Just a simple intro to typescript.
Most of the contents are of any OO language, hence slide contents are minimal. If anyone need any further help, reach me out - akhil2369492@gmail.com
-Akhil
The slides I was using when delivering a one day seminar for learning the TypeScript programming language. The seminar was delivered as part of the Expert Days 4 days conference that took place in 12/2015 in Israel.
More information about the Front End Development course I deliver can be found at fed.course.lifemichael.com
Just a simple intro to typescript.
Most of the contents are of any OO language, hence slide contents are minimal. If anyone need any further help, reach me out - akhil2369492@gmail.com
-Akhil
The slides I was using when delivering a one day seminar for learning the TypeScript programming language. The seminar was delivered as part of the Expert Days 4 days conference that took place in 12/2015 in Israel.
More information about the Front End Development course I deliver can be found at fed.course.lifemichael.com
The course gives a professional and academic introduction to computer and information security using the ethical hacking approach, which enables improved defence thanks to adopting an attacker mindset when discovering vulnerabilities, hands-on experience with different attacks, facilitates linking theory and practice in significant areas of one’s digital literacy, and can therefore be utilized by (future) security professionals, (informed) decision-makers, (savvy) users and developers alike.
Learn Python Programming | Python Programming - Step by Step | Python for Beg...Edureka!
( Python Training : https://www.edureka.co/python )
This Edureka “Python Programming" introduces you to Python by giving you enough reasons to learn it. It will then take you to its various fundamentals along with a practical demonstrating the various libraries such as Numpy, Pandas, Matplotlib and Seaborn. This video helps you to learn the below topics:
1. Why should you go for Python?
2. Introduction to Python Programming Language
3. How to work with Jupyter?
4. Python Programming Fundamentals: Operators & Data Types
5. Libraries: Numpy, Pandas, Matplotlib, Seaborn
Why DevOps?
DevOps principles
DevOps concepts
DevOps practices
DevOps people
DevOps controls
DevOps training and further reading
Where do you start with DevOps?
CNIT 123: Ch 3: Network and Computer AttacksSam Bowne
Slides for a college course based on "Hands-On Ethical Hacking and Network Defense, Third Edition" by Michael T. Simpson, Kent Backman, and James Corley -- ISBN: 9781285454610
Teacher: Sam Bowne
Twitter: @sambowne
Website: https://samsclass.info/123/123_S18.shtml
Basic concept of Deep Learning with explaining its structure and backpropagation method and understanding autograd in PyTorch. (+ Data parallism in PyTorch)
Don't Be Mocked by your Mocks - Best Practices using MocksVictor Rentea
Do you ❤️ Mocks? When you write your first unit tests, especially on older codebases, mocking foreign code is key to survival. But as you grow older in the craft, you start piling up hours and days wasted to refactor fragile tests or to fix bugs that those heavy mock-based tests didn't catch. And so you start looking at Mocks differently.
Let's go through the key factors to consider to strike the optimal balance between what needs to be mocked away and what code should be tested in integration. There's sometimes a fine line there, often interwoven with strong emotions:
"Why am I testing this?"
"Argh… these tests take too long"
"Can this ever really break?"
etc...
Among the points that we'll touch on:
- Mocks vs Refactoring
- Mocks vs Reliability
- Fine vs Coarse Mocks
- Reproducibility
- Partial Mocks
- Mocking Statics
- Alternatives to Mocks
Speakers: Victor Rentea
Victor is a Java Champion and Independent Trainer with an impressive experience: thousands of developers in dozens of companies trained in dedicated company sessions. He is the founder of one of the largest developer communities in Romania, Bucharest Software Craftsmanship Community and a top international conference speaker.
To find more about him, join a live masterclass or call him in for a company dedicated training: victorrentea.ro
Tests are hard to write if the production design is crappy - goes an old saying. Indeed, writing unit tests gives you one of the most comprehensive, yet brutal, feedback about the design of your production code, but if it comes too late, many developers can’t take it and they will either stop testing or test superficially. At the other end, others struggle to write contrieved, fragile tests full of mocks that end up frustrating more than helping them. This talk reviews the main hints that unit tests provide you, from the most obvious improvements to some of the most subtle design principles.
The Nullable type allows you to assign a null value to a variable. Nullable types can only work with Value Type not with Reference Type because it already contains a null value. The Nullable type is an instance of System.Nullable<T> struct.
As computer systems become more sophisticated, process injection techniques also evolve. These techniques are notorious for their use by "malicious software" to hide code execution and avoid detection. In this presentation we dive deep into the Windows runtime and we demonstrate these techniques. Besides, we also learn how to code construction and design patterns that relate to perform hidden code can recognize.
The course gives a professional and academic introduction to computer and information security using the ethical hacking approach, which enables improved defence thanks to adopting an attacker mindset when discovering vulnerabilities, hands-on experience with different attacks, facilitates linking theory and practice in significant areas of one’s digital literacy, and can therefore be utilized by (future) security professionals, (informed) decision-makers, (savvy) users and developers alike.
Learn Python Programming | Python Programming - Step by Step | Python for Beg...Edureka!
