Wajdi Khattel presented a proposal for a terrorist detection model in social networks. The model uses a multi-dimensional network as input and consists of three sub-models: a text classification model, image classification model, and general information classification model. The sub-models each output a score that is then used by a decision making module to classify a user as a terrorist or not based on a threshold. The implementation involved collecting offline training data from banned Twitter accounts, Google images, and a public dataset. Online data was also collected from Facebook, Instagram, and Twitter using their APIs. Several machine learning models were tested for each sub-model and the proposed full model uses a neural network for text, CNN with data augmentation and
This thesis proposal aims to develop a system called Eureka to efficiently discover training data for visual machine learning tasks. Eureka combines early discard filters, just-in-time machine learning, and the ability to create more accurate filters without writing new code. The goal is to reduce the manual effort required of domain experts to find and label rare phenomena in large unlabeled visual datasets. The proposal outlines research thrusts to apply Eureka in different computing environments like edge, cloud, and smart storage, as well as different problem domains including images, videos, and other multidimensional data. Initial experiments show Eureka can discover more true positives per unit time compared to naive hand-labeling.
The document discusses privacy concerns related to big data. It notes that as individuals leave large digital trails through online activities like social media, this data is being collected and analyzed by companies. While this data collection can help with marketing, it also raises privacy issues as digital behavior can be used to infer identities even when data is anonymized. The document explores these tensions and how privacy regulations are aiming to protect individual anonymity, but this is challenging given how useful data loses anonymity.
Robust Feature Learning with Deep Neural Networks
http://snu-primo.hosted.exlibrisgroup.com/primo_library/libweb/action/display.do?tabs=viewOnlineTab&doc=82SNU_INST21557911060002591
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
This thesis presents research on using deep learning methods for feature extraction from satellite imagery to identify landslide pixels. The objectives are to classify land cover using machine learning algorithms like SVM and random forests in Google Earth Engine, design and evaluate a deep neural network for landslide identification, and compare performance of deep learning models in MATLAB. Results show that a neural network achieved over 98% accuracy at identifying landslide pixels. Future work proposes developing new indices for improved identification and an automatic landslide monitoring platform.
Wajdi Khattel presented a proposal for a terrorist detection model in social networks. The model uses a multi-dimensional network as input and consists of three sub-models: a text classification model, image classification model, and general information classification model. The sub-models each output a score that is then used by a decision making module to classify a user as a terrorist or not based on a threshold. The implementation involved collecting offline training data from banned Twitter accounts, Google images, and a public dataset. Online data was also collected from Facebook, Instagram, and Twitter using their APIs. Several machine learning models were tested for each sub-model and the proposed full model uses a neural network for text, CNN with data augmentation and
This thesis proposal aims to develop a system called Eureka to efficiently discover training data for visual machine learning tasks. Eureka combines early discard filters, just-in-time machine learning, and the ability to create more accurate filters without writing new code. The goal is to reduce the manual effort required of domain experts to find and label rare phenomena in large unlabeled visual datasets. The proposal outlines research thrusts to apply Eureka in different computing environments like edge, cloud, and smart storage, as well as different problem domains including images, videos, and other multidimensional data. Initial experiments show Eureka can discover more true positives per unit time compared to naive hand-labeling.
The document discusses privacy concerns related to big data. It notes that as individuals leave large digital trails through online activities like social media, this data is being collected and analyzed by companies. While this data collection can help with marketing, it also raises privacy issues as digital behavior can be used to infer identities even when data is anonymized. The document explores these tensions and how privacy regulations are aiming to protect individual anonymity, but this is challenging given how useful data loses anonymity.
Robust Feature Learning with Deep Neural Networks
http://snu-primo.hosted.exlibrisgroup.com/primo_library/libweb/action/display.do?tabs=viewOnlineTab&doc=82SNU_INST21557911060002591
Talk @ ACM SF Bayarea Chapter on Deep Learning for medical imaging space.
The talk covers use cases, special challenges and solutions for Deep Learning for Medical Image Analysis using Tensorflow+Keras. You will learn about:
- Use cases for Deep Learning in Medical Image Analysis
- Different DNN architectures used for Medical Image Analysis
- Special purpose compute / accelerators for Deep Learning (in the Cloud / On-prem)
- How to parallelize your models for faster training of models and serving for inferenceing.
- Optimization techniques to get the best performance from your cluster (like Kubernetes/ Apache Mesos / Spark)
- How to build an efficient Data Pipeline for Medical Image Analysis using Deep Learning
- Resources to jump start your journey - like public data sets, common models used in Medical Image Analysis
Artificial Intelligence is increasingly playing an integral role in determining our day-to-day experiences. Moreover, with proliferation of AI based solutions in areas such as hiring, lending, criminal justice, healthcare, and education, the resulting personal and professional implications of AI are far-reaching. The dominant role played by AI models in these domains has led to a growing concern regarding potential bias in these models, and a demand for model transparency and interpretability. In addition, model explainability is a prerequisite for building trust and adoption of AI systems in high stakes domains requiring reliability and safety such as healthcare and automated transportation, and critical industrial applications with significant economic implications such as predictive maintenance, exploration of natural resources, and climate change modeling.
As a consequence, AI researchers and practitioners have focused their attention on explainable AI to help them better trust and understand models at scale. The challenges for the research community include (i) defining model explainability, (ii) formulating explainability tasks for understanding model behavior and developing solutions for these tasks, and finally (iii) designing measures for evaluating the performance of models in explainability tasks.
In this tutorial, we present an overview of model interpretability and explainability in AI, key regulations / laws, and techniques / tools for providing explainability as part of AI/ML systems. Then, we focus on the application of explainability techniques in industry, wherein we present practical challenges / guidelines for effectively using explainability techniques and lessons learned from deploying explainable models for several web-scale machine learning and data mining applications. We present case studies across different companies, spanning application domains such as search & recommendation systems, sales, lending, and fraud detection. Finally, based on our experiences in industry, we identify open problems and research directions for the data mining / machine learning community.
This thesis presents research on using deep learning methods for feature extraction from satellite imagery to identify landslide pixels. The objectives are to classify land cover using machine learning algorithms like SVM and random forests in Google Earth Engine, design and evaluate a deep neural network for landslide identification, and compare performance of deep learning models in MATLAB. Results show that a neural network achieved over 98% accuracy at identifying landslide pixels. Future work proposes developing new indices for improved identification and an automatic landslide monitoring platform.
Tutorial on 'Explainability for NLP' given at the first ALPS (Advanced Language Processing) winter school: http://lig-alps.imag.fr/index.php/schedule/
The talk introduces the concepts of 'model understanding' as well as 'decision understanding' and provides examples of approaches from the areas of fact checking and text classification.
Exercises to go with the tutorial are available here: https://github.com/copenlu/ALPS_2021
This document discusses interpretability and explainable AI (XAI) in neural networks. It begins by providing motivation for why explanations of neural network predictions are often required. It then provides an overview of different interpretability techniques, including visualizing learned weights and feature maps, attribution methods like class activation maps and guided backpropagation, and feature visualization. Specific examples and applications of each technique are described. The document serves as a guide to interpretability and explainability in deep learning models.
