A description of SocialSensor project motivation, objectives and use cases in news and infotainment. First results in topic discovery in tweeter streams.
Social Data and Multimedia Analytics for News and Events ApplicationsYiannis Kompatsiaris
The keynote discusses a framework enabling real-time multimedia indexing and search across multiple social media sources. It places particular emphasis on the real-time, social and contextual nature of content and information consumption in order to integrate topic and event detection, mining, search and retrieval, based on aggregation and indexing of shared user-generated multimedia content. User-friendly applications for the News and Events domains have been developed based on these approaches, incorporating novel user-centric media visualisation and browsing methods. The research and development is part of the FP7 EU project SocialSensor.
Content:
Introduction
Motivation – Challenges
SocialSensor Project and Use Cases
Research Approaches
Large-Scale visual search
Clustering
Verification
Demos – Applications
MM News Demo
Clusttour
Thessfest
Conclusions
Social Data and Multimedia Analytics for News and Events ApplicationsYiannis Kompatsiaris
The keynote discusses a framework enabling real-time multimedia indexing and search across multiple social media sources. It places particular emphasis on the real-time, social and contextual nature of content and information consumption in order to integrate topic and event detection, mining, search and retrieval, based on aggregation and indexing of shared user-generated multimedia content. User-friendly applications for the News and Events domains have been developed based on these approaches, incorporating novel user-centric media visualisation and browsing methods. The research and development is part of the FP7 EU project SocialSensor.
Content:
Introduction
Motivation – Challenges
SocialSensor Project and Use Cases
Research Approaches
Large-Scale visual search
Clustering
Verification
Demos – Applications
MM News Demo
Clusttour
Thessfest
Conclusions
Computing for Human Experience: Sensors, Perception, Semantics, Social Comput...Amit Sheth
Keynote at the 3rd Asian Semantic Web Conference (ASWC2008), Bangkok, Thailand, Feb 2-5, 2009. http://aswc2008.ait.ac.th/invitedspeaker2.html
More details: http://wiki.knoesis.org/index.php/Computing_For_Human_Experience
Using Behaviour Analysis to Detect Cultural Aspects in Social Web SystemsMatthew Rowe
Presented at:
-Aston Business School, Birmingham, UK. 2011
-Keynote presentation at Detecting and Exploiting Cultural Diversity on the Social Web Workshop, 20th Annual Conference on Information and Knowledge Management 2011
Bye, Bye Research. Hello Data Mining! Tapping into Social MediaAlterian
Consumers are talking about your brand at length on social networks and this data is finally able to be ‘tapped’ and utilized. Social Media Monitoring provides a different type of data that is unlike what marketing has seen in the past and figuring how to best use it is the challenge. Presentation based on recent cases study and social media monitoring data through the use of Alterian SM2.
Social Data and Multimedia Analytics for News and Events Applications lecture given at 2015 IEEE SPS Italy Chapter Summer School on Signal Processing (S3P)
The DemaWare Service-Oriented AAL Platform for People with DementiaYiannis Kompatsiaris
This work presents DemaWare, an Ambient Intelligence platform that targets Ambient Assisted Living for people with Dementia. DemaWare seamlessly integrates diverse hardware (wearable and ambient sensors), as well as soft- ware components (semantic interpretation, reasoning), involved in such context. It also enables both online and offline processes, including sensor analysis and storage of context semantics in a Knowledge Base. Consequently, it orchestrates semantic interpretation which incorporated defeasible logics for uncertainty handling. Overall, the underlying functionality aids clinicians and carers to timely assess and diagnose patients in the context of lab trials, homes or nursing homes.
DATA, TEXT, AND WEB MINING FOR BUSINESS INTELLIGENCE: A SURVEYijdkp
The Information and Communication Technologies revolution brought a digital world with huge amounts
of data available. Enterprises use mining technologies to search vast amounts of data for vital insight and
knowledge. Mining tools such as data mining, text mining, and web mining are used to find hidden
knowledge in large databases or the Internet. Mining tools are automated software tools used to achieve
business intelligence by finding hidden relations, and predicting future events from vast amounts of data.
This uncovered knowledge helps in gaining completive advantages, better customers’ relationships, and
even fraud detection. In this survey, we’ll describe how these techniques work, how they are implemented.
Furthermore, we shall discuss how business intelligence is achieved using these mining tools. Then look
into some case studies of success stories using mining tools. Finally, we shall demonstrate some of the main
challenges to the mining technologies that limit their potential.
