The document provides an overview of the teaching and research activities of the ELIS Multimedia Lab at Ghent University and Ghent University Global Campus. It discusses various research projects involving multimedia data analysis using techniques such as deep learning, neural networks, and computer vision. Specific projects summarized include terrain classification using convolutional neural networks, video content understanding using 3D CNNs and LSTMs, video event detection using reservoir computing networks, Twitter micropost modeling using word2vec and feedforward neural networks, humor detection on Twitter, and multimodal condition monitoring of wind turbines.
Ψηφιακές βιβλιοθήκες, ψηφιακά αποθετήρια, υποδομές δεδομένων: θεμέλια της νέα...kebepcy
Παρουσίαση από τη διάλεξη με θέμα
«Ψηφιακές βιβλιοθήκες, ψηφιακά αποθετήρια, υποδομές δεδομένων: θέτοντας τις βάσεις για επιστήμες βασισμένες στα δεδομένα» του Kαθηγητή του τμήματος Πληροφορικής και Τηλεπικοινωνιών του Πανεπιστημίου Αθηνών Γιάννη Ιωαννίδη,
που πραγματοποιήθηκε την Τρίτη 29 Ιουνίου στο Πανεπιστήμιο Λευκωσίας Την εκδήλωση διοργάνωσαν η Βιβλιοθήκη και το Τμήμα Πληροφορικής Πανεπιστημίου Λευκωσίας, η Βιβλιοθήκη και το Τμήμα Πληροφορικής Πανεπιστημίου Κύπρου και η Κυπριακή Ένωση Βιβλιοθηκονόμων - Επιστημόνων Πληροφόρησης (ΚΕΒΕΠ).
Continual/Lifelong Learning with Deep ArchitecturesVincenzo Lomonaco
Humans have the extraordinary ability to learn continually from experience. Not only can we apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of AI is building an artificial continually learning agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex skills and knowledge.
"Continual Learning" (CL) is indeed a fast emerging topic in AI concerning the ability to efficiently improve the performance of a deep model over time, dealing with a long (and possibly unlimited) sequence of data/tasks. In this workshop, after a brief introduction of the topic, we’ll implement different Continual Learning strategies and assess them on common vision benchmarks. We’ll conclude the workshop with a look at possible real world applications of CL.
Identification of Learning Goals in Forum-based CommunitiesMilos Kravcik
When Internet users search for information, surf on websites or discuss with others, their actions are driven by certain goals. Extraction of users' goals can enable higher effectiveness and accuracy of web services. Supporting users in based on their goals can be highly beneficial, especially supporting of learners in the preparation for an exam as a learning process, Different phases of learning are identified when users learn collaboratively. We scrutinize how goals are constructed and achieved within a community, examining not only social activities based on patterns of behavior, but also emotions and intents users express in their posts. As a result we elicit users’ goals. We achieved good accuracy in defining emotions of users and recognizing their intents and social patterns in our case. Here we discuss how the obtained results contribute to mining of learning community goals.
Knowledge mining and Semantic Models: from Cloud to Smart CityPaolo Nesi
Course for the Doctorate in Information Technologies, DIST, at DINFO UNIFI
Pierfrancesco Bellini, Paolo Nesi, DISIT Lab
Dipartimento di Ingegneria dell’Informazione, DINFO
Università degli Studi di Firenze,Via S. Marta 3, 50139, Firenze, Italy
Tel: +39-055-2758511, fax: +39-055-2758570
http://www.disit.dinfo.unifi.it alias http://www.disit.org
Pierfrancesco.bellini@unifi.it , Paolo.nesi@unifi.it
.
program:
From RDF to OWL
Knowledge engineering for Beginners
Smart Cloud Application (ICARO Case)
Big Data Smart City Architecture
Smart-city Ontology
Data Ingestion and Mining
Distributed and real time processes
RDF processing
Smart City Engine
Development Interfaces
Sii-Mobility
Continual Learning: Another Step Towards Truly Intelligent MachinesVincenzo Lomonaco
Humans have the extraordinary ability to learn continually from experience. Not only we can apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of Artificial Intelligence (AI) is building an artificial continual learning agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex knowledge and skills. However, current AI systems greatly suffer from the exposure to new data or environments which even slightly differ from the ones for which they have been trained for. Moreover, the learning process is usually constrained on fixed datasets within narrow and isolated tasks which may hardly lead to the emergence of more complex and autonomous intelligent behaviors. In essence, continual learning and adaptation capabilities, while more than often thought as fundamental pillars of every intelligent agent, have been mostly left out of the main AI research focus. In this talk, we explore the application of these ideas in the context of Vision with a focus on (deep) continual learning strategies for object recognition running at the edge on highly-constrained hardware devices.
