Like Partying? Your Face Says It All. Predicting Place AMBIANCE From Profile Pictures
Miriam Redi, Daniele Quercia, Lindsay Graham, Samuel Gosling
paper http://arxiv.org/abs/1505.07522
Do the survey! http://bit.ly/prez2010
A multidimensional scaling (MDS) approach to Filipino Presidentiable Leadership Perception using a Presidentiable Leadership Similarity Survey (PLSS)
cf. city flows - A comparative visualization of bike sharing systemsTill Nagel
cf. city flows is a comparative visualization environment of urban bike mobility designed to help citizens casually analyze three bike-sharing systems in the context of a public exhibition space.
By Till Nagel and Christopher Pietsch.
Urban Complexity Lab, FH Potsdam
<a>http://uclab.fh-potsdam.de/</a>
This talk introduces the project and some of its goals and visualizations, and shows our design process in analyzing the data and designing the visualizations.
cf. city flows was exhibited at the Streams and Traces in November 2015 in Berlin. Find more information at http://streamsandtraces.com/
More information coming soon.
La ville sans urbanistes ? Urbanisme tactique en Amérique du Nord. Mémoire de...Rémi Crombez
Ce mémoire s’intéresse aux nouveaux activismes urbains qui sont apparus avec l’urbanisme tactique dans les villes d’Amérique du Nord. Les habitants interviennent directement sur leur environnement avec des aménagements légers, temporaires et flexibles. Ces interventions tactiques remettent en question la capacité de la planification urbaine traditionnelle à répondre aux attentes des habitants. Il se dessine alors une mutation du rôle de l’urbaniste vers une nouvelle forme de médiation urbaine qui n’invite plus seulement les citadins à donner leur avis mais à s’investir dans la conception et la réalisation d’un projet commun. Si les perspectives semblent enthousiasmantes, l’urbanisme tactique s’inscrit néanmoins dans un contexte de crise des ressources financières et d’économie urbaine néo-libérale. Son rôle moteur dans la gentrification des quartiers est d’or et déjà perceptible.
Do the survey! http://bit.ly/prez2010
A multidimensional scaling (MDS) approach to Filipino Presidentiable Leadership Perception using a Presidentiable Leadership Similarity Survey (PLSS)
cf. city flows - A comparative visualization of bike sharing systemsTill Nagel
cf. city flows is a comparative visualization environment of urban bike mobility designed to help citizens casually analyze three bike-sharing systems in the context of a public exhibition space.
By Till Nagel and Christopher Pietsch.
Urban Complexity Lab, FH Potsdam
<a>http://uclab.fh-potsdam.de/</a>
This talk introduces the project and some of its goals and visualizations, and shows our design process in analyzing the data and designing the visualizations.
cf. city flows was exhibited at the Streams and Traces in November 2015 in Berlin. Find more information at http://streamsandtraces.com/
More information coming soon.
La ville sans urbanistes ? Urbanisme tactique en Amérique du Nord. Mémoire de...Rémi Crombez
Ce mémoire s’intéresse aux nouveaux activismes urbains qui sont apparus avec l’urbanisme tactique dans les villes d’Amérique du Nord. Les habitants interviennent directement sur leur environnement avec des aménagements légers, temporaires et flexibles. Ces interventions tactiques remettent en question la capacité de la planification urbaine traditionnelle à répondre aux attentes des habitants. Il se dessine alors une mutation du rôle de l’urbaniste vers une nouvelle forme de médiation urbaine qui n’invite plus seulement les citadins à donner leur avis mais à s’investir dans la conception et la réalisation d’un projet commun. Si les perspectives semblent enthousiasmantes, l’urbanisme tactique s’inscrit néanmoins dans un contexte de crise des ressources financières et d’économie urbaine néo-libérale. Son rôle moteur dans la gentrification des quartiers est d’or et déjà perceptible.
Trend Makers and Trend Spotters in a Mobile ApplicationDaniele Quercia
WHO creates trends in a mobile sharing app? accidentals or influentials?
