Short presentation at Dagstuhl seminar on Physical-Cyber-Social Computing, September 29 to October 4, 2013.
http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=13402
Gave this talk at SSSW'13; The 10th Summer School on Ontology Engineering and the Semantic Web
7 - 13 July, 2013. Cercedilla, Spain. http://sssw.org/2013/
Harith Alani is a researcher at the Knowledge Media Institute (KMi) at the Open University. KMi focuses on research and development related to the future internet, knowledge management, multimedia systems, and more. Alani's work involves analyzing behavior and health of online communities through semantic profiling, social network analysis, and machine learning models. He has published over 100 papers on topics such as predicting valuable community members, detecting influential content, and understanding community evolution over time.
Mining and Comparing Engagement Dynamics Across Multiple Social Media Platfor...The Open University
Understanding what attracts users to engage with social media content is important in domains such as market analytics, advertising, and community management.
To date, many pieces of work have examined engagement dynamics in isolated platforms with little consideration or assessment of how these dynamics might vary between disparate social media systems. Additionally, such explorations have often used different features and notions of engagement, thus rendering the cross-platform comparison of engagement dynamics limited. In this paper we define a common framework of engagement analysis and examine and compare engagement dynamics across five social media platforms: Facebook, Twitter, Boards.ie, Stack Overflow and the SAP Community Network. We define a variety of common features (social and content) to capture the dynamics that correlate with engagement in multiple social media platforms, and present an evaluation pipeline intended to enable cross-platform comparison. Our comparison results demonstrate the varying factors at play in different platforms, while also exposing several similarities.
This document discusses monitoring and analyzing online communities. It begins by outlining tools for monitoring social media mentions, sentiment, discussion activity and more. It then discusses measuring social media usage in companies and tools for analyzing community features like influence, opinions and geolocation. The document explores merging offline and online social networks using sensors and integrating physical presence data with online profiles and semantic analysis. It provides examples of tracking face-to-face contact networks and analyzing characteristics of offline social networks.
This document discusses social network analysis and its applications. It defines a social network as being composed of actors (people or groups) connected by social relationships. Social network analysis can be used to map these relationships visually using sociograms, understand information flow and community structure, and identify influential actors through metrics like centrality and betweenness. Tools like NodeXL and Gephi enable network extraction, visualization, and analysis to glean strategic insights from social networks.
More than ever, we need to learn how to harness the power of networks to tackle the complex issues we're facing as a society. Here's a quick guide to the basics of social network analysis.
Interested? Sign up at http://kumu.io
Practical Applications for Social Network Analysis in Public Sector Marketing...Mike Kujawski
This document provides an overview of a presentation on practical applications of social network analysis. It discusses the growth of social data, defines social network analysis, and provides several use cases. It then outlines the presentation topics which include basics of reading sociograms, refining data, and applying SNA to public sector marketing. Examples of SNA applications to specific organizations are provided. Both free and paid tools for conducting SNA are also mentioned.
Gave this talk at SSSW'13; The 10th Summer School on Ontology Engineering and the Semantic Web
7 - 13 July, 2013. Cercedilla, Spain. http://sssw.org/2013/
Harith Alani is a researcher at the Knowledge Media Institute (KMi) at the Open University. KMi focuses on research and development related to the future internet, knowledge management, multimedia systems, and more. Alani's work involves analyzing behavior and health of online communities through semantic profiling, social network analysis, and machine learning models. He has published over 100 papers on topics such as predicting valuable community members, detecting influential content, and understanding community evolution over time.
Mining and Comparing Engagement Dynamics Across Multiple Social Media Platfor...The Open University
Understanding what attracts users to engage with social media content is important in domains such as market analytics, advertising, and community management.
To date, many pieces of work have examined engagement dynamics in isolated platforms with little consideration or assessment of how these dynamics might vary between disparate social media systems. Additionally, such explorations have often used different features and notions of engagement, thus rendering the cross-platform comparison of engagement dynamics limited. In this paper we define a common framework of engagement analysis and examine and compare engagement dynamics across five social media platforms: Facebook, Twitter, Boards.ie, Stack Overflow and the SAP Community Network. We define a variety of common features (social and content) to capture the dynamics that correlate with engagement in multiple social media platforms, and present an evaluation pipeline intended to enable cross-platform comparison. Our comparison results demonstrate the varying factors at play in different platforms, while also exposing several similarities.
This document discusses monitoring and analyzing online communities. It begins by outlining tools for monitoring social media mentions, sentiment, discussion activity and more. It then discusses measuring social media usage in companies and tools for analyzing community features like influence, opinions and geolocation. The document explores merging offline and online social networks using sensors and integrating physical presence data with online profiles and semantic analysis. It provides examples of tracking face-to-face contact networks and analyzing characteristics of offline social networks.
