Affective priming (positive and negative emotion) is shown to significantly influence accuracy in visual judgment tasks for several common chart types.
This document discusses two-system models of decision making and presents results from experiments investigating the relationship between executive function and decisions in the Ultimatum Game. It finds that updating ability, but not switching or inhibition, is positively related to more rational decision making. However, this relationship is fragile and depends on factors like whether unfair feedback primes affective responses. Overall, the evidence for a connection between executive function and System 2 decision making is mixed, possibly due to limitations of the within-subject paradigm used.
The document discusses two-system models of decision making that propose an automatic, emotional System 1 and a controlled, rational System 2. It reports on several experiments that tested how executive function and working memory load affect decisions in the Ultimatum Game. The results provided little evidence that executive function is directly related to System 2 decision making. Relationships found were fragile and dependent on other factors like feedback received. Further research is needed to better understand how different cognitive systems interact in decision making.
Using RealTime fMRI Based Neurofeedback To Probe Default Network RegulationCameron Craddock
Talk given at the 63rd Annual Meeting of the American Academy of Child & Adolescent Psychiatry. Describes an experiment using realtime fMRI neurofeedback to probe participants ability to modulate default network regulation along with preliminary results.
Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Rec...Evgeny Frolov
This document proposes a new tensor factorization model called CoFFee to improve top-N recommendations by incorporating negative feedback from users. CoFFee treats ratings as ordinal rather than cardinal and is equally sensitive to positive and negative feedback. It provides more granular preferences for cold-start users with just one feedback. Standard recommendation evaluation metrics are biased towards positive effects, so new precision, recall, and loss metrics are introduced accounting for relevance thresholds. CoFFee outperforms standard matrix factorization on cold-start scenarios using both positive and negative signals.
The study used temporal order judgment (TOJ) tasks to directly measure how motivationally significant stimuli like faces capture attention. In Experiment 1, upright faces showed prior entry over inverted faces. Experiment 2 found emotional faces had greater prior entry than neutral faces. Experiment 3 ruled out low-level feature differences by finding prior entry for inverted emotional faces. Experiment 4 eliminated prior entry for inverted faces, supporting the role of holistic processing. Experiment 5 extended these effects to realistic faces. Experiment 6 addressed response biases. Across experiments, the study provides evidence that faces and emotional expressions capture attention through visual prior entry.
This document provides an overview of a workshop on root cause analysis using the Theory of Constraints thinking tools. It begins with a brief introduction to the Theory of Constraints and then demonstrates how to build a Current Reality Tree and Future Reality Tree to analyze a problem and proposed solution. The workshop includes practicing this process as a group activity and sharing insights. Key Theory of Constraints thinking tools are introduced for understanding problems and developing solutions, including Current Reality Trees, Future Reality Trees, and Evaporating Clouds.
Matching Attentional Draw with Utility in Interruptionjengluck
This research examines a design guideline that aims to increase the positive perception of interruptions. The guideline advocates matching the amount of attention attracted by an interruption’s notification method (attentional draw) to the utility of the interruption content. Our first experiment examined a set of 10 visual notification signals in terms of their detection times and established a set of three significantly different signals along the spectrum of attentional draw. Our second experiment investigated matching these different signals to interruption content with different levels of utility. Results indicate that the matching strategy decreases annoyance and increases perception of benefit compared to a strategy that uses the same signal regardless of interruption utility, with no significant impact on workload or performance. Design implications arising from the second experiment as well as recommendations for future work are discussed.
This document discusses two-system models of decision making and presents results from experiments investigating the relationship between executive function and decisions in the Ultimatum Game. It finds that updating ability, but not switching or inhibition, is positively related to more rational decision making. However, this relationship is fragile and depends on factors like whether unfair feedback primes affective responses. Overall, the evidence for a connection between executive function and System 2 decision making is mixed, possibly due to limitations of the within-subject paradigm used.
The document discusses two-system models of decision making that propose an automatic, emotional System 1 and a controlled, rational System 2. It reports on several experiments that tested how executive function and working memory load affect decisions in the Ultimatum Game. The results provided little evidence that executive function is directly related to System 2 decision making. Relationships found were fragile and dependent on other factors like feedback received. Further research is needed to better understand how different cognitive systems interact in decision making.
