Introduction to Machine Learning talk (part-2) focused on the applications of machine learning in the disaster response domain. In the first part of the talk, we presented different machine learning approaches.
Extracting Information Nuggets from Disaster-Related Messages in Social MediaMuhammad Imran
This presentation describes our work presented at the 10th International Conference on Information Systems on Crisis Response and Management (ISCRAM) in Baden-Baden, Germany. The work shows the importance of microblogging websites such as Twitter, and huge number of informative messages that can contribute to situational awareness at the time of disasters. Specifically, the work shows the classification, and information extractions of those valuable, actionable informative messages that people post during emergencies.
The Role of Social Media and Artificial Intelligence for Disaster ResponseMuhammad Imran
Keynote slides for ISCRAM 2016.
"Social Media platforms such as Twitter are invaluable sources of time-critical information. Information on social media communicated during emergencies convey timely and actionable information. For rapid crisis response, real-time insights are important for emergency responders. Although, many humanitarian organizations would like to use this information, however they struggle due a number of issues such as information overload, information vagueness, less credible and misinformation. In this talk, I will describe the role of social media and potential artificial intelligence computational techniques useful for humanitarian organizations and decision makers to make sense of social media data for rapid crisis response."
Coordinating Human and Machine Intelligence to Classify Microblog Communica0o...Muhammad Imran
An emerging paradigm for the processing of data streams involves human and machine computation working together, allowing human intelligence to process large-scale data. We apply this approach to the classification of crisis-related messages in microblog streams. We begin by describing the platform AIDR (Artificial Intelligence for Disaster Response), which collects human annotations over time to create and maintain automatic supervised classifiers for social media messages. Next, we study two significant challenges in its design: (1) identifying which elements must be labeled by humans, and (2) determining when to ask for such annotations to be done. The first challenge is selecting the items to be labeled by crowdsourcing workers to maximize the productivity of their work. The second challenge is to schedule the work in order to reliably maintain high classification accuracy over time. We provide and validate answers to these challenges by extensive experimentation on real- world datasets.
Presentation at the Tow Center for Digital Journalism, Columbia University. November 14th, 2013.
VIDEO: http://new.livestream.com/accounts/1079539/events/2542929
http://towcenter.org/events/conversation-with-carlos-castillo/
Overview of Social Media During Disaster and Crowd Power in Disaster Response
Prepared for Otago University, COMP113 Social Media and Online
Presented by Catherine Graham
January 29, 2013
Extracting Information Nuggets from Disaster-Related Messages in Social MediaMuhammad Imran
This presentation describes our work presented at the 10th International Conference on Information Systems on Crisis Response and Management (ISCRAM) in Baden-Baden, Germany. The work shows the importance of microblogging websites such as Twitter, and huge number of informative messages that can contribute to situational awareness at the time of disasters. Specifically, the work shows the classification, and information extractions of those valuable, actionable informative messages that people post during emergencies.
The Role of Social Media and Artificial Intelligence for Disaster ResponseMuhammad Imran
Keynote slides for ISCRAM 2016.
"Social Media platforms such as Twitter are invaluable sources of time-critical information. Information on social media communicated during emergencies convey timely and actionable information. For rapid crisis response, real-time insights are important for emergency responders. Although, many humanitarian organizations would like to use this information, however they struggle due a number of issues such as information overload, information vagueness, less credible and misinformation. In this talk, I will describe the role of social media and potential artificial intelligence computational techniques useful for humanitarian organizations and decision makers to make sense of social media data for rapid crisis response."
Coordinating Human and Machine Intelligence to Classify Microblog Communica0o...Muhammad Imran
An emerging paradigm for the processing of data streams involves human and machine computation working together, allowing human intelligence to process large-scale data. We apply this approach to the classification of crisis-related messages in microblog streams. We begin by describing the platform AIDR (Artificial Intelligence for Disaster Response), which collects human annotations over time to create and maintain automatic supervised classifiers for social media messages. Next, we study two significant challenges in its design: (1) identifying which elements must be labeled by humans, and (2) determining when to ask for such annotations to be done. The first challenge is selecting the items to be labeled by crowdsourcing workers to maximize the productivity of their work. The second challenge is to schedule the work in order to reliably maintain high classification accuracy over time. We provide and validate answers to these challenges by extensive experimentation on real- world datasets.
Presentation at the Tow Center for Digital Journalism, Columbia University. November 14th, 2013.