Privacy-preserving Data Mining in Industry: Practical Challenges and Lessons ...Krishnaram Kenthapadi
Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple’s differential privacy deployment for iOS, Google’s RAPPOR, and LinkedIn Salary. We will also discuss various open source as well as commercial privacy tools, and conclude with open problems and challenges for data mining / machine learning community.
[Phd Thesis Defense] CHAMELEON: A Deep Learning Meta-Architecture for News Re...Gabriel Moreira
Presentation of the Phd. thesis defense of Gabriel de Souza Pereira Moreira at Instituto Tecnológico de Aeronáutica (ITA), on Dec. 09, 2019, in São José dos Campos, Brazil.
Abstract:
Recommender systems have been increasingly popular in assisting users with their choices, thus enhancing their engagement and overall satisfaction with online services. Since the last decade, recommender systems became a topic of increasing interest among machine learning, human-computer interaction, and information retrieval researchers.
News recommender systems are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, it is a challenging scenario for recommendations. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated and irrelevant to most readers. News readers exhibit more unstable consumption behavior than users in other domains such as entertainment. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous, with no past behavior tracked.
Since 2016, Deep Learning methods and techniques have been explored in Recommender Systems research. In general, they can be divided into methods for: Deep Collaborative Filtering, Learning Item Embeddings, Session-based Recommendations using Recurrent Neural Networks (RNN), and Feature Extraction from Items' Unstructured Data such as text, images, audio, and video.
The main contribution of this research was named CHAMELEON a meta-architecture designed to tackle the specific challenges of news recommendation. It consists of a modular reference architecture which can be instantiated using different neural building blocks.
As information about users' past interactions is scarce in the news domain, information such as the user context (e.g., time, location, device, the sequence of clicks within the session), static and dynamic article features like the article textual content and its popularity and recency, are explicitly modeled in a hybrid session-based recommendation approach using RNNs.
The recommendation task addressed in this work is the next-item prediction for user sessions, i.e., "what is the next most likely article a user might read in a session?". A temporal offline evaluation is used for a realistic offline evaluation of such task, considering factors that affect global readership interests like popularity, recency, and seasonality.
Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based algorithms.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Road to NODES Workshop Series - Intro to Neo4jNeo4j
The document provides an introduction to Neo4j and graphs. It discusses what graphs are, why they are useful, and how to identify graph problems. It then introduces the Neo4j property graph model including nodes, relationships, and properties. The document demonstrates querying graphs using Cypher and includes hands-on examples using the movie graph on Neo4j AuraDB. It also summarizes the Neo4j graph data platform and ecosystem and provides resources for continuing to learn about Neo4j.
This document discusses the YOLO object detection algorithm and its applications in real-time object detection. YOLO frames object detection as a regression problem to predict bounding boxes and class probabilities in one pass. It can process images at 30 FPS. The document compares YOLO versions 1-3 and their improvements in small object detection, resolution, and generalization. It describes implementing YOLO with OpenCV and its use in self-driving cars due to its speed and contextual awareness.
How Graph Data Science can turbocharge your Knowledge GraphNeo4j
Knowledge Graphs are becoming mission-critical across many industries. More recently, we are witnessing the application of Graph Data Science to Knowledge Graphs, offering powerful outcomes. But how do we define Knowledge Graphs in industry and how can they be useful for your project? In this talk, we will illustrate the various methods and models of Graph Data Science being applied to Knowledge Graphs and how they allow you to find implicit relationships in your graph which are impossible to detect in any other way. You will learn how graph algorithms from PageRank to Embeddings drive ever deeper insights in your data.
image classification is a common problem in Artificial Intelligence , we used CIFR10 data set and tried a lot of methods to reach a high test accuracy like neural networks and Transfer learning techniques .
you can view the source code and the papers we read on github : https://github.com/Asma-Hawari/Machine-Learning-Project-
Big Data Helsinki v 3 | "Federated Learning and Privacy-preserving AI" - Oguz...Dataconomy Media
"Machine learning algorithms require significant amounts of training data which has been centralized on one machine or in a datacenter so far. For numerous applications, such need of collecting data can be extremely privacy-invasive. Recent advancements in AI research approach this issue by a new paradigm of training AI models, i.e., Federated Learning.
In federated learning, edge devices (phones, computers, cars etc.) collaboratively learn a shared AI model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. From personal data perspective, this paradigm enables a way of training a model on the device without directly inspecting users’ data on a server. This talk will pinpoint several examples of AI applications benefiting from federated learning and the likely future of privacy-aware systems."
This document provides an overview of computer vision techniques including classification and object detection. It discusses popular deep learning models such as AlexNet, VGGNet, and ResNet that advanced the state-of-the-art in image classification. It also covers applications of computer vision in areas like healthcare, self-driving cars, and education. Additionally, the document reviews concepts like the classification pipeline in PyTorch, data augmentation, and performance metrics for classification and object detection like precision, recall, and mAP.
Scene recognition using Convolutional Neural NetworkDhirajGidde
The document discusses scene recognition using convolutional neural networks. It begins with an abstract stating that scene recognition allows context for object recognition. While object recognition has improved due to large datasets and CNNs, scene recognition performance has not reached the same level of success. The document then discusses using a new scene-centric database called Places with over 7 million images to train CNNs for scene recognition. It establishes new state-of-the-art results on several scene datasets and allows visualization of network responses to show differences between object-centric and scene-centric representations.
Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior. Major AI researchers and textbooks define this field as "the study and design of intelligent agents", in which an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.
John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines".
AI research is highly technical and specialized, and is deeply divided into sub fields that often fail to communicate with each other. Some of the division is due to social and cultural factors: sub fields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some sub fields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. General intelligence is still among the field's long-term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are a large number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, mathematics, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology.
The field was founded on the claim that a central property of humans, human intelligence—the sapience of Homo sapiens sapiens—"can be so precisely described that a machine can be made to simulate it." This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity. Artificial intelligence has been the subject of tremendous optimism but has also suffered stunning setbacks. Today AI techniques have become an essential part of the technology industry, providing the heavy lifting for many of the most challenging problems in computer science.
How Graph Algorithms Answer your Business Questions in Banking and BeyondNeo4j
This document provides an agenda and overview for a presentation on using graph algorithms in banking. The presentation introduces graphs and the Neo4j graph database, demonstrates sample banking data modeled as a graph, and reviews several graph algorithms that could be used for applications like fraud detection, including PageRank, weakly connected components, node similarity, and Louvain modularity. The document concludes with a demo and Q&A section.