Ψηφιακές βιβλιοθήκες, ψηφιακά αποθετήρια, υποδομές δεδομένων: θεμέλια της νέα...kebepcy
Παρουσίαση από τη διάλεξη με θέμα
«Ψηφιακές βιβλιοθήκες, ψηφιακά αποθετήρια, υποδομές δεδομένων: θέτοντας τις βάσεις για επιστήμες βασισμένες στα δεδομένα» του Kαθηγητή του τμήματος Πληροφορικής και Τηλεπικοινωνιών του Πανεπιστημίου Αθηνών Γιάννη Ιωαννίδη,
που πραγματοποιήθηκε την Τρίτη 29 Ιουνίου στο Πανεπιστήμιο Λευκωσίας Την εκδήλωση διοργάνωσαν η Βιβλιοθήκη και το Τμήμα Πληροφορικής Πανεπιστημίου Λευκωσίας, η Βιβλιοθήκη και το Τμήμα Πληροφορικής Πανεπιστημίου Κύπρου και η Κυπριακή Ένωση Βιβλιοθηκονόμων - Επιστημόνων Πληροφόρησης (ΚΕΒΕΠ).
Continual/Lifelong Learning with Deep ArchitecturesVincenzo Lomonaco
Humans have the extraordinary ability to learn continually from experience. Not only can we apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of AI is building an artificial continually learning agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex skills and knowledge.
"Continual Learning" (CL) is indeed a fast emerging topic in AI concerning the ability to efficiently improve the performance of a deep model over time, dealing with a long (and possibly unlimited) sequence of data/tasks. In this workshop, after a brief introduction of the topic, we’ll implement different Continual Learning strategies and assess them on common vision benchmarks. We’ll conclude the workshop with a look at possible real world applications of CL.
Identification of Learning Goals in Forum-based CommunitiesMilos Kravcik
When Internet users search for information, surf on websites or discuss with others, their actions are driven by certain goals. Extraction of users' goals can enable higher effectiveness and accuracy of web services. Supporting users in based on their goals can be highly beneficial, especially supporting of learners in the preparation for an exam as a learning process, Different phases of learning are identified when users learn collaboratively. We scrutinize how goals are constructed and achieved within a community, examining not only social activities based on patterns of behavior, but also emotions and intents users express in their posts. As a result we elicit users’ goals. We achieved good accuracy in defining emotions of users and recognizing their intents and social patterns in our case. Here we discuss how the obtained results contribute to mining of learning community goals.
Knowledge mining and Semantic Models: from Cloud to Smart CityPaolo Nesi
Course for the Doctorate in Information Technologies, DIST, at DINFO UNIFI
Pierfrancesco Bellini, Paolo Nesi, DISIT Lab
Dipartimento di Ingegneria dell’Informazione, DINFO
Università degli Studi di Firenze,Via S. Marta 3, 50139, Firenze, Italy
Tel: +39-055-2758511, fax: +39-055-2758570
http://www.disit.dinfo.unifi.it alias http://www.disit.org
Pierfrancesco.bellini@unifi.it , Paolo.nesi@unifi.it
.
program:
From RDF to OWL
Knowledge engineering for Beginners
Smart Cloud Application (ICARO Case)
Big Data Smart City Architecture
Smart-city Ontology
Data Ingestion and Mining
Distributed and real time processes
RDF processing
Smart City Engine
Development Interfaces
Sii-Mobility
Continual Learning: Another Step Towards Truly Intelligent MachinesVincenzo Lomonaco
Humans have the extraordinary ability to learn continually from experience. Not only we can apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of Artificial Intelligence (AI) is building an artificial continual learning agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex knowledge and skills. However, current AI systems greatly suffer from the exposure to new data or environments which even slightly differ from the ones for which they have been trained for. Moreover, the learning process is usually constrained on fixed datasets within narrow and isolated tasks which may hardly lead to the emergence of more complex and autonomous intelligent behaviors. In essence, continual learning and adaptation capabilities, while more than often thought as fundamental pillars of every intelligent agent, have been mostly left out of the main AI research focus. In this talk, we explore the application of these ideas in the context of Vision with a focus on (deep) continual learning strategies for object recognition running at the edge on highly-constrained hardware devices.
