Talk given at Delft University speaker series on "Crowd Computing & Human-Centered AI" (https://www.academicfringe.org/). November 23, 2020. Covers two 2020 works:
(1) Anubrata Das, Brandon Dang, and Matthew Lease. Fast, Accurate, and Healthier: Interactive Blurring Helps Moderators Reduce Exposure to Harmful Content. In Proceedings of the 8th AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2020.
Alexander Braylan and Matthew Lease. Modeling and Aggregation of Complex Annotations via Annotation Distances. In Proceedings of the Web Conference, pages 1807--1818, 2020.
Recommender Systems and Misinformation: The Problem or the Solution?Alejandro Bellogin
Presentation at Workshop on Online Misinformation- and Harm-Aware Recommender Systems co-located with the 14th ACM Conference on Recommender Systems (RecSys 2020).
Deepfake detection is a critical and evolving field aimed at identifying and mitigating the risks associated with manipulated multimedia content created using artificial intelligence (AI) techniques. Deepfakes involve the use of advanced machine learning algorithms, particularly generative models like Generative Adversarial Networks (GANs), to create highly convincing fake videos, audio recordings, or images that can deceive viewers into believing they are genuine.
One prevalent approach to deepfake detection involves leveraging advancements in computer vision and pattern recognition. Researchers and developers employ sophisticated algorithms to analyze various visual and auditory cues that may indicate the presence of deepfake manipulation. For instance, anomalies in facial expressions, inconsistent lighting and shadows, or unnatural lip sync in videos can be indicative of deepfake content. Additionally, deepfake detectors may examine metadata, such as inconsistencies in timestamps or editing artifacts, to identify alterations in the content's authenticity.
Machine learning plays a central role in deepfake detection, with models being trained on diverse datasets that include both authentic and manipulated content. Supervised learning techniques involve training models on labeled datasets, enabling them to recognize patterns associated with deepfake manipulation. Researchers also explore unsupervised and semi-supervised learning methods, allowing detectors to identify anomalies without explicit labels for every training instance.
As the field progresses, deepfake detectors are increasingly adopting advanced neural network architectures to enhance their accuracy. Ensembling multiple models, each specialized in detecting specific types of manipulations, is another strategy employed to improve overall detection performance. Furthermore, the integration of explainable AI techniques enables better understanding of the detection process and provides insights into the features contributing to the decision-making process of the models.
Despite these advancements, deepfake detection remains a challenging task due to the constant evolution of deepfake generation techniques. Adversarial training, where detectors are trained on data that includes adversarial examples, is one method to improve robustness against sophisticated manipulation attempts. Continuous research efforts are required to stay ahead of emerging deepfake technologies and to develop detectors capable of identifying novel manipulation methods.
In conclusion, deepfake detection is a multidimensional challenge that requires a combination of computer vision, machine learning, and data analysis techniques. Researchers and practitioners are actively developing and refining methods to detect manipulated content by examining visual and auditory cues, leveraging machine learning models, and staying vigilant against evolving deepfake technologies. As the threat landscape evolves, ongoing innovati
1. Virtual reality is an artificial 3D environment that is created with software and presented to users in a way that makes them feel like they are experiencing a real environment.
2. Early VR systems included head mounted displays, tracking systems, and input devices. Modern VR uses these components along with powerful computers and sophisticated sensors.
3. VR has applications in many fields including gaming, product design, architecture, medicine, and more. However, challenges remain around creating realistic environments, avoiding health issues, and developing natural human interaction.
Depression Analysis from Social Media Data in Bangla Language using Long Shor...A. Hasib Uddin
Human emotions like depression are inner
sentiments of human beings which expose actual behaviors of a person. Analyzing and determining these
type of emotions from people’s social activities in virtual world can be very helpful to understand their
behaviors. Existing approaches may be useful for analyzing common sentiments, such as positive, negative
or neutral expressions. However, human emotions, such
as depression, are very critical and sometimes almost
impossible to analyze using these approaches. In this
work, we deployed Long Short Term Memory (LSTM)
Deep Recurrent Network for depression analysis on
Bangla social media data. We created a small dataset
of Bangla tweets and stratified it. In this paper, we
have shown the effects of hyper-parameter tuning and
how it can be helpful for depression analysis on a small
Bangla social media dataset. The result shows that 5
layered LSTM of size 128 with batch size 25, learning
rate 0.0001 over 20 epochs, the depression detection
accuracy is high for stratified dataset with repeated
sampling. This result will help psychologists and other
researchers to detect depression of individuals from
their social activities in virtual world and help them to
take necessary measures to prevent undesirable doings
resulted from depression.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
This document outlines an agenda for a presentation on ubiquitous computing and context-aware computing systems. The presentation will cover ubiquitous computing concepts like devices, research areas, and great moments in the field's history. It will then discuss context-aware computing systems, including definitions of context, real-world usage scenarios, and interest from industry and academia. Sensors that enable context awareness will also be discussed. The presentation will conclude with a Q&A section.
Recommender Systems and Misinformation: The Problem or the Solution?Alejandro Bellogin
Presentation at Workshop on Online Misinformation- and Harm-Aware Recommender Systems co-located with the 14th ACM Conference on Recommender Systems (RecSys 2020).
Deepfake detection is a critical and evolving field aimed at identifying and mitigating the risks associated with manipulated multimedia content created using artificial intelligence (AI) techniques. Deepfakes involve the use of advanced machine learning algorithms, particularly generative models like Generative Adversarial Networks (GANs), to create highly convincing fake videos, audio recordings, or images that can deceive viewers into believing they are genuine.
One prevalent approach to deepfake detection involves leveraging advancements in computer vision and pattern recognition. Researchers and developers employ sophisticated algorithms to analyze various visual and auditory cues that may indicate the presence of deepfake manipulation. For instance, anomalies in facial expressions, inconsistent lighting and shadows, or unnatural lip sync in videos can be indicative of deepfake content. Additionally, deepfake detectors may examine metadata, such as inconsistencies in timestamps or editing artifacts, to identify alterations in the content's authenticity.
Machine learning plays a central role in deepfake detection, with models being trained on diverse datasets that include both authentic and manipulated content. Supervised learning techniques involve training models on labeled datasets, enabling them to recognize patterns associated with deepfake manipulation. Researchers also explore unsupervised and semi-supervised learning methods, allowing detectors to identify anomalies without explicit labels for every training instance.