Improve My City: App for Citizens Reporting Issues in Municipalities – RegionsYiannis Kompatsiaris
The application enables citizens to report local problems such as potholes, illegal trash dumping, faulty street lights, broken tiles on sidewalks, and illegal advertising boards. The submitted issues are displayed on the city's map. Users may add photos and comments. Moreover, they can suggest solutions for improving the environment of their neighbourhood.
Understanding Data Mining in the Social Media Marketing AgeSherman Mohr Jr.
Our preferences, comments, sharing, and online community involvement is being analyzed. The analysis is so subtle that most participants don't even notice. We learn in this session how marketers are gathering, extracting, analyzing and then building advertising campaigns around social media participation.
Social Media Verification Challenges, Approaches and ApplicationsYiannis Kompatsiaris
As grassroots and social media-based journalism becomes more widespread, the need to verify information coming from such channels becomes imperative. The objective of this talk is to explore the challenges involved in social media computational verification to automatically classify unreliable media content as fake or real. After presenting a generic conceptual architecture, there will be a focus on tweets around big events linking to images (fake or real) of which the reliability could be verified by independent online sources. The REVEALr platform will be demonstrated, a scalable and efficient content-based media crawling and indexing framework featuring a novel and resilient near-duplicate detection approach and intelligent content- and context-based aggregation capabilities (e.g. clustering, named entity extraction)
Social Media Analytics: The Value PropositionContent Savvy
Rohini K. Srihari delivers her powerful presentation at the KDD 2010 Workshop on Social Media Analytics.
Overview:
-What is Social Media?
-Value Proposition: Why mine social media?
-Business Analytics
-Counterterrorism
-Challenges
-Technology, Challenges
-Multilingual social media mining
Social media mining and multimedia analysis research and applicationsYiannis Kompatsiaris
In this talk, research and applications in social media mining and multimedia analysis are going to be presented. Social media sharing websites host billions of images and videos, which have been annotated and shared among friends, or published in groups that cover a specific topic of interest. The fact that users annotate and comment on the content in the form of tags, ratings, preferences and so on, and that these activities are performed on a daily basis, gives such social media data source an extremely dynamic nature that reflects topics of interests, events and the evolution of community opinion and focus.
The talk will present research challenges and activities and will focus on multi-modal graph-based community detection methods for social media mining, concept and event detection. Clusttour, a mobile and web application integrating research results with appropriate interface design will be demonstrated as a relevant use case. The talk will also include approaches for object/region classifiers learned using the self-training paradigm with loosely annotated training samples automatically selected from social media.
The pervasive use of Online Social Networks (OSN) for networking, communication and search in tandem with the ubiquitous availability of smartphones, which enables real-time multimedia capturing and sharing, have led to massive amounts of user-generated content and activities being amassed online, and made publicly available for analysis and mining. Each content item is associated with an abundance of metadata and related information such as location, tags, comments, favorites and mood indicators, access logs, and so on. At the same time, all this information is implicitly or explicitly interconnected based on various properties such as social links among users, groups, communities, and sharing patterns. These properties transform social media into data sources of an extremely dynamic nature that reflect topics of interests, events, and the evolution of community opinion and focus. Social media processing offers a unique opportunity to structure and extract information and to benefit multiple areas ranging from new media experiences to psychology and marketing. The objective of this talk is to provide an overview of the current research in emerging topics related to applications where social media can act as sensors of real-life phenomena and case studies that reveal valuable insights. After discussing challenges and presenting a generic conceptual architecture, there will be a focus on efficient processing and indexing algorithms that can handle massive amounts of content with application to graph-based event detection and summarization in social media streams.
Computing for Human Experience: Sensors, Perception, Semantics, Social Comput...Amit Sheth
Keynote at the 3rd Asian Semantic Web Conference (ASWC2008), Bangkok, Thailand, Feb 2-5, 2009. http://aswc2008.ait.ac.th/invitedspeaker2.html
More details: http://wiki.knoesis.org/index.php/Computing_For_Human_Experience
Using Behaviour Analysis to Detect Cultural Aspects in Social Web SystemsMatthew Rowe
Presented at:
-Aston Business School, Birmingham, UK. 2011
-Keynote presentation at Detecting and Exploiting Cultural Diversity on the Social Web Workshop, 20th Annual Conference on Information and Knowledge Management 2011
Bye, Bye Research. Hello Data Mining! Tapping into Social MediaAlterian
Consumers are talking about your brand at length on social networks and this data is finally able to be ‘tapped’ and utilized. Social Media Monitoring provides a different type of data that is unlike what marketing has seen in the past and figuring how to best use it is the challenge. Presentation based on recent cases study and social media monitoring data through the use of Alterian SM2.
