AIDR Tutorial (Artificial Intelligence for Disaster Response)

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This is a short tutorial of AIDR.

AIDR Tutorial
Muhammad Imran
Research Scien1st
Qatar Compu1ng Research Ins1tute, HBKU
Doha, Qatar
h"p://aidr.qcri.org/

Outline
•  Data collec2on in AIDR
•  Data classiﬁca2on in AIDR
•  Data view/download in AIDR

Data Collec2on in AIDR
•  Twi:er data collec2on strategies that AIDR supports
–  By keywords
–  By geographical regions
•  Strict: coordinates strictly inside geo boundaries
•  Approximate: tweets from a place that overlaps with the geo
boundaries.
–  By following Twi:er users
–  By keywords + regions
•  Tweets that match any of the keywords and within the geo
boundaries.

Data Collec2on Using Keywords
•  Keywords limit = 400
•  One keyword could a single word like
“Suffolk” or a phrase “Suffolk accident”
•  1 keyword/phrase cannot be more than 60
bytes (1 char = 1 byte)
•  Generic keywords collect irrelevant tweets
•  Specific keywords most likely collect relevant
tweets

Keywords Examples

Loca2on-based Collec2on
•  Bounding boxes do not act as ﬁlters for other ﬁlter
parameters. For example :
keyword=twi:er&loca2ons=-122.75,36.8,-121.75,37.8
would match any tweets containing the term Twi:er (even
non-geo tweets) OR coming from the San Francisco area.

Following Twi:er Users
For each user speciﬁed, the tool will collect:
•  Tweets created by the user.
•  Tweets which are retweeted by the user.
•  Replies to any Tweet created by the user.
•  Retweets of any Tweet created by the user.
•  Manual replies, created without pressing a reply bu:on (e.g.
“@twi:erapi I agree”).
The tool will not contain:
•  Tweets men2oning the user (e.g. “Hello @twi:erapi!”).
•  Manual Retweets created without pressing a Retweet bu:on (e.g.
“RT @twi:erapi The API is great”).
•  Tweets by protected users.
Use comma-separated list of TwiFer user id (hFp://geFwiFerid.com/)

Classiﬁer UI

Detailed Informa2on of Classiﬁers

Data Classifica2on in AIDR
•  Define classifiers (name, descrip2on)
– Define labels (name, descrip2on)
– Having a “miscellaneous” category will be helpful
•  Wait around 15-20 minutes (for fast
collec2ons) and 30-40 minutes (for slow
collec2on)
•  Start tagging

Classifier Genera2on
•  Check the classifier status (UI)
–  First classifier/model will be up ager 50 labeled
tweets, ideally equally distributed among labels
–  If no model appears ager 50 tags, keep tagging
•  Human-tagged items (the more the be:er)
•  40 more needed to re-train (next classifier target)
•  Machine-tagged items (keep an eye on
misclassifica2ons)
•  Quality (ideally should be 90 < AUC != 100)

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Retrieval augmented generation (RAG) is the most popular style of large language model application to emerge from 2023. The most basic style of RAG works by vectorizing your data and injecting it into a vector database like Milvus for retrieval to augment the text output generated by an LLM. This is just the beginning. One of the ways that we can extend RAG, and extend AI, is through multilingual use cases. Typical RAG is done in English using embedding models that are trained in English. In this talk, we’ll explore how RAG could work in languages other than English. We’ll explore French, Chinese, and Polish.

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Tracing the root cause of a performance issue requires a lot of patience, experience, and focus. It’s so hard that we sometimes attempt to guess by trying out tentative fixes, but that usually results in frustration, messy code, and a considerable waste of time and money. This talk explains how to correctly zoom in on a performance bottleneck using three levels of profiling: distributed tracing, metrics, and method profiling. After we learn to read the JVM profiler output as a flame graph, we explore a series of bottlenecks typical for backend systems, like connection/thread pool starvation, invisible aspects, blocking code, hot CPU methods, lock contention, and Virtual Thread pinning, and we learn to trace them even if they occur in library code you are not familiar with. Attend this talk and prepare for the performance issues that will eventually hit any successful system. About authorWith two decades of experience, Victor is a Java Champion working as a trainer for top companies in Europe. Five thousands developers in 120 companies attended his workshops, so he gets to debate every week the challenges that various projects struggle with. In return, Victor summarizes key points from these workshops in conference talks and online meetups for the European Software Crafters, the world’s largest developer community around architecture, refactoring, and testing. Discover how Victor can help you on victorrentea.ro : company training catalog, consultancy and YouTube playlists.

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AIDR Tutorial (Artificial Intelligence for Disaster Response)

1. AIDR Tutorial Muhammad Imran Research Scien1st Qatar Compu1ng Research Ins1tute, HBKU Doha, Qatar h"p://aidr.qcri.org/

2. Outline •  Data collec2on in AIDR •  Data classiﬁca2on in AIDR •  Data view/download in AIDR

3. Data Collec2on in AIDR •  Twi:er data collec2on strategies that AIDR supports –  By keywords –  By geographical regions •  Strict: coordinates strictly inside geo boundaries •  Approximate: tweets from a place that overlaps with the geo boundaries. –  By following Twi:er users –  By keywords + regions •  Tweets that match any of the keywords and within the geo boundaries.

4. Data Collec2on Using Keywords •  Keywords limit = 400 •  One keyword could a single word like “Suffolk” or a phrase “Suffolk accident” •  1 keyword/phrase cannot be more than 60 bytes (1 char = 1 byte) •  Generic keywords collect irrelevant tweets •  Specific keywords most likely collect relevant tweets

5. Keywords Examples

6. Loca2on-based Collec2on •  Bounding boxes do not act as ﬁlters for other ﬁlter parameters. For example : keyword=twi:er&loca2ons=-122.75,36.8,-121.75,37.8 would match any tweets containing the term Twi:er (even non-geo tweets) OR coming from the San Francisco area.

7. Following Twi:er Users For each user speciﬁed, the tool will collect: •  Tweets created by the user. •  Tweets which are retweeted by the user. •  Replies to any Tweet created by the user. •  Retweets of any Tweet created by the user. •  Manual replies, created without pressing a reply bu:on (e.g. “@twi:erapi I agree”). The tool will not contain: •  Tweets men2oning the user (e.g. “Hello @twi:erapi!”). •  Manual Retweets created without pressing a Retweet bu:on (e.g. “RT @twi:erapi The API is great”). •  Tweets by protected users. Use comma-separated list of TwiFer user id (hFp://geFwiFerid.com/)

8.

9. Classiﬁer UI

10. Detailed Informa2on of Classiﬁers

11. Data Classifica2on in AIDR •  Define classifiers (name, descrip2on) – Define labels (name, descrip2on) – Having a “miscellaneous” category will be helpful •  Wait around 15-20 minutes (for fast collec2ons) and 30-40 minutes (for slow collec2on) •  Start tagging

12. Classifier Genera2on •  Check the classifier status (UI) –  First classifier/model will be up ager 50 labeled tweets, ideally equally distributed among labels –  If no model appears ager 50 tags, keep tagging •  Human-tagged items (the more the be:er) •  40 more needed to re-train (next classifier target) •  Machine-tagged items (keep an eye on misclassifica2ons) •  Quality (ideally should be 90 < AUC != 100)