Utilizing Social Health Websites for Cognitive Computing and Clinical Decisio...CrowdTruth
Crowdsourced annotations data offers cognitive computing systems insights in lay semantics. This is especially important in health care, where medical terminology is often not aligned with patients `lay' language. However, the general crowd often has limited medical knowledge. Therefore this research investigated the opportunities of social health websites for obtaining ground truth annotations data for cognitive computing systems including clinical decision support systems. By identifying these websites and analyzing their data, it offers a starting point for the future utilization of user-generated health content for cognitive systems. However, the opportunities of social health data are currently limited by various legal regulations. Therefore this paper also dwells on the legal aspects of implementing social health data for cognitive computing systems.
Visualization of Disagreement-based Quality Metrics of Crowdsourcing DataCrowdTruth
Crowdsourcing represents a significant source of data which needs to be analyzed and interpreted. These tasks influence the quality of the output as well as the efficiency of the process. Visualization proved to be an effective way of dealing with large amount of data. In this paper we propose a visualization analytic model in the context of the CrowdTruth framework and CrowdTruth metrics for optimizing the crowdsourcing process and improving its data quality. The requirements for the dynamic, scalable and interactive visualizations were extracted through literature and interviews with users of the framework.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Utilizing Social Health Websites for Cognitive Computing and Clinical Decisio...CrowdTruth
Crowdsourced annotations data offers cognitive computing systems insights in lay semantics. This is especially important in health care, where medical terminology is often not aligned with patients `lay' language. However, the general crowd often has limited medical knowledge. Therefore this research investigated the opportunities of social health websites for obtaining ground truth annotations data for cognitive computing systems including clinical decision support systems. By identifying these websites and analyzing their data, it offers a starting point for the future utilization of user-generated health content for cognitive systems. However, the opportunities of social health data are currently limited by various legal regulations. Therefore this paper also dwells on the legal aspects of implementing social health data for cognitive computing systems.
Visualization of Disagreement-based Quality Metrics of Crowdsourcing DataCrowdTruth
Crowdsourcing represents a significant source of data which needs to be analyzed and interpreted. These tasks influence the quality of the output as well as the efficiency of the process. Visualization proved to be an effective way of dealing with large amount of data. In this paper we propose a visualization analytic model in the context of the CrowdTruth framework and CrowdTruth metrics for optimizing the crowdsourcing process and improving its data quality. The requirements for the dynamic, scalable and interactive visualizations were extracted through literature and interviews with users of the framework.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
This article is all about what AI trends will emerge in the field of creative operations in 2024. All the marketers and brand builders should be aware of these trends for their further use and save themselves some time!
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
You’ll learn:
- The latest trends in AI and automation, and what this means for an evolving paid search ecosystem.
- New developments in privacy and data regulation.
- Emerging ad formats that are expected to make an impact next year.
Watch Sreekant Lanka from iQuanti and Irina Klein from OneMain Financial as they dive into the future of paid search and explore the trends, strategies, and technologies that will shape the search marketing landscape.
If you’re looking to assess your paid search strategy and design an industry-aligned plan for 2024, then this webinar is for you.
5 Public speaking tips from TED - Visualized summarySpeakerHub
From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Time Management & Productivity - Best PracticesVit Horky
Here's my presentation on by proven best practices how to manage your work time effectively and how to improve your productivity. It includes practical tips and how to use tools such as Slack, Google Apps, Hubspot, Google Calendar, Gmail and others.
The six step guide to practical project managementMindGenius
The six step guide to practical project management
If you think managing projects is too difficult, think again.
We’ve stripped back project management processes to the
basics – to make it quicker and easier, without sacrificing
the vital ingredients for success.
“If you’re looking for some real-world guidance, then The Six Step Guide to Practical Project Management will help.”
Dr Andrew Makar, Tactical Project Management
Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...Applitools
During this webinar, Anand Bagmar demonstrates how AI tools such as ChatGPT can be applied to various stages of the software development life cycle (SDLC) using an eCommerce application case study. Find the on-demand recording and more info at https://applitools.info/b59
Key takeaways:
• Learn how to use ChatGPT to add AI power to your testing and test automation
• Understand the limitations of the technology and where human expertise is crucial
• Gain insight into different AI-based tools
• Adopt AI-based tools to stay relevant and optimize work for developers and testers
* ChatGPT and OpenAI belong to OpenAI, L.L.C.
2. Problem Statement
Large number of devices which can take pictures and videos lead to an increase in uploaded
multimedia content
300 hours of video uploaded to YouTube each hour
3.25 billion hours of YouTube videos watched every month
By 2020, Cisco forecasts it would take 5 million years for a person to watch every online video
3. Difference between video topic and description
Recipe :
relevant
for video
Subscriber channels
- not related to video
subject
4. Same video type - different categories
Both cooking-related videos, yet appear in different categories
6. Findability Problem
Problem : content becomes less and less findable
How can we fix this?