Social Data and Multimedia Analytics for News and Events Applications lecture given at 2015 IEEE SPS Italy Chapter Summer School on Signal Processing (S3P)
The DemaWare Service-Oriented AAL Platform for People with DementiaYiannis Kompatsiaris
This work presents DemaWare, an Ambient Intelligence platform that targets Ambient Assisted Living for people with Dementia. DemaWare seamlessly integrates diverse hardware (wearable and ambient sensors), as well as soft- ware components (semantic interpretation, reasoning), involved in such context. It also enables both online and offline processes, including sensor analysis and storage of context semantics in a Knowledge Base. Consequently, it orchestrates semantic interpretation which incorporated defeasible logics for uncertainty handling. Overall, the underlying functionality aids clinicians and carers to timely assess and diagnose patients in the context of lab trials, homes or nursing homes.
DATA, TEXT, AND WEB MINING FOR BUSINESS INTELLIGENCE: A SURVEYijdkp
The Information and Communication Technologies revolution brought a digital world with huge amounts
of data available. Enterprises use mining technologies to search vast amounts of data for vital insight and
knowledge. Mining tools such as data mining, text mining, and web mining are used to find hidden
knowledge in large databases or the Internet. Mining tools are automated software tools used to achieve
business intelligence by finding hidden relations, and predicting future events from vast amounts of data.
This uncovered knowledge helps in gaining completive advantages, better customers’ relationships, and
even fraud detection. In this survey, we’ll describe how these techniques work, how they are implemented.
Furthermore, we shall discuss how business intelligence is achieved using these mining tools. Then look
into some case studies of success stories using mining tools. Finally, we shall demonstrate some of the main
challenges to the mining technologies that limit their potential.
Improve My City: App for Citizens Reporting Issues in Municipalities – RegionsYiannis Kompatsiaris
The application enables citizens to report local problems such as potholes, illegal trash dumping, faulty street lights, broken tiles on sidewalks, and illegal advertising boards. The submitted issues are displayed on the city's map. Users may add photos and comments. Moreover, they can suggest solutions for improving the environment of their neighbourhood.
Understanding Data Mining in the Social Media Marketing AgeSherman Mohr Jr.
Our preferences, comments, sharing, and online community involvement is being analyzed. The analysis is so subtle that most participants don't even notice. We learn in this session how marketers are gathering, extracting, analyzing and then building advertising campaigns around social media participation.
Social Media Verification Challenges, Approaches and ApplicationsYiannis Kompatsiaris
As grassroots and social media-based journalism becomes more widespread, the need to verify information coming from such channels becomes imperative. The objective of this talk is to explore the challenges involved in social media computational verification to automatically classify unreliable media content as fake or real. After presenting a generic conceptual architecture, there will be a focus on tweets around big events linking to images (fake or real) of which the reliability could be verified by independent online sources. The REVEALr platform will be demonstrated, a scalable and efficient content-based media crawling and indexing framework featuring a novel and resilient near-duplicate detection approach and intelligent content- and context-based aggregation capabilities (e.g. clustering, named entity extraction)
Social Media Analytics: The Value PropositionContent Savvy
Rohini K. Srihari delivers her powerful presentation at the KDD 2010 Workshop on Social Media Analytics.
Overview:
-What is Social Media?
-Value Proposition: Why mine social media?
-Business Analytics
-Counterterrorism
-Challenges
-Technology, Challenges
-Multilingual social media mining
Social media mining and multimedia analysis research and applicationsYiannis Kompatsiaris
In this talk, research and applications in social media mining and multimedia analysis are going to be presented. Social media sharing websites host billions of images and videos, which have been annotated and shared among friends, or published in groups that cover a specific topic of interest. The fact that users annotate and comment on the content in the form of tags, ratings, preferences and so on, and that these activities are performed on a daily basis, gives such social media data source an extremely dynamic nature that reflects topics of interests, events and the evolution of community opinion and focus.