VIDEO: http://new.livestream.com/accounts/1079539/events/2542929
http://towcenter.org/events/conversation-with-carlos-castillo/
Overview of Social Media During Disaster and Crowd Power in Disaster Response
Prepared for Otago University, COMP113 Social Media and Online
Presented by Catherine Graham
January 29, 2013
Ignite talk at ICCM-2013 at United Nations (UN) Nairobi by NSF SoCS project researcher, Hemant Purohit - 'How to Leverage Social Media Communities for Crisis Response Coordination' using Human+Machine computing
Key-message: We need to extract smart actionable data out of big crisis data to assist response coordination, by focusing on demand-supply centric technology.
More at Kno.e.sis' SOCS project page: http://knoesis.org/research/semsoc/projects/socs
Also, Crisis Informatics at Kno.e.sis: http://j.mp/CrisisRes
There are many different technologies available for use in disasters. This page highlights the different technologies and categorizes them by type.
The SlideShare below was originally created in response to a number of presentation requests I have had. I will continue to add new technologies as I come across them! Feel free to send any leads you may have!
Automatically Rank Social Media Requests for Emergency Services using Service...Hemant Purohit
Public expects a prompt response from online services, including emergency response organizations to requests for help posted on social media. However, the information overload experienced by these organizations, coupled with their limited human resources, challenges them to timely identifying and prioritizing such requests. We present a novel model to formally characterize social media requests and then, develop a Learning-to-Rank system using this model.
Paper: Purohit, H., Castillo, C., Imran, M., and Pandey, R. (2018). Social-EOC: Serviceability Model To Rank Social Media Requests for Emergency Operation Centers. ASONAM 2018.
Slideshare lost the previous upload which had nearly 70K views. Re-uploading. http://knoesis.org/?q=node/2633
With the explosion in social media (1B+ Facebook users, 500M+ Twitter users) and ubiquitous mobile access (6B+ mobile phone subscribers) sharing their observations and opinions, we have unprecedented opportunities to extract social signals, create spatio-temporal mappings, perform analytics on social data, and support applications that vary from situational awareness during crisis response, preparedness and rebuilding phases to advanced analytics on social data, and gaining valuable insights to support improved decision making.This tutorial weaves three themes and corresponding relevant topics- a.) citizen sensing and crisis mapping, b.) technical challenges and recent research for leveraging citizen sensing to improve crisis response coordination, and c.) experiences in building robust and scalable platforms/systems. It will couple technical insights with identification of computational techniques and algorithms along with real-world examples. We will also do exemplary demos of the features in the Sahana, CrowdMap (Ushahidi's version) and Twitris platforms while elaborating on the practical issues and pitfalls of the development and operation of these large-scale platforms, especially during the real-time crisis response
NY VOST Response to the Second Avenue Building Collapse, East Village, NYCJoanna Lane
New York Virtual Operations Support Team
SECOND AVENUE BUILDING COLLAPSE, EAST VILLAGE, NYC
Response/Exercise: March 26, 2015 – March 30,2015
Situation Summary
On March 26, 2015 at 1517 hrs, FDNY units were dispatched to 123 2nd Ave between 7th Street and St. Mark’s Place for a fire and possible explosion. At 1522 hrs a structural fire and building collapse were reported by arriving units. By 1554 the incident had reached a 7th alarm. Two 5-story buildings were fully involved with fire, as well as 3 floors of a neighboring 7-story building. Surrounding occupancies were evacuated and a perimeter was established. Outside operations commenced due to the large volume of fire. All three buildings eventually collapsed.
Integrating social media into Situational Awareness supports the enhanced quality of decision making and risk management processes. During the operational period, the NYVOST employed a range of emerging technologies to support an integrated Situational Awareness toolset for the FDNY Incident Management Team by:
Monitoring social media for mission-critical information from the public
Amplifying public safety messaging from the FDNY and related NYC sources
Performing quality assurance and reputation management for the activating agency
Archiving and AAR reporting
Operational personnel can gain much from practicing the actual tasks that would be executed in the event of a major disaster, such as Superstorm Sandy. NYVOST members (VOSTies) gained ninety six (96) hours of activation experience in total over the operational period. Individual practitioners gained between (4) hours and forty four (44) hours each, depending on their voluntary availability.
NYVOST Incident Response March 26 -30, 2015
Summary
The New York Virtual Operations Support Team (NYVOST) activated at 21:27 on March 26, 2015 to provide social media emergency management (SMEM) support to the FDNY Type II All Hazard Incident Management Team at the scene. The FDNY-IMT’s Public Information Officer (PIO) liaised with the NYVOST’s Team Lead remotely, who coordinated the VOST response to perform the following missions:
* Monitoring for issues requiring a 911 response, smoke conditions, displaced people,
rumors and hoaxes.