This document discusses social network analysis and its applications. It defines a social network as being composed of actors (people or groups) connected by social relationships. Social network analysis can be used to map these relationships visually using sociograms, understand information flow and community structure, and identify influential actors through metrics like centrality and betweenness. Tools like NodeXL and Gephi enable network extraction, visualization, and analysis to glean strategic insights from social networks.
More than ever, we need to learn how to harness the power of networks to tackle the complex issues we're facing as a society. Here's a quick guide to the basics of social network analysis.
Interested? Sign up at http://kumu.io
Practical Applications for Social Network Analysis in Public Sector Marketing...Mike Kujawski
This document provides an overview of a presentation on practical applications of social network analysis. It discusses the growth of social data, defines social network analysis, and provides several use cases. It then outlines the presentation topics which include basics of reading sociograms, refining data, and applying SNA to public sector marketing. Examples of SNA applications to specific organizations are provided. Both free and paid tools for conducting SNA are also mentioned.
2010-November-8-NIA - Smart Society and Civic Culture - Marc SmithMarc Smith
This document discusses how social media and social networks are enabling new forms of civic participation and collective action. It notes that citizens are increasingly using social media to find government services, engage in discussions, and measure public opinion. The document also discusses how social network analysis can be used to analyze patterns in social media networks and identify influential users. It provides an overview of various social media platforms and the types of social networks and connections that exist within them.
interacting with social media content about eventsmor
This document discusses research into creating new experiences for consuming social media content about events. It describes three systems created: Vox Civitas for journalistic inquiry into events using Twitter data, Multiplayer for organizing YouTube videos from live events, and an evaluation of social multimedia experiences. Evaluations of Vox Civitas and Multiplayer provided insights into how journalists and users interact with such systems and tasks they support. The research aims to understand user interactions beyond a specific implementation and produce generalizable insights.
This document provides an overview of social network analysis (SNA). It defines social networks as sets of nodes (individuals) connected by links, with SNA having roots in sociology, economics, physics and mathematics emerging in the 1930s. The document discusses software used to perform SNA, and how networks can be analyzed by their shapes, types, and measures at the node and network levels. It provides examples of how SNA can be used across sectors and industries, and for organizations in Cambodia specifically. A case study example is also presented.
The Key Success Factor in Knowledge Management... What Else? Change ManagementPatti Anklam
Presented at SLA 2013, on a panel with Ethel Salonen of MITRE Corporation. Provides perspective on change management and how it is used in understanding and creating interventions in knowledge networks.
This thesis proposes to help analyzing the characteristics of the heterogeneous social networks that emerge from the use of web-based social applications, with an original contribution that leverages Social Network Analysis with Semantic Web frameworks. Social Network Analysis (SNA) proposes graph algorithms to characterize the structure of a social network and its strategic positions. Semantic Web frameworks allow representing and exchanging knowledge across web applications with a rich typed graph model (RDF), a query language (SPARQL) and schema definition frameworks (RDFS and OWL). In this thesis, we merge both models in order to go beyond the mining of the flat link structure of social graphs by integrating a semantic processing of the network typing and the emerging knowledge of online activities. In particular we investigate how (1) to bring online social data to ontology-based representations, (2) to conduct a social network analysis that takes advantage of the rich semantics of such representations, and (3) to semantically detect and label communities of online social networks and social tagging activities.
A high-level overview of social network analysis, providing background on how it came into the knowledge management field. Includes an example and core concepts pertinent to the audience, online community managers.
This document discusses analyzing a social network on Facebook. It begins by introducing the project team and advisor. It then provides definitions of key social network analysis (SNA) concepts like nodes, edges, degree centrality, betweenness centrality, and clustering coefficient. The document outlines analyzing a sample Facebook network to identify high degree nodes and understand their behavior. The goal is to explore how Facebook uses basic SNA elements to recommend potential friends.
This short set of slides summarizes the characteristics of people who play specific roles in networks. In a social network analysis, people in these roles can be discovered by running mathematical algorithms through the social graphs. But you don't need to be an algorithm to spot some of these people in your networks!
NetWorkShop: Boston Facilitators RoundtablePatti Anklam
1. The document summarizes a presentation on networks and network analysis. It discusses how networks are important in the 21st century and how understanding network structure can provide insights.
2. Various types of network metrics and analyses are introduced, including structural metrics about the overall network and centrality metrics about individual nodes. Mapping networks can reveal informal relationships and raise good questions.
3. Understanding value networks and exchanges within them is discussed, differentiating tangible from intangible exchanges. Mapping value networks analyzes how work gets done and where there are opportunities to improve value and efficiency.
