Edinburgh Data-Intensive Research Data-intensive refers to huge volumes of data, complex patterns of data integration and analysis, and intricate interactions between data and users. Current methods and tools are failing to address data-intensive challenges effectively. They fail for several reasons, all of which are aspects of scalability. The deluge of computational methods and plethora of computational systems prevents effective and efficient use of resources, user interfaces are not adopted at a sufficient rate to satisfy demand for scientific computing and data and knowledge is created outside suitable contexts for collaborative research to be effective. The Edinburgh Data-Intensive Research group addresses these scalability issues by providing mappings from abstract formulations to concrete and optimised executions of research challenges, by developing intuitive interfaces to enable access to steer these executions and by developing systems to aid in creating new research challenges. In this talk I will present several exemplars where we have dealt with scalability issues in scientific scenarios.
Self Attested Images for Secured Transactions using Superior SOMIDES Editor
Separate digital signals are usually used as the
digital watermarks. But this paper proposes rebuffed
untrained minute values of vital image as a digital watermark,
since no host image is needed to hide the vital image for its
safety. The vital images can be transformed with the self
attestation. Superior Self Organized Maps is used to derive
self signature from the vital image. This analysis work
constructs framework with Superior Self Organizing Maps
(SSOM) against Counter Propagation Network for watermark
generation and detection. The required features like
robustness, imperceptibility and security was analyzed to prove
that which neural network is appropriate for mining watermark
from the host image. SSOM network is proved as an efficient
neural trainer for the proposed watermarking technique. The
paper presents one more contribution to the watermarking
area.
The document discusses the DAME (Data Mining & Exploration) project, which aims to implement data mining applications and services for massive data analysis and exploration using a distributed computing environment. It seeks to standardize data mining methods and make them interoperable within the virtual observatory. The project has developed several web applications and investigates using a plugin architecture and standardized accounting to improve interoperability between applications and minimize data transfer requirements. The goal is to develop a unified data mining application approach for the virtual observatory.
Issues of Information Semantics and Granularity in Cross-Media PublishingBeat Signer
CAiSE 2003, Conference on Advanced Information Systems Engineering, Klagenfurt/Velden, Austria, June 2003
ABSTRACT: While there have been dramatic increases in the use of digital technologies for the storage and processing of information, the affordances of paper have ensured its retention as a key information medium. Recent developments in digitally augmented paper provide the potential to embed active links within printed documents, thereby turning paper into an interactive medium. In this paper, we address the issues of information granularity and semantics that arise in integrating paper as a first-class interactive information medium in hypermedia systems and show that the information server is vital in realising the true potential of this vision. Further, we discuss the authoring issues of cross-media information environments and the forms of tools required to support the various categories of authoring activity.
IEEE P2P 2009 - Kalman Graffi - Monitoring and Management of Structured Peer-...Kalman Graffi
The peer-to-peer paradigm shows the potential to provide the same functionality and quality like client/server based systems, but with much lower costs. In order to control the quality of peer-to-peer systems, monitoring and management mechanisms need to be applied. Both tasks are challenging in large-scale networks with autonomous, unreliable nodes. In this paper we present a monitoring and management framework for structured peer-to-peer systems. It captures the live status of a peer-to-peer network in an exhaustive statistical representation. Using principles of autonomic computing, a preset system state is approached through automated system re-configuration in the case that a quality deviation is detected. Evaluation shows that the monitoring is very precise and lightweight and that preset quality goals are reached and kept automatically.
1) The document discusses VLSI architecture and implementation for 3D neural network based image compression. It proposes developing new hardware architectures optimized for area, power, and speed for implementing 3D neural networks for image compression.
2) A block diagram is presented showing the overall process of image acquisition, preprocessing, compression using a 3D neural network, and encoding for transmission.
3) The proposed 3D neural network architecture uses multiple hidden layers with lower dimensions than the input and output layers to perform compression and decompression. The network is trained using backpropagation.
Application scenarios in streaming oriented embedded-system designMr. Chanuwan
This document introduces the concept of application scenarios for streaming-oriented embedded system design. It defines application scenarios as sets of similar operation modes grouped by their resource usage. The document outlines a three-step methodology for incorporating application scenarios into the design process: 1) discovering scenarios by identifying and clustering similar operation modes, 2) deriving predictors to determine the active scenario, and 3) exploiting scenarios to optimize design aspects like energy efficiency. It also discusses different ways to classify and discover scenarios, and provides examples of how previous works have used scenarios to optimize memory usage, voltage scaling, and multi-task scheduling.
IRJET - An Robust and Dynamic Fire Detection Method using Convolutional N...IRJET Journal
This document proposes a new fire detection method using convolutional neural networks (CNNs). Specifically, it uses the YOLOv3 object detection algorithm, which can detect objects like fire in images or videos quickly and accurately. The proposed method aims to reduce computational time and costs compared to other CNN-based approaches, while also improving detection accuracy and reducing false alarms. It discusses implementing the method using four main modules: data exploration, pre-processing, feature engineering, and model selection. The workflow involves exploring data, pre-processing images, extracting features, and selecting the YOLOv3 CNN model for fire detection. The goal is to develop a robust and dynamic fire detection system using computer vision techniques to help prevent accidents.
IRJET- An Efficient VLSI Architecture for 3D-DWT using Lifting SchemeIRJET Journal
This document proposes an efficient VLSI architecture for 3D discrete wavelet transform (DWT) using the lifting scheme. The lifting scheme implementation of DWT has lower area, power consumption and computational complexity compared to convolution-based DWT. The proposed architecture achieves reductions in total area and power compared to existing convolution DWT and discrete cosine transform architectures. It evaluates the performance in terms of area analysis, timing reports, and output matrices after 1D, 2D and 3D DWT using both convolution and lifting schemes. The results show that the lifting scheme provides better compression performance with less area and delay.
Self Attested Images for Secured Transactions using Superior SOMIDES Editor
Separate digital signals are usually used as the
digital watermarks. But this paper proposes rebuffed
untrained minute values of vital image as a digital watermark,
since no host image is needed to hide the vital image for its
safety. The vital images can be transformed with the self
attestation. Superior Self Organized Maps is used to derive
self signature from the vital image. This analysis work
constructs framework with Superior Self Organizing Maps
(SSOM) against Counter Propagation Network for watermark
generation and detection. The required features like
robustness, imperceptibility and security was analyzed to prove
that which neural network is appropriate for mining watermark
from the host image. SSOM network is proved as an efficient
neural trainer for the proposed watermarking technique. The
paper presents one more contribution to the watermarking
area.
The document discusses the DAME (Data Mining & Exploration) project, which aims to implement data mining applications and services for massive data analysis and exploration using a distributed computing environment. It seeks to standardize data mining methods and make them interoperable within the virtual observatory. The project has developed several web applications and investigates using a plugin architecture and standardized accounting to improve interoperability between applications and minimize data transfer requirements. The goal is to develop a unified data mining application approach for the virtual observatory.
Issues of Information Semantics and Granularity in Cross-Media PublishingBeat Signer
CAiSE 2003, Conference on Advanced Information Systems Engineering, Klagenfurt/Velden, Austria, June 2003
ABSTRACT: While there have been dramatic increases in the use of digital technologies for the storage and processing of information, the affordances of paper have ensured its retention as a key information medium. Recent developments in digitally augmented paper provide the potential to embed active links within printed documents, thereby turning paper into an interactive medium. In this paper, we address the issues of information granularity and semantics that arise in integrating paper as a first-class interactive information medium in hypermedia systems and show that the information server is vital in realising the true potential of this vision. Further, we discuss the authoring issues of cross-media information environments and the forms of tools required to support the various categories of authoring activity.