The talk will present research challenges and activities and will focus on multi-modal graph-based community detection methods for social media mining, concept and event detection. Clusttour, a mobile and web application integrating research results with appropriate interface design will be demonstrated as a relevant use case. The talk will also include approaches for object/region classifiers learned using the self-training paradigm with loosely annotated training samples automatically selected from social media.
The pervasive use of Online Social Networks (OSN) for networking, communication and search in tandem with the ubiquitous availability of smartphones, which enables real-time multimedia capturing and sharing, have led to massive amounts of user-generated content and activities being amassed online, and made publicly available for analysis and mining. Each content item is associated with an abundance of metadata and related information such as location, tags, comments, favorites and mood indicators, access logs, and so on. At the same time, all this information is implicitly or explicitly interconnected based on various properties such as social links among users, groups, communities, and sharing patterns. These properties transform social media into data sources of an extremely dynamic nature that reflect topics of interests, events, and the evolution of community opinion and focus. Social media processing offers a unique opportunity to structure and extract information and to benefit multiple areas ranging from new media experiences to psychology and marketing. The objective of this talk is to provide an overview of the current research in emerging topics related to applications where social media can act as sensors of real-life phenomena and case studies that reveal valuable insights. After discussing challenges and presenting a generic conceptual architecture, there will be a focus on efficient processing and indexing algorithms that can handle massive amounts of content with application to graph-based event detection and summarization in social media streams.
Production model lifecycle management 2016 09Greg Makowski
This talk covers going over the various stages of building data mining models, putting them into production and eventually replacing them. A common theme throughout are three attributes of predictive models: accuracy, generalization and description. I assert you can have it all, and having all three is important for managing the lifecycle. A subtle point is that this is a step to developing embedded, automated data mining systems which can figure out themselves when they need to be updated.
Datamine’s campaign management platform stands as a unified environment for designing and executing complex campaigns, of several types, across a wide range of channels.
Campaign Data Analysis
Based on datamine’s Segment Designer as the rule definition toolkit for the formation of complex target groups and the interactive customer profiler, it sets new standards in target group design and analysis. Analyze your campaigns through extensive performance analysis & reporting – including OLAP models. Get insight on factors, demographics, resources or other attributes affecting campaign performance. Via suitable integration, CM extends existing Business Intelligence/ Customer Analytics infrastructure through systematic campaign and customer response data feeds.
Campaign Response Modeling
Improve your campaigns through systematic campaign response analysis & modeling: our data-mining models can unveil patterns explaining responses or even inspiring new contact & communication strategies.
The growth of social media over the last decade has revolutionized the way individuals interact and industries conduct business. Individuals produce data at an unprecedented rate by interacting, sharing, and consuming content through social media. Understanding and processing this new type of data to glean actionable patterns presents challenges and opportunities for interdisciplinary research, novel algorithms, and tool development. Social Media Mining integrates social media, social network analysis, and data mining to provide a convenient and coherent platform for students, practitioners, researchers, and project managers to understand the basics and potentials of social media mining. It introduces the unique problems arising from social media data and presents fundamental concepts, emerging issues, and effective algorithms for network analysis and data mining. Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts, principles, and methods in various scenarios of social media mining.
Details at: http://dmml.asu.edu/smm/
Mining Social media for Product Development & Marketing Insightsjuliannacole
Highlights of final project for Big Data & NoSQL Programming to analyze Twitter streams for select hashtags in effort to determine product development and marketing insights.
SocialSensor Project: Sensing User Generated Input for Improved Media Discove...Yiannis Kompatsiaris
SocialSensor: Sensing User Generated Input for Improved Media Discovery and Experience
Social Multimedia Crawling & Mining
EventSense: Capturing the Pulse of Large-scale Events by Mining Social Media Streams
Citizen Sensor Data Mining, Social Media Analytics and ApplicationsAmit Sheth
Opening talk at Singapore Symposium on Sentiment Analysis (S3A), February 6, 2015, Singapore. http://s3a.sentic.net/#s3a2015
Abstract
With the rapid rise in the popularity of social media, and near ubiquitous mobile access, the sharing of observations and opinions has become common-place. This has given us an unprecedented access to the pulse of a populace and the ability to perform analytics on social data to support a variety of socially intelligent applications -- be it for brand tracking and management, crisis coordination, organizing revolutions or promoting social development in underdeveloped and developing countries.