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
This document summarizes a research paper about blockchain technology from a sustainability perspective. It discusses how blockchain could help achieve the 17 sustainable development goals set by the UN, such as increasing transparency, reducing fraud and corruption, and enabling new funding opportunities. However, it also notes blockchain has sustainability drawbacks. The energy intensive "proof of work" algorithm used by Bitcoin requires massive electricity consumption from fossil fuel power sources, undermining climate goals. While blockchain aims to increase accessibility, its current infrastructure poses environmental risks that could threaten sustainability if left unaddressed.
Impact of big data congestion in IT: An adaptive knowledgebased Bayesian networkIJECEIAES
Recent progress on real-time systems are growing high in information technology which is showing importance in every single innovative field. Different applications in IT simultaneously produce the enormous measure of information that should be taken care of. In this paper, a novel algorithm of adaptive knowledge-based Bayesian network is proposed to deal with the impact of big data congestion in decision processing. A Bayesian system show is utilized to oversee learning arrangement toward all path for the basic leadership process. Information of Bayesian systems is routinely discharged as an ideal arrangement, where the examination work is to find a development that misuses a measurably inspired score. By and large, available information apparatuses manage this ideal arrangement by methods for normal hunt strategies. As it required enormous measure of information space, along these lines it is a tedious method that ought to be stayed away from. The circumstance ends up unequivocal once huge information include in hunting down ideal arrangement. A calculation is acquainted with achieve quicker preparing of ideal arrangement by constraining the pursuit information space. The proposed algorithm consists of recursive calculation intthe inquiry space. The outcome demonstrates that the ideal component of the proposed algorithm can deal with enormous information by processing time, and a higher level of expectation rates.
Tutorial on 'Explainability for NLP' given at the first ALPS (Advanced Language Processing) winter school: http://lig-alps.imag.fr/index.php/schedule/
The talk introduces the concepts of 'model understanding' as well as 'decision understanding' and provides examples of approaches from the areas of fact checking and text classification.
Exercises to go with the tutorial are available here: https://github.com/copenlu/ALPS_2021
This document discusses interpretability and explainable AI (XAI) in neural networks. It begins by providing motivation for why explanations of neural network predictions are often required. It then provides an overview of different interpretability techniques, including visualizing learned weights and feature maps, attribution methods like class activation maps and guided backpropagation, and feature visualization. Specific examples and applications of each technique are described. The document serves as a guide to interpretability and explainability in deep learning models.
Privacy-preserving Data Mining in Industry: Practical Challenges and Lessons ...Krishnaram Kenthapadi
Preserving privacy of users is a key requirement of web-scale data mining applications and systems such as web search, recommender systems, crowdsourced platforms, and analytics applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. In this tutorial, we will first present an overview of privacy breaches over the last two decades and the lessons learned, key regulations and laws, and evolution of privacy techniques leading to differential privacy definition / techniques. Then, we will focus on the application of privacy-preserving data mining techniques in practice, by presenting case studies such as Apple’s differential privacy deployment for iOS, Google’s RAPPOR, and LinkedIn Salary. We will also discuss various open source as well as commercial privacy tools, and conclude with open problems and challenges for data mining / machine learning community.
[Phd Thesis Defense] CHAMELEON: A Deep Learning Meta-Architecture for News Re...Gabriel Moreira
Presentation of the Phd. thesis defense of Gabriel de Souza Pereira Moreira at Instituto Tecnológico de Aeronáutica (ITA), on Dec. 09, 2019, in São José dos Campos, Brazil.
Abstract:
Recommender systems have been increasingly popular in assisting users with their choices, thus enhancing their engagement and overall satisfaction with online services. Since the last decade, recommender systems became a topic of increasing interest among machine learning, human-computer interaction, and information retrieval researchers.
News recommender systems are aimed to personalize users experiences and help them discover relevant articles from a large and dynamic search space. Therefore, it is a challenging scenario for recommendations. Large publishers release hundreds of news daily, implying that they must deal with fast-growing numbers of items that get quickly outdated and irrelevant to most readers. News readers exhibit more unstable consumption behavior than users in other domains such as entertainment. External events, like breaking news, affect readers interests. In addition, the news domain experiences extreme levels of sparsity, as most users are anonymous, with no past behavior tracked.
Since 2016, Deep Learning methods and techniques have been explored in Recommender Systems research. In general, they can be divided into methods for: Deep Collaborative Filtering, Learning Item Embeddings, Session-based Recommendations using Recurrent Neural Networks (RNN), and Feature Extraction from Items' Unstructured Data such as text, images, audio, and video.
The main contribution of this research was named CHAMELEON a meta-architecture designed to tackle the specific challenges of news recommendation. It consists of a modular reference architecture which can be instantiated using different neural building blocks.
As information about users' past interactions is scarce in the news domain, information such as the user context (e.g., time, location, device, the sequence of clicks within the session), static and dynamic article features like the article textual content and its popularity and recency, are explicitly modeled in a hybrid session-based recommendation approach using RNNs.
The recommendation task addressed in this work is the next-item prediction for user sessions, i.e., "what is the next most likely article a user might read in a session?". A temporal offline evaluation is used for a realistic offline evaluation of such task, considering factors that affect global readership interests like popularity, recency, and seasonality.
Experiments performed with two large datasets have shown the effectiveness of the CHAMELEON for news recommendation on many quality factors such as accuracy, item coverage, novelty, and reduced item cold-start problem, when compared to other traditional and state-of-the-art session-based algorithms.
http://imatge-upc.github.io/telecombcn-2016-dlcv/
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Road to NODES Workshop Series - Intro to Neo4jNeo4j
The document provides an introduction to Neo4j and graphs. It discusses what graphs are, why they are useful, and how to identify graph problems. It then introduces the Neo4j property graph model including nodes, relationships, and properties. The document demonstrates querying graphs using Cypher and includes hands-on examples using the movie graph on Neo4j AuraDB. It also summarizes the Neo4j graph data platform and ecosystem and provides resources for continuing to learn about Neo4j.
This document discusses the YOLO object detection algorithm and its applications in real-time object detection. YOLO frames object detection as a regression problem to predict bounding boxes and class probabilities in one pass. It can process images at 30 FPS. The document compares YOLO versions 1-3 and their improvements in small object detection, resolution, and generalization. It describes implementing YOLO with OpenCV and its use in self-driving cars due to its speed and contextual awareness.
How Graph Data Science can turbocharge your Knowledge GraphNeo4j
Knowledge Graphs are becoming mission-critical across many industries. More recently, we are witnessing the application of Graph Data Science to Knowledge Graphs, offering powerful outcomes. But how do we define Knowledge Graphs in industry and how can they be useful for your project? In this talk, we will illustrate the various methods and models of Graph Data Science being applied to Knowledge Graphs and how they allow you to find implicit relationships in your graph which are impossible to detect in any other way. You will learn how graph algorithms from PageRank to Embeddings drive ever deeper insights in your data.
image classification is a common problem in Artificial Intelligence , we used CIFR10 data set and tried a lot of methods to reach a high test accuracy like neural networks and Transfer learning techniques .
you can view the source code and the papers we read on github : https://github.com/Asma-Hawari/Machine-Learning-Project-
Big Data Helsinki v 3 | "Federated Learning and Privacy-preserving AI" - Oguz...Dataconomy Media
"Machine learning algorithms require significant amounts of training data which has been centralized on one machine or in a datacenter so far. For numerous applications, such need of collecting data can be extremely privacy-invasive. Recent advancements in AI research approach this issue by a new paradigm of training AI models, i.e., Federated Learning.
