1) Mangaki is a French startup that uses algorithms to recommend Japanese manga and anime works based on user preferences. Their goal is to help users discover new works from Japanese culture.
2) Mangaki uses collaborative filtering and matrix completion techniques to estimate a user's preferences based on their ratings. They represent users and items in a shared vector space to find similar users and recommend items close to a user.
3) Mangaki is developing preference elicitation techniques to ask new users a minimal set of questions to estimate their preferences. They are exploring using determinantal point processes to sample a diverse set of items for users to rate to efficiently build their preference profile.
Snap!, a powerful tool for studying algorithms in high schoolNathalie Carrié
Long talk given at the first Snap! Conference .
SnapCon19, Heidelberg, September 22 - 25, 2019
https://www.didaktik-aktuell.de/index.php/snapcon19
My Snap! projects: https://snap.berkeley.edu/user?user=nathalierun
This work is delivered under the Creative Common License:
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
https://creativecommons.org/licenses/by-sa/4.0/
Rutgers Invited Talk: Creative Expression to Motivate Interest in ComputingMark Guzdial
Efforts in the US to promote learning about computer science and computational thinking emphasize the vocational benefits. Research on end-user programming suggests that for every professional software developer in the United States, there are four more professionals who program as part of doing their job. Efforts in other countries (UK, Denmark, New Zealand) instead emphasize the value of computing as a rigorous discipline providing insight into our world. We offer a third motivation: computing as a powerful medium for creative expression. We have used computational media to motivate children to study computing, to go beyond thinking about “geeks” in computing. We use media computation to encourage teachers and introductory students at college. The approach draws in a different audience than we normally get in computer science The BS in Computational Media at Georgia Tech is the most gender-balanced, ABET-accredited undergraduate computing degree in the United States. We use these examples to paint a picture of using creative expression to motivate interest in computing.
Snap!, a powerful tool for studying algorithms in high schoolNathalie Carrié
Long talk given at the first Snap! Conference .
SnapCon19, Heidelberg, September 22 - 25, 2019
https://www.didaktik-aktuell.de/index.php/snapcon19
My Snap! projects: https://snap.berkeley.edu/user?user=nathalierun
This work is delivered under the Creative Common License:
Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
https://creativecommons.org/licenses/by-sa/4.0/
Rutgers Invited Talk: Creative Expression to Motivate Interest in ComputingMark Guzdial
Efforts in the US to promote learning about computer science and computational thinking emphasize the vocational benefits. Research on end-user programming suggests that for every professional software developer in the United States, there are four more professionals who program as part of doing their job. Efforts in other countries (UK, Denmark, New Zealand) instead emphasize the value of computing as a rigorous discipline providing insight into our world. We offer a third motivation: computing as a powerful medium for creative expression. We have used computational media to motivate children to study computing, to go beyond thinking about “geeks” in computing. We use media computation to encourage teachers and introductory students at college. The approach draws in a different audience than we normally get in computer science The BS in Computational Media at Georgia Tech is the most gender-balanced, ABET-accredited undergraduate computing degree in the United States. We use these examples to paint a picture of using creative expression to motivate interest in computing.
Multi Task DPP for Basket Completion by Romain WARLOP, Fifty Fiverecsysfr
Determinantal point processes (DPPs) have received significant attention in the recent years as an elegant model for a variety of machine learning tasks, due to their ability to elegantly model set diversity and item quality or popularity. Recent work has shown that DPPs can be effective models for product recommendation and basket completion tasks. We present an enhanced DPP model that is specialized for the task of basket completion, the multi-task DPP. We view the basket completion problem as a multi-class classification problem, and leverage ideas from tensor factorization and multi-class classification to design the multi-task DPP model. We evaluate our model on several real-world datasets, and find that the multi-task DPP provides significantly better predictive quality than a number of state-of-the-art models.
Building a recommender system with Annoy and Word2Vec by Cristian PEREZ, Kern...recsysfr
The Kernix Lab will talk about the development of a recommender engine at the RecSys MeetUp. We will discuss both strategic and technical considerations for a production ready system. Technically, how we handle cold start, misspelled words and content high renewal rates will be shared.
Artem Kozhevnikov, lead Data Scientist, will present some quality metrics commonly followed @ tinyclues in order to evaluate the model predictive power. Those metrics are going beyond well known technical metrics like AUC or RMSE and seem to be important in the context of CRM campaigns targeting.
