The document provides instructions for writing a news article, including the key elements and their purpose. It explains that the most important information should be at the top to engage readers. The main elements are the headline, byline, lead statement, and body. The headline should grab attention and summarize the main idea. The byline includes the author and date. The lead statement introduces the who, what, when, where, why and how. The body provides more details, facts, and quotations to fully explain the event or story. Examples are given for each element to illustrate proper news article structure and composition.
The document outlines the main elements typically found in a news article, including the headline, byline, location, lead paragraph, and supporting paragraphs. The headline is meant to summarize the main idea of the article in a short, attention-grabbing way. The byline includes the author's name and publication. The location provides details about where the news event took place. The lead paragraph answers basic questions about the news topic. Supporting paragraphs then give additional context and details to develop the story.
We estimate that nearly one third of news articles contain references to future events. While this information can prove crucial to understanding news stories and how events will develop for a given topic, there is currently no easy way to access this information. We propose a new task to address the problem of retrieving and ranking sentences that contain mentions to future events, which we call ranking related news predictions. In this paper, we formally define this task and propose a learning to rank approach based on 4 classes of features: term similarity, entity-based similarity, topic similarity, and temporal similarity. Through extensive evaluations using a corpus consisting of 1.8 millions news articles and 6,000 manually judged relevance pairs, we show that our approach is able to retrieve a significant number of relevant predictions related to a given topic.
Brand Strategy and Super Bowl Twitter AnalyticsImage Sou.docxAASTHA76
Brand Strategy and Super
Bowl Twitter Analytics
Image Source: Adweek.com
Twitter popularity as 2nd screen
Infographic Source: Fortune, 2015
About Twitter
3
Twitter Stats (cont’d)
4
About Super Bowl Ads
5
Motivation
• Cost per 30 seconds slot – 5 mil (CNNMoney, 2017).
• Brand strategy in Super Bowl ads? Enhance &
reach.
• The role of IS tools in this strategy?
– Analytics of audience (demographics, locations etc.).
– Supplementary info for future decision making.
• What is the relationship between Social Media
buzz around Super Bowl ads with ad ratings?
– Twitter activities surrounding SB ads with USA Today
Index ratings (USA Today)?
3 Tweet Examples
Those Budweiser commercials had me balling my eyes
out!! #Budweiser #SuperBowl #horsepuppy #tearjerker
Thanks #CocaCola & #Cheerios for showing U.S.
multicultural families and successfully including
diverse markets #adbowl #AmericaIsBeautiful
Scarlett Johansson should realize that the only real
flavor of #SodaStream is oppression
http://t.co/QLpDD7vkXA #superbowl
Objective
• Learn to extract valuable metrics from social
media data using MS Excel.
• Know what insights are valuable to brands.
• Simple introduction to (review on) descriptive
statistics, correlation, charts, regressions, and
word clouds.
• Lab instructions become more vague as lab
progresses to encourage student self learning.
8
Lab Setup
• Download this presentation.
• Download the SB 2014 spreadsheet ‐‐ This is a
summary of actual tweets *downloaded using
PHP and MySQL scripts (done prior).
• Download the doritos.txt tweet text file*.
• You will need MS Excel with Data Analysis
ToolPak.
• Prepare a Word doc for submission.
Introduction
• Start with research question:
– Is social media (Twitter) an effective tool for measuring
brand performance (SB ads)?
• How?
– Find a relationship between Twitter metrics and SB
performance.
– If a relationship exists then social media may be a valuable
(real time, low cost) monitoring mechanism in addition to
existing tools.
– In addition, social media provides rich feedback (WOM,
influencer, competitors, etc.).
Analysis
1. Descriptive Statistics
2. Correlation Analysis
3. Charts (Bar chart and scatter plot)
4. 3 Sets of OLS Regressions
5. Word Cloud diagrams
SB 2014* Dataset
• Super Bowl 2014 Ads
• 51 Ads
• Contains social media measures and USA
Today ratings for each ad.
• Measures extracted from downloading and
analyzing tweets for each ad.
*Seattle Seahawks beat Denver Broncos (43‐8) 12
Descriptive Statistics
1. Open MS Excel
2. Open SB 2014 spreadsheet
3. Highlight all the number cells (including titles) but
excluding official# and brand columns.
4. Go to Data > Data Analysis
Descriptive Statistics
5. Select Descriptive Statistics.
6. Select the shown choices below.
Descriptive Statistics
7. Results appear in next Sheet.
8. Copy Result to Word doc.
Descriptive Statistics
9. Discuss result.
10.Focus ...