* Amplifying @FDNY twitter and other official response agencies and verified sources.
* Identifying possible FDNY-IMT response gaps on social media or complaints as
reported by the public.
These primary missions remained throughout the operational period, with a special instruction added on the second day to source, verify and curate images for FDNY use. The team’s Pinterest board, entitled #NYC Explosion March 26th 2015, fulfilled this need.
This is a 22 page After Action Report about NYVOST’s support of the FDNY Incident Management Team in this incident.
International Day for Disaster Reduction at the World Bank
Disaster Risk Management in the Information Age
A joint training workshop by GICT, GFDRR, infoDev and LCSUW to mark the International Day for Disaster Reduction
Machine learning lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
Topics covered:
1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
Ignite talk at ICCM-2013 at United Nations (UN) Nairobi by NSF SoCS project researcher, Hemant Purohit - 'How to Leverage Social Media Communities for Crisis Response Coordination' using Human+Machine computing
Key-message: We need to extract smart actionable data out of big crisis data to assist response coordination, by focusing on demand-supply centric technology.
More at Kno.e.sis' SOCS project page: http://knoesis.org/research/semsoc/projects/socs
Also, Crisis Informatics at Kno.e.sis: http://j.mp/CrisisRes
There are many different technologies available for use in disasters. This page highlights the different technologies and categorizes them by type.
The SlideShare below was originally created in response to a number of presentation requests I have had. I will continue to add new technologies as I come across them! Feel free to send any leads you may have!
Automatically Rank Social Media Requests for Emergency Services using Service...Hemant Purohit
Public expects a prompt response from online services, including emergency response organizations to requests for help posted on social media. However, the information overload experienced by these organizations, coupled with their limited human resources, challenges them to timely identifying and prioritizing such requests. We present a novel model to formally characterize social media requests and then, develop a Learning-to-Rank system using this model.
Paper: Purohit, H., Castillo, C., Imran, M., and Pandey, R. (2018). Social-EOC: Serviceability Model To Rank Social Media Requests for Emergency Operation Centers. ASONAM 2018.
Slideshare lost the previous upload which had nearly 70K views. Re-uploading. http://knoesis.org/?q=node/2633
With the explosion in social media (1B+ Facebook users, 500M+ Twitter users) and ubiquitous mobile access (6B+ mobile phone subscribers) sharing their observations and opinions, we have unprecedented opportunities to extract social signals, create spatio-temporal mappings, perform analytics on social data, and support applications that vary from situational awareness during crisis response, preparedness and rebuilding phases to advanced analytics on social data, and gaining valuable insights to support improved decision making.This tutorial weaves three themes and corresponding relevant topics- a.) citizen sensing and crisis mapping, b.) technical challenges and recent research for leveraging citizen sensing to improve crisis response coordination, and c.) experiences in building robust and scalable platforms/systems. It will couple technical insights with identification of computational techniques and algorithms along with real-world examples. We will also do exemplary demos of the features in the Sahana, CrowdMap (Ushahidi's version) and Twitris platforms while elaborating on the practical issues and pitfalls of the development and operation of these large-scale platforms, especially during the real-time crisis response
NY VOST Response to the Second Avenue Building Collapse, East Village, NYCJoanna Lane
New York Virtual Operations Support Team
SECOND AVENUE BUILDING COLLAPSE, EAST VILLAGE, NYC
Response/Exercise: March 26, 2015 – March 30,2015
Situation Summary
On March 26, 2015 at 1517 hrs, FDNY units were dispatched to 123 2nd Ave between 7th Street and St. Mark’s Place for a fire and possible explosion. At 1522 hrs a structural fire and building collapse were reported by arriving units. By 1554 the incident had reached a 7th alarm. Two 5-story buildings were fully involved with fire, as well as 3 floors of a neighboring 7-story building. Surrounding occupancies were evacuated and a perimeter was established. Outside operations commenced due to the large volume of fire. All three buildings eventually collapsed.