As the field progresses, deepfake detectors are increasingly adopting advanced neural network architectures to enhance their accuracy. Ensembling multiple models, each specialized in detecting specific types of manipulations, is another strategy employed to improve overall detection performance. Furthermore, the integration of explainable AI techniques enables better understanding of the detection process and provides insights into the features contributing to the decision-making process of the models.
Despite these advancements, deepfake detection remains a challenging task due to the constant evolution of deepfake generation techniques. Adversarial training, where detectors are trained on data that includes adversarial examples, is one method to improve robustness against sophisticated manipulation attempts. Continuous research efforts are required to stay ahead of emerging deepfake technologies and to develop detectors capable of identifying novel manipulation methods.
In conclusion, deepfake detection is a multidimensional challenge that requires a combination of computer vision, machine learning, and data analysis techniques. Researchers and practitioners are actively developing and refining methods to detect manipulated content by examining visual and auditory cues, leveraging machine learning models, and staying vigilant against evolving deepfake technologies. As the threat landscape evolves, ongoing innovati
1. Virtual reality is an artificial 3D environment that is created with software and presented to users in a way that makes them feel like they are experiencing a real environment.
2. Early VR systems included head mounted displays, tracking systems, and input devices. Modern VR uses these components along with powerful computers and sophisticated sensors.
3. VR has applications in many fields including gaming, product design, architecture, medicine, and more. However, challenges remain around creating realistic environments, avoiding health issues, and developing natural human interaction.
Depression Analysis from Social Media Data in Bangla Language using Long Shor...A. Hasib Uddin
Human emotions like depression are inner
sentiments of human beings which expose actual behaviors of a person. Analyzing and determining these
type of emotions from people’s social activities in virtual world can be very helpful to understand their
behaviors. Existing approaches may be useful for analyzing common sentiments, such as positive, negative
or neutral expressions. However, human emotions, such
as depression, are very critical and sometimes almost
impossible to analyze using these approaches. In this
work, we deployed Long Short Term Memory (LSTM)
Deep Recurrent Network for depression analysis on
Bangla social media data. We created a small dataset
of Bangla tweets and stratified it. In this paper, we
have shown the effects of hyper-parameter tuning and
how it can be helpful for depression analysis on a small
Bangla social media dataset. The result shows that 5
layered LSTM of size 128 with batch size 25, learning
rate 0.0001 over 20 epochs, the depression detection
accuracy is high for stratified dataset with repeated
sampling. This result will help psychologists and other
researchers to detect depression of individuals from
their social activities in virtual world and help them to
take necessary measures to prevent undesirable doings
resulted from depression.
This Presentation were Made By BugsBusters team from faculty of Computers and information, Helwan University - Egypt
IMPORTANT NOTE !!!
Do not view this online or it will not be compatible Download it to view videos and see original slides :))
This document outlines an agenda for a presentation on ubiquitous computing and context-aware computing systems. The presentation will cover ubiquitous computing concepts like devices, research areas, and great moments in the field's history. It will then discuss context-aware computing systems, including definitions of context, real-world usage scenarios, and interest from industry and academia. Sensors that enable context awareness will also be discussed. The presentation will conclude with a Q&A section.
Virtual reality (VR) uses computer-generated environments to simulate realistic experiences. VR headsets use displays and lenses to create stereoscopic 3D images that trick the brain into perceiving the virtual environment as reality. Special hardware like head-mounted displays, motion trackers, and controllers allow users to interact with and immerse themselves in virtual worlds. The history of VR began in the 1930s with science fiction concepts, and continued to evolve with early head-mounted displays in the 1960s. Today, VR is used across many industries like gaming, real estate, education, and more.
Word embeddings are a technique for converting words into vectors of numbers so that they can be processed by machine learning algorithms. Words with similar meanings are mapped to similar vectors in the vector space. There are two main types of word embedding models: count-based models that use co-occurrence statistics, and prediction-based models like CBOW and skip-gram neural networks that learn embeddings by predicting nearby words. Word embeddings allow words with similar contexts to have similar vector representations, and have applications such as document representation.
This document discusses cross-lingual information retrieval. It presents approaches for translating queries from other languages to the document language, including using online machine translation systems and developing a statistical machine translation system. It describes experiments on reranking translations to select the one most effective for retrieval and on adapting the reranking model to new languages. Results show the reranking approach improves over baselines and online translation systems. The document also explores document translation and query expansion techniques.
Artificial intelligence (AI) can be defined as machines that can mimic human intelligence and behavior. The document discusses different types of AI like robots, which are programmed to perform tasks, while AI can learn from human behavior. Tests for AI are also described, including the Turing Test which involves determining if a human or computer is behind different conversations. Examples of current AI technologies like drones, cars and IBM's Watson are provided. While AI is advancing, issues around job losses, costs and how AI may impact humanity are controversies that still need addressing as AI will continue growing in the future.
Microsoft Hololens is the technology that combines the VR with the real world. The company claims that this so-called computer over the head, HoloLens can process TBs of data per second which is insanely huge number. This technology has a lot many application which can not be explained simply as such.
Now, the time is not very far when the world will be more like the sci-fi movie.
Sentiment Analysis Using Hybrid Structure of Machine Learning AlgorithmsSangeeth Nagarajan
Sentiment Analysis is the process used to determine the attitude/ opinion/ emotion expressed by a person about a particular topic. The presentation dealt with general approach and different machine learning based classification alogorithms. The slides is based on the work "Sentiment analysis using Neuro-Fuzzy and Hidden Markov models of text" by Rustamov S , Mustafayev E and Clements M A.
The training content covers:
- Basics of Artificial Intelligence
- Penetration of AI in our daily lives
- Few examples and Use cases
- A brief on how future with AI looks like
This document discusses evaluating hypotheses and estimating hypothesis accuracy. It provides the following key points:
- The accuracy of a hypothesis estimated from a training set may be different from its true accuracy due to bias and variance. Testing the hypothesis on an independent test set provides an unbiased estimate.
- Given a hypothesis h that makes r errors on a test set of n examples, the sample error r/n provides an unbiased estimate of the true error. The variance of this estimate depends on r and n based on the binomial distribution.
- For large n, the binomial distribution can be approximated by the normal distribution. Confidence intervals for the true error can then be determined based on the sample error and standard deviation
Introduction to Artificial Intelligence and Machine Learning Emad Nabil
Ant colony optimization is an example of taking inspiration from nature for AI. It is inspired by how ants find the shortest path between their colony and a food source. Individual ants deposit pheromones along the paths they follow; other ants are more likely to follow a path with a stronger pheromone concentration and less likely to follow one with a weaker concentration, with the result that the shortest path is identified and reinforced through positive feedback over multiple ant trips between the colony and food source. This decentralized process was abstracted and applied to solve combinatorial optimization problems in computer science.
