Deep learning is a collection of machine learning algorithms utilizing multiple layers, with which higher levels of raw data are slowly removed. For example, lower layers can recognize edges in image processing whereas higher layers may define concepts for humans such as numbers or letters or faces. In this paper we have done a literature survey of some other papers to know how useful is Deep Learning and how to define other Artificial Intelligence things using Deep Learning. Anirban Chakraborty "A Study of Deep Learning Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31629.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31629/a-study-of-deep-learning-applications/anirban-chakraborty
This study used signal detection theory to examine how neuroscientists identify the default mode network compared to other prominent resting-state networks. Twenty participants were asked to distinguish the default mode network from three other networks in a rapid forced-choice task, where the networks were presented at different signal thresholds. Results showed that participants more accurately identified the default mode network when it was presented at the most stringent threshold, and made the most conservative decisions when networks were not thresholded. These findings suggest that thresholding fMRI data improves accuracy in identifying brain networks.
Literature Review on DNA based Audio Steganographic TechniquesRashmi Tank
This document summarizes 10 different techniques for DNA-based audio steganography that have been proposed in previous research papers. It discusses how DNA sequences can be used to encode secret messages due to their large storage capacity and randomness. Various methods are described that encode messages into DNA sequences and then hide the DNA sequences in audio files in an imperceptible manner using techniques like least significant bit modification. The techniques aim to provide secure transmission of secret data using the properties of DNA sequences while maintaining high audio quality after embedding.
Automated Analysis of Microscopy Images using Deep Convolutional Neural NetworkAdetayoOkunoye
This document summarizes research on using deep convolutional neural networks to automatically analyze microscopy images. The goals are to expedite the analysis of high-content microscopy data and automate tasks like cell counting and classification. The researchers trained and tested models using TensorFlow on microscopy images to classify cells, achieving over 75% accuracy. This level of automation could benefit biological research by reducing human errors and speeding up analysis of large image datasets.
This document discusses stacked convolutional neural networks as a model for imagined speech in EEG data. It outlines several challenges in studying imagined speech, including the difficulty of obtaining clean EEG data without external stimuli interfering, and the brain's complex and diffuse representation of imagination. The document argues that progress requires a robust language model that accounts for the brain's organizational structure, and that understanding imagined speech could have applications like restoring communication for paralyzed patients.
This document provides an overview of the applications of error-control coding over the past 50 years. It begins by discussing early applications in deep space communications, where coding provided significant power savings. The first coding scheme used for deep space, known as the Mariner code, achieved a power gain of 3.2 dB over uncoded BPSK but required higher bandwidth. More advanced codes were later developed that achieved gains close to the theoretical limits. The document then discusses how coding has been widely used in other areas such as satellite communications, data transmission, data storage, mobile communications and more to improve performance.
Deep learning is a collection of machine learning algorithms utilizing multiple layers, with which higher levels of raw data are slowly removed. For example, lower layers can recognize edges in image processing whereas higher layers may define concepts for humans such as numbers or letters or faces. In this paper we have done a literature survey of some other papers to know how useful is Deep Learning and how to define other Artificial Intelligence things using Deep Learning. Anirban Chakraborty "A Study of Deep Learning Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31629.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/31629/a-study-of-deep-learning-applications/anirban-chakraborty
This study used signal detection theory to examine how neuroscientists identify the default mode network compared to other prominent resting-state networks. Twenty participants were asked to distinguish the default mode network from three other networks in a rapid forced-choice task, where the networks were presented at different signal thresholds. Results showed that participants more accurately identified the default mode network when it was presented at the most stringent threshold, and made the most conservative decisions when networks were not thresholded. These findings suggest that thresholding fMRI data improves accuracy in identifying brain networks.
Literature Review on DNA based Audio Steganographic TechniquesRashmi Tank
This document summarizes 10 different techniques for DNA-based audio steganography that have been proposed in previous research papers. It discusses how DNA sequences can be used to encode secret messages due to their large storage capacity and randomness. Various methods are described that encode messages into DNA sequences and then hide the DNA sequences in audio files in an imperceptible manner using techniques like least significant bit modification. The techniques aim to provide secure transmission of secret data using the properties of DNA sequences while maintaining high audio quality after embedding.
Automated Analysis of Microscopy Images using Deep Convolutional Neural NetworkAdetayoOkunoye
This document summarizes research on using deep convolutional neural networks to automatically analyze microscopy images. The goals are to expedite the analysis of high-content microscopy data and automate tasks like cell counting and classification. The researchers trained and tested models using TensorFlow on microscopy images to classify cells, achieving over 75% accuracy. This level of automation could benefit biological research by reducing human errors and speeding up analysis of large image datasets.
This document discusses stacked convolutional neural networks as a model for imagined speech in EEG data. It outlines several challenges in studying imagined speech, including the difficulty of obtaining clean EEG data without external stimuli interfering, and the brain's complex and diffuse representation of imagination. The document argues that progress requires a robust language model that accounts for the brain's organizational structure, and that understanding imagined speech could have applications like restoring communication for paralyzed patients.
This document provides an overview of the applications of error-control coding over the past 50 years. It begins by discussing early applications in deep space communications, where coding provided significant power savings. The first coding scheme used for deep space, known as the Mariner code, achieved a power gain of 3.2 dB over uncoded BPSK but required higher bandwidth. More advanced codes were later developed that achieved gains close to the theoretical limits. The document then discusses how coding has been widely used in other areas such as satellite communications, data transmission, data storage, mobile communications and more to improve performance.
Semantic Annotation of the Cyttron DatabaseDavid Graus
Final Presentation for my MSc Graduation Project.
Abstract:
"Semantic annotation uses human knowledge formalized in ontologies to enrich texts, by providing structured and machine-understandable information of its content. This paper proposes an approach for automatically annotating texts of the Cyttron Scientific Image Database, using the NCI Thesaurus ontology. Several frequency-based keyword extraction algorithms were implemented and evaluated, aiming to extract important concepts and exclude less relevant ones. Furthermore, topic classification algorithms were applied to identify important concepts which do not occur in the text. The algorithms were evaluated by comparison to annotations provided by experts. Semantic networks were generated from these annotations and an ontology-based similarity metric was applied to perform the comparison. Finally the networks were visualized to provide further insights into the differences of the semantic structure generated by humans, and the algorithms."
More information: http://graus.nu/category/thesis
The document discusses the Science Collaboration Framework (SCF) which aims to replicate biomedical web communities like Alzforum. The SCF leverages existing linked open data and uses shared ontologies to semantically annotate scientific articles. This semantic annotation allows for powerful semantic search capabilities across communities. The SCF uses text mining to suggest terms for semantic tagging and editors refine the annotations, achieving both high recall and precision.
The document describes a project to semantically annotate research papers with ACM classification categories. It discusses using cosine similarity, latent Dirichlet allocation, and a proposed model combining labeled LDA and doc2vec. The proposed model trains a supervised topic model to learn document representations that capture semantic relationships between papers and categories. The model achieved 59.31% mean average precision and 45.03% NDCG on a test dataset, demonstrating an improvement over baselines.
