The US Federal Trade Commission has issued a warning to search engines that they need to clearly mark all results that are advertisements. The FTC issued letters 24 June 2013 to firms including AOL, Bing, DuckDuckGo, Google, and Yahoo—plus companies offering specialized search in areas such as local businesses, travel, and shopping—stating that they need to clearly distinguish between paid advertising and routine search results. The agency said paid search results have become increasingly less distinguishable as advertising. Search engine companies, particularly Google, have recently faced similar complaints from European government agencies. The FTC can levy fines for noncompliance with regulations regarding deceptive advertisements. (http://www.siliconvalley.com/news/ci_23542318/google-yahoo-microsoft-told-label-ads-better-search)(CNET)(United States Federal Trade Commission)
Sunday, July 28, 2013
US Agency: Search Engines Must Clearly Label Ads
Friday, February 1, 2013
Smart search engines for news videos
Anyone who has visited one of the big online video portals or TV broadcasters’ media libraries to search for a video clip is already familiar with the search engines tasked with seeking out and flagging video footage. However, these engines have their weaknesses. Their results are based on automatic search algorithms that often go by text-based information alone. Although they can be used to locate and identify videos, a comparison of individual sequences is still very difficult. To make search engines even smarter, the Fraunhofer Institute for Digital Media Technology IDMT in Ilmenau has developed a piece of software called “NewsHistory” that will now make full use of user knowledge as well. Researchers will be presenting an initial demonstration version of the smart video search engine at the CeBIT trade fair in Hannover.
Technology learns from users
“NewsHistory provides users with search algorithms, a data model and a web-based user interface so that they can locate identical sequences within various news videos,” explains Patrick Aichroth from Fraunhofer IDMT. He is responsible for coordinating the institute’s R&D work within the EU’s CUbRIK project. Here, researchers are harnessing user knowledge to optimize and extend the capabilities of automated analysis techniques. “The search engine learns from each individual user, allowing it to keep improving search results. Not only does this improve the quality of results, but the resources needed to undertake the analysis are also cut down,” Aichroth continues.
NewsHistory allows each user to add additional information to the results generated by the search engine, including production and broadcast date, sources and keywords for videos. It is also possible to rate the results. Finally, the user’s search itself is a source of information, providing data that is incorporated into the search engine; the metadata of a newly uploaded video, for instance, passes into the database.
“Comparing digital video data online or within video databases is very complex,” explains Christian Weigel from the Audio-Visual Systems research group at the IDMT. “Videos that share the same content have for the most part been edited, meaning that they are scaled and encoded in a variety of formats. Also, search engines are often unable to distinguish images cropped from a larger picture, lower thirds or the zoom shots so popular with US news channels.”
The demonstration version being presented at CeBIT will investigate how a selection of TV channels have made use of film footage, changed its form and broadcast it. The user interface displays commonalities and appraises them in graphic form. The search itself is conducted either by inputting text or by directly uploading individual video sequences. The researchers’ aim is to make the software sufficiently robust that it could also be used in the future to compare the multimedia content found on big online media portals. The scientists do not imagine archivists or journalists will be the only users. “NewsHistory is of particular interest to media and market researchers, say if they want to assess the televised political duels coming up this year,” concludes Weigel.
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Wednesday, September 19, 2012
Search technology that can gauge opinion and predict the future
'Do a search for the word "climate" on Google or another search engine and what you will get back is basically a list of results featuring that word: there's no categorisation, no specific order, no context. Current search engines do not take into account the dimensions of diversity: factors such as when the information was published, if there is a bias toward one opinion or another inherent in the content and structure, who published it and when,' explains Fausto Giunchiglia, a professor of computer science at the University of Trento in Italy.
But can search technology be made to identify and embrace diversity? Can a search engine tell you, for example, how public opinion about climate change has changed over the last decade? Or how hot the weather will be a century from now, by aggregating current and past estimates from different sources?