Using RealTime fMRI Based Neurofeedback To Probe Default Network RegulationCameron Craddock
Talk given at the 63rd Annual Meeting of the American Academy of Child & Adolescent Psychiatry. Describes an experiment using realtime fMRI neurofeedback to probe participants ability to modulate default network regulation along with preliminary results.
Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Rec...Evgeny Frolov
This document proposes a new tensor factorization model called CoFFee to improve top-N recommendations by incorporating negative feedback from users. CoFFee treats ratings as ordinal rather than cardinal and is equally sensitive to positive and negative feedback. It provides more granular preferences for cold-start users with just one feedback. Standard recommendation evaluation metrics are biased towards positive effects, so new precision, recall, and loss metrics are introduced accounting for relevance thresholds. CoFFee outperforms standard matrix factorization on cold-start scenarios using both positive and negative signals.
The study used temporal order judgment (TOJ) tasks to directly measure how motivationally significant stimuli like faces capture attention. In Experiment 1, upright faces showed prior entry over inverted faces. Experiment 2 found emotional faces had greater prior entry than neutral faces. Experiment 3 ruled out low-level feature differences by finding prior entry for inverted emotional faces. Experiment 4 eliminated prior entry for inverted faces, supporting the role of holistic processing. Experiment 5 extended these effects to realistic faces. Experiment 6 addressed response biases. Across experiments, the study provides evidence that faces and emotional expressions capture attention through visual prior entry.
This document provides an overview of a workshop on root cause analysis using the Theory of Constraints thinking tools. It begins with a brief introduction to the Theory of Constraints and then demonstrates how to build a Current Reality Tree and Future Reality Tree to analyze a problem and proposed solution. The workshop includes practicing this process as a group activity and sharing insights. Key Theory of Constraints thinking tools are introduced for understanding problems and developing solutions, including Current Reality Trees, Future Reality Trees, and Evaporating Clouds.
Matching Attentional Draw with Utility in Interruptionjengluck
This research examines a design guideline that aims to increase the positive perception of interruptions. The guideline advocates matching the amount of attention attracted by an interruption’s notification method (attentional draw) to the utility of the interruption content. Our first experiment examined a set of 10 visual notification signals in terms of their detection times and established a set of three significantly different signals along the spectrum of attentional draw. Our second experiment investigated matching these different signals to interruption content with different levels of utility. Results indicate that the matching strategy decreases annoyance and increases perception of benefit compared to a strategy that uses the same signal regardless of interruption utility, with no significant impact on workload or performance. Design implications arising from the second experiment as well as recommendations for future work are discussed.
Social proof is a cognitive bias where people determine what is correct by finding cues about what other people think is correct. If many people believe in something or engage in a behavior, others are more likely to accept it as well. This psychological phenomenon helps explain why trends and popular opinions can spread rapidly in a society through conformity with what others perceive as normal or popular.
Priming is a psychological phenomenon where exposure to a stimulus influences a person's response to a later stimulus, without their conscious awareness. Priming works because the first stimulus prepares the brain to identify and process related information from the second stimulus more quickly and efficiently. Research has shown that priming can influence people's behaviors, judgments, and decision making in subtle yet meaningful ways.
Video Priming – How to Give Your Acquisition Campaigns an Unfair AdvantageGrow.co
Mobile Apps Unlocked Vegas 2016
Thursday, May 5
Acquisition Breakout Session — 10:20am - 10:40am
Let’s get down to brass tacks: If you want more users then your acquisition costs will increase. This creates a shift from a low average cost to a potentially high marginal cost. Historically, there’s been no way around it. However, If we start to change our thinking from only focusing on today’s results to thinking about the users of tomorrow, then we can create scale without experiencing the same efficiency tradeoff. Our data has shown us that users exposed to rich, engaging content (such as video) makes them easier to acquire through other sources down the line. In this session, Fetch will share exclusive learnings from our data dashboard, Fetchme, and explain how to use data to prime behaviors in mobile video and TV to drive tomorrow’s users, while also hitting today’s targets.