( Python Training : https://www.edureka.co/python )
This Edureka “Python Programming" introduces you to Python by giving you enough reasons to learn it. It will then take you to its various fundamentals along with a practical demonstrating the various libraries such as Numpy, Pandas, Matplotlib and Seaborn. This video helps you to learn the below topics:
1. Why should you go for Python?
2. Introduction to Python Programming Language
3. How to work with Jupyter?
4. Python Programming Fundamentals: Operators & Data Types
5. Libraries: Numpy, Pandas, Matplotlib, Seaborn
Why DevOps?
DevOps principles
DevOps concepts
DevOps practices
DevOps people
DevOps controls
DevOps training and further reading
Where do you start with DevOps?
CNIT 123: Ch 3: Network and Computer AttacksSam Bowne
Slides for a college course based on "Hands-On Ethical Hacking and Network Defense, Third Edition" by Michael T. Simpson, Kent Backman, and James Corley -- ISBN: 9781285454610
Teacher: Sam Bowne
Twitter: @sambowne
Website: https://samsclass.info/123/123_S18.shtml
Basic concept of Deep Learning with explaining its structure and backpropagation method and understanding autograd in PyTorch. (+ Data parallism in PyTorch)
Don't Be Mocked by your Mocks - Best Practices using MocksVictor Rentea
Do you ❤️ Mocks? When you write your first unit tests, especially on older codebases, mocking foreign code is key to survival. But as you grow older in the craft, you start piling up hours and days wasted to refactor fragile tests or to fix bugs that those heavy mock-based tests didn't catch. And so you start looking at Mocks differently.
Let's go through the key factors to consider to strike the optimal balance between what needs to be mocked away and what code should be tested in integration. There's sometimes a fine line there, often interwoven with strong emotions:
"Why am I testing this?"
"Argh… these tests take too long"
"Can this ever really break?"
etc...
Among the points that we'll touch on:
- Mocks vs Refactoring
- Mocks vs Reliability
- Fine vs Coarse Mocks
- Reproducibility
- Partial Mocks
- Mocking Statics
- Alternatives to Mocks
Speakers: Victor Rentea
Victor is a Java Champion and Independent Trainer with an impressive experience: thousands of developers in dozens of companies trained in dedicated company sessions. He is the founder of one of the largest developer communities in Romania, Bucharest Software Craftsmanship Community and a top international conference speaker.
To find more about him, join a live masterclass or call him in for a company dedicated training: victorrentea.ro
Tests are hard to write if the production design is crappy - goes an old saying. Indeed, writing unit tests gives you one of the most comprehensive, yet brutal, feedback about the design of your production code, but if it comes too late, many developers can’t take it and they will either stop testing or test superficially. At the other end, others struggle to write contrieved, fragile tests full of mocks that end up frustrating more than helping them. This talk reviews the main hints that unit tests provide you, from the most obvious improvements to some of the most subtle design principles.
The Nullable type allows you to assign a null value to a variable. Nullable types can only work with Value Type not with Reference Type because it already contains a null value. The Nullable type is an instance of System.Nullable<T> struct.
As computer systems become more sophisticated, process injection techniques also evolve. These techniques are notorious for their use by "malicious software" to hide code execution and avoid detection. In this presentation we dive deep into the Windows runtime and we demonstrate these techniques. Besides, we also learn how to code construction and design patterns that relate to perform hidden code can recognize.
Presented at Hypertext'13.
Topic classification (TC) of short text messages o↵ers an ef- fective and fast way to reveal events happening around the world ranging from those related to Disaster (e.g. Sandy hurricane) to those related to Violence (e.g. Egypt revolu- tion). Previous approaches to TC have mostly focused on exploiting individual knowledge sources (KS) (e.g. DBpedia or Freebase) without considering the graph structures that surround concepts present in KSs when detecting the top- ics of Tweets. In this paper we introduce a novel approach for harnessing such graph structures from multiple linked KSs, by: (i) building a conceptual representation of the KSs, (ii) leveraging contextual information about concepts by exploiting semantic concept graphs, and (iii) providing a principled way for the combination of KSs. Experiments evaluating our TC classifier in the context of Violence detec- tion (VD) and Emergency Responses (ER) show promising results that significantly outperform various baseline models including an approach using a single KS without linked data and an approach using only Tweets.
Online paedophile activity in social media has become a major concern in society as Internet access is easily available to a broader younger population. One common form of online child exploitation is child grooming, where adults and minors exchange sexual text and media via social media platforms.
Such behaviour involves a number of stages performed by a predator (adult) with the final goal of approaching a victim (minor) in person. This paper presents a study of such online grooming stages from a machine learning perspective. We
propose to characterise such stages by a series of features covering sentiment polarity, content, and psycho-linguistic and discourse patterns. Our experiments with online chatroom conversations show good results in automatically classifying chatlines into various grooming stages. Such a deeper understanding and tracking of predatory behaviour is vital for building robust systems for detecting grooming conversations and potential predators on social media.