Semantic annotation with Pundit: Enriching the Web of ScienceFrancesca Di Donato
Agorà Final Conference: Digitizing Philosophy. Towards new paradigms and methods in editing, publishing and querying philosophical texts, Accademia Nazionale dei Lincei, Roma, 19 marzo 2014.
Semantic annotation is done through first representing words and documents in the vector space model using Word2Vec and Doc2Vec implementations, the vectors are taken as features into a classifier, trained and a model is made which can classify a document with ACM classification tree categories, with the help of Wikipedia corpus.
Project Presentation: https://youtu.be/706HJteh1xc
Project Webpage: http://rohitsakala.github.io/semanticAnnotationAcmCategories/
Source Code: https://github.com/rohitsakala/semanticAnnotationAcmCategories
References:
Quoc V. Le, and Tomas Mikolov, ''Distributed Representations of Sentences and Documents ICML", 2014
This study aims to compare the cerebrospinal fluid spaces of normal rabbits and hydrocephalus models using image reconstruction software. Both manual and automated segmentation methods were used to perform 3D reconstruction of the ventricular system in vivo and ex vivo. The goal is to reveal the normal and hydrocephalus subarachnoid spaces using these software applications to improve hydrocephalus treatment. Imaging modalities like MRI and 3D angiography were used along with image reconstruction software to analyze hydrocephalus. There are still challenges to address regarding small animal ex vivo MRI acquisition and tissue preparation.
USING SINGULAR VALUE DECOMPOSITION IN A CONVOLUTIONAL NEURAL NETWORK TO IMPRO...ijcsit
A brain tumor consists of cells showing abnormal brain growth. The area of the brain tumor significantly
affects choosing the type of treatment and following the course of the disease during the treatment. At the
same time, pictures of Brain MRIs are accompanied by noise. Eliminating existing noises can significantly
impact the better segmentation and diagnosis of brain tumors. In this work, we have tried using the
analysis of eigenvalues. We have used the MSVD algorithm, reducing the image noise and then using the
deep neural network to segment the tumor in the images. The proposed method's accuracy was increased
by 2.4% compared to using the original images. With Using the MSVD method, convergence speed has
also increased, showing the proposed method's effectiveness.
The document discusses using singular value decomposition (SVD) to reduce noise in MRI images before using a convolutional neural network (CNN) for brain tumor segmentation. SVD is applied using multiresolution SVD (MSVD) to decompose images into sub-bands and remove noise from high-frequency sub-bands. A U-Net CNN is then used to segment tumors. Results found MSVD improved segmentation accuracy by 2.4% over original images and increased CNN convergence speed. The proposed method effectively combined MSVD denoising with CNN segmentation for improved and faster brain tumor detection.
This seminar report summarizes multimodal deep learning. It was submitted by Sangeetha Mathew to fulfill requirements for a Bachelor of Technology degree in computer science and engineering. The report was guided by Ms. Divya Madhu of the computer science department at Muthoot Institute of Technology and Science. The report provides an overview of traditional models like SVM and LDA for multimodal data classification. It then discusses various deep learning architectures for multimodal data, including restricted Boltzmann machines, deep belief networks, deep Boltzmann machines, and deep autoencoders. The report focuses on these energy-based probabilistic graphical models and their use of restricted Boltzmann machines as building blocks for multimodal feature learning.
from pq import from search import class InformedNode(NoJeanmarieColbert3
from pq import *
from search import *
class InformedNode(Node):
"""
Added the goal state as a parameter to the constructor. Also
added a new method to be used in conjunction with a priority
queue.
"""
def __init__(self, goal, state, parent, operator, depth):
Node.__init__(self, state, parent, operator, depth)
self.goal = goal
def priority(self):
"""
Needed to determine where the node should be placed in the
priority queue. Depends on the current depth of the node as
well as the estimate of the distance from the current state to
the goal state.
"""
return self.depth + self.state.heuristic(self.goal)
class InformedSearch(Search):
"""
A general informed search class that uses a priority queue and
traverses a search tree containing instances of the InformedNode
class. The problem domain should be based on the
InformedProblemState class.
"""
def __init__(self, initialState, goalState):
self.expansions = 0
self.clearVisitedStates()
self.q = PriorityQueue()
self.goalState = goalState
self.q.enqueue(InformedNode(goalState, initialState, None, None, 0))
solution = self.execute()
if solution == None:
print("Search failed")
else:
self.showPath(solution)
print("Expanded", self.expansions, "nodes during search")
def execute(self):
while not self.q.empty():
current = self.q.dequeue()
self.expansions += 1
if self.goalState.equals(current.state):
return current
else:
successors = current.state.applyOperators()
operators = current.state.operatorNames()
for i in range(len(successors)):
if not successors[i].illegal():
n = InformedNode(self.goalState,
successors[i],
current,
operators[i],
current.depth+1)
if n.repeatedState():
del(n)
else:
self.q.enqueue(n)
return None
class InformedProblemState(ProblemState):
"""
An interface class for problem domains used with informed search.
"""
def heuristic(self, goal):
"""
For use with informed search. Returns the estimated
cost of reaching the goal from this state.
"""
abstract()
Read and complete the activities in Module 4.04. You will look at the advantages and disadvantages of using various types of media to communicate your ideas:
1. What does the term "medium" mean when used in the text?
2. What are the tw ...
From pq import from search import class informed node(nojoney4
The document describes an interactive presentation and text article about DNA fingerprinting. The interactive uses visuals and audio to convey information over multiple slides, while the text provides more in-depth details in paragraphs. Both aim to educate about DNA analysis techniques used in forensics and other fields, but the interactive engages learners through multiple mediums whereas the text requires sustained reading. The document analyzes the advantages and disadvantages of each format for communicating ideas.
Image Steganography Technique By Using Braille Method of Blind People (LSBrai...CSCJournals
The document proposes a new image steganography method called LSBraille that represents secret message characters using the 6-dot Braille coding system. This allows more secret data to be hidden in images compared to traditional LSB encoding that uses 8-bit ASCII codes. The method constructs a table to represent each of the 63 Braille characters as a 6-bit binary value. Experimental results show the proposed method achieves higher visual quality than LSB as measured by peak signal-to-noise ratio despite embedding more secret bits in the cover image.
Brain Image Segmentation Methods using Image Processing Techniques to Analysi...IIRindia
Attention Deficit Hyperactivity Disorder (ADHD) is a neurological state that involves problems in inattention, hyperactivity and impulsivity that are developed inconsistent with the age. ADHD may occur due to brain disorder namely Brain injury, Brain damage and Brain abnormalities. Brain injury is a more expressive term than “Head Injury” in which Caudate nucleus will be affected. The abnormality of Caudate nucleus is to be found by its size and volume. The grey and white matter of brain also is abnormal due to brain damage. The main aim to detect and diagnose ADHD depends on the parts of the brain. By means of efficient Brain segmentation techniques, it can be easily identified. So, in this paper, to extract the brain parts various brain segmentation techniques are surveyed and discussed. A simple thresholding technique is proposed to extract Gray and white matter as well as “Active contour with region based Techniques”is implemented to extract the Caudate nucleus portion. The experimental results of various images are examined and discussed.