It seems that it can, thanks to a pioneering combination of modern science and a decades-old classification method, brought together by European researchers in the LivingKnowledge (1) project. Supported by EUR 4.8 million in funding from the European Commission, the LivingKnowledge team, coordinated by Prof. Giunchiglia, adopted a multidisciplinary approach to developing new search technology, drawing on fields as diverse as computer science, social science, semiotics and library science.
Indeed, the so-called father of library science, Sirkali Ramamrita Ranganathan, an Indian librarian, served as a source of inspiration for the researchers. In the 1920s and 1930s, Ranganathan developed the first major analytico-synthetic, or faceted, classification system. Using this approach, objects -- books, in the case of Ranganathan; web and database content, in the case of the LivingKnowlege team -- are assigned multiple characteristics and attributes (facets), enabling the classification to be ordered in multiple ways, rather than in a single, predetermined, taxonomic order. Using the system, an article about the effects on agriculture of climate change written in Norway in 1990 might be classified as 'Geography; Climate; Climate change; Agriculture; Research; Norway; 1990.'
In order to understand the classification system better and implement it in search engine technology, the LivingKnowledge researchers turned to the Indian Statistical Institute, a project partner, which uses faceted classification on a daily basis.
'Using their knowledge we were able to turn Ranganathan's pseudo-algorithm into a computer algorithm and the computer scientists were able to use it to mine data from the web, extract its meaning and context, assign facets to it, and use these to structure the information based on the dimensions of diversity,' Prof. Giunchiglia says.
Researchers at the University of Pavia in Italy, another partner, drew on their expertise in extracting meaning from web content -- not just from text and multimedia content, but also from the way the information is structured and laid out -- in order to infer bias and opinions, adding another facet to the data.
'We are able to identify the bias of authors on a certain subject and whether their opinions are positive or negative,' the LivingKnowledge coordinator says. 'Facts are facts, but any information about an event, or on any subject, is often surrounded by opinions and bias.'
From libraries of the 1930s to space travel in 2034...
The technology was implemented in a testbed, now available as open source software, and used for trials based around two intriguing application scenarios.
Working with Austrian social research institute SORA, the team used the LivingKnowledge system to identify social trends and monitor public opinion in both quantitative and qualitative terms. Used for media content analysis, the system could help a company understand the impact of a new advertising campaign, showing how it has affected brand recognition over time and which social groups have been most receptive. Alternatively, a government might use the system to gauge public opinion about a new policy, or a politician could use it to respond in the most publicly acceptable way to a rival candidate's claims.
With Barcelona Media, a non-profit research foundation supported by Yahoo!, and with the Netherlands-based Internet Memory Foundation, the LivingKnowledge team looked not only at current and past trends, but extrapolated them and drew on forecasts extracted from existing data to try to predict the future. Their Future Predictor application is able to make searches based on questions such as 'What will oil prices be in 2050?' or 'How much will global temperatures rise over the next 100 years?' and find relevant information and forecasts from today's web. For example, a search for the year 2034 turns up 'space travel' as the most relevant topic indexed in today's news.
'More immediately, this application scenario provides functionality for detecting trends even before these trends become apparent in daily events -- based on integrated search and navigation capabilities for finding diverse, multi-dimensional information depending on content, bias and time,' Prof. Giunchiglia explains.
Several of the project partners have plans to implement the technology commercially, and the project coordinator intends to set up a non-profit foundation to build on the LivingKnowledge results at a time when demand for this sort of technology is only likely to increase.
As Prof. Giunchiglia points out, Google fundamentally changed the world by providing everyone with access to much of the world's information, but it did it for people: currently only humans can understand the meaning of all that data, so much so that information overload is a common problem. As we move into a 'big data' age in which information about everything and anything is available at the touch of a button, the meaning of that information needs to be understandable not just by humans but also by machines, so quantity must come combined with quality. The LivingKnowledge approach addresses that problem.
