Creating A Multi-wavelength Galactic Plane Atlas With Amazon Web ServicesG. Bruce Berriman
This is a talk given at the "Tools for Astronomical Big Data" Workshop, hosted by NOAO and held in Tucson, AZ. March 9-11, 2015.
Visit http://www.noao.edu/meetings/bigdata/. The talk is on-line at http://www.noao.edu/meetings/bigdata/files. /Berriman.pdf
Creating A Multi-wavelength Galactic Plane Atlas With Amazon Web ServicesG. Bruce Berriman
This is a talk given at the "Tools for Astronomical Big Data" Workshop, hosted by NOAO and held in Tucson, AZ. March 9-11, 2015.
Visit http://www.noao.edu/meetings/bigdata/. The talk is on-line at http://www.noao.edu/meetings/bigdata/files. /Berriman.pdf
MSc Proposal Presentation: A comparison of TLS and PhotogrammetryPeter McCready
MSc Geospatial and Mapping Sciences final project:
MSc project proposal presentation slides.
Produced in fulfilment of MSc Geospatial & Mapping Sciences at the University of Glasgow (2015).
Object extraction from satellite imagery using deep learningAly Abdelkareem
Presentation for extract objects from satellite imagery using deep learning techniques. you find a comparison between state-of-art approaches in computer vision.
Efficient data reduction and analysis of DECam images using multicore archite...Roberto Muñoz
A talk I gave in the workshop "Tools for astronomical big data" held in Tucson, Arizona on March 2015. My talk was about how to do data science and big data in Astronomy having a small budget.
AI and Deep Learning for On-Board Satellite Image Analysis, OW2con'19, June 1...OW2
This presentation presents how OW2 ProActive is accelerating the development of AI Image Analysis with Deep Learning.
The objective is to on-board and launch in a Satellite analysis that automatically detect image differences between two passes of a satellite over the same spot on the earth. This further accelerate notification of the ground station that something potentially abnormal or dangerous is going-on on our planet.
Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save people's lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visual data collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework.
Links:
http://infolab.usc.edu/DocsDemos/to_ieeebigdata2015.pdf
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7363814
MSc Proposal Presentation: A comparison of TLS and PhotogrammetryPeter McCready
MSc Geospatial and Mapping Sciences final project:
MSc project proposal presentation slides.
Produced in fulfilment of MSc Geospatial & Mapping Sciences at the University of Glasgow (2015).
Object extraction from satellite imagery using deep learningAly Abdelkareem
Presentation for extract objects from satellite imagery using deep learning techniques. you find a comparison between state-of-art approaches in computer vision.
Efficient data reduction and analysis of DECam images using multicore archite...Roberto Muñoz
A talk I gave in the workshop "Tools for astronomical big data" held in Tucson, Arizona on March 2015. My talk was about how to do data science and big data in Astronomy having a small budget.
AI and Deep Learning for On-Board Satellite Image Analysis, OW2con'19, June 1...OW2
This presentation presents how OW2 ProActive is accelerating the development of AI Image Analysis with Deep Learning.
The objective is to on-board and launch in a Satellite analysis that automatically detect image differences between two passes of a satellite over the same spot on the earth. This further accelerate notification of the ground station that something potentially abnormal or dangerous is going-on on our planet.
Efficient and thorough data collection and its timely analysis are critical for disaster response and recovery in order to save people's lives during disasters. However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the popularity and pervasiveness of mobile devices, crowdsourcing data collection and analysis has emerged as an effective and scalable solution. This paper addresses the problem of crowdsourcing mobile videos for disasters by identifying two unique challenges of 1) prioritizing visual data collection and transmission under bandwidth scarcity caused by damaged communication networks and 2) analyzing the acquired data in a timely manner. We introduce a new crowdsourcing framework for acquiring and analyzing the mobile videos utilizing fine granularity spatial metadata of videos for a rapidly changing disaster situation. We also develop an analytical model to quantify the visual awareness of a video based on its metadata and propose the visual awareness maximization problem for acquiring the most relevant data under bandwidth constraints. The collected videos are evenly distributed to off-site analysts to collectively minimize crowdsourcing efforts for analysis. Our simulation results demonstrate the effectiveness and feasibility of the proposed framework.
Links:
http://infolab.usc.edu/DocsDemos/to_ieeebigdata2015.pdf
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7363814
DEEP LEARNING APPROACH FOR EVENT MONITORING SYSTEMIJMIT JOURNAL
With an increasing number of extreme events and complexity, more alarms are being used to monitor
control rooms. Operators in the control rooms need to monitor and analyze these alarms to take suitable
actions to ensure the system’s stability and security. Security is the biggest concern in the modern world. It
is important to have a rigid surveillance that should guarantee protection from any sought of hazard.