Brain Image Segmentation Methods using Image Processing Techniques to Analysi...IIRindia
Attention Deficit Hyperactivity Disorder (ADHD) is a neurological state that involves problems in inattention, hyperactivity and impulsivity that are developed inconsistent with the age. ADHD may occur due to brain disorder namely Brain injury, Brain damage and Brain abnormalities. Brain injury is a more expressive term than “Head Injury” in which Caudate nucleus will be affected. The abnormality of Caudate nucleus is to be found by its size and volume. The grey and white matter of brain also is abnormal due to brain damage. The main aim to detect and diagnose ADHD depends on the parts of the brain. By means of efficient Brain segmentation techniques, it can be easily identified. So, in this paper, to extract the brain parts various brain segmentation techniques are surveyed and discussed. A simple thresholding technique is proposed to extract Gray and white matter as well as “Active contour with region based Techniques” is implemented to extract the Caudate nucleus portion. The experimental results of various images are examined and discussed.
This study investigated differences in brain structural connectivity and the functional default mode network between deaf and hearing individuals using MRI. Results found increased activation in the posterior cingulate cortex, precuneus, and medial temporal lobes in the deaf group's default mode network. Analysis of structural connectivity found differences in node degree and fiber density in these areas and the motor cortex for the deaf group, suggesting neuronal plasticity related to sign language processing. Preliminary results provide new insights into brain network adaptations related to deafness and sign language use.
High Resolution Mri Brain Image Segmentation Technique Using Holder Exponent ijsc
Image segmentation is a technique to locate certain objects or boundaries within an image. Image segmentation plays a crucial role in many medical imaging applications. There are many algorithms and techniques have been developed to solve image segmentation problems. Spectral pattern is not sufficient in high resolution image for image segmentation due to variability of spectral and structural information. Thus the spatial pattern or texture techniques are used. Thus the concept of Holder Exponent for segmentation of high resolution medical image is an efficient image segmentation technique. The proposed method is implemented in Matlab and verified using various kinds of high resolution medical images. The experimental results shows that the proposed image segmentation system is efficient than the existing segmentation systems.
HIGH RESOLUTION MRI BRAIN IMAGE SEGMENTATION TECHNIQUE USING HOLDER EXPONENTijsc
Image segmentation is a technique to locate certain objects or boundaries within an image. Image
segmentation plays a crucial role in many medical imaging applications. There are many algorithms and
techniques have been developed to solve image segmentation problems. Spectral pattern is not sufficient in
high resolution image for image segmentation due to variability of spectral and structural information.
Thus the spatial pattern or texture techniques are used. Thus the concept of Holder Exponent for
segmentation of high resolution medical image is an efficient image segmentation technique. The proposed
method is implemented in Matlab and verified using various kinds of high resolution medical images. The
experimental results shows that the proposed image segmentation system is efficient than the existing
segmentation systems.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
Segmentation and Classification of Brain MRI Images Using Improved Logismos-B...IJERA Editor
Automated reconstruction and diagnosis of brain MRI images is one of the most challenging problems in medical imaging. Accurate segmentation of MRI images is a key step in contouring during radiotherapy analysis. Computed tomography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis and treatment planning. Segmentation techniques used for the brain Magnetic Resonance Imaging (MRI) is one of the methods used by the radiographer to detect any abnormality specifically in brain. The method also identifies important regions in brain such as white matter (WM), gray matter (GM) and cerebrospinal fluid spaces (CSF). These regions are significant for physician or radiographer to analyze and diagnose the disease. We propose a novel clustering algorithm, improved LOGISMOS-B to classify tissue regions based on probabilistic tissue classification, generalized gradient vector flows with cost and distance function. The LOGISMOS graph segmentation framework. Expand the framework to allow regionally-aware graph construction and segmentation.
Visual character n grams for classification and retrieval of radiological imagesijma
Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases
would help the inexperienced radiologist in the interpretation process. Character n-gram model has been
effective in text retrieval context in languages such as Chinese where there are no clear word boundaries.
We propose the use of visual character n-gram model for representation of image for classification and
retrieval purposes. Regions of interests in mammographic images are represented with the character ngram
features. These features are then used as input to back-propagation neural network for classification
of regions into normal and abnormal categories. Experiments on miniMIAS database show that character
n-gram features are useful in classifying the regions into normal and abnormal categories. Promising
classification accuracies are observed (83.33%) for fatty background tissue warranting further
investigation. We argue that Classifying regions of interests would reduce the number of comparisons
necessary for finding similar images from the database and hence would reduce the time required for
retrieval of past similar cases.
Visual Character N-Grams for Classification and Retrieval of Radiological Imagesijma
Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases
would help the inexperienced radiologist in the interpretation process. Character n-gram model has been
effective in text retrieval context in languages such as Chinese where there are no clear word boundaries.
We propose the use of visual character n-gram model for representation of image for classification and
retrieval purposes. Regions of interests in mammographic images are represented with the character ngram features. These features are then used as input to back-propagation neural network for classification
of regions into normal and abnormal categories. Experiments on miniMIAS database show that character
n-gram features are useful in classifying the regions into normal and abnormal categories. Promising
classification accuracies are observed (83.33%) for fatty background tissue warranting further
investigation. We argue that Classifying regions of interests would reduce the number of comparisons
necessary for finding similar images from the database and hence would reduce the time required for
retrieval of past similar cases
Semantic Annotation of the Cyttron DatabaseDavid Graus
Final Presentation for my MSc Graduation Project.
Abstract:
"Semantic annotation uses human knowledge formalized in ontologies to enrich texts, by providing structured and machine-understandable information of its content. This paper proposes an approach for automatically annotating texts of the Cyttron Scientific Image Database, using the NCI Thesaurus ontology. Several frequency-based keyword extraction algorithms were implemented and evaluated, aiming to extract important concepts and exclude less relevant ones. Furthermore, topic classification algorithms were applied to identify important concepts which do not occur in the text. The algorithms were evaluated by comparison to annotations provided by experts. Semantic networks were generated from these annotations and an ontology-based similarity metric was applied to perform the comparison. Finally the networks were visualized to provide further insights into the differences of the semantic structure generated by humans, and the algorithms."
More information: http://graus.nu/category/thesis
The document discusses the Science Collaboration Framework (SCF) which aims to replicate biomedical web communities like Alzforum. The SCF leverages existing linked open data and uses shared ontologies to semantically annotate scientific articles. This semantic annotation allows for powerful semantic search capabilities across communities. The SCF uses text mining to suggest terms for semantic tagging and editors refine the annotations, achieving both high recall and precision.