Tim Villanueva, Head of Media Partnerships @ Fetch
Dan Wilson, Head of Data @ Fetch
This document discusses different types of priming, which is an implicit memory effect where exposure to a stimulus influences responses to a later stimulus. It describes verbal priming, where exposure to a word primes the reader to complete a word stem with the same word. It also discusses behavioral priming, where exposure to images or phrases associated with a stereotype can unconsciously elicit that behavior. Additionally, it mentions moral priming, where people are more likely to donate money if they feel they are being watched. Finally, it explains negative priming, where being primed with conflicting color words and responses can slow a person's reaction time.
- Anchoring is a cognitive bias where people rely too heavily on the first piece of information (the "anchor") when making decisions. Initial anchors shape subsequent judgments and estimates.
- Experiments show that arbitrary and irrelevant anchors influence people's judgments. For example, students were more likely to attend a free poetry reading depending on whether they were initially asked if they would pay $2 or be paid $2 to attend.
- Anchors are difficult to adjust from, even when the anchor is clearly irrelevant. This can lead people to make suboptimal decisions based on arbitrary initial values rather than objective information.
Long-term memory can be declarative (explicit) or non-declarative (implicit). Declarative memory involves remembering facts and events and can be impaired through amnesia. Non-declarative memory is implicit and involves skills and habits. Studies of amnesiac patients like H.M. showed they could improve skills through practice but not remember learning them, and priming effects could influence word recall but not conscious remembering. Effective learning strategies like elaboration and relating ideas aid deep understanding and memory retention, while forgetting serves an important function in managing memory overload.
This is a companion module that I use to help students understand the nature of priming and spread activation. The 'sample schema' created in this presentation was created using Inspiration.com software. This module can be viewed as a narrated movie off my website for the course:
http://courses.ncsu.edu/edp304/lec/002/
The document discusses memory in early childhood and provides definitions and types of memory, including explicit memory (episodic and semantic) and implicit memory (priming and procedural). It then outlines various techniques to improve early childhood memory, such as early musical training, mnemonics, engaging children in detailed conversations about past events, playing memory games, suggesting learning strategies, practicing repeatedly, and using rhymes, acronyms, and acrostics.
This document discusses priming theory and how entertainment media like TV shows can prime viewers' political perceptions. It presents research on how shows like "The West Wing" positively portrayed the US presidency while "Scandal" may negatively prime viewers' views of the president. The study hypothesized that viewers of "Scandal" would have more negative perceptions of Barack Obama than those watching "Full House." Students were surveyed before and after watching an episode of either show to test if perceptions changed. The results supported that "Scandal" primed more negative political views compared to the control show.
This document discusses different types of memory including short-term memory, long-term memory, procedural memory, priming memory, episodic memory, and semantic memory. It describes key aspects of memory such as encoding, storage, and retrieval. Different causes of memory loss are also outlined including alcohol blackout, dissociative fugue, Korsakoff's psychosis, post-traumatic amnesia, and repressed memory.
Memory works through encoding, storage, and retrieval according to an information processing model. The Atkinson-Shiffrin model proposes that information moves from sensory memory to short-term memory and then long-term memory, though more recent models recognize additional processing in working memory and some automatic processing into long-term memory. Memories can be formed through effortful, explicit processing or implicit, automatic processing. Encoding involves strategies like chunking, mnemonics, rehearsal, deep processing, and relating information to oneself. Memories are stored throughout the brain in overlapping neural networks rather than isolated locations. Explicit and implicit memories are processed in different brain areas, and emotions can strengthen memory formation through the amygdala. Retrieval is affected
1) The study analyzed a dataset of 30 students who took a math test before and after playing a video game. It investigated if playing an action video game improved math scores and if gender played a role.
2) Statistical analyses found a significant difference in math scores between those who played an action game versus a non-action game. Playing an action game was associated with higher scores.
3) Gender was also found to significantly impact math scores, with ANOVA and contrasts analyses showing differences between male and female scores. However, linear regression found no clear linear relationship between gender and scores.
The document summarizes a study that aimed to determine the characteristics of an effective smile through computer animated facial models and ratings from over 800 participants. Key findings were that smiles with moderate to high dental show were most effective, and effectiveness depended on the combination of smile angle and extent rather than single factors. The most effective smiles showed diversity in their representations. Surgeons should aim for effective combinations of angle and extent, such as medium angles around 12-17 degrees.