Highlights on most interesting RecSys papers - Elena Smirnova, Lowik Chanusso...recsysfr
RecSys conference was held in Como at the end of August. We will summarize for you the most trendy techniques and results presented at this conference.
APNIC Foundation, presented by Ellisha Heppner at the PNG DNS Forum 2024APNIC
Ellisha Heppner, Grant Management Lead, presented an update on APNIC Foundation to the PNG DNS Forum held from 6 to 10 May, 2024 in Port Moresby, Papua New Guinea.
This 7-second Brain Wave Ritual Attracts Money To You.!nirahealhty
Discover the power of a simple 7-second brain wave ritual that can attract wealth and abundance into your life. By tapping into specific brain frequencies, this technique helps you manifest financial success effortlessly. Ready to transform your financial future? Try this powerful ritual and start attracting money today!
Multi-cluster Kubernetes Networking- Patterns, Projects and GuidelinesSanjeev Rampal
Talk presented at Kubernetes Community Day, New York, May 2024.
Technical summary of Multi-Cluster Kubernetes Networking architectures with focus on 4 key topics.
1) Key patterns for Multi-cluster architectures
2) Architectural comparison of several OSS/ CNCF projects to address these patterns
3) Evolution trends for the APIs of these projects
4) Some design recommendations & guidelines for adopting/ deploying these solutions.
# Internet Security: Safeguarding Your Digital World
In the contemporary digital age, the internet is a cornerstone of our daily lives. It connects us to vast amounts of information, provides platforms for communication, enables commerce, and offers endless entertainment. However, with these conveniences come significant security challenges. Internet security is essential to protect our digital identities, sensitive data, and overall online experience. This comprehensive guide explores the multifaceted world of internet security, providing insights into its importance, common threats, and effective strategies to safeguard your digital world.
## Understanding Internet Security
Internet security encompasses the measures and protocols used to protect information, devices, and networks from unauthorized access, attacks, and damage. It involves a wide range of practices designed to safeguard data confidentiality, integrity, and availability. Effective internet security is crucial for individuals, businesses, and governments alike, as cyber threats continue to evolve in complexity and scale.
### Key Components of Internet Security
1. **Confidentiality**: Ensuring that information is accessible only to those authorized to access it.
2. **Integrity**: Protecting information from being altered or tampered with by unauthorized parties.
3. **Availability**: Ensuring that authorized users have reliable access to information and resources when needed.
## Common Internet Security Threats
Cyber threats are numerous and constantly evolving. Understanding these threats is the first step in protecting against them. Some of the most common internet security threats include:
### Malware
Malware, or malicious software, is designed to harm, exploit, or otherwise compromise a device, network, or service. Common types of malware include:
- **Viruses**: Programs that attach themselves to legitimate software and replicate, spreading to other programs and files.
- **Worms**: Standalone malware that replicates itself to spread to other computers.
- **Trojan Horses**: Malicious software disguised as legitimate software.
- **Ransomware**: Malware that encrypts a user's files and demands a ransom for the decryption key.
- **Spyware**: Software that secretly monitors and collects user information.
### Phishing
Phishing is a social engineering attack that aims to steal sensitive information such as usernames, passwords, and credit card details. Attackers often masquerade as trusted entities in email or other communication channels, tricking victims into providing their information.
### Man-in-the-Middle (MitM) Attacks
MitM attacks occur when an attacker intercepts and potentially alters communication between two parties without their knowledge. This can lead to the unauthorized acquisition of sensitive information.
### Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) Attacks
Bridging the Digital Gap Brad Spiegel Macon, GA Initiative.pptxBrad Spiegel Macon GA
Brad Spiegel Macon GA’s journey exemplifies the profound impact that one individual can have on their community. Through his unwavering dedication to digital inclusion, he’s not only bridging the gap in Macon but also setting an example for others to follow.
1.Wireless Communication System_Wireless communication is a broad term that i...JeyaPerumal1
Wireless communication involves the transmission of information over a distance without the help of wires, cables or any other forms of electrical conductors.
Wireless communication is a broad term that incorporates all procedures and forms of connecting and communicating between two or more devices using a wireless signal through wireless communication technologies and devices.
Features of Wireless Communication
The evolution of wireless technology has brought many advancements with its effective features.
The transmitted distance can be anywhere between a few meters (for example, a television's remote control) and thousands of kilometers (for example, radio communication).
Wireless communication can be used for cellular telephony, wireless access to the internet, wireless home networking, and so on.