Social Network Analysis and Partnerships SNA presentation Guevara 2015Sophia Guevara
Social network analysis (SNA) is a methodology for studying relationships and how individuals fit within networks. SNA can help funders achieve better impact through relationships by identifying opportunities, potential obstacles, and influential individuals. Examples show how foundations have used SNA to scale impact, understand grant outcomes, and inform future funding. SNA software like NodeXL and Gephi allow users to analyze and visualize their networks from professional sites like LinkedIn to understand network density, hierarchy, and connections. Resources are provided for continuing SNA learning.
Part 1: Concepts and Cases (the language of networks, networks in organizations, case studies and key concepts)
Part 2: (Starts on #44) Mapping Organizational, Personal, and Enterprise Networks: Tools
An update to last year's Social Network Analysis Introduction and Tools...
The document provides an overview of building and sustaining networks. It discusses key concepts like:
- Understanding networks through their purpose, structure, style, and value properties.
- Examining networks using tools like organizational network analysis and value network analysis to assess relationships and flows of value.
- Designing networks by defining their purpose, structure, style, and value up front.
- Using collaboration tools and social media to facilitate interaction and information sharing within a network.
Revision of Previous Show on SNA and Introduction to Tools
The Language of Networks
Introduction to Social Network Analysis/ Cases
Tools for Analyzing social networks, including graphing Facebook, LinkedIn, and Twitter networks
AAPOR - comparing found data from social media and made data from surveysCliff Lampe
This presentation was for the 2014 AAPOR conference, and deals with specific components of how "big data" from social media is different from data acquired through surveys.
Social network analysis & Big Data - Telecommunications and moreWael Elrifai
Social Network Analysis: Practical Uses and Implementation is a presentation that discusses social network analysis and its uses. It covers key topics such as defining social networks and social network analysis, why social network analysis is important, identifying influencers in social networks, roles in social networks, graph theory concepts used in social network analysis, calculating metrics from social networks, and recommended approaches to social network analysis. The presentation provides an overview of social network analysis concepts and their practical applications.
Social Network Analysis Introduction including Data Structure Graph overview. Doug Needham
Social Network Analysis Introduction including Data Structure Graph overview. Given in Cincinnati August 18th 2015 as part of the DataSeed Meetup group.
2007-JOSS-Visualizing the signatures of social roles in online discussion groupsMarc Smith
The document discusses identifying social roles in online discussion groups based on behavioral and structural signatures. It focuses on distinguishing the role of "answer people", who primarily respond to others' questions. Three signatures are identified for answer people: 1) responding to isolated members, 2) having few intense ties and triangles in their local networks, and 3) typically contributing only one or two messages per thread. Regression analysis shows these signatures strongly predict being an answer person, explaining 72% of variation. The study advances understanding of social roles and identification methods, which can benefit online community managers.
The document presents results from a multivariate probit estimation examining factors that influence households' adoption of various climate change adaptation strategies. It finds that gender structure, climate variables, capital stocks, risk aversion, health status, and demographics all significantly impact the likelihood of adopting certain strategies. For example, female-headed households are more likely to change planting/harvesting dates or increase rainwater harvesting. Exposure to moderate increases in dry spells increases the likelihood of various strategies. Higher education and physical/natural capital also increase the likelihood of some strategies.
Using Community Management to Drive Engagement in Higher Ed Enterprise Hive
The document discusses how Oral Roberts University used the social engagement platform HiveSocial to build and manage its online community. It describes HiveSocial as a cloud-based SaaS solution that provides collaboration and networking tools to encourage social engagement. The presentation then discusses how ORU defined the purpose and goals of its community, analyzed its maturity level, and created a roadmap to engage members and measure the impact on business objectives. Speakers from ORU and The Community Roundtable share their experience using HiveSocial to connect students, faculty and staff into a productive online community.
2010-November-8-NIA - Smart Society and Civic Culture - Marc SmithMarc Smith
This document discusses how social media and social networks are enabling new forms of civic participation and collective action. It notes that citizens are increasingly using social media to find government services, engage in discussions, and measure public opinion. The document also discusses how social network analysis can be used to analyze patterns in social media networks and identify influential users. It provides an overview of various social media platforms and the types of social networks and connections that exist within them.
interacting with social media content about eventsmor
This document discusses research into creating new experiences for consuming social media content about events. It describes three systems created: Vox Civitas for journalistic inquiry into events using Twitter data, Multiplayer for organizing YouTube videos from live events, and an evaluation of social multimedia experiences. Evaluations of Vox Civitas and Multiplayer provided insights into how journalists and users interact with such systems and tasks they support. The research aims to understand user interactions beyond a specific implementation and produce generalizable insights.
