This document compares semantic similarity measures for detecting near-duplicate video clips (NDVCs) using semantic features. It finds that semantic NDVC detection is most effective when similarity is measured using tag statistics from Flickr, rather than WordNet-based measures that are limited to concepts in the English WordNet. Experiments show lower NDVR (better detection) using tag co-occurrence statistics compared to semantic similarity measures based on WordNet concepts and hierarchies.
An Exploration based on Multifarious Video Copy Detection Strategiesidescitation
We co-exist in an era, where tonnes and tonnes of
videos are uploaded every day. Video copy detection has become
the need for the hour as most of them are user generated
Internet videos through popular sites such as YouTube. It acts
as a medium to restrain piracy and prove whether the contents
are legitimate. The usual procedure adopted in video copy
detection techniques include discovering whether a query
video is copied from a database of videos or not. This paper
acquaints different Video copy detection techniques that have
been adopted to ensure robust and secure videos along some
applications of video fingerprinting.
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.
Video has become ubiquitous on the Internet, TV, as well as personal devices. Recognition of video content has been a fundamental challenge in computer vision for decades, where previous research predominantly focused on recognizing videos using a predefined yet limited vocabulary. Thanks to the recent development of deep learning techniques, researchers in multiple communities are now striving to bridge videos with natural language in order to move beyond classification to interpretation, which should be regarded as the ultimate goal of video understanding. We will present recent advances in exploring the synergy of video understanding and language processing techniques.
Video has become ubiquitous on the Internet, TV, as well as personal devices. Recognition of video content has been a fundamental challenge in computer vision for decades, where previous research predominantly focused on understanding videos using a predefined yet limited vocabulary. Thanks to the recent development of deep learning techniques, researchers in both computer vision and multimedia communities are now striving to bridge videos with natural language, which can be regarded as the ultimate goal of video understanding. We will present recent advances in exploring the synergy of video understanding and language processing techniques, including video-language alignment and video captioning.
These slides summarize the main trends in deep neural networks for video encoding. Including single frame models, spatiotemporal convolutionals, long term sequence modeling with RNNs and their combinaction with optical flow.
An Exploration based on Multifarious Video Copy Detection Strategiesidescitation
We co-exist in an era, where tonnes and tonnes of
videos are uploaded every day. Video copy detection has become
the need for the hour as most of them are user generated
Internet videos through popular sites such as YouTube. It acts
as a medium to restrain piracy and prove whether the contents
are legitimate. The usual procedure adopted in video copy
detection techniques include discovering whether a query
video is copied from a database of videos or not. This paper
acquaints different Video copy detection techniques that have
been adopted to ensure robust and secure videos along some
applications of video fingerprinting.
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.
Video has become ubiquitous on the Internet, TV, as well as personal devices. Recognition of video content has been a fundamental challenge in computer vision for decades, where previous research predominantly focused on recognizing videos using a predefined yet limited vocabulary. Thanks to the recent development of deep learning techniques, researchers in multiple communities are now striving to bridge videos with natural language in order to move beyond classification to interpretation, which should be regarded as the ultimate goal of video understanding. We will present recent advances in exploring the synergy of video understanding and language processing techniques.
Video has become ubiquitous on the Internet, TV, as well as personal devices. Recognition of video content has been a fundamental challenge in computer vision for decades, where previous research predominantly focused on understanding videos using a predefined yet limited vocabulary. Thanks to the recent development of deep learning techniques, researchers in both computer vision and multimedia communities are now striving to bridge videos with natural language, which can be regarded as the ultimate goal of video understanding. We will present recent advances in exploring the synergy of video understanding and language processing techniques, including video-language alignment and video captioning.
These slides summarize the main trends in deep neural networks for video encoding. Including single frame models, spatiotemporal convolutionals, long term sequence modeling with RNNs and their combinaction with optical flow.
Unlike full reading, 'skim-reading' involves the process of looking quickly over information in an attempt to cover more material whilst still being able to retain a superficial view of the underlying content. Within this work, we specifically emulate this natural human activity by providing a dynamic graph-based view of entities automatically extracted from text using superficial text parsing / processing techniques. We provide a preliminary web-based tool (called `SKIMMR') that generates a network of inter-related concepts from a set of documents. In SKIMMR, a user may browse the network to investigate the lexically-driven information space extracted from the documents. When a particular area of that space looks interesting to a user, the tool can then display the documents that are most relevant to the displayed concepts. We present this as a simple, viable methodology for browsing a document collection (such as a collection scientific research articles) in an attempt to limit the information overload of examining that document collection.
https://mcv-m6-video.github.io/deepvideo-2018/
Overview of deep learning solutions for video processing. Part of a series of slides covering topics like action recognition, action detection, object tracking, object detection, scene segmentation, language and learning from videos.
Prepared for the Master in Computer Vision Barcelona:
http://pagines.uab.cat/mcv/
Providing tools that insure excellent Cell Based Assays is a cornerstone of our business strategy. Lauren McGillicuddy and her team at Essen Bioscience have been using our E18 Primary Rat Cortical Neurons to develop NeuroTrakTM assays enabling kinetic quantification of neurite dynamics (initiation, branching, extension, retraction). NeuroTrack is one of several CellPlayerTM assays that can be run in IncuCyte ZoomTM.