The document describes a project to semantically annotate research papers with ACM classification categories. It discusses using cosine similarity, latent Dirichlet allocation, and a proposed model combining labeled LDA and doc2vec. The proposed model trains a supervised topic model to learn document representations that capture semantic relationships between papers and categories. The model achieved 59.31% mean average precision and 45.03% NDCG on a test dataset, demonstrating an improvement over baselines.
Semantic annotation with Pundit: Enriching the Web of ScienceFrancesca Di Donato
Agorà Final Conference: Digitizing Philosophy. Towards new paradigms and methods in editing, publishing and querying philosophical texts, Accademia Nazionale dei Lincei, Roma, 19 marzo 2014.
Semantic annotation is done through first representing words and documents in the vector space model using Word2Vec and Doc2Vec implementations, the vectors are taken as features into a classifier, trained and a model is made which can classify a document with ACM classification tree categories, with the help of Wikipedia corpus.
Project Presentation: https://youtu.be/706HJteh1xc
Project Webpage: http://rohitsakala.github.io/semanticAnnotationAcmCategories/
Source Code: https://github.com/rohitsakala/semanticAnnotationAcmCategories
References:
Quoc V. Le, and Tomas Mikolov, ''Distributed Representations of Sentences and Documents ICML", 2014
This study aims to compare the cerebrospinal fluid spaces of normal rabbits and hydrocephalus models using image reconstruction software. Both manual and automated segmentation methods were used to perform 3D reconstruction of the ventricular system in vivo and ex vivo. The goal is to reveal the normal and hydrocephalus subarachnoid spaces using these software applications to improve hydrocephalus treatment. Imaging modalities like MRI and 3D angiography were used along with image reconstruction software to analyze hydrocephalus. There are still challenges to address regarding small animal ex vivo MRI acquisition and tissue preparation.
USING SINGULAR VALUE DECOMPOSITION IN A CONVOLUTIONAL NEURAL NETWORK TO IMPRO...ijcsit
A brain tumor consists of cells showing abnormal brain growth. The area of the brain tumor significantly
affects choosing the type of treatment and following the course of the disease during the treatment. At the
same time, pictures of Brain MRIs are accompanied by noise. Eliminating existing noises can significantly
impact the better segmentation and diagnosis of brain tumors. In this work, we have tried using the
analysis of eigenvalues. We have used the MSVD algorithm, reducing the image noise and then using the
deep neural network to segment the tumor in the images. The proposed method's accuracy was increased
by 2.4% compared to using the original images. With Using the MSVD method, convergence speed has
also increased, showing the proposed method's effectiveness.
The document discusses using singular value decomposition (SVD) to reduce noise in MRI images before using a convolutional neural network (CNN) for brain tumor segmentation. SVD is applied using multiresolution SVD (MSVD) to decompose images into sub-bands and remove noise from high-frequency sub-bands. A U-Net CNN is then used to segment tumors. Results found MSVD improved segmentation accuracy by 2.4% over original images and increased CNN convergence speed. The proposed method effectively combined MSVD denoising with CNN segmentation for improved and faster brain tumor detection.
This seminar report summarizes multimodal deep learning. It was submitted by Sangeetha Mathew to fulfill requirements for a Bachelor of Technology degree in computer science and engineering. The report was guided by Ms. Divya Madhu of the computer science department at Muthoot Institute of Technology and Science. The report provides an overview of traditional models like SVM and LDA for multimodal data classification. It then discusses various deep learning architectures for multimodal data, including restricted Boltzmann machines, deep belief networks, deep Boltzmann machines, and deep autoencoders. The report focuses on these energy-based probabilistic graphical models and their use of restricted Boltzmann machines as building blocks for multimodal feature learning.
from pq import from search import class InformedNode(NoJeanmarieColbert3
from pq import *
from search import *
class InformedNode(Node):
"""
Added the goal state as a parameter to the constructor. Also
added a new method to be used in conjunction with a priority
queue.
"""
def __init__(self, goal, state, parent, operator, depth):
Node.__init__(self, state, parent, operator, depth)
self.goal = goal
def priority(self):
"""
Needed to determine where the node should be placed in the
priority queue. Depends on the current depth of the node as
well as the estimate of the distance from the current state to
the goal state.
"""
return self.depth + self.state.heuristic(self.goal)
class InformedSearch(Search):
"""
A general informed search class that uses a priority queue and
traverses a search tree containing instances of the InformedNode
class. The problem domain should be based on the
InformedProblemState class.
"""
def __init__(self, initialState, goalState):
self.expansions = 0
self.clearVisitedStates()
self.q = PriorityQueue()
self.goalState = goalState
self.q.enqueue(InformedNode(goalState, initialState, None, None, 0))
solution = self.execute()
if solution == None:
print("Search failed")
else:
self.showPath(solution)
print("Expanded", self.expansions, "nodes during search")
def execute(self):
while not self.q.empty():
current = self.q.dequeue()
self.expansions += 1
if self.goalState.equals(current.state):
return current
else:
successors = current.state.applyOperators()
operators = current.state.operatorNames()
for i in range(len(successors)):
if not successors[i].illegal():
n = InformedNode(self.goalState,
successors[i],
current,
operators[i],
current.depth+1)
if n.repeatedState():
del(n)
else:
self.q.enqueue(n)
return None
class InformedProblemState(ProblemState):
"""
An interface class for problem domains used with informed search.
"""
def heuristic(self, goal):
"""
For use with informed search. Returns the estimated
cost of reaching the goal from this state.
"""
abstract()
Read and complete the activities in Module 4.04. You will look at the advantages and disadvantages of using various types of media to communicate your ideas:
1. What does the term "medium" mean when used in the text?
2. What are the tw ...
From pq import from search import class informed node(nojoney4
The document describes an interactive presentation and text article about DNA fingerprinting. The interactive uses visuals and audio to convey information over multiple slides, while the text provides more in-depth details in paragraphs. Both aim to educate about DNA analysis techniques used in forensics and other fields, but the interactive engages learners through multiple mediums whereas the text requires sustained reading. The document analyzes the advantages and disadvantages of each format for communicating ideas.
Image Steganography Technique By Using Braille Method of Blind People (LSBrai...CSCJournals
The document proposes a new image steganography method called LSBraille that represents secret message characters using the 6-dot Braille coding system. This allows more secret data to be hidden in images compared to traditional LSB encoding that uses 8-bit ASCII codes. The method constructs a table to represent each of the 63 Braille characters as a 6-bit binary value. Experimental results show the proposed method achieves higher visual quality than LSB as measured by peak signal-to-noise ratio despite embedding more secret bits in the cover image.
