Tom M. Mitchell, a professor of computer science at Carnegie Mellon University, will give a talk on December 9th at 1170 TMCB at 11:00 AM. He will discuss his research using machine learning and brain imaging to study cognitive processes. Specifically, he will cover how his team has trained machine learning classifiers to distinguish between cognitive subprocesses based on fMRI data, such as reading words about tools versus buildings. He will also discuss more complex models that can track multiple overlapping cognitive processes in the brain.
USING THE MANDELBROT SET TO GENERATE PRIMARY POPULATIONS IN THE GENETIC ALGOR...csandit
Nowadays, finding a way to secure media is common with the growth of digital media. An
effective method for the secure transmission of images can be found in the field of visual
cryptography. There is a growing interest in the use of visual cryptography in security
application. Since this method is used for secure transmission of images, many of the methods
are developed based on the original algorithm proposed by Naor and Shamir in 1994. In this
paper, a new hybrid model is used in cryptography of images which is composed of Mandelbrot
algorithm and genetic algorithm. In the early stages of proposal, a number of encrypted images
are made by using the Mandelbrot algorithm and the original picture and in the next stage,
these encrypted images are used as the initial population for the genetic algorithm. At each
stage of the genetic algorithm, the answer of previous iterations is optimized to get the best
encoding image. Also, in the proposed method, we can achieve the decoded image by a reverse
operation from the genetic algorithm. The best encrypted image is an image with high entropy
and low correlation coefficient. According to the entropy and correlation coefficient of the
proposed method compared with existing methods, it is observed that our method gets better
results in both of them.
USING THE MANDELBROT SET TO GENERATE PRIMARY POPULATIONS IN THE GENETIC ALGOR...csandit
Nowadays, finding a way to secure media is common with the growth of digital media. An
effective method for the secure transmission of images can be found in the field of visual
cryptography. There is a growing interest in the use of visual cryptography in security
application. Since this method is used for secure transmission of images, many of the methods
are developed based on the original algorithm proposed by Naor and Shamir in 1994. In this
paper, a new hybrid model is used in cryptography of images which is composed of Mandelbrot
algorithm and genetic algorithm. In the early stages of proposal, a number of encrypted images
are made by using the Mandelbrot algorithm and the original picture and in the next stage,
these encrypted images are used as the initial population for the genetic algorithm. At each
stage of the genetic algorithm, the answer of previous iterations is optimized to get the best
encoding image. Also, in the proposed method, we can achieve the decoded image by a reverse
operation from the genetic algorithm. The best encrypted image is an image with high entropy
and low correlation coefficient. According to the entropy and correlation coefficient of the
proposed method compared with existing methods, it is observed that our method gets better
results in both of them.
Studies about mobility and mobile interaction help researchers and practitioners in the social sciences to make sense of emergent working and living practices in an increasingly mobilised world. This paper aims to present a reflective analysis of the recommended methodological approaches for mobile studies based on three case studies. Following mobile workers across the different dimensions of time and space is a major challenge researchers have to face. The paper discusses these challenges, and highlights areas of interest for researchers interested in the study of mobility and mobile interaction.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
A HUMAN-CENTRIC APPROACH TO GROUP-BASED CONTEXT-AWARENESSIJNSA Journal
The emerging need for qualitative approaches in context-aware information processing calls for proper modelling of context information and efficient handling of its inherent uncertainty resulted from human interpretation and usage. Many of the current approaches to context-awareness either lack a solid theoretical basis for modelling or ignore important requirements such as modularity, high-order uncertainty management and group-based context-awareness. Therefore, their real-world application and extendibility remains limited. In this paper, we present f-Context as a service-based contextawareness framework, based on language-action perspective (LAP) theory for modelling. Then we identify some of the complex, informational parts of context which contain high-order uncertainties due to differences between members of the group in defining them. An agent-based perceptual computer architecture is proposed for implementing f-Context that uses computing with words (CWW) for handling uncertainty. The feasibility of f-Context is analyzed using a realistic scenario involving a group of mobile users. We believe that the proposed approach can open the door to future research on context-awareness by offering a theoretical foundation based on human communication, and a service-based layered architecture which exploits CWW for context-aware, group-based and platform-independent access to information systems.
Brain reading, compressive sensing, fMRI and statistical learning in PythonGael Varoquaux
Talk given at Gipsa-lab on using machine learning to learn from fMRI brain patterns and regions related to behavior. This talks focuses on the signal and inverse-problem aspects of the equation, as well as on the software.
A simple introduction to fMRI study design for social science and other researchers outside the field who might want to design a study using fMRI brain scanning technology
These are slides for an introductory lecture on fMRI/MRI and analysis of fMRI data. The corresponding tutorial is available on my website kathiseidlrathkopf.com
Deep Learning for Computer Vision: A comparision between Convolutional Neural...Vincenzo Lomonaco
In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general.