IEEE P2P 2009 - Kalman Graffi - Monitoring and Management of Structured Peer-...Kalman Graffi
The peer-to-peer paradigm shows the potential to provide the same functionality and quality like client/server based systems, but with much lower costs. In order to control the quality of peer-to-peer systems, monitoring and management mechanisms need to be applied. Both tasks are challenging in large-scale networks with autonomous, unreliable nodes. In this paper we present a monitoring and management framework for structured peer-to-peer systems. It captures the live status of a peer-to-peer network in an exhaustive statistical representation. Using principles of autonomic computing, a preset system state is approached through automated system re-configuration in the case that a quality deviation is detected. Evaluation shows that the monitoring is very precise and lightweight and that preset quality goals are reached and kept automatically.
1) The document discusses VLSI architecture and implementation for 3D neural network based image compression. It proposes developing new hardware architectures optimized for area, power, and speed for implementing 3D neural networks for image compression.
2) A block diagram is presented showing the overall process of image acquisition, preprocessing, compression using a 3D neural network, and encoding for transmission.
3) The proposed 3D neural network architecture uses multiple hidden layers with lower dimensions than the input and output layers to perform compression and decompression. The network is trained using backpropagation.
Application scenarios in streaming oriented embedded-system designMr. Chanuwan
This document introduces the concept of application scenarios for streaming-oriented embedded system design. It defines application scenarios as sets of similar operation modes grouped by their resource usage. The document outlines a three-step methodology for incorporating application scenarios into the design process: 1) discovering scenarios by identifying and clustering similar operation modes, 2) deriving predictors to determine the active scenario, and 3) exploiting scenarios to optimize design aspects like energy efficiency. It also discusses different ways to classify and discover scenarios, and provides examples of how previous works have used scenarios to optimize memory usage, voltage scaling, and multi-task scheduling.
IRJET - An Robust and Dynamic Fire Detection Method using Convolutional N...IRJET Journal
This document proposes a new fire detection method using convolutional neural networks (CNNs). Specifically, it uses the YOLOv3 object detection algorithm, which can detect objects like fire in images or videos quickly and accurately. The proposed method aims to reduce computational time and costs compared to other CNN-based approaches, while also improving detection accuracy and reducing false alarms. It discusses implementing the method using four main modules: data exploration, pre-processing, feature engineering, and model selection. The workflow involves exploring data, pre-processing images, extracting features, and selecting the YOLOv3 CNN model for fire detection. The goal is to develop a robust and dynamic fire detection system using computer vision techniques to help prevent accidents.
IRJET- An Efficient VLSI Architecture for 3D-DWT using Lifting SchemeIRJET Journal
This document proposes an efficient VLSI architecture for 3D discrete wavelet transform (DWT) using the lifting scheme. The lifting scheme implementation of DWT has lower area, power consumption and computational complexity compared to convolution-based DWT. The proposed architecture achieves reductions in total area and power compared to existing convolution DWT and discrete cosine transform architectures. It evaluates the performance in terms of area analysis, timing reports, and output matrices after 1D, 2D and 3D DWT using both convolution and lifting schemes. The results show that the lifting scheme provides better compression performance with less area and delay.
PREVENTING COPYRIGHTS INFRINGEMENT OF IMAGES BY WATERMARKING IN TRANSFORM DOM...ijistjournal
1) The document discusses a method for preventing copyright infringement of images using watermarking in the transform domain and a full counter propagation neural network.
2) It aims to encode the host image before watermark embedding to enhance security. The fast and effective full counter propagation neural network then helps successfully embed the watermark without deteriorating the image quality.
3) Previous techniques embedded watermarks directly in images, but the authors find neural network synapses provide a better way to reduce distortion and increase message capacity when embedding watermarks.
IRJET- Reversible Image Data Hiding in an Encrypted Domain with High Level of...IRJET Journal
The document proposes a reversible image data hiding scheme that operates in an encrypted domain. It embeds data through public key modulation without needing access to the secret encryption key. It uses a support vector machine classifier at the decoder to jointly decode the embedded message and reconstruct the original image by distinguishing encrypted from non-encrypted image patches. Experimental results on 100 test images validate that the proposed approach provides higher embedding capacity while perfectly reconstructing the original image and embedded message.
Presentación de Mihai Datcu sobre web semantica, data mining y web 3.0 en la Jornada de Web Semántica organizada por la AEI del Conocimiento de Asturias y el Cluster TIC.
Celebrada el 4 de junio de 2010.
The document summarizes a meeting of the Computer Science Research Training Group METRIK (GRK 1324) held on June 19, 2012. METRIK focuses on model-based development of technologies for self-organizing decentralized information systems in disaster management and smart cities. The group has been running research for six years, starting with disaster management and earthquake early warning, and now applying previous findings to smart cities topics. It involves 11 doctoral candidates and makes use of its Humboldt Wireless Lab testbed for wireless sensor network and wireless mesh network experiments.
Dr. Ara V. Nefian is seeking a challenging research position in areas including computer vision, 3D reconstruction, robotics, and signal processing. She has over 10 years of research experience and 40 peer-reviewed publications. Her education includes a PhD in Electrical Engineering from Georgia Tech, focusing on face detection and recognition using HMMs. Her professional experience includes senior research roles at Carnegie Mellon, Nokia, Ooyala, and Intel, where she led teams and projects in areas like 3D terrain reconstruction, Mars surface reconstruction, pipeline threat detection, and web image clustering. She has 20 patents filed and 10 issued in areas like face recognition, audio-visual processing, and machine learning.
The International Journal of Engineering and Science (The IJES)theijes
This document summarizes and compares different techniques for moving object detection in video surveillance systems. It discusses background subtraction, background estimation, and adaptive contrast change detection methods. It finds that while traditional methods work for single objects, correlation between frames performs better for multiple objects or poor lighting conditions, as it detects changes between frames. The document evaluates several algorithms and concludes correlation significantly improves output and performance even with multiple moving objects, making it suitable for night-time surveillance applications.
Video Denoising using Transform Domain MethodIRJET Journal
This document presents a proposed method for video denoising using dictionary learning and transform domain techniques. It begins with an abstract describing how traditional video denoising models based on Gaussian noise do not account for real-world noise sources. The proposed method then learns basis functions adaptively from input video frames using dictionary learning, providing a sparse representation. Hard thresholding is applied in the transform domain to compute denoised frames. Experimental results on standard test videos show the method achieves competitive performance compared to other approaches in terms of peak signal-to-noise ratio.
Indoor Point Cloud Processing - Deep learning for semantic segmentation of in...CubiCasa
This document discusses using deep learning techniques for semantic segmentation of indoor point clouds. It provides an overview of initial ideas for using deep learning models trained on 3D CAD models to classify and label points in an indoor point cloud. It also discusses pre-processing the point cloud through techniques like denoising, upsampling, and finding planar surfaces to simplify the input before semantic segmentation. The order of semantic segmentation and 3D reconstruction is noted as something that could potentially be swapped.
Image restoration techniques covered such as denoising, deblurring and super-resolution for 3D images and models.
From classical computer vision techniques to contemporary deep learning based processing for both ordered and unordered point clouds, depth maps and meshes.
NEURAL NETWORKS FOR HIGH PERFORMANCE TIME-DELAY ESTIMATION AND ACOUSTIC SOURC...csandit
Time-delay estimation is an essential building block of many signal processing applications.This paper follows up on earlier work for acoustic source localization and time delay estimation
using pattern recognition techniques in the adverse environment such as reverberant rooms or underwater; it presents unprecedented high performance results obtained with supervised training of neural networks which challenge the state of the art and compares its performance to that of well-known methods such as the Generalized Cross-Correlation or Adaptive Eigenvalue Decomposition.
The document proposes a selective data pruning-based compression scheme to improve rate-distortion performance. It involves pruning original frames to a smaller size before compression by dropping rows or columns. After decoding, frames are interpolated back to the original size using an edge-directed interpolation method. A novel high-order interpolation is also introduced to adapt to multiple edge directions. Simulation results validate the effectiveness of the proposed methods in image interpolation and video coding applications by achieving high quality from lower bitrates compared to existing techniques.