This document provides an overview of social network analysis (SNA). It defines social networks as sets of nodes (individuals) connected by links, with SNA having roots in sociology, economics, physics and mathematics emerging in the 1930s. The document discusses software used to perform SNA, and how networks can be analyzed by their shapes, types, and measures at the node and network levels. It provides examples of how SNA can be used across sectors and industries, and for organizations in Cambodia specifically. A case study example is also presented.
The Key Success Factor in Knowledge Management... What Else? Change ManagementPatti Anklam
Presented at SLA 2013, on a panel with Ethel Salonen of MITRE Corporation. Provides perspective on change management and how it is used in understanding and creating interventions in knowledge networks.
This thesis proposes to help analyzing the characteristics of the heterogeneous social networks that emerge from the use of web-based social applications, with an original contribution that leverages Social Network Analysis with Semantic Web frameworks. Social Network Analysis (SNA) proposes graph algorithms to characterize the structure of a social network and its strategic positions. Semantic Web frameworks allow representing and exchanging knowledge across web applications with a rich typed graph model (RDF), a query language (SPARQL) and schema definition frameworks (RDFS and OWL). In this thesis, we merge both models in order to go beyond the mining of the flat link structure of social graphs by integrating a semantic processing of the network typing and the emerging knowledge of online activities. In particular we investigate how (1) to bring online social data to ontology-based representations, (2) to conduct a social network analysis that takes advantage of the rich semantics of such representations, and (3) to semantically detect and label communities of online social networks and social tagging activities.
A high-level overview of social network analysis, providing background on how it came into the knowledge management field. Includes an example and core concepts pertinent to the audience, online community managers.
This document discusses analyzing a social network on Facebook. It begins by introducing the project team and advisor. It then provides definitions of key social network analysis (SNA) concepts like nodes, edges, degree centrality, betweenness centrality, and clustering coefficient. The document outlines analyzing a sample Facebook network to identify high degree nodes and understand their behavior. The goal is to explore how Facebook uses basic SNA elements to recommend potential friends.
This short set of slides summarizes the characteristics of people who play specific roles in networks. In a social network analysis, people in these roles can be discovered by running mathematical algorithms through the social graphs. But you don't need to be an algorithm to spot some of these people in your networks!
NetWorkShop: Boston Facilitators RoundtablePatti Anklam
1. The document summarizes a presentation on networks and network analysis. It discusses how networks are important in the 21st century and how understanding network structure can provide insights.
2. Various types of network metrics and analyses are introduced, including structural metrics about the overall network and centrality metrics about individual nodes. Mapping networks can reveal informal relationships and raise good questions.
3. Understanding value networks and exchanges within them is discussed, differentiating tangible from intangible exchanges. Mapping value networks analyzes how work gets done and where there are opportunities to improve value and efficiency.
Social Network Analysis and Partnerships SNA presentation Guevara 2015Sophia Guevara
Social network analysis (SNA) is a methodology for studying relationships and how individuals fit within networks. SNA can help funders achieve better impact through relationships by identifying opportunities, potential obstacles, and influential individuals. Examples show how foundations have used SNA to scale impact, understand grant outcomes, and inform future funding. SNA software like NodeXL and Gephi allow users to analyze and visualize their networks from professional sites like LinkedIn to understand network density, hierarchy, and connections. Resources are provided for continuing SNA learning.
Part 1: Concepts and Cases (the language of networks, networks in organizations, case studies and key concepts)
Part 2: (Starts on #44) Mapping Organizational, Personal, and Enterprise Networks: Tools
An update to last year's Social Network Analysis Introduction and Tools...
The document provides an overview of building and sustaining networks. It discusses key concepts like:
- Understanding networks through their purpose, structure, style, and value properties.
- Examining networks using tools like organizational network analysis and value network analysis to assess relationships and flows of value.
- Designing networks by defining their purpose, structure, style, and value up front.
- Using collaboration tools and social media to facilitate interaction and information sharing within a network.
Revision of Previous Show on SNA and Introduction to Tools
The Language of Networks
Introduction to Social Network Analysis/ Cases
Tools for Analyzing social networks, including graphing Facebook, LinkedIn, and Twitter networks
AAPOR - comparing found data from social media and made data from surveysCliff Lampe
This presentation was for the 2014 AAPOR conference, and deals with specific components of how "big data" from social media is different from data acquired through surveys.
Social network analysis & Big Data - Telecommunications and moreWael Elrifai
Social Network Analysis: Practical Uses and Implementation is a presentation that discusses social network analysis and its uses. It covers key topics such as defining social networks and social network analysis, why social network analysis is important, identifying influencers in social networks, roles in social networks, graph theory concepts used in social network analysis, calculating metrics from social networks, and recommended approaches to social network analysis. The presentation provides an overview of social network analysis concepts and their practical applications.
Social Network Analysis Introduction including Data Structure Graph overview. Doug Needham
Social Network Analysis Introduction including Data Structure Graph overview. Given in Cincinnati August 18th 2015 as part of the DataSeed Meetup group.