Integrating social media into Situational Awareness supports the enhanced quality of decision making and risk management processes. During the operational period, the NYVOST employed a range of emerging technologies to support an integrated Situational Awareness toolset for the FDNY Incident Management Team by:
Monitoring social media for mission-critical information from the public
Amplifying public safety messaging from the FDNY and related NYC sources
Performing quality assurance and reputation management for the activating agency
Archiving and AAR reporting
Operational personnel can gain much from practicing the actual tasks that would be executed in the event of a major disaster, such as Superstorm Sandy. NYVOST members (VOSTies) gained ninety six (96) hours of activation experience in total over the operational period. Individual practitioners gained between (4) hours and forty four (44) hours each, depending on their voluntary availability.
NYVOST Incident Response March 26 -30, 2015
Summary
The New York Virtual Operations Support Team (NYVOST) activated at 21:27 on March 26, 2015 to provide social media emergency management (SMEM) support to the FDNY Type II All Hazard Incident Management Team at the scene. The FDNY-IMT’s Public Information Officer (PIO) liaised with the NYVOST’s Team Lead remotely, who coordinated the VOST response to perform the following missions:
* Monitoring for issues requiring a 911 response, smoke conditions, displaced people,
rumors and hoaxes.
* Amplifying @FDNY twitter and other official response agencies and verified sources.
* Identifying possible FDNY-IMT response gaps on social media or complaints as
reported by the public.
These primary missions remained throughout the operational period, with a special instruction added on the second day to source, verify and curate images for FDNY use. The team’s Pinterest board, entitled #NYC Explosion March 26th 2015, fulfilled this need.
This is a 22 page After Action Report about NYVOST’s support of the FDNY Incident Management Team in this incident.
International Day for Disaster Reduction at the World Bank
Disaster Risk Management in the Information Age
A joint training workshop by GICT, GFDRR, infoDev and LCSUW to mark the International Day for Disaster Reduction
Machine learning lets you make better business decisions by uncovering patterns in your consumer behavior data that is hard for the human eye to spot. You can also use it to automate routine, expensive human tasks that were previously not doable by computers. In the business to business space (B2B), if your competitors can make wiser business decisions based on data and automate more business operations but you still base your decisions on guesswork and lack automation, you will lose out on business productivity. In this introduction to machine learning tech talk, you will learn how to use machine learning even if you do not have deep technical expertise on this technology.
Topics covered:
1.What is machine learning
2.What is a typical ML application architecture
3.How to start ML development with free resource links
4.Key decision factors in ML technology selection depending on use case scenarios
What is sensitive skin?
It is a large area flexible array of sensors, with data processing capabilities, with the ability to sense the surroundings.
It make possible the use of unsupervised machine in our midst.
Machines in unstructured environments
Societal needs and concerns
a) Health industry
b) Eco friendly
c) Difficulties of acceptance
Examples of Real-World Big Data Application Specific examples of velocity challenge and how it is addressed in disaster coordination scenario (e.g., Jammu&Kashmir Floods).
Prof Amit Sheth - Kno.e.sis
Emerging Communications Tech: Lessons from Hurricane Sandy and Super Typhoon ...Cisco Crisis Response
This presentation takes a look at two recent Cisco TacOps deployments, Hurricane Sandy and Super Typhoon Haiyan, and examines the emerging communications technologies that are bringing innovation to disasters and humanitarian crises.
Engaging the Community in Disaster Response - July 2013 VolunteerMatch BPN We...VolunteerMatch
When disaster strikes a community, individuals, corporations and community groups want to be a part of the solution and help their neighbors in need. Join us for this encore presentation from the 2013 VolunteerMatch Client Summit to learn how to mobilize the power of volunteers and the generosity of donors and employees during and after a disaster. Hear from three unique vantage points: American Red Cross, Microsoft and Network for Good. Together they will arm you with the most recent trends, opportunities, challenges and solutions to engage your audience in compelling response and recovery efforts.
BackLifeUp is an investment tool for anticipating funds for SDG goals and allocating funds into tokenized natural assets. Please contact me if you have any questions!
CASA UASSC Meeting May 2016- Presentation by Industry Chair, Terry MartinTerrence Martin (PhD)
This is the presentation given by Dr Terry Martin at the CASA UASSC in May 2016. He is the current Industry Chair. Note that the original presentation contains extensive animation. In order to maintain the animation effect, the presentation has been split into individual slides however this now makes it more than 270 slides. This may be offputting to some!
Please note that the contents are the view of Dr Martin and are not necessarily endorsed by CASA.