Annotating the videos could improve findability
7. User annotation
2 problems :
● Small number of tags (average 9 per video)
● May contain irrelevant tags - to gain more views
Alternative : automatically annotate the videos
8. Solution : Automatic annotation
● Process video streams (Google Video Intelligence, Clarifai API)
● Process video subtitles (Alchemy API, Google Natural Language)
● Tools processing the same type of data - likely yield different results - COMBINE THEM
● Disadvantage : video tools only able to provide content information, text tools only able to
provide context information
● Best approach - Combine tools which process different video dimensions
10. Related work
● Concept detection in video relies
mostly on using low level image
attributes (e.g. color
histograms)-Lin et al, Chang et al
● Detecting concepts in subtitles -
used to assign categories to videos
(Katsiouli et al) or as basis for
finding other relevant entities
(Garcia et al)
● Crowdsourcing concepts -
encourage users to play games
and draw outlines of objects in
the video (Di Salvo et al) or
(Kavasidis et al)
Color histograms for similar images
11. Research Question
Main Research Question : How can we identify key topics in a video through processing
of the video stream and its textual description?
Two sub-research questions:
● How can we determine if certain concepts are more relevant than others ? (RQ1)
● How can we best align the concepts from the input sources(video stream and
transcript) ? (RQ2)
12. Dataset
● YouTube videos
● Of various types and lengths
● Aimed to select videos which do
not fit in more than one category
● In total 519 videos
13. Tools
● 1 subtitle processing tool - Google
Natural Language [1]
○ Outputs detected concepts in
the order of appearance
● 2 video processing tools - Clarifai[2]
and Google Video Intelligence[3]
○ We chose these tools because
the alternatives break down the
video into keyframes and
perform concept detection on
images, rather than video
Clarifai GVI
Output format JSON JSON
Tags per second yes no
Tags ordered
alphabetically
no yes
Occurrences of
same tags grouped
together
no yes
Confidence score
for tag
yes yes
‘Video relevant’
label for tags
no yes
[1] https://cloud.google.com/natural-language/
[2] https://www.clarifai.com/
[3] https://cloud.google.com/video-intelligence/
14. GVI Sample Output
TAG with single
occurence
Multiple occurrences of
same tag are grouped
Tag relevant at video
level
15. Clarifai Sample Output - list of vectors
Vector of seconds
Vector of
concepts for
each second
Vector of probabilitie
for each concept
16. After running the tools on the dataset...
● Clarifai - highest number of tags
● Subtitles - lowest number of tags
● Large amount of unique tags between the tools - thus overlap is low
18. Research Methodology
Tag Processing (for each topl separately)
Step 1 : calculate number of occurrences and
longest time interval of each tag
In Figure 6:
Black
● Number of occurrences = 1
● Longest time interval = 5 (last - first)
Classroom
● Number of occurrences = 3
● Longest time interval = 5 (last - first)
19. Research Methodology
Step 2 : Transform confidence scales so
the tag with highest confidence score
ends up having confidence = 1 (highest
confidence score becomes divisor)
a. Recalculate confidence for all
other tags
In the example to the right : text has the
highest confidence score - use that as
divisor
20. Research Methodology
Step 3 : calculate relevance score for each tag
Sum of confidence scores / video length in
seconds
Step 4 : combine tags from the three different
outputs
● Use average formula
● If tag detected by tool, use relevance, if not,
use 0
Combining tags from the three tools
21. Evaluation Goals
We have identified 4 evaluation goals
● Confirm our computations (EV1)
● Check for bias towards one of the tools (EV2)
● Check for any correlation between bias and video characteristics (EV3)
● Check if the automatic tools may have missed something (EV4)
Evaluate using crowdsourcing
22. Strategy for Selecting Videos to Evaluate
Choose a sample of videos which have high overlap between the 3 tools
Because it was concluded that shorter videos are more suitable for crowdsourcing (workers tend to lose focus
for longer videos) we decided to show 10 second segments of video
From the sample of videos - pick 10 second segments to evaluate
Pick those segments from each video in which highly relevant (as resulted after combining the outputs of the
tools) tags occur
In total, 2169 segments to be evaluated, from 213 videos
23. Selecting Tags to display
For each segment of video - compose a list of most relevant, maybe relevant and not so relevant tags from the tags for the
overall video
AT most 10 tags in each category
3 variables help to assign tags to categories:
1. Max relevance score for segment (MaxConf)
2. Tag’s relevance score (Rel)
3. A relevance threshold (Thresh - is 0.2 is MaxConf > 0.2 and is = 0.02 if MaxConf <=0.2)
Assign tags in categories:
1. If MaxConf - Thresh < Rel < MaxConf AND less than 10 tags in category => put tag in that category
2. Repeat until rule no longer holds or more than 10 tags in category
3. MaxConf = MaxConf - Thresh
4. Repeat until categories full or no more tags
24. Crowdsourcing Task
● Ask users to watch 10 seconds of video
● Users can then select tags related to the video from the list
● Users can add any other tags they think are relevant to the segment
● Each task is evaluated by 15 workers
● Each worker gets 2 cents for each completed task
● Workers cannot submit the results without watching all 10 seconds
26. Evaluation Results - EV1
At segment level, an average of 41.74% of highly relevant tags (as evaluated by the crowdsourcing
workers) were correctly detected by the algorithm
Maybe relevant tags - smallest overlap of all
Additional subtitle tags (not detected by any tool other than subtitles) have highest overlap - BUT we
counted each tag chosen by at least one worker in the same category (perhaps relevance is low ?)
