Agenda:
Introduction
Supercomputers for Scientific Research
Covid-19 Tracking and Prediction
Covid-19 Research and Diagnosis
Use Case 1 NLP and BERT to answer scientific questions
Use Case 2 Covid-19 Data Lake and Platform
Multitask Adversarial Learning of Deep Neural Networks for Medical Imaging an...Debdoot Sheet
A comprehensive review and summary of some of the recent works in the area of adversarial learning of deep neural networks carried out at the Kharagpur Learning, Imaging and Visualization (KLIV) Research Group.
Using Mask R CNN to Isolate PV Panels from Background Object in Imagesijtsrd
Identifying foreground objects in an image is one of the most common operations used in image processing. In this work, Mask R CNN algorithm is used to identify solar photovoltaic PV panels in aerial images and create a mask that can be used to remove the background from the images. This allows processing the PV panels separately. Using ML to solve this problem can generate more accurate results in comparison to more traditional image processing techniques like using edge detection or Gaussian filtering especially in images where the view might not be easily separable from the objects of interest. The trained model was found to be successful in detecting the PV panels and selecting the pixels that belong to them while ignoring the background pixels. This kind of work can be useful in collecting information about PV installation present in aerial or satellite imagery, or in analyzing the health and integrity of PV modules in large scale installations e.g., in a solar power plant. The results show that this method is effective with a high potential for improved results if the model is trained using larger and more diverse datasets. Muhammet Sait | Atilla Erguzen | Erdal Erdal "Using Mask R-CNN to Isolate PV Panels from Background Object in Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38173.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38173/using-mask-rcnn-to-isolate-pv-panels-from-background-object-in-images/muhammet-sait
Short presentation for a special lecture on Medicine Graduation Course in Hospital de Clínicas (https://www.hcpa.edu.br/), as part of a one-day special discipline on Machine Learning and Healthcare. The goal was introducing the importance of Deep Learning for Healthcare as well as showing some of the recent impact.
Agenda:
Introduction
Supercomputers for Scientific Research
Covid-19 Tracking and Prediction
Covid-19 Research and Diagnosis
Use Case 1 NLP and BERT to answer scientific questions
Use Case 2 Covid-19 Data Lake and Platform
Multitask Adversarial Learning of Deep Neural Networks for Medical Imaging an...Debdoot Sheet
A comprehensive review and summary of some of the recent works in the area of adversarial learning of deep neural networks carried out at the Kharagpur Learning, Imaging and Visualization (KLIV) Research Group.
Using Mask R CNN to Isolate PV Panels from Background Object in Imagesijtsrd
Identifying foreground objects in an image is one of the most common operations used in image processing. In this work, Mask R CNN algorithm is used to identify solar photovoltaic PV panels in aerial images and create a mask that can be used to remove the background from the images. This allows processing the PV panels separately. Using ML to solve this problem can generate more accurate results in comparison to more traditional image processing techniques like using edge detection or Gaussian filtering especially in images where the view might not be easily separable from the objects of interest. The trained model was found to be successful in detecting the PV panels and selecting the pixels that belong to them while ignoring the background pixels. This kind of work can be useful in collecting information about PV installation present in aerial or satellite imagery, or in analyzing the health and integrity of PV modules in large scale installations e.g., in a solar power plant. The results show that this method is effective with a high potential for improved results if the model is trained using larger and more diverse datasets. Muhammet Sait | Atilla Erguzen | Erdal Erdal "Using Mask R-CNN to Isolate PV Panels from Background Object in Images" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38173.pdf Paper URL : https://www.ijtsrd.com/engineering/computer-engineering/38173/using-mask-rcnn-to-isolate-pv-panels-from-background-object-in-images/muhammet-sait
Short presentation for a special lecture on Medicine Graduation Course in Hospital de Clínicas (https://www.hcpa.edu.br/), as part of a one-day special discipline on Machine Learning and Healthcare. The goal was introducing the importance of Deep Learning for Healthcare as well as showing some of the recent impact.