The presentation covers 5 key areas:
Part 1: (Slides 1-65) Covers some innovative UAV applications such as NASA UTM, and Google Loon, before discussing the impact of automation on the jobs market, internationally and Nationally. The key point is that disruption and automation mean that many jobs are at risks, and Australia could be vulnerable if it continues on its current path
Part 2 (Slides 67-82) then provides an overview of the recently amended RPAS regulatory framework
Part 3 (Slides 83- 171 ) then works through whats broken in the current RPAS UASSC before describing the efforts of Europe and the US, the implications of Australian having no UAV Roadmap, the need for a pragmatic appreciation of our national capacity, particularly when it comes to the expertise necessary to making assessments about risk, safety and equipage requirements.
Part 4 (172 - 243 ) Selected elements of Detect and Avoid and Command and Non Payload Communications are outlined
Part 5 (244- 286) proposed steps we could take to identify our priorities, identify the operational and technical shortcomings that are hindering UAV integration and work with Industry more effectively to achieve that end state. This includes the restructure plans for the UASSC Working Groups.
Heather Blanchard, Co Founder of CrisisCommons, presentation at the Fleming Europe's 2nd Annual Geospatial Conference (http://www.flemingeurope.com/aviation-and-defence-conferences/europe/2nd-annual-geospatial-intelligence-summit)
Given the growth of social media and rapid evolution of Web of Data, we have unprecedented opportunities to improve crisis response by extracting social signals, creating spatio-temporal mappings, performing analytics on social and Web of Data, and supporting a variety of applications. Such applications can help provide situational awareness during an emergency, improve preparedness, and assist during the rebuilding/recovery phase of a disaster. Data mining can provide valuable insights to support emergency responders and other stakeholders during crisis. However, there are a number of challenges and existing computing technology may not work in all cases. Therefore, our objective here is to present the characterization of such data mining tasks, and challenges that need further research attention for leveraging social media and Web of Data to assist crisis response coordination.
Emerging Technologies for Fundraising Optimisation Colin Habberton
Prepared for Resource Alliance's Fundraising Online 2014 conference, this presentation suggests the Five Forces of the Digital Age adapting them into Michael Porter's 1979 model.
Collecting and Coding Twitter Data in DiscoverTextJill Hopke
These are the slides to a workshop I presented on September 23, 2014 to the University of Wisconsin-Madison Digital Humanities Research Network (http://dhresearchnetwork.wordpress.com/). The workshop covered an overview of my research using DiscoverText, steps to collect data in the cloud-based big data analytics software DiscoverText (https://discovertext.com/), and coding data, as well as limitations, challenges and other resources for social media data collection and analysis.
Processing Social Media Messages in Mass Emergency: A SurveyMuhammad Imran
Millions of people use social media to share information during disasters and mass emergencies. Information available on social media, particularly in the early hours of an event when few other sources are available, can be extremely valuable for emergency responders and decision makers, helping them gain situational awareness and plan relief efforts. Processing social media content to obtain such information involves solving multiple challenges, including parsing brief and informal messages, handling information overload, and prioritizing different types of information. These challenges can be mapped to information processing operations such as filtering, classifying, ranking, aggregating, extracting, and summarizing. This work highlights these challenges and presents state of the art computational techniques to deal with social media messages, focusing on their application to crisis scenarios.
Damage Assessment from Social Media Imagery Data During DisastersMuhammad Imran
Rapid access to situation-sensitive data through social media networks creates new opportunities to address a number of real-world problems. Damage assessment during dis- asters is a core situational awareness task for many humanitarian organizations that traditionally takes weeks and months. In this work, we analyze images posted on social media platforms during natural disasters to determine the level of damage caused by the disasters. We employ state-of-the-art machine learning techniques to perform an extensive experimentation of damage assessment using images from four major natural disasters. We show that the domain-specific fine-tuning of deep Convolutional Neural Networks (CNN) outperforms other state-of-the-art techniques such as Bag-of-Visual-Words (BoVW). High classification ac- curacy under both event-specific and cross-event test settings demonstrate that the proposed approach can effectively adapt deep-CNN features to identify the severity of destruction from social media images taken after a disaster strike.
Image4Act: Online Social Media Image Processing for Disaster ResponseMuhammad Imran
We present an end-to-end social media image processing system called Image4Act. The system aims at collecting, denoising, and classifying imagery content posted on social media platforms to help humanitarian organizations in gaining situational awareness and launching relief operations. The system combines human computation and machine learning techniques to process high-volume social media imagery content in real time during natural and human-made disasters. To cope with the noisy nature of the social media imagery data, we use a deep neural network and perceptual hashing techniques to filter out irrelevant and duplicate images. Furthermore, we present a specific use case to assess the severity of infrastructure damage incurred by a disaster. The evaluations of the system on existing disaster datasets as well as a real-world deployment during a recent cyclone prove the effectiveness of the system.