27. Evaluation Results - EV1
At video level, an average of 46.19% of the tags which were evaluated as being highly relevant
by the workers were also detected by the algorithm as being highly relevant
Same as segment level, medium relevance tags have lowest overlap
Low relevance tags slightly higher overlap than high relevance tags - for very short videos, there
is higher overlap for highly relevant videos
28. Evaluation Results - EV2
● Clarifai - mainly low relevance tags
● Most high relevance tags were
detected by both visual processing
tools
● Bias towards choosing tags
detected by more than one tool
29. Evaluation Results - EV3
By assigning numerical values to the time
distribution and tag category, we were able to
calculate correlation with the help of the
corresponding Excel function.
Assignment: {100, 200, 300, 400} corresponds to
{under 3 min, 3-5 min., 5-10 min and 10-15 min}
{10, 20, 30, 40, 50} corresponds to {clarifai + gvi,
clarifai + gvi + sub, clarifai, gvi, sub}
High correlation score for cooking between time
distribution and processing tool.
Inexistent or not very strong correlation for the
other 4 categories (for nature there is a
correlation, but very light)
30. Evaluation Results - EV3
Using the same assignment as in
the previous slide for the tag
detection tools, we assigned
{1,2,3,4,5} to be the alias of
{cooking, culture, nature,
travel,other}
For all time distributions,
correlation factor is negative
No apparent correlation between
categories and detection tools in
any time distribution.
31. Evaluation Results - EV4
● Only about 20% additional tags found in out lists
● Most of them with low relevance
32. Evaluation Result - RQ1
● Identified a bias towards choosing tags detected by more than one tool
● These should be higher up in the list
● Better alignment strategy : instead of simple average, use a weighted average
● Assign higher weight to tags detected by more than one tool
33. Evaluation Result - RQ2
● Current alignment detects 46.19% of highly relevant tags for the sampled videos
(comparison between the highly relevant tags detected by our algorithm and the highly
relevant tags chosen by crowdsourcing workers)
● There is a percentage of tags detected as being of medium relevance which have been
promoted to high relevance after crowdsourcing
● Find a better relevance threshold
34. Evaluation result - RQ2
Examined users choice behaviour for each category (the other three categories on next slide) to see
whether combining tools results in more accurate results
● For each category, tags selected by GVI + Clarifai are chosen more often that either Clarifai or GVI
separately
● Adding subtitles does not make much of a difference (highest overlap score for highly relevant tags
happens for tags detected by GVI+Clarifai
● Subtitles have the least chosen amount of tags ( remember that subtitle tags included here are not
detected by any other tool)
35. Combining visual tools - better than
using them individually
Combining visual tags with subtitle -
better than using just subtitles
Linear increase in tags - as relevance
decreases - number of tags increases
36. Conclusion and Future Work
● Our alignment strategy correctly detects around 46% of relevant tags for sampled videos
● Wanted to find out whether combining tools would yield better results
○ Tags from GVI are chosen more often than Clarifai tags
○ Most tags for sampled videos come from GVI + Clarifai - more relevant
○ Adding subtitles to visual tags -better than using just subtitle tags
● Differences between video categories are not that many - can use them as one single
dataset
● Related work deals mostly with one source of information, whereas we deal with
information from 3 different sources
○ Also mostly concerned with aligning tags to parts of the video, whereas we tried to find tags
relevant to the whole video.
● Our algorithm can be improved
● Include crowdsourcing to identify better threshold, not just for confirmation
● Use weighted average as part of alignment
38. References
C. Y. Lin et al ‘VideoAL: A novel End-To-End MPEG-7 Video Automatic Labeling System’(2003)
Chang, S. F. and Ellis, D. and Jiang, W. and Lee, K. and Yanagawa, A. and Loui, A. C. and Luo, J :
‘Large-Scale Multimodal Semantic Concept Detection for Consumer Video ‘ (2007)
Katsiouli, P. and Tsetsos, V. and Hadjiefthymiades, S. : Semantic Video Classification Based on
Subtitles and Domain Terminologies (2007)
Garcia, J. L. R. and Vocht, L. and Troncy, R. and Mannens, E. and Van de Walle, R. : Describing and
contextualizing events in TV news shows (2014)
Di Salvo, R. and Giordano, D. and Kavasidi, I : A Crowdsourcing Approach to Support Video
Annotation (2014)
Kavasidis, I. and Palazzo, S. and Di Salvo, R. and Giordano, D. and Spampinato, C. : An innovative
web-based collaborative platform for video annotation (2013)