This document provides guidance on labeling fundus images for classification models. It recommends using optimized labeling tools to annotate optic disc positions more efficiently than manual drawing. Popular tools include Labelbox and VGG Image Annotator. The document estimates that labeling 1,000 fundus images with a single object each could take around 1 hour and 20 minutes. It also notes that pre-trained non-medical networks can be built upon for "small data" sets of 1,000 images.
This document summarizes a research paper on Fashion AI. It proposes a new Group Decreasing Network (GroupDNet) that uses group convolutions in the generator and gradually reduces the percentage of groups in the decoder's convolutions. This allows the model to have more control over generating images from semantic labels and produce high-quality, multi-modal outputs. The paper describes GroupDNet's architecture, compares it to other approaches like using multiple generators, and shows it outperforms other methods on challenging datasets based on metrics like FID and mIoU. Potential applications discussed include mixed fashion styles, semantic manipulation, and tracking fashion trends over time. The conclusion discusses GroupDNet's performance but notes room for improving computational efficiency
This document discusses optimal image compression techniques based on wavelet transforms. It proposes classifying images as artificial or natural and then applying different wavelet-based compression methods. Haar, Daubechies, Coiflet, and Discrete Meyer wavelet transforms are compared for compressing artificial and natural images. A novel classifier is developed that extracts features from images like entropy, color, and saturation to categorize them. Compression performance and ratio are found to be significantly influenced by first classifying images and then applying the appropriate wavelet technique.
A NOVEL BACKGROUND SUBTRACTION ALGORITHM FOR PERSON TRACKING BASED ON K-NN csandit
Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful
information. It is indeed a challenging problem and it’s an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, and the last one is temporal
differencing. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper.Our proposed method is implemented and evaluated using CAVIAR database.
Journal club done with Vid Stojevic for PointNet:
https://arxiv.org/abs/1612.00593
https://github.com/charlesq34/pointnet
http://stanford.edu/~rqi/pointnet/
Deep learning for Indoor Point Cloud processing. PointNet, provides a unified architecture operating directly on unordered point clouds without voxelisation for applications ranging from object classification, part segmentation, to scene semantic parsing.
Alternative download link:
https://www.dropbox.com/s/ziyhgi627vg9lyi/3D_v2017_initReport.pdf?dl=0
Image Watermarking in Spatial Domain Using QIM and Genetic Algorithmijsrd.com
Digital watermarking is one of the proposed solutions for copyright protection of multimedia data. A watermark is a form of image or text that is impressed onto paper, which provides evidence of its authenticity. A digital watermark is digital data embedded in some host document so as to later prove the ownership of the document. Digital image watermarking refers to digital data embedding in images. Robust image watermarking systems are required so that watermarked images can resist geometric attacks in addition to common image processing tasks, such as JPEG compression. Least Significant Bit (LSB) watermarking, is one of the most traditional method of watermarking which changes the LSB of individual pixels in correlation with the watermark. However, pure LSB scheme provides a fragile watermarking technique which is not acceptable in practical applications. Also, robustness against geometric attacks, such as rotation, scaling and translation, still remains one of the most challenging research topics in pixel based image watermarking. In this paper, a new pixel-based watermarking system is proposed, in which a binary logo is embedded, a bit per pixel, in the pixel domain of an image. The LSB based watermarking is then quantized using QIM, augmented with genetic algorithm to produce a watermarking scheme which is highly robust against geometrical attacks.
Falling costs with rising quality via hardware innovations and deep learning.
Technical introduction for scanning technologies from Structure-from-Motion (SfM), Range sensing (e.g. Kinect and Matterport) to Laser scanning (e.g. LiDAR), and the associated traditional and deep learning-based processing techniques.
Note! Due to small font size, and bad rendering by SlideShare, better to download the slides locally to your device
Alternative download link for the PDF:
https://www.dropbox.com/s/eclyy45k3gz66ve/proptech_emergingScanningTech.pdf?dl=0
The document discusses various topics in a disjointed manner, moving between discussions of interview questions, music albums, football stadiums, and business realities. It questions whether distraction by trivial matters keeps people from addressing real issues, and criticizes the marketing of mediocrity as greatness and the prioritization of sports over community issues.
O documento descreve o Programa Nascentes, um programa do governo de São Paulo para incentivar a recuperação de matas ciliares e a recomposição de vegetação em bacias hidrográficas. O programa tem como objetivos proteger os recursos hídricos, otimizar investimentos ambientais e apoiar produtores rurais na recuperação de matas ciliares. Participantes podem cadastrar áreas elegíveis para projetos de restauração florestal ou financiar projetos existentes por meio de um banco de projetos.
PREVENTING COPYRIGHTS INFRINGEMENT OF IMAGES BY WATERMARKING IN TRANSFORM DOM...ijistjournal
1) The document discusses a method for preventing copyright infringement of images using watermarking in the transform domain and a full counter propagation neural network.
2) It aims to encode the host image before watermark embedding to enhance security. The fast and effective full counter propagation neural network then helps successfully embed the watermark without deteriorating the image quality.
3) Previous techniques embedded watermarks directly in images, but the authors find neural network synapses provide a better way to reduce distortion and increase message capacity when embedding watermarks.
IRJET- Reversible Image Data Hiding in an Encrypted Domain with High Level of...IRJET Journal
The document proposes a reversible image data hiding scheme that operates in an encrypted domain. It embeds data through public key modulation without needing access to the secret encryption key. It uses a support vector machine classifier at the decoder to jointly decode the embedded message and reconstruct the original image by distinguishing encrypted from non-encrypted image patches. Experimental results on 100 test images validate that the proposed approach provides higher embedding capacity while perfectly reconstructing the original image and embedded message.
Presentación de Mihai Datcu sobre web semantica, data mining y web 3.0 en la Jornada de Web Semántica organizada por la AEI del Conocimiento de Asturias y el Cluster TIC.
Celebrada el 4 de junio de 2010.
The document summarizes a meeting of the Computer Science Research Training Group METRIK (GRK 1324) held on June 19, 2012. METRIK focuses on model-based development of technologies for self-organizing decentralized information systems in disaster management and smart cities. The group has been running research for six years, starting with disaster management and earthquake early warning, and now applying previous findings to smart cities topics. It involves 11 doctoral candidates and makes use of its Humboldt Wireless Lab testbed for wireless sensor network and wireless mesh network experiments.
Dr. Ara V. Nefian is seeking a challenging research position in areas including computer vision, 3D reconstruction, robotics, and signal processing. She has over 10 years of research experience and 40 peer-reviewed publications. Her education includes a PhD in Electrical Engineering from Georgia Tech, focusing on face detection and recognition using HMMs. Her professional experience includes senior research roles at Carnegie Mellon, Nokia, Ooyala, and Intel, where she led teams and projects in areas like 3D terrain reconstruction, Mars surface reconstruction, pipeline threat detection, and web image clustering. She has 20 patents filed and 10 issued in areas like face recognition, audio-visual processing, and machine learning.
The International Journal of Engineering and Science (The IJES)theijes
This document summarizes and compares different techniques for moving object detection in video surveillance systems. It discusses background subtraction, background estimation, and adaptive contrast change detection methods. It finds that while traditional methods work for single objects, correlation between frames performs better for multiple objects or poor lighting conditions, as it detects changes between frames. The document evaluates several algorithms and concludes correlation significantly improves output and performance even with multiple moving objects, making it suitable for night-time surveillance applications.
Video Denoising using Transform Domain MethodIRJET Journal
This document presents a proposed method for video denoising using dictionary learning and transform domain techniques. It begins with an abstract describing how traditional video denoising models based on Gaussian noise do not account for real-world noise sources. The proposed method then learns basis functions adaptively from input video frames using dictionary learning, providing a sparse representation. Hard thresholding is applied in the transform domain to compute denoised frames. Experimental results on standard test videos show the method achieves competitive performance compared to other approaches in terms of peak signal-to-noise ratio.