A Robust Framework for Classifying Evolving Document Streams in an Expert-Mac...Muhammad Imran
An emerging challenge in the online classification of social media data streams is to keep the categories used for classification up-to-date. In this paper, we propose an innovative framework based on an Expert-Machine-Crowd (EMC) triad to help categorize items by continuously identifying novel concepts in heterogeneous data streams often riddled with outliers. We unify constrained clustering and outlier detection by formulating a novel optimization problem: COD-Means. We design an algorithm to solve the COD-Means problem and show that COD-Means will not only help detect novel categories but also seamlessly discover human annotation errors and improve the overall quality of the categorization process. Experiments on diverse real data sets demonstrate that our approach is both effective and efficient.
Summarizing Situational Tweets in Crisis ScenarioMuhammad Imran
During mass convergence events such as natural disasters, microblogging platforms like Twitter are widely used by affected people to post situational awareness messages. These crisis related messages disperse among multiple categories like infrastructure damage, information about missing, injured, and dead people etc. The challenge here is to extract important situational updates from these messages, assign them appropriate informational categories, and finally summarize big trove of information in each category. In this paper, we propose a novel framework which first assigns tweets into different situational classes and then summarize those tweets. In the summarization phase, we propose a two stage summarization framework which first extracts a set of important tweets from the whole set of information through an Integer-linear programming (ILP) based optimization technique and then follows a word graph and content word based abstractive summarization technique to produce the final summary. Our method is time and memory efficient and outperforms the baseline in terms of quality, coverage of events, locations et al., effectiveness, and utility in disaster scenarios.
Artificial Intelligence for Disaster ResponseMuhammad Imran
We present AIDR (Artificial Intelligence for Disaster Response), a platform designed to perform automatic classification of crisis-related microblog communications. AIDR enables humans and machines to work together to apply human intelligence to large-scale data at high speed. The objective of AIDR is to classify messages that people
post during disasters into a set of user-defined categories of information (e.g., “needs”, “damage”, etc.) For this purpose, the system continuously ingests data from Twitter, processes it (i.e., using machine learning classification techniques) and leverages human-participation (through crowdsourcing) in real-time. AIDR has been successfully tested to classify informative vs. non-informative tweets posted during the 2013 Pakistan Earthquake. Overall, we achieved a classification quality (measured using AUC) of 80%. AIDR is available at http://aidr.qcri.org/.
A Real-time Heuristic-based Unsupervised Method for Name Disambiguation in Di...Muhammad Imran
This paper addresses the baffling problem of name disam- biguation in the context of digital libraries that administer bibliographic citations. The problem emanates when multi- ple authors share a common name or when multiple name variations of an author appear in citation records. Name dis- ambiguation is not trivial to solve, and most of the digital libraries do not provide an efficient way to accurately iden- tify the citation records of an author. Furthermore, lack of complete meta-data information in digital libraries hinders the existence of generic algorithm that can be applicable on any dataset. We propose a heuristic-based, unsupervised and adaptive method that also embraces users’ interaction to count users’ feedback in disambiguation process. Moreover, the method exploits important features associated with an author and citation records such as co-authors, affiliation, publication title, venue etc., and contrives a conspicuous multilayer hierarchical clustering algorithm, which tunes it- self according to the available information and form clusters of unambiguous records. Our experiments on a set of re- searchers that are contemplated to be highly ambiguous de- cisively produced high precision and recall results and affirm the viability of our algorithm.
Tweet4act: Using Incident-Specific Profiles for Classifying Crisis-Related Me...Muhammad Imran
This work describes our work presented at the ISCRAM-2013 conference. We presented Tweet4act system, which is used to detect and classify crisis-related messages communicated over a microblogging platform. Our system relies on extracting content features from each message. These features and the use of an incident-specific dictionary allow us to determine the period type of an incident that each message belongs to.
We are going to represent a Mashup platform for the research evaluation. This talk was given at 2nd Search computing workshop in Como, italy on 27-may-2010.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Richard's aventures in two entangled wonderlandsRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Introduction to Machine Learning: An Application to Disaster Response
1. Introduction to Machine Learning:
An Application to Disaster Response
Muhammad Imran & Shafiq Joty
Qatar Computing Research Institute
Hamad Bin Khalifa University
Doha, Qatar
2. DISASTERS - SOCIAL MEDIA – RESPONSE EFFORTS
Humans suffering from the impacts of disasters, crises, and armed conflicts.