SocInfo14 - On the Feasibility of Predicting News Popularity at Cold StartTelefonica Research
We perform a study on cold-start news popularity prediction using a collection of 13,319 news articles obtained from Yahoo News. We characterise the online popularity of news articles by two different metrics and try to predict them using machine learning techniques. Contrary to a prior work on the same topic, our findings indicate that predicting the news popularity at cold start is a difficult task and the previously published results may be superficial.
Assignment 3 Team Project Outline(Complete by Sunday, Nov. .docxSusanaFurman449
Assignment 3: Team Project Outline
(Complete by Sunday, Nov. 20)
Due No Due Date Points 50 Submitting a file upload
Start Assignment
This assignment is due in this Module.
This assignment aligns with Learning Outcomes 1, 2 and 4.
Directions
For this project you will work in a collaborative team to identify a political issue. Your
team will then research, analyze, develop, and defend a position within that issue. The
end product will be a paper that describes the issue and supports your team’s
position. Remember, your goal is to select an issue, take a position, and develop a
paper to support that position.
Political issues could include discussions on healthcare, immigration reform,
American’s marijuana debate, tax reform, religious freedom act, LGBTQ rights, and,
etc. If you are unsure of what constitutes a valid political issue, please check with the
instructor for guidance in this area.
The outline should minimally contain the following sections:
1. Brief introduction and thesis statement
2. Overview of the issue (research including at least 5 references)
3. The team's position
11/15/22, 11:25 PM
Page 1 of 4
Team Project Outline Rubric
Criteria Ratings Pts
25 pts
4. Rationale for your position (research including at least 5 references)
5. Summary, conclusions and recommendations
Submission
A designated member will be the one who submits this assignment. This assignment
requires a file upload submission. After you have reviewed the assignment instructions
and rubric, as applicable, complete your submission by selecting the Submit
Assignment button next to the assignment title. Browse for your file and remember to
select the Submit Assignment button below the file to complete your submission.
Review the confirmation annotation that presents after submission.
Grading
This assignment is worth 50 points toward your final grade and will be graded using
the Project Outline Rubric. Please use it as a guide toward successful completion of
this assignment.
(1)
Organization 25 to >22.0 pts
Exemplary
The outline is
logically
organized with a
well-written
introduction,
thesis statement
and conclusion.
22 to >19.0 pts
Meets
Expectations
The outline is
logically
organized with a
well-written
introduction,
thesis statement
19 to >13.0 pts
Developing
The outline is
logically
organized with a
well-written
introduction,
thesis statement
and conclusion.
13 to >0 pts
Beginning
The outline is
loosely
organized with
an
introduction,
thesis
statement, and
11/15/22, 11:25 PM
Page 2 of 4
15 pts
10 pts
and conclusion.
One of these
requires
improvement.
One or more of
the introduction,
thesis statement,
and/or
conclusion
require
improvement.
conclusion.
The
introduction,
thesis
statement,
and/or
conclusion
require much
improvement.
Topic
Overview
15 to >13.0 pts
Exemplary
Provides an
overview that
comprehensively
describes the
selected
political issue.
Th.
1. The document summarizes a project that analyzed social media data from Facebook and Twitter about 2016 US presidential candidates. It collected data using APIs and natural language processing, then stored statistics in a DynamoDB database and displayed analyses on a website.
2. It implemented features to retrieve and analyze data from Facebook and Twitter, including sentiment analysis and aggregating hashtags, adjectives, and engagement statistics. Challenges included data volume limitations and lack of a public Facebook API.
3. Future work proposed including more international data sources and using machine learning to extrapolate sentiment analysis trends over time.
This document discusses data modeling approaches for MongoDB. It begins by comparing relational and document modeling methodologies. Key considerations for MongoDB modeling include the application's read/write patterns and how the data model may evolve. The document then outlines several design patterns for MongoDB including linking vs embedding, schema versioning, buckets, and computed fields. It also provides a step-by-step methodology for iterative data modeling. Finally, it applies these concepts through a sample data model for an online shopping application.
The document provides instructions for writing a news article, including the key elements and their purpose. It explains that the most important information should be at the top to engage readers. The main elements are the headline, byline, lead statement, and body. The headline should grab attention and summarize the main idea. The byline includes the author and date. The lead statement introduces the who, what, when, where, why and how. The body provides more details, facts, and quotations to fully explain the event or story. Examples are given for each element to illustrate proper news article structure and composition.
The document outlines the main elements typically found in a news article, including the headline, byline, location, lead paragraph, and supporting paragraphs. The headline is meant to summarize the main idea of the article in a short, attention-grabbing way. The byline includes the author's name and publication. The location provides details about where the news event took place. The lead paragraph answers basic questions about the news topic. Supporting paragraphs then give additional context and details to develop the story.