In federated learning, edge devices (phones, computers, cars etc.) collaboratively learn a shared AI model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. From personal data perspective, this paradigm enables a way of training a model on the device without directly inspecting users’ data on a server. This talk will pinpoint several examples of AI applications benefiting from federated learning and the likely future of privacy-aware systems."
This document provides an overview of computer vision techniques including classification and object detection. It discusses popular deep learning models such as AlexNet, VGGNet, and ResNet that advanced the state-of-the-art in image classification. It also covers applications of computer vision in areas like healthcare, self-driving cars, and education. Additionally, the document reviews concepts like the classification pipeline in PyTorch, data augmentation, and performance metrics for classification and object detection like precision, recall, and mAP.
Scene recognition using Convolutional Neural NetworkDhirajGidde
The document discusses scene recognition using convolutional neural networks. It begins with an abstract stating that scene recognition allows context for object recognition. While object recognition has improved due to large datasets and CNNs, scene recognition performance has not reached the same level of success. The document then discusses using a new scene-centric database called Places with over 7 million images to train CNNs for scene recognition. It establishes new state-of-the-art results on several scene datasets and allows visualization of network responses to show differences between object-centric and scene-centric representations.
Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior. Major AI researchers and textbooks define this field as "the study and design of intelligent agents", in which an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success.
John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines".
AI research is highly technical and specialized, and is deeply divided into sub fields that often fail to communicate with each other. Some of the division is due to social and cultural factors: sub fields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some sub fields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. General intelligence is still among the field's long-term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are a large number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, mathematics, psychology, linguistics, philosophy and neuroscience, as well as other specialized fields such as artificial psychology.
The field was founded on the claim that a central property of humans, human intelligence—the sapience of Homo sapiens sapiens—"can be so precisely described that a machine can be made to simulate it." This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity. Artificial intelligence has been the subject of tremendous optimism but has also suffered stunning setbacks. Today AI techniques have become an essential part of the technology industry, providing the heavy lifting for many of the most challenging problems in computer science.
How Graph Algorithms Answer your Business Questions in Banking and BeyondNeo4j
This document provides an agenda and overview for a presentation on using graph algorithms in banking. The presentation introduces graphs and the Neo4j graph database, demonstrates sample banking data modeled as a graph, and reviews several graph algorithms that could be used for applications like fraud detection, including PageRank, weakly connected components, node similarity, and Louvain modularity. The document concludes with a demo and Q&A section.
Modern Data Challenges require Modern Graph TechnologyNeo4j
This session focuses on key data trends and challenges impacting enterprises. And, how graph technology is evolving to future-proof data strategy and architectures.
This document summarizes a research paper about blockchain technology from a sustainability perspective. It discusses how blockchain could help achieve the 17 sustainable development goals set by the UN, such as increasing transparency, reducing fraud and corruption, and enabling new funding opportunities. However, it also notes blockchain has sustainability drawbacks. The energy intensive "proof of work" algorithm used by Bitcoin requires massive electricity consumption from fossil fuel power sources, undermining climate goals. While blockchain aims to increase accessibility, its current infrastructure poses environmental risks that could threaten sustainability if left unaddressed.
Impact of big data congestion in IT: An adaptive knowledgebased Bayesian networkIJECEIAES
Recent progress on real-time systems are growing high in information technology which is showing importance in every single innovative field. Different applications in IT simultaneously produce the enormous measure of information that should be taken care of. In this paper, a novel algorithm of adaptive knowledge-based Bayesian network is proposed to deal with the impact of big data congestion in decision processing. A Bayesian system show is utilized to oversee learning arrangement toward all path for the basic leadership process. Information of Bayesian systems is routinely discharged as an ideal arrangement, where the examination work is to find a development that misuses a measurably inspired score. By and large, available information apparatuses manage this ideal arrangement by methods for normal hunt strategies. As it required enormous measure of information space, along these lines it is a tedious method that ought to be stayed away from. The circumstance ends up unequivocal once huge information include in hunting down ideal arrangement. A calculation is acquainted with achieve quicker preparing of ideal arrangement by constraining the pursuit information space. The proposed algorithm consists of recursive calculation intthe inquiry space. The outcome demonstrates that the ideal component of the proposed algorithm can deal with enormous information by processing time, and a higher level of expectation rates.
Analysis of IT Monitoring Using Open Source Software Techniques: A ReviewIJERD Editor
The Network administrators usually rely on generic and built-in monitoring tools for network
security. Ideally, the network infrastructure is supposed to have carefully designed strategies to scale up
monitoring tools and techniques as the network grows, over time. Without this, there can be network
performance challenges, downtimes due to failures, and most importantly, penetration attacks. These can lead to
monetary losses as well as loss of reputation. Thus, there is a need for best practices to monitor network
infrastructure in an agile manner. Network security monitoring involves collecting network packet data,
segregating it among all the 7 OSI layers, and applying intelligent algorithms to get answers to security-related
questions. The purpose is to know in real-time what is happening on the network at a detailed level, and
strengthen security by hardening the processes, devices, appliances, software policies, etc. The Multi Router
Traffic Grapher, or just simply MRTG, is free software for monitoring and measuring the traffic load
on network links. It allows the user to see traffic load on a network over time in graphical form.
The document discusses the Internet of Things (IoT) and some of the key challenges. It notes that IoT data is multi-modal, distributed, heterogeneous, noisy and incomplete. It raises issues around data management, actuation and feedback, service descriptions, real-time analysis, and privacy and security. The document outlines research challenges around transforming raw data to actionable information, machine learning for large datasets, making data accessible and discoverable, and energy efficient data collection and communication. It emphasizes that IoT data integration requires solutions across physical, cyber and social domains.
Analytics of Performance and Data Quality for Mobile Edge Cloud ApplicationsHong-Linh Truong
The document discusses performance and data quality analytics for mobile edge cloud applications. It presents MECCA, a mobile edge cloud application for providing cornering recommendations to cars. MECCA has a complex architecture using microservices and third party services. Analyzing MECCA's performance and data quality across different edge and cloud deployments is challenging due to dependencies between application parameters, streaming processing, and third party services. Future work aims to develop toolsets and datasets to better evaluate performance and data quality metrics for mobile edge cloud applications.
The document summarizes the evolution of the semantic grid from its origins in 2001 to the present. It describes how early work on the semantic grid aimed to close the gap between grid applications and the vision of global e-science collaboration. Key developments included linking grid services with semantic web technologies to enable automation and advanced functionality through machine-processable descriptions. The semantic grid is now seen as an important approach for virtual research environments that support both formal and informal scientific processes through collaborative tools and persistent representations of discussions.