I will review: 1) understanding and analysis of informal text, esp. microblogs (e.g., issues of cultural entity extraction and role of semantic/background knowledge enhanced techniques), and 2) how we built Twitris, a comprehensive social media analytics (social intelligence) platform.
I will describe the analysis capabilities along three dimensions: spatio-temporal-thematic, people-content-network, and sentiment-emption-intent. I will couple technical insights with identification of computational techniques and real-world examples using live demos of Twitris (http://twitris2.knoesis.org).
From Research to Applications: What Can We Extract with Social Media Sensing?Yiannis Kompatsiaris
SIGMAP22 Keynote Presentation:
Social media have transformed the Web into an interactive sharing platform where users upload data and media, comment on, and share this content within their social circles. The large-scale availability of user-generated content in social media platforms has opened up new possibilities for studying and understanding real-world phenomena, trends and events. Social media and websites provide an access to public opinions on certain aspects and therefore play an important role in getting insights on targeted audiences. The objective of this talk is to provide an overview of social media mining, including key aspects such as data collection, multimodal analysis and visualization. Challenges, such as fighting misinformation, will be presented together with applications, results and demonstrations from multiple areas including: news, environment, security, interior and urban design.
Visual Information Analysis for Crisis and Natural Disasters Management and R...Yiannis Kompatsiaris
Invited talk at the Ninth International Conference on Image Processing Theory, Tools and Applications IPTA 2019 (http://www.ipta-conference.com/ipta19/)
Crises and natural disasters are unwelcome, but also unavoidable features of modern society, affecting more communities than ever. Visual information analysis plays an important role in efficient pre-event (e.g. risk modeling), during the event (response) and post-event (recovery) emergency situation management. This talk will describe the potential role of visual information sources including satellite images, surveillance and traffic cameras, social multimedia and aerial video in applications such as floods, fires, and oil spills. Multimodal and fusion techniques will be presented combining satellite and social data and how deep neural networks can be applied in this domain. The talks will include demos and results from the relevant BeAware and EOPEN projects and from our participation in the 2018 Multimedia Satellite Task of the MediaEval Benchmarking Initiative.
How can we mine, analyse and visualise the Social Web?
In this lecture, you will learn about mining social web data for analysis. Data preparation and gathering basic statistics on your data.
Workshop session given at the Institutional Web Management Workshop 2012 (IWMW 2012) event held at the University of Edinburgh on 18th - 20th June 2012.
Myths and challenges in knowledge extraction and analysis from human-generate...Marco Brambilla
For centuries, science (in German "Wissenschaft") has aimed to create ("schaften") new knowledge ("Wissen") from the observation of physical phenomena, their modelling, and empirical validation. Recently, a new source of knowledge has emerged: not (only) the physical world any more, but the virtual world, namely the Web with its ever-growing stream of data materialized in the form of social network chattering, content produced on demand by crowds of people, messages exchanged among interlinked devices in the Internet of Things. The knowledge we may find there can be dispersed, informal, contradicting, unsubstantiated and ephemeral today, while already tomorrow it may be commonly accepted. The challenge is once again to capture and create knowledge that is new, has not been formalized yet in existing knowledge bases, and is buried inside a big, moving target (the live stream of online data). The myth is that existing tools (spanning fields like semantic web, machine learning, statistics, NLP, and so on) suffice to the objective. While this may still be far from true, some existing approaches are actually addressing the problem and provide preliminary insights into the possibilities that successful attempts may lead to.
The talk explores the mixed realistic-utopian domain of knowledge extraction and reports on some tools and cases where digital and physical world have brought together for better understanding our society.
Smarter Cities pillars: Internet of Things, Web of Data, Crowdsourcing
Interdependence analysis: Society ageing and Societal urbanisation
Enablement of Smarter Inclusive Cities
Social media mining for sensing and responding to real-world trends and eventsYiannis Kompatsiaris
Social media have transformed the Web into an interactive sharing platform where users upload data and media, comment on, and share this content within their social circles. The large-scale availability of user-generated content in social media platforms has opened up new possibilities for studying and understanding real-world phenomena, trends and events. The objective of this talk is to provide an overview of social media mining, which offers a unique opportunity to to discover, collect, and extract relevant information in order to provide useful insights. It will include key challenges and issues, such as fighting misinformation, data collection, analysis and visualization components, applications, results and demos from multiple areas ranging from news to environmental and security ones.
