In-Image Advertising

2011 ◽  
pp. 49-66
Author(s):  
Lusong Li ◽  
Xian-Sheng Hua
Keyword(s):  
Face Detection ◽  
Real World ◽  
Large Scale ◽  
Online Advertising ◽  
Visual Similarity ◽  
Primary Sources ◽  
Advertising Strategy ◽  
Advertising Model ◽  
The Web

The daunting volumes of images on the Web have become one of the primary sources for online advertising. This work presents a contextual in-image advertising strategy driven by images, which automatically associates relevant ads with an image and seamlessly inserts the ads in the nonintrusive areas within each individual image. In in-image advertising platform, the ads are selected based on not only textual relevance but also visual similarity. The ad insertion positions are detected based on image salience, as well as face detection, to minimize intrusiveness to the user. In addition to general in-image advertising, we also provide a special game-like in-image advertising model dedicated to image on the basis of gaming form, called GameSense, which supports creating a game from an online image and associates relevant ads within the game. We evaluate in-image advertising model on a large-scale real-world images, and demonstrate the effectiveness of in-image advertising platform.

scholarly journals Detecting Anti Ad-blockers in the Wild

2017 ◽  
Vol 2017 (3) ◽  
pp. 130-146 ◽  
Author(s):  
Muhammad Haris Mughees ◽  
Zhiyun Qian ◽  
Zubair Shafiq
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Online Advertising ◽  
Arms Race ◽  
Third Party ◽  
Measurement Study ◽  
Economic Threat ◽  
In The Wild ◽  
Automated Machine Learning ◽  
The Web

Abstract The rise of ad-blockers is viewed as an economic threat by online publishers who primarily rely on online advertising to monetize their services. To address this threat, publishers have started to retaliate by employing anti ad-blockers, which scout for ad-block users and react to them by pushing users to whitelist the website or disable ad-blockers altogether. The clash between ad-blockers and anti ad-blockers has resulted in a new arms race on the Web. In this paper, we present an automated machine learning based approach to identify anti ad-blockers that detect and react to ad-block users. The approach is promising with precision of 94.8% and recall of 93.1%. Our automated approach allows us to conduct a large-scale measurement study of anti ad-blockers on Alexa top-100K websites. We identify 686 websites that make visible changes to their page content in response to ad-block detection. We characterize the spectrum of different strategies used by anti ad-blockers. We find that a majority of publishers use fairly simple first-party anti ad-block scripts. However, we also note the use of third-party anti ad-block services that use more sophisticated tactics to detect and respond to ad-blockers.

Scalable Authoritative OWL Reasoning for the Web

2011 ◽  
pp. 2206-2249
Author(s):  
Aidan Hogan ◽  
Andreas Harth ◽  
Axel Polleres
Keyword(s):  
Real World ◽  
Large Scale ◽  
Scale Up ◽  
Web Data ◽  
Rule Based ◽  
Forward Chaining ◽  
The Web

In this article the authors discuss the challenges of performing reasoning on large scale RDF datasets from the Web. Using ter-Horst’s pD* fragment of OWL as a base, the authors compose a rulebased framework for application to web data: they argue their decisions using observations of undesirable examples taken directly from the Web. The authors further temper their OWL fragment through consideration of “authoritative stheirces” which counter-acts an observed behavitheir which we term “ontology hijacking”: new ontologies published on the Web re-defining the semantics of existing entities resident in other ontologies. They then present their system for performing rule-based forward-chaining reasoning which they call SAOR: Scalable Authoritative OWL Reasoner. Based upon observed characteristics of web data and reasoning in general, they design their system to scale: the system is based upon a separation of terminological data from assertional data and comprises of a lightweight in-memory index, on-disk sorts and file-scans. The authors evaluate their methods on a dataset in the order of a hundred million statements collected from real-world Web stheirces and present scale-up experiments on a dataset in the order of a billion statements collected from the Web.

Approaches to Large-Scale User Opinion Summarization for the Web

2014 ◽  
pp. 163-184
Author(s):  
William Darling
Keyword(s):  
Real World ◽  
Domain Knowledge ◽  
Large Scale ◽  
Text Summarization ◽  
User Comments ◽  
Opinion Summarization ◽  
Real World Problem ◽  
World Opinion ◽  
Summarization System ◽  
The Web

This chapter discusses approaches to applying text summarization research to the real-world problem of opinion summarization of user comments. Following a brief overview of the history of research in text summarization, the authors consider large scale user opinion summarization on the Web, a summarization problem that is distinct from the traditional domain that the research has focused on until very recently. More specifically, they consider opinion summarization of large datasets that generally include large degrees of noise and little editorial structure. To deal with this kind of real-world problem, the chapter addresses three major areas that must be considered and adhered to when designing systems for this type of problem: simple techniques, domain knowledge, and evaluative testing. Each area is covered in detail, and throughout the chapter, the lessons are applied to a case study that aims to apply the recommendations to designing a real-world opinion summarization system for a fictional book publisher.