Indoor Point Cloud Processing - Deep learning for semantic segmentation of in...CubiCasa
This document discusses using deep learning techniques for semantic segmentation of indoor point clouds. It provides an overview of initial ideas for using deep learning models trained on 3D CAD models to classify and label points in an indoor point cloud. It also discusses pre-processing the point cloud through techniques like denoising, upsampling, and finding planar surfaces to simplify the input before semantic segmentation. The order of semantic segmentation and 3D reconstruction is noted as something that could potentially be swapped.
Image restoration techniques covered such as denoising, deblurring and super-resolution for 3D images and models.
From classical computer vision techniques to contemporary deep learning based processing for both ordered and unordered point clouds, depth maps and meshes.
NEURAL NETWORKS FOR HIGH PERFORMANCE TIME-DELAY ESTIMATION AND ACOUSTIC SOURC...csandit
Time-delay estimation is an essential building block of many signal processing applications.This paper follows up on earlier work for acoustic source localization and time delay estimation
using pattern recognition techniques in the adverse environment such as reverberant rooms or underwater; it presents unprecedented high performance results obtained with supervised training of neural networks which challenge the state of the art and compares its performance to that of well-known methods such as the Generalized Cross-Correlation or Adaptive Eigenvalue Decomposition.
The document proposes a selective data pruning-based compression scheme to improve rate-distortion performance. It involves pruning original frames to a smaller size before compression by dropping rows or columns. After decoding, frames are interpolated back to the original size using an edge-directed interpolation method. A novel high-order interpolation is also introduced to adapt to multiple edge directions. Simulation results validate the effectiveness of the proposed methods in image interpolation and video coding applications by achieving high quality from lower bitrates compared to existing techniques.
This document provides guidance on labeling fundus images for classification models. It recommends using optimized labeling tools to annotate optic disc positions more efficiently than manual drawing. Popular tools include Labelbox and VGG Image Annotator. The document estimates that labeling 1,000 fundus images with a single object each could take around 1 hour and 20 minutes. It also notes that pre-trained non-medical networks can be built upon for "small data" sets of 1,000 images.
This document summarizes a research paper on Fashion AI. It proposes a new Group Decreasing Network (GroupDNet) that uses group convolutions in the generator and gradually reduces the percentage of groups in the decoder's convolutions. This allows the model to have more control over generating images from semantic labels and produce high-quality, multi-modal outputs. The paper describes GroupDNet's architecture, compares it to other approaches like using multiple generators, and shows it outperforms other methods on challenging datasets based on metrics like FID and mIoU. Potential applications discussed include mixed fashion styles, semantic manipulation, and tracking fashion trends over time. The conclusion discusses GroupDNet's performance but notes room for improving computational efficiency
This document discusses optimal image compression techniques based on wavelet transforms. It proposes classifying images as artificial or natural and then applying different wavelet-based compression methods. Haar, Daubechies, Coiflet, and Discrete Meyer wavelet transforms are compared for compressing artificial and natural images. A novel classifier is developed that extracts features from images like entropy, color, and saturation to categorize them. Compression performance and ratio are found to be significantly influenced by first classifying images and then applying the appropriate wavelet technique.
A NOVEL BACKGROUND SUBTRACTION ALGORITHM FOR PERSON TRACKING BASED ON K-NN csandit
Object tracking can be defined as the process of detecting an object of interest from a video scene and keeping track of its motion, orientation, occlusion etc. in order to extract useful
information. It is indeed a challenging problem and it’s an important task. Many researchers are getting attracted in the field of computer vision, specifically the field of object tracking in video surveillance. The main purpose of this paper is to give to the reader information of the present state of the art object tracking, together with presenting steps involved in Background Subtraction and their techniques. In related literature we found three main methods of object tracking: the first method is the optical flow; the second is related to the background subtraction, which is divided into two types presented in this paper, and the last one is temporal
differencing. We present a novel approach to background subtraction that compare a current frame with the background model that we have set before, so we can classified each pixel of the image as a foreground or a background element, then comes the tracking step to present our object of interest, which is a person, by his centroid. The tracking step is divided into two different methods, the surface method and the K-NN method, both are explained in the paper.Our proposed method is implemented and evaluated using CAVIAR database.
Journal club done with Vid Stojevic for PointNet:
https://arxiv.org/abs/1612.00593
https://github.com/charlesq34/pointnet
http://stanford.edu/~rqi/pointnet/
Deep learning for Indoor Point Cloud processing. PointNet, provides a unified architecture operating directly on unordered point clouds without voxelisation for applications ranging from object classification, part segmentation, to scene semantic parsing.
Alternative download link:
https://www.dropbox.com/s/ziyhgi627vg9lyi/3D_v2017_initReport.pdf?dl=0
Image Watermarking in Spatial Domain Using QIM and Genetic Algorithmijsrd.com
Digital watermarking is one of the proposed solutions for copyright protection of multimedia data. A watermark is a form of image or text that is impressed onto paper, which provides evidence of its authenticity. A digital watermark is digital data embedded in some host document so as to later prove the ownership of the document. Digital image watermarking refers to digital data embedding in images. Robust image watermarking systems are required so that watermarked images can resist geometric attacks in addition to common image processing tasks, such as JPEG compression. Least Significant Bit (LSB) watermarking, is one of the most traditional method of watermarking which changes the LSB of individual pixels in correlation with the watermark. However, pure LSB scheme provides a fragile watermarking technique which is not acceptable in practical applications. Also, robustness against geometric attacks, such as rotation, scaling and translation, still remains one of the most challenging research topics in pixel based image watermarking. In this paper, a new pixel-based watermarking system is proposed, in which a binary logo is embedded, a bit per pixel, in the pixel domain of an image. The LSB based watermarking is then quantized using QIM, augmented with genetic algorithm to produce a watermarking scheme which is highly robust against geometrical attacks.
Falling costs with rising quality via hardware innovations and deep learning.
Technical introduction for scanning technologies from Structure-from-Motion (SfM), Range sensing (e.g. Kinect and Matterport) to Laser scanning (e.g. LiDAR), and the associated traditional and deep learning-based processing techniques.
Note! Due to small font size, and bad rendering by SlideShare, better to download the slides locally to your device
Alternative download link for the PDF:
https://www.dropbox.com/s/eclyy45k3gz66ve/proptech_emergingScanningTech.pdf?dl=0
The document discusses various topics in a disjointed manner, moving between discussions of interview questions, music albums, football stadiums, and business realities. It questions whether distraction by trivial matters keeps people from addressing real issues, and criticizes the marketing of mediocrity as greatness and the prioritization of sports over community issues.
O documento descreve o Programa Nascentes, um programa do governo de São Paulo para incentivar a recuperação de matas ciliares e a recomposição de vegetação em bacias hidrográficas. O programa tem como objetivos proteger os recursos hídricos, otimizar investimentos ambientais e apoiar produtores rurais na recuperação de matas ciliares. Participantes podem cadastrar áreas elegíveis para projetos de restauração florestal ou financiar projetos existentes por meio de um banco de projetos.
A employment notification as Malda District Court Recruitment 2016 had been declared by the Organization to hire 38 applicants for various posts such as English Stenographer (Grade-III), Lower Division Clerk (Group-C), Process Server, Group D & Sweeper (Karma Bandhu) (Group D) on a temporary basis.Get further details at official portal.