2007-JOSS-Visualizing the signatures of social roles in online discussion groupsMarc Smith
The document discusses identifying social roles in online discussion groups based on behavioral and structural signatures. It focuses on distinguishing the role of "answer people", who primarily respond to others' questions. Three signatures are identified for answer people: 1) responding to isolated members, 2) having few intense ties and triangles in their local networks, and 3) typically contributing only one or two messages per thread. Regression analysis shows these signatures strongly predict being an answer person, explaining 72% of variation. The study advances understanding of social roles and identification methods, which can benefit online community managers.
The document presents results from a multivariate probit estimation examining factors that influence households' adoption of various climate change adaptation strategies. It finds that gender structure, climate variables, capital stocks, risk aversion, health status, and demographics all significantly impact the likelihood of adopting certain strategies. For example, female-headed households are more likely to change planting/harvesting dates or increase rainwater harvesting. Exposure to moderate increases in dry spells increases the likelihood of various strategies. Higher education and physical/natural capital also increase the likelihood of some strategies.
Using Community Management to Drive Engagement in Higher Ed Enterprise Hive
The document discusses how Oral Roberts University used the social engagement platform HiveSocial to build and manage its online community. It describes HiveSocial as a cloud-based SaaS solution that provides collaboration and networking tools to encourage social engagement. The presentation then discusses how ORU defined the purpose and goals of its community, analyzed its maturity level, and created a roadmap to engage members and measure the impact on business objectives. Speakers from ORU and The Community Roundtable share their experience using HiveSocial to connect students, faculty and staff into a productive online community.
Creating a Big data Strategy with Tactics for Quick ImplementationLewandog, Inc,
The document provides an overview of creating a big data strategy with quick implementation tactics. It discusses establishing centers of excellence, empowering users to access and analyze data, and transforming organizations to be data-driven. It also notes that only a small percentage of data is currently analyzed and highlights opportunities that additional analysis could provide through increased profits and insights.
The document discusses the benefits of Internet of Things (IoT) technology for the Muslim world. It describes how connecting physical assets through sensors can help monitor things like health, transportation, and resource usage. The data collected from these connected devices and sensors can provide insights and optimization opportunities. New business models are emerging around monetizing sensor data through "Sensing as a Service" where data is licensed to applications and organizations. The document advocates for building an IoT ecosystem in Malaysia as a testbed for connecting infrastructure and developing smart city applications.
This document discusses service science and its importance in creating smarter product-service systems to improve quality of life. It outlines IBM's focus on service innovation and growth, as well as key priorities and challenges in developing service science as an interdisciplinary field. Global trends like urbanization, aging populations, and new technologies are driving opportunities in business, education, and government.
Social computing is an interdisciplinary field that studies how technology can facilitate social interaction and communication. It has three main goals: 1) Develop better social software to facilitate online interaction; 2) Computerize aspects of human society through modeling and simulation; 3) Forecast how changing technologies may impact social behavior. Social computing draws from fields like sociology, computer science, and applied physics. It covers topics such as social networks, recommendation systems, virtual worlds, and using technology to study social dynamics. Methods include social network analysis, simulation, and data mining of social media. Learning social computing would help information scientists develop technologies that consider social factors and behaviors.
My speech to the Hong Kong IoT Association about how instantly shared real-time IoT data can transform companies and allow highly efficient and creative circular organizations
Here are the key points about the course:
- The course number is NS-600 and the title is Theoretical Foundations for Nursing Practice. It is a 3 credit course.
- The instructor is Dr. Elaine Jackson from the Department of Nursing at Southeast Missouri State University.
- The course description indicates it will examine theory construction and the role of theory in providing the scientific basis for nursing practice.
- Requirements include assigned readings, class participation, and a written assignment analyzing a nursing theory.
- Grading is based on a point system and letter grades.
- Incompletes require a written contract between the student and instructor specifying the work to be completed and deadline.
- Cell
IOT/IOP Dalam Pengurusan oleh Dr. Zahrah MokhtarPersatuan Uitm
Pembentangan IOT/IOP Dalam Pengurusan oleh Dr. Zahrah Mokhtar, Mantan Pendaftar UiTM pada Seminar Pengurusan Pentadbir (SePP) 2019 anjuran Anjuran Persatuan Pentadbir UiTM di ILD Bandar Enstek, Negeri Sembilan pada 22 - 24 Disember 2019
Objektif Seminar SePP 2019
1. Menjelaskan dan menerapkan teknik pembentukan dan pelaksanaan Pengurusan Tangkas.
2. Menjelaskan manfaat Internet of Thing (IoT)/Internet of People (IoP) dalam pengurusan.
3. Merealisasikan minda Cepat, Tepat dan Integriti (CTI) - Produktiviti, Kreatviti dan Inovasi (PCI).
4. Mempelajari teknik mengurus imej penampilan profesional serta penekanan dalam tatacara beretika dan adab dalam tugasan dan dalam setiap acara dan urusan.