Persuasive communication is the process of shaping, reinforcing and changing others’ responses. In political debates, speakers ex- press their views towards the debated topics by choosing both the content of their discourse and the argumentation process. In this work we study the use of semantic frames for modelling argumentation in speakers’ discourse. We investigate the impact of a speaker’s argumentation style and their effect in influencing an audience in supporting their candidature. We model the influence index of each candidate based on their relative standings in the polls released prior to the debate and present a system which ranks speakers in terms of their relative influence using a combination of content and persuasive argumentation features. Our results show that although con- tent alone is predictive of a speaker’s influence rank, persuasive argumentation also affects such indices.
Location sharing services(LSS) like Foursquare, Gowalla and Face- book Places gather information from millions of users who leave trails in loca- tions (i.e. chekins) in the form of micro-posts. These footprints provide a unique opportunity to explore the way in which users engage and perceive a point of interest (POI). A POI is as a human construct which describes information about locations (e.g restaurants, cities). In this work we investigate whether the collec- tive perception of a POI can be used as a real-time dataset from which POI’s transient features can be extracted. We introduce a graph-based model for profil- ing geographical areas based on social awareness streams. Based on this model we define a set of measures that can characterise a location-based social aware- ness stream as well as act as indicators of volatile events occurring at a POI. We applied the model and measures on a dataset consisting of a collection of tweets generated at the city of Sheffield and registered over three week-ends. The model and measures introduced in this paper are relevant for design of future location-based services, real-time emergency-response models, as well as traffic forecasting. Our empirical findings demonstrate that social awareness streams not only can act as an event-sensor but also can enrich the profile of a location-entity.
Psyc 12 a description of relevant course theory/tutorialoutletBinksz
FOR MORE CLASSES VISIT
tutorialoutletdotcom
• Psychology of Prejudice—PSYC12
Term Paper Grading Scheme ____ / 25 General
Eloquent and clear writing
Sentences are well constructed and varied in structure
Paper organized and well argued
Economy of language
Choose one media program or article that deals with an issue r.docxnancy1113
Choose
one
media program or article that deals with an issue related to the variables we are exploring in this course: race, ethnicity, gender, sexual orientation, religion, age, or social class. Examples of acceptable media include a newspaper article, a radio program, a television show, or a movie. Find these via the Capella library or online. Feel free to choose a topic or media source that is either domestic or global in content.
Then, write an essay that includes a brief summary of the main event or issues in your article or program, clarifying how the event or issue connects to this course. Your essay should accomplish the following:
Discuss sociological theories appropriate for promoting understanding of a diversity concept.
Include relevant examples from the article or program to illustrate your points.
Discuss how a media piece may affect or might have been influenced by policy and power.
Consider how individuals in power influence decisions regarding how issues are portrayed. This can include corporate executives, lobbyists, special interest groups, or those who own a particular newspaper, channel, or media conglomerate.
Discuss how minority and dominant groups are portrayed in a media piece to understand influences on discrimination.
Relate your discussion to Merton's typology of prejudice and discrimination from the
Merton's Typology of Prejudice and Discrimination
interactive.
Analyze data that is appropriate for supporting or refuting the central tenets of your media piece.
If data (statistical information) is cited in your media selection, you may go to that source and analyze it yourself; then consult an additional source that supports or refutes the data presented.
Apply in text the standard writing conventions for the discipline, including structure, voice, person, tone, and citation formatting.
Additional Requirements
Written communication:
Ensure written communication is free of errors that detract from the overall message.
Length of paper:
Submit 2–4 typed, double-spaced pages (title and reference page not included).
Format:
Format resources and citations according to current
APA Style and Format
guidelines.
Font and font size:
Use Times New Roman, 12-point font.
Sources:
Cite at least two sources. You may cite your media piece as one source and a course material or another scholarly source as the other. If you refer to concepts or theories covered in the course content, be sure to appropriately cite the applicable course reading or video.
.
For more course tutorials visit
www.newtonhelp.com
CRJ 305 Week 1 Discussion Deterring Crime
Deterring Crime. 1st Post Due by Day 3. In Chapter 2 of The Prevention of Crime, the concepts of general and specific deterrence are discussed. In this week’s discussion please cover the following:
• Think about a type of crime that interests you such as homicide, robbery, etc. Which theory covered in
Do you know what we call opinion in the absence of evidence We call.docxblossomblackbourne
Do you know what we call opinion in the absence of evidence? We call it prejudice.
—Michael Crichton,
State of Fear
Prejudice is an underlying complex mix of mental perceptions and associated emotions and attitudes toward members of another group that often result in social distance and manifest in overt acts of discrimination. An important first step in addressing the roots of prejudice and discrimination is to trace the historical sources and the psychological reinforcements.
In this Assignment, you will explore historical examples of prejudice and discrimination, drawing on how prejudice is perpetuated across generations, and analyzing the implications of persistent prejudice in society.
To prepare:
Identify
one historical example
and
one contemporary example
of discrimination. Consider the underlying causes and the consequences of discrimination in these cases.
Submit a 3- to 4-page paper
in which you do the following for
both
your historical example and your contemporary example of discrimination:
Analyze the major cause(s) of discrimination.
Analyze how discrimination has been manifested.
Analyze the consequences of the discrimination. How has discrimination benefitted one group over another?
Apply one or more of the theories of prejudice and discrimination (covered in Chapter 3 of the course text (Marger, 2015)) to analyze each example.