Este silabo describe el plan de estudios para el tercer bimestre de informática para quinto año de secundaria. Incluye objetivos como el desarrollo de habilidades lógicas y de pensamiento crítico a través del uso de funciones avanzadas en Excel. El plan contempla 5 actividades como repaso de funciones lógicas, creación de fórmulas anidadas, elaboración de consultas, creación de reportes y un proyecto final utilizando lo aprendido. La evaluación considera participación en clase, prácticas de laboratorio y un
Este documento describe las prendas tradicionales y los bailes típicos de la cultura santeña de Colombia. Detalla las diferentes piezas de la pollera santeña, como el cabestrillo, el escapulario y la basquiña. También explica bailes como el punto, el tamborito y la cumbia, e instrumentos musicales como la murga y el pindín. Concluye resaltando las características distintivas de la cultura santeña.
El diagrama muestra como se va estructurar una red en un área especifica y a igual forma muestra como y que tipo de material usar para pasar el cableado de Internet
Знаки компании (исследование корпоративной культуры)Бути Ковец
Корпоративные артефакты — это «искренние» знаки компании, которые можно увидеть, услышать или почувствовать при взаимодействии с организацией, т. е. «следы» действия сотрудников и среды их обитания. Артефакт — «след жизни живущих». И, следовательно, можно идти по следам корпоративных артефактов и восстанавливать по фрагменту системы — более целостное представление о всей организационной системе, а также — видеть «точки напряжения и рассогласования» между словом и делом внутри компании. Работа с артефактами — один из технологических приемов, который реализует в своей практике сопровождения организационных проектов компания «Тренинг-Бутик»
спикер
Светлана Баронене, кандидат философских наук, партнер компании Тренинг-Бутик, бизнес-тренер, коуч, консультант по развитию корпоративной культуры, лидер темы Управление изменениями, доцент НИУ ВШЭ-Санкт-Петербург
6 сентября 2015 года, ПиР ОТУМКИ 2015
The document outlines a 10-step process for technical writing:
1. Conduct research first to understand the topic before writing about it.
2. Take notes during research to aid the writing process.
3. Write a first draft to get initial ideas on paper.
4. Rewrite and refine the draft, correcting errors and ensuring clarity.
5. Add visual elements like graphs, diagrams and photos to enhance understanding.
6. Use lists to organize information in an easy to comprehend manner.
Культура в пространстве. Убедительные посланияБути Ковец
Предновогодняя декабрьская Бутиковская булавка 16 декабря. «Культура в пространстве. Убедительные послания» — клуб о проектировании корпоративных сред, о сфокусированных культурных утверждениях — памятниках: зачем компании ставят памятники, кому и когда.
А еще HR 2016 года по версии Тренинг-Бутика, кот Бегомот и новогодняя елка.
Este documento presenta el silabo de la unidad de Adobe Flash para quinto año de secundaria. El silabo describe las capacidades y destrezas que los estudiantes desarrollarán, como razonamiento lógico, pensamiento crítico creativo y socialización. Incluye cinco actividades prácticas para aprender funciones de Flash como interpolación, máscaras y animación de personajes, culminando en un proyecto final. La evaluación considera participación, procesos de aprendizaje y productos evaluados con rúbricas.
Presentation from Professor Trevor Drage on behalf of the UKCCSRC at the National CCS Week conference in Sydney, Australia on 1 September 2014. http://www.nationalccsweek.com.au/
This document discusses public security expectations from the Punjab Police and assesses their performance. It begins with definitions of security, police, and public security. It then outlines expectations like maintaining law and order, preventing and detecting crime, and ensuring citizens feel safe. The document evaluates Punjab Police initiatives such as recruiting educated officers, establishing a dolphin force, and automating records. It concludes that while the police face challenges like rising population and crime, their vision is to become a professional, accountable force that protects communities through initiatives, political support, and engaging the public.
The knitting section has 20 circular knitting machines of different diameters and gauges from different brands like Pailung and Liskey. The machines can produce different knit fabrics like single jersey, 1x1 rib etc with a total daily capacity of around 6,000 kg.
1. The document discusses various anatomical structures and developmental stages in embryology. It lists over 100 terms related to anatomy and early embryonic development.
2. Many terms relate to specific anatomical structures and tissues like the endoderm, mesoderm, neural crest, heart, gut, and nervous system that develop early in embryogenesis.
3. The list also includes various stages of embryonic development from the one-cell stage through organ systems and includes many specific anatomical features that develop during embryogenesis.
This document discusses marketing transformation and the use of marketing automation. It outlines strategies for digital B2B marketing, including focusing on target audiences through various stages of the customer buying journey. The document recommends dividing marketing responsibilities between central and local teams to optimize demand generation, product awareness, and lead generation across different media channels and countries. Budgets and key performance indicators are also addressed.
Susie Almaneih's Top 15 Inspiring Quotes From Oprah Susie Almaneih
The document provides 15 inspiring quotes from Oprah Winfrey about success, dreams, life, and personal growth. It discusses how Oprah has given hope and happiness to viewers over 25 years through her charitable efforts and focus on creating a more tolerant world. The quotes offer wisdom on finding significance over success, following your passions, and having confidence to keep learning and growing.
The document discusses model-based visual software specification. It proposes a tool-chain for bridging different development disciplines through domain-specific modeling languages. The tool-chain would allow designers, ergonomists and programmers to work with a single model for specification and simulation. This model could then generate visual specifications and prototypes to facilitate early verification. Creating a domain-specific language involves identifying domain concepts, defining constraints, adding a graphical notation, and generating code templates. The benefits of this approach include increased flexibility, standardization and early identification of conceptual problems.
Tim Malthus_Towards standards for the exchange of field spectral datasetsTERN Australia
This document discusses the development of standards for the exchange of field spectral datasets. It notes the importance of metadata for determining the quality and representativeness of spectral data obtained in the field. A workshop was held in 2012 to discuss best practices for data collection and exchange and key conclusions included the need for standards to facilitate accurate comparison across studies and the role of thorough metadata. Work is ongoing to enhance the SPECCHIO system for hosting spectral libraries and metadata and establishing it as the international tool for storage and exchange of spectral datasets.
Big Data Beyond Hadoop*: Research Directions for the FutureOdinot Stanislas
Michael Wrinn
Research Program Director, University Research Office,
Intel Corporation
Jason Dai
Engineering Director and Principal Engineer,
Intel Corporation
This document summarizes a presentation about high performance computing (HPC) in the petroleum industry given by Dr. Leonid Sheremetov of the Mexican Petroleum Institute (IMP). It outlines the challenges of HPC in petroleum exploration and production. It provides an overview of IMP's research program in applied mathematics and computing, including their use of HPC. It then summarizes several of IMP's research projects applying HPC to problems in petroleum such as reservoir simulation, data mining of petroleum data, and distributed computing applications.
The document summarizes a presentation about high performance computing applications in the petroleum industry given by Dr. Leonid Sheremetov of the Mexican Petroleum Institute. It discusses the challenges of exploration and production for PEMEX and outlines IMP's research program including grid-based simulation, data mining, and task optimization on clusters and desktop grids. Specific applications mentioned include reservoir simulation, seismic analysis, data mining of production data, electron microscopy, and a data mining project between IMP and other Mexican institutions.
In this presentation we review some of the research problems we address at EPFL in the area of sensor data management. At the level of infrastructure we have developed a middleware to seamlessly integrate, aggregate and analyze heterogeneous sensor data streams in real-time, a WIKI based repository supporting the cooperative management of the metadata associated with sensor deployments and cloud-based storage infrastructure. An important problem in managing sensor data is their efficient storage and transmission using compression techniques. To that end we apply model-based compression methods. For analyzing sensor data, we have developed methods to dynamically estimate the variability, which can be readily used for outlier detection, and to extract semantic features from GPS sensor data streams. We also investigate techniques for trading off between the accuracy of the sensor data obtained and the degree of privacy preservation that can be maintained.
The Sensor Data Management presentation was presented by Karl Aberer (Ecole Polytechnique Federale de Lausanne) at the PlanetData project Meeting on February 28 - March 4, 2011 in Innsbruck, Austria.