2. France is the 2nd
manga consumer in the world
1. Japan - 500M volumes
2. France - 13M volumes
3. US - 9M volumes
Japan Expo 2015, summer festival about Japanese culture
250k tickets over 4 days
e130 per person
More than e8M every year
2 / 21
3. Mangaki
Let’s use algorithms to discover
pearls of Japanese culture!
23 members:
3 mad developers
1 graphic designer
1 intern
Feb 2014 - PhD about Adaptive Testing
Oct 2014 - Mangaki
Oct 2015 - Student Demo Cup winner, Microsoft prize
Feb 2016 - Japanese Culture Embassy Prize, Paris
We’re flying to Tokyo!
3 / 21
4. Mangaki
2000 users
150 → 14000 works anime / manga / OST
290000 ratings fav / like / dislike / neutral / willsee / wontsee
People rate a few works Preference Elicitation
And receive recommendations Collaborative Filtering
4 / 21
5. Collaborative Filtering
Problem
Users u = 1, . . . , n and items i = 1, . . . , m
Every user u rates some items Ru
(rui: rating of user u on item i)
How to guess unknown ratings?
k-nearest neighbor
Similarity score between users:
score(u, v) =
Ru · Rv
||Ru|| · ||Rv ||
.
Let us find the k nearest neighbors of the user
And recommend what they liked that he did not rate
5 / 21
6. Preference Elicitation
Problem
What questions to ask adaptively to a new user?
4 decks
Popularity
Controversy (Reddit)
controversy(L, D) = (L + D)min(L/D,D/L)
Most liked
Precious pearls: few rates but almost no dislike
Problem: most people cannot rate the controversial items
6 / 21
8. Matrix Completion
If the matrix of ratings is assumed low rank:
M =
R1
R2
...
Rn
= = C
P
Every line Ru is a linear combination of few profiles P.
1. Explicable profiles
If P P1 : adventure P2 : romance P3 : plot twist
And Cu 0,2 −0,5 0,6
⇒ u likes a bit adventure, dislikes romance, loves plot twists.
2. We get user2vec & item2vec in the same space!
⇒ An user likes items that are close to him.
8 / 21
9. Mangaki’s Explained Profiles
P1 loves stories for adults (Ghibli/SF), hates stories for teens
Paprika : In the near future, a machine that allows to enter
another person’s dreams for psychotherapy treatment has been
robbed. The doctors investigate.
P2 likes the most popular works, hates really weird works
Same-family homosexual romances:
- Mira is a high school student in love with his father, Ky¯osuke.
- They are involved both in a romantic and sexual relationship.
- Trouble arises when Mira finds adoption papers.
- Mira thinks [his dad] is cheating on him with a girl, which turns out to
be his mother.
- Meanwhile, Mira is chased by his best friend, who is also in love
with him.
- And the end, it turns out that Ky¯osuke is his uncle and everything is
right again.
9 / 21
12. Modelling Diversity: Determinantal Point Processes
Let’s sample a few items that are far from each other and ask
them to the user.
12 / 21
13. Determinantal Point Processes
We want to sample over n items
K : n × n similarity matrix over items (positive semidefinite)
P is a determinantal point process if Y is drawn such that:
∀A ⊂ Y, P(A ⊆ Y) ∝ det(KA)
Example
K =
1 2 3 4
2 5 6 7
3 6 8 9
4 7 9 0
A = {1, 2, 4} will be included
with probability prop. to
KA = det
1 2 4
2 5 7
4 7 0
13 / 21
14. Link with diversity
The determinant is the volume of the vectors
Non-correlated (diverse) vectors will increase the volume
We need to sample k diverse elements efficiently
14 / 21
15. Results
We assume the eigenvalues of K are known.
Kulesza & Taskar, ICML 2011
Sampling k diverse elements from a DPP of size n has
complexity O(nk2).
Kang (Samsung), NIPS 2013
We found an algorithm ϵ-close with complexity O(k3 log(k/ϵ)).
Rebeschini & Karbasi, COLT 2015
No, you did not.
Your proof of complexity is false, but at least your algorithm
samples correctly.
Vie, RecSysFR 2016
Please calm down guys, we’re all friends here.
15 / 21
16. Work in progress: Preference Elicitation in Mangaki
Keep the 100 most popular works
Pick 5 diverse works using k-DPP
Ask the user to rate them
Estimate the user’s vector
Filter less informative works accordingly
Repeat.
16 / 21