Analyze any social policies that have emerged in order to address the acts of discrimination.
Support your assertions by making
at least two
documented references to your course readings. Please use proper APA formatting to cite each of your sources.
Length:
3–4 pages
Marger, M. N. (2015).
Race and ethnic relations: American and global perspectives
(10th ed.). Stamford, CT: Cengage Learning.
Chapter 2, “Ethnic Stratification: Majority and Minority” (pp. 27–48)
Chapter 3, “Tools of Dominance: Prejudice and Discrimination” (pp. 49–78)
Media
Ted Conferences (Producer). (2014c).
Paul Bloom:
Can prejudice ever be a good thing?
[Video file]. Retrieved from
https://www.ted.com/talks/paul_bloom_can_prejudice_ever_be_a_good_thing
Note:
The approximate length of this media piece is 16 minutes.
“We often think of bias and prejudice as rooted in ignorance. But as psychologist Paul Bloom seeks to show, prejudice is often natural, rational... even moral. The key, says Bloom, is to understand how our own biases work—so we can take control when they go wrong.”
Understanding Prejudice. (2016b).
The lunch date
[Video file]. Retrieved from
http://www.understandingprejudice.org/multimedia/stereo.htm
Understanding Prejudice. (2016c).
Implicit associations and hidden biases
[Video file]. Retrieved from
http://www.understandingprejudice.org/multimedia/stereo.htm
Understanding Prejudice. (2016d).
The psychological effects of stereotype threat
[Video file]. Retrieved from
http://www.understandingprejudice.org/multimedia/stereo.htm
.
This presentation on Diversification is part of the ARCOMEM training curriculum. Feel free to roam around or contact us on Twitter via @arcomem to learn more about ARCOMEM training on archiving Social Media.
Discussion thread: computer science background/ experience
In this discussion, describe any and all computer experience you have entering this course. Include items such as (but not limited to):
· Experience in office products (i.e. Word, Excel, PowerPoint).
· Social media experience.
· Mobile applications experience.
· Any Programming Experience.
· Any Database Experience.
· Any Data Networking experience.
· Would you classify yourself as a Novice user, Competent user of Highly competent user? And explain why.
· Any certifications.
You will not see other student posts until after you post your initial thread. Your post needs to be a complete essay type response.
Please review the Discussion Assignment InstructionsDownload Discussion Assignment Instructionsprior to posting. You may also click the three dots in the upper corner to Show Rubric.
Post-First: This course utilizes the Post-First feature in all Discussions. This means you will only be able to read and interact with your classmates’ threads after you have submitted your thread in response to the provided prompt.
2
American Public University
Charles Town, WV
Introduction Comment by Chris Martinez: You need an introduction to your study. Think of it as a background, current situation, or setting the table for your problem, purpose, and question you wan to explore Comment by Ronald Punzalan: Resolved
Terrorism has become a significant issue in the United States since the 2001 attack (Wright, 2016). Terrorism and religion have been connected for as long as human history can be traced. Civilizations and empires in ancient times are perfect examples of true extremist believers who have engaged in wars to defend, promote, and spread their faith. Rink and Sharma (2016) asserted that the contemporary era is witnessing escalating religious terrorism in its frequency, the scale of violence, and global reach. Indeed, previous studies show that religious radicalization is the genesis of violence justification (Rink & Sharma, 2016). The choice of target is influenced by existing and loose interpretations of religious doctrine or based on defending one's religious group. Religious radicalization is globalized, unlike ethnic radicalization, and involves sophisticated networks, making it challenging to tackle the militia groups (Rink & Sharma, 2016). The United States is not immune to terrorism resulting from religious radicalization. Some authors have found a relationship between religious radicalization and individual-level psychological trauma related to social relations and process-oriented factors (Rink & Sharma, 2016). Comment by Chris Martinez: This is an improvement Comment by Ronald Punzalan: Resolved
A report published by the Center for Strategic and International studies indicated that out of the 893 terror incidents analyzed between 1994 and 2020, 15.6% were committed by religious terrorists (Jones, 2022). The same data showed that religious terrorism significantly increased ...
Children’s Critical Thinking When Learning from Others.docxmccormicknadine86
Children’s Critical Thinking When Learning from Others:
A Critique Submitted by
XXXXXXXXX
El Centro College
Psychology 2301, Section 53xxx, Spring 2013
Running head: CHILDREN AND CRITICAL THINKING 1
Running head: CHIDREN AND CRITICAL THINKING
1
Abstract
Children’s Critical Thinking When Learning from Others
Introduction
Everyday children must decide for themselves about what is a reliable source of information. They must critically evaluate a source, be it a cartoon watched on television or a conversation held with another child or adult. Children must also determine if a resource is reliable and credible or if it is lacking in real information, then think critically about the information that is given. Heyman’s (2008) meta-analytic study explores how critical thinking skills can be taught to children and defines critical thinking and analyses how early and how well these critical thinking skills develop in children. Heyman (2008) also explores how social experiences shape the development of these skills, including a comparison of responses in Chinese and American children. The researcher hopes that information from this study, and prior studies discussed in this article, can be used by parents as a means for guiding their children along a path toward critical thinking (Heyman, 2008). ReviewCritique
CHILDREN AND CRITICAL THINKING 4
References
Heyman, G. D. (2008). Children’s critical thinking when learning from others. Current Directions in Psychological Science, 17(5), pp. 344-347.