Answer: influentials DO exist, yet they are not few but many!
http://profzero.org/publications/trend13sha.pdf
Social computing broadly refers to supporting social behaviours using computational systems. In the last decade, the advent of Web 2.0 and its social networking services, wikis, blogs, and social bookmarking has revolutionised social computing, creating new online contexts within which people interact socially (social networking). With the pervasiveness of mobile devices and embedded sensors, we stand at the brink of another major revolution, where the boundary between online and offline social behaviours blurs, providing opportunities for (re)defining social conventions and contexts once again. But opportunities come with challenges: can middleware foster the engineering of social software? We identify three societal grand challenges that are likely to drive future research in social computing and elaborate on how the middleware community can help address them.
Auralist: Introducing Serendipity into Music RecommendationDaniele Quercia
Recommendation systems exist to help users discover content in a large body of items. An ideal recommendation system should mimic the actions of a trusted friend or expert, producing a personalised collection of recommendations that balance between the desired goals of accuracy, diversity, novelty and serendipity. We introduce the Auralist recommendation framework, a system that - in contrast to previous work - attempts to balance and improve all four factors simultaneously. Using a collection of novel algorithms inspired by principles of ‘serendipitous discovery’, we demonstrate a method of successfully injecting serendipity, novelty and diversity into recommendations whilst limiting the impact on accuracy. We evaluate Auralist quantitatively over a broad set of metrics and, with a user study on music recommendation, show that Auralist’s emphasis on serendipity indeed improves user satisfaction.
Recommending Social Events from Mobile Phone Location Data
A city offers thousands of social events a day, and it is difficult for dwellers to make choices. The combination of mobile phones and recommender systems can change the way one deals with such abundance. Mobile phones with positioning technology are now widely available, making it easy for people to broadcast their whereabouts; recommender systems can now identify patterns in people’s movements in order to, for example, recommend events. To do so, the system relies on having mobile users who share their attendance at a large number of social events: cold-start users, who have no location history, cannot receive recommendations. We set out to address the mobile cold-start problem by answering the following research question: how can social events be recommended to a cold-start user based only on his home location?
To answer this question, we carry out a study of the rela- tionship between preferences for social events and geography, the first of its kind in a large metropolitan area. We sample location estimations of one million mobile phone users in Greater Boston, combine the sample with social events in the same area, and infer the social events attended by 2,519 residents. Upon this data, we test a variety of algorithms for recommending social events. We find that the most effective algorithm recommends events that are popular among residents of an area. The least effective, instead, recommends events that are geographically close to the area. This last result has interesting implications for location-based services that emphasize recommending nearby events.
"FriendSensing: Recommending Friends Using Mobile Phones" - Talk for this RecSys paper
http://web.mit.edu/quercia/www/publications/friendSensing_short.pdf
"Sybil Attacks Against Mobile Users: Friends and Foes to the Rescue". Presentation at INFOCOM 2010 of this paper
http://eprints.ucl.ac.uk/18812/1/18812.pdf
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Trend Makers and Trend Spotters in a Mobile ApplicationDaniele Quercia
WHO creates trends in a mobile sharing app? accidentals or influentials?
Answer: influentials DO exist, yet they are not few but many!
http://profzero.org/publications/trend13sha.pdf
Social computing broadly refers to supporting social behaviours using computational systems. In the last decade, the advent of Web 2.0 and its social networking services, wikis, blogs, and social bookmarking has revolutionised social computing, creating new online contexts within which people interact socially (social networking). With the pervasiveness of mobile devices and embedded sensors, we stand at the brink of another major revolution, where the boundary between online and offline social behaviours blurs, providing opportunities for (re)defining social conventions and contexts once again. But opportunities come with challenges: can middleware foster the engineering of social software? We identify three societal grand challenges that are likely to drive future research in social computing and elaborate on how the middleware community can help address them.
Auralist: Introducing Serendipity into Music RecommendationDaniele Quercia
Recommendation systems exist to help users discover content in a large body of items. An ideal recommendation system should mimic the actions of a trusted friend or expert, producing a personalised collection of recommendations that balance between the desired goals of accuracy, diversity, novelty and serendipity. We introduce the Auralist recommendation framework, a system that - in contrast to previous work - attempts to balance and improve all four factors simultaneously. Using a collection of novel algorithms inspired by principles of ‘serendipitous discovery’, we demonstrate a method of successfully injecting serendipity, novelty and diversity into recommendations whilst limiting the impact on accuracy. We evaluate Auralist quantitatively over a broad set of metrics and, with a user study on music recommendation, show that Auralist’s emphasis on serendipity indeed improves user satisfaction.