The document outlines long-term research goals for uncertainty propagation algorithms and data assimilation algorithms over 5, 10, 15, and 20 years at the Lab for Autonomous & Intelligent Robotic Systems (LAIRS). The goals include developing Bayesian and belief theory frameworks, centralized and distributed data assimilation algorithms for sensor networks, and transitioning from static to dynamic sensor architectures. Potential applications are listed for areas like orbit determination, attitude estimation, and epidemic spread modeling.
Cassandra framework a service oriented distributed multimediaJoão Gabriel Lima
This document describes the CASSANDRA framework, a distributed multimedia content analysis system. It uses a service-oriented architecture that allows individual analysis components to be integrated and upgraded easily. The system is modular, self-organizing, and real-time. It can dynamically distribute workloads across available devices. The framework allows for flexible integration of new analysis algorithms and coordination of existing algorithms from different domains.
This document summarizes a method for using cloud computing resources to efficiently explore large model spaces for quantitative structure-activity relationship (QSAR) modeling. Key points:
- The method uses e-Science Central and Windows Azure to run QSAR modeling workflows in parallel across many nodes, allowing exploration of large model spaces.
- Over 250,000 models were generated exploring different modeling methods (e.g. linear regression, neural networks) across 460,000 workflow executions and 4.4 million service calls.
- Scaling to 200 nodes reduced modeling time from over 11 days to under 2 hours, demonstrating near-linear speedups from additional nodes.
Presented at the Intel Global IoT DevFest (Oct 2017)
- Real-world use cases: healthcare, building management, retail, smart cities, transportation
- Time-series analysis
- AI / ML overview & applications
The document presents a novel multi-view multi-level network (MMNet) for fault diagnosis that accommodates feature transferability. Existing deep transfer learning solutions for fault diagnosis focus on domain adaptation by minimizing data distribution discrepancies, but neglect unique domain-specific features. MMNet constructs two network channels - one for learning common cross-domain features using multi-kernel maximum mean discrepancy, and another for learning domain-specific features using combined domain and fault classification. MMNet also adopts a few-shot learning approach using two modules for feature extraction and relation computation, enabling zero-shot fault classification in the target domain. Experimental results demonstrate MMNet achieves state-of-the-art performance on transfer tasks for fault diagnosis.
IRJET- Hand Sign Recognition using Convolutional Neural NetworkIRJET Journal
1) The document presents a study on using a convolutional neural network (CNN) to recognize American Sign Language (ASL) alphabets captured in real-time via a webcam.
2) The researchers trained a CNN model on 1600 images of 5 ASL alphabets (E, F, I, L, V) and tested it on 320 unlabeled images, achieving a validation accuracy of 74.8%.
3) While the model showed potential, the researchers acknowledged limitations like overfitting due to the small dataset and noted areas for improvement like recognizing a broader range of ASL letters and full sentences.
Kuncoro Wastuwibowo is the Vice Chair of IEEE Indonesia Section and has experience in multimedia services creation at Telkom Indonesia. He has also served as Chairman of IEEE Communications Society Indonesia Chapter from 2009-2011 and Vice Chair from 2007-2008. He currently works as a Senior Service Creation at Telkom Indonesia Multimedia Division and can be contacted by email at kuncoro@computer.org or on Twitter @kuncoro.
MICE is a tool for monitoring context evolution and updating context models at runtime. It consists of three main components: a Monitor that collects contextual data from applications, a Context Data Repository that stores the data, and a Modeling Component that retrieves the data and updates context models. The tool was demonstrated by collecting battery data from Android devices using an open-source monitor, storing it in Cosm, an open-source repository, and updating awareness manager models. Future work includes combining multiple data streams and context attributes into more complex models and integrating context and design models at runtime.
Application-Aware Big Data Deduplication in Cloud EnvironmentSafayet Hossain
The document proposes AppDedupe, a distributed deduplication framework for cloud environments that exploits application awareness, data similarity, and locality. AppDedupe uses a two-tiered routing scheme with application-aware routing at the director level and similarity-aware routing at the client level. It builds application-aware similarity indices with super-chunk fingerprints to speed up intra-node deduplication efficiently. Evaluation results show that AppDedupe consistently outperforms state-of-the-art schemes in deduplication efficiency and achieving high global deduplication effectiveness.
Architectural decisions in designing data and computation intensive systems can have a major impact on the ability of these systems to perform statistical and other complex calculations efficiently. The storage, processing, tools, and associated databases coupled with the networking and compute infrastructure make some kinds of computations easier, and other harder. This talk will provide an introduction to software and data systems components that are important for understanding how these choices impact data analysis uncertainties and costs, and thus for developing system and software designs best suited to statistical analyses.
The document discusses the Virtual Atomic and Molecular Data Center (VAMDC) project, which aims to build an interoperable infrastructure for exchanging atomic and molecular data. VAMDC involves partners from multiple European countries and integrates several existing research databases. It intends to allow users to seamlessly search and retrieve data from over 20 atomic and molecular databases. VAMDC is developing standards like XSAMS, an XML schema, and technologies like the TAP protocol to enable interoperable data exchange between the distributed databases. The goal is to provide a virtual data warehouse to serve the needs of the atomic and molecular research community.
Unity: Because the Sum is Greater than the PartsInside Analysis
The Briefing Room with Krish Krishnan and Teradata
Live Webcast on Dec. 4, 2012
The current Holy Grail of analytical systems is the Unified Data Architecture: a management layer that connects people and systems to all the appropriate information sources, both structured and unstructured, across the entire enterprise. The idea is to provide a strategic view of information assets, ideally with an intelligent metadata layer that helps users align salient data sets, thus enabling a big-picture perspective that fosters insight, collaboration and improvement.
Check out this episode of The Briefing Room to hear veteran Analyst Krish Krishnan explain why we're closer than ever to achieving this holistic perspective for data assets. He'll be briefed by Imad Birouty of Teradata, who will tout his company's Unity offering, a management environment for connecting Teradata systems of all shapes and sizes. He'll explain how Unity allows users to navigate through complex information architectures, route queries to specific data sources, even access Big Data in Hadoop via AsterSQL. Birouty will also offer a sneak peek at Shark, a new project spun out of the Kickfire acquisition, which promises super-high performance queries on specific workloads.
Visit: http://www.insideanalysis.com
Impact of Soft Errors in Silicon on Reliability and Availability of ServersIshwar Parulkar
This document discusses the impact of soft errors on the reliability and availability of servers used for internet computing. It outlines how soft errors can lead to silent data corruption or unscheduled system interruptions. While memory is a major source of soft errors, the number of logic gates and pipelines in processors is increasing, thereby increasing their potential soft error rate over time. Techniques like error correction codes help mitigate soft errors but ongoing improvements are needed to meet high reliability targets for internet infrastructure.
Chi2011 Case Study: Interactive, Dynamic SparklinesLeo Frishberg
This document describes the development of interactive sparklines to help electronic engineers debug circuits. Sparklines condense hundreds of data points into a small visual space. Initially designed for static displays, the author's team created interactive sparklines to assist with debugging circuits in real-time. Through user research, they identified engineers' needs for fast, accurate data acquisition and visualizations that quickly detect problems. The team developed iterative designs based on user feedback to refine the sparklines for interactive debugging of high-speed serial data circuits.
20130227 supa-pals-challenges-in-retinal-imagingJano van Hemert
This document discusses challenges in retinal imaging. Specifically, it notes that eye health care is a rapidly expanding market and retinal imaging requires constant innovation in physics and computer science. It also summarizes that the demand for eye care services is increasing significantly due to growing rates of diabetic retinopathy and the associated economic costs. Technologies like Optos SLO/OCT aim to address this increasing demand through wider retinal imaging capabilities.