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Presentation Slides from the Awareness Inaugural Meeting Amsterdam 2010. Awareness is a Future and Emerging Technologies Proactive Initiative funded by the European Commission under FP7
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
Continual/Lifelong Learning with Deep Architectures, Vincenzo LomonacoData Science Milan
Humans have the extraordinary ability to learn continually from experience. Not only can we apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of AI is building an artificial continually learning agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex skills and knowledge.
"Continual Learning" (CL) is indeed a fast emerging topic in AI concerning the ability to efficiently improve the performance of a deep model over time, dealing with a long (and possibly unlimited) sequence of data/tasks. In this workshop, after a brief introduction of the topic, we’ll implement different Continual Learning strategies and assess them on common vision benchmarks. We’ll conclude the workshop with a look at possible real world applications of CL.
Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna and founder of ContinualAI.org. He is also the PhD students representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. Previously, he was a Machine Learning software engineer at IDL in-line Devices and a Master Student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: a Comparison Between CNNs and HTMs on Object Recognition Tasks".
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Presentation made at SemTech2010 detailing the Calit2 Research Intelligence system for faculty expertise profile and our experience with semantics in this space.
Are we ready for disruption in Translational Research through Digital Medicine?Ashish Atreja, MD, MPH
This is the slide deck that was presented at Translational Science 2016. Touches upon evidence generation as one of the most desired but expensive process in medical science. Provides examples of how Social Media, medical apps, quantified self movement are leading to patient generated data that can disrupt evidence generation process.
Summary, outcomes and action plan presented by Dr. Angela Christiano at the end of the two-day Alopecia Areata Research Summit held November 14-15, 2016 in New York, NY.
Effectiveness of the current dominant approach to integrated care in the NHSNuffield Trust
Jonathan Stokes of the Greater Manchester Primary Care Patient Safety Translational Research Centre presents a systematic review of case management in integrated care.
Science dissemination 2.0: Social media for researchers. Practical workshop. Xavier Lasauca i Cisa
This practical workshop complements the lecture that I gave in University of Barcelona's Faculty of Medicine (Master in Translational Medicine-MSc Cellex, University of Barcelona-Hospìtal Clínic, 9 March 2016) where I summarised the benefits which can be gained from use of social media (specially blogs, Twitter and other socialnetwork sites) to support research activities, and I provided examples of these innovative emerging resources as tools for scientific communication related to translational medicine, as well as discussed their implications for digital scholarship. You can access to the lecture at: http://www.slideshare.net/xavierlasauca/science-dissemination-20-social-media-for-researchers-59551716
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of big annotated data and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which had been addressed until now with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks and Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles and applications of deep learning to computer vision problems, such as image classification, object detection or text captioning.
Presentation Slides from the Awareness Inaugural Meeting Amsterdam 2010. Awareness is a Future and Emerging Technologies Proactive Initiative funded by the European Commission under FP7
https://telecombcn-dl.github.io/2017-dlsl/
Winter School on Deep Learning for Speech and Language. UPC BarcelonaTech ETSETB TelecomBCN.
The aim of this course is to train students in methods of deep learning for speech and language. Recurrent Neural Networks (RNN) will be presented and analyzed in detail to understand the potential of these state of the art tools for time series processing. Engineering tips and scalability issues will be addressed to solve tasks such as machine translation, speech recognition, speech synthesis or question answering. Hands-on sessions will provide development skills so that attendees can become competent in contemporary data analytics tools.
Continual/Lifelong Learning with Deep Architectures, Vincenzo LomonacoData Science Milan
Humans have the extraordinary ability to learn continually from experience. Not only can we apply previously learned knowledge and skills to new situations, we can also use these as the foundation for later learning. One of the grand goals of AI is building an artificial continually learning agent that constructs a sophisticated understanding of the world from its own experience through the autonomous incremental development of ever more complex skills and knowledge.
"Continual Learning" (CL) is indeed a fast emerging topic in AI concerning the ability to efficiently improve the performance of a deep model over time, dealing with a long (and possibly unlimited) sequence of data/tasks. In this workshop, after a brief introduction of the topic, we’ll implement different Continual Learning strategies and assess them on common vision benchmarks. We’ll conclude the workshop with a look at possible real world applications of CL.
Vincenzo Lomonaco is a Deep Learning PhD student at the University of Bologna and founder of ContinualAI.org. He is also the PhD students representative at the Department of Computer Science of Engineering (DISI) and teaching assistant of the courses “Machine Learning” and “Computer Architectures” in the same department. Previously, he was a Machine Learning software engineer at IDL in-line Devices and a Master Student at the University of Bologna where he graduated cum laude in 2015 with the dissertation “Deep Learning for Computer Vision: a Comparison Between CNNs and HTMs on Object Recognition Tasks".