However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of “intelligence”.
The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them.
CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems.
HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain.
In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.
Artificial intelligence (AI) is a commonly used term as a result of adopting an overly generalized representation.
The main problem is definitions of “intelligence,” which often misinterpret practical notions that the term indicates.
The word “artificial,” from medical and biological points of view, quite naturally designates a non-natural property.
Studies about mobility and mobile interaction help researchers and practitioners in the social sciences to make sense of emergent working and living practices in an increasingly mobilised world. This paper aims to present a reflective analysis of the recommended methodological approaches for mobile studies based on three case studies. Following mobile workers across the different dimensions of time and space is a major challenge researchers have to face. The paper discusses these challenges, and highlights areas of interest for researchers interested in the study of mobility and mobile interaction.
The increased potential of the ontologies to reduce the human interference has wide range of applications. This paper identifies requirements for an ontology development platform to innovate artificially intelligent web. To facilitate this process, RDF and OWL have been developed as standard formats for the sharing and integration of data and knowledge. The knowledge in the form of rich conceptual schemas called ontologies. Based on the framework, an architectural paradigm is put forward in view of ontology engineering and development of ontology applications and a development portal designed to support ontology engineering, content authoring and application development with a view to maximal scalability in size and complexity of semantic knowledge and flexible reuse of ontology models and ontology application processes in a distributed and collaborative engineering environment.
A HUMAN-CENTRIC APPROACH TO GROUP-BASED CONTEXT-AWARENESSIJNSA Journal
The emerging need for qualitative approaches in context-aware information processing calls for proper modelling of context information and efficient handling of its inherent uncertainty resulted from human interpretation and usage. Many of the current approaches to context-awareness either lack a solid theoretical basis for modelling or ignore important requirements such as modularity, high-order uncertainty management and group-based context-awareness. Therefore, their real-world application and extendibility remains limited. In this paper, we present f-Context as a service-based contextawareness framework, based on language-action perspective (LAP) theory for modelling. Then we identify some of the complex, informational parts of context which contain high-order uncertainties due to differences between members of the group in defining them. An agent-based perceptual computer architecture is proposed for implementing f-Context that uses computing with words (CWW) for handling uncertainty. The feasibility of f-Context is analyzed using a realistic scenario involving a group of mobile users. We believe that the proposed approach can open the door to future research on context-awareness by offering a theoretical foundation based on human communication, and a service-based layered architecture which exploits CWW for context-aware, group-based and platform-independent access to information systems.
Brain reading, compressive sensing, fMRI and statistical learning in PythonGael Varoquaux
Talk given at Gipsa-lab on using machine learning to learn from fMRI brain patterns and regions related to behavior. This talks focuses on the signal and inverse-problem aspects of the equation, as well as on the software.
A simple introduction to fMRI study design for social science and other researchers outside the field who might want to design a study using fMRI brain scanning technology
These are slides for an introductory lecture on fMRI/MRI and analysis of fMRI data. The corresponding tutorial is available on my website kathiseidlrathkopf.com
Deep Learning for Computer Vision: A comparision between Convolutional Neural...Vincenzo Lomonaco
In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general.
However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of “intelligence”.
The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them.
CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems.
HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain.
In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.
Artificial intelligence (AI) is a commonly used term as a result of adopting an overly generalized representation.
The main problem is definitions of “intelligence,” which often misinterpret practical notions that the term indicates.
The word “artificial,” from medical and biological points of view, quite naturally designates a non-natural property.
Artificial intelligence - Approach and MethodRuchi Jain
Human natural intelligence is ubiquitous with human activities, such as solving problems, playing chess, guessing puzzles. AI is new mean to solve such complex problems. We NuAIg is a AI consulting firm, who will help you to create a AI road-map for your business and process automation.
Chaps29 the entirebookks2017 - The Mind MahineSyedVAhamed
In this chapter, we take bold step and propose the unthinkable: The genesis of a Customizable Mind Machine.
Thought that stems from the mind is deeply seated in a biological framework of neurons. The biological origin lies
in the marvel of evolution over the eons and refined ever so fast, faster than in the prior centuries. Three (a, b and
c), triadic objects are ceaselessly at work. At a personal level (a) Mind, knowledge and machines have been
intertwined like inspiration, words and language since the dawn of the human evolution and more recently (b)
technology, manufacturing and economics have formed a web for (c) wealth, global marketing and insatiable needs
of humans and civilization. These triadic cycles of nine essential objects of human existence are spinning quicker
and quicker every year. The Internet offers the mind no choice but to leap and soar over history and over the globe.