Recommending Social Events from Mobile Phone Location Data
A city offers thousands of social events a day, and it is difficult for dwellers to make choices. The combination of mobile phones and recommender systems can change the way one deals with such abundance. Mobile phones with positioning technology are now widely available, making it easy for people to broadcast their whereabouts; recommender systems can now identify patterns in people’s movements in order to, for example, recommend events. To do so, the system relies on having mobile users who share their attendance at a large number of social events: cold-start users, who have no location history, cannot receive recommendations. We set out to address the mobile cold-start problem by answering the following research question: how can social events be recommended to a cold-start user based only on his home location?
To answer this question, we carry out a study of the rela- tionship between preferences for social events and geography, the first of its kind in a large metropolitan area. We sample location estimations of one million mobile phone users in Greater Boston, combine the sample with social events in the same area, and infer the social events attended by 2,519 residents. Upon this data, we test a variety of algorithms for recommending social events. We find that the most effective algorithm recommends events that are popular among residents of an area. The least effective, instead, recommends events that are geographically close to the area. This last result has interesting implications for location-based services that emphasize recommending nearby events.
"FriendSensing: Recommending Friends Using Mobile Phones" - Talk for this RecSys paper
http://web.mit.edu/quercia/www/publications/friendSensing_short.pdf
"Sybil Attacks Against Mobile Users: Friends and Foes to the Rescue". Presentation at INFOCOM 2010 of this paper
http://eprints.ucl.ac.uk/18812/1/18812.pdf
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
19. STEREOTYPES: Man and Machine Disagree
FEMALE, BRIGHTNESS
FEMALE
Romantic
Attractive
(Pick-Up)
Open-Minded BRIGHTNESS
RED
WARMER COLORS
BOTH GENDERS
PRESENCE OF CIRCLES
PLACES FEATURESFEATURES
ALGORITHM HUMAN
20. STEREOTYPES: Man and Machine Agree
OLDER, READING
GLASSES
Studying, Reading
FriendlySMILING
OLDER, READING
GLASSES
SMILING
StrangeYELLOW, NO FACE YELLOW
PLACES FEATURESFEATURES
ALGORITHM HUMAN
I am Miriam and I am a vision researcher. What happens when you put together a computational social scientist, 2 psychologists from UT Austin, a computer vision researcher? We thought of a machine vision system that can read face images under an new, unexpected point of view.
Computer vision researchers, we love to put the sentence &quot;an image is worth a thousand words&quot; pretty much everywhere in our presentations.
But what we often forget to say is that, when a picture is a portrait of a person, that face image is worth an entire book of words.
A face image is a book telling the biography of the person depicted, his history, his personality traits, his emotions. Computer vision systems have been devloped in the recent years to automatically infer some aspects of this biography, such as the identity for example. But in this work, we go beyond this intuitive dimensions, building an algorithm able to estimate the AMBIANCE of the place where people go based on their profile pictures.
So for example, our algorithm could see a group of profile pictures and say: ‘oh those guys might like geek places, or party places’
We are inspired by a study that was presented four years ago by our co-authors at this same conference.
In this study, they selected 49 FS venues in Austin, and asked a group of students to physically go to each of those places and rate their AMBIANCEs, according to 72 AMBIANCE types such as ‘party’ or creative. They then collected from FS the profile pictures of 25 users that frequently go to those venues. They showed these profile images to another team of students, and asked them to guess the preferences of the subjects in the pictures. And, by correlating such face-driven scores with the on-the-spot ratings, they showed that actually the students were able to correctly assess the AMBIANCE of the place where people would go, by just looking at their profile pictures.
We are inspired by a study that was presented four years ago by our co-authors at this same conference.
In this study, they selected 49 FS venues in Austin, and asked a group of students to physically go to each of those places and rate their AMBIANCEs, according to 72 AMBIANCE types such as ‘party’ or creative. They then collected from FS the profile pictures of 25 users that frequently go to those venues. They showed these profile images to another team of students, and asked them to guess the preferences of the subjects in the pictures. And, by correlating such face-driven scores with the on-the-spot ratings, they showed that actually the students were able to correctly assess the AMBIANCE of the place where people would go, by just looking at their profile pictures.