5. Mempelajari teknik penyampaian dan komunikasi berkesan.
read more
http://ppuitmsa.blogspot.com/2019/11/seminar-pengurusan-pentadbir-2019.html
Altmetrics and the publisher discusses altmetrics and how publishers like Elsevier are incorporating altmetrics into their platforms and analyses. It notes that altmetrics measures non-traditional impacts like saves, shares and mentions on social media. The presentation provides examples of altmetric data on articles and journals and discusses open challenges and future directions for altmetrics to provide more robust interpretations of impact beyond just counts.
Whether you’re embracing the hype or eagerly waiting to see how things evolve, there’s no question the “Internet of Things” is creating excitement from living room to C- Suite
Of course, as with every new technology wave, there are those on the front lines shaping the discussion, influencing decision making, and charting the course for what the Internet of Things will mean to each of us in the not-too-distant future.
And these IoT thought leaders come from diverse industries and disciplines. There are the analysts, authors, and speakers who help us understand the opportunities and implications, senior executives that champion enterprise and startup initiatives, and those responsible for turning the Internet of Things into a daily reality.
But who are these people and what’s influencing their own perspective on IoT?
This is where social insights come in... as social media activity can give us a truly unique lens through which to gain insights into the people leading the conversation about the Internet of Things.
That’s why we’re excited to collaborate with Neustar to develop a social insights report analyzing these IoT thought leaders. What did we discover in researching and preparing this report?
Here’s what we learned.
This document provides an introduction to Stafford Beer's Viable System Model (VSM). It discusses key cybernetics concepts like variety, variety attenuators and amplifiers, self-organizing systems, and Ashby's Law of Requisite Variety. The VSM aims to identify the necessary structures for a system to remain viable and cope with its environment by matching its variety. It can be applied to analyze organizations, help diagnose problems, and improve functioning.
The document provides an overview of the industrial internet of things (IIoT). It discusses how IIoT can help businesses by connecting physical assets via sensors and data collection. The document also notes that IIoT growth will be driven by expanded connectivity, high mobile adoption rates, lower sensor costs, and corporate investment. However, security, privacy, implementation challenges, and fragmentation pose barriers to IIoT growth. The document highlights how IIoT data and analytics can provide operational insights and high value business intelligence when combined with operational context.
This document discusses how nonprofits can become more data-driven and networked. It recommends that nonprofits (1) adopt a networked mindset with decentralized decision-making and open communication, (2) recognize different levels of maturity in using data and social networks, starting with small pilots before more advanced strategies, and (3) make measurement, data analysis, and learning central to their culture from senior leadership down. Nonprofits should go beyond just counting metrics and instead focus on learning from data through experimentation and continuous improvement.
Certus Accelerate - User Centred Everything by Sam WilliamsCertus Solutions
The document describes the Certus Accelerate app. It provides the following key information:
- The app will give access to event related content such as videos, infographics, and blogs.
- To download the app, the user will receive a text with a link to tap and follow instructions.
- New content and features can be added after the event concludes, and users will continue to receive updates.
Steve Giannoni Accessibility, Discoverability and Interoperability: If it’s w...sherif user group
This document discusses EBSCO's focus on accessibility, discoverability, and interoperability of information. It provides an overview of EBSCO's history and values, including being privately owned and donating profits to charity. It describes EBSCO's focus on users and innovation, and discusses their work to provide accessible discovery tools and content on a wide range of platforms and devices. This includes efforts towards responsive design, accessibility testing, support for ebooks and screen readers, and partnerships to provide seamless access to content.
Similar to From smart meters to smart behaviour (20)
Keynote delivered at ACM Hypertext conference on 6th of September 2023.
Abstract: You’re probably getting a bit worn out from all these talks about misinformation and Twitter-based experiments. The fact that Twitter is now called Platform X is probably not enough of a change to keep you awake during my talk! But I think, or hope, to bring up a few things in this talk that you might not have come across or thought about much before. I believe that having fact-checks that call out false or misleading claims is very important in our fight against misinformation. But we’re still not quite sure if and how they impact the spread of wrong information and how they could help set things right online. So, in this talk, I’ll dive into how we’re all prone to falling for misinformation and make a case for needing data and tools to help us see how both ourselves and others engage with false or unreliable information over long periods of time. I’ll also share what we’ve learnt from our research about how these fact-checks affect how wrong info spreads, and I’ll give you the scoop on what happened when we tried using automatic replies to correct misinforming posts on Twitter, oops, I mean platform X. If all of this still feels like old news to you, well, there’s always that email inbox to keep you awake during my keynote.