Satellite and Land Cover Image Classification using Deep Learningijtsrd
Satellite imagery is very significant for many applications including disaster response, law enforcement and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic area to be covered are great and the analysts available to conduct the searches are few, automation is required. The traditional object detection and classification algorithms are too inaccurate, takes a lot of time and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that can be used for the automation of such tasks. It has achieved success in image classification by using convolutional neural networks. The problem of object and facility classification in satellite imagery is considered. The system is developed by using various facilities like Tensor Flow, XAMPP, FLASK and other various deep learning libraries. Roshni Rajendran | Liji Samuel "Satellite and Land Cover Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32912.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32912/satellite-and-land-cover-image-classification-using-deep-learning/roshni-rajendran
Copernicus is one of the largest Earth Observation data providers. Copernicus’ archives are growing data repositories containing a wealth of data and information that is of utmost importance for policy support and many economic and industrial domains.
AI needs vast amounts of data to be developed. AI works by identifying patterns in available data and then applying this knowledge to new data. The larger the data set is, the better AI can learn and discover. Bringing AI technologies that scale at the Petabyte level to Copernicus operations is an opportunity that Europe shall seize to maximise return on investment and to develop a new generation of products and services based on Copernicus data assets.
This workshop will present EU programmes, challenges and opportunities to connect Copernicus, its data assets and stakeholders to the digital world.
Women Who Code-HSV Event:
'An Introduction to Machine Learning and Genomics'. Dr. Lasseigne will introduce the R programming language and the foundational concepts of machine learning with real-world examples including applications in the field of genomics with an emphasis on complex human disease research.
Brittany Lasseigne, PhD, is a postdoctoral fellow in the lab of Dr. Richard Myers at the HudsonAlpha Institute for Biotechnology and a 2016-2017 Prevent Cancer Foundation Fellow. Dr. Lasseigne received a BS in biological engineering from the James Worth Bagley College of Engineering at Mississippi State University and a PhD in biotechnology science and engineering from The University of Alabama in Huntsville. As a graduate student, she studied the role of epigenetics and copy number variation in cancer, identifying novel diagnostic biomarkers and prognostic signatures associated with kidney cancer. In her current position, Dr. Lasseigne’s research focus is the application of genetics and genomics to complex human diseases. Her recent work includes the identification of gene variants linked to ALS, characterization of gene expression patterns in schizophrenia and bipolar disorder, and development of non-invasive biomarker assays. Dr. Lasseigne is currently focused on integrating genomic data across cancers with functional annotations and patient information to explore novel mechanisms in cancer etiology and progression, identify therapeutic targets, and understand genomic changes associated with patient survival. Based upon those analyses, she is creating tools to share with the scientific community.
Machine Learning for Chemistry: Representing and InterveningIchigaku Takigawa
Joint Symposium of Engineering & Information Science & WPI-ICReDD in Hokkaido University
Apr. 26 (Mon), 2021
https://www.icredd.hokudai.ac.jp/event/5430
MOST READ ARTICLES IN ARTIFICIAL INTELLIGENCE - International Journal of Arti...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
This workshop is a hands-on introduction to machine learning with R and was presented on December 8, 2017 at the University of South Carolina for the 2017 Computational Biology Symposium held by the International Society for Computational Biology Regional Student Group-Southeast USA.
Medical image is an important parameter for diagnosis to many diseases. Now day’s
telemedicine is major treatment based on medical images. The World Health Organization
(WHO) established the Global Observatory for eHealth (GOe) to review the benefits that
Information and communication technologies (ICTs) can bring to health care and patients’
wellbeing. Securing medical images is important to protect the privacy of patients and assure
data integrity. In this paper a new self-adaptive medical image encryption algorithm is proposed
to improve its robustness. A corresponding size of matrix in the top right corner was created by
the pixel gray-scale value of the top left corner under Chebyshev mapping. The gray-scale value
of the top right corner block was then replaced by the matrix created before. The remaining
blocks were encrypted in the same manner in clockwise until the top left corner block was finally
encrypted. This algorithm is not restricted to the size of image and it is suitable to gray images
and color images, which leads to better robustness. Meanwhile, the introduction of gray-scale value diffusion system equips this algorithm with powerful function of diffusion and disturbance.