We estimate that nearly one third of news articles contain references to future events. While this information can prove crucial to understanding news stories and how events will develop for a given topic, there is currently no easy way to access this information. We propose a new task to address the problem of retrieving and ranking sentences that contain mentions to future events, which we call ranking related news predictions. In this paper, we formally define this task and propose a learning to rank approach based on 4 classes of features: term similarity, entity-based similarity, topic similarity, and temporal similarity. Through extensive evaluations using a corpus consisting of 1.8 millions news articles and 6,000 manually judged relevance pairs, we show that our approach is able to retrieve a significant number of relevant predictions related to a given topic.
Brand Strategy and Super Bowl Twitter AnalyticsImage Sou.docxAASTHA76
Brand Strategy and Super
Bowl Twitter Analytics
Image Source: Adweek.com
Twitter popularity as 2nd screen
Infographic Source: Fortune, 2015
About Twitter
3
Twitter Stats (cont’d)
4
About Super Bowl Ads
5
Motivation
• Cost per 30 seconds slot – 5 mil (CNNMoney, 2017).
• Brand strategy in Super Bowl ads? Enhance &
reach.
• The role of IS tools in this strategy?
– Analytics of audience (demographics, locations etc.).
– Supplementary info for future decision making.
• What is the relationship between Social Media
buzz around Super Bowl ads with ad ratings?
– Twitter activities surrounding SB ads with USA Today
Index ratings (USA Today)?
3 Tweet Examples
Those Budweiser commercials had me balling my eyes
out!! #Budweiser #SuperBowl #horsepuppy #tearjerker
Thanks #CocaCola & #Cheerios for showing U.S.
multicultural families and successfully including
diverse markets #adbowl #AmericaIsBeautiful
Scarlett Johansson should realize that the only real
flavor of #SodaStream is oppression
http://t.co/QLpDD7vkXA #superbowl
Objective
• Learn to extract valuable metrics from social
media data using MS Excel.
• Know what insights are valuable to brands.
• Simple introduction to (review on) descriptive
statistics, correlation, charts, regressions, and
word clouds.
• Lab instructions become more vague as lab
progresses to encourage student self learning.
8
Lab Setup
• Download this presentation.
• Download the SB 2014 spreadsheet ‐‐ This is a
summary of actual tweets *downloaded using
PHP and MySQL scripts (done prior).
• Download the doritos.txt tweet text file*.
• You will need MS Excel with Data Analysis
ToolPak.
• Prepare a Word doc for submission.
Introduction
• Start with research question:
– Is social media (Twitter) an effective tool for measuring
brand performance (SB ads)?
• How?
– Find a relationship between Twitter metrics and SB
performance.
– If a relationship exists then social media may be a valuable
(real time, low cost) monitoring mechanism in addition to
existing tools.
– In addition, social media provides rich feedback (WOM,
influencer, competitors, etc.).
Analysis
1. Descriptive Statistics
2. Correlation Analysis
3. Charts (Bar chart and scatter plot)
4. 3 Sets of OLS Regressions
5. Word Cloud diagrams
SB 2014* Dataset
• Super Bowl 2014 Ads
• 51 Ads
• Contains social media measures and USA
Today ratings for each ad.
• Measures extracted from downloading and
analyzing tweets for each ad.
*Seattle Seahawks beat Denver Broncos (43‐8) 12
Descriptive Statistics
1. Open MS Excel
2. Open SB 2014 spreadsheet
3. Highlight all the number cells (including titles) but
excluding official# and brand columns.
4. Go to Data > Data Analysis
Descriptive Statistics
5. Select Descriptive Statistics.
6. Select the shown choices below.
Descriptive Statistics
7. Results appear in next Sheet.
8. Copy Result to Word doc.
Descriptive Statistics
9. Discuss result.
10.Focus ...
SocInfo14 - On the Feasibility of Predicting News Popularity at Cold StartTelefonica Research
We perform a study on cold-start news popularity prediction using a collection of 13,319 news articles obtained from Yahoo News. We characterise the online popularity of news articles by two different metrics and try to predict them using machine learning techniques. Contrary to a prior work on the same topic, our findings indicate that predicting the news popularity at cold start is a difficult task and the previously published results may be superficial.
Assignment 3 Team Project Outline(Complete by Sunday, Nov. .docxSusanaFurman449
Assignment 3: Team Project Outline
(Complete by Sunday, Nov. 20)
Due No Due Date Points 50 Submitting a file upload
Start Assignment
This assignment is due in this Module.
This assignment aligns with Learning Outcomes 1, 2 and 4.