Keynote at the 2nd International Workshop on Knowledge Graphs for Online Discourse Analysis (BeyondFacts’22) – April 26, 2022
Talk abstract: Misinformation has always been part of humankind’s information ecosystem. The development of tools and methods for automatically detecting the reliability of information has received a great deal of attention in recent years, such as calculating the authenticity of images, calculating the likelihood of claims, and assessing the credibility of sources. Unfortunately, there is little evidence that the presence of these advanced technologies or the constant effort of fact-checkers worldwide can help stop the spread of misinformation. I will try to convince you that you also hold various false beliefs, and argue for the need for technologies and processes to assess the information shared by ourselves or by others, over a longer period of time, in order to improve our knowledge of our information credibility and vulnerability, as well as those of the people we listen to. Also, I will describe the benefits, challenges, and risks of automated information corrective actions, both for the target recipients and their wider audience.
Talk delivered at the Paris Peace Forum, Nov 12-13, where I presented the H2020 Co-Inform project that aims at researching and developing socio-technical tools to tackle misinformation.
SASIG Workshop on “Improving the digital landscape for our children”The Open University
Reflections on the Online Harms White Paper published in April 2019. https://www.gov.uk/government/consultations/online-harms-white-paper
Presented these slides as part of a panel. Agenda of the workshop: https://gallery.mailchimp.com/6a29a22efa92c19681485a0ee/files/f3d318a3-978e-4977-be85-971ecb97ca13/Child_Safety_Online_Agenda_v33.pdf
The COMRADES project aims to develop an intelligent platform to empower communities to respond to and recover from crisis situations using social media. Led by Harith Alani at the Open University, the consortium includes Ushahidi, Delft University of Technology, and University of Sheffield. The project will extract requirements, identify emergency events on social media, assess information validity, and deploy the COMRADES platform to support communities during live crises. It will run in two periods over multiple crisis types and aims to produce 30 peer-reviewed publications and 40 scientific outputs to empower local communities through information sharing.
Co-Inform (Co-Creating Misinformation Resilient Societies)The Open University
This document summarizes a presentation on co-creating resilience to misinformation. It discusses the challenges fact checkers face in keeping up with misinformation spread on social media and the unclear impact of fact checks. The goals of the Co-Inform project are outlined, including understanding misinformation flow, creating detection tools, and making recommendations. The presentation notes several existing misinformation detection tools and plugins. It promotes developing self-awareness through tools assessing an individual's interactions with and spread of misinformation within their own network.
The document discusses using artificial intelligence and machine learning techniques to automatically process and classify information from social media during crisis events. It describes research on classifying tweets and social media posts as related or not related to a crisis, identifying the type of crisis, and determining the type of information in the posts. The research compares traditional machine learning classifiers to deep learning models and finds that semantic features and cross-lingual capabilities improve classification. The goal is to develop tools that can help emergency responders more effectively manage information during disasters.
The document discusses the COMRADES project, which aims to develop an intelligent platform to help communities reconnect, respond to, and recover from crisis situations using social media. The project is funded by the European Union's Horizon 2020 program and involves several European universities and organizations. It plans to extract requirements for resilience platforms, identify emergency events on social media, assess crisis information validity, and deploy the platform for communities during real disasters.
This document discusses detecting online radicalization through social media. It presents several approaches for identifying signals of radicalization using machine learning techniques, including lexicon-based approaches that analyze language use and knowledge graphs that extract semantic relationships between entities and concepts. The goal is to automatically classify social media accounts as non-violent radical, non-radical, or violent radical in order to more effectively and less biased detect the radicalization process.
The document discusses online child grooming and radicalization. It begins by defining child grooming as premeditated behavior intended to secure the trust of a minor for future sexual conduct. It then provides an example conversation between a predator and minor to demonstrate grooming techniques. Next, it discusses approaches for automatically identifying the stages of grooming (approach, trust development, isolation, physical approach) using machine learning classifiers. It achieves up to 88% accuracy. Finally, it discusses detecting online radicalization, including approaches using semantic analysis, knowledge graphs, and classifying social media accounts as radical or non-radical using machine learning.
The document discusses detecting online grooming and radicalization through social media analytics. It provides background on child grooming and statistics on its prevalence from sources like the NSPCC and CEOP. Examples of online grooming conversations are presented, showing how predators use approaches like compliments, requests for photos, developing trust and isolation to groom their victims. Methods for automatically identifying the stages of grooming through classifiers trained on annotated datasets are described, achieving over 80% precision and recall. The document also covers online radicalization, presenting models of radicalization and research on detecting signals of radicalization on social media through machine learning and lexicon-based approaches.