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/12/structures-as-sensors-smaller-data-learning-in-the-physical-world-a-presentation-from-carnegie-mellon-university/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Pei Zhang, Associate Research Professor at Carnegie Mellon University, presents the “Structures as Sensors: Smaller-Data Learning in the Physical World” tutorial at the September 2020 Embedded Vision Summit.
Machine learning has become a useful tool for many data-rich problems. However, its use in cyber-physical systems has been limited because of its need for large amounts of well-labeled data that must be tailored for each deployment, and the large number of variables that can affect data in the physical space (e.g. weather, time).
This talk introduces the problem through the concept of Structures as Sensors (SaS), in which the infrastructure (e.g. a building, or vehicle fleets) acts as the physical elements of the sensor, and the response is interpreted to obtain information about the occupants and about the environment. Zhang presents three physical-based approaches to reduce the data demand for robust learning in SaS: 1) generate data through the use of physical models, 2) improve sensed data through actuation of the sensing system and 3) combine and transfer data from multiple deployments using physical understanding.
June 2020: Most Downloaded Article in Soft Computing ijsc
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
Top 1 cited papers - COMPUTER SCIENCE & ENGINEERING: AN INTERNATIONAL JOURNAL...cseij
Computer Science & Engineering: An International Journal (CSEIJ) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science & Computer Engineering. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science and computer Engineering.
Satellite and Land Cover Image Classification using Deep Learningijtsrd
Satellite imagery is very significant for many applications including disaster response, law enforcement and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic area to be covered are great and the analysts available to conduct the searches are few, automation is required. The traditional object detection and classification algorithms are too inaccurate, takes a lot of time and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that can be used for the automation of such tasks. It has achieved success in image classification by using convolutional neural networks. The problem of object and facility classification in satellite imagery is considered. The system is developed by using various facilities like Tensor Flow, XAMPP, FLASK and other various deep learning libraries. Roshni Rajendran | Liji Samuel "Satellite and Land Cover Image Classification using Deep Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32912.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32912/satellite-and-land-cover-image-classification-using-deep-learning/roshni-rajendran
Copernicus is one of the largest Earth Observation data providers. Copernicus’ archives are growing data repositories containing a wealth of data and information that is of utmost importance for policy support and many economic and industrial domains.
AI needs vast amounts of data to be developed. AI works by identifying patterns in available data and then applying this knowledge to new data. The larger the data set is, the better AI can learn and discover. Bringing AI technologies that scale at the Petabyte level to Copernicus operations is an opportunity that Europe shall seize to maximise return on investment and to develop a new generation of products and services based on Copernicus data assets.
This workshop will present EU programmes, challenges and opportunities to connect Copernicus, its data assets and stakeholders to the digital world.
Women Who Code-HSV Event:
'An Introduction to Machine Learning and Genomics'. Dr. Lasseigne will introduce the R programming language and the foundational concepts of machine learning with real-world examples including applications in the field of genomics with an emphasis on complex human disease research.
Brittany Lasseigne, PhD, is a postdoctoral fellow in the lab of Dr. Richard Myers at the HudsonAlpha Institute for Biotechnology and a 2016-2017 Prevent Cancer Foundation Fellow. Dr. Lasseigne received a BS in biological engineering from the James Worth Bagley College of Engineering at Mississippi State University and a PhD in biotechnology science and engineering from The University of Alabama in Huntsville. As a graduate student, she studied the role of epigenetics and copy number variation in cancer, identifying novel diagnostic biomarkers and prognostic signatures associated with kidney cancer. In her current position, Dr. Lasseigne’s research focus is the application of genetics and genomics to complex human diseases. Her recent work includes the identification of gene variants linked to ALS, characterization of gene expression patterns in schizophrenia and bipolar disorder, and development of non-invasive biomarker assays. Dr. Lasseigne is currently focused on integrating genomic data across cancers with functional annotations and patient information to explore novel mechanisms in cancer etiology and progression, identify therapeutic targets, and understand genomic changes associated with patient survival. Based upon those analyses, she is creating tools to share with the scientific community.