The natural question that came to our mind when looking at this study was: can we use computer vision techniques to build a machine that is able to do the same as the students were doing in this study, and automatically guess the AMBIANCE preferences given the profile pictures of patrons. And if such a machine existed, what are the visual attributes that the machine evaluates when predicting the AMBIANCE of a place, and how does it differ from humans when performing this task?
Let’s go step by step. To build such a system, we need a learning algorithm that is able to associate AMBIANCE ratings with attributes of the profile pictures. To do so, we resort to the dataset collected by our co-authors in their study. For each of the 49 FS venues, we get 25 profile pictures and we compute a set of visual features. Visual features, based on signal processing techniques can tell us (and the machine) something about the properties of the picture and its subject. We then associate a set of AMBIANCE labels to each place. These AMBIANCE labels correspond to the on-the-spot AMBIANCE ratings that the students gave when visitng the venues. We then feed a regression algorithm with both features and ratings, and make it learn how to associate these 2 dimensions.
Let’s take a look at the visual features we design for AMBIANCE prediction.
The first group of visual features is inspired by our previous work in compuational aesthetic. Aesthetic features tell us something about the image composition and its quality. For example, we extract some brightness, contrast and saturation metrics, some structural information such as the symmetry or the number of circles, - here we have ... one.
We then look at the color distribution, since visual perception theory tells us that image colors relate to some specific feelings or atmosphere depicted in the scene. For example, yellow is associated with cheerfulness
We then use computer vision to understand the facial expressions of a subject. So that we can know whether the subject is sad, happy, or whether she is having a sugar shock for example!
Based Face ++ Face analysis software we can record the demographic traits of the subject, age gender and race. For example, this is a picture of a white mail when he was still a young researcher ;)
Lastly, in this work we are not dealing with simple portraits. Ours are profile pictures from online social networks, and we want to understand the appearance choices that users make when presenting themselves to the online society thorough their profile pictures. We look at the presence of glasses, whether the image shows a real face or not, the position/orientation of the face, and we compute a uniqueness score based on spectral analysis, that can tell us how original the image composition is.
Now we build an algorithm that combines these features in a variety of ways. What is itsaccuracy?
And it turns out that it works! When we saw the framework&apos;s performances we were actually surprised! This plot shows the MSE between the real AMBIANCE scores and the AMBIANCE scores predicted by the machine in a leave-one-out fashion. The error is always lower than 12%! Sometimes, the machine finds it harder to guess the AMBIANCE of places, especially for AMBIANCEs such as creative and party, where the corresponding profile pictures are very diverse, introducing more noise in the system.
We then compared the scores given by our system when “looking” at the pictures versus the scores given by the students when looking at the pictures, and, we see that, for 10 out of 18 AMBIANCEs, our algorithm is better than the students in guessing AMBIANCE preferences!
How is this possible! Our intuition is that the machine directly learns how to associate faces with AMBIANCEs, without any cultural mediation. On the other hand, humans when looking at faces necessarily attach values influenced by their cultural background and stereotypes.
And indeed, when we look at the importance of features that machines and humans evaluate when performing this task, we find that, on one hand, the algorithm tends to look more at signal statistics and structural element of the picture, while humans are much more influenced by color attributes and demographic traits.
Also, the machine at times is able to overcome basic human gender stereotypes. In the study the students would associate the presence of women with romantic and pick-up places. However, the algorithm simply says that people going to romantic places tend to put warm colors in their pictures, and that you can find both genders in pick-up places, which makes sense.
Sometimes, humans and machine agree in their stereotpyes. As expected, friendly AMBIANCEs are associated with people that smile. Patrons of strange places seem to choose yellow to represent themselves, and, at least for the machine, they don&apos;t want to show their faces too much. And finally, nerds going to reading places like to wear glasses, and the machine confirms it!
faces tell us stories. And we have some powerful machine vision techniques to read those stories. We made the choice of selecting features that are interpretable [PAUSE]. That’s the only way to counter algorithmic stereotyping.
But one of the most take-home messages from this study is that Visual features can help us understanding not only what faces say, but also how humans perceive them. Visual features are glass-boxes through which we are able to detect stereotypes. And if we know which visual patterns relate to sterotypes, we might also know how to modify such patterns, encourage relativism and discourage stereotyping.