Brain Image Segmentation Methods using Image Processing Techniques to Analysi...IIRindia
Attention Deficit Hyperactivity Disorder (ADHD) is a neurological state that involves problems in inattention, hyperactivity and impulsivity that are developed inconsistent with the age. ADHD may occur due to brain disorder namely Brain injury, Brain damage and Brain abnormalities. Brain injury is a more expressive term than “Head Injury” in which Caudate nucleus will be affected. The abnormality of Caudate nucleus is to be found by its size and volume. The grey and white matter of brain also is abnormal due to brain damage. The main aim to detect and diagnose ADHD depends on the parts of the brain. By means of efficient Brain segmentation techniques, it can be easily identified. So, in this paper, to extract the brain parts various brain segmentation techniques are surveyed and discussed. A simple thresholding technique is proposed to extract Gray and white matter as well as “Active contour with region based Techniques”is implemented to extract the Caudate nucleus portion. The experimental results of various images are examined and discussed.
Brain Image Segmentation Methods using Image Processing Techniques to Analysi...IIRindia
Attention Deficit Hyperactivity Disorder (ADHD) is a neurological state that involves problems in inattention, hyperactivity and impulsivity that are developed inconsistent with the age. ADHD may occur due to brain disorder namely Brain injury, Brain damage and Brain abnormalities. Brain injury is a more expressive term than “Head Injury” in which Caudate nucleus will be affected. The abnormality of Caudate nucleus is to be found by its size and volume. The grey and white matter of brain also is abnormal due to brain damage. The main aim to detect and diagnose ADHD depends on the parts of the brain. By means of efficient Brain segmentation techniques, it can be easily identified. So, in this paper, to extract the brain parts various brain segmentation techniques are surveyed and discussed. A simple thresholding technique is proposed to extract Gray and white matter as well as “Active contour with region based Techniques” is implemented to extract the Caudate nucleus portion. The experimental results of various images are examined and discussed.
This study investigated differences in brain structural connectivity and the functional default mode network between deaf and hearing individuals using MRI. Results found increased activation in the posterior cingulate cortex, precuneus, and medial temporal lobes in the deaf group's default mode network. Analysis of structural connectivity found differences in node degree and fiber density in these areas and the motor cortex for the deaf group, suggesting neuronal plasticity related to sign language processing. Preliminary results provide new insights into brain network adaptations related to deafness and sign language use.
High Resolution Mri Brain Image Segmentation Technique Using Holder Exponent ijsc
Image segmentation is a technique to locate certain objects or boundaries within an image. Image segmentation plays a crucial role in many medical imaging applications. There are many algorithms and techniques have been developed to solve image segmentation problems. Spectral pattern is not sufficient in high resolution image for image segmentation due to variability of spectral and structural information. Thus the spatial pattern or texture techniques are used. Thus the concept of Holder Exponent for segmentation of high resolution medical image is an efficient image segmentation technique. The proposed method is implemented in Matlab and verified using various kinds of high resolution medical images. The experimental results shows that the proposed image segmentation system is efficient than the existing segmentation systems.
HIGH RESOLUTION MRI BRAIN IMAGE SEGMENTATION TECHNIQUE USING HOLDER EXPONENTijsc
Image segmentation is a technique to locate certain objects or boundaries within an image. Image
segmentation plays a crucial role in many medical imaging applications. There are many algorithms and
techniques have been developed to solve image segmentation problems. Spectral pattern is not sufficient in
high resolution image for image segmentation due to variability of spectral and structural information.
Thus the spatial pattern or texture techniques are used. Thus the concept of Holder Exponent for
segmentation of high resolution medical image is an efficient image segmentation technique. The proposed
method is implemented in Matlab and verified using various kinds of high resolution medical images. The
experimental results shows that the proposed image segmentation system is efficient than the existing
segmentation systems.
International Journal of Research in Engineering and Science is an open access peer-reviewed international forum for scientists involved in research to publish quality and refereed papers. Papers reporting original research or experimentally proved review work are welcome. Papers for publication are selected through peer review to ensure originality, relevance, and readability.
Segmentation and Classification of Brain MRI Images Using Improved Logismos-B...IJERA Editor
Automated reconstruction and diagnosis of brain MRI images is one of the most challenging problems in medical imaging. Accurate segmentation of MRI images is a key step in contouring during radiotherapy analysis. Computed tomography (CT) and Magnetic resonance (MR) imaging are the most widely used radiographic techniques in diagnosis and treatment planning. Segmentation techniques used for the brain Magnetic Resonance Imaging (MRI) is one of the methods used by the radiographer to detect any abnormality specifically in brain. The method also identifies important regions in brain such as white matter (WM), gray matter (GM) and cerebrospinal fluid spaces (CSF). These regions are significant for physician or radiographer to analyze and diagnose the disease. We propose a novel clustering algorithm, improved LOGISMOS-B to classify tissue regions based on probabilistic tissue classification, generalized gradient vector flows with cost and distance function. The LOGISMOS graph segmentation framework. Expand the framework to allow regionally-aware graph construction and segmentation.
Visual character n grams for classification and retrieval of radiological imagesijma
Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases
would help the inexperienced radiologist in the interpretation process. Character n-gram model has been
effective in text retrieval context in languages such as Chinese where there are no clear word boundaries.
We propose the use of visual character n-gram model for representation of image for classification and
retrieval purposes. Regions of interests in mammographic images are represented with the character ngram
features. These features are then used as input to back-propagation neural network for classification
of regions into normal and abnormal categories. Experiments on miniMIAS database show that character
n-gram features are useful in classifying the regions into normal and abnormal categories. Promising
classification accuracies are observed (83.33%) for fatty background tissue warranting further
investigation. We argue that Classifying regions of interests would reduce the number of comparisons
necessary for finding similar images from the database and hence would reduce the time required for
retrieval of past similar cases.
Visual Character N-Grams for Classification and Retrieval of Radiological Imagesijma
Diagnostic radiology struggles to maintain high interpretation accuracy. Retrieval of past similar cases
would help the inexperienced radiologist in the interpretation process. Character n-gram model has been
effective in text retrieval context in languages such as Chinese where there are no clear word boundaries.
We propose the use of visual character n-gram model for representation of image for classification and
retrieval purposes. Regions of interests in mammographic images are represented with the character ngram features. These features are then used as input to back-propagation neural network for classification
of regions into normal and abnormal categories. Experiments on miniMIAS database show that character
n-gram features are useful in classifying the regions into normal and abnormal categories. Promising
classification accuracies are observed (83.33%) for fatty background tissue warranting further
investigation. We argue that Classifying regions of interests would reduce the number of comparisons
necessary for finding similar images from the database and hence would reduce the time required for
retrieval of past similar cases
This document summarizes a research paper that introduces a user-friendly program developed in MATLAB for early detection of Alzheimer's disease using brain MRI scans. The program provides quantitative and clinical analysis of digital medical images by identifying the cerebral cortex region of the brain and measuring the cavity area in brain folds, known as sulci. This is done using bi-cubic interpolation for more accurate results than other interpolation techniques. The paper also provides background on Alzheimer's disease, describing it as a neurodegenerative disorder causing memory loss and cognitive decline due to brain cell death. Common imaging techniques used to study the brain like MRI, CT, PET, EEG and MEG are also summarized.