Directions
For this project you will work in a collaborative team to identify a political issue. Your
team will then research, analyze, develop, and defend a position within that issue. The
end product will be a paper that describes the issue and supports your team’s
position. Remember, your goal is to select an issue, take a position, and develop a
paper to support that position.
Political issues could include discussions on healthcare, immigration reform,
American’s marijuana debate, tax reform, religious freedom act, LGBTQ rights, and,
etc. If you are unsure of what constitutes a valid political issue, please check with the
instructor for guidance in this area.
The outline should minimally contain the following sections:
1. Brief introduction and thesis statement
2. Overview of the issue (research including at least 5 references)
3. The team's position
11/15/22, 11:25 PM
Page 1 of 4
Team Project Outline Rubric
Criteria Ratings Pts
25 pts
4. Rationale for your position (research including at least 5 references)
5. Summary, conclusions and recommendations
Submission
A designated member will be the one who submits this assignment. This assignment
requires a file upload submission. After you have reviewed the assignment instructions
and rubric, as applicable, complete your submission by selecting the Submit
Assignment button next to the assignment title. Browse for your file and remember to
select the Submit Assignment button below the file to complete your submission.
Review the confirmation annotation that presents after submission.
Grading
This assignment is worth 50 points toward your final grade and will be graded using
the Project Outline Rubric. Please use it as a guide toward successful completion of
this assignment.
(1)
Organization 25 to >22.0 pts
Exemplary
The outline is
logically
organized with a
well-written
introduction,
thesis statement
and conclusion.
22 to >19.0 pts
Meets
Expectations
The outline is
logically
organized with a
well-written
introduction,
thesis statement
19 to >13.0 pts
Developing
The outline is
logically
organized with a
well-written
introduction,
thesis statement
and conclusion.
13 to >0 pts
Beginning
The outline is
loosely
organized with
an
introduction,
thesis
statement, and
11/15/22, 11:25 PM
Page 2 of 4
15 pts
10 pts
and conclusion.
One of these
requires
improvement.
One or more of
the introduction,
thesis statement,
and/or
conclusion
require
improvement.
conclusion.
The
introduction,
thesis
statement,
and/or
conclusion
require much
improvement.
Topic
Overview
15 to >13.0 pts
Exemplary
Provides an
overview that
comprehensively
describes the
selected
political issue.
Th.
1. The document summarizes a project that analyzed social media data from Facebook and Twitter about 2016 US presidential candidates. It collected data using APIs and natural language processing, then stored statistics in a DynamoDB database and displayed analyses on a website.
2. It implemented features to retrieve and analyze data from Facebook and Twitter, including sentiment analysis and aggregating hashtags, adjectives, and engagement statistics. Challenges included data volume limitations and lack of a public Facebook API.
3. Future work proposed including more international data sources and using machine learning to extrapolate sentiment analysis trends over time.
This document discusses data modeling approaches for MongoDB. It begins by comparing relational and document modeling methodologies. Key considerations for MongoDB modeling include the application's read/write patterns and how the data model may evolve. The document then outlines several design patterns for MongoDB including linking vs embedding, schema versioning, buckets, and computed fields. It also provides a step-by-step methodology for iterative data modeling. Finally, it applies these concepts through a sample data model for an online shopping application.
Research Opportunities in India & Keyword Search Over Dynamic Categorized Inf...VNIT-ACM Student Chapter
The document summarizes different types of software companies in India and provides career advice. It discusses services companies that do outsourced work, product development companies that build software products, and research and development companies that conceptualize new products. It recommends gaining experience through internships and networking to find jobs in product development companies, which offer more interesting work than services roles.
The document describes a methodology for using traffic and citizen tweets to manage transportation incidents for a city department of transportation. Key aspects include:
- Extracting keywords from historical traffic tweets and using them to search for and extract relevant tweets about current incidents.
- Ranking extracted tweets based on the occurrence of keywords and the influence of the tweeting user to determine relevance to potential incidents.
- Storing extracted tweets and associated data in MongoDB for scalability and accessibility.
- Displaying results to transportation management center operators through a front-end interface using frameworks like Bootstrap and D3.js for visualization.
KSurvey is survey software that offers:
1) Easy workflow management and drag-and-drop question building.
2) Powerful data management, analysis, and visualization tools including multiple output file formats and graph libraries.
3) Flexible deployment options for surveys including print, email, and web with customizable fronts ends and compatibility with various websites.
KSurvey is survey software that offers:
1) Easy workflow management and drag-and-drop question building.
2) Powerful data management, analysis, and visualization tools including multiple output files and graph libraries.
3) Flexible deployment options for surveys including printing, email distribution, and hosting surveys on the web or mobile apps.