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
Artificial Intelligence: Challenges and OpportunitiesMarco Neves
1) The document discusses artificial intelligence (AI) and the challenges and opportunities it presents, focusing on cognitive machines.
2) It explores definitions of intelligence from thinkers like Einstein and Gardner, and discusses early concepts of AI from thinkers like Turing and McCarthy.
3) The document outlines some of the impacts of AI through technologies like the Internet of Things, big data, advanced algorithms, 5G networks, and quantum computing, and how these are changing industries like entertainment, transportation, e-commerce, social media, and hiring.
Artificial intelligence and first order logicparsa rafiq
The document discusses knowledge representation and first order logic. It defines knowledge representation as how knowledge is encoded in artificial systems. It discusses representing objects, events, performance, meta-knowledge and facts. It also discusses types of knowledge like meta knowledge, heuristic knowledge, procedural knowledge and declarative knowledge. The document then discusses first order logic syntax including logical symbols, terms, formulas, quantifiers and predicates. It also discusses semantics and the uses and history of first order logic.
Presentation by Mark Billinghurst on Collaborative Immersive Analytics at the BDVA conference on November 7th 2017. This talk provides an overview of the topic of Collaborative Immersive Analytics
Deep Reinforcement Learning from Human Preferencestaeseon ryu
심화 강화학습 시스템이 현실 세계 환경과 유용하게 상호작용하려면 복잡한 목표를 이 시스템에게 전달해야 합니다. 이 연구에서는 여러분이 결정한 경로 세그먼트들 사이의 복잡한 목표를 시스템에게 전달하는 방법을 탐구합니다. 이러한 방식으로 우리는 보상 함수에 대한 액세스 없이 Atari 게임 및 시뮬레이션 로봇 이동 등 복잡한 강화학습 과제를 효과적으로 해결할 수 있음을 보여줍니다. 이는 환경과 상호작용하는 에이전트의 인터랙션 중 1% 미만에 대한 피드백을 제공하면서 인간 감독 비용을 줄이는 것을 의미합니다. 이 방법의 유연성을 증명하기 위해, 논문은 약 1시간 동안 복잡한 새로운 행동을 성공적으로 훈련시킬 수 있었습니다.
Fuzzy logic is a flexible machine learning technique that mimics human thought by allowing intermediate values between true and false. It provides a mechanism for interpreting and executing commands based on approximate or uncertain reasoning. Unlike binary logic which can only have true or false values, fuzzy logic uses linguistic variables and degrees of membership to represent concepts that may have a partial truth. Fuzzy systems find applications in automatic control, prediction, diagnosis and user interfaces.
Ai and robotics: Past, Present and FutureHongmei He
Abstract: Artificial Intelligence (AI) has been a topic of research since the term was first coined by John McCarthy in 1956. In the last six decades, development of AI has experienced an uneven ride. Recently, the successful application of deep learning in Google AlphaGo triggered a wave of revolutionary advances in AI.
Robotics and AI have developed as inseparable twins. This presentation will briefly trace the history of the relationship between the two, survey various types of robots, and identify the contribution of AI to robot intelligence. In particular, we will consider the robot system architecture and how AI techniques are associated with its various capacities and functions.
Technology is replacing people in many jobs, but also creating new and better work and conditions in some cases. Scientists have estimated that machines could take 50% of our jobs in the next 30 years. Who will own the machines? Join me to explore the future challenges and issues of AI and robotics.
This document appears to be a presentation on sentiment analysis by M. Almenea and M. Albidah. It discusses the need for sentiment analysis to study emotions and opinions expressed in text. It also mentions that companies can use sentiment analysis to understand customer opinions without surveys. The presentation outlines the system design process for sentiment analysis and discusses some of the challenges, such as named entity recognition and language complexity.
Designing at the Intersection of HCI & AI: Misinformation & Crowdsourced Anno...Matthew Lease
This document summarizes a presentation about designing human-AI partnerships for fact-checking misinformation. It discusses using crowdsourced rationales to improve the accuracy and cost-efficiency of annotation tasks. It also addresses challenges in designing interfaces for automatic fact-checking models, such as integrating human knowledge and reasoning to correct errors and account for bias. The goal is to develop mixed-initiative systems where humans and AI can jointly reason and personalize fact-checking.
Virtual reality (VR) uses computer-generated environments to simulate realistic experiences. VR headsets use displays and lenses to create stereoscopic 3D images that trick the brain into perceiving the virtual environment as reality. Special hardware like head-mounted displays, motion trackers, and controllers allow users to interact with and immerse themselves in virtual worlds. The history of VR began in the 1930s with science fiction concepts, and continued to evolve with early head-mounted displays in the 1960s. Today, VR is used across many industries like gaming, real estate, education, and more.
Word embeddings are a technique for converting words into vectors of numbers so that they can be processed by machine learning algorithms. Words with similar meanings are mapped to similar vectors in the vector space. There are two main types of word embedding models: count-based models that use co-occurrence statistics, and prediction-based models like CBOW and skip-gram neural networks that learn embeddings by predicting nearby words. Word embeddings allow words with similar contexts to have similar vector representations, and have applications such as document representation.
This document discusses cross-lingual information retrieval. It presents approaches for translating queries from other languages to the document language, including using online machine translation systems and developing a statistical machine translation system. It describes experiments on reranking translations to select the one most effective for retrieval and on adapting the reranking model to new languages. Results show the reranking approach improves over baselines and online translation systems. The document also explores document translation and query expansion techniques.
Artificial intelligence (AI) can be defined as machines that can mimic human intelligence and behavior. The document discusses different types of AI like robots, which are programmed to perform tasks, while AI can learn from human behavior. Tests for AI are also described, including the Turing Test which involves determining if a human or computer is behind different conversations. Examples of current AI technologies like drones, cars and IBM's Watson are provided. While AI is advancing, issues around job losses, costs and how AI may impact humanity are controversies that still need addressing as AI will continue growing in the future.
Microsoft Hololens is the technology that combines the VR with the real world. The company claims that this so-called computer over the head, HoloLens can process TBs of data per second which is insanely huge number. This technology has a lot many application which can not be explained simply as such.
