trained and tested, you can use it to make predictions on unknown data based on the previous knowledge it has learned.
How does supervised learning work?
The data used in supervised learning is labeled — meaning that it contains examples of both inputs (called features) and correct outputs (labels). The algorithms analyze a large dataset of these training pairs to infer what a desired output value would be when asked to make a prediction on new data.
For instance, let’s pretend you want to teach a model to identify pictures of trees. You provide a labeled dataset that contains many different examples of types of trees and the names of each species. You let the algorithm try to define what set of characteristics belongs to each tree based on the labeled outputs. You can then test the model by showing it a tree picture and asking it to guess what species it is. If the model provides an incorrect answer, you can continue training it and adjusting its parameters with more examples to improve its accuracy and minimize errors.
Once the model has been trained and tested, you can use it to make predictions on unknown data based on the previous knowledge it has learned.
Types of supervised learning
Supervised learning in machine learning is generally divided into two categories: classification and regre
Toward Tractable AGI: Challenges for System Identification in Neural CircuitryRandal Koene
This is the presentation I gave at AGI-12 (also called the Winter Intelligence 2012 conferece) in Oxford, UK, on Dec.11, 2012. There is an AGI-12 proceedings paper that accompanies this talk. I will make that available on my publications page at http://randalkoene.com and I will put both together on the http://carboncopies.org page about this event. The video (recorded by Adam Ford) should also appear soon.
Abstract. Feasible and practical routes to Artificial General Intelligence involve short-cuts tailored to environments and challenges. A prime example of a system with built-in short-cuts is the human brain. Deriving from the brain the functioning system that implements intelligence and generality at the level of neurophysiology is interesting for many reasons, but also poses a set of specific challenges. Representations and models demand that we pick a constrained set of signals and behaviors of interest. The systematic and iterative process of model building involves what is known as System Identification, which is made feasible by decomposing the overall problem into a collection of smaller System Identification problems. There is a roadmap to tackle that includes structural scanning (a way to obtain the “connectome”) as well as new tools for functional recording. We examine the scale of the endeavor, and the many challenges that remain, as we consider specific approaches to System Identification in neural circuitry.
AI&BigData Lab 2016. Артем Чернодуб: Обучение глубоких, очень глубоких и реку...GeeksLab Odessa
4.6.16 AI&BigData Lab
Upcoming events: goo.gl/I2gJ4H
В докладе представлен обзор новых подходов к обучению глубоких и рекуррентных нейросетей. Обсуждаются ортогональная инициализация весов для сверточных и рекуррентных нейросетей и ее влияние на проблему исчезновения градиентов (vanishing gradient effect), нормализацию мини-пакетов (batch normalization), разностное обучение (residual learning).
Image Splicing Detection involving Moment-based Feature Extraction and Classi...IDES Editor
In the modern age, the digital image has taken
the place of the original analog photograph, and the forgery
of digital images has become increasingly easy, and harder
to detect. Image splicing is the process of making a
composite picture by cutting and joining two or more
photographs. An approach to efficient image splicing
detection is proposed here. The spliced image often
introduces a number of sharp transitions such as lines,
edges and corners. Phase congruency is a sensitive measure
of these sharp transitions and is hence proposed as a
feature for splicing detection. Statistical moments of
characteristic functions of wavelet sub-bands have been
examined to detect the differences between the authentic
images and spliced images. Image splicing detection can be
treated as a two-class pattern recognition problem, which
builds the model using moment features and some other
parameters extracted from the given test image. Artificial
neural network (ANN) is chosen as a classifier to train and
test the given images.
Fast Object Recognition from 3D Depth Data with Extreme Learning MachineSoma Boubou
Object recognition from RGB-D sensors has recently emerged as a renowned and challenging research topic. The current systems often require large amounts of time to train the models and to classify new data. We proposed an effective and fast object recognition approach from 3D data acquired from depth sensors such as Structure or Kinect sensors.
Our contribution in this work} is to present a novel fast and effective approach for real-time object recognition from 3D depth data:
- First, we extract simple but effective frame-level features, which we name as differential frames, from the raw depth data.
- Second, we build a recognition system based on Extreme Learning Machine classifier with a Local Receptive Field (ELM-LRF).
Similar to Marked Point Process For Neurite Tracing (20)
C for Cuda - Small Introduction to GPU computingIPALab
In this talk, we are presenting a short introduction to CUDA and GPU computing to help anyone who reads it to get started with this technology.
At first, we are introducing the GPU from the hardware point of view: what is it? How is it built? Why use it for General Purposes (GPGPU)? How does it differ from the CPU?