IRJET- Facial Emotion Detection using Convolutional Neural NetworkIRJET Journal
The document describes a study that aims to design a convolutional neural network (CNN) model to classify facial expressions in images into seven emotions (angry, disgust, fear, happy, sad, surprise, neutral) using deep learning techniques. The proposed CNN architecture contains five convolutional layers and five max pooling layers, along with two fully connected layers and a softmax output layer. The model is trained and evaluated on the FER-2013 dataset from Kaggle, achieving an accuracy of 65.34%. The goal of the research is to develop a model that can automatically and accurately detect emotions from facial expressions using CNNs.
Global credit risk cycles, lending standards, and limits to cross border risk...SYRTO Project
Global credit risk cycles, lending standards, and limits to cross border risk diversification. Bernd Schwaab, Siem Jan Koopman, André Lucas.
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
This study examines the difficulty of forging signatures by collecting data from surveys of semi-experts in biometrics. The surveys focused on specific aspects of signatures that forgers would consider when trying to replicate a signature. The results of the surveys were then analyzed to determine which features were most difficult for forgers to replicate. The goal was to better understand forgers' perceptions of difficulty before examining actual forged signatures.
This lesson discusses hypothesis testing using the Chi2 test to compare proportions between groups. The Chi2 test can be used for goodness-of-fit tests to compare observed data to expected proportions. It can also be used for tests of association to compare proportions between two or more factors. Examples are provided to demonstrate Chi2 tests for goodness-of-fit on coin toss and die rolling data, as well as a test of association on call center volume data.
This document describes a study that used convolutional neural networks (CNNs) to detect facial emotions and recommend movies based on the detected emotion. The researchers collected facial images from an online database and split them into training and test sets. They used a CNN model to extract features from the images and classify them into 7 emotion categories. The CNN achieved 49.4% accuracy on the test set after training for 100 epochs. The goal is to detect a user's emotion from their facial expression and recommend movies tailored to their detected mood.
This document summarizes a Kaggle competition to develop an algorithm for detecting facial keypoints like the eyes, nose, and mouth in images. The author trained an algorithm using a dataset of images labeled with keypoints, calculating average locations. Image patches around keypoints were extracted and averaged to create templates. The algorithm then compared templates to test images to predict keypoints. While achieving adequate results for the competition, the author notes this simple algorithm could not handle more complex real-world images and a more advanced approach would be needed.
Social proof is a cognitive bias where people determine what is correct by finding cues about what other people think is correct. If many people believe in something or engage in a behavior, others are more likely to accept it as well. This psychological phenomenon helps explain why trends and popular opinions can spread rapidly in a society through conformity with what others perceive as normal or popular.
Priming is a psychological phenomenon where exposure to a stimulus influences a person's response to a later stimulus, without their conscious awareness. Priming works because the first stimulus prepares the brain to identify and process related information from the second stimulus more quickly and efficiently. Research has shown that priming can influence people's behaviors, judgments, and decision making in subtle yet meaningful ways.
Video Priming – How to Give Your Acquisition Campaigns an Unfair AdvantageGrow.co
Mobile Apps Unlocked Vegas 2016
Thursday, May 5
Acquisition Breakout Session — 10:20am - 10:40am
Let’s get down to brass tacks: If you want more users then your acquisition costs will increase. This creates a shift from a low average cost to a potentially high marginal cost. Historically, there’s been no way around it. However, If we start to change our thinking from only focusing on today’s results to thinking about the users of tomorrow, then we can create scale without experiencing the same efficiency tradeoff. Our data has shown us that users exposed to rich, engaging content (such as video) makes them easier to acquire through other sources down the line. In this session, Fetch will share exclusive learnings from our data dashboard, Fetchme, and explain how to use data to prime behaviors in mobile video and TV to drive tomorrow’s users, while also hitting today’s targets.