ABBREVIATED TITLE IN CAPITAL LETTERS 4
Full Title in Upper and Lower Case Letters
A Critiqué Submitted by
Name of Student
El Centro College
Psychology 2301, Section 53005, Spring 2012
ABBREVIATED TITLE IN CAPITAL LETTERS 2
Running head: ABBREVIATED TITLE IN CAPITAL LETTERS 1
Abstract
The abstract is a summary of YOUR paper but for this assignment you will not be creating much content, so an abstract will not be required. Consequently, you will center the word Abstract as above (the content under the abstract is left justified) and leave this section blank. You will however notice that the header on this and the following pages does not include the words “Running head:” but do include the actual running head which is an abbreviated title that is 50 characters or less in length.
Note: It is advisable for you to use this template as much of the formatting has already been done. Also, when you submit your paper for grading, it will be automatically sent to SafeAssign which analyses your paper for similarities found in other papers that have been submitted, research articles and websites. It is highly functional and will most often catch plagiarism, so please do not plagiarize.
Full Title in Upper and Lower Case LettersSummary
Read the author’s abstract as an example of how to write a summary of the article but do NOT copy their abstract. For the Summary section you will summarize the author’s article in your own words ...
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/
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
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.
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.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
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.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...
Violence det ijcnlp13-slideshare
1. A Weakly Supervised Bayesian Model for
Click to edit Master subtitle style
Violence Detection in Social Media
Elizabeth Cano*, Yulan He*, Kang Liu+, Jun Zhao+
*School
of Engineering and Applied Science Aston University, UK
+Institute of Automation Chinese Academy of Sciences, China
2. Outline
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o
Introduction
o
Research Challenges
o
Violence Detection Model
o
Deriving word priors
o
Experiments
2
5. Introduction
Objectives
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Objectives
Identification of
suspicious tweets
Violence-related Topic
detection
Extraction of violent and
criminal events appearing
in social media
5
6. Introduction
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Violence-related content analysis
Violence-related content
Characterised by the use of terms expressing aggression and
attitudes towards violence
Violence-related content Analysis
Identifying violence polarity in piece of text (violence-related or
non-violence related)
Involves the detection of particular types of sentiments not
necessarily negative (e.g. anger, shame, excitement)
6
7. Introduction
Click to edit Master subtitle violence-related tweets
Characterisingstyle
Challenges
Restricted number of characters
Irregular and ill-formed words
Wide variety of language
Evolving jargon (e.g. slang and teenage lingo)
Event-dependent vocabulary characterising violence-related content
•
Volatile jargon relevant to particular events. While sentiment and affect
lexicon rarely changes in time, words relevant to violence tend to be event
dependent
E.g., “fire” and “flame” are negative during the UK riots 2011, but appear to be
positive in the London Olympics 2012.
E.g. “#Jan25” violence-related during the Egyptian revolution
7
8. Related Work
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Violence-related classification in Social Media
Topic Classification of short texts
Standard supervised machine learning methods [Milne-etal 2008][Gabrilovich-et-al 2006][Munoz-et-al 2011][Meij-et-al 2012]
Alleviate micropost sparsity by making use of external
knowledge sources (e.g. DBpedia)[Michelson-et-al
2010][Cano-et-al 2013]
Weakly Supervised approaches
JST model [Lin&He 2009][Lin&He2012]
Partially-Labeled LDA (PLDA) [Ramage et al., 2011]
8
9. Related Work
Click to edit Master subtitle style
Violence-related classification in Social Media
Rely on supervised classification techniques or do not cater
for the violence detection challenges.
Do not perform discover topics with an associated document
category.
9
10. Related Work
Click to edit Master subtitle style
Violence-related classification in Social Media
Topic Classification of short texts
Standard supervised machine learning methods [Milne-et-al
2008][Gabrilovich-et-al 2006][Munoz-et-al 2011][Meij-et-al 2012]
Alleviate micropost sparsity by making use of external
Since violence-related (e.g. DBpedia)[Michelson-et-al 2010][Canoknowledge sources events tend to occur during short to
medium 2013]
et-al life-spans, methods relying only on labeled data can
rapidly become outdated.
Rely on supervised classification techniques or do not cater
for the violence detection challenges.
Do not perform discover topics with an associated document
category.
10
11. Violence-related classification in Social Media
Click to edit Master subtitle
Challenges style
How to characterise violence-polarity?
How to build a model to discriminate across documents to
identify violence-related content?
How to provide overall information to understand the type of
violence-related events?