Deep learning technologies are at the core of the current revolution in artificial intelligence for multimedia data analysis. The convergence of large-scale annotated datasets and affordable GPU hardware has allowed the training of neural networks for data analysis tasks which were previously addressed with hand-crafted features. Architectures such as convolutional neural networks, recurrent neural networks or Q-nets for reinforcement learning have shaped a brand new scenario in signal processing. This course will cover the basic principles of deep learning from both an algorithmic and computational perspectives.
Presentation made at SemTech2010 detailing the Calit2 Research Intelligence system for faculty expertise profile and our experience with semantics in this space.
Are we ready for disruption in Translational Research through Digital Medicine?Ashish Atreja, MD, MPH
This is the slide deck that was presented at Translational Science 2016. Touches upon evidence generation as one of the most desired but expensive process in medical science. Provides examples of how Social Media, medical apps, quantified self movement are leading to patient generated data that can disrupt evidence generation process.
Summary, outcomes and action plan presented by Dr. Angela Christiano at the end of the two-day Alopecia Areata Research Summit held November 14-15, 2016 in New York, NY.
Effectiveness of the current dominant approach to integrated care in the NHSNuffield Trust
Jonathan Stokes of the Greater Manchester Primary Care Patient Safety Translational Research Centre presents a systematic review of case management in integrated care.
Science dissemination 2.0: Social media for researchers. Practical workshop. Xavier Lasauca i Cisa
This practical workshop complements the lecture that I gave in University of Barcelona's Faculty of Medicine (Master in Translational Medicine-MSc Cellex, University of Barcelona-Hospìtal Clínic, 9 March 2016) where I summarised the benefits which can be gained from use of social media (specially blogs, Twitter and other socialnetwork sites) to support research activities, and I provided examples of these innovative emerging resources as tools for scientific communication related to translational medicine, as well as discussed their implications for digital scholarship. You can access to the lecture at: http://www.slideshare.net/xavierlasauca/science-dissemination-20-social-media-for-researchers-59551716
biotechnology and its applications
application s of biotechnology, bt.cotton, cloning, dna, dna fingerprinting, dna isolation, gene manipulation, genetic engineering, goldenrice., r dnatechnology, recombinant vaccines, transgenic, vectors
Evaluation of the Integrated Care and Support Pioneers ProgrammeNuffield Trust
Nick Mays of the Policy Innovation Research Unit presents some conclusions from the early evaluation of the Integrated Care and Support Pioneers Programme.
This slideshow was used in a Preparing Your Research Material for the Future course for the Humanities Division, University of Oxford, on 2017-02-22. It provides an overview of some key issues, focusing on the long-term management of data and other research material, including sharing and curation.
This presentation at CERN during the IT Technical Forum on 24 Nov 2017 highlighted the achievement of the Up2University Project (https://up2university.eu/, funded under the EC Call ICT-22-2016: Technologies for Learning and Skills), which aims at bridging the gap between secondary schools, higher education, and the research domain adopting learning technology and methodology to let high school students use the very same tools & services used by real researchers doing Big Science at CERN.
In order to provide concrete example of CERN core technologies running in containers, the Up2U cloud based education services have been ported to the HNSciCloud prototype systems provided by T-Systems and IBM.
Remote Experimentation from Research to Education: A European RoadmapJohann Marquez-Barja
Keynote @ 13th International Conference on Remote Engineering and Virtual Instrumentation REV16
The European Commission has funded, through different programmes such as FP6, FP7 and H2020, several high-performance testbed facilities towards empowering telecommunications research in academia and industry. Such a wide range of experimentation facilities provides cutting-edge technologies, from optical networks to wireless communication technologies, for research within the Future Internet and Research Experimentation (FIRE) initiative. The explosive growth of online learning technologies and, in particular, the rise of remote laboratories for education, enables the use of such research facilities for technology-enhanced learning purposes. The FP7 project FORGE (Forging Online Education through FIRE) has developed a framework that exposes the testbed facilities in an easy manner for remote experimentation that can be integrated into both traditional classroom-based and online learning courses. Moreover, FORGE facilitates the use of those federated facilities by providing widgets, adapters, and courses that can be used/reused anywhere around the world and delivered through different platforms, such as tablets, laptops, PCs or smartphones. This talk will present an overview of such facilities, the FORGEBox framework and its components. Furthermore, it will cover the methodology for using such free resources and to create new courses using FORGE remote labs. Finally a roadmap for adapting experimentation from research to education will be discussed.