VEKG: Video Event Knowledge Graph to Represent Video Streams for Complex Even...Piyush Yadav
This work was presented at IEEE Graph Computing Conference 2019 at Laguna Hills California. The work focused on graph based structured representation of video streams and complex event rules creation for pattern matching.
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.
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.
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/
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
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
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.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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.
Comparison of Semantic Similarity Measures for NDVC Detection Using Semantic Features
1. Comparison of Semantic Similarity Measures for
NDVC Detection Using Semantic Features
Hyun-seok Min, Jae Young Choi, Wesley De Neve, and Yong Man Ro
Image and Video Systems Lab
Korea Advanced Institute of Science and Technology (KAIST)
Daejeon, South Korea
e-mail: ymro@ee.kaist.ac.kr website: http://ivylab.kaist.ac.kr
I. INTRODUCTION 1.3. Jiang–Conrath : based on the conditional probability of encountering
- Observations an instance of a child concept in a certain corpus
- an increasing number of near-duplicate video clips (NDVCs) can be
found on websites for video sharing
1
simJC (ti , t j ) = .
- content transformations tend to preserve semantic information log( p(ti )) + log( p(t j )) - log(p(lso(ti , t j )))
- Novel idea
- NDVC detection by means of semantic features and adaptive 1.4. Lin : follows from his theory of similarity between arbitrary objects
semantic distance measurement
- Objective 2 × log p(lso(ti , t j ))
- to answer the question: ‘which semantic similarity measure is most simL (ti , t j ) = .
effective in the context of NDVC detection using semantic features?’ log p(ti ) + log p(t j )
II. SEMANTIC NDVC DETECTION 2. Similarity measurement using Flickr tag occurrence and co-occurrence
Input: query video clip statistics
Video shot segmentation
Image folksonomy
I ti ∩ j I ti ∩ j : the set of images annotated with both
t
t ti and tj
simTC (ti , t j ) = ,
... ...
I ti I ti : the set of images annotated with tag ti
Tag relevance learning
Shot 1 ... Shot i ... Shot N using neighbor voting
IV. EXPERIMENTS
Semantic concept detection
1. Experimental setup
... ...
- Use of TRECVID 2009 for creating NDVCs and reference video clips
Creation of a semantic video signature - Use of MIRFLICKR-25000 as a source of collective knowledge
- Use of Toolbox and the Natural Language Toolkit (NLTK) for WordNet-
Matching of semantic video signatures based semantic similarity measurement
Reference video 2. Experimental results
Output: NDVC identification database - Semantic NDVC detection is, in general, most effective when similarity
measurement makes use of tag statistics derived from Flickr
Fig. 1. NDVC detection by means of semantic video signatures.
- similarity measurement using Flickr-based tag statistics is able to
exploit an unrestricted concept vocabulary, whereas the WordNet-
Ai ti , j , wi , j , j 1,..., Ai , wi , j is a weight value for tag ti,j based similarity measures are only able to make use of semantic
concepts that are part of the English-language version of WordNet
0.8
q r q r q r q r T Tag statistics Leacock–Chodorow
Dshot (S , S ) = SQFD( A , A ) = w | -w G w | -w , 0.7 Jiang–Conrath Lin
0.6 Resnik
SQFD: Signature Quadratic Form Distance 0.5
NDCR
W: vector of weight values for the tags t under consideration 0.4
G: matrix of ground distances (computed using tag statistics) 0.3
III. SEMANTIC SIMILARITY MEASURES 0.2
0.1
1. Similarity measurement using the WordNet knowledge base 0
blur crop pattern change in mirroring resize shift average
1.1. Leacock–Chodorow : relies on the length of the shortest path insertion brightness
between two concepts Transformations
len(ti , t j )
simLC (ti , t j ) = log , Fig. 2. Influence of semantic similarity measurement on the effectiveness of semantic
2E NDVC detection. The lower the NDCR, the more effective NDVC detection.
len(ti , t j ) : the shortest path between two concepts (ti, tj)
V. CONCLUSIONS
E : the overall depth of the taxonomy used
- We presented a novel technique for NDVC detection
1.2. Resnik : measures the information content of the most specific - takes advantage of the collective knowledge in an image folksonomy,
common ancestor of two concepts thus allowing for the use of an unrestricted concept vocabulary
- We quantified the influence of several semantic similarity measures on
simR (ti , t j ) = log p(lso(ti , t j )), the effectiveness of NDVC detection using semantic features
- semantic NDVC detection is most effective when semantic similarity
lso(ti , t j ) : the lowest super-ordinate of ti and tj measurement takes advantage of tag occurrence and co-occurrence
statistics derived from Flickr (an unstructured source of knowledge),
p(t ) : the probability of encountering an instance of a concept t outperforming semantic similarity measurement that takes advantage
in a certain corpus
of WordNet (a knowledge base with a hierarchical structure)
The International Conference on Multimedia Information Technology and Applications (MITA), July 2012, Beijing (China)