11
12. Click to edit Master subtitle style
Violence Detection Model (VDM)
Problem Formulation and Proposed Method
12
13. Accessing Topics via Word Distributions
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o
Novel Bayesian Modelling Approach for:
Identifying violent content in social media
No need of labelled data
Inspired by the previous work on sentiment analysis, in
particular on the JST model[Lin&He 2009][Lin&He2012]
o
Use of knowledge sources (e.g. DBpedia)
Priors derivation strategies
13
15. Accessing Topics via Word Distributions
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Each Tweet can involve multiple topics
Topics
15
16. Accessing Topics via Word Distributions
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Each tweet involves as well words with different violencepolarity
Violence Polarity
Casting these intuitions into a generative probabilistic
process [Blei-et-al 2003]
- Each document is a random mixture of corpus-wide topics
- Each word is drawn from one of those topics
16
17. Accessing Topics via Word Distributions
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Document
Violence polarity
non-violence-related
violence-related
Text
non-violence-related
Document
Violence polarity
non-violence-related
violence-related
Text
violence-related
17
18. Violence Detection Model (VDM)
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violenceLabel/
topic probability
word
topic
Violabel/topic
language model
word
Violence
probability
vioLabel
Nd
D
18
19. Violence Detection Model (VDM)
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•
Choose ω ∼ Beta(ε), φ0 ∼ Dir(β0), φ
∼ Dir(β).
• For each category (violent or nonviolent) c
For each topic z under the
document category c
o Choose θcz ~ Dir(α)
• For each doc m
Choose πm ~ Dir(γ)
For each word wi in doc m
o choose xm,n ∼ Mult (ω);
o If xm,n =0,
choose a word wm,n ∼ Mult(φ0);
o if xm,n =1,
choose a tweet category label
cm,n ∼ Mult (πm ),
choose a topic zm,n ∼ Mult(θcm,n
),
choose a word wm,n ∼ Mult(φcm,n
,zm,n ).
19
20. Violence Detection Model (VDM)
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• Single document category-topic distribution shared across all the
documents.
• Assumes words are generated either from a category-specific topic
distribution or from a general background model.
20
21. Click to edit Master subtitle style
Deriving Word Priors
21
22. Violence Lexicon
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•
Violence Lexicon Preparation
•
•
DBpedia articles from violent related topics
Twitter Data for Jan-Dec 2010 (10% Twitter Firehose)
Violence-related
Non-Violence-related
fight
war
protest
riots
conflict
bomb
trouble
fear
twilight
sandwich
award
moon
record
common
excited
great
22
23. Deriving Priors
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Using DBpedia Categories
• Structured Semantic Web
Representation of data derived from
Wikipedia
Maintained by thousand of editors
Evolves and adapts as knowledge
changes [Syed et al, 2008]
• Cover a broad range of topics
• Characterise topics with a large
number of resources
DBpedia*
Yago2
Freebase
Resources
2.35 million
447million
3.6 million
Classes
359
562,312
1,450
Properties
1,820
253,213,84
2
7,000
23
24. Deriving Priors
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Using DBpedia Categories
Revolutionary Terror
Terrorism
Violence
War
….
Military Operations
Guerrilla Warfare
…
….
24
25. Obtaining Priors from Tweets
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1 million Tweets annotated with OpenCalais derived topics
including:
• Business & Finance
• Disaster & Accident
• Education
• Entertainment & Culture
• Environment
• Health & Medical
• Hospital & Recreation
• Labor
• Law &Crime
•Politics
• Religion & Belief
• Social Issues
• Sports
• Technology &Internet
• War & Conflict 8,338
tweets
25
26. Datasets for Priors
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•
Use OpenCalais to annotate tweets
•
•
•
Extracted tweets labelled as “War & Conflict” and
considered them as violence-related annotations
OpenCalais has low F-measure of 38% when evaluated on
our manually annotated test set
DBpedia abstracts have longer sentences than tweets
•
Generated tweet size documents by chunking the abstracts
into 9 or less words
Tweets (TW)
DBpedia (DB)
DBpedia chunked
(DCH)
Violent-related
10,432
4,082
32,174
Non violent-related
11,411
11,411
11,411
26
27. Relative Word Entropy
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•
Corpus Word Entropy captures the dispersion of the usage
of word w in the corpus SD
•
Class Word Entropy characterises the usage of a word in
a particular document class
•
Relative Word Entropy provides information on the relative
importance of that word to a given document class
27
28. Word Priors Obtained using RWE
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DBpedia-Chunked
Priors
DBpedia-derived Priors
Tweets-derived Priors
Violent
NotViolent
Violent
NotViolent
Violent
NotViolent
group
customer
group
gop
rebel
ey
alleg
win
power
lov
destro
nnw
armour
diff
suffer
back
sectar
vot
resid
good
soc
good
anti
soc
cult
sen
palest
twees
mortat
aid
separat
eat
knif
interest
amnest
job
influ
surve
rebel
right
drug
good
democr
afford
campaign
answer
fighter
congrat
28
30. Datasets for Experiments
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•
TREC Microblog 2011 corpus
•
•
Comprises over 16 million tweets sampled over a two week
period (January 23rd to February 8th, 2011)
includes 49 different events
•
•
violence-related ones such as Egyptian revolution, and
Moscow airport bombing
non-violence related such as the Super Bowl seating fiasco
Training set
Violence-related
Non violence-related
10,581
Testing set
759
1,000
30
31. Baselines
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• Learned from labelled features
• Word priors are used as labelled feature constraints
• Train MaxEnt classifier with Generalized Expectation (GE) [Druck
et al., 2008] or Posterior Regularization (PR) [Ganchev et al., 2010]
• Joint Sentiment-Topic (JST) model [Lin&He 2009][Lin&He2012]
• Set the number of sentiment classes to 2 (violent or non-violent)
• Partially-Labeled LDA (PLDA) [Ramage et al., 2011]
• Assume that some document labels are observed and model perlabel latent topics
• Supervised information is incorporated at the document level rather
than at the word level
• The training set is labelled as violent or non-violent using
OpenCalais
31
32. Violence Classification Results
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• ME-GE and ME-PR perform poorly