Tim Villanueva, Head of Media Partnerships @ Fetch
Dan Wilson, Head of Data @ Fetch
This document discusses different types of priming, which is an implicit memory effect where exposure to a stimulus influences responses to a later stimulus. It describes verbal priming, where exposure to a word primes the reader to complete a word stem with the same word. It also discusses behavioral priming, where exposure to images or phrases associated with a stereotype can unconsciously elicit that behavior. Additionally, it mentions moral priming, where people are more likely to donate money if they feel they are being watched. Finally, it explains negative priming, where being primed with conflicting color words and responses can slow a person's reaction time.
- Anchoring is a cognitive bias where people rely too heavily on the first piece of information (the "anchor") when making decisions. Initial anchors shape subsequent judgments and estimates.
- Experiments show that arbitrary and irrelevant anchors influence people's judgments. For example, students were more likely to attend a free poetry reading depending on whether they were initially asked if they would pay $2 or be paid $2 to attend.
- Anchors are difficult to adjust from, even when the anchor is clearly irrelevant. This can lead people to make suboptimal decisions based on arbitrary initial values rather than objective information.
Long-term memory can be declarative (explicit) or non-declarative (implicit). Declarative memory involves remembering facts and events and can be impaired through amnesia. Non-declarative memory is implicit and involves skills and habits. Studies of amnesiac patients like H.M. showed they could improve skills through practice but not remember learning them, and priming effects could influence word recall but not conscious remembering. Effective learning strategies like elaboration and relating ideas aid deep understanding and memory retention, while forgetting serves an important function in managing memory overload.
This is a companion module that I use to help students understand the nature of priming and spread activation. The 'sample schema' created in this presentation was created using Inspiration.com software. This module can be viewed as a narrated movie off my website for the course:
http://courses.ncsu.edu/edp304/lec/002/
The document discusses memory in early childhood and provides definitions and types of memory, including explicit memory (episodic and semantic) and implicit memory (priming and procedural). It then outlines various techniques to improve early childhood memory, such as early musical training, mnemonics, engaging children in detailed conversations about past events, playing memory games, suggesting learning strategies, practicing repeatedly, and using rhymes, acronyms, and acrostics.
This document discusses priming theory and how entertainment media like TV shows can prime viewers' political perceptions. It presents research on how shows like "The West Wing" positively portrayed the US presidency while "Scandal" may negatively prime viewers' views of the president. The study hypothesized that viewers of "Scandal" would have more negative perceptions of Barack Obama than those watching "Full House." Students were surveyed before and after watching an episode of either show to test if perceptions changed. The results supported that "Scandal" primed more negative political views compared to the control show.
This document discusses different types of memory including short-term memory, long-term memory, procedural memory, priming memory, episodic memory, and semantic memory. It describes key aspects of memory such as encoding, storage, and retrieval. Different causes of memory loss are also outlined including alcohol blackout, dissociative fugue, Korsakoff's psychosis, post-traumatic amnesia, and repressed memory.
Memory works through encoding, storage, and retrieval according to an information processing model. The Atkinson-Shiffrin model proposes that information moves from sensory memory to short-term memory and then long-term memory, though more recent models recognize additional processing in working memory and some automatic processing into long-term memory. Memories can be formed through effortful, explicit processing or implicit, automatic processing. Encoding involves strategies like chunking, mnemonics, rehearsal, deep processing, and relating information to oneself. Memories are stored throughout the brain in overlapping neural networks rather than isolated locations. Explicit and implicit memories are processed in different brain areas, and emotions can strengthen memory formation through the amygdala. Retrieval is affected
1) The study analyzed a dataset of 30 students who took a math test before and after playing a video game. It investigated if playing an action video game improved math scores and if gender played a role.
2) Statistical analyses found a significant difference in math scores between those who played an action game versus a non-action game. Playing an action game was associated with higher scores.
3) Gender was also found to significantly impact math scores, with ANOVA and contrasts analyses showing differences between male and female scores. However, linear regression found no clear linear relationship between gender and scores.
The document summarizes a study that aimed to determine the characteristics of an effective smile through computer animated facial models and ratings from over 800 participants. Key findings were that smiles with moderate to high dental show were most effective, and effectiveness depended on the combination of smile angle and extent rather than single factors. The most effective smiles showed diversity in their representations. Surgeons should aim for effective combinations of angle and extent, such as medium angles around 12-17 degrees.
