Enviar búsqueda
Cargar
microposts2015presentation-150518124457-lva1-app6892.pdf
•
0 recomendaciones
•
1 vista
S
SunnySam26
Seguir
Aiml
Leer menos
Leer más
Datos y análisis
Denunciar
Compartir
Denunciar
Compartir
1 de 21
Descargar ahora
Descargar para leer sin conexión
Recomendados
SplunkLive! New York Dec 2012 - SNAP Interactive
SplunkLive! New York Dec 2012 - SNAP Interactive
Splunk
Presentation for the defense of my Master Thesis
Semantic Analysis to Compute Personality Traits from Social Media Posts
Semantic Analysis to Compute Personality Traits from Social Media Posts
Giulio Carducci
AAAI/IAAI2018@New Orleansに採録された論文(Mobille Network Failure Event Detection and Forecasting with Multiple User Activity Data Sets)の発表スライド
18.02.05_IAAI2018_Mobille Network Failure Event Detection and Forecasting wit...
18.02.05_IAAI2018_Mobille Network Failure Event Detection and Forecasting wit...
LINE Corp.
Invited talks at UC Santa Barbara and UC Santa Cruz, May 2013.
Scalable Topic-Specific Influence Analysis on Microblogs
Scalable Topic-Specific Influence Analysis on Microblogs
Yuanyuan Tian
presentation on recommendation algorithms given by Manuel Garcia (University of Lisbon)à at RecSys2016 workshop on TV in Boston.
A flexible recommenndation system for Cable TV
A flexible recommenndation system for Cable TV
IntoTheMinds
3rd Workshop on Recommendation Systems for Television and online Video (RecSysTV), At Boston, MA, USA
A Flexible Recommendation System for Cable TV
A Flexible Recommendation System for Cable TV
Francisco Couto
Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques are dependent on the features extracted from the website samples. However, few studies have actually considered efficient feature selection for detecting phishing attacks. In this work, we investigate an agreement on the definitive features which should be used in phishing detection. We apply Fuzzy Rough Set (FRS) theory as a tool to select most effective features from three benchmarked data sets. The selected features are fed into three often used classifiers for phishing detection. To evaluate the FRS feature selection in developing a generalizable phishing detection, the classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained by FRS feature selection is 95% using Random Forest classification. Also, there are 9 universal features selected by FRS over all the three data sets. The F-measure value using this universal feature set is approximately 93% which is a comparable result in contrast to the FRS performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a faster phishing detection which is also robust toward zero-day attacks.
Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection
Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection
Wright State University, Dayton, OH, USA
Feature based Opinion Mining from Amazon Customer Reviews
Feature Based Opinion Mining from Amazon Reviews
Feature Based Opinion Mining from Amazon Reviews
Ravi Kiran Holur Vijay
Recomendados
SplunkLive! New York Dec 2012 - SNAP Interactive
SplunkLive! New York Dec 2012 - SNAP Interactive
Splunk
Presentation for the defense of my Master Thesis
Semantic Analysis to Compute Personality Traits from Social Media Posts
Semantic Analysis to Compute Personality Traits from Social Media Posts
Giulio Carducci
AAAI/IAAI2018@New Orleansに採録された論文(Mobille Network Failure Event Detection and Forecasting with Multiple User Activity Data Sets)の発表スライド
18.02.05_IAAI2018_Mobille Network Failure Event Detection and Forecasting wit...
18.02.05_IAAI2018_Mobille Network Failure Event Detection and Forecasting wit...
LINE Corp.
Invited talks at UC Santa Barbara and UC Santa Cruz, May 2013.
Scalable Topic-Specific Influence Analysis on Microblogs
Scalable Topic-Specific Influence Analysis on Microblogs
Yuanyuan Tian
presentation on recommendation algorithms given by Manuel Garcia (University of Lisbon)à at RecSys2016 workshop on TV in Boston.
A flexible recommenndation system for Cable TV
A flexible recommenndation system for Cable TV
IntoTheMinds
3rd Workshop on Recommendation Systems for Television and online Video (RecSysTV), At Boston, MA, USA
A Flexible Recommendation System for Cable TV
A Flexible Recommendation System for Cable TV
Francisco Couto
Phishing as one of the most well-known cybercrime activities is a deception of online users to steal their personal or confidential information by impersonating a legitimate website. Several machine learning-based strategies have been proposed to detect phishing websites. These techniques are dependent on the features extracted from the website samples. However, few studies have actually considered efficient feature selection for detecting phishing attacks. In this work, we investigate an agreement on the definitive features which should be used in phishing detection. We apply Fuzzy Rough Set (FRS) theory as a tool to select most effective features from three benchmarked data sets. The selected features are fed into three often used classifiers for phishing detection. To evaluate the FRS feature selection in developing a generalizable phishing detection, the classifiers are trained by a separate out-of-sample data set of 14,000 website samples. The maximum F-measure gained by FRS feature selection is 95% using Random Forest classification. Also, there are 9 universal features selected by FRS over all the three data sets. The F-measure value using this universal feature set is approximately 93% which is a comparable result in contrast to the FRS performance. Since the universal feature set contains no features from third-part services, this finding implies that with no inquiry from external sources, we can gain a faster phishing detection which is also robust toward zero-day attacks.
Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection
Fuzzy Rough Set Feature Selection to Enhance Phishing Attack Detection
Wright State University, Dayton, OH, USA
Feature based Opinion Mining from Amazon Customer Reviews
Feature Based Opinion Mining from Amazon Reviews
Feature Based Opinion Mining from Amazon Reviews
Ravi Kiran Holur Vijay
There has been much effort on studying how social media sites, such as Twitter, help propagate information in differ- ent situations, including spreading alerts and SOS messages in an emergency. However, existing work has not addressed how to actively identify and engage the right strangers at the right time on social media to help effectively propagate intended information within a desired time frame. To ad- dress this problem, we have developed two models: (i) a feature-based model that leverages peoples’ exhibited social behavior, including the content of their tweets and social interactions, to characterize their willingness and readiness to propagate information on Twitter via the act of retweeting; and (ii) a wait-time model based on a user's previous retweeting wait times to predict her next retweeting time when asked. Based on these two models, we build a recommender system that predicts the likelihood of a stranger to retweet information when asked, within a specific time window, and recommends the top-N qualified strangers to engage with. Our experiments, including live studies in the real world, demonstrate the effectiveness of our work. Presented at Intelligent User Interfaces 2014, Haifa, Israel. February 27, 2014.
Who will RT this?: Automatically Identifying and Engaging Strangers on Twitte...
Who will RT this?: Automatically Identifying and Engaging Strangers on Twitte...
Jeffrey Nichols
The widespread location-based social networks (LBSNs) have immersed into our daily life. As an open platform, LBSNs typically allow all kinds of users to register accounts. Malicious attackers can easily join and post misleading information, often with the intention of influencing the users' decision in urban computing environments. To provide reliable information and improve the experience for legitimate users, we design and implement DeepScan, a malicious account detection system for LBSNs. Different from existing approaches, DeepScan leverages emerging deep learning technologies to learn users' dynamic behavior. In particular, we introduce the long short-term memory (LSTM) neural network to conduct time series analysis of user activities. DeepScan combines newly introduced time series features and a set of conventional features extracted from user activities, and exploits a supervised machine learning-based model for detection. Using the real traces collected from Dianping, a representative LBSN, we demonstrate that DeepScan can achieve an excellent prediction performance with an F1-score of 0.964. We also find that the time series features play a critical role in the detection system.
DeepScan: Exploiting Deep Learning for Malicious Account Detection in Locatio...
DeepScan: Exploiting Deep Learning for Malicious Account Detection in Locatio...
yeung2000
In this talk we explore how to build Machine Learning Systems that can that can learn "continuously" from their mistakes (feedback loop) and adapt to an evolving data distribution. The youtube link to video of the talk is here: https://www.youtube.com/watch?v=VtBvmrmMJaI
Building Continuous Learning Systems
Building Continuous Learning Systems
Anuj Gupta
In this study, we focus on the creation and evaluation of domain-specific web corpora. To this purpose, we propose a two-step approach, namely the (1) the automatic extraction and evaluation of term seeds from personas and use cases/scenarios; (2) the creation and evaluation of domain-specific web corpora bootstrapped with term seeds automatically extracted in step 1. Results are encouraging and show that: (1) it is possible to create a fairly accurate term extractor for relatively short narratives; (2) it is straightforward to evaluate a quality such as domain-specificity of web corpora using well-established metrics.
Towards a Quality Assessment of Web Corpora for Language Technology Applications
Towards a Quality Assessment of Web Corpora for Language Technology Applications
Marina Santini
Mobile Recommendation Engine collaborative filtering and content based approach in hybrid manner then Genetic Algorithm for Enhancement of the Recommendation Engine. by this marketers also will get the unique characteristics of the product that must be created and also recommend to the user.
Recommendation engine Using Genetic Algorithm
Recommendation engine Using Genetic Algorithm
Vaibhav Varshney
authors: Alison Hitchens and Gail Sperling Presented at ELUNA Salt Lake City, 2012
Primo Reporting: Using 3rd Party Software to Create Primo Reports & Analyze P...
Primo Reporting: Using 3rd Party Software to Create Primo Reports & Analyze P...
Alison Hitchens
basic Function and Terminology of Recommendation Systems. Some Algorithmic Implementation with some sample Dataset for Understanding. It contains all the Layers of RS Framework well explained.
Recommendation Systems
Recommendation Systems
Robin Reni
Jump-start your machine learning project by using the crowd to build your training set. Before you can train your machine learning algorithm, you need to take your raw inputs and label, annotate, or tag them to build your ground truth. Learn how to use the Amazon Mechanical Turk marketplace to perform these tasks. We share Amazon's best practices, developed while training our own machine learning algorithms, and walk you through quickly getting affordable and high-quality training data.
AWS re:Invent 2016: Getting to Ground Truth with Amazon Mechanical Turk (MAC201)
AWS re:Invent 2016: Getting to Ground Truth with Amazon Mechanical Turk (MAC201)
Amazon Web Services
Talk at ASONAM / FOSINT 2015
Real-time Classification of Malicious URLs on Twitter using Machine Activity ...
Real-time Classification of Malicious URLs on Twitter using Machine Activity ...
Pete Burnap
A two step ranking solution to the RecSys 2014 Challenge presented at the RecSys 2014 conference on Oct 10, in Foster City (CA, USA) by Behnoush Abdollahi
A Two Step Ranking Solution for Twitter User Engagement