This is a presentation on Sensor Based Ambient Assisted Living architecture and approaches developed by the Multimedia Knowledge and Social Media Analytics Lab of CERTH-ITI. It includes sensors used for monitoring Activities of Daily Living of elders and persons with mild Dementia at home. Visual and sensor data analytics are combined with formal representations (ontology), fusion, reasoning techniques and visualizations in order to provide an objective view of everyday activities. Example projects and pilots are included. Clinical assessment show improvement in cognitive abilities of participants.
"Μια πόλη από το μέλλον": Πως ο πολίτης μπορεί να γίνει συμμέτοχος μέσω της χ...Yiannis Kompatsiaris
Παρουσιάζονται παραδείγματα και αποτελέσματα του Εργαστηρίου Γνώσης, Πολυμέσων και Ανάλυσης Κοινωνικών Δικτύων του ΕΚΕΤΑ-ΙΠΤΗΛ (http://mklab.iti.gr) που αφορούν εφαρμογές που υποστηρίζουν τη συμμετοχή των πολιτών σε τομείς όπως: Αυτο-οργάνωση εθελοντικών κινημάτων, Συλλογική καταγραφή προβλημάτων, Συμμετοχή στη διαχείριση προβλημάτων και δημοσίου χώρου, Σχεδιασμό σε μελλοντικές δράσεις και έργα στην πόλη. Η παρουσίαση δόθηκε στα πλαίσια του συνεδρίου "Μια πόλη από το μέλλον", που διοργανώθηκε από την Παράλλαξη: http://www.parallaximag.gr/parallax-view/elate-na-deite-mia-poli-apo-mellon
Τεχνικές Αναγνώρισης Προτύπων και Μηχανικής Μάθησης για Εφαρμογές Ανάλυσης Πο...Yiannis Kompatsiaris
Η ομιλία περιλαμβάνει εφαρμογές τεχνικών αναγνώρισης προτύπων και μηχανικής μάθησης σε ανάλυση πολυμέσων και κοινωνικών δικτύων. Πιο συγκεκριμένα, θα παρουσιαστούν τεχνικές και εφαρμογές κατάτμησης εικόνων με χρήση Κ-Μέσων και επεκτάσεων, χρήση Support Vector Ma-chines για μάθηση εννοιών σε εικόνες καθώς και τεχνικές ανάλυσης γράφων από κοινωνικά δίκτυα. Θα παρουσιαστούν σχετικές εφαρμογές που αξιοποιούν τα αποτελέσματα της ανάλυσης, όπως αναζήτηση πολυμέσων και εφαρμογή για τουρισμό και ενημέρωση από κοινωνικά δίκτυα. Θα αναφερθούν τρέχοντα ερευνητικά προβλήματα και περιοχές.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
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.
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.
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…
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
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.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Monitoring Java Application Security with JDK Tools and JFR Events
Socialsensor project overview and topic discovery in tweeter streams
1. SocialSensor: Sensing User Generated Input for
Improved Media Discovery and Experience
Topic discovery in tweeter streams
Dr. Yiannis Kompatsiaris, Project Coordinator
International Workshop on Search Computing, Brussels, 25 September 2012
2. Overview
• Motivation
• Objectives
• Architecture
• Use Cases and Requirements
– News
– Infotainment
• Research Activities and Results
– Topic Discovery in Tweeter Streams
• Conclusions
#2
3. What is SocialSensor?
• 3-year FP7 European Integrated Project
– http://www.socialsensor.eu
• Members: CERTH, ATC (Greece), Deutsche Welle,
University Koblenz, Research Center for Artificial
Intelligence (Germany), The City University London,
Alcatel – Lucent Bell Labs, JCP Consult (France),
University of Klagenfurt (Austria), IBM Israel, Yahoo
Iberia
• 1 year into the project (Development of user
requirements, use case scenarios, architecture study and
implementation and first R&D prototypes)
#3
4. Motivation: Social Networks as Sensors
• Social Networks is a data source with an
extremely dynamic nature that reflects
events and the evolution of community
focus (user’s interests)
• Transform individually rare but
collectively frequent media to meaningful
topics, events, points of
interest, emotional states and social
connections
• Mine the data and their relations and
exploit them in the right context
• Scalable mining and indexing approaches
taking into account the content and social
context of social networks
5. Relevant Applications
Xin Jin, Andrew Gallagher, Liangliang Cao, Jiebo Luo, and
Jiawei Han. The wisdom of social multimedia:
using flickr for prediction and
forecast, International conference on Multimedia (MM
'10). ACM.