The document provides an introduction to big data, including:
1) It defines big data and discusses its key characteristics of volume, velocity, and variety.
2) It describes sources of big data like sensors, social media, and purchase transactions.
3) It discusses big data analytics including descriptive, predictive, and prescriptive analytics and the stages of capture, organize, analyze, and act.
The document discusses using machine learning for efficient attack detection in IoT devices without feature engineering. It proposes a feature-engineering-less machine learning (FEL-ML) process that uses raw packet byte streams as input instead of engineered features. This approach is lighter weight and faster than traditional methods. The FEL-ML model is trained directly on unprocessed packet data to perform malware detection on resource-constrained IoT devices. Prior research that used engineered features or complex deep learning models are not suitable for IoT due to limitations of memory and processing power. The proposed FEL-ML approach aims to enable effective network traffic security for IoT using minimal resources.
EFFICIENT ATTACK DETECTION IN IOT DEVICES USING FEATURE ENGINEERING-LESS MACH...ijcsit
Through the generalization of deep learning, the research community has addressed critical challenges in
the network security domain, like malware identification and anomaly detection. However, they have yet to
discuss deploying them on Internet of Things (IoT) devices for day-to-day operations. IoT devices are often
limited in memory and processing power, rendering the compute-intensive deep learning environment
unusable. This research proposes a way to overcome this barrier by bypassing feature engineering in the
deep learning pipeline and using raw packet data as input. We introduce a feature- engineering-less
machine learning (ML) process to perform malware detection on IoT devices. Our proposed model,”
Feature engineering-less ML (FEL-ML),” is a lighter-weight detection algorithm that expends no extra
computations on “engineered” features. It effectively accelerates the low-powered IoT edge. It is trained
on unprocessed byte-streams of packets. Aside from providing better results, it is quicker than traditional
feature-based methods. FEL-ML facilitates resource-sensitive network traffic security with the added
benefit of eliminating the significant investment by subject matter experts in feature engineering.
Big Data in Distributed Analytics,Cybersecurity And Digital ForensicsSherinMariamReji05
This document provides an overview of big data and its applications in distributed analytics, cyber security, and digital forensics. It discusses how big data can reduce the processing time of large volumes of data in distributed computing environments using Hadoop. Examples of big data applications include using social media, search engine, and aircraft black box data for analysis. The document also outlines the challenges of traditional systems and how distributed big data architectures help address them by allowing data to be processed across clustered computers.
Concept Drift Identification using Classifier Ensemble Approach IJECEIAES
Abstract:-In Internetworking system, the huge amount of data is scattered, generated and processed over the network. The data mining techniques are used to discover the unknown pattern from the underlying data. A traditional classification model is used to classify the data based on past labelled data. However in many current applications, data is increasing in size with fluctuating patterns. Due to this new feature may arrive in the data. It is present in many applications like sensornetwork, banking and telecommunication systems, financial domain, Electricity usage and prices based on its demand and supplyetc .Thus change in data distribution reduces the accuracy of classifying the data. It may discover some patterns as frequent while other patterns tend to disappear and wrongly classify. To mine such data distribution, traditionalclassification techniques may not be suitable as the distribution generating the items can change over time so data from the past may become irrelevant or even false for the current prediction. For handlingsuch varying pattern of data, concept drift mining approach is used to improve the accuracy of classification techniques. In this paper we have proposed ensemble approach for improving the accuracy of classifier. The ensemble classifier is applied on 3 different data sets. We investigated different features for the different chunk of data which is further given to ensemble classifier. We observed the proposed approach improves the accuracy of classifier for different chunks of data.
This document discusses using data mining techniques to help with crime investigation by analyzing large amounts of crime data. It compares the performance of three data mining algorithms (J48, Naive Bayes, JRip) on a sample criminal database to identify the best performing algorithm. The best algorithm would then be used on the criminal database to help identify possible suspects for a crime based on evidence and attributes. The document provides details on each of the three algorithms and evaluates them based on classification accuracy and other metrics to select the best technique for the criminal investigation application.
Enhanced Privacy Preserving Accesscontrol in Incremental Datausing Microaggre...rahulmonikasharma
In microdata releases, main task is to protect the privacy of data subjects. Microaggregation technique use to disclose the limitation at protecting the privacy of microdata. This technique is an alternative to generalization and suppression, which use to generate k-anonymous data sets. In this dataset, identity of each subject is hidden within a group of k subjects. Microaggregation perturbs the data and additional masking allows refining data utility in many ways, like increasing data granularity, to avoid discretization of numerical data, to reduce the impact of outliers. If the variability of the private data values in a group of k subjects is too small, k-anonymity does not provide protection against attribute disclosure. In this work Role based access control is assumed. The access control policies define selection predicates to roles. Then use the concept of imprecision bound for each permission to define a threshold on the amount of imprecision that can be tolerated. So the proposed approach reduces the imprecision for each selection predicate. Anonymization is carried out only for the static relational table in the existing papers. Privacy preserving access control mechanism is applied to the incremental data.
IRJET- Fault Detection and Prediction of Failure using Vibration AnalysisIRJET Journal
This document discusses fault detection and prediction of failures in rotating equipment using vibration analysis. It begins by introducing vibration analysis as a method to monitor machines and detect faults in rotating components that may cause failures. It then discusses how motor vibration is measured and analyzed using techniques like spectrum analysis to identify faults like unbalance, bearing issues, or broken rotor bars. The document proposes decomposing vibration signals using intrinsic mode functions and calculating the Gabor representation's frequency marginal to identify fault types using classifiers like support vector machines or random forests. It provides context on data mining techniques relevant to this type of fault prediction problem.
Data Mining Framework for Network Intrusion Detection using Efficient TechniquesIJAEMSJORNAL
The implementation measures the classification accuracy on benchmark datasets after combining SIS and ANNs. In order to put a number on the gains made by using SIS as a strategic tool in data mining, extensive experiments and analyses are carried out. The predicted results of this investigation will have implications for both theoretical and applied settings. Predictive models in a wide variety of disciplines may benefit from the enhanced classification accuracy enabled by SIS inside ANNs. An invaluable resource for scholars and practitioners in the fields of AI and data mining, this study adds to the continuing conversation about how to maximize the efficacy of machine learning methods.
IDENTITY DISCLOSURE PROTECTION IN DYNAMIC NETWORKS USING K W – STRUCTURAL DIV...IJITE
The data mining figures out accurate information for requesting user after the raw data is analyzed. Among
lots of developments, data mining face hot issues on security, privacy and integrity. Data mining use one of the latest technique called privacy preserving data publishing (PPDP), which enforces security for the digital information provided by governments, corporations, companies and individuals in social networks. People become embarrassed when adversary tries to know the sensitive information shared. Sensitive information is gathered through the vertex and multi community identities of the user. Vertex identity denotes the self-information of user like name, address, mobile number, etc. Multi community identity denotes the community group in which the user participates. To prevent such identity disclosures, this paper proposes KW -structural diversity anonymity technique, for the protection of vertex and multi community identity disclosure. In KW -structural diversity anonymity technique, k is privacy level applied for users and W is an adversary monitoring time.