Machine Learning for Chemistry: Representing and InterveningIchigaku Takigawa
Joint Symposium of Engineering & Information Science & WPI-ICReDD in Hokkaido University
Apr. 26 (Mon), 2021
https://www.icredd.hokudai.ac.jp/event/5430
MOST READ ARTICLES IN ARTIFICIAL INTELLIGENCE - International Journal of Arti...gerogepatton
The International Journal of Artificial Intelligence & Applications (IJAIA) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Artificial Intelligence & Applications (IJAIA). It is an international journal intended for professionals and researchers in all fields of AI for researchers, programmers, and software and hardware manufacturers. The journal also aims to publish new attempts in the form of special issues on emerging areas in Artificial Intelligence and applications.
This workshop is a hands-on introduction to machine learning with R and was presented on December 8, 2017 at the University of South Carolina for the 2017 Computational Biology Symposium held by the International Society for Computational Biology Regional Student Group-Southeast USA.
Medical image is an important parameter for diagnosis to many diseases. Now day’s
telemedicine is major treatment based on medical images. The World Health Organization
(WHO) established the Global Observatory for eHealth (GOe) to review the benefits that
Information and communication technologies (ICTs) can bring to health care and patients’
wellbeing. Securing medical images is important to protect the privacy of patients and assure
data integrity. In this paper a new self-adaptive medical image encryption algorithm is proposed
to improve its robustness. A corresponding size of matrix in the top right corner was created by
the pixel gray-scale value of the top left corner under Chebyshev mapping. The gray-scale value
of the top right corner block was then replaced by the matrix created before. The remaining
blocks were encrypted in the same manner in clockwise until the top left corner block was finally
encrypted. This algorithm is not restricted to the size of image and it is suitable to gray images
and color images, which leads to better robustness. Meanwhile, the introduction of gray-scale value diffusion system equips this algorithm with powerful function of diffusion and disturbance.
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2020/12/structures-as-sensors-smaller-data-learning-in-the-physical-world-a-presentation-from-carnegie-mellon-university/
For more information about edge AI and computer vision, please visit:
https://www.edge-ai-vision.com
Pei Zhang, Associate Research Professor at Carnegie Mellon University, presents the “Structures as Sensors: Smaller-Data Learning in the Physical World” tutorial at the September 2020 Embedded Vision Summit.
Machine learning has become a useful tool for many data-rich problems. However, its use in cyber-physical systems has been limited because of its need for large amounts of well-labeled data that must be tailored for each deployment, and the large number of variables that can affect data in the physical space (e.g. weather, time).
This talk introduces the problem through the concept of Structures as Sensors (SaS), in which the infrastructure (e.g. a building, or vehicle fleets) acts as the physical elements of the sensor, and the response is interpreted to obtain information about the occupants and about the environment. Zhang presents three physical-based approaches to reduce the data demand for robust learning in SaS: 1) generate data through the use of physical models, 2) improve sensed data through actuation of the sensing system and 3) combine and transfer data from multiple deployments using physical understanding.
June 2020: Most Downloaded Article in Soft Computing ijsc
Soft computing is likely to play an important role in science and engineering in the future. The successful applications of soft computing and the rapid growth suggest that the impact of soft computing will be felt increasingly in coming years. Soft Computing encourages the integration of soft computing techniques and tools into both everyday and advanced applications. This Open access peer-reviewed journal serves as a platform that fosters new applications for all scientists and engineers engaged in research and development in this fast growing field.
Top 1 cited papers - COMPUTER SCIENCE & ENGINEERING: AN INTERNATIONAL JOURNAL...cseij
Computer Science & Engineering: An International Journal (CSEIJ) is a bi-monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Computer Science & Computer Engineering. The journal is devoted to the publication of high quality papers on theoretical and practical aspects of computer science and computer Engineering.
Enabling Data-Intensive Science Through Data InfrastructuresLIBER Europe
These slides are from a talk given at LIBER's 42nd annual conference by Carlos Morais Pires of the European Commission.
In light of the current data deluge, and plans by the European Commission to harness this deluge through the implementation of e-infrastructures for data driven science under Horizon 2020, Pires issued a call to action to libraries to engage in the data infrastructure and bring their own unique, and now much needed competencies, to bear in bringing meaning to, and spreading the word about, data-driven science.