Scalable Authoritative OWL Reasoning for the Web*

2011 ◽  
pp. 131-177 ◽  
Author(s):  
Aidan Hogan ◽  
Andreas Harth ◽  
Axel Polleres
Keyword(s):  
Real World ◽  
Large Scale ◽  
Scale Up ◽  
Original Publication ◽  
Web Data ◽  
Rule Based ◽  
Recent Developments ◽  
Observed Behaviour ◽  
Extended Discussion ◽  
The Web

In this chapter, the authors discuss the challenges of performing reasoning on large scale RDF datasets from the Web. Using ter-Horst’s pD* fragment of OWL as a base, the authors compose a rule-based framework for application to Web data: they argue their decisions using observations of undesirable examples taken directly from the Web. The authors further temper their OWL fragment through consideration of “authoritative sources” which counter-acts an observed behaviour which they term “ontology hijacking”: new ontologies published on the Web re-defining the semantics of existing entities resident in other ontologies. They then present their system for performing rule-based forward-chaining reasoning which they call SAOR: Scalable Authoritative OWL Reasoner. Based upon observed characteristics of Web data and reasoning in general, they design their system to scale: the system is based upon a separation of terminological data from assertional data and comprises of a lightweight in-memory index, on-disk sorts and file-scans. The authors evaluate their methods on a dataset in the order of a hundred million statements collected from real-world Web sources and present scale-up experiments on a dataset in the order of a billion statements collected from the Web. In this republished version, the authors also present extended discussion reflecting upon recent developments in the area of scalable RDFS/OWL reasoning, some of which has drawn inspiration from the original publication (Hogan, et al., 2009).

1588-P: Therapy Trends in Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1588-P ◽  
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
AMBRISH MITHAL ◽  
SHASHANK JOSHI ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Real World ◽  
Large Scale ◽  
Nationwide Study

2258-PUB: Impact of Cardiovascular (CV) Risk Factors during the Initial 6 Months of the First Large-Scale Longitudinal Nationwide Study on Management and Real-World Outcomes of Diabetes in India (LANDMARC)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 2258-PUB
Author(s):  
ROMIK GHOSH ◽  
ASHOK K. DAS ◽  
SHASHANK JOSHI ◽  
AMBRISH MITHAL ◽  
K.M. PRASANNA KUMAR ◽  
...  
Keyword(s):  
Risk Factors ◽  
Real World ◽  
Large Scale ◽  
Nationwide Study

Mapping global water projects: improving access to donor investment information on the web

Water Policy ◽  
2003 ◽  
Vol 5 (3) ◽  
pp. 203-212
Author(s):  
J. Lisa Jorgensona
Keyword(s):  
Water Resources ◽  
Web Sites ◽  
Large Scale ◽  
Technical Assistance ◽  
Water Withdrawal ◽  
Small Scale ◽  
Water Projects ◽  
Essential Information ◽  
Water Project ◽  
The Web

This paper discusses a series of discusses how web sites now report international water project information, and maps the combined donor investment in more than 6000 water projects, active since 1995. The maps show donor investment:  • has addressed water scarcity,  • has improved access to improvised water resources,  • correlates with growth in GDP,  • appears to show a correlation with growth in net private capital flow,  • does NOT appear to correlate with growth in GNI. Evaluation indicates problems in the combined water project portfolios for major donor organizations: •difficulties in grouping projects over differing Sector classifications, food security, or agriculture/irrigation is the most difficult.  • inability to map donor projects at the country or river basin level because 60% of the donor projects include no location data (town, province, watershed) in the title or abstracts available on the web sites.  • no means to identify donor projects with utilization of water resources from training or technical assistance.  • no information of the source of water (river, aquifer, rainwater catchment).  • an identifiable quantity of water (withdrawal amounts, or increased water efficiency) is not provided.  • differentiation between large scale verses small scale projects. Recommendation: Major donors need to look at how the web harvests and combines their information, and look at ways to agree on a standard template for project titles to include more essential information. The Japanese (JICA) and the Asian Development Bank provide good models.

scholarly journals The graph neural networking challenge

2021 ◽  
Vol 51 (3) ◽  
pp. 9-16
Author(s):  
José Suárez-Varela ◽  
Miquel Ferriol-Galmés ◽  
Albert López ◽  
Paul Almasan ◽  
Guillermo Bernárdez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Networks ◽  
Real World ◽  
Large Scale ◽  
Lessons Learned ◽  
Educational Resources ◽  
Global Competition ◽  
International Telecommunication Union ◽  
International Telecommunication ◽  
Broad Audience

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

Face Detection for Real World Application

2021 ◽  
Author(s):  
Dilpreet Singh Brar ◽  
Amit Kumar ◽  
Pallavi ◽  
Usha Mittal ◽  
Pooja Rana
Keyword(s):  
Face Detection ◽  
Real World ◽  
Real World Application
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