Big & Personal: the data and the models behind Netflix recommendations by Xa...BigMine
Since the Netflix $1 million Prize, announced in 2006, our company has been known for having personalization at the core of our product. Even at that point in time, the dataset that we released was considered “large”, and we stirred innovation in the (Big) Data Mining research field. Our current product offering is now focused around instant video streaming, and our data is now many orders of magnitude larger. Not only do we have many more users in many more countries, but we also receive many more streams of data. Besides the ratings, we now also use information such as what our members play, browse, or search.
In this talk I will discuss the different approaches we follow to deal with these large streams of data in order to extract information for personalizing our service. I will describe some of the machine learning models used, as well as the architectures that allow us to combine complex offline batch processes with real-time data streams.
Conor Hayes - Topics, tags and trends in the blogosphereDERIGalway
This document summarizes a presentation on analyzing topics, tags, and trends in the blogosphere. It discusses how blogs are organized around topics in a user-centered way using tags, unlike Usenet which is topic-centered. The presentation covers analyzing user and topic drift over time through clustering blogs and measuring entropy. It finds that most consistent, topic-relevant blogs are "A-bloggers" that tag posts similarly over time and remain closely related to topic definitions.
This document describes a project to predict the popularity of New York Times blog articles using logistic regression, text mining, and topic modeling. The methodology involved data preprocessing including removing punctuation and stop words, then creating a document term matrix. Logistic regression was used to predict popularity based on terms and variables like publication date, word count, and section. Topic modeling with LDA identified important topics. The model achieved 86.4% accuracy in predicting popular articles, defined as having 25 or more comments.
Machine Learning for Q&A Sites: The Quora ExampleXavier Amatriain
Machine learning is used extensively at Q&A sites like Quora to improve user experience. Some applications include answer ranking to determine the best answers, feed ranking to present the most interesting stories, asking users to answer questions, recommending new topics and users, detecting duplicate questions, and moderating content. Quora uses a variety of machine learning models and does extensive experimentation and A/B testing to optimize different metrics.
Text is one of the most used forms of communication and ubiquitous in the Internet. Social networks like Facebook and Twitter mainly contain unstructured text; the same is true for content-driven websites.
For humans it is easy to grasp the meaning of text - much more difficult so for computers. Used correctly, computers can help humans tremendously in structuring and classifying huge amounts of text. This "symbiosis" can help humans work more efficiently, reduce repetitve work and help them make use of the uncovered structure.
Our talk starts with visualizations giving us ideas how to automatically classify texts. Then we will demonstrate that manual intervention is sometimes necessary and how this can be used as a basis for machine learning. We introduce the concept of classification (taxonomies). We then explain how text classification works with machine learning (TF/IDF, BoW etc.) and how we can deterministically extend training sets.
All our examples use data which is openly available and already pre-categorized. This reduces the amount of manual work, gives us more opportunities in experimenting and helps significantly in classifying more complicated cases.
As software tools we use R, Apache Solr, D3.js, and several NLP and ML tools from the ASF.
Prediction of Reaction towards Textual Posts in Social NetworksMohamed El-Geish
Posting on social networks could be a gratifying or a terrifying experience depending on the reaction the post and its author —by association— receive from the readers. To better understand what makes a post popular, this project inquires into the factors that determine the number of likes, comments, and shares a textual post gets on LinkedIn; and finds a predictor function that can estimate those quantitative social gestures.
The document summarizes research on blog search tasks conducted as part of the TREC Blog Track, including opinion finding, blog distillation, and identifying top news stories. It describes the tasks, approaches taken, and conclusions. Opinion finding aimed to find blog posts expressing opinions about targets and determine polarity. Blog distillation retrieved relevant blogs in response to topics. Identifying top news stories ranked the most important news articles based on blog post relevance and volume. A variety of approaches were explored, including classification, language modeling, and voting models. The Blog Track played an important role in initiating research on social search and blog search.
This document provides an overview of how to use the Quid platform to identify emerging trends in news and blogs. It discusses how to break questions down, visualize data through different views and filters, tag content, and analyze trends over time to understand shifting topics, sentiment, and engagement. The goal is to surface insightful conversations and map relationships to inform business decisions. Strategic tagging, timeline views, and comparing coverage to social metrics help spot emerging areas and identify topics with the most consumer interest versus media focus.
Agile Patterns: Agile Estimation
We’re agile, so we don’t have to estimate and have no deadlines, right? Wrong! This session will consist of review of the problem with estimation in projects today and then an overview of the concept of agile estimation and the notion of re-estimation. We’ll learn about user stories, story points, team velocity, how to apply them all to estimation and iterative re-estimation. We will take a look at the cone of uncertainty and how to use it to your advantage. We’ll then take a look at the tools we will use for Agile Estimation, including planning poker, Visual Studio Team System, and much more. This is a very interactive session, so bring a lot of questions!