This talk presents areas of investigation underway at the Rensselaer Institute for Data Exploration and Applications. First presented at Flipkart, Bangalore India, 3/2015.
BIMCV, Banco de Imagen Medica de la Comunidad Valenciana. María de la IglesiaMaria de la Iglesia
Según Hal Varian (experto en microeconomía y economía de la información y, desde el año 2002, Chief Economist de Google) “En los próximos años, el trabajo más atractivo será el de los estadísticos: La capacidad de recoger datos, comprenderlos, procesarlos, extraer su valor, visualizarlos, comunicarlos serán todas habilidades importantes en las próximas décadas. Ahora disponemos de datos gratuitos y omnipresentes. Lo que aún falta es la capacidad de comprender estos datos“.
Big Data and Data Science: The Technologies Shaping Our LivesRukshan Batuwita
Big Data and Data Science have become increasingly imperative areas in both industry and academia to the extent that every company wants to hire a Data Scientist and every university wants to start dedicated degree programs and centres of excellence in Data Science. Big Data and Data Science have led to technologies that have already shaped different aspects of our lives such as learning, working, travelling, purchasing, social relationships, entertainments, physical activities, medical treatments, etc. This talk will attempt to cover the landscape of some of the important topics in these exponentially growing areas of Data Science and Big Data including the state-of-the-art processes, commercial and open-source platforms, data processing and analytics algorithms (specially large scale Machine Learning), application areas in academia and industry, the best industry practices, business challenges and what it takes to become a Data Scientist.
Structuring Big Data results to create new information: Smart Data. These Smart Data can be used to advance knowledge and support decision-making processes.
A close cooperation between industry and science creates better conditions for cutting-edge research in Data Engineering/Smart Data.
Webinar - INSPIRE 2020 Virtual Conference
----
How Spatial Data Infrastructures (SDIs) can evolve into Data Ecosystems?
This is the main question that the ongoing study addressing “Data ecosystems for geospatial data - Evolution of Spatial Data Infrastructures” (JRC/IPR/2019/MVP/2781) is addressing. It is performed by the Luxembourg Institute of Science and Technology (LIST) in close collaboration with the Joint Research Centre of the European Commission.
The purpose of this study is to identify and analyse a set of successful data ecosystems and to address recommendations in support of the implementation of data-driven innovation in line with the recently published European Strategy for Data. It investigates factors such as relevant actors, their responsibilities and data value chains, emerging data sources (e.g. the Internet of Things) and technical/architectural approaches (e.g. digital platforms, mobile-by-default, Application Programming Interfaces). It also addresses the interoperability between data ecosystems in different sectors and/or different countries and crosscutting requirements for geospatial data.
This session is intended to share with the audience the study approach, methodological approach and first identified Data Ecosystems, and to learn from their experiences with Data Ecosystems: emergence, barriers, opportunities, sustainability, interoperability between ecosystems, etc.
AGENDA
14.00 - Welcome, Introduction to the context of the study (JRC)
14.10 - Study approach and methodological framework (LIST)
14.20 - Identified data ecosystems and selection criteria (LIST)
14.25 - Illustration of data ecosystem analysis (LIST)
Ghislain Delabie, Simon Saint-Georges, Urban Rennes Data Interface
Sean Wiid, UP42
Charles Moszkowicz, ENEO • Interactive session (All, 20)
Next activities, Goodbye.
dkNET Webinar "Integrative Artificial Intelligence Approach to Predict T1D" 0...dkNET
Presenter: Kenneth Young, Ph.D. Assistant Professor, Health Informatics Institute, University of South Florida
Abstract
Type 1 diabetes (T1D) is a complex and heterogenous autoimmune disease that is no longer considered a clear-cut clinically diagnosed disease. T1D is multifaceted and the efficacy of therapeutic interventions varies greatly. With the evidence of etiological differences in T1D and the availability of high-dimensional multi-omics data in combination with clinical and environmental data, this project aims to use an artificial intelligence (AI) exploratory approach that may aid in the identification of new markers to predict IA and T1D.