IRJET- Facial Emotion Detection using Convolutional Neural NetworkIRJET Journal
The document describes a study that aims to design a convolutional neural network (CNN) model to classify facial expressions in images into seven emotions (angry, disgust, fear, happy, sad, surprise, neutral) using deep learning techniques. The proposed CNN architecture contains five convolutional layers and five max pooling layers, along with two fully connected layers and a softmax output layer. The model is trained and evaluated on the FER-2013 dataset from Kaggle, achieving an accuracy of 65.34%. The goal of the research is to develop a model that can automatically and accurately detect emotions from facial expressions using CNNs.
Global credit risk cycles, lending standards, and limits to cross border risk...SYRTO Project
Global credit risk cycles, lending standards, and limits to cross border risk diversification. Bernd Schwaab, Siem Jan Koopman, André Lucas.
SYRTO Code Workshop
Workshop on Systemic Risk Policy Issues for SYRTO (Bundesbank-ECB-ESRB)
Head Office of Deustche Bundesbank, Guest House
Frankfurt am Main - July, 2 2014
This study examines the difficulty of forging signatures by collecting data from surveys of semi-experts in biometrics. The surveys focused on specific aspects of signatures that forgers would consider when trying to replicate a signature. The results of the surveys were then analyzed to determine which features were most difficult for forgers to replicate. The goal was to better understand forgers' perceptions of difficulty before examining actual forged signatures.
This lesson discusses hypothesis testing using the Chi2 test to compare proportions between groups. The Chi2 test can be used for goodness-of-fit tests to compare observed data to expected proportions. It can also be used for tests of association to compare proportions between two or more factors. Examples are provided to demonstrate Chi2 tests for goodness-of-fit on coin toss and die rolling data, as well as a test of association on call center volume data.
This document describes a study that used convolutional neural networks (CNNs) to detect facial emotions and recommend movies based on the detected emotion. The researchers collected facial images from an online database and split them into training and test sets. They used a CNN model to extract features from the images and classify them into 7 emotion categories. The CNN achieved 49.4% accuracy on the test set after training for 100 epochs. The goal is to detect a user's emotion from their facial expression and recommend movies tailored to their detected mood.
This document summarizes a Kaggle competition to develop an algorithm for detecting facial keypoints like the eyes, nose, and mouth in images. The author trained an algorithm using a dataset of images labeled with keypoints, calculating average locations. Image patches around keypoints were extracted and averaged to create templates. The algorithm then compared templates to test images to predict keypoints. While achieving adequate results for the competition, the author notes this simple algorithm could not handle more complex real-world images and a more advanced approach would be needed.
This presentation discusses the procedure involved in two-way mixed ANOVA design. The procedure has been discussed by solving a problem using SPSS functionality.
IRJET- Characteristics and Mood Prediction of Human by Signature and Facial E...IRJET Journal
This document discusses techniques for predicting human mood and behavior through analysis of signatures and facial expressions. It proposes using the Improved Susan method to recognize facial expressions based on mouth features extracted using edge detection. Eigenvector approach with principal component analysis is also used for facial expression recognition. Signature analysis examines features like pen pressure and alignment extracted from signatures to predict behavior using support vector machines and radial basis functions. The methods are tested on standard datasets and experimental results demonstrate their ability to accurately recognize different expressions and predict behavior.
The document discusses the steps for conducting a response surface methodology (RSM) experiment using central composite design (CCD). It involves determining independent and dependent variables, selecting an appropriate CCD, conducting the experiment runs according to the design, analyzing the data using statistical methods to develop a mathematical model and check its adequacy, and using the model to optimize responses. Key aspects of RSM and CCD covered include developing the design, analyzing results through ANOVA and regression, and checking model validity.
Hypothesis Testing: Central Tendency – Non-Normal (Compare 1:Standard)Matt Hansen
An extension on hypothesis testing, this lesson reviews the 1 Sample Sign & Wilcoxon tests as central tendency measurements for non-normal distributions.