How to Use Data Effectively by Abra Sr. Business AnalystProduct School
Key Takeaways from this presentation include:
- How data is used to run day to day operations
- How data is used to influence product decisions and marketing strategies
- Which skills are necessary to become self-serving in data tasks regardless of core responsibilities
This document provides an overview of the data science process for predicting the NBA MVP winner for the upcoming season. It discusses framing the question, collecting relevant stats data from basketball-reference.com, cleaning and formatting the data, exploring it with Python libraries like Pandas and NumPy, building and evaluating decision tree and random forest models, and discussing ways to improve the model's performance, such as modifying feature selection.
The document discusses SQL query optimization and why it is so difficult. It begins by noting that there are often many possible execution plans for a given query, sometimes numbering in the millions. The query optimizer must select the most efficient plan by estimating the cost of each possible plan. This involves estimating predicate selectivities using statistics like histograms, and calculating estimates of the CPU and I/O costs for each operator. While query optimization is a fundamental task, after decades of research it remains challenging due to the complexity of modern hardware, software and queries.
This document describes a project to develop a system that searches for the top-k social media users from large amounts of geo-tagged social media data. The system would take as input a location, distance, and keywords, and output the top users who have posted content relevant to the keywords near the given location. It discusses collecting and analyzing Twitter data using APIs, parsing and storing the data, generating a user interaction graph, analyzing and scoring data based on location, keyword relevance, and popularity to identify top users, and processing top-k queries from users. The project is estimated to take two months to complete and will be developed using Python, PostgreSQL, and web front-end tools.
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.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/temporal-event-neural-networks-a-more-efficient-alternative-to-the-transformer-a-presentation-from-brainchip/
Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
Integration with BrainChip’s Akida neuromorphic hardware IP further enhances TENNs’ capabilities, enabling the realization of highly capable, portable and passively cooled edge devices. This presentation delves into the technical innovations underlying TENNs, presents real-world benchmarks, and elucidates how this cutting-edge approach is positioned to revolutionize edge AI across diverse applications.
More Related Content
Similar to News Article Ranking : Leveraging the Wisdom of Bloggers
Research Opportunities in India & Keyword Search Over Dynamic Categorized Inf...VNIT-ACM Student Chapter
The document summarizes different types of software companies in India and provides career advice. It discusses services companies that do outsourced work, product development companies that build software products, and research and development companies that conceptualize new products. It recommends gaining experience through internships and networking to find jobs in product development companies, which offer more interesting work than services roles.
The document describes a methodology for using traffic and citizen tweets to manage transportation incidents for a city department of transportation. Key aspects include:
- Extracting keywords from historical traffic tweets and using them to search for and extract relevant tweets about current incidents.
- Ranking extracted tweets based on the occurrence of keywords and the influence of the tweeting user to determine relevance to potential incidents.
- Storing extracted tweets and associated data in MongoDB for scalability and accessibility.
- Displaying results to transportation management center operators through a front-end interface using frameworks like Bootstrap and D3.js for visualization.
KSurvey is survey software that offers:
1) Easy workflow management and drag-and-drop question building.
2) Powerful data management, analysis, and visualization tools including multiple output file formats and graph libraries.
3) Flexible deployment options for surveys including print, email, and web with customizable fronts ends and compatibility with various websites.
KSurvey is survey software that offers:
1) Easy workflow management and drag-and-drop question building.
2) Powerful data management, analysis, and visualization tools including multiple output files and graph libraries.
3) Flexible deployment options for surveys including printing, email distribution, and hosting surveys on the web or mobile apps.
Big & Personal: the data and the models behind Netflix recommendations by Xa...BigMine
Since the Netflix $1 million Prize, announced in 2006, our company has been known for having personalization at the core of our product. Even at that point in time, the dataset that we released was considered “large”, and we stirred innovation in the (Big) Data Mining research field. Our current product offering is now focused around instant video streaming, and our data is now many orders of magnitude larger. Not only do we have many more users in many more countries, but we also receive many more streams of data. Besides the ratings, we now also use information such as what our members play, browse, or search.
In this talk I will discuss the different approaches we follow to deal with these large streams of data in order to extract information for personalizing our service. I will describe some of the machine learning models used, as well as the architectures that allow us to combine complex offline batch processes with real-time data streams.
Conor Hayes - Topics, tags and trends in the blogosphereDERIGalway
This document summarizes a presentation on analyzing topics, tags, and trends in the blogosphere. It discusses how blogs are organized around topics in a user-centered way using tags, unlike Usenet which is topic-centered. The presentation covers analyzing user and topic drift over time through clustering blogs and measuring entropy. It finds that most consistent, topic-relevant blogs are "A-bloggers" that tag posts similarly over time and remain closely related to topic definitions.