The second part of the presentation is dealing with the software abstraction and the use of CUDA to implement parallel computing. The software architecture, the kernels and the different types of memories are tackled in this part.
Finally, and to illustrate what has been presented previously, examples of codes are given. These examples are also highlighting the issues that may occur while using parallel-computing.
Semantic Reasoning in Context-Aware Assistive Environments to Support Ageing ...IPALab
Robust solutions for ambient assisted living are numerous, yet predominantly specific in their scope of usability. In this paper, we de- scribe the potential contribution of semantic web technologies to building more versatile solutions — a step towards adaptable context-aware en- gines and simplified deployments. Our conception and deployment work in hindsight, we highlight some implementation challenges and require- ments for semantic web tools that would help to ease the development of context-aware services and thus generalize real-life deployment of se- mantically driven assistive technologies. We also compare available tools with regard to these requirements and validate our choices by providing some results from a real-life deployment.
The MICO Project: COgnitive MIcroscopy For Breast Cancer GradingIPALab
In close collaboration with AGFA Healthcare and La Pitié Salpêtrière Hospital, Paris, France, IPAL’s MICO (COgnitive virtual MIcroscopy)platform aims at developing a cognition-driven visual explorer for histopathology, particularly for breast cancer grading, supported by dynamic semantic annotation and medical ontology. The analysis capabilities and results are made available to the pathologist through a platform combining virtual microscopy and cognitive reasoning. This allows the medical staff to interact with the platform at the appropriate level of abstraction. The platform should combine multi-modal histopathological images, multi-scale whole slide image (WSI) exploration analysis, and medical knowledge representation inference using ontologies.
Semantic tools should be used to drive image exploration & analysis. A semantic profile should be provided to each algorithm, allowing high flexibility and good knowledge gathering. Medical knowledge should also be integrated into MICO, improving it’s abilities to interact with the histopathologist users, helping them to make the right choices.
Using Formal Models For Analysis Of Biological PathwaysIPALab
We will first describe the biological problems we are facing (building a model of a Biological Pathway), before talking about the usual methods used by computer scientist to tackle such problems.
We will finally describe the stochastic modeling used in IPAL for the biological pathways, and explain the decomposition framework we are developing to speed up the computations.
A New In-Camera Imaging Model For Color Computer Vision And Its ApplicationIPALab
We present two applications of interactive computer vision. The first involves an efficient method for producing picture legends for group photos. This approach combines face detection with human shape priors into an interactive selection framework to allow users to quickly segment the individuals in a group photo. Results obtained by our method are better than those obtained by general selection tools and can be produced in a fraction of the time. Our second method is a tool for correcting errors in panoramic images. In particular, we describe two features:
1. a seam-editing tool that allows the user to modify blending seams in a local manner
2. a content-aware snapping tool to help the user better align local image content between overlapping images
We demonstrate the effectiveness of our tool on several examples that are tedious to achieve using existing photo-editing softwares.
We present a study of the in-camera image processing through an extensive analysis of more than 10,000 images from over 30 cameras. The goal of this work is to investigate if image values can be transformed to physically meaningful values, and if so, when and how this can be done. From our analysis, we found a major limitation of the imaging model employed in conventional radiometric calibration methods and propose a new in-camera imaging model that fits well with today’s cameras. With the new model, we present associated calibration procedures that allow us to convert sRGB images back to their original CCD RAW responses in a manner that is significantly more accurate than any existing methods. Additionally, we show how this new imaging model can be used to build an image correction application that converts an sRGB input image captured with the wrong camera settings to an sRGB output image that would have been recorded under the correct settings of a specific camera.
We also describe a method to construct a sparse lookup table (LUT) that is effective in modeling the camera imaging pipeline that maps a RAW camera image to its sRGB output based on the new aforementioned color processing model. We show how to construct a LUT using a novel nonuniform lattice regression method that adapts the LUT lattice to better fit the underlying 3D function which was previously formulated as a RBF function. Our method offers not only a performance speedup of an order of magnitude faster than RBF, but also a compact mechanism to describe the imaging pipeline.
Robust solutions for ambient assisted living are numerous, yet predominantly specific in their scope of usability. This presentation describes the contribution of the service oriented architecture combined with semantic web technologies in order to build more versatile solutions — a step towards adaptable context-aware engines and simplified deployments. Our design and deployment work in hindsight, we highlight some requirements for semantic web tools that would help with real-life deployment of semantically driven assistive technologies. We also describe our current service platform developed as an integrated solution for AAL.
-- A research work from IPAL Lab, Singapore.