Affective Metacognitive Scaffolding and User Model Augmentation for Experient...Adam Moore
The ImREAL project (http://www.imreal-project.eu) is researching how to meaningfully augment and extend existing experiential training simulators. The services developed support self-regulated, goal-, and application-oriented learning in adult training. We present results from a study evaluating a medical interview training simulator that has been augmented by an affective metacognitive scaffolding service and by user modelling exploiting social digital traces. Data from 152 medical students participating in this user trial were compared to the results of a prior trial on an earlier technology version. Findings show that students perceived the learning simulator positively and that the enhanced simulator led to increased feelings of success, less frustration, higher technical flow, and more reflection on learning. Interestingly, this cohort of users proved reluctant to provide their open social IDs to enrich their user models.
Unobtrusive sensors for measuring well-being at workWessel Kraaij
This document summarizes research on using unobtrusive sensors to measure well-being at work. It discusses two experiments - a controlled study that collected rich sensor data but low-frequency labels, and a pseudo real-life study that used selected sensors and collected high-frequency affect ratings. The research found that sensors can distinguish between neutral and stressful conditions. Facial expressions and posture were found to best predict mental effort and stress levels. However, performance was low when applying models to new users due to individual differences. Overall, the research demonstrated the potential of unobtrusive sensors for monitoring well-being at work.
The document discusses issues with current evaluation practices in machine learning and proposes ways to improve them. It notes that evaluation has not been a primary concern, unlike in other fields. Common performance measures like accuracy, precision, and ROC analysis each have shortcomings. Confidence estimation using t-tests can also be problematic if assumptions are not met. The document recommends borrowing evaluation measures from other disciplines, constructing new measures, and considering all evaluation steps carefully.
This is slides used at Arithmer seminar given by Dr. Masaaki Uesaka at Arithmer inc.
It is a summary of recent methods for quality assurance of machine learning model.
Arithmer Seminar is weekly held, where professionals from within our company give lectures on their respective expertise.
Arithmer株式会社は東京大学大学院数理科学研究科発の数学の会社です。私達は現代数学を応用して、様々な分野のソリューションに、新しい高度AIシステムを導入しています。AIをいかに上手に使って仕事を効率化するか、そして人々の役に立つ結果を生み出すのか、それを考えるのが私たちの仕事です。
Arithmer began at the University of Tokyo Graduate School of Mathematical Sciences. Today, our research of modern mathematics and AI systems has the capability of providing solutions when dealing with tough complex issues. At Arithmer we believe it is our job to realize the functions of AI through improving work efficiency and producing more useful results for society.
Hypothesis Testing: Central Tendency – Normal (Compare 2+ Factors)Matt Hansen
An extension on a series about hypothesis testing, this lesson reviews the ANOVA test as a central tendency measurement for normal distributions. It also explains what residuals and boxplots are and how to use them with the ANOVA test.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Nunit vs XUnit vs MSTest Differences Between These Unit Testing Frameworks.pdfflufftailshop
When it comes to unit testing in the .NET ecosystem, developers have a wide range of options available. Among the most popular choices are NUnit, XUnit, and MSTest. These unit testing frameworks provide essential tools and features to help ensure the quality and reliability of code. However, understanding the differences between these frameworks is crucial for selecting the most suitable one for your projects.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
5. Affect influences
creativity.
Affect alters how we decide
under uncertainty.
Affect modulates visual
perception.
(Lewis et al., 2011)
(Fredrickson, et al., 2005)
(Vuilleumier et al., 2007)
13. How do we induce Emotion?
Stories
Visual memory != verbal
memory
Commonly used in web-
based studies. (Goeritz., 2007)
(Shackman et al., 2006)
An excerpt...
14. No one could say for sure how long she would live, but
continued hospital care was clearly pointless. Nor could
she go home: she needed more attention than her
family could provide...
The problem was, she had no place to go. There was a
hospice facility near her house, but it would accept her
only if she would die within six days...
- Excerpt from Looking for a Place to Die,
Theresa Brown
15. How do we quantify Emotion?
How do we induce Emotion?
Stories
Visual memory != verbal
memory
Commonly used in web-
based studies. (Goeritz., 2007)
(Shackman et al., 2006)
(Lang et al., 2008, Lewis et al., 2011)
9-point
Self-Assessment
Manikin (SAM)
16. Pilot 2: how effective is the
priming?