This document describes a project to predict the popularity of New York Times blog articles using logistic regression, text mining, and topic modeling. The methodology involved data preprocessing including removing punctuation and stop words, then creating a document term matrix. Logistic regression was used to predict popularity based on terms and variables like publication date, word count, and section. Topic modeling with LDA identified important topics. The model achieved 86.4% accuracy in predicting popular articles, defined as having 25 or more comments.
Machine Learning for Q&A Sites: The Quora ExampleXavier Amatriain
Machine learning is used extensively at Q&A sites like Quora to improve user experience. Some applications include answer ranking to determine the best answers, feed ranking to present the most interesting stories, asking users to answer questions, recommending new topics and users, detecting duplicate questions, and moderating content. Quora uses a variety of machine learning models and does extensive experimentation and A/B testing to optimize different metrics.
Text is one of the most used forms of communication and ubiquitous in the Internet. Social networks like Facebook and Twitter mainly contain unstructured text; the same is true for content-driven websites.
For humans it is easy to grasp the meaning of text - much more difficult so for computers. Used correctly, computers can help humans tremendously in structuring and classifying huge amounts of text. This "symbiosis" can help humans work more efficiently, reduce repetitve work and help them make use of the uncovered structure.
Our talk starts with visualizations giving us ideas how to automatically classify texts. Then we will demonstrate that manual intervention is sometimes necessary and how this can be used as a basis for machine learning. We introduce the concept of classification (taxonomies). We then explain how text classification works with machine learning (TF/IDF, BoW etc.) and how we can deterministically extend training sets.
All our examples use data which is openly available and already pre-categorized. This reduces the amount of manual work, gives us more opportunities in experimenting and helps significantly in classifying more complicated cases.
As software tools we use R, Apache Solr, D3.js, and several NLP and ML tools from the ASF.
Prediction of Reaction towards Textual Posts in Social NetworksMohamed El-Geish
Posting on social networks could be a gratifying or a terrifying experience depending on the reaction the post and its author —by association— receive from the readers. To better understand what makes a post popular, this project inquires into the factors that determine the number of likes, comments, and shares a textual post gets on LinkedIn; and finds a predictor function that can estimate those quantitative social gestures.
The document summarizes research on blog search tasks conducted as part of the TREC Blog Track, including opinion finding, blog distillation, and identifying top news stories. It describes the tasks, approaches taken, and conclusions. Opinion finding aimed to find blog posts expressing opinions about targets and determine polarity. Blog distillation retrieved relevant blogs in response to topics. Identifying top news stories ranked the most important news articles based on blog post relevance and volume. A variety of approaches were explored, including classification, language modeling, and voting models. The Blog Track played an important role in initiating research on social search and blog search.
This document provides an overview of how to use the Quid platform to identify emerging trends in news and blogs. It discusses how to break questions down, visualize data through different views and filters, tag content, and analyze trends over time to understand shifting topics, sentiment, and engagement. The goal is to surface insightful conversations and map relationships to inform business decisions. Strategic tagging, timeline views, and comparing coverage to social metrics help spot emerging areas and identify topics with the most consumer interest versus media focus.
Agile Patterns: Agile Estimation
We’re agile, so we don’t have to estimate and have no deadlines, right? Wrong! This session will consist of review of the problem with estimation in projects today and then an overview of the concept of agile estimation and the notion of re-estimation. We’ll learn about user stories, story points, team velocity, how to apply them all to estimation and iterative re-estimation. We will take a look at the cone of uncertainty and how to use it to your advantage. We’ll then take a look at the tools we will use for Agile Estimation, including planning poker, Visual Studio Team System, and much more. This is a very interactive session, so bring a lot of questions!
How to Use Data Effectively by Abra Sr. Business AnalystProduct School
Key Takeaways from this presentation include:
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This document provides an overview of the data science process for predicting the NBA MVP winner for the upcoming season. It discusses framing the question, collecting relevant stats data from basketball-reference.com, cleaning and formatting the data, exploring it with Python libraries like Pandas and NumPy, building and evaluating decision tree and random forest models, and discussing ways to improve the model's performance, such as modifying feature selection.
The document discusses SQL query optimization and why it is so difficult. It begins by noting that there are often many possible execution plans for a given query, sometimes numbering in the millions. The query optimizer must select the most efficient plan by estimating the cost of each possible plan. This involves estimating predicate selectivities using statistics like histograms, and calculating estimates of the CPU and I/O costs for each operator. While query optimization is a fundamental task, after decades of research it remains challenging due to the complexity of modern hardware, software and queries.