Considering security, Closed Circuit TV (CCTV) cameras are being utilized for reconnaissance, but these
CCTV cameras require a person for supervision. As a human being, there can be a possibility to be tired
off in supervision at any point of time. So, we need a system to detect automatically. Thus, we came up with
a solution using YOLO V5. We have taken a data set and used robo-flow framework to enhance the existing
images into numerous variations where it will create a copy of grey scale image, a copy of its rotation and
a copy of its blurred version which will be used to get an enlarged data set. This work mainly focuses on
providing a secure environment using CCTV live footage as a source to detect the weapons. Using YOLO
algorithm, it divides an image from the video into grid system and each grid detects an object within itself
Disaster Monitoring using Unmanned Aerial Vehicles and Deep LearningAndreas Kamilaris
Monitoring and identification of disasters are crucial for mitigating their effects on the
environment and on human population, and can be facilitated by the use of unmanned aerial vehicles
(UAV), equipped with camera sensors which can produce frequent aerial photos of the areas of interest. A
modern, promising technique for recognition of events based on aerial photos is deep learning. In this paper,
we present the state of the art work related to the use of deep learning techniques for disaster monitoring
and identification. Moreover, we demonstrate the potential of this technique in identifying disasters
automatically, with high accuracy, by means of a relatively simple deep learning model. Based on a small
dataset of 544 images (containing images of disasters such as fires, earthquakes, collapsed buildings,
tsunami and flooding, as well as “non-disaster” scenes), our preliminary results show an accuracy of 91%
achieved, indicating that deep learning, combined with UAV equipped with camera sensors, have the
potential to predict disasters with high accuracy in the near future. Presented at the EnviroInfo 2017 Conference in Luxembourg.
Detecting anomalies in security cameras with 3D-convolutional neural network ...IJECEIAES
This paper presents a novel deep learning-based approach for anomaly detec- tion in surveillance films. A deep network that has been trained to recognize objects and human activity in movies forms the foundation of the suggested ap- proach. In order to detect anomalies in surveillance films, the proposed method combines the strengths of 3D-convolutional neural network (3DCNN) and con- volutional long short-term memory (ConvLSTM). From the video frames, the 3DCNN is utilized to extract spatiotemporal features,while ConvLSTM is em- ployed to record temporal relationships between frames. The technique was evaluated on five large-scale datasets from the actual world (UCFCrime, XD- Violence, UBIFights, CCTVFights, UCF101) that had both indoor and outdoor video clips as well as synthetic datasets with a range of object shapes, sizes, and behaviors. The results further demonstrate that combining 3DCNN with Con- vLSTM can increase precision and reduce false positives, achieving a high ac- curacy and area under the receiver operating characteristic-area under the curve (ROC-AUC) in both indoor and outdoor scenarios when compared to cutting- edge techniques mentioned in the comparison.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Preliminary Evaluation of TinyYOLO on a New Dataset for Search-And-Rescue with Drones
1. Preliminary Evaluation of TinyYOLO on a New
Dataset for Search-And-Rescue with Drones
Giovanna Castellano, Ciro Castiello, Corrado Mencar, Gennaro Vessio
CILAB, Department of Computer Science, University of Bari, Italy
gennaro.vessio@uniba.it
ISCMI 2020
2. Context
Drones can provide a cost-efficient aid to
emergency rescue operations:
● swarms of aerial vehicles can be rapidly
spread across a disaster area providing
mobile ad-hoc networks
● they can rapidly overfly and traverse
difficult to reach regions, such as
mountains, islands, etc.
● they can deliver rescue apparatus, such as
medications, much faster than rescue
teams
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3. Motivations
However, in such a scenario, a manual search performed by a flight operator
(based on the aerial video captured by the drone) can prove extremely difficult:
● it requires a long concentration to perform the flight operation and the
searching task at the same time
● the operator could work in poor conditions, because of the small size of the
monitor he is equipped with, as well as the brightness of the screen outdoor
The use of autonomous drones can reduce manual human intervention, thereby
increasing detection rate, while reducing rescue time
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4. Goal
This opportunity motivates research efforts
towards the development of real-time intelligent
tools to be mounted directly on-board drones
Nowadays, drones embed quite powerful GPUs,
so even a simple UAV can be transformed into
an advanced computer vision flying machine
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5. Proposed method
During a real-world SAR operation,
both high performance on-board
computing systems and high-speed
network connections are unlikely to be
available
Therefore, a lightweight and fast neural
network model is required to efficiently
process each video frame
For this reason, we used the
well-known lightweight TinyYOLOv3
model to implement an object (people)
detection system
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6. ● Largest dataset of aerial images ever published
● Bounding boxes of different object categories
● Various weather and lighting conditions
● Sparse and crowded scenes
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VisDrone - Task 1 dataset (fine-tuning)
7. New SAR dataset
(testing only)
Unfortunately, most of the existing
works make use of images captured by
drones depicting everyday-life
scenarios that are unrealistic for SAR
At present, we propose a new dataset
including two different SAR scenarios:
● mountains (200 frames, 100
with annotated people)
● beaches (110 frames, 55 with
annotated people)
Video frames were retrieved querying
YouTube and manually annotated in
accordance with well-known standards
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8. Setting
● Software:
○ Darknet library
● Hardware:
○ Google Colab NVIDIA Tesla K80 (training)
○ NVIDIA Jetson TX2 (testing) → 7 fps
● Hyper-parameter setting:
○ Mini-batch size: 64
○ Learning rate: 0.001
○ Early stopping on a validation set
○ Input: 416×416
○ IoU: 50%
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10. Conclusion
This work represents a first step in our research whose long-term goal is to develop
a large, challenging dataset to promote advances in this research direction
To this end, the dataset has been made publicly available in its current version and
we solicit contributions to make it bigger and bigger:
https://doi.org/10.5281/zenodo.3924925
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