Now, the time is not very far when the world will be more like the sci-fi movie.
Sentiment Analysis Using Hybrid Structure of Machine Learning AlgorithmsSangeeth Nagarajan
Sentiment Analysis is the process used to determine the attitude/ opinion/ emotion expressed by a person about a particular topic. The presentation dealt with general approach and different machine learning based classification alogorithms. The slides is based on the work "Sentiment analysis using Neuro-Fuzzy and Hidden Markov models of text" by Rustamov S , Mustafayev E and Clements M A.
The training content covers:
- Basics of Artificial Intelligence
- Penetration of AI in our daily lives
- Few examples and Use cases
- A brief on how future with AI looks like
This document discusses evaluating hypotheses and estimating hypothesis accuracy. It provides the following key points:
- The accuracy of a hypothesis estimated from a training set may be different from its true accuracy due to bias and variance. Testing the hypothesis on an independent test set provides an unbiased estimate.
- Given a hypothesis h that makes r errors on a test set of n examples, the sample error r/n provides an unbiased estimate of the true error. The variance of this estimate depends on r and n based on the binomial distribution.
- For large n, the binomial distribution can be approximated by the normal distribution. Confidence intervals for the true error can then be determined based on the sample error and standard deviation
Introduction to Artificial Intelligence and Machine Learning Emad Nabil
Ant colony optimization is an example of taking inspiration from nature for AI. It is inspired by how ants find the shortest path between their colony and a food source. Individual ants deposit pheromones along the paths they follow; other ants are more likely to follow a path with a stronger pheromone concentration and less likely to follow one with a weaker concentration, with the result that the shortest path is identified and reinforced through positive feedback over multiple ant trips between the colony and food source. This decentralized process was abstracted and applied to solve combinatorial optimization problems in computer science.
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
Artificial Intelligence: Challenges and OpportunitiesMarco Neves
1) The document discusses artificial intelligence (AI) and the challenges and opportunities it presents, focusing on cognitive machines.
2) It explores definitions of intelligence from thinkers like Einstein and Gardner, and discusses early concepts of AI from thinkers like Turing and McCarthy.
3) The document outlines some of the impacts of AI through technologies like the Internet of Things, big data, advanced algorithms, 5G networks, and quantum computing, and how these are changing industries like entertainment, transportation, e-commerce, social media, and hiring.
Artificial intelligence and first order logicparsa rafiq
The document discusses knowledge representation and first order logic. It defines knowledge representation as how knowledge is encoded in artificial systems. It discusses representing objects, events, performance, meta-knowledge and facts. It also discusses types of knowledge like meta knowledge, heuristic knowledge, procedural knowledge and declarative knowledge. The document then discusses first order logic syntax including logical symbols, terms, formulas, quantifiers and predicates. It also discusses semantics and the uses and history of first order logic.
Presentation by Mark Billinghurst on Collaborative Immersive Analytics at the BDVA conference on November 7th 2017. This talk provides an overview of the topic of Collaborative Immersive Analytics
Deep Reinforcement Learning from Human Preferencestaeseon ryu
심화 강화학습 시스템이 현실 세계 환경과 유용하게 상호작용하려면 복잡한 목표를 이 시스템에게 전달해야 합니다. 이 연구에서는 여러분이 결정한 경로 세그먼트들 사이의 복잡한 목표를 시스템에게 전달하는 방법을 탐구합니다. 이러한 방식으로 우리는 보상 함수에 대한 액세스 없이 Atari 게임 및 시뮬레이션 로봇 이동 등 복잡한 강화학습 과제를 효과적으로 해결할 수 있음을 보여줍니다. 이는 환경과 상호작용하는 에이전트의 인터랙션 중 1% 미만에 대한 피드백을 제공하면서 인간 감독 비용을 줄이는 것을 의미합니다. 이 방법의 유연성을 증명하기 위해, 논문은 약 1시간 동안 복잡한 새로운 행동을 성공적으로 훈련시킬 수 있었습니다.
Fuzzy logic is a flexible machine learning technique that mimics human thought by allowing intermediate values between true and false. It provides a mechanism for interpreting and executing commands based on approximate or uncertain reasoning. Unlike binary logic which can only have true or false values, fuzzy logic uses linguistic variables and degrees of membership to represent concepts that may have a partial truth. Fuzzy systems find applications in automatic control, prediction, diagnosis and user interfaces.
Ai and robotics: Past, Present and FutureHongmei He
Abstract: Artificial Intelligence (AI) has been a topic of research since the term was first coined by John McCarthy in 1956. In the last six decades, development of AI has experienced an uneven ride. Recently, the successful application of deep learning in Google AlphaGo triggered a wave of revolutionary advances in AI.
Robotics and AI have developed as inseparable twins. This presentation will briefly trace the history of the relationship between the two, survey various types of robots, and identify the contribution of AI to robot intelligence. In particular, we will consider the robot system architecture and how AI techniques are associated with its various capacities and functions.
Technology is replacing people in many jobs, but also creating new and better work and conditions in some cases. Scientists have estimated that machines could take 50% of our jobs in the next 30 years. Who will own the machines? Join me to explore the future challenges and issues of AI and robotics.
This document appears to be a presentation on sentiment analysis by M. Almenea and M. Albidah. It discusses the need for sentiment analysis to study emotions and opinions expressed in text. It also mentions that companies can use sentiment analysis to understand customer opinions without surveys. The presentation outlines the system design process for sentiment analysis and discusses some of the challenges, such as named entity recognition and language complexity.
Designing at the Intersection of HCI & AI: Misinformation & Crowdsourced Anno...Matthew Lease
This document summarizes a presentation about designing human-AI partnerships for fact-checking misinformation. It discusses using crowdsourced rationales to improve the accuracy and cost-efficiency of annotation tasks. It also addresses challenges in designing interfaces for automatic fact-checking models, such as integrating human knowledge and reasoning to correct errors and account for bias. The goal is to develop mixed-initiative systems where humans and AI can jointly reason and personalize fact-checking.
TOO4TO Module 7 / Artificial Intelligence and Sustainability: Part 3TOO4TO
1) The document discusses future predictions about AI including how researchers imagine AI may develop over the next 10 years and some potential negative environmental and social impacts of AI solutions.
2) AI techniques commonly require large amounts of energy to run equipment and process data, which can produce substantial greenhouse gas emissions. Training a single AI system can emit over 110,000 kilograms of carbon dioxide.