Rao Mikkilineni discusses the emergence of cognitive computing models and a new cognitive infrastructure. He argues that increasing data volumes and the need for real-time insights are driving the need for intelligent, sentient, and resilient systems. The new cognitive infrastructure will include a cognitive and infrastructure agnostic control overlay, composable services, and cognitive deep learning integration. It will enable a post-hypervisor cognitive computing era with intelligent, distributed systems.
Review Paper on Shared and Distributed Memory Parallel Algorithms to Solve Bi...JIEMS Akkalkuwa
This document presents a review of parallel algorithms to solve big data problems in biological, social network, and spatial domains using shared and distributed memory. It discusses sequential and parallel algorithms for community detection in protein-protein interaction networks and social networks. It also discusses techniques for processing and analyzing large LiDAR point cloud data for applications like forest monitoring and 3D modeling. The document reviews relevant literature on algorithms for community detection, network partitioning, and LiDAR data reduction and interpolation. It then describes the BLLP algorithm for community detection in biological networks and discusses how it could be extended to distributed memory systems.
Image Recognition Expert System based on deep learningPRATHAMESH REGE
The document summarizes literature on image recognition expert systems and deep learning. It discusses two papers:
1. The Low-Power Image Recognition Challenge which established a benchmark for comparing low-power image recognition solutions based on both accuracy and energy efficiency using datasets like ILSVRC.
2. The role of knowledge-based systems and expert systems in automatic interpretation of aerial images. It discusses techniques like semantic networks, frames and logical inference used to solve ill-defined problems with limited information. Frameworks like the blackboard model, ACRONYM and SIGMA are discussed.
Integrated Analytics for IIoT Predictive Maintenance using IoT Big Data Cloud...Hong-Linh Truong
For predictive maintenance of equipment with In-
dustrial Internet of Things (IIoT) technologies, existing IoT Cloud
systems provide strong monitoring and data analysis capabilities
for detecting and predicting status of equipment. However, we
need to support complex interactions among different software
components and human activities to provide an integrated analyt-
ics, as software algorithms alone cannot deal with the complexity
and scale of data collection and analysis and the diversity of
equipment, due to the difficulties of capturing and modeling
uncertainties and domain knowledge in predictive maintenance.
In this paper, we describe how we design and augment complex
IoT big data cloud systems for integrated analytics of IIoT
predictive maintenance. Our approach is to identify various
complex interactions for solving system incidents together with
relevant critical analytics results about equipment. We incorpo-
rate humans into various parts of complex IoT Cloud systems
to enable situational data collection, services management, and
data analytics. We leverage serverless functions, cloud services,
and domain knowledge to support dynamic interactions between
human and software for maintaining equipment. We use a real-
world maintenance of Base Transceiver Stations to illustrate our
engineering approach which we have prototyped with state-of-
the art cloud and IoT technologies, such as Apache Nifi, Hadoop,
Spark and Google Cloud Functions.
Similaire à Detection of fraud in financial blockchain-based transactions through big data analytics (20)
El documento describe una investigación sobre la monitorización de redes sociales y la desinformación en Europa. El proyecto busca desarrollar una plataforma híbrida que utilice técnicas deterministas e inteligencia artificial para clasificar y analizar contenidos en redes, detectar bots y medir la viralidad. El objetivo final es ayudar a verificar la información y combatir la desinformación.
This document discusses engineering digitalization through task automation and reuse in the development lifecycle. It proposes a knowledge-centric approach to systems engineering using a knowledge management strategy. This includes defining a controlled vocabulary, relating terms through relationships and clusters, representing textual patterns for matching, and combining rules and tasks to infer information. This knowledge graph could then enable capabilities like requirements extraction, model population, quality checking, and reuse of system artifacts. The approach aims to automate tasks, link different artifact types, and leverage semantics and AI/ML to better understand and exploit knowledge embedded in systems artifacts.
Presentation adapted from the ProSTEP symposium to present the concept and advances in the digitalization of the lifecyle with focus on task automation and reuse.
1) The document discusses how systems engineering methods can be integrated with the AI/ML lifecycle to engineer intelligent systems. It identifies 10 major challenges for this integration, including describing AI/ML model needs and capabilities, integrating AI/ML into specification, verification, and other systems engineering processes.
2) The document proposes concepts for tackling each challenge, such as using standards to describe AI/ML model lifecycles and digital twin environments for verification. It also discusses opportunities like reusing existing AI/ML models and the need to educate new professionals.
3) Key points are that research is active in integrating systems engineering and AI/ML to build safer, more cost-effective cyber-physical systems, and
This document discusses digitalizing the engineering lifecycle through task automation and reuse. It proposes a knowledge-centric systems engineering approach using a knowledge management strategy called "Sailing the V". This involves defining a controlled vocabulary and formalizing relationships between terms, textual patterns, and rules to infer information and link system artifacts like requirements, models, and simulations. The goal is to automate tasks, enable reuse, ensure quality, and provide a more integrated environment for engineers. Future work will focus on data integration, semantics, artificial intelligence, and enhancing engineering methods.
Este documento presenta una introducción a Deep Learning. Comienza con una agenda que incluye una visión general de Deep Learning, Keras y ejemplos de casos de uso. Luego cubre arquitecturas y configuraciones de redes neuronales profundas, incluidas funciones de activación, pérdida y ejemplos de redes como AlexNet y ResNet. También describe el entorno tecnológico, incluidos frameworks como TensorFlow y Keras, e infraestructura en la nube. Finalmente, proporciona una metodología de trabajo y una lista de ejemplos práct
This presentation is a keynote in the AI4SE International Workshop exploring the challenges and opportunities of bringing Systems Engineering the development of AI/ML functions for safety-critical systems.
This is the presentation of the paper about the integration of artificial intelligence and the systems engineering lifecycle.
You can find more information in the following link: https://event.conflr.com/IS2019/sessiondetail_395325
The objective of this presentation to present some challenges and opportunities in the integration of Systems Engineering and the Artificial Intelligence/Machine Learning model lifecycle.
A presentation of the on-going work on interoperability within the toolchain. A new domain OSLC KM is introduced, some experiments for reusing models are also presented and, some videos are also used to present some user stories.
This document introduces software architecture and provides examples using GitHub. It defines software architecture as the fundamental concepts or properties of a system embodied in its elements, relationships, and design principles. The document outlines Philippe Kruchten's 4+1 view model for describing software architecture, including logical, process, physical and development views in addition to scenarios. Diagrams for GitHub's class, component, sequence and deployment architectures are presented as examples.
This is the final degree project of Eduardo Cibrián that has developed a semantic system to generate news headlines for several sports based on a set of patterns
In this presentation, a an overview of the blockchain foundations are presented. The presentation introduces the use of blockchain in the music industry. To do so, a good number of platforms are presented. It mainly reviews the use of blockchain for intellectual property management, digital identity, monetization, etc.