Presenting the EOSCpilot Science DemonstratorsEOSCpilot .eu
This presentation was held at the 1st EOSC Stakeholder Forum 28-29/11/2017 in Brussels by Hermann Lederer, Max Planck Gesellschaft.
For more information on the 1st EOSC Stakeholder Forum visit: https://eoscpilot.eu/eosc-stakeholder-forum-shaping-future-eosc
Follow EOSCpilot on Twitter: https://twitter.com/eoscpilot
and LinkedIn: https://uk.linkedin.com/in/eoscpiloteu
A full description of the molecular autoencoder for automated exploration of chemical compound space using neural nets and machine learning architectures, developed by the Aspuru-Guzik group at Harvard. Talk given to Prof. Peter W. Chung's research group at the University of Maryland, College Park, August 2017.
Ralf Klamma
Advanced Community Information Systems (ACIS)RWTH Aachen University, Germany
klamma@dbis.rwth-aachen.de
Dresden, January 22, 2015
las2peer is a distributed, highly reliable and secure platform for creating community information systems and community services.
The main goal of las2peer is to provide a fast and flexible way to create services which may communicate with each other and their users through standard protocols. The used and stored information is handled in a trustworthy way and within full control of the communities.
Why Researchers are Using Advanced NetworksLarry Smarr
07.07.03
Remote Talk from Calit2 to:
Building KAREN Communities for Collaboration Forum
KIWI Advanced Research and Education Network
University of Auckland, Auckland City, New Zealand
Title: Why Researchers are Using Advanced Networks
La Jolla, CA
Similar to Ghent University and GUGC-K: Overview of Teaching and Research Activities (20)
Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional ne...Wesley De Neve
Towards diagnosis of rotator cuff tears in 3-D MRI using 3-D convolutional neural networks. Paper presented at the Workshop on Computational Biology at the International Conference on Machine Learning, Long Beach, USA, 2019.
Investigating the biological relevance in trained embedding representations o...Wesley De Neve
Investigating the biological relevance in trained embedding representations of protein sequences. Paper presented at the Workshop on Computational Biology at the International Conference on Machine Learning, Long Beach, USA, 2019.
Towards reading genomic data using deep learning-driven NLP techniquesWesley De Neve
Towards reading genomic data using deep learning-driven NLP techniques. Slides presented at BIOINFO 2016 – Precision Bioinformatics & Machine Learning.
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to...Wesley De Neve
Deep Machine Learning for Making Sense of Biotech Data - From Clean Energy to Smart Farming. Presentation given at the Korea-Europe International Conference on the 4th Industry Revolution.
Biotech Data Science @ GUGC in Korea: Deep Learning for Prediction of Drug-Ta...Wesley De Neve
Biotech Data Science @ GUGC in Korea: Deep Learning for Prediction of Drug-Target Interaction and DNA Analysis.
Poster presented at the BIG N2N Symposium 2016.
Towards using multimedia technology for biological data processingWesley De Neve
Towards using multimedia technology for biological data processing.
Presentation given during the Ghent University Global Campus (GUGC) Research Seminar on 19/1/2014.
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
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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.
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.
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Ghent University and GUGC-K: Overview of Teaching and Research Activities
1. ELIS – Multimedia Lab
Ghent University and GUGC-K:
Overview of Teaching and Research Activities
Research Seminar
KAIST, 18 August 2015
Wesley De Neve
@wmdeneve
Ghent University – iMinds & KAIST
2. 2
ELIS – Multimedia Lab
• Teaching activities
- Ghent University Global Campus
- Ghent University Home Campus
• Research activities
- Ghent University Home Campus
- Ghent University Global Campus
Outline
3. 3
ELIS – Multimedia Lab
• Teaching activities
- Ghent University Global Campus
- Ghent University Home Campus
• Research activities
- Ghent University Home Campus
- Ghent University Global Campus
Outline
5. 5
ELIS – Multimedia Lab
Ghent University, Belgium
Rector: Prof. Anne De Paepe
Vice-rector: Prof. Freddy Mortier
Ghent University Global Campus, Korea
Campus President: Prof. Jozef Vercruysse
Campus Vice-president: Dr. Thomas Buerman
8. 8
ELIS – Multimedia Lab
Incheon Global Campus (IGC)
University of Utah
George Mason University
Ghent University
SUNY at Stonybrook
University of Nevada
10. 10
ELIS – Multimedia Lab
Molecular
Biotechnology
Food
Technology
Bachelor Master
PhD
Double Accreditation
Resident and
Flying Faculty
Ghent University Degree
Quality Control
Ghent University Appointment
Integrated Research Plan
Environmental
Technology
11. 11
ELIS – Multimedia Lab
Research-focused program