3) If AI systems are trained on biased data, they can introduce unwanted biases that lead to discrimination against certain groups. Ensuring AI is developed and applied fairly is important for social well-being.
Presentation given at the Linguistic Data Consortium (LDC), University of Pennsylvania, April 2019. Based on presentations at the 6th ACM Collective Intelligence Conference, 2018 and the 6th AAAI Conference on Human Computation & Crowdsourcing (HCOMP), 2018. Blog post: https://blog.humancomputation.com/?p=9932.
The challenges of the Digital Age creates a sea of opportunities for technologists. Developing software transforms the economic, political, cultural, and social reality of countries.
On the one hand, a larger part of the population does not know the downside of IT, which does not decrease our great responsibility. On the other hand, technologists do not always know how to make ethical decisions in day-to-day systems development. There is also a long discussion about the role of technology in the sustainability of the planet: after all, when IT is good or bad?
This lecture is an introduction to ethics and sustainability aimed at technologists who want to learn how to position themselves as professionals in the face of so many challenges and opportunities of the 21st century.
Don't look at me that way! - Understanding User Attitudes Towards Data Glasse...EISLab
Data glasses do carry promising potential for hands-free interaction, but also raise various concerns amongst their potential users. In order to gain insights into the nature of those concerns, we investigate how potential usage scenarios are perceived by device users and their peers. We present results of a two-step approach: a focus group discussion with 7 participants, and a user study with 38 participants. In particular, we look into differences between the usage of data glasses and more established devices such as smart phones. We provide quantitative measures for scenario-related social acceptability and point out factors that can influence user attitudes. Based on our quantitative and qualitative results, we derive design implications that might support the development of head-worn devices and applications with an improved social acceptability.
Please cite this work as follows: M. Koelle, M. Kranz, A. Möller: Don't look at me that way! - Understanding User Attitudes Towards Data Glasses Usage. In: Proceedings of the 17th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI '15), Copenhagen, Denmark, 2015
This document provides an overview and planning considerations for a mobile learning project. It discusses defining mobile learning and understanding learner needs and behaviors. Key aspects to address in planning include objectives, audience, instructional strategies, content development, technical requirements, evaluation, challenges and opportunities. Testing, sustainability, and taking advantage of mobile capabilities are emphasized. Resources for mobile learning research and tools are also provided.
Violence Detection: Introducing a Machine Learning Based Novel Method Arindam Paul
Human Facial Expressions plays an important role for identifying human action or intention. Facial
expressions can represents any specific action of any person and the pattern of violent behavior of
any person strongly depends on the geographic region. Here we’ve designed an automated system
by using a Convolutional Neural Network which can detect weather a person have any intention to
commit any violence or not. Here we proposed a new method that can identify violence intensions or violent behavior of any person before executing violences more efficiently by using a very little data of facial expressions before executing violence or any violent tasks. Instead of using image features which is a time-consuming and faulty method we used an automated feature selector CNN (Convolutional Neural Network) model which can capture exact facial expressions for training and then can predict that target facial expressions more accurately. Here we used only the facial data of a specific geographic region which can represent the violent and before violence facial patterns of the people of whole region.
Explainable AI is not yet Understandable AIepsilon_tud
Keynote of Dr. Nava Tintarev at RCIS'2020. Decision-making at individual, business, and societal levels is influenced by online content. Filtering and ranking algorithms such as those used in recommender systems are used to support these decisions. However, it is often not clear to a user whether the advice given is suitable to be followed, e.g., whether it is correct, whether the right information was taken into account, or if the user’s best interests were taken into consideration. In other words, there is a large mismatch between the representation of the advice by the system versus the representation assumed by its users. This talk addresses why we (might) want to develop advice-giving systems that can explain themselves, and how we can assess whether we are successful in this endeavor. This talk will also describe some of the state-of-the-art in explanations in a number of domains (music, tweets, and news articles) that help link the mental models of systems and people. However, it is not enough to generate rich and complex explanations; more is required in order to understand and be understood. This entails among other factors decisions around which information to select to show to people, and how to present that information, often depending on the target users and contextual factors
Digital data is increasingly being used to track and analyze human activities like work, learning, and living. This document discusses how the "datafication" of these areas is redistributing responsibilities between humans and algorithms. It explores issues around accountability, control, and transparency when important decisions are made based on data. The author advocates developing new "literacies" to ensure data practices align with public interests and values, and calls for a posthuman perspective that sees humans and technology as deeply entangled.
The Global Learn Conference in March 2011 featured several presentations on 21st century skills, new media, and elearning:
1. A talk discussed the shift from manual to critical thinking skills in workplaces, and the increase of abstract tasks over routine manual jobs. 21st century skills like collaboration and problem solving are key.
2. A project aims to develop metrics for measuring 21st century skills like collaboration, problem solving, and networking. Games may help develop and assess collaborative skills.
3. Augmented reality is an emerging technology that combines virtual elements with the real world. It is predicted to be adopted within 2-3 years for educational uses.
4. A "no-fuss" model
This panel at CPDP 2020 discussed emotional AI and empathic technologies, focusing on rights, children, and domestication. The panelists were Ben Bland, Frederike Kaltheuner, Giovanna Mascheroni, and Gilad Rosner, moderated by Andrew McStay. They addressed issues such as how children interact with social robots, the liveliness ascribed to such technologies, and concerns about affect recognition capabilities.
The document describes a presentation on introducing a machine learning based method for violence detection. It discusses combining social theory, behavioral theory and statistical reports to build an image dataset for identifying patterns of violence. A convolutional neural network model is used for prediction and is evaluated on a small dataset with over 65% accuracy. The goal is to build an interpretable model for both explaining and predicting violence by analyzing how media portrayal influences behaviors.
Detection and Minimization Influence of Rumor in Social NetworkIRJET Journal
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Smart Data for you and me: Personalized and Actionable Physical Cyber Social ...Amit Sheth
Featured Keynote at Worldcomp'14, July 2014: http://www.world-academy-of-science.org/worldcomp14/ws/keynotes/keynote_sheth
Video of the talk at: http://youtu.be/2991W7OBLqU
Big Data has captured a lot of interest in industry, with the emphasis on the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity, and their applications to drive value for businesses. Recently, there is rapid growth in situations where a big data challenge relates to making individually relevant decisions. A key example is human health, fitness, and well-being. Consider for instance, understanding the reasons for and avoiding an asthma attack based on Big Data in the form of personal health signals (e.g., physiological data measured by devices/sensors or Internet of Things around humans, on the humans, and inside/within the humans), public health signals (information coming from the healthcare system such as hospital admissions), and population health signals (such as Tweets by people related to asthma occurrences and allergens, Web services providing pollen and smog information, etc.). However, no individual has the ability to process all these data without the help of appropriate technology, and each human has different set of relevant data!