This document discusses brain tumor segmentation from MRI images using fuzzy c-means clustering. It begins with an introduction to brain tumors and MRI imaging. Next, it reviews existing methods for brain tumor segmentation such as thresholding, region growing, and clustering. It then discusses preprocessing MRI images, including converting images to grayscale and filtering. Finally, it describes fuzzy c-means clustering, which is an unsupervised learning technique used to segment and classify pixels in MRI images to detect tumor regions. The goal is to develop an accurate and automated method for brain tumor segmentation to assist medical experts.
FAKE NEWS DETECTION WITH SEMANTIC FEATURES AND TEXT MININGijnlc
Nearly 70% of people are concerned about the propagation of fake news. This paper aims to detect fake news in online articles through the use of semantic features and various machine learning techniques. In this research, we investigated recurrent neural networks vs. the naive bayes classifier and random forest classifiers using five groups of linguistic features. Evaluated with real or fake dataset from kaggle.com, the best performing model achieved an accuracy of 95.66% using bigram features with the random forest classifier. The fact that bigrams outperform unigrams, trigrams, and quadgrams show that word pairs as opposed to single words or phrases best indicate the authenticity of news.
FAKE NEWS DETECTION WITH SEMANTIC FEATURES AND TEXT MININGkevig
This document summarizes a research paper that aims to detect fake news articles through machine learning techniques. It compares random forest, naive bayes, and recurrent neural network classifiers using linguistic features including n-grams and word embeddings. The best performing model was random forest with bigram features, achieving 95.66% accuracy on a real or fake news dataset. Bigrams were found to outperform other n-grams as indicators of news authenticity.
Similar to Semantic annotation, clustering and visualization (20)
Pragmatic ethical and fair AI for data scientistsDavid Graus
1. David Graus presented on pragmatic and fair AI for recruitment and news recommendations.
2. He discussed how algorithms can unintentionally learn and reflect human biases around gender and race. However, AI may also help address these biases, such as through representational ranking in recruitment to achieve demographic parity.
3. Graus also explored using editorial values like diversity, dynamism and serendipity to guide news recommendations, and found their system could increase dynamism without loss of accuracy through constrained intervention.
Slidedeck of my lecture at SIKS Course "Advances in Information Retrieval"
Read more here: https://graus.nu/blog/bias-in-recommendations-lecture-siks-course-on-advances-in-ir/
RecSys in the Media Industry: Relevance, Recency, Popularity, and Diversity.David Graus
The document summarizes research on recommender systems in the media industry. It discusses how FD Mediagroup uses recommender systems for their SMART Radio and SMART Journalism products. Key aspects of building a recommender system that FD focuses on include relevance, usefulness, and trust. Relevance is evaluated using metrics like NDCG, MAP, and R-Precision. Usefulness considers both algorithmic goals like diversity and business goals. Trust is evaluated based on whether users engage with the recommender system.
Zoeken, vinden, en aanbevelen: personalisatie vs. privacyDavid Graus
Lezing op de VOGIN-IP-lezing op 28 maart 2018 bij de Openbare Bibliotheek Amsterdam.
DISCLAIMER: dit praatje is een mooi stukje ouderwetse (menselijke) manipulatie: expert komt met een 5-tal aanbevelingen :-).
"Tegenwoordig kijkt men steeds vaker met argusogen naar technologiebedrijven die op grote schaal gebruikersgedrag verzamelen. In dit praatje zet ik uiteen waarom het inzetten van gebruikersgedrag van belang is, en hoe het wordt gebruikt om informatie effectief te kunnen ontsluiten en doorzoekbaar maken, of het nu gaat om een zoekmachine als Google, die zich een weg moet banen door een web van miljarden pagina’s, of een service als Spotify, die haar gebruikers graag de juiste muziek blijft aanbieden."
Layman's Talk: Entities of Interest --- Discovery in Digital TracesDavid Graus
The document outlines a program that includes a committee grilling a speaker at 10:00, the committee retreating afterwards, a ceremony at 10:15, and a reception downstairs from 11:00 to 12:30.
Slides of the talk I gave at PyData Amsterdam.
Abstract:
"The FD Mediagroep collects, analyses and filters valuable and relevant information, 24/7, for an influential group of professionals, business executives and high net worth individuals. Company.info (part of FDMG) provides complete, reliable, up-to-date company information and business news about no less than 2.7 million companies and other legal entities in the Netherlands. For Company.info we continuously monitor and crawl hundreds of (online) news sources, resulting in a large archive of (Dutch) business-related news, spanning hundreds of thousands of articles. These articles are automatically enriched, by linking the profiles of companies that are mentioned in the articles, using a custom in-house entity linking framework built in Python. In this talk, I will briefly explain the entity linking task, I will detail the implementation of our custom entity linking framework, and our pipeline for crawling and enriching news articles."
De Macht van Data --- Hoe algoritmen ons leven vormgevenDavid Graus
Slides of the introductory talk I gave at an event at De Balie: "De macht van data" on June 18th, 2017.
For a video recording of the talk see: http://graus.co/blog/mini-college-algoritmen/
Talk I gave at the Data Science Northeast Netherlands Meetup, where I detail the custom in-house entity linking framework, sentiment analysis, and entity salience scoring model we developed for Company.info, in addition to showing some example applications of our corpus of news articles linked to organization profiles.
Dynamic Collective Entity Representations for Entity RankingDavid Graus
This document proposes using collective intelligence to dynamically enrich entity representations from multiple sources like knowledge bases, anchors, tags, and tweets. It presents an adaptive ranking model that learns optimal weights for ranking features like field similarity and importance over time. An experiment on query logs shows expanding entities with different sources improves ranking and retraining the ranker with new content further enhances performance.
Dynamic Collective Entity Representations for Entity RankingDavid Graus
This document proposes using dynamic collective entity representations to improve entity ranking. It describes enriching static entity representations from knowledge bases with descriptions from dynamic sources like tweets, queries, and tags. An adaptive ranking model individually weights each description source and retrains over time using clicks. Experimental results show expanding representations and retraining the ranker improves ranking performance compared to a non-adaptive model, with different sources providing varying benefits depending on their dynamic nature and entity coverage.
David Graus presents his research on using semantic search techniques to improve information retrieval for digital forensic evidence from emails and other electronic documents. He discusses using social network analysis of communication patterns and language models of email content to predict likely recipients of emails. By combining these approaches, he is able to more accurately rank potential recipients than using either technique alone. Future work includes incorporating organizational structure and decay of communication patterns over time.