The document discusses detecting online grooming behavior on social media. It provides definitions of child grooming and online grooming as premeditated behaviors to gain a minor's trust to engage in future sexual conduct. Statistics are presented on the number of UK children who experience sexual abuse. Theories of online grooming behaviors are described, including establishing rapport, isolating the child, and introducing sexual topics. An example conversation shows the gradual process of an online predator engaging in grooming language and attempts to escalate contact over several messages.
This document discusses research on analyzing social networks using radio-frequency identification (RFID) devices to track face-to-face interactions between individuals in real-world settings like conferences. It describes deployments of RFID badge systems to log social contacts at various events and outlines approaches for merging offline contact networks with online social networking data to generate semantic profiles of users and communities. Key findings from analyzing face-to-face contact data include characteristics of interaction patterns, correlations between scientific experience and networking behavior, and a lack of strong correlation between offline social activity and size of online social networks.
The document summarizes the Live Social Semantics (LSS) platform, which aims to integrate users' physical interactions at events with their online social networking and interests. LSS collects data on face-to-face interactions at conferences using RFID badges and links it to users' social media profiles and tagging activities to generate semantic user profiles. It addresses challenges like ambiguous tags by cleaning data and linking tags to semantic concepts. The platform has been deployed at several conferences to test merging online and offline social graphs and providing personalized recommendations.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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.
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.
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
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.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
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.
5. Table 1: Correlation Coefficients of dimensions
Dispersion Engagement Contribution Initiation Quality Popularity
Dispersion 1.000 0.277 0.168 0.389 0.086 0.356
Engagement 0.277 1.000 0.939** 0.284 0.151 0.926**
Contribution 0.168 0.939** 1.000 0.274 0.086 0.909**
Initiation 0.389 0.284 0.274 1.000 -0.059 0.513
Quality 0.086 0.151 0.086 -0.059 1.000 0.065
Popularity 0.356 0.926** 0.909** 0.513 0.065 1.000
Behaviour analysis of online communities
§ Bottom Up analysis
§ Every community member is
classified into a “role”
§ Unknown roles might be
identified
§ Copes with role changes
over time
ini#ators
lurkers
followers
leaders
Structural, social network,
reciprocity, persistence,
participation
Feature levels change with the
dynamics of the community
Associations of roles with a collection of
feature-to-level mappings
e.g. in-degree -> high, out-degree -> high
Run rules over each user’s features
and derive the community role
composition
Table 1: Correlation Coefficients of dimensions
Dispersion Engagement Contribution Initiation Quality Popularity
Dispersion 1.000 0.277 0.168 0.389 0.086 0.356
Engagement 0.277 1.000 0.939** 0.284 0.151 0.926**
Contribution 0.168 0.939** 1.000 0.274 0.086 0.909**
Initiation 0.389 0.284 0.274 1.000 -0.059 0.513
Quality 0.086 0.151 0.086 -0.059 1.000 0.065
Popularity 0.356 0.926** 0.909** 0.513 0.065 1.000
Table 1: Correlation Coefficients of dimensions
Dispersion Engagement Contribution Initiation Quality Popularity
Dispersion 1.000 0.277 0.168 0.389 0.086 0.356
Engagement 0.277 1.000 0.939** 0.284 0.151 0.926**
Contribution 0.168 0.939** 1.000 0.274 0.086 0.909**
Initiation 0.389 0.284 0.274 1.000 -0.059 0.513
Quality 0.086 0.151 0.086 -0.059 1.000 0.065
Popularity 0.356 0.926** 0.909** 0.513 0.065 1.000
Figure 7: Cumulative density functions of each dimension showing Figure 8: Boxplots of the feature distributions
6. Correlations
§ Between different behaviour
roles
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
Churn Rate
FPR
TPR
0.0 0.2 0.4 0.6 0.8 1.0
0.00.20.40.60.81.0
User Count
FPR
TPR
0.00.20.40.60.81.0
TPR
§ Between behaviour and activity
§ Between behaviours and community
health
15. Need to change consumption behaviour
Nov 2012
• Behaviour can be changed
• Individual/community approaches
• Multiple motivating factors
• Behaviour change is sustainable
key findings
• Quantitative impact of specific changes
• Socio-demographic factors
• Gas vs electricity vs water
• Cost-effectiveness of interventions
• Longevity of change
gaps
August 2012
16. • Personal energy-saving targets
• Community/social initiative lead to long-term change
• Dynamic pricing schemes don’t always work
• The “rebound effect” can emerge from short-term measures
• Role of technology, age, economic situation, culture, marketing, etc.
• Consumer ability to handle new technology, capital cost, trade-offs, and
expected convenience
18. Feedback
§ What’s the optimal level of
detail ?
§ What feedback is suitable
for what type of
consumer?
§ What feedback tools?
What visualisations?