Alternatively, human mind can sink deeper and deeper into ignorance and oblivion. More recently, the Artificial
Intelligence at work in the Internet had challenged the natural intelligence at the cognizance level in the mind to find
its way to breakthroughs and innovations.
We integrate functions of the mind with the processing of knowledge in the hardware of machines by freely
traversing the neural, mental, physical, psychological, social, knowledge, and computational spaces. The laws of
neural biology and mind, laws of knowledge and social sciences and finally the laws of physics and mechanics, in
each of the spaces are unique and executed by distinctive processors for each space. Much as mind rules over
matter, the triad of mind, space and time creates a human-space that rules over the Relativistic-space of matter,
space and time.
Keywords—Mind, Knowledge, Machines, Technology, Human Needs, Knowledge Windows, Perceptual Spaces
Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Cl...BIJIAM Journal
Facial expression recognition is a vital research topic in most fields ranging from artificial intelligence and gaming to human–computer interaction (HCI) and psychology. This paper proposes a hybrid model for facial expression recognition, which comprises a deep convolutional neural network (DCNN) and a Haar Cascade deep learning architecture. The objective is to classify real-time and digital facial images into one of the seven facial emotion categories considered. The DCNN employed in this research has more convolutional layers, ReLU activation functions, and multiple kernels to enhance filtering depth and facial feature extraction. In addition, a Haar Cascade model was also mutually used to detect facial features in real-time images and video frames. Grayscale images from the Kaggle repository (FER-2013) and then exploited graphics processing unit (GPU) computation to expedite the training and validation process. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. The experimental results show a significantly improved classification performance compared to state-of-the-art (SoTA) experiments and research. Also, compared to other conventional models, this paper validates that the proposed architecture is superior in classification performance with an improvement of up to 6%, totaling up to 70% accuracy, and with less execution time of 2,098.8 s.
Toward enhancement of deep learning techniques using fuzzy logic: a survey IJECEIAES
Deep learning has emerged recently as a type of artificial intelligence (AI) and machine learning (ML), it usually imitates the human way in gaining a particular knowledge type. Deep learning is considered an essential data science element, which comprises predictive modeling and statistics. Deep learning makes the processes of collecting, interpreting, and analyzing big data easier and faster. Deep neural networks are kind of ML models, where the non-linear processing units are layered for the purpose of extracting particular features from the inputs. Actually, the training process of similar networks is very expensive and it also depends on the used optimization method, hence optimal results may not be provided. The techniques of deep learning are also vulnerable to data noise. For these reasons, fuzzy systems are used to improve the performance of deep learning algorithms, especially in combination with neural networks. Fuzzy systems are used to improve the representation accuracy of deep learning models. This survey paper reviews some of the deep learning based fuzzy logic models and techniques that were presented and proposed in the previous studies, where fuzzy logic is used to improve deep learning performance. The approaches are divided into two categories based on how both of the samples are combined. Furthermore, the models' practicality in the actual world is revealed.
1. Tom M. Mitchell
Fredkin Professor of Computer Science
Carnegie Mellon University
Thursday December 9, 2004
1170 TMCB, 11:00 AM
Using Machine Learning and Brain Imaging to
Study Cognitive Processes
Over the past decade, functional Magnetic Resonance Imaging (fMRI) has emerged as an
important new method for studying cognitive processes in the human brain. A typical fMRI
experiment captures a sequence of three-dimensional images of brain activity, once per second,
at a spatial resolution of a few millimeters. This talk will present our recent research exploring
the question of how best to analyze fMRI data to build models of human cognitive processes.
We will first describe our recent successes training machine learning classifiers to distinguish
cognitive subprocesses based on observed fMRI images. For example, we have been able to
train classifiers to discriminate whether a person is reading words about tools, or words about
buildings, based on their observed fMRI brain activation. We will then describe our more recent
research on learning more complex models capable of tracking multiple cognitive processes that
overlap in time and space within the brain.
Biography
Tom M. Mitchell is the Fredkin Professor of Computer Science at Carnegie Mellon University.
His research lies in the areas of machine learning, artificial intelligence, and cognitive
neuroscience. Mitchell is author of the textbook "Machine Learning," Past President of the
American Association of Artificial Intelligence (AAAI), and a member of the US National
Research Council's Computer Science and Telecommunications Board. In 2002 he received
the Debye Prize from the Edmund Hustinx Foundation for his research in computer science.
Mitchell is the founding director of CMU's Center for Automated Learning and Discovery, an
interdisciplinary research center specializing in statistical machine learning and data mining, and
the first institution to offer a Ph.D. program specifically in this area. Mitchell's recent research
has focused on machine learning approaches to analyzing human brain function based on fMRI
data, and on machine learning for intelligent personal assistants.
Donuts will be provided