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/
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
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.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3
Marked Point Process For Neurite Tracing
1. Marked Point Process for Neurite
Tracing
Sreetama Basu
NUS-IPAL
Supervised by
Prof. OOI Wei Tsang
And
Prof. Daniel Racoceanu
2. Presentation topics
• Neurite tracing
• Marked Point Process(MPP)
• MPP model for neurite tracing
• Results
3. Digital reconstruction
Segmentation followed by quantification
•Structure determines functions;
linked to higher order cognitive functions,
neuro-degenerative diseases
(Alzheimer’s, Parkinson’s etc).
•Brain Atlas of common lab animals;
•Huge volume of (sparse) data, Manual reconstruction takes days and months
• C.elegans ~ 15 years; EM connectomic data ~800 terabytes
•Digital reconstruction : Easier to archive, exchange, analyze
Figure adapted from : Ascoli, G. A., J. L. Krichmar, et al. (2001). "Generation, description and storage of dendritic morphology
data." Philos Trans R Soc Lond B Biol Sci 356(1412): 1131-1145.
4. Challenges of automated reconstruction
Tile stitching artifacts Fuzzy structures
False connections, Halos around branches
overlaps, crossing mistaken as parallel
processes
Intra operator
Branch gaps And inter operator
And Variability in manual
discontinuities reconstruction
by experts
Brown, K. M., G. Barrionuevo, et al. (2011). "The DIADEM data sets: representative light microscopy images of neuronal
morphology to advance automation of digital reconstructions.“ Neuroinformatics 9(2-3): 143-157
5. Presentation topics
• Neurite tracing
• Marked Point Process
• MPP model for neurite tracing
• Results
6. Marked Point Process
Extraction of tree crowns
Extraction of arbitrarily-shaped objects using stochastic multiple birth-and-death dynamics and active contours, Maria S. Kulikova, Ian H. Jermyn,
Xavier Descombes, Elena Zhizhina, and JosianeZerubia, IS&T/SPIE Electronic Imaging 2010 meeting, San Jose, California, USA
Extraction of
road networks
A Point Process for Fully Automatic Road Network Detection in Satellite and Aerial Images Pierre, C. Descombes, X. Zhizhina, E. Problems of
Information Transmission 10 3 247-256 2010
7. Marked Point Process
Intermediate Image data
0th iteration
iteration MPP Objects
Energy optimization through
A B A B Birth and death dynamics
C C Sampling embedded in
D E E
D Simulated Annealing
F F
A B A B
C C
D E D E
F F
Iteration stops when all objects Intermediate
are detected, corresponds to minimum energy iteration
8. Marked Point Process
• Set of objects= realization of a MPP
K: point process whose realization is a
random number of points given by a
random variable
• Marked point process objects defined in S= K x M
M: mark space
of the object
• Example: M= [Rmin,Rmax]
• And (x,y, R) defines a disc object
A K
B
centre radius C
D E
F
9. Marked point process
• image is viewed as a Gibbs energy model, where the maximum
object density corresponds to minimum energy
1
P ( X x=
= ) exp( − β E ( x))
K
Energy
Inverse temperature
• Optimization: Multiple Birth and Death dynamics embedded in
Simulated Annealing
Advantages:
Unsupervised extraction not involving any specific configuration
initialization (unknown number of objects)
Allows incorporation of prior – geometric and interaction constraints
among objects
Takes into account data at macroscopic scale , at object level instead of
pixels (HR images)
10. Presentation topics
• Neurite tracing
• Marked Point Process
• MPP model for neurite tracing
• Results
11. MPP model for neuronal network
extraction
• Aim: unsupervised 3D neuronal network extraction
• Model: object process (objects: sphere ~ U ([rmin , rmax ])
specified by an Energy:
U = Ud + Ui + Up
Prior term
Data term
•Favors connectivity
• internal homogeneity
•Contrast with background Interaction term
•Prevents overlap of objects
•Prevents crowding together of objects
• Optimization: Multiple Birth and Death dynamics
embedded in Simulated annealing
12. MPP model for neuronal network
extraction
Red circle =
cut of
spherical
object on a
Data Energy response slice of the
image stack
Internal homogeneity Both constraints
No contrast are satisfied
constraint is violated
with background
13. MPP model for neuronal network
extraction
Pair-wise interaction constraints
Considering radiometric properties + interaction constraints among objects
14. MPP model for neuronal network
extraction
Prior Energy: based on number of neighbors,
Models multi-object interaction; favors
connectivity and elongation of network
15. Presentation topics
• Neurite tracing
• Marked Point Process
• MPP model for neurite tracing
• Results
18. Preliminary Results
Approximation of the neuronal network using Construction of Minimum Spanning Tree from
MPP with spheres as objects object centres on 2D projection (max intensity)
of the Olfactory Projection data stack