'When we started the project, no one was talking about big data. Now everyone is and there is increasing interest in this sort of technology,' Prof. Giunchiglia says. 'The future will be all about big data -- we can't say whether it will be good or bad, but it will certainly be different.'
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The above story is reprinted from materials provided by CORDIS Features, formerly ICT Results.
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Monday, September 10, 2012
Search engine for social networks based on the behavior of ants
One of the main technical questions in the field of social networks, whose use is becoming more and more generalized, consists in locating the chain of reference that leads from one person to another, from one node to another. The greatest challenges that are presented in this area is the enormous size of these networks and the fact that the response must be rapid, given that the final user expects results in the shortest time possible. In order to find a solution to this problem, these researchers from UC3M have developed an algorithm SoSACO, which accelerates the search for routes between two nodes that belong to a graph that represents a social network.
The way SoSACO works was inspired by behavior that has been perfected over thousands of years by one of the most disciplined insects on the planet when they search for food. In general, the algorithms used by colonies of ants imitate how they are capable of finding the path between the anthill and the source of food by secreting and following a chemical trail, called a pheromone, which is deposited on the ground.
"In this study -- the authors explain -- other scented trails are also included so that the ants can follow both the pheromone as well as the scent of the food, which allows them to find the food source much more quickly." The main results of this research, which was carried out by Jessica Rivero in UC3M's Laboratorio de Bases de Datos Avanzadas (The Advanced Data Bases Laboratory -- LABDA) as part of her doctoral thesis, are summarized in a scientific article published in the journal Applied Intelligence. "The early results show that the application of this algorithm to real social networks obtains an optimal response in a very short time (tens of milliseconds)," Jessica Rivero states.
Multiple applications
Thanks to this new search algorithm, the system can find these routes more easily, and without modifying the structure of graph (an image that uses nodes and links to represents the relationships among a set of elements). "This advance allows us to solve many problems that we find in the real world, because the scenarios in which they occur can be modeled by a graph," the researchers explain. Thus, it could be applied in many different scenarios, such as to improve locating routes in FPS systems or in on-line games, to plan deliveries for freight trucks, to know if two words are somehow related or to simply know exactly which affinities two Facebook or Twitter users, for example, have in common.
This research, which has received support from the Autonomous Community of Madrid (MA2VICMR, S2009/TIC-1542) and the Ministry of Education and Science (Ministerio de Educación y Ciencia), began as part of the SOPAT project (TSI-020110-2009-419), in response to the need to guide a hotel's clients using a natural interaction system. Jessica Rivero's doctoral thesis, which deals with this subject, is titled "Búsqueda Rápida de Caminos en Grafos de Alta Cardinalidad Estáticos y Dinámicos" ("A Quick Search for Routes in Static and Dynamic Graphs of High Cardinality"); it was directed by Francisco Javier Calle y Mª Dolores Cuadra, professors in the LABDA of the Computer Science Department, and received a grade of Apto-Cum Laude (Pass-Cum Laude).
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The above story is reprinted from materials provided by Universidad Carlos III de Madrid - Oficina de Información Científica, via AlphaGalileo.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Note: If no author is given, the source is cited instead.
Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.
Sunday, August 26, 2012
Google goes cancer: Search engine algorithm finds cancer biomarkers
The researcher's own version of the Google algorithm has been used in this study to find new cancer biomarkers, which are molecules produced by cancer cells. Biomarkers can help to detect cancer earlier in body fluids or directly in the cancer tissue obtained in an operation or biopsy. Finding these biomarkers is often difficult and time consuming. Another problem is that markers found in different studies for the same types of cancer almost never overlap.
This problem has been circumvented using the Google strategy, which takes into account the content of a web page and also how these pages are connected via hyperlinks. With this strategy as the model, the authors made use of the fact that proteins in a cell are connected through a network of physical and regulatory interactions; the 'protein Facebook' so to speak.