Federal Emergency Management Agency plans to
engage the public more in disaster response by
sharing data and leveraging reports from mobile
phones and social media
“…if you're more than 100 km away from the epicenter
[of an earthquake] you can read about the quake on
twitter before it hits you…”
5
6. Objective
SocialSensor quickly surfaces trusted and relevant material
from social media – with context.
Social media mining
Aggregation & indexing
content time
usage DySCO location
social context behaviour
Massive social media Personalised access
and unstructured web Ad-hoc P2P networks News - Infotainment
7. The SocialSensor Vision
SocialSensor quickly surfaces trusted and relevant
material from social media – with context.
•“quickly”: in real time
•“surfaces”: automatically discovers, clusters and searches
•“trusted”: automatic support in verification process
•“relevant”: to the users, personalized
•“material”: any material (text, image, audio, video =
multimedia), aggregated with other sources (e.g. web)
•“social media”: across all relevant social media platforms
•“with context”: location, time, sentiment, influence
#7
8. Conceptual Architecture and Main components
• Real time dynamic topic Public In-project
and event clustering Data Data
• Trend, popularity DATA COLLECTION / CRAWLING
and sentiment analysis
• Calculate trust/influence STORAGE
scores around people MINING INDEXING
• Personalized search,
SEARCH & RECOMMENDATION
access & presentation
based on social network SEMANTIC MIDDLEWARE
interactions
USER MODELLING & PRESENTATION
• Semantic enrichment
and discovery of services
#10
9. “Social media is transforming the way we do journalism”
(New York Times)
“Social media is the key place for emerging stories –
internationally, nationally, locally” (BBC)
“It has changed the way we do
news”(MSN)
Source: picture alliance / dpa
#12
11. “It’s really hard to find the nuggets of useful stuff
in an ocean of content” (BBC)
“Things that aren’t relevant crowd out the content
you are looking for” (MSN)
“The filters aren’t configurable
enough” (CNN)
Source: Getty
Images
#14
13. An example: BBC Verification Procedure: Arab
Spring Coverage
• Referencing locations against maps and existing images
from, in particular, geo-located ones.
• Working with our colleagues in BBC Arabic and BBC
Monitoring to ascertain that accents and language are
correct for the location.
• Searching for the original source of the
upload/sequences as an indicator of date.
• Examining weather reports and shadows to confirm that
the conditions shown fit with the claimed date and time.
• Maintaining lists of previously verified material to act as
reference for colleagues covering the stories.
• Checking scenery, weaponry, vehicles and licence plates
against those known for the given country.
#16
14. Infotainment
• Events with large numbers
of visitors
• Thessaloniki International
Film Festival
– 80,000 viewers / 100,000
visitors in 10 days
– 150 films, 350 screenings
• Fete de la Musique Berlin
– 100,000 visitors every year
• Discovery and presentation
of relevant aggregated
social media (e.g. film
ratings from tweets)
#18
15. User Requirements
• Social media aggregation
• Sentiment analysis for
screenings and events
• Real-time check-in
heatmaps
• AR 3D interfaces with
RT information layers
30
• Social media-based film 25
recommendations 20
15 Positive
10 Negative
• Smart location-based 5 Interest
recommendations 0
-5 Fri Mar Fri Mar Fri Mar Fri Mar Fri Mar
06 07 08 09 10
#19
16. ThessFest
• Thessaloniki ThessFest
International Film
Festival
• Support
twitter/comment
usage within the app
• Ratings and
comments per film
• Feedback
aggregation
– Votes
– Tweets
• Real-time feedback
to the organisation
and visitors
#20
17. ThessFest
• Gather “realistic” user requirements
• Early showcase and evaluation of SocialSensor
technologies in real-world event scale
• Engage users and create an informed user basis
• TDF14: 9-18 March 2012
– 400+ users
– 6500+ user sessions
– positive response to social media
• Next version
– Updated features
– Android version
#21
18. Topic Discovery in Tweeter Streams
Giorgos Petkos, Symeon Papadopoulos, Yiannis Kompatsiaris,
Carlos Martin, David Corney, Ryan Skraba, Luca Aiello, Alex Jaimes, Yosi Mass
#22
19. Problem Formulation
• Detect trending topics in a stream of Tweets
• Topics are represented as sets of keywords that
capture the essence of an emerging news story
– Ex: “ramires”, “goal”, “1-0”, “chelsea”, “score”
in the case of a soccer match
• Due to evolving nature of social interest, topics are
extracted per timeslot (e.g. per minute, hour, etc.)