In this talk, I will forward the concept of Smart Data that is realized by extracting value from Big Data, to benefit not just large companies but each individual. If I am an asthma patient, for all the data relevant to me with the four V-challenges, what I care about is simply, “How is my current health, and what is the risk of having an asthma attack in my personal situation, especially if that risk has changed?” As I will show, Smart Data that gives such personalized and actionable information will need to utilize metadata, use domain specific knowledge, employ semantics and intelligent processing, and go beyond traditional reliance on ML and NLP.
For harnessing volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration. For Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships, using them to better understand new cues in the data that capture rapidly evolving events and situations.
Smart Data applications in development at Kno.e.sis come from the domains of personalized health, energy, disaster response, and smart city. I will present examples from a couple of these.
University Public Driven Applications - Big Data and Organizational Design maria chiara pettenati
This document discusses improving access to and use of big data for university and public applications. It summarizes the discussions of a working group on this topic. The group examined current approaches to big data, potential future applications, and challenges. Recommendations focus on developing interdisciplinary education programs to train experts, providing open access to large datasets, and establishing frameworks and standards to support big data analysis. The goal is to leverage big data for addressing societal problems in areas like healthcare, transportation and the environment.
The document summarizes Joe McCarthy's presentation about his research on proactive displays, which aim to bridge online social networks and shared physical spaces. It provides a brief history of McCarthy's work in this area over multiple generations of proactive display systems. It then describes McCarthy's most recent project, the Context, Content & Community Collage, which uses a large display to share coworkers' social media content in a workplace setting to potentially foster greater community.
Here are the key steps for conducting a trade area analysis:
1. Define the trade area. Determine the geographic boundaries that encompass the majority (e.g. 75%+) of a store's customers based on factors like drive time, road networks, geographical barriers. Common trade areas are 5, 10, 15 minute drives.
2. Analyze demographic data. Obtain census data on population, income levels, age distribution, household types etc. within the trade area and compare to national averages. This provides insights into customer base.
3. Examine competitor analysis. Identify and locate any competing stores or brands within the trade area. Analyzing their strengths, weaknesses and customer value propositions helps determine opportunities.
UCL joint Institute of Education (London Knowledge Lab) & UCL Interaction Centre seminar, 20th April 2016. Replay: https://youtu.be/0t0IWvcO-Uo
Algorithmic Accountability & Learning Analytics
Simon Buckingham Shum
Connected Intelligence Centre, University of Technology Sydney
ABSTRACT. As algorithms pervade societal life, they are moving from the preserve of computer science to becoming the object of far wider academic and media attention. Many are now asking how the behaviour of algorithms can be made “accountable”. But why are they “opaque” and to whom? As this vital discussion unfolds in relation to Big Data in general, the Learning Analytics community must articulate what would count as meaningful questions and satisfactory answers in educational contexts. In this talk, I propose different lenses that we can bring to bear on a given learning analytics tool, to ask what it would mean for it to be accountable, and to whom. From a Human-Centred Informatics perspective, it turns out that algorithmic accountability may be the wrong focus.
BIO. Simon Buckingham Shum is Professor of Learning Informatics at the University of Technology Sydney, which he joined in August 2014 to direct the new Connected Intelligence Centre. Prior to that he was at The Open University’s Knowledge Media Institute 1995-2014. He brings a Human-Centred Informatics (HCI) approach to his work, with a background in Psychology (BSc, York), Ergonomics (MSc, London) and HCI (PhD, York) where he worked with Rank Xerox Cambridge EuroPARC on Design Rationale. He co-edited Visualizing Argumentation (2003) followed by Knowledge Cartography (2008, 2nd Edn. 2014), and with Al Selvin wrote Constructing Knowledge Art (2015). He is active in the emerging field of Learning Analytics and is a co-founder of the Society for Learning Analytics Research, Compendium Institute and Learning Emergence network.
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Power Grid Model
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Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
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1. Matt Lease
School of Information
The University of Texas at Austin
Adventures in Crowdsourcing :
Toward Safer Content Moderation & Better
Supporting Complex Annotation Tasks
1
Lab: ir.ischool.utexas.edu
@mattlease
Slides: slideshare.net/mattlease
2. Roadmap
• Context: UT Good Systems & iSchool
• Two parts to talk today
– Content Moderation
– Aggregating Complex Annotations
2
3. 3
Goal: Design a future of Artificial Intelligence (AI)
technologies to meet society’s needs and values.
.
http://goodsystems.utexas.edu
Good Systems: an 8-year, $10M
UT Austin Grand Challenge
4. “The place where people & technology meet”
~ Wobbrock et al., 2009
“iSchools” now exist at over 100 universities around the world
4
What’s an Information School?
5. Anubrata Das, Brandon Dang and Matthew Lease
School of Information
The University of Texas at Austin
Fast, Accurate, and Healthier:
Interactive Blurring Helps Moderators
Reduce Exposure to Harmful Content
5
Lab: ir.ischool.utexas.edu
@mattlease
Slides: slideshare.net/mattlease
6. Today’s Talk: Content Moderation
- Social media platforms are hubs of user generated content
- Some types of content are unacceptable or may cause harm
- pornography & nudity, depictions of violence, hate speech, mis/disinformation
- What is considered acceptable varies by platform and region
- Further issues of free speech & due process in content removal & remediation
- e.g., Moderate Globally, Impact Locally: The Global Impacts of Content Moderation (Yale, Nov. 2020)
6
Alon Halevy et al. "Preserving integrity in online social networks." arXiv preprint, September 25, 2020.
7. Scale of Content Moderation
7Paul M. Barrett. (2020). Who Moderates the Social Media Giants? A Call to End Outsourcing.