• Best result obtained using VDM with word priors derived from TW using
RWE
• Source data for deriving word priors
•
DB does not improve over TW
•
DCH boosts F-measure in JST and is close to TW for VDM
• RWE consistently outperforms IG for both JST and VDM
32
35. Example Violence-Related Topics
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Protest in
Tahrir Square
Middle East
uprise
Moscow Airport
bombing
Government shut
down Facebook
Topic 1
Topic 2
Topic 3
Topic 4
egypt
middle
internet
crash
tahrir
east
egypt
kill
cair
give
phone
moscow
strees
power
block
bomb
police
idea
word
airport
protester
government
service
tweets
square
spread
government
injure
arm
uprise
shut
arrest
report
fall
facebook
dead
35
36. Questions?
Click to edit Master subtitle style
Elizabeth Cano
Yulan He
Kang Liu
Jun Zhao
a.cano_basave@aston.ac.uk
y.he@cantab.net
kliu@nlpr.ia.ac.cn
jzhao@nlpr.ia.ac.cn
Slides available at http://www.slideshare.net/ampaeli
36
Editor's Notes
During the last 2 years we have witnessed the use of social media platforms as medium to express different emotions within society; Inlcuding for example:Middle East revolutions.2011 Japan Earthquake these services have become a proxy of information which communicates the social perception of situations regarding for exampleTerrorismSocial Crisis RacismAs well as Extremist groups propagandaThis project aims to leverage this continuous streaming of information for detecting and tracking of violent radicalization and extremism in social media, becoming therefore a sensor of the social perception of violent activities. The project aim to help in the prompt detection of situations which can lead to the diffusion of messages which can potentially become influential triggers of violence.
In this work we focus on Twitter data; in particular we aim at creating models which can identify suspicious tweets which can give an insight of violent or criminal events happening at the moment. We seek to detect and extract topics related to violent and criminal activities from large-scale social media data in real-time, and constantly track any events that are identified as suspicious. . Owing to the fast- evolving nature of social media, such a system will be very important for the forces of law to respond to and deal with the potential security risks timely.This work aims to develop efficient computational tools for detecting violent radicalization and extremism from social media, which will ultimately help improving the national security capability with the online monitoring function offered by the system. Specifically, the tools seek to detect and extract topics relating to violent and criminal activities from large-scale social media data in real-time, and constantly track any events that are identified suspicious
But also positive sentiments such as excitement can appear in criminal activities like for example rioting
Characterising violence-related content in tweets present different challenges, including the:The constantredefinition of the vocabulary used to represent current events, and the generation of new jargon in this channels of communications, introduce new difficulties for the use of traditional supervised models, which make use of labelled data. Traditional classification methods which rely on labelled data for training their models do not necessarily work with social media, since of what we see is event driven having short life spans. This means that in order to maintain tuned models it is necessary the continuous learning from social media for re-chacaracterising the feature representation of an event.
There has been a large body of work in topic classification of short texts Weakly supervised approaches include the JST model and the partially-labelled LDA model. These two models will be part of our baselines and we’ll talk about them in more detail later on. To the best of our knowledge very few have been devoted to violent content analysis of Twitter, and none has car- ried out deep violence-related topic analysis.
Previous approaches rely only on..To the best of our knowledge very few have been devoted to violent content analysis of Twitter, none of which has carried out deep violence-related topic analysis.
One of the main challenges in detecting violence-related content is that this type of content is event-related, tending to occur during short to medium life-spans, therefore methods which rely only on labeled data can rapidly become outdated.
There has been a large body of work in topic classification of short textsTo the best of our knowledge very few have been devoted to vio- lent content analysis of Twitter, and none has car- ried out deep violence-related topic analysis.
Rather than using traditional machine learning models, in this project we propose the use of a Bayesian model which allows the detection of violence-related topics from social media without the use of labelled data. In particular, prior knowledge capturing words typically expressing violence is derived from external knowledge sources and incorporated into model learning.
Consider the following tweet which is contains information about Travis Kvapil who is an NASCAR racing driver, and who seem to have been involved in a domestic dispute
The existing framework of LDA has three hierarchical layers, where topics are associated with documents, and words are associated with topics. In order to model document violence-polarity, we construct a violence detection model (VDM) by adding an additional violence label layer between the document and the topic layer. Hence, VDM is effectively a four-layer hierarchical Bayesian model, where violence labels are associated with documents, under which topics are associated with violence labels and words are associated with both violence labels and topics.
Although the model does not require labelled documents for learning, it does require as an input a collection of words which are dominant on the topic of interest. Such a list of words is often called as a lexicon. In our study, we explore two different types of sources for deriving violence related lexicons which are DBpedia and Twitter.