[4:55 p.m.] Bryan Oates
OJPs are becoming a critical resource for policy-makers and researchers who study the labour market. LMIC continues to work with Vicinity Jobs’ data on OJPs, which can be explored in our Canadian Job Trends Dashboard. Valuable insights have been gained through our analysis of OJP data, including LMIC research lead
Suzanne Spiteri’s recent report on improving the quality and accessibility of job postings to reduce employment barriers for neurodivergent people.
Decoding job postings: Improving accessibility for neurodivergent job seekers
Improving the quality and accessibility of job postings is one way to reduce employment barriers for neurodivergent people.
OJP data from firms like Vicinity Jobs have emerged as a complement to traditional sources of labour demand data, such as the Job Vacancy and Wages Survey (JVWS). Ibrahim Abuallail, PhD Candidate, University of Ottawa, presented research relating to bias in OJPs and a proposed approach to effectively adjust OJP data to complement existing official data (such as from the JVWS) and improve the measurement of labour demand.
Fabular Frames and the Four Ratio ProblemMajid Iqbal
Digital, interactive art showing the struggle of a society in providing for its present population while also saving planetary resources for future generations. Spread across several frames, the art is actually the rendering of real and speculative data. The stereographic projections change shape in response to prompts and provocations. Visitors interact with the model through speculative statements about how to increase savings across communities, regions, ecosystems and environments. Their fabulations combined with random noise, i.e. factors beyond control, have a dramatic effect on the societal transition. Things get better. Things get worse. The aim is to give visitors a new grasp and feel of the ongoing struggles in democracies around the world.
Stunning art in the small multiples format brings out the spatiotemporal nature of societal transitions, against backdrop issues such as energy, housing, waste, farmland and forest. In each frame we see hopeful and frightful interplays between spending and saving. Problems emerge when one of the two parts of the existential anaglyph rapidly shrinks like Arctic ice, as factors cross thresholds. Ecological wealth and intergenerational equity areFour at stake. Not enough spending could mean economic stress, social unrest and political conflict. Not enough saving and there will be climate breakdown and ‘bankruptcy’. So where does speculative design start and the gambling and betting end? Behind each fabular frame is a four ratio problem. Each ratio reflects the level of sacrifice and self-restraint a society is willing to accept, against promises of prosperity and freedom. Some values seem to stabilise a frame while others cause collapse. Get the ratios right and we can have it all. Get them wrong and things get more desperate.
Discover the Future of Dogecoin with Our Comprehensive Guidance36 Crypto
Learn in-depth about Dogecoin's trajectory and stay informed with 36crypto's essential and up-to-date information about the crypto space.
Our presentation delves into Dogecoin's potential future, exploring whether it's destined to skyrocket to the moon or face a downward spiral. In addition, it highlights invaluable insights. Don't miss out on this opportunity to enhance your crypto understanding!
https://36crypto.com/the-future-of-dogecoin-how-high-can-this-cryptocurrency-reach/
Economic Risk Factor Update: June 2024 [SlideShare]Commonwealth
May’s reports showed signs of continued economic growth, said Sam Millette, director, fixed income, in his latest Economic Risk Factor Update.
For more market updates, subscribe to The Independent Market Observer at https://blog.commonwealth.com/independent-market-observer.
Enhancing Asset Quality: Strategies for Financial Institutionsshruti1menon2
Ensuring robust asset quality is not just a mere aspect but a critical cornerstone for the stability and success of financial institutions worldwide. It serves as the bedrock upon which profitability is built and investor confidence is sustained. Therefore, in this presentation, we delve into a comprehensive exploration of strategies that can aid financial institutions in achieving and maintaining superior asset quality.
New Visa Rules for Tourists and Students in Thailand | Amit Kakkar Easy VisaAmit Kakkar
Discover essential details about Thailand's recent visa policy changes, tailored for tourists and students. Amit Kakkar Easy Visa provides a comprehensive overview of new requirements, application processes, and tips to ensure a smooth transition for all travelers.
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A toxic combination of 15 years of low growth, and four decades of high inequality, has left Britain poorer and falling behind its peers. Productivity growth is weak and public investment is low, while wages today are no higher than they were before the financial crisis. Britain needs a new economic strategy to lift itself out of stagnation.
Scotland is in many ways a microcosm of this challenge. It has become a hub for creative industries, is home to several world-class universities and a thriving community of businesses – strengths that need to be harness and leveraged. But it also has high levels of deprivation, with homelessness reaching a record high and nearly half a million people living in very deep poverty last year. Scotland won’t be truly thriving unless it finds ways to ensure that all its inhabitants benefit from growth and investment. This is the central challenge facing policy makers both in Holyrood and Westminster.
What should a new national economic strategy for Scotland include? What would the pursuit of stronger economic growth mean for local, national and UK-wide policy makers? How will economic change affect the jobs we do, the places we live and the businesses we work for? And what are the prospects for cities like Glasgow, and nations like Scotland, in rising to these challenges?
University of North Carolina at Charlotte degree offer diploma Transcripttscdzuip
办理美国UNCC毕业证书制作北卡大学夏洛特分校假文凭定制Q微168899991做UNCC留信网教留服认证海牙认证改UNCC成绩单GPA做UNCC假学位证假文凭高仿毕业证GRE代考如何申请北卡罗莱纳大学夏洛特分校University of North Carolina at Charlotte degree offer diploma Transcript
Independent Study - College of Wooster Research (2023-2024) FDI, Culture, Glo...AntoniaOwensDetwiler
"Does Foreign Direct Investment Negatively Affect Preservation of Culture in the Global South? Case Studies in Thailand and Cambodia."
Do elements of globalization, such as Foreign Direct Investment (FDI), negatively affect the ability of countries in the Global South to preserve their culture? This research aims to answer this question by employing a cross-sectional comparative case study analysis utilizing methods of difference. Thailand and Cambodia are compared as they are in the same region and have a similar culture. The metric of difference between Thailand and Cambodia is their ability to preserve their culture. This ability is operationalized by their respective attitudes towards FDI; Thailand imposes stringent regulations and limitations on FDI while Cambodia does not hesitate to accept most FDI and imposes fewer limitations. The evidence from this study suggests that FDI from globally influential countries with high gross domestic products (GDPs) (e.g. China, U.S.) challenges the ability of countries with lower GDPs (e.g. Cambodia) to protect their culture. Furthermore, the ability, or lack thereof, of the receiving countries to protect their culture is amplified by the existence and implementation of restrictive FDI policies imposed by their governments.
My study abroad in Bali, Indonesia, inspired this research topic as I noticed how globalization is changing the culture of its people. I learned their language and way of life which helped me understand the beauty and importance of cultural preservation. I believe we could all benefit from learning new perspectives as they could help us ideate solutions to contemporary issues and empathize with others.