Facebook, Youtube
8. Can’t we just use AI?
• High cost of errors -> very high accuracy required
• Continually evolving content and moderation policies
– also regional variants, cultural issues, and adversarial attacks
• While AI systems are often advertised/perceived as fully-automated, in
practice, human labor is typically required and often hidden
– Gray and Suri (2019) “ghost work”, Ekbia and Nardi (2014) ”heteromation”,
Irani and Silberman (2013) “invisible work”
• Human moderators today: Facebook ~15K, Youtube ~10K
• No free lunch: human annotators still needed to create training data 8
9. Barr & Cabrera, ACM Queue 2006
9
“Software developers with innovative ideas for businesses
and technologies are constrained by the limits of artificial
intelligence… If software developers could programmatically
access and incorporate human intelligence into their
applications, a whole new class of innovative businesses
and applications would be possible. This is the goal of
Amazon Mechanical Turk… people are freer to innovate because
they can now imbue software with real human intelligence.”
11. Implication on Moderators
“The psychological effects of viewing harmful content is well
documented, with reports of moderators experiencing
posttraumatic stress disorder (PTSD) symptoms and other
mental health issues as a result of the disturbing content they
are exposed to.” (Cambridge Consultants, 2019)
11
“From my own interviews with more than 100 moderators… a
significant number [get PTSD]. And many other employees
develop long- lasting mental health symptoms that stop short
of full-blown PTSD, including depression, anxiety, and
insomnia.” (Casey Newton, 2020)
Volume quotas (akin to a call center) - “constant measurement
for accuracy is as pressurizing as a quota” (Dwoskin 2019)
Image Source: The Verge
12. The Great Irony
12
The sort of task we most want an algorithm to do
(emotionally disturbing) is what people are doing
because the algorithm isn’t good enough
13. BUT WHO PROTECTS THE
MODERATORS? (HCOMP 2018)
BRANDON DANG1, MARTIN J. RIEDL2, AND MATTHEW LEASE1
1School of Information & 2School of Journalism (both students contributed equally)
The University of Texas at Austin
AAAI HCOMP -&- ACM Collective Intelligence
July 2018, Zurich, Switzerland
14. Research Question
14
By revealing less of an image, can we reduce the emotional
labor of image moderation without compromising
moderator accuracy and efficiency?
15. Design and Demo
http://ir.ischool.utexas.edu/CM/demo/
15Dang, Brandon, Martin J. Riedl, and Matthew Lease. "But who protects the moderators? the case of crowdsourced image
moderation." arXiv preprint arXiv:1804.10999 (2018).
Code: https://github.com/budang/content-moderation
16. Exposure and Control
“shielding moderators from harm begins with giving them
more control of what they’re seeing and how they’re seeing it,
so just the existence of ...preferences helps” (Sullivan 2019)
16
“Scientifically, do we know how much [exposure] is too much?
The answer is no, we don’t... If there’s something that were to
keep me up at night... it’s that question”
(Facebook psychologist Chris Harrison)
“Finding the right balance between content reviewer well-
being and resiliency, quality, and productivity is very
challenging at the scale we operate in. We are continually
working to get this balance right.” (Facebook’s Carolyn
Glanville)
Source: https://images.fastcompany.net/image/upload/w_596,c_limit,q_auto:best,f_auto/wp-cms/uploads/2019/06/Quick-Settings.png
17. Exposure and Control
- Industry moving towards establishing best practices for providing control & tools
17
19. Exposure and Control
- Industry moving towards establishing best practices for providing control & tools
- Such interventions include greyscaling, muting videos, and blurring
- Not well understood how effective such practices are
- Google: Ramakrishnan and Karunakaran (HCOMP 2019) report grayscaling of
images and videos reduces harm. Also study static blurring.
19
21. Survey: Well-being and Usability
21
Usefulness04
Perceived usefulness and
perceived ease of use
(Davis 1989; Venkatesh and Davis 2000)
Emotional Exhaustion03
Slightly modified version of emotional
exhaustion scale
(Wharton 1993) (Cates and Howe 2015)
Positive and Negative
Affect02
7-point Likert scale what emotions they are
currently feeling (I-PANAS-SF)
(Thompson 2007)
Positive and Negative
Experience01
5-point Likert scale how often they experience
the following emotions: positive, negative,
good, bad, pleasant, unpleasant, etc. (SPANE)
(Diener et al, 2010)
22. Experiment
22
- Random sample of 60 synthetic & real images
across categories: 180 total images
- Divided into groups of 9, balanced over classes
- 20 HITs, Five workers/ HIT
- Workers restricted to a single HIT
- Adult content qualification, >98% approval rate
with 300+ submitted HITs
- $7.25/hour
23. Results
Performance
- Accuracy
- Time taken
- Effort*
- # Clicks
- # Mouse Movement
Well-being
- Worker comfort
- Experience
- Affect
- Emotional Exhaustion
- Usefulness
*Brandon Dang, Miles Hutson, & Matthew Lease. MmmTurkey: A Crowdsourcing Framework for Deploying Tasks
and Recording Worker Behavior on Amazon Mechanical Turk. HCOMP 2016. https://github.com/budang/turkey-lite
24. Speed and Accuracy is not Impacted in Interactive Blurring
24
Worker Accuracy Time
30. Increased mean positive affect with increasing level of blur
31
Positive and Negative Affect
31. Summary: Hover is the Champion for Adoption
32
B: Baseline, **p< 0.05, ***p< 0.005
- Slider and hover are both top performers
- Hover shows significantly low emotional exhaustion with comparatively high accuracy
- If key goal is to keep accuracy intact & reduce emotional impact, we recommend hover design
32. 33
Future Work03
• Qualitative Analysis
• Intelligent Unblurring
• Early warning for severity
Conclusion02
As opposed to static blurring that
decreases accuracy, Interactive
blurring, improves well-being without
sacrificing accuracy and speed
Contribution01
Proposed and extensively evaluated
intervention that improves moderator
well-being
33. Alex Braylan1
and Matthew Lease2
1
Dept. of Computer Science & 2
School of Information
The University of Texas at Austin
Modeling and Aggregation of Complex
Annotations via Annotation Distance
34
ml@utexas.edu
@mattlease
Slides: slideshare.net/mattlease
Encore: Dec 11 talk @NeurIPS Crowd Science Workshop (https://research.yandex.com/workshops/crowd/neurips-2020)
Code & Data: https://github.com/Praznat/annotationmodeling
34. Simple annotation & aggregation
• Classification
– sentiment analysis
– image categorization
• Ordinal rating
– product & movie reviews
– search relevance
• Multiple choice selection
– quizzes
Aggregation
• Crowdsourcing: quality control
• Experts: wisdom of crowds
• Goal: select best label available
for each item (no label fusion)
35
38. Caption this image:
When majority voting falls short
Problem: large label space, exact match doesn’t work!