This document describes a project to develop a system that searches for the top-k social media users from large amounts of geo-tagged social media data. The system would take as input a location, distance, and keywords, and output the top users who have posted content relevant to the keywords near the given location. It discusses collecting and analyzing Twitter data using APIs, parsing and storing the data, generating a user interaction graph, analyzing and scoring data based on location, keyword relevance, and popularity to identify top users, and processing top-k queries from users. The project is estimated to take two months to complete and will be developed using Python, PostgreSQL, and web front-end tools.
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Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
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How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
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Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
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6. Kȍniget al. & Sayyadiet al. have exploited the blogosphere for event detectionObama Victory Number of blog posts Day (November 2008) M. Thelwall WWW’06 Kȍnig et al. SIGIR’09 Sayyadi et al. ICWSM’09
7.
8. Every day newspaper editors select articles for placement within their newspapers.
10. Rank articles by readership interestFront Page Page 2 Newspaper Editor . . . We investigate how such a ranking can be approximated using evidence from the blogosphere
19. Given a day of interest dQ we wish to score each news article a by its predicted importance, score(a,dQ) using evidence from the blogosphere.=29 Day dQ =23 =14 =13 News Article Ranker =4 =4 Importance Scores
20.
21. Score by blog post volumeApproach Two Stages: Score each news article a for all days d based on related blog post volume for day d. News articles are represented by their headlines Given a query day dQ rank A based on the score for each news article on day dQ, i.e. score(a, dQ) -> a voting process The Votes Approach
22. Votes Approach : Stage 1 Stage 1: Score days for each news story 1 1 2 3 4 2 3 4 Ranking of days for a blog post ranking 4) Rank days by votes received 2) Select the top 1000 blog posts for a 3) Each post votes for a day Days votes = 2 votes = 1 votes = 2 votes = 2 For each news articlea 1) Use its representation (headline) as a query votes = 0 votes = 1 votes = 2 votes = 0 Terrier Votes Voting Model : Count * Craig Macdonald PhD thesis 2009
60. Votes Performance Better performance than TREC 2009 best systems Results: BM25<DPH (DFR) Votes + extras Hyperlink evidence is of less value than textual evidence Votes Approach TREC 2009 Best Systems
83. Weights downward the scores for each day dependent on w.ScoreGaussBoost(B,4) = (1*4)+(0.79*1)+(0.18*1) = 4.970 ScoreGaussBoost(A,4) = (1*4)+(0.79*4)+(0.18*3) = 7.700 dQ N = -2 Score =7.700 Num Votes Score=11 Score =4.970 Score=6 Days
84.
85. Does the quality of evidence decrease as distance from dQ increases?
86. Is historical or future (before or after dQ) blog post evidence more useful?Research Questions
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88. The parameter w determines the width of the Gaussian curve, and as such, the weights ∆d for the days.( n = -2, w = 0.5 ) ScoreGaussBoost(A,4) = (1*4)+(0.38*4)+(0.01*3) = 4.608 ScoreGaussBoost(B,4) = (1*4)+(0.38*1)+(0.01*1) = 4.390 ( n = -2, w = 1 ) ScoreGaussBoost(A,4) = (1*4)+(0.79*4)+(0.18*3) = 7.700 ScoreGaussBoost(B,4) = (1*4)+(0.79*1)+(0.18*1) = 4.970 Temporal Promotion
89. NDayBoost Performance Future blog postings does provide useful evidence Baseline DPH+Votes MAP Historical evidence is not useful for NDayBoost n value (days)
90. GaussBoost Performance Future blog postings provide stronger evidence than historical postings Historical blog postings are useful for days close to dQ Baseline DPH+Votes MAP w value (not days!)
91.
92. Both historical and future evidence is useful to improve Votes ranking performance
93. Can use this evidence to generate a better ranking for editors if the data is available
113. Prune headlines less likely to be news-worthyImproving the Article Representation
114.
115. Add related terms (counter sparsity)Approach: Select retrieve top 3 blog posts from: Blogs08 (query expansion , K. L. Kwok and M. S. Chan. SIGIR 1998) Wikipedia (collection enrichment, F. Diaz and D. Metzler. SIGIR 2006) using DPH (DFR) Expand query with the top 10 terms identified using Bo1 (G. Amati, Thesis 2003) from those documents. a Terrier Top Terms DPH Bo1 Blogs08/Wikipedia Query expansion/External Query expansion/Collection Enrichment
118. Collection enrichment helps find the blog posts that are related.Article Improvement Performance Collection enrichment with Wikipedia significantly increases performance MAP