Our first experiment users two corpora, the TRECMicrobloging, and DBPediaDBpedia is the semantified version of Wikipedia. The latest version of DBpedia consists of over 1.8 million resources, which have been classified into 740 thousand Wikipedia categories, and over 18 million Yago categories. Social Knowledge sources constitute one of the largest repositories built in a collaborative manner. They provide an up-to-date channel of information and knowledge over a large number of topics.These ontologies enable a broad coverage of entities in the world ,and allow entities to bear multiple overlapping types. One of the main advantages of using this knowledge sources for topic classification, is that each particular topic is associated with a large number of resources.We created out violence related corpus by querying DBepdia for all articles belonging to categories and subcategories under the Violence Category.
We created out violence related corpus by querying DBepdia for all articles belonging to categories and subcategories under the Violence Category.After removing those categories with less than 1000 articles, we obtained a set of 14 categories
In the case of the Twitter dataset we selected those documents which were annotated by OpenCalais as been relevant to the topic of War and conflict, and the collection of other tweets as the ones for deriving the non-violent lexicon.
In the case of the Twitter dataset we selected those documents which were annotated by OpenCalais as been relevant to the topic of War and conflict, and the collection of other tweets as the ones for deriving the non-violent lexicon.
Here is an example of the type of violent and non-violent lexicon derived from these two sources using RWE. In the firs column we present a lexicon derived using the dbpedia corpus combined with the twitter corpus. In this case we chunked all those article’s abstract related to violent categories, in order to obtain documents which were of the same average size of a tweet and use the non-violent documents from the Twitter corpus as the non-violent documents. The second column present lexicons derived from the DBpedia corpus and the third those from the Twitter corpus.We compare the performance of the propose RWE metric against word priors derived IG.After filtering features using Information Gain, we obtained the probability of a word given a category.P(W|C) = P(C|W)*P(W) / P(C)This measure weights the word as been relevant or not to the violent and non-violent categories.
Whenanalysing the TREC corpus we observed that as expected there are very few violence related documents as opposed to the massive amount of tweets discussing other matters. The violent related tweets are event oriented, therefore some of the existing dates may not contain violent tweets at all. This is interesting to notice when thinking on the implementation of evolving models which depend on previous violence-related occurrences. Our proposed model is not epoch dependent at the moment and was tested on the collection of tweets taken a particular epoch.
We proposed two different strategies for deriving priors the first one is based on information gain while the second one is based on word entropy. We compare the performance of the proposed approach with three other models. The first two models are unsupervised approaches, namely the maximum entropy trained with generalised expectation, and the maximum entropy trained with posterior regularisation. The third one is a weakly supervised approach which also makes use of prior lexicons. We can see that the proposed approach VDM, outperforms existing approaches, and that the entropy based strategy for the lexicon derivation from Twitter is the one providing the best performance in precision. It is important to notice that the use of DBPedia as a source for deriving lexicon priors turned out to be quite effective, since it reduces the need of having Twitter annotations with can be costly.
We proposed two different strategies for deriving priors:the first one is based on information gain while the second one is based on word entropy. We compare the performance of the proposed approach with three other models.:The first two models are unsupervised approaches, namely the maximum entropy trained with generalised expectation, and the maximum entropy trained with posterior regularisation. The third one is a weakly supervised approach which also makes use of prior lexicons. We can see that the proposed approach VDM, outperforms existing approaches, and that the entropy based strategy for the lexicon derivation from Twitter is the one providing the best performance in precision. It is important to notice that the use of DBPedia as a source for deriving lexicon priors turned out to be quite effective, since it reduces the need of having Twitter annotations with can be costly.
We proposed two different strategies for deriving priors the first one is based on information gain while the second one is based on word entropy. We compare the performance of the proposed approach with three other models. The first two models are unsupervised approaches, namely the maximum entropy trained with generalised expectation, and the maximum entropy trained with posterior regularisation. The third one is a weakly supervised approach which also makes use of prior lexicons. We can see that the proposed approach VDM, outperforms existing approaches, and that the entropy based strategy for the lexicon derivation from Twitter is the one providing the best performance in precision. It is important to notice that the use of DBPedia as a source for deriving lexicon priors turned out to be quite effective, since it reduces the need of having Twitter annotations with can be costly.
We proposed two different strategies for deriving priors the first one is based on information gain while the second one is based on word entropy. We compare the performance of the proposed approach with three other models. The first two models are unsupervised approaches, namely the maximum entropy trained with generalised expectation, and the maximum entropy trained with posterior regularisation. The third one is a weakly supervised approach which also makes use of prior lexicons. We can see that the proposed approach VDM, outperforms existing approaches, and that the entropy based strategy for the lexicon derivation from Twitter is the one providing the best performance in precision. It is important to notice that the use of DBPedia as a source for deriving lexicon priors turned out to be quite effective, since it reduces the need of having Twitter annotations with can be costly.
Whenanalysing the set of topics derived from the VDM model, we notice that this collection of clustered words are very good indicators of current events been discussed in the Twitter-sphere. Our next goal is to enable the automatic labelling of these topics in order to enrich the context of current events been discussed.