39
A cat is
eating
The cat
eats
A beautiful
picture
39. What about complex annotations?
Ranked lists
Parse trees
A1: A cat is eating
A2: The cat eats
A3: A beautiful picture
Image captions
Range sequences
40
41. Aggregating Simple Labels
• Hundreds of papers
• Multiple benchmarking studies
• Rich body of Bayesian modeling
• General-purpose aggregation
models for simple labels don’t
support complex labels!
Dawid-Skene MACE
Hierarchical Dawid-Skene
Item Difficulty
Logistic Random Effects
Source:
Paun et al 2018
“Comparing bayesian
models of annotation”
42
42. Task-specific models
• Pros:
– Task specialization
maximizes accuracy
• Cons:
– Need new model for
every task
– Complicated, difficult
to formulate
Nguyen et al 2017 (Sequences)
Lin, Mausam, and Weld 2012 (Math)
43
43. Task-specific workflows
• Pros:
– Empower workers
for complex tasks
• Cons:
– Need new workflow
for every task
– Complicated, difficult
to formulate
Noronha et al 2011
(image analysis)
Lasecki et al 2012
(transcription)
44
44. Our goals
• We want aggregation for complex data types
– Build on ideas from simple label aggregation models
• We want to generalize across many labeling tasks
– Can we reduce problem to common simpler state space?
45
46. Key Insight
• Partial credit matching via task-specific distance function
– Encapsulate task-specific label features into requester distance function
– Model annotation distances rather than annotations
– Distance functions already exist for most tasks because people need
evaluation functions to compare predicted labels vs gold
47
47. Distance functions
48
Properties of distance functions
Non-negativity
Symmetry
Triangle inequality
Data Free Text Rankings
Example
evaluation fn
BLEU(x, y)
Example
distance fn
Non-negativity ✓ ✓
Symmetry ✓ ✓
Triangle
inequality
✓ ✓
48. Calculate distances
“a cat is eating” “cat is eating”
“a beautiful picture” “the cat eats”
49
• Example task: text annotation
• Example distance function:
string edit distance
49. Calculate distances
“a cat is eating” “cat is eating”
“a beautiful picture” “the cat eats”
0.05
0.1
0.1
50
• Example task: text annotation
• Example distance function:
string edit distance
50. Calculate distances
“a cat is eating” “cat is eating”
“a beautiful picture” “the cat eats”
0.8
0.82
0.05
0.1
0.1
51
0.82
• Example task: text annotation
• Example distance function:
string edit distance
51. A1: A cat is eating
A2: The cat eats
A3: A beautiful
picture
0.1 0.6
0.3
52
All tasks reduce to matrices of
annotation distances
52. How to aggregate given distances
• Local selection model
• Global selection model
• Combined
53
Current item
Other items
53. Local approach: Smallest Avg Distance
• For each item:
1. Compute average distance between
annotations for the item
2. Choose annotation with smallest
average distance
• Generalization of majority vote
• Independence between items
• Local approach does not model
annotator reliability
54
Current item
Other items
54. Global approach: Best Available User
• For each annotator:
– Score by average distance over full dataset
• For each item:
– Choose label by best-scoring annotator
• Fixed annotator reliability
• Global approach does not model how
well annotators did on specific items
55
Current item
Other items
55. Can we get best of both worlds?
• Want a method that combines:
– Best available user (global)
– Smallest avg distance (local)
• Should build on rich history of work on Bayesian annotation modeling
• Need a principled framework for modeling annotation distance matrices
weights
votes weighted voting
56
56. Multidimensional Annotation Scaling (MAS)
• Based on Multidimensional
Scaling (Kruskal & Wish 1978)
• Probabilistic model of multi-
item distance matrices
• “Hierarchical Bayesian”
– Additional learned parameters
represent crowd effects such as
worker reliability
A cat is
eating
The cat
eats
A beautiful
picture
58
65. Tasks & datasets
SYNTHETIC DATASETS
• Syntactic parse trees
– Distance function: evalb
• Ranked lists
– Distance function: Kendall’s tau
REAL DATASETS
• Biomedical text sequences
– Distance function: Span F1
• Urdu-English translations
– Distance function: GLEU
67
Nguyen et al 2017
Zaidan and Callison-Burch 2011
66. Methods
Baselines:
• Random User (RU): pick one label randomly
• ZenCrowd (ZC) (Demartini et al. 2012)
– Weighted voting based on exact match (rare!)
• Crowd Hidden Markov Model (CHMM) (Nguyen et al. 2017)
– Sequence annotation task only
Upper bound: Oracle (OR) (always picks best label)
• Even if 5 workers answer, limited by best answer any of them gave
68
67. Results
Task Metric RU ZC CHMM MAS Oracle
Translations GLEU 0.185 0.246
Sequences F1 0.561 0.827
Parses EVALB 0.812 0.939
Rankings 0.491 0.724
69
• Diverse complex label datasets
70. Results
Task Metric RU ZC CHMM MAS Oracle
Translations GLEU 0.185 0.188 - 0.217 0.246
Sequences F1 0.561 0.569 0.702 0.709 0.827
Parses EVALB 0.812 0.819 - 0.932 0.939
Rankings 0.491 0.495 - 0.710 0.724
72
• Diverse complex label datasets
• MAS aggregation is best way to get closer to ground truth with no
model alteration between datasets
71. Conclusion
• Goal: general-purpose probabilistic model to aggregate complex annotations
– Categorical-based methods insufficient
– Custom models difficult to design for new annotation types
• Solution: Model annotation distances via task-specific distance functions
– Transforms problem into general-purpose variable space
• Multi-dimensional Annotation Scaling (MAS)
– Allows unsupervised weighted voting with inferred annotator reliability
• Not covered in talk (see paper)
– Semi-supervised learning
– Partial credit 73
72. Ongoing work
• Generalization to more tasks (e.g., image bounding boxes & keypoints)
• Generalization to simple annotation tasks (”one ring to rule them all”)
• Support for multiple latent objects per item
• Merging annotations rather than selecting best one
– e.g. guessing weight of an ox
– MAS vs. non-embedding EM model, varying noise, fewer annotations, …
74
Code & Data: https://github.com/Praznat/annotationmodeling
A1: A cat is eating
A2: The cat eats
A3: A beautiful picture
73. Thank you!
75
Matt Lease (University of Texas at Austin)
Lab: ir.ischool.utexas.edu
@mattlease
Slides: slideshare.net/mattlease
We thank our many talented crowd workers for their contributions to our research!