Pilot 1: is the stimuli valid?
Full-study: all 8 chart types
Study components:
17. Pilot 1: Validate emotional
content of stories
Stories selected from the
New York Times
n = 40 on Mechanical Turk
SAM given only as post-test
Significant difference in emotion
(valence).
18. Pilot 2: Effectiveness of the
prime.
Purpose: does priming exposure
guarantee a performance impact?
n = 234 on Mechanical Turk
SAM given as pre- and post- test
Absolute values of SAM are
subjective, but change in SAM-
score indicates successful
priming*
19. Full-study: 8 chart types
Purpose: test whether affect influences
graphical perception.
Design:
- n = 963 on Mechanical Turk
- 1 random prime, 1 random chart
- 5 perception tasks per participant
- between subjects
Measures:
- performance: log-error
- subjective: 9-point SAM
20. Experiment procedure (Full study & Pilot 2)
Pre-Valence
Pre-Arousal
Post-Valence
Post-ArousalAccuracyVerification Question
Measure Emotion Random Priming
The patient was a fairly
young woman and she'd
had cancer for as long as
her youngest child had
been alive...
During this past year I've
had three instances of
car trouble: a blowout on
a freeway, a bunch of
blown fuses and an
out-of-gas situation...
A B
100
0
A
B
100
0
A B
100
0
A
B
A
B
tree
A
B
A
B
1000
A
B
Random Chart
V1
V2
V3
V4
V5
V6
V7
V8
Tasks
Which of the two (A
or B) is SMALLER?
What percentage is
the SMALLER of the
LARGER?
Measure Emotion
21. Full-study: 8 chart types
Cleanup:
- 299 removed for junk answers
- n = 664 total...
- n = 207 successfully primed
Analysis:
- log absolute errors
- 95% confidence-intervals
Two cases: by priming group (664)
and by SAM-change (207)...
22. Mean of all participants, regardless of final SAM
(n = 664):
0 1 2 3 4
Positively Primed
Negatively Primed
Means of All
Participants
No significant difference in error:
t(662) = 1.8318; p = .067
23. All participants, regardless of final SAM (n =
664):
A B
100
0
A
B
100
0
A B
100
0
A
B
A
B
tree
A
B
A
B
1000
A
B
V1
V2
V3
V4
V5
V6
V7
V8
0 1 2 3 4
Positively Primed
Negatively Primed
24. Means of primed participants (n = 207):
0 1 2 3 4
Positively Primed
Negatively Primed
Means of Primed
Participants
Significant difference in error:
t(205) = 3.1560; p = .0018
25. Primed participants (n = 207):
A B
100
0
A
B
100
0
A B
100
0
A
B
A
B
tree
A
B
A
B
1000
A
B
V1
V2
V3
V4
V5
V6
V7
V8
0 1 2 3 4
Positively Primed
Negatively Primed
>
26. Primed participants, (change in SAM):
A B
100
0
A
B
100
0
A B
100
0
A
B
A
B
tree
A
B
A
B
1000
A
B
V1
V2
V3
V4
V5
V6
V7
V8
0 1 2 3 4
Positively Primed
Negatively Primed
27. Expert Discussion:
Steven Franconeri, Northwestern
Positive moods can expand the
scope of the perceptual
spotlight of attention.
Encourage an observer to
process a larger spatial area of
the world in a single glance.
Negative or anxious moods can
constrict this spatial area.
(Eriksen & St. James, 1986)
(Gasper & Clore, 2002; Rowe et al., 2007)
(Eysenck & Calvo, 1992)
To summarize...
29. A B
100
0
A
B
100
0
A B
100
0
A
B
A
B
tree
A
B
A
B
1000
A
B
V1
V2
V3
V4
V5
V6
V7
V8
0 1 2 3 4
Positively Primed
Negatively Primed
Ltharri1@uncc.edu @LaneHarrison codementum.org
Thanks!
co-authors:
Drew Skau, Steven
Franconeri, Aidong Lu,
Remco Chang
sponsors:
National Science
Foundation,
Visual.ly
colleagues:
Evan Peck, Alvitta Ottley
Dan Afergan, Jordan Crouser