In the last two decades, 218 million people each year were affected by disasters;
At an annual cost to the global economy that exceeds $300 billion. (Source: UN)
3. @NYGovCuomo orders closing of NYC bridges. Only Staten Island
bridges unaffected at this time. Bridges must close by 7pm. #Sandy
#NYC.
rt @911buff: public help needed: 2 boys 2 & 4 missing nearly 24 hours
after they got separated from their mom when car submerged in si.
#sandy #911buff
freaking out. home alone. will just watch tv #Sandy #NYC.
400 Volunteers are needed for areas that #Sandy destroyed.
SANDY HURRICANE TWEETS
4. @NYGovCuomo orders closing of NYC bridges. Only Staten Island
bridges unaffected at this time. Bridges must close by 7pm. #Sandy
#NYC.
rt @911buff: public help needed: 2 boys 2 & 4 missing nearly 24 hours
after they got separated from their mom when car submerged in si.
#sandy #911buff
freaking out. home alone. will just watch tv #Sandy #NYC.
400 Volunteers are needed for areas that #Sandy destroyed.
Personal
Informative
SANDY HURRICANE TWEETS
5. @NYGovCuomo orders closing of NYC bridges. Only Staten Island
bridges unaffected at this time. Bridges must close by 7pm. #Sandy
#NYC.
rt @911buff: public help needed: 2 boys 2 & 4 missing nearly 24 hours
after they got separated from their mom when car submerged in si.
#sandy #911buff
freaking out. home alone. will just watch tv #Sandy #NYC.
400 Volunteers are needed for areas that #Sandy destroyed.
Personal
Informative
Caution and Advice
Reports of missing people
Help/volunteers needed
SANDY HURRICANE TWEETS
6. @NYGovCuomo orders closing of NYC bridges. Only Staten Island
bridges unaffected at this time. Bridges must close by 7pm. #Sandy
#NYC.
rt @911buff: public help needed: 2 boys 2 & 4 missing nearly 24 hours
after they got separated from their mom when car submerged in si.
#sandy #911buff
freaking out. home alone. will just watch tv #Sandy #NYC.
400 Volunteers are needed for areas that #Sandy destroyed.
Personal
Informative
Caution and Advice
Reports of missing people
Help/volunteers needed
SANDY HURRICANE TWEETS
7. Personal
Informative
(Direct & Indirect)
Other
Caution and advice
Casualties and damage
Donations
People missing, found, or seen
Information source
Siren heard, warning issued/lifted etc.
People dead, injured, damage etc.
Money, shelter, blood, goods, or services
Webpages, photos, videos information sources
…
FINDING TACTICAL AND ACTIONABLE INFORMATION
8. USEFUL INFORMATION ON TWITTER
Caution
and advice
Information
source
Donations
Causalities
& damage
A siren heard
Tornado warning issued/lifted
Tornado sighting/touchdown
42%
50%
30%
12%
18%
Photos as info. source
Webpages info. source
Videos as info. source
44%
20%
16%
Other donations
Money
Equipment, shelter,
Volunteers, Blood
38%
8%
54%
People injured
People dead
Damage
44%
44%
2%
16%
10%
% of informative tweets
Ref: “Extracting Information Nuggets from Disaster-Related Messages in Social Media”. Imran et al. ISCRAM-2013, Baden-Baden, Germany.
9. INFORMATION PROCESSING PIPELINE (SUPERVISED LEARNING):
OFFLINE APPROACH
Data collection
1 2
Human annotations
on sample data
Machine training
3
Classification
4
Disaster Timeline:
DATA COLLECTION
10. IMPACT AND RESPONSE TIMELINE
Department of Community Safety, Queensland Govt. & UNOCHA, 2011
Disaster response (today) Disaster response (target)
Target disaster response requires real-time processing of data.