David Graus - Entity Linking (at SEA), Search Engines Amsterdam, Fri June 27thDavid Graus
David Graus from the University of Amsterdam gave a presentation on entity linking at the Search Engines Amsterdam conference on June 27th. He began by defining entity linking as linking mentions of entities in text to their corresponding entities in a knowledge base. He then gave an example of entity linking and discussed ranking entity candidates based on their prior probabilities like link probability and commonness. Finally, he described using both local and global features in supervised learning models to improve entity linking accuracy.
This document discusses understanding email traffic patterns through recipient recommendation. It explores using social network analysis and language models of email content to predict likely recipients of a given email. Specifically, it examines using measures of node importance in the network, strength of connections between nodes, and similarity between language models of communication profiles to rank and select recipient nodes. The findings indicate that combining social network analysis and language modeling performs better than either approach individually, and that language model similarity is most important for interpersonal communication, while network metrics are more informative for highly active users. Recipient recommendation could help with applications like anomaly detection in e-discovery.
Generating Pseudo-ground Truth for Detecting New Concepts in Social StreamsDavid Graus
The manual curation of knowledge bases is a bottleneck in fast paced domains where new concepts constantly emerge. Identification of nascent concepts is important for improving early entity linking, content interpretation, and recommendation of new content in real-time applications. We present an unsupervised method for generating pseudo-ground truth for training a named entity recognizer to specifically identify entities that will become concepts in a knowledge base in the setting of social streams. We show that our method is able to deal with missing labels, justifying the use of pseudo-ground truth generation in this task. Finally, we show how our method significantly outperforms a lexical-matching baseline, by leveraging strategies for sampling pseudo-ground truth based on entity confidence scores and textual quality of input documents.
yourHistory - entity linking for a personalized timeline of historic eventsDavid Graus
The document describes an entity linking approach to generate a personalized timeline of historic events for a user. It involves 4 main parts: (1) fetching candidate historic events from DBpedia, (2) generating a user profile based on information extracted from the user's Facebook profile, (3) matching the candidate events to the user's interests in their profile, and (4) scoring and ranking the events to produce the final personalized timeline. The approach uses entity linking techniques to associate mentions of entities in the user's profile with the corresponding entries in a knowledge base, in order to identify the user's interests.
This document discusses research on applying text mining and information retrieval techniques for fact finding in regulatory investigations from electronic documents. The researchers are developing methods for semantic search in e-discovery to iteratively retrieve relevant evidence from emails, forums, and other sources by integrating structural context and extracting knowledge from unstructured text. Their current work includes using Twitter mining as a form of conversational search and entity linking to semantically enrich documents.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
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Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
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GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Freshworks Rethinks NoSQL for Rapid Scaling & Cost-EfficiencyScyllaDB
Freshworks creates AI-boosted business software that helps employees work more efficiently and effectively. Managing data across multiple RDBMS and NoSQL databases was already a challenge at their current scale. To prepare for 10X growth, they knew it was time to rethink their database strategy. Learn how they architected a solution that would simplify scaling while keeping costs under control.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
FREE A4 Cyber Security Awareness Posters-Social Engineering part 3Data Hops
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Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
1. Semantic annotation, clustering
and visualization
Media Technology Msc Programme
David Graus Graduation Project
Supervisor: Joris Slob
2. David Graus Media Technology Msc Programme 07/02/2012
Introduction
3. David Graus Media Technology Msc Programme 07/02/2012
Cyttron DB entry
"The volume of the brain evaluated in this study. The
color scale represents the number of 4-mm voxels with
data in at least 7 subjects along a 3-cm deep line into
the brain. A three-dimensional rendering of a brain is
shown in regions where insufficient data were
obtained. The most superior regions of the frontal and
parietal lobes and the most inferior regions of the
temporal lobes were not evaluated. Imaging artifacts
may also compromise the significance of results in the
most inferior portions of the frontal lobe."
4. David Graus Media Technology Msc Programme 07/02/2012
Tasks
1. Semantic annotation
Identify and tag most important concepts from text [NLP]
2. Topic extraction
Relate concepts and find clusters [Linked Data]
3. Visualization
Draw resulting graphs and clusters [Datavisualization]
5. David Graus Media Technology Msc Programme 07/02/2012
1. Semantic Annotation
Method I: Find words Method II: Compare texts
6. David Graus Media Technology Msc Programme 07/02/2012
Semantic Annotation: Method I
"The volume of the brain evaluated in this study. The
color scale represents the number of 4-mm voxels with
data in at least 7 subjects along a 3-cm deep line into
the brain. A three-dimensional rendering of a brain is
shown in regions where insufficient data were
obtained. The most superior regions of the frontal and
parietal lobes and the most inferior regions of the
temporal lobes were not evaluated. Imaging artifacts
may also compromise the significance of results in the
most inferior portions of the frontal lobe."
7. David Graus Media Technology Msc Programme 07/02/2012
Formal knowledge: Biomedical Ontology
8. David Graus Media Technology Msc Programme 07/02/2012
NCI Thesaurus
89.129 unique concepts
50.804 definitions
258.051 synonyms
Relations!
Concept Agrobacterium tumefaciens
Definition A species of Gram negative, rod shaped bacteria
assigned to the phylum Proteobacteria. This
bacteria is motile by flagella and mediates the
horizontal gene transfer of its Ti plasmid to
infect plants. A. tumefaciens is commonly found
in soil and around the root surfaces of plants
and is the causative agent of crown gall disease.
Synonyms RHIZOBIUM RADIOBACTER
CDC GROUP VD-3
9. David Graus Media Technology Msc Programme 07/02/2012
Semantic Annotation: Method I
"The volume of the brain evaluated in this study. The
color scale represents the number of 4-mm voxels with
data in at least 7 subjects along a 3-cm deep line into
the brain. A three-dimensional rendering of a brain is
shown in regions where insufficient data were
obtained. The most superior regions of the frontal and
parietal lobes and the most inferior regions of the
temporal lobes were not evaluated. Imaging artifacts
may also compromise the significance of results in the
most inferior portions of the frontal lobe."
10. David Graus Media Technology Msc Programme 07/02/2012
Semantic Annotation: Method I
"The volume of the brain evaluated in this study. The
color scale represents the number of 4-mm voxels with
data in at least 7 subjects along a 3-cm deep line into
the brain. A three-dimensional rendering of a brain is
shown in regions where insufficient data were
obtained. The most superior regions of the frontal and
parietal lobes and the most inferior regions of the
temporal lobes were not evaluated. Imaging artifacts
may also compromise the significance of results in the
most inferior portions of the frontal lobe."
11. David Graus Media Technology Msc Programme 07/02/2012
Semantic Annotation: Method I
"The volume of the brain evaluated in this study. The
color scale represents the number of 4-mm voxels with
data in at least 7 subjects along a 3-cm deep line into
the brain. A three-dimensional rendering of a brain is
shown in regions where insufficient data were
obtained. The most superior regions of the frontal and
parietal lobes and the most inferior regions of the
temporal lobes were not evaluated. Imaging artifacts
may also compromise the significance of results in the
most inferior portions of the frontal lobe."