Practical excersises
in laboratories
Graduation project
Double accreditation
NVAO
January - August 2013
MoE
March – November 2013
Ghent University degree
Company internships
One semester in Belgium
16. 16
ELIS – Multimedia Lab
• Teaching activities
- Ghent University Global Campus
- Ghent University Home Campus
• Research activities
- Ghent University Home Campus
- Ghent University Global Campus
Outline
17. 17
ELIS – Multimedia Lab
• Course content
- management, analysis,
and visualization of large-
scale datasets
• Lecture on the art of (deep)
machine learning
• Hands-on session
- word2vec for natural
language processing (NLP)
- Apache Spark
Teaching Activities
Big Data Science
(Spring term)
18. 18
ELIS – Multimedia Lab
• Teaching activities
- Ghent University Global Campus
- Ghent University Home Campus
• Research activities
- Ghent University Home Campus
- Ghent University Global Campus
Outline
19. 19
ELIS – Multimedia Lab
TERRAIN CLASSIFICATION FOR
HYPERSPECTRAL IMAGES
Viktor Slavkovikj
20. 20
ELIS – Multimedia Lab
• Hyperspectral images
- each pixel contains hundreds of measurements of the
electromagnetic spectrum
- often captured through remote sensing
• e.g., through a camera mounted on an airplane
• Problem: how to do terrain classification?
- e.g., corn, wheat, and woods
Problem Statement
21. 21
ELIS – Multimedia Lab
Architecture Convolutional Neural Network
input layer
convolutional layer
convolutional layer
convolutional layer
fully connected layer
fully connected layer
output layer
output: one out of
16 terrain classes
800 hidden units
(hyperbolic tangent)
800 hidden units
(hyperbolic tangent)
filter size: 9x16
filter size: 1x16
filter size: 1x16
input: 9 pixels and
their spectral bands
implementation: by means of Python and Lasagne, a lightweight library to quickly
build and train neural networks in Theano
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ELIS – Multimedia Lab
• Data augmentation through the addition of Gaussian noise
- minor impact
- similar observation for max-pooling, ReLUs, and DropOut
• Classification results on par with the state-of-the-art
- overall accuracy between 80% and 95%
Experimental Results
Indian Pines
Test results
5%
training data
10%
training data
20%
training data
Non-augmented
Overall
accuracy (%)
85.46 ± 1.73 92.76 ± 0.93 96.54 ± 0.47
Augmented
Overall
accuracy (%)
86.54 ± 0.30 92.70 ± 1.00 96.58 ± 0.55
25. 25
ELIS – Multimedia Lab
Goals
Representation
Learning using
Neural Networks
Spatial &
Temporal Feature
Construction
Generation of
Fine-grained
Descriptions
Focus on Video
Content
Understanding
objects, actions,
& scenes
26. 26
ELIS – Multimedia Lab
Techniques
Main focus is on neural network techniques
that are able to capture temporal behaviour
3-D Convolutional
Neural Networks
Recurrent Long
Short-Term
Memory Networks
“Convolve over spatial
(2D) and/or temporal
domain (3D) to acquire
knowledge of input”
“Process sequence of
inputs and acquire
knowledge based on
memory cells”
Recurrent Reservoir
Computing
Networks
“Randomly assigned
weights in the reservoir,
combined with a
readout layer using
linear regression”
baseline video features: IDTF, AlexNet (ImageNet), C3D (FAIR)
implementation: Theano, Caffe, and Lasagne
27. 27
ELIS – Multimedia Lab
Data
Focus on
Action recognition dataset Crawled Vine videos
‘Realistic action videos’ Social and mobile content
Well-known and widely used Noisy and short-form data
UCF101
28. 28
ELIS – Multimedia Lab
First Exemplary Approach
Convolutional
Neural Network
Long Short-Term
Memory Network
f1 … fnf2
video
Representation f2
…
Representation f1
Representation fn
Video
Representation
Classification
29. 29
ELIS – Multimedia Lab
Second Exemplary Approach
Convolutional
Neural Network
Classification
Convolutional
Neural Network
f1
…
fn
f2
m1
…
mk
m2
raw frames motion flows
Fusion
Video Representation
30. 30
ELIS – Multimedia Lab
RESERVOIR COMPUTING FOR
VIDEO EVENT DETECTION
Azarakhsh Jalalvand
31. 31
ELIS – Multimedia Lab
• Goal
- detect the status of a door: open, closed, half-open
- use of a simple, efficient, and effective system
• Approach
- use of a fixed low-resolution camera (30×30 pixels)
• privacy reasons: people are not recognizable
• low bandwidth needed to stream the data
- use of Reservoir Computing Networks (RCNs)
• good in modeling temporal information (cf. speech)
• good in dealing with noisy data
Video Event Detection (1/2)
32. 32
ELIS – Multimedia Lab
• Implemented solution: small neural network of 200 nodes
- fast training
• reservoir: random assignment of connection weights
• readout layer: gradient descent for linear regression
- real-time response
- robust against noise
• low light conditions & people occurring
Video Event Detection (2/2)
Reservoir
36. 36
ELIS – Multimedia Lab
Problem statement
Current Natural Language Processing (NLP) research focuses
on “clean” text: news articles, Wikipedia articles…
What about noisy, short-form, and unstructured microposts?