The demands of data-intensive science represent a challenge for diverse scientific communities. Data volumes from various sources are increasing exponentially, creating data management challenges. New approaches and technologies are needed to enable scientists to effectively analyze and store massive amounts of data.
Advanced Data Mining and Integration Research for Europe (ADMIRE)Jano van Hemert
The document discusses the challenges of data-intensive science across diverse fields. As experiments and simulations generate ever-larger data volumes, a new research paradigm of data-intensive science is emerging. This involves techniques for performing science at extreme scales, such as computer clusters optimized for data analysis. Many fields now face hundred- to thousand-fold increases in data from various instruments. Effectively managing, analyzing, and archiving these "digital deluges" presents significant challenges for scientists.
Rapid Giving Computational Science A Friendly FaceJano van Hemert
Rapid is a unique approach to quickly designing and delivering web portal interfaces for applications that require large amounts of computing resources or that need to run on specific servers. We will demonstrate the success of Rapid in a number of projects across a wide range of disciplines: brain imaging, chemistry, microscopy, engineering and seismology.
The Rapid approach consists of defining the resources, application use and user interface in one configuration file. This file is then validated and translated directly into a live portlet that can be inserted into a portal container. The whole process can be performed without any conventional programming. Rapid provides all the necessary components for handling compute-jobs. It knows how to handle remote files stores, monitor jobs, validate input, talk to Sun Grid Engine, Condor, PBS or just use a plain SSH connection.
Presentation for the Digital Repositories e-Science Network to introduce the new JISC-funded project which aims to deliver Google Maps for Developmental Biology.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
In the realm of cybersecurity, offensive security practices act as a critical shield. By simulating real-world attacks in a controlled environment, these techniques expose vulnerabilities before malicious actors can exploit them. This proactive approach allows manufacturers to identify and fix weaknesses, significantly enhancing system security.
This presentation delves into the development of a system designed to mimic Galileo's Open Service signal using software-defined radio (SDR) technology. We'll begin with a foundational overview of both Global Navigation Satellite Systems (GNSS) and the intricacies of digital signal processing.
The presentation culminates in a live demonstration. We'll showcase the manipulation of Galileo's Open Service pilot signal, simulating an attack on various software and hardware systems. This practical demonstration serves to highlight the potential consequences of unaddressed vulnerabilities, emphasizing the importance of offensive security practices in safeguarding critical infrastructure.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
https://www.wask.co/ebooks/digital-marketing-trends-in-2024
Feeling lost in the digital marketing whirlwind of 2024? Technology is changing, consumer habits are evolving, and staying ahead of the curve feels like a never-ending pursuit. This e-book is your compass. Dive into actionable insights to handle the complexities of modern marketing. From hyper-personalization to the power of user-generated content, learn how to build long-term relationships with your audience and unlock the secrets to success in the ever-shifting digital landscape.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
2. Efficient distributed
systems
Computer Science
Research
Effective
algorithms Data-intensive
computing
3. Efficient distributed Reusable computational
systems models
Computer Science Interdisciplinary
Research Applications
Effective Intuitive
algorithms Data-intensive Collaborative interfaces
computing environments
New conceptual
models for systems
4. Developmental Medical Emergency
Chemistry Response
Biology Genetics
Reusable computational
models
alpha release of a combined earth-
quake selection and waveform selec-
tion service combining the EMSC and
Real-time access to European BB
data successively increasing
The Virtual European Broad-band
the ORFEUS services. The web por- Seismograph Network (VEBSN) is
tal also includes a first test version steadily increasing its size. Currently
of the underlying software structure more then 270 stations are contrib-
Interdisciplinary
of the distributed archive services of uting data to the VEBSN in near real-
the Integrated European Distributed time. For some tens of these stations
Archive (EIDA) for waveform data. we still need to compile the instru-
The alpha release implies that a mentation and data details (data-
test version of the current service is less Seed volumes). An example of
made accessible for a selected group the earthquake in Greece on Febru-
Applications
of scientist that are willing to test it ary 14, 2008 illustrates the available
and recommend modifications. In- data. The VEBSN is a joint initiative
terested seismologists, student, re- of European-Mediterranean seismo-
searcher or network operator, are logical networks. More information
encouraged to contact the NERIES can be obtained from www.orfeus-
Project Office if they are interested eu.org/Data-info/vebsn.html.
to test the services. A short video
Intuitive
presentation is available (http:// Figure 3. The Greek earthquake of February 14, 2008
as recorded by the vertical component of broadband
www.neries-eu.org/main.php/demo. stations of the VEBSN (mainly in the European-Medi-
terranean area) and made available by ORFEUS. The
wmv?fileitem=8798210). Alessan- VEBSN is currently still expanding.
Collaborative Brain
dro Spinuso, Sergio Rives, Luca Tra-
Neuro- Quantitative
ni, Phetaphone Thomy, Rémy Bossu,
interfaces
Seismology
Torild van Eck. (See figure 2 below.)
informatics Genetics Imaging
environments
10. !
Figure 3: Screenshots of the DGEMap Web Portal, showing the facility for adding new project
details to the database.
Page 2 Deliverable D2.8
Design Study Contract number 011993
!
11. ?
!
Figure 3: Screenshots of the DGEMap Web Portal, showing the facility for adding new project
details to the database.
Page 2 Deliverable D2.8
Design Study Contract number 011993
!
?
12.
13.
14.
15.
16. Scaling
• More users able to join in
• Deal with more experiments
• Better reproducibility (in progress)
Want your own scientific computing portal?
Ask me!
28. Data mining results
Table 1. The preliminary result of classification performance using 10-fold validation
hhhh
h hhClassification Performance
hhhh
hhhh Sensitivity Specificity
Gene expression hh h
Humerus 0.7525 0.7921
Handplate 0.7105 0.7231
Fibula 0.7273 0.718
Tibia 0.7467 0.7451
Femur 0.7241 0.7345
Ribs 0.5614 0.7538
Petrous part 0.7903 0.7538
Scapula 0.7882 0.7099
Head mesenchyme 0.7857 0.5507
Note: Sensitivity: true positive rate. Specificity: true negative rate.
How good we can predict it is there
5 Conclusion and Future Work
How good we can predict it is not there
29. Scaling
• Size of experiment
• Volume of data
• Available resources
Want your own (distributed) data integration & mining?
Ask me!
35. Scaling
• Larger collaborations
• Handle more & diverse knowledge
• Speed-up “Fourth Paradigm”
(http://bit.ly/dwQzYe)
Want your own 3D visualisation & annotation?
Ask me!
36. Multi-disciplinary
[1] D. Rodr´ıguez Gonz´lez, T. Carpenter, J.I. van Hemert, and J. Wardlaw. An open source toolkit for
a
medical imaging de-identification. European Radiology, page First Online, 2010.
[2] R.R. Kitchen, V.S. Sabine, A.H. Sims, E.J. Macaskill, L. Renshaw, J.S. Thomas, J.I. van Hemert,
J.M. Dixon, and J.M.S. Bartlett. Correcting for intra-experiment variation in illumina beadchip data is
necessary to generate robust gene-expression profiles. BMC Genomics, 11, 2010.
[3] C.A. Morrison, N. Robertson, A. Turner, J. van Hemert, and J. Koetsier. Molecular Orbital Calculations
of Inorganic Compounds, chapter 3.33, pages 261–267. Wiley-VCH, 3 edition, 2010.
[4] Ales Tichopad, Tzachi Bar, Ladislav Pecen, Robert R. Kitchen, Mikael Kubista, and Michael W. Pfaffl.
Quality control for quantitative pcr based on amplification compatibility test. Methods, 50:308–312, 2010.
[5] Robert R. Kitchen, Mikael Kubista, and Ales Tichopad. Statistical aspects of quantitative real-time pcr
experiment design. Methods, 50:231–236, 2010.