– in contrast to previous work on topic detection, we are not
only interested in a posteriori detecting topics in a static
corpus, but in detecting them at the time they are
discussed
#23
20. Background
Two basic classes of methods to discover topics:
• Feature-pivot (Feat-p):
1. Select important (trending) terms in a stream
2. Cluster them with related terms into topics
• Document-pivot (Doc-p):
1. Cluster documents (tweets) into groups based on
similarity
2. Extract most characteristic terms for each group
#24
21. Proposed Methods
• Doc-p based on Locality Sensitive Hashing (LSH)
– Efficient stream clustering cluster filtering term selection
• Graph-based feat-p approach
– Term selection term graph creation based on co-occurrence
graph clustering
• Feat-p based on frequent pattern detection
– Parallel FP-Growth algorithm (Hadoop implementation) frequent
pattern ranking
• “Soft” frequent itemset mining
– Greedy search for incrementally detecting maximal term sets of co-
occurrence
• DF-IDFt approach
– Select n-grams based on burstiness (increase in frequency relative to
recent past) hierarchical clustering of n-grams
#25
22. Leveraging Influence
• Exploiting social context in the analysis procedure
• Assumption: Tweets coming from influential users
are expected to be more relevant / less noisy
• Extract a first set of topics with previous TD methods
• Identify a set of influencers for each of those topics
– Create content citation graph [nodes: users, edges:
retweets], term profile for each edge based on RT text
– Term- and topic-based subgraphs
– Influence computation based on random walks
(distributed computation on top of Hadoop)
• Filter original stream of Tweets taking into account
only Tweets coming from influencers
#26
23. Evaluation
• Two datasets
– Super Tuesday (ST): event in presidential nomination race for US
Republican party [3.5M tweets, average 131 tweets/min]
– FA Cup (FA): Chelsea vs. Liverpool [444K tweets, av. 148 tweets/min]
• Manual ground truth construction of set of interesting topics
– Source: mainstream media reports
– Representation: Set of topics (keywords + short headline) per timeslot
[1-hour timeslot for ST, 1-minute for FA]
• Metrics
– Topic recall (number of found target topics) [TRec]
– Keyword-level recall [KRec] & precision [KPrec]
• Baseline
– Latent Dirichlet Allocation (LDA)
#27
26. Evaluation
FA cup:
•Time: 17:16, Ramires scores for Chelsea.
– goal, 1-0, ramires, #cfc
•Time: 18:56, Andy Caroll takes a header but Cech saves Chelsea.
– #facupfinal, saved, carroll, claiming, header, cech, @chelseafc, #cfcwembley
Super Tuesday:
•Time: 19:00: ABC/NBC/CNN project Newt Gingrich as the winner of the
Georgia Primary
– georgia, newt, wins, primary, republican, breaking, gingrich
•Time: 19:00, Newt Gingrich says “Thank you Georgia! It is gratifying to win
my home state so decisively to launch our March Momentum”
– @newtgingrich, win, georgia, launch, #marchmo, gratifying, march, momentum
#30
28. Mobile photo browsing
& tagging on the go
HIERARCHICAL
CITY PROFILE MINING OWN IMAGE TAGGING
PHOTO BROWSING
Areas: cluster geotagging
information (BIRCH)
Image clustering:
community detection
(SCAN) on image similarity
graphs
Cluster processing: CONTEXTUAL TAG
- classify landmarks-events RECOMMENDATION
- extract titles and tags
ClustTour
#32
30. Hierarchical exploration through mobile
Top-level spatial clustering
(areas) Adaptive marker clustering
Scrollable photo clusters
#34
31. Conclusions
• Great interest in both use cases
• In news social media have transformed both news generation and
consumption
• Social media data mining can provide interesting results in
many applications
• Not all data always available (e.g. User queries, fb)
– Infrastructure, Policy issues
• Technical challenges
– Fusion (multi-modality, context), real-time, noise, big data,
aggregation (web, Linked Open Data)
• Applications challenges
• User engagement, privacy, copyright, commercialization