Lack of correct spelling, a lot of slang
Lack of context
Lack of consistent grammar rules (~structure)
37. 37
ELIS – Multimedia Lab
A simple, general but effective
neural network architecture (1)
Use Google’s word2vec (=simplified neural network) to generate
good feature representations for words (=unsupervised learning)
Feed word representations to another neural network (NN) for any
classification task (=supervised learning)
Tweet
Feature
representation
Machine learning:
classification
Label
Learn word2vec
word representations
once in advance
Train a new NN
for any NLP task
38. 38
ELIS – Multimedia Lab
A simple, general but effective
neural network architecture (2)
W(t-1)
W(t)
W(t+1)
L
o
o
k
u
p
N-dim
N-dim
N-dim
Feed
forward
neural
network
Label(W(t))
Tweet
Feature
representation
Machine learning:
classification
Label
Concatenate (3N-dim)Window = 3
from
Seoul
to
Im going from Seoul to Daejeon. #KTX
39. 39
ELIS – Multimedia Lab
Word2vec: automatically learning good features
Model trained on 400 million tweets having 5 billion words
2-D projection of a 400-D space of the top 1000 words used on Twitter
40. 40
ELIS – Multimedia Lab
Part-of-Speech tagging: is it a verb, noun or article?
Im
going
from
L
o
o
k
u
p
400D
400D
400D
FFNN:
400 hidden
nodes
Verb
slang
NIPS Workshop on Modern Machine Learning Methods and Natural Language Processing
41. 41
ELIS – Multimedia Lab
Named Entity Recognition:
is it a location, company or TV show (1)?
from
Seoul
to
L
o
o
k
u
p
400D
400D
400D
FFNN:
400 hidden
nodes
Location
The same word representations
The same network, but with different weights
42. 42
ELIS – Multimedia Lab
Named Entity Recognition:
is it a location, company or TV show (2)?
Used both
“standard” features
as word
representations
Only using word
representations
ACL 2015 Workshop on Noisy User-generated Text
43. 43
ELIS – Multimedia Lab
Next Steps
Replace word2vec word representations with character
representations
Use Convolution Neural Networks as pattern filters, to prevent a
huge increase in vocabulary size (e.g., a convolutional filter should be
able to map “the" and "da" onto the same pattern)
Combine character representations to form word representations
that can be classified
45. 45
ELIS – Multimedia Lab
• Observation
- lots of humor on Twitter
• Question
- can we automatically detect
humorous tweets?
• Motivation
- humor is engaging (ads!)
- creation of intelligent agents
with social & emotional skills
Humor Detection on Twitter
46. 46
ELIS – Multimedia Lab
• Different kinds of humor
- sarcastic humor
- black humor
- self-deprecating humor
- satire
- parody
• Personal context
• Multimodal tweets
• Language usage
Why Humor Detection on Twitter Is Challenging
47. 47
ELIS – Multimedia Lab
• Binary classification problem: humorous or non-humorous
• Collection of tweets in English
- tweets containing #lol, #rofl, #lmao, #funny, #hilarious, …
- dataset of 373,498 tweets
• 50/50 humorous and non-humorous
• Features
- word2vec
• Classification technique
- feed-forward neural network with ReLUs
Approach (1/2)
49. 49
ELIS – Multimedia Lab
Classification accuracy: 81.07%
Preliminary Results
Humorous Tweets
You know you're at a Croatian jam whn your uncle forces
you to take shots .....
I've finally learned how to play spades
Watermelon inside of a watermelon!! My fav vine!