"Once we added the network information in our analysis, our biomarkers became more reproducible," said Christof Winter, the paper's first author. Using this network information and the Google Algorithm, a significant overlap was found with an earlier study from the University of North Carolina. There, a connection was made with a protein which can assess aggressiveness in pancreatic cancer.
Although the new biomarkers seem to mark an improvement over currently used diagnostic tools, they are far from perfect and still need to be validated in a larger follow-up study before they can be used in clinical practice. It remains an open problem to turn these insights into novel drugs which slow down cancer progression. A first step in this direction is the group's cooperation with the Dresden-based biotech company RESprotect, who are running a clinical trial on a pancreas cancer drug.
TU Dresden is a leading German university, whose Center for Regenerative Therapies was awarded excellence status in the national excellence initiative. The work was a cooperation between the bioinformatics group of Prof. Dr. Michael Schroeder and the medical groups of Dr. Christian Pilarsky and Prof. Robert Grützmann.
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The above story is reprinted from materials provided by Public Library of Science.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Journal Reference:
Christof Winter, Glen Kristiansen, Stephan Kersting, Janine Roy, Daniela Aust, Thomas Knösel, Petra Rümmele, Beatrix Jahnke, Vera Hentrich, Felix Rückert, Marco Niedergethmann, Wilko Weichert, Marcus Bahra, Hans J. Schlitt, Utz Settmacher, Helmut Friess, Markus Büchler, Hans-Detlev Saeger, Michael Schroeder, Christian Pilarsky, Robert Grützmann. Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes. PLoS Computational Biology, 2012; 8 (5): e1002511 DOI: 10.1371/journal.pcbi.1002511Note: If no author is given, the source is cited instead.
Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.
Saturday, August 18, 2012
Quantum computers could help search engines keep up with the Internet's growth
Behind the scenes, a lot of math goes into figuring out exactly what qualifies as most relevant web page for your search. Google, for example, uses a page ranking algorithm that is rumored to be the largest numerical calculation carried out anywhere in the world. With the web constantly expanding, researchers at USC have proposed -- and demonstrated the feasibility -- of using quantum computers to speed up that process.
"This work is about trying to speed up the way we search on the web," said Daniel Lidar, corresponding author of a paper on the research that appeared in the journal Physical Review Letters on June 4.
As the Internet continues to grow, the time and resources needed to run the calculation -- which is done daily -- grow with it, Lidar said.
Lidar, who holds appointments at the USC Viterbi School of Engineering and the USC Dornsife College of Letters, Arts and Sciences, worked with colleagues Paolo Zanardi of USC Dornsife and first author Silvano Garnerone, formerly a postdoctoral researcher at USC and now of the University of Waterloo, to see whether quantum computing could be used to run the Google algorithm faster.
As opposed to traditional computer bits, which can encode distinctly either a one or a zero, quantum computers use quantum bits or "qubits," which can encode a one and a zero at the same time. This property, called superposition, some day will allow quantum computers to perform certain calculations much faster than traditional computers.
Currently, there isn't a quantum computer in the world anywhere near large enough to run Google's page ranking algorithm for the entire web. To simulate how a quantum computer might perform, the researchers generated models of the web that simulated a few thousand web pages.
The simulation showed that a quantum computer could, in principle, return the ranking of the most important pages in the web faster than traditional computers, and that this quantum speedup would improve the more pages needed to be ranked. Further, the researchers showed that to simply determine whether the web's page rankings should be updated, a quantum computer would be able to spit out a yes-or-no answer exponentially faster than a traditional computer.
This research was funded by number of sources, including the National Science Foundation, the NASA Ames Research Center, the Lockheed Martin Corporation University Research Initiative program, and a Google faculty research award to Lidar.
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The above story is reprinted from materials provided by University of Southern California.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Journal Reference:
Silvano Garnerone, Paolo Zanardi, Daniel Lidar. Adiabatic Quantum Algorithm for Search Engine Ranking. Physical Review Letters, 2012; 108 (23) DOI: 10.1103/PhysRevLett.108.230506Note: If no author is given, the source is cited instead.
Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.
Thursday, July 19, 2012
Search engine for social networks based on the behavior of ants
One of the main technical questions in the field of social networks, whose use is becoming more and more generalized, consists in locating the chain of reference that leads from one person to another, from one node to another. The greatest challenges that are presented in this area is the enormous size of these networks and the fact that the response must be rapid, given that the final user expects results in the shortest time possible. In order to find a solution to this problem, these researchers from UC3M have developed an algorithm SoSACO, which accelerates the search for routes between two nodes that belong to a graph that represents a social network.
The way SoSACO works was inspired by behavior that has been perfected over thousands of years by one of the most disciplined insects on the planet when they search for food. In general, the algorithms used by colonies of ants imitate how they are capable of finding the path between the anthill and the source of food by secreting and following a chemical trail, called a pheromone, which is deposited on the ground.
"In this study -- the authors explain -- other scented trails are also included so that the ants can follow both the pheromone as well as the scent of the food, which allows them to find the food source much more quickly." The main results of this research, which was carried out by Jessica Rivero in UC3M's Laboratorio de Bases de Datos Avanzadas (The Advanced Data Bases Laboratory -- LABDA) as part of her doctoral thesis, are summarized in a scientific article published in the journal Applied Intelligence. "The early results show that the application of this algorithm to real social networks obtains an optimal response in a very short time (tens of milliseconds)," Jessica Rivero states.
Multiple applications
Thanks to this new search algorithm, the system can find these routes more easily, and without modifying the structure of graph (an image that uses nodes and links to represents the relationships among a set of elements). "This advance allows us to solve many problems that we find in the real world, because the scenarios in which they occur can be modeled by a graph," the researchers explain. Thus, it could be applied in many different scenarios, such as to improve locating routes in FPS systems or in on-line games, to plan deliveries for freight trucks, to know if two words are somehow related or to simply know exactly which affinities two Facebook or Twitter users, for example, have in common.
This research, which has received support from the Autonomous Community of Madrid (MA2VICMR, S2009/TIC-1542) and the Ministry of Education and Science (Ministerio de Educación y Ciencia), began as part of the SOPAT project (TSI-020110-2009-419), in response to the need to guide a hotel's clients using a natural interaction system. Jessica Rivero's doctoral thesis, which deals with this subject, is titled "Búsqueda Rápida de Caminos en Grafos de Alta Cardinalidad Estáticos y Dinámicos" ("A Quick Search for Routes in Static and Dynamic Graphs of High Cardinality"); it was directed by Francisco Javier Calle y Mª Dolores Cuadra, professors in the LABDA of the Computer Science Department, and received a grade of Apto-Cum Laude (Pass-Cum Laude).
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The above story is reprinted from materials provided by Universidad Carlos III de Madrid - Oficina de Información Científica, via AlphaGalileo.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Note: If no author is given, the source is cited instead.
Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.
Sunday, July 15, 2012
Quantum computers could help search engines keep up with the Internet's growth
Behind the scenes, a lot of math goes into figuring out exactly what qualifies as most relevant web page for your search. Google, for example, uses a page ranking algorithm that is rumored to be the largest numerical calculation carried out anywhere in the world. With the web constantly expanding, researchers at USC have proposed -- and demonstrated the feasibility -- of using quantum computers to speed up that process.
"This work is about trying to speed up the way we search on the web," said Daniel Lidar, corresponding author of a paper on the research that appeared in the journal Physical Review Letters on June 4.
As the Internet continues to grow, the time and resources needed to run the calculation -- which is done daily -- grow with it, Lidar said.
Lidar, who holds appointments at the USC Viterbi School of Engineering and the USC Dornsife College of Letters, Arts and Sciences, worked with colleagues Paolo Zanardi of USC Dornsife and first author