This project utilizes data from NIDDK funded by The Environmental Determinants of Diabetes in the Young (TEDDY) study. TEDDY has generated over 900TB of diverse data types including multi-omics data, deep phenotyping, and environmental factor measurements every three-six months for fifteen years. We utilize deep learning methods, such as convolutional neural networks (CNN) and recurrent neural networks (RNN) that apply bidirectional long short-term memory (LSTM), in combination with multi-layer perceptron (MLP), to evaluate the prediction of IA and T1D. To aid in T1D predication, this project uses innovative and transformative AI approaches that combine temporal and static data, which may ultimately provide insights into the complex heterogeneity, diversity, and pathogenesis of T1D. The knowledge gained from this project may not only help advance the T1D community, but may have a broad impact on a variety of autoimmune diseases such as celiac and thyroid diseases which frequently coexist and share genetic susceptibility to T1D.
Præsentation v/Marianne Dybkjær, DBC på Infodag om DBC's projekter og udvikling af nye produkter. Afholdt 14.9 2017 i Ballerup. 15.9 2017 i Aalborg og 28.9 i Vejle.
Præsentation v/Bettina Andersen, DBC på Infodag om DBC's projekter og udvikling af nye produkter. Afholdt 14.9 2017 i Ballerup. 15.9 2017 i Aalborg og 28.9 i Vejle.
Præsentation v/Line Jung Lindhard, DBC på Infodag om DBC's projekter og udvikling af nye produkter. Afholdt 14.9 2017 i Ballerup. 15.9 2017 i Aalborg og 28.9 i Vejle.
Præsentation v/Laura Holm, DBC på Infodag om DBC's projekter og udvikling af nye produkter. Afholdt 14.9 2017 i Ballerup. 15.9 2017 i Aalborg og 28.9 i Vejle.
Præsentation v/Nanna Agergaard, DBC på Infodag om DBC's projekter og udvikling af nye produkter. Afholdt 14.9 2017 i Ballerup. 15.9 2017 i Aalborg og 28.9 i Vejle.
Præsentation v/Nanna Agergaard, DBC på Infodag om DBC's projekter og udvikling af nye produkter. Afholdt 14.9 2017 i Ballerup. 15.9 2017 i Aalborg og 28.9 i Vejle.
Oplæg v/Hanne Hørl Hansen på temadag i Odense den 8. juni 2017 arrangeret af faggrupperne META og Metaforum i samarbejde med Bibliografisk Råd og Slots- og Kulturstyrelsen.
Oplæg v/Line Jung Lindhard, DBC på temadag i Odense den 8. juni 2017 arrangeret af faggrupperne META og Metaforum i samarbejde med Bibliografisk Råd og Slots- og Kulturstyrelsen.
Oplæg v/Bo Weymann på temadag i Odense den 8. juni 2017 arrangeret af faggrupperne META og Metaforum i samarbejde med Bibliografisk Råd og Slots- og Kulturstyrelsen.
Slides fra workshoppen den 15. april 2016: "Den demokratiske søgefunktion" på Bibliotekspolitisk topmøde i Horsens den 14. - 15. april 2016. Oplægsholde: chefstrateg Bo Weymann og udviklet Christian Boesgaard, begge fra DBC.
Slides fra kurset Webservices på biblioteket afholdt af Anders Vestergaard på Gentofte Bibliotekerne den 11. oktober 2013 og på Vejle Bibliotekerne den 30. oktober 2013.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4
Big Data - A view
1. 1
Big Data – a view
DBC
14 January 2016
Bjarne Kjær Ersbøll / bker@dtu.dk
2 DTU Compute, Technical University of Denmark
Acknowledgements
This slide deck is compiled from material from a lot of my colleagues and
people I collaborate with at DTU. The following list is incomplete:
• Jakob Eg Larsen
• Mark Riis
• Mads Odgaard
• Knut Conradsen
• Tage Thyrsted
• Lone Falsig Hansen
• Elena Guarneri
• And many more…
2. 2
3 DTU Compute, Technical University of Denmark
So, what is Big Data anyway?