Some boys will wear dark sunglasses in Church, then be
blaming God later when they end up as Welders
It's so weird to thing that over in the other side of the
country there are people going to sleep while I'm getting
up
Got a new TV set for downstairs and my dad said "I bet I
can do this in 15 minutes" and almost 1 hour later it's
nearly finished
#RapLikeLilWayne I walk while I sleep. Call that Sleep
walkin!!!! #whaddup
50. 50
ELIS – Multimedia Lab
• Collect more training data by making use of Reddit
• Experimentation with recurrent neural network techniques
• Multimodal word/concept vector representations,
integrating both textual and visual information
Next Steps
52. 52
ELIS – Multimedia Lab
Healthy wind turbine Broken wind turbine
• Multi-sensor monitoring of bearings to detect faults early on
- infrared imaging, vibration data, and temperature data
• Classification
- white box models: random decision forests and SVMs
- black box models: CNNs
Condition Monitoring: Failure Prevention
54. 54
ELIS – Multimedia Lab
• Infrared imaging analysis
- handcrafted features + SVM: accuracy of 88.25%
• Vibration data analysis
- handcrafted features + RDF: accuracy of 87.25%
- CNN: accuracy of 91.77%
• Ongoing research: ensembling
- creation of a multimodal system using early and/or late fusion
Some Observations
56. 56
ELIS – Multimedia Lab
• Challenge: data handling
- DNA sequencing is outrunning
DNA storage, transmission, and
analysis
• Research question
- how about compressing DNA by making use of video coding
tools in order to alleviate storage, transmission, and analysis
problems?
Problem Statement
57. 57
ELIS – Multimedia Lab
• Modular and extensible
- thanks to the use of the pipes and filters design pattern
• Block-based compression
- allows selecting the best coding tool per block (adaptivity)
- enables random access, streaming, and parallel processing
Codec Architecture (1/2)
Input filter Encoding filter
Pipe
Output filter
Pipe PipePipe
Statistics
58. 58
ELIS – Multimedia Lab
Codec Architecture (2/2)
Efficiency
FunctionalityEffectiveness
Proposed
solution
SOTA
allowing for a flexible trade-off between
efficiency, effectiveness, and functionality
has always been a major design goal
59. 59
ELIS – Multimedia Lab
• Effectiveness: compression of the human Y chromosome
• Efficiency
- < 3 minutes: 4.30 MB
- 10 minutes: 4.21 MB
- 7 hours: 3.75 MB
Experimental Results
Format File size (MB)
No compression (FASTA) 18.70
Binary 7.01
Huffman 5.16
Proposed framework (December 2014) 4.26
Proposed framework with CABAC (August 2015) 3.75
60. 60
ELIS – Multimedia Lab
• Compression
- support for the protein alphabet
- performance optimizations (I/O, GPU)
• Privacy protection and streaming
- encryption
• Compressed-domain manipulation
- only download and decode that part of the compressed
genome that belongs to a particular gene (region-of-interest)
• DCC + MPEG standardization
Future Activities
Past
Future
61. 61
ELIS – Multimedia Lab
• Teaching activities
- Ghent University Global Campus
- Ghent University Home Campus
• Research activities
- Ghent University Home Campus
- Ghent University Global Campus
Outline
62. 62
ELIS – Multimedia Lab
Deep Learning for Biotech Data
Deep machine
learning
Multimedia
data
Biotech
data
SongdoGhent
important: unique (specialized) use cases and corresponding data sets,
given the current speed of change in the field of deep learning
64. 64
ELIS – Multimedia Lab
• What?
- commonly used in chemistry to create a fingerprint by
which molecules can be identified
• Applications
- medical diagnosis and food analysis (a/o)
Use Case 2: Raman Spectroscopy (1/2)
65. 65
ELIS – Multimedia Lab
• Challenges?
- data: vast amounts of data
- device: different devices,
different characteristics
- noise: environment, side effects
- composite materials:
overlapping signals
• Goal
- noise-robust automatic Raman spectrum identification
using signal processing and machine learning techniques
Use Case 2: Raman Spectroscopy (2/2)
66. 66
ELIS – Multimedia Lab
iMinds & ETRI
R&D collaboration in the field of IoT,
Big Data, and network communication (5G)
joint international research labs
(in Songdo?)
67. 67
ELIS – Multimedia Lab
• Memoranda of Understanding (MoU)
• Joint research projects (legal status GUGC-K?)
• Joint doctoral degrees
• Visits of master’s and Ph.D. students in Spring 2016?
- GPU cluster in Songdo (for deep CNNs, a/o)
• 4 Xeon CPUs
• 8 Titan Black GPUs with 96 GB of memory
• 128 GB of system memory
• 2 TB SSD + 16 TB of storage capacity
• 3200 Watt of power consumption
Further Ideas