[6] J. Koetsier, A. Turner, P. Richardson, and J.I. van Hemert. Rapid chemistry portals through engaging
researchers. In IEEE 5th International Conference on e-Science, page In press, 2009.
[7] Liangxiu Han, Jano van Hemert, Richard Baldock, and Malcolm P. Atkinson. Automating gene expression
annotation for mouse embryo. In Ronghuai Huang; Qiang Yang; Jian Pei et al., editor, Advanced Data
Mining and Applications, 5th International Conference, volume LNAI 5678. Springer, 2009.
[8] J. O’Donoghue and J.I. van Hemert. Using the DCC Lifecycle Model to curate a gene expression database:
A case study. International Journal of Digital Curation, page In press, 2009.
[9] J.D. Armstrong and J.I. van Hemert. Towards a virtual fly brain. Philosophical Transactions A,
367(1896):2387–2397, June 2009.
37. Multi-disciplinary
[1] D. Rodr´ıguez Gonz´lez, T. Carpenter, J.I. van Hemert, and J. Wardlaw. An open source toolkit for
a
medical imaging de-identification. European Radiology, page First Online, 2010.
[2] R.R. Kitchen, V.S. Sabine, A.H. Sims, E.J. Macaskill, L. Renshaw, J.S. Thomas, J.I. van Hemert,
J.M. Dixon, and J.M.S. Bartlett. Correcting for intra-experiment variation in illumina beadchip data is
necessary to generate robust gene-expression profiles. BMC Genomics, 11, 2010.
[3] C.A. Morrison, N. Robertson, A. Turner, J. van Hemert, and J. Koetsier. Molecular Orbital Calculations
of Inorganic Compounds, chapter 3.33, pages 261–267. Wiley-VCH, 3 edition, 2010.
[4] Ales Tichopad, Tzachi Bar, Ladislav Pecen, Robert R. Kitchen, Mikael Kubista, and Michael W. Pfaffl.
Quality control for quantitative pcr based on amplification compatibility test. Methods, 50:308–312, 2010.
[5] Robert R. Kitchen, Mikael Kubista, and Ales Tichopad. Statistical aspects of quantitative real-time pcr
experiment design. Methods, 50:231–236, 2010.
[6] J. Koetsier, A. Turner, P. Richardson, and J.I. van Hemert. Rapid chemistry portals through engaging
researchers. In IEEE 5th International Conference on e-Science, page In press, 2009.
[7] Liangxiu Han, Jano van Hemert, Richard Baldock, and Malcolm P. Atkinson. Automating gene expression
annotation for mouse embryo. In Ronghuai Huang; Qiang Yang; Jian Pei et al., editor, Advanced Data
Mining and Applications, 5th International Conference, volume LNAI 5678. Springer, 2009.
[8] J. O’Donoghue and J.I. van Hemert. Using the DCC Lifecycle Model to curate a gene expression database:
A case study. International Journal of Digital Curation, page In press, 2009.
[9] J.D. Armstrong and J.I. van Hemert. Towards a virtual fly brain. Philosophical Transactions A,
367(1896):2387–2397, June 2009.
38. "'()*+!,&
Jano van Hemert—j.vanhemert@ed.ac.uk '$-$.()-")#(/"
!"#"$!%&
Academics
Malcolm Atkinson
Research Assistants
Jos Koetsier
Liangxiu Han
David Rodriguez
Gagarine Yaikhom
Laura Valkonen
PhD Students
Thomas French
Luna De Ferrari
Rob Kitchen
Chee-Sun Liew IDEA Lab 29:
Fan Zhu
Research Students
A scientific gateway for real time
Gary, Vijay, Hwee, Yue, geophysical experiments
Charalampos, Jeff,
Gideon, Charis, Gareth,
Harika, Andrejs http://research.nesc.ac.uk/partners/
Editor's Notes
* Research focuses on progressing computer science
* by evaluating both generic and tailored methodologies
* in a multidisciplinary context with
* rich use cases to test hypotheses
* Research focuses on progressing computer science
* by evaluating both generic and tailored methodologies
* in a multidisciplinary context with
* rich use cases to test hypotheses
* Research focuses on progressing computer science
* by evaluating both generic and tailored methodologies
* in a multidisciplinary context with
* rich use cases to test hypotheses
* Research focuses on progressing computer science
* by evaluating both generic and tailored methodologies
* in a multidisciplinary context with
* rich use cases to test hypotheses
* Research focuses on progressing computer science
* by evaluating both generic and tailored methodologies
* in a multidisciplinary context with
* rich use cases to test hypotheses
* Research focuses on progressing computer science
* by evaluating both generic and tailored methodologies
* in a multidisciplinary context with
* rich use cases to test hypotheses
* Research focuses on progressing computer science
* by evaluating both generic and tailored methodologies
* in a multidisciplinary context with
* rich use cases to test hypotheses
* Research focuses on progressing computer science
* by evaluating both generic and tailored methodologies
* in a multidisciplinary context with
* rich use cases to test hypotheses
* Research focuses on progressing computer science
* by evaluating both generic and tailored methodologies
* in a multidisciplinary context with
* rich use cases to test hypotheses
* Research focuses on progressing computer science
* by evaluating both generic and tailored methodologies
* in a multidisciplinary context with
* rich use cases to test hypotheses
* Research focuses on progressing computer science
* by evaluating both generic and tailored methodologies
* in a multidisciplinary context with
* rich use cases to test hypotheses
* Research focuses on progressing computer science
* by evaluating both generic and tailored methodologies
* in a multidisciplinary context with
* rich use cases to test hypotheses
* Research focuses on progressing computer science
* by evaluating both generic and tailored methodologies
* in a multidisciplinary context with
* rich use cases to test hypotheses
* Formulation = an abstract description of the data-intensive challenge
* Execution = an implementation of the challenge that runs on a computational platform
* Interaction = necessary to manage the formulation process and to steer the execution
* Formulation = an abstract description of the data-intensive challenge
* Execution = an implementation of the challenge that runs on a computational platform
* Interaction = necessary to manage the formulation process and to steer the execution
* Formulation = an abstract description of the data-intensive challenge
* Execution = an implementation of the challenge that runs on a computational platform
* Interaction = necessary to manage the formulation process and to steer the execution
* Formulation = an abstract description of the data-intensive challenge
* Execution = an implementation of the challenge that runs on a computational platform
* Interaction = necessary to manage the formulation process and to steer the execution
* scaling 1: rapid to portal building
* scaling 2: portal to gaussian use (140 students)
* mention myExperiment
* scaling 1: rapid to portal building
* scaling 2: portal to gaussian use (140 students)
* mention myExperiment
* scaling 1: rapid to portal building
* scaling 2: portal to gaussian use (140 students)
* mention myExperiment
* scaling 1: rapid to portal building
* scaling 2: portal to gaussian use (140 students)
* mention myExperiment
* Formulation = an abstract description of the data-intensive challenge
* Execution = an implementation of the challenge that runs on a computational platform
* Interaction = necessary to manage the formulation process and to steer the execution
* Formulation = an abstract description of the data-intensive challenge
* Execution = an implementation of the challenge that runs on a computational platform
* Interaction = necessary to manage the formulation process and to steer the execution
* Formulation = an abstract description of the data-intensive challenge
* Execution = an implementation of the challenge that runs on a computational platform
* Interaction = necessary to manage the formulation process and to steer the execution
* Formulation = an abstract description of the data-intensive challenge
* Execution = an implementation of the challenge that runs on a computational platform
* Interaction = necessary to manage the formulation process and to steer the execution
* Formulation = an abstract description of the data-intensive challenge
* Execution = an implementation of the challenge that runs on a computational platform
* Interaction = necessary to manage the formulation process and to steer the execution
* Formulation = an abstract description of the data-intensive challenge
* Execution = an implementation of the challenge that runs on a computational platform
* Interaction = necessary to manage the formulation process and to steer the execution