4 DTU Compute, Technical University of Denmark
The 4 V’s
3. 3
5 DTU Compute, Technical University of Denmark
Data
explosion
6 DTU Compute, Technical University of Denmark
4. 4
7 DTU Compute, Technical University of Denmark
Crowds, Bluetooth and Rock n’ Roll:
Understanding Music Festival Participant
Behavior
8 DTU Compute, Technical University of Denmark
5. 5
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10 DTU Compute, Technical University of Denmark
6. 6
BIG1
Den 3. december 2013
12 DTU Compute, Technical University of Denmark
BIG1 purpose
• Identify technological challenges associated with exploiting the
potential of Big Data / Data-driven business development - to
improve animal health and higher food quality and safety.
7. 7
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BIG1 participants
• DTU Compute
• DTU National Food Institute
• DTU Veterinary Institute
• DTU Management
• DTU Biosys
• DTU Administration
14 DTU Compute, Technical University of Denmark
Big Data Value-chain
Data
Origins
The Internet,
sensors,
machines,
etc.
Data
Collection
Web log,
sensor data,
images/au‐
dio, RFID and
videos etc.
Data
Storage
Technologies
supporting
data storage
Analytics
Predictive
analytics,
patterns in
data,
decision
making
Consumers
Business
processes,
humans, and
applications
Sense Think Act
8. 8
15 DTU Compute, Technical University of Denmark
Feed/plants Animals Processing Consumers
Value chain
Actors
Data
Feed producers
Plant producers
Equipm. producers
Farmers
Abbatoir
Dairy
Retail sector
Export
Eg feed quality Eg growth rate
of animals
Eg efficiency in
slaughtering
process
Consumer
patterns and
food quality
Big Data
Stakeholders in BIG1 value-chain
16 DTU Compute, Technical University of Denmark
Optimere/speede algoritmernes funktionalitet og gøre beregningerne billigere
GenericBigData
problemtopics
Domain / application areas
Cattle Pigs Nutritional
composition
… and other
applications
Collection of data, eg sensors on individuals (eg RFID or image analysis)
Storage, manipulation, real-time data
Establising a dynamic Big Data cloud
Structuring data, distributed data and data-sharing
Merging and integration of databases
Pattern recognition, machine learning, artificial intelligence, query-algorithms
Multivariat analsis and advanced statistics and data analysis
Privacy/ethics regarding data
Visualisation of data wrt descision support
Platform project
Targeted projects
Optimation/speed-up algorithm functionality and lower cost of calculation
BIG1: What can we do?
9. 9
17 DTU Compute, Technical University of Denmark
18 DTU Compute, Technical University of Denmark
Sensors and data generation
10. 10
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Hardware and software
DTU Compute, Technical University of Denmark
Big Data – 1991 – Economic Geology
20 18.01.2016
11. 11
DTU Compute, Technical University of Denmark
Data
• Landsat satellite (common reference) – 4 scenes – 8 tapes
– Geometric rectification, mosaicking, ratios, factor scores,
• Geological – geological maps, topographic maps
– Structural information, lineaments converted to concentrations in 10
directions
• Geochemical – K, Rb, Sr, U, Nb, Y, Ga, Fe in stream sediments.
– Kriging to a 1 km grid, interpolation by bicubic spline to Landsat
pixels
• Radiometric – helicoptor-bourne gamma-spectrometric measurements,
U, Th, K, and Total concentration.
– Max in 1 km grid interpolated by minimum curvature and further by
bicubic spline
• Aeromagnetic data – 11 map sheets
– Manually digitized and interpolated
• Resulting in 40 variables on a pixel level (50.8m x 50.8m)
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DTU Compute, Technical University of Denmark
Data
• Converted to a 5km x 5km grid – trying to preserve information by
taking (when relevant):
– Min, max, 1%, 5%, median, 95%, 99%, mean, stddev, %land-cover
– 240 variables in all in 1084 squares
• Training set of
– 17 mineralized, central
– 21 mineralized, marginal
– 14 barren, central
– 5 barren, marginal
• Discriminant analysis using stepwise selection
– 1084 squares classified
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12. 12
DTU Compute, Technical University of Denmark23 18.01.2016
DTU Compute, Technical University of Denmark24 18.01.2016
13. 13
DTU Compute, Technical University of Denmark
Big Data ?