12. REQUIREMENS & CHALLENGES
• Real-time analysis of data is required
• For rapid crisis response
• To reduce community harm
• Combine human and machine intelligence
• Usable and useful for end-users (mostly non-technical)
• End-users (stakeholders)
• Crisis managers (policy makers)
• Crisis responders (field workers)
13. REQUIREMENS & CHALLENGES
Other key challenges:
• Volume
Scale of data (20m tweets in 5 days)
• Velocity
Analysis of streaming data (16k/min)
• Variety
Different forms/types of data (information types)
• Veracity
Uncertainty of data
14. STREAM PROCESSING USING SUPERVISED ML
Combining human and machine computation
Quality assurance loops: human processing elements
do the work, automatic processing elements check for
consistency
Process-verify: work is done automatically, humans
check low-confidence or borderline cases
Online supervised learning: humans train the machine
to do the work automatically
15. Data collection
1 2
Human annotations Machine training
3
Classification
4
ONLINE APPROACH
DATA COLLECTION
H
A
Learning-1
CLASSIFICATION OF DATA & DECISION MAKING PROCESS
Learning-2 Learning-3 … Learning-n
Human
annotation - 1
Human
annotation - 2
Human
annotation - 3 …
Human
annotation - n
First few hours
INFORMATION PROCESSING PIPELINE: ONLINE APPROACH
(REAL-TIME)
16. http://aidr.qcri.org/
AIDR —Artificial Intelligence for Disaster Response— is a free, open-source, and easy-to-use
platform to automatically filter and classify relevant tweets posted during humanitarian crises.
1 2 3
Collect Curate Classify
17. AIDR: FROM END-USERS PERSPECTIVE
Collection Classifier(s)
• Keywords, hashtags
• Geographical bounding box
• Languages
• Follow specific set of users
A collection is a set of filters A classifier is a set of tags
• Donations requests & offers
• Damage & causalities
• Eyewitness accounts
• …
2 step approach
1 2
http://aidr.qcri.org/
19. AIDR: HIGH-LEVEL ARCHTECTURE
http://aidr.qcri.org/
Items Collector Feature Extractor Classifier(s)
Learner
Crowdsourcing
Task GeneratorStream of incoming
items from data sources
Item &
featuresItem
An expert defines
classifiers by giving
a name and description
for each category
Expert
Items
Crowd workers/volunteers
Model
parameter
Classified
Item
A list of classified items by category
and classifier’s confidence
Labeling
tasks
Labeled
item
Data
source
Data
source
20. QUALITY VS. COST
http://aidr.qcri.org/
• Gaining acceptable quality
• Quality (classification accuracy)
• Cost (human labels: monetary in case of paid-workers, time in
case of volunteers)
Quality vs. cost using passive learning Quality vs. cost using active learning
21. PERFORMANCE
http://aidr.qcri.org/
• In terms of throughput and latency
Throughput of feature extractor, classifier, and the system
Latency of feature extractor, classifier, and the system
22. CHALLENGES: DOMAIN ADAPTATION
http://aidr.qcri.org/
• Crisis-specific labels are necessary
• Contrasting vocabulary use
• Differences in public concerns, affected infrastructure
• New labels should be collected for each new crisis
[ Imran et al. 2013b ]
• Domain adaptation
• Train models using all past labeled data (all types of events)
• Train on labeled data from past similar events
• Train on data from neighboring countries on similar events
31. AIDR has been awarded the Grand Prize in the
Open Source Software World Challenge 2015
32. http://aidr.qcri.org/
AIDR —Artificial Intelligence for Disaster Response— is a free, open-source, and easy-to-use
platform to automatically filter and classify relevant tweets posted during humanitarian crises.
Thank you!
Editor's Notes
Finding tactical and actionable information from a millions of messages that people post on social media is a complex and challenging task. For this purpose, specifically for disasters we came up with a sensible ontology that has mainly three stages. Every stage refine a piece of information that thus can highly contribute to disaster management. In order to get to the actionable information it is required that we first categories a coming message to a predefined category that is of disaster-specific.
I would like to start with showing you the results of our last year paper in ISCRAM. These charts show that a significant amount of valuable information is available can be extracted from tweets. In this work, we used state-of-art supervised machine learning techniques to classify tweets posted during disaster situations.
Our conclusion from that work was Social media platforms like Twitter contain useful information. Now the big question is how one can get that useful information during an on-going disaster for an effective disaster response?
One of the big challenges in the crowdsourcing
AIDR tagger is a machine computational component. Users define classifiers by specifying categories they like tweets to classified against. AIDR tagger requires tagged examples for its learning process.
On the left side, screenshot list training examples of a particular collection. One can review, and remove if required. On the right side, screenshot shows the classified output. That is, tweets classified into categories with confidence score.
During disasters, nothing better than helping those who are affected to save lives. That’s exactly what we did during the Typhoon Hagupit that struck the Philippines in early December 2014 on a request from UN to help them find requests of help/needs, infrastructure damage, aid needs and provided using Social Media data. On your left side, the guardian page covering the whole story, and on your right side a map generated by our platforms AIDR, MicroMappers.