How Non-Banking Financial Companies Empower Startups With Venture Debt Financing
Detection of fraud in financial blockchain-based transactions through big data analytics
1. Detection of fraud in financial blockchain-based
transactions through big data analytics
Jessica P´aez Bonilla
Director: Jose Maria ´Alvarez Rodr´ıguez
Universidad Carlos III de Madrid
Master in Big Data Analytics
2017-2018
July 11,2018
Jessica P´aez Bonilla (UC3M) Master Thesis July 11,2018 1 / 27
2. Overview
1 Introduction
2 Project Objectives
3 System Design
4 Implementation
5 Experiment
6 Project Budget and Plan
7 Legal Framework and socio-economic environment
8 Conclusions and Future works
Jessica P´aez Bonilla (UC3M) Master Thesis July 11,2018 2 / 27
3. Introduction
Using analytical techniques -data gathering, preprocessing, and model
building- it could be possible to detect and prevent financial fraud.
The aim to describe complex fraud in terms of patterns suitable for
system-driven detection and analysis.
Network analysis can provide useful insight into large datasets based
on the interconnectedness of the agents in the network being
analyzed.
Jessica P´aez Bonilla (UC3M) Master Thesis July 11,2018 3 / 27
4. Introduction
Network: shows relationships among the blockchain users and flux of
money. It enables the fraud patterns discovery.
Network graph analysis offers a method for capturing the context
of fraud in a standard, machine readable and transferable format.
Associations learned from visually observing fraudulent transactions,
could be used as knowledge input to create analytical models.
Jessica P´aez Bonilla (UC3M) Master Thesis July 11,2018 4 / 27
5. Project Objectives
1 Research techniques used for fraud detection and explore blockchain
data.
2 Design a system that could take into account the patterns
surrounding the fraudulent transactions.
3 Implement a system using big data analytic tools like R and Python.
4 Experiment and validate the designed system.
Jessica P´aez Bonilla (UC3M) Master Thesis July 11,2018 5 / 27
7. System Design - Network Metrics
Metric Interpretation
Degree Influence on the network
Closeness How quick is the access to other nodes in the network
Betweeness Node location. Is it in the shortest path to other nodes?
Density Level of linkage among the nodes
Modularity How modular the network is
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8. Implementation - Technology used
BigQuery, R
(igraph) and
Python have been
used in the
development of
this system.
Table 1: Used Packages Versions
Package Used Version
matplotlib 1.5.1
pandas 0.19.2
networkx 1.11
community 0.9
numpy 1.11.3
scipy 0.18.1
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9. Experiment - Steps
1 Data Exploration.
2 Network metrics and extraction of communities.
3 Features and ML algorithms selection.
4 Performance Measures.
5 Execution.
6 Analysis of Results.
7 Experiment Limitations.
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10. Experiment - 1. Data Exploration
Bitcoin blockchain data was explored using BigQuery. A data segment
containing fraudulent movements was chosen as sample for analysis in this
project.
Figure 1: Blocks over time Figure 2: Transactions in the sample
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12. Experiment - 3. Features and ML algorithms selection
Figure 4: Selected features
ML Algorithms
1 Decision Tree
1 White-box modeled. Can be
interpreted.
2 Perform well on imbalanced
datasets.
2 Random Forest
1 Ensemble: combine the
predictions of several base
estimators in order to improve
robustness over a single
estimator.
2 Each tree in the ensemble is
built from a sample drawn
with replacement
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13. Experiment - 4. Performance Measures
Classification Precision
It gives the percentage of correct predictions.
Confusion Matrix
It is a 2x2 matrix that tells us the types of errors that the classifier is
making.
AUC - Area Under the (ROC) Curve
It is a single number summary of classifier performance, useful even when
there is class imbalance.
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14. Experiment - 5. Execution
Once the features (transaction network metrics) are obtained, and ML
algortithms and its performance metrics are defined, 2 main tasks need to
be run before fitting the system.
Observations Labeling
Analysis of a real fraudulent transaction.
Dataset Balancing
Once the dataset is labeled, there were many more observations of one
class. An oversampling technique was applied in order to balance it.
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15. Experiment- 5.1. Analysis of a fraudulent transaction
Figure 5: Fraudster Neighbours
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16. Experiment - 5.2. Dataset Balancing
The dataset used
has around 30k
observations in
the training set
and around 7k in
the test set.
Python package
Imbalanced-learn
was used. It
applies an
oversampling on
the minority class.
Table 2: Proportion of classes
Dataset Class Proportion
Train Suspicious 0.498627
Train Non-suspicious 0.501373
Test Suspicious 0.500343
Test Non-suspicious 0.499657
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17. Experiment - 6. Analysis of Results
The obtained metrics of the selected ML algorithms are summarized in the
table below:
Table 3: Classification Metrics Comparison
Model Class. Accuracy Sensitivity AUC
Decision Tree 0.9989 0.9979 0.9994
Random Forest 0.9619 0.9752 0.9974
The selected method was the Random Forest, as was the one giving more
weight to the different network metrics and still achieving a high accuracy.
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18. Experiment - 6. Analysis of Results
The weight given to each of the features of Random Forest is presented in
this barchart.
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21. Experiment - 7. Limitations
Studying more known cases of fraud within the bitcoin blockchain, it
could be possible to increase the known fraudulent transaction
patterns.
Having more data will also help to prevent the overfitting with
decision trees, as the tree design would not be able to cover all the
training data.
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22. Project Budget
A summary of the project budget is presented in the table.
Cost Total (AC)
Direct Costs 8,827.5
Indirect Costs 882,75
Total Costs 9,710.25
Profit (10%) 971.025
Cost + Profit 10,681.275
IVA (21%) 2,243.06
TOTAL + IVA 12,924.343
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24. Legal Framework and socio-economic environment
Legal Framework: The Bitcoin blockchain data is now available for
exploration with BigQuery, using Google Cloud services. Data is
public and no licensing is required.
Socio-economic environment: Blockchain technology is rapidly
evolving and will be widely used in the finance world in the coming
years.
10 % of world GDP will be stored in blockchains by 2020.
IoT era also promotes the Fintech revolution.
It creates the challenge to develop and apply different sets of
techniques in order to detect fraud in these new digital platforms.
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25. Conclusions
1 Business: Detecting and flagging activity suspicious of fraud before it
actually takes place could save billions annually in both developed and
non-developed economies.
2 Technical: The proposed system can flag a suspicious blockchain
transaction with a high accuracy taking into account network metrics
resulting of modeling the giant components of the transactions.
3 Personal: Learning of a ongrowing sector (”Fintech”) that combines
finance and technology as well as of how the analytic techniques can
be applied to it.
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26. Future works
1 Create a software platform that could access and integrate both
environments R and Python.
2 This platform could be running continuously and flag by means of
an UI whenever the model classifies a new observation as Suspicious.
3 Knowing more patterns of fraudulent transactions can help to
avoid the overfitting in the models.
4 Try other network metrics (like mean neighbour degree, node
correlation similarity etc..) as features for the classification model.
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27. Thank you for your attention
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