25 18.01.2016
DTU Compute, Technical University of Denmark
Other Big Data cases
ELIXIR Data describing the human
genetic variation
Development of personal
medical drugs which take
variation between patients
into account
Global Microbial Identifier Global system on genome-
sequence data from micro-
organismes to improve
national clinical diagnostics
and international
surveillance of diseases
CITIES IT-solutions for analysis,
operation and development
of integrated energy-
systems (electricity, gas,
district heating and bio-
masse) in cities to achieve
higher flexibility in eg
energy-storage
14. 14
Data Science (Big Data)
Profile at DTU Compute
28 DTU Compute, Technical University of Denmark
Data Science – main elements
Ambitious – courses: 45 ECTS (4/6
core) + thesis: A further 30-35 ECTS
Pioneering – across the Big Data
value chain and competences
Application oriented:
o Work with concrete data sets
o Collaboration with companies
15. 15
29 DTU Compute, Technical University of Denmark
Entry via all 3 DTU Compute programs
• Computer Science and Engineering
• Mathematical Modelling and Computation
• Digital Media Engineering
• …and now also: IT & Health (combination education btw KU & DTU)
• Cross-educational skills
30 DTU Compute, Technical University of Denmark
Big Data Value chain
data BIG data model
analysis
Data Origins
The Internet, sensors,
machines, etc.
Data Collection
Web log, sensor data,
images/audio, RFID and
videos, etc.
Data Storage
Technologies
supporting data storage
Analytics:
Predictive analytics,
patterns in data,
decision making
Consumers:
Business processes,
humans, and
applications
Sense Think Act
16. 16
31 DTU Compute, Technical University of Denmark
Courses in Data Science specialization
Origin Collection Storage Analytics Consumers
01227 Graph theory (5) 1 3
01405 Error correcting codes 2 1 1
01617 Dynamical Systems 1 2
02170 Database systems (5) 4
02232 Applied Cryptography (5) 2 3 1 1
Core 02239 Data Security 1 4 1
02249 Computationally hard problems (7.5) 1 1 4
02266 User experience engineering 1 1 5
02281 Data Logic (5) 1 2 1 1
Core 02282 Algorithms for Massive Data Sets (7.5) 2 3 3
Core 02288 Missing a course on “Advanced databases/w arehouses”? 2
02407 Stochastic Processes (5) 3
02409 Multivariate Statistics (5) 4
02417 Time Series Analysis (5) 4
02443 Stochastic Simulation (5) 4 1
02450 Introduction to Machine Learning and Data Modeling (5) 3 1
02457 Non-linear signal processing 1 1
02458 Cognitive Modelling (5) 3 2
02460 Advanced Machine Learning (5) 1 3 1
02506 Advanced Image Analysis 3
02515 Health technology 1 2
Core 02582 Computational dataanalysis 3
02586 Statistical Genetics (5) 2
Core 02806 Social data analysis and visualization(5) 2 3
Core 02819 Data Mining using Python (5) 1 3 1
30530 Geographical information systems 1 1 1
25303 Mathematical Biology 1 1 1 1
27411 Biological data analysis and chemometrics 1
27625 Algorithms in bioinformatics 1 1
42112 Mathematical Programming w ith Modelling Softw are 1 1
32 DTU Compute, Technical University of Denmark
Big Data
Hackathon
65 students
10 groups
48 hours
DTU's Skylab
Funding
1-2 start up companies
17. 17
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Big Data solutions for Lyngby-Taarbæk
municipality
”Smart City app” to make it a better place to
live
34 DTU Compute, Technical University of Denmark
Projects!
Energy utilization in buildings
Optimization of Bus-routes
Smart Traffic-regulation
Smart Energy renovation
Personalized Care for elderly
Smart tests for the Schools
Flexible collection of Waste
18. 18
35 DTU Compute, Technical University of Denmark
36 DTU Compute, Technical University of Denmark
Implementation of first recommendation:
Big Data•DTU