12. David Graus Media Technology Msc Programme 07/02/2012
Example
"The volume of the brain evaluated in this study. The color scale represents the number of
4-mm voxels with data in at least 7 subjects along a 3-cm deep line into the brain. A three-
dimensional rendering of a brain is shown in regions where insufficient data were obtained.
The most superior regions of the frontal and parietal lobes and the most inferior regions of
the temporal lobes were not evaluated. Imaging artifacts may also compromise the
significance of results in the most inferior portions of the frontal lobe."
Most, Brain, A, Inferior, Data, And, With, Volume, Volume, Three,
Temporal, Superior, Study, Scale, Parietal, Number, Lobe, Line, Into,
Frontal Lobe, Deep, Color, At
13. David Graus Media Technology Msc Programme 07/02/2012
Example
"The volume of the brain evaluated in this study. The color scale represents the number of
4-mm voxels with data in at least 7 subjects along a 3-cm deep line into the brain. A three-
dimensional rendering of a brain is shown in regions where insufficient data were obtained.
The most superior regions of the frontal and parietal lobes and the most inferior regions of
the temporal lobes were not evaluated. Imaging artifacts may also compromise the
significance of results in the most inferior portions of the frontal lobe."
14. David Graus Media Technology Msc Programme 07/02/2012
Semantic Annotation: Method I
2 ‘Modifiers’ of representations:
1. (Porter) Stemming (text & ontologyconcepts)
Lobes – lobe
Brains – brain
Etc…
2. Generate synonyms (using WordNet)
15. David Graus Media Technology Msc Programme 07/02/2012
Different text representations
Most frequent 'brain, regions, data, evaluated, frontal, inferior, lobes, along, also, artifacts,
words
color, compromise, deep, dimensional, imaging, insufficient, least, line, lobe‘
Most frequent 'brain, color, deep, imaging, insufficient, line, lobe, number, rendering, scale,
nouns
significance, study, volume‘
Bigrams 'also compromise, artifacts may, cm deep, color scale, compromise significance,
deep line, dimensional rendering, imaging artifacts, may also, mm voxels,
represents number, scale represents, significance results, subjects along, data
least, data obtained, evaluated study, frontal lobe, frontal parietal, inferior
portions‘
Trigrams 'also compromise significance, artifacts may also, cm deep line, color scale
represents, compromise significance results, imaging artifacts may, may also
compromise, scale represents number, insufficient data obtained, mm voxels
data, portions frontal lobe, […]
Combo 'brain, regions, data, evaluated, frontal, inferior, lobes, along, also, artifacts,
color, compromise, deep, dimensional, imaging, insufficient, least, line, lobe.
brain, color, deep, imaging, insufficient, […]
16. David Graus Media Technology Msc Programme 07/02/2012
Semantic Annotation: Method I
6 Representations (literal + 5 keyword variations)
4 Treatments (literal + stem + synonyms + both)
24 results
17. David Graus Media Technology Msc Programme 07/02/2012
Method II: Text Comparison
Find concepts that might not occur in text
"The volume of the brain evaluated in
this study. The color scale represents the
number of 4-mm voxels with data in at
least 7 subjects along a 3-cm deep line
into the brain. A three-dimensional
rendering of a brain is shown in regions
where insufficient data were obtained.
The most superior regions of the frontal
and parietal lobes and the most inferior
regions of the temporal lobes were not
evaluated. Imaging artifacts may also
compromise the significance of results
in the most inferior portions of the
frontal lobe."
18. David Graus Media Technology Msc Programme 07/02/2012
Compare text to definitions
Find relevant concepts based on their (textual) definitions
"The volume of the
brain evaluated in
this study. The
color scale
represents the Parietal Lobe: One
number of 4-mm of the lobes of the
voxels with data in cerebral hemisphere
at least 7 subjects located superiorly to
along a 3-cm deep the occipital lobe and
line into the brain. posteriorly to the
A three-dim frontal lobe.
Cognition and
visuospatial
processing are its
Cyttron entry main functions.
NCI Thesaurus
definitions
19. David Graus Media Technology Msc Programme 07/02/2012
Method II: Text Comparison
Find concepts that might not occur in text
"The volume of the brain evaluated in
this study. The color scale represents the
number of 4-mm voxels with data in at
least 7 subjects along a 3-cm deep line
into the brain. A three-dimensional
rendering of a brain is shown in regions
where insufficient data were obtained.
The most superior regions of the frontal
and parietal lobes and the most inferior
regions of the temporal lobes were not
evaluated. Imaging artifacts may also
compromise the significance of results
in the most inferior portions of the
frontal lobe."
20. David Graus Media Technology Msc Programme 07/02/2012
Compare how?
Bag of Words + TF-IDF
Dictionary: BioMedCentral Corpus
> 100.000 articles
> 8GB raw data
Process Corpus
Clean (strip tags, store only article body)
Tokenize (create list of words)
Remove common words (stopwords)
Stem remaining words
21. David Graus Media Technology Msc Programme 07/02/2012
Method II: Text Comparison
Convert both texts to vector space using dictionary, compute similarity.
Return most similar concepts.
"The volume of the brain evaluated in this study. 1. Frontotemporal Dementia
The color scale represents the number of 4-mm
voxels with data in at least 7 subjects along a 3-
2. Parietal Lobe
cm deep line into the brain. A three-dimensional 3. Area of Broca
rendering of a brain is shown in regions where 4. Anterior Cranial Fossa
insufficient data were obtained. The most
superior regions of the frontal and parietal lobes 5. Brain Lobectomy
and the most inferior regions of the temporal 6. Anterior Parietal Artery
lobes were not evaluated. Imaging artifacts may
also compromise the significance of results in the
7. Mammary Gland
most inferior portions of the frontal lobe." 8. Frontal Lobe
9. Interlobar
10. Lobar
22. David Graus Media Technology Msc Programme 07/02/2012
Method II: Text Comparison
Different cut-off rules:
1. Anything over x% similar
2. 5 most similar
3. 10 most similar
4. 20% most similar
5. 10% most similar
23. David Graus Media Technology Msc Programme 07/02/2012
Result
Long list of (linked) concepts
Relevancy?
24. David Graus Media Technology Msc Programme 07/02/2012
Find clusters
Measure semantic similarity between concepts
- Shortest paths
- Shared parents
- Node’s ‘depth’
25. David Graus Media Technology Msc Programme 07/02/2012
26. David Graus Media Technology Msc Programme 07/02/2012
To do
Get data!
Analyse algorithms
Editor's Notes
So these are the 10 most similar concepts returned
Example of a connectedgraph.I want to explore the possibilities of visualizing the results, withvarying node (circle) sizesfor more and less important concepts.Colored and transparant circlesforliteral and non-literalconcepts.Conveying the information from the text in a graph.This might also help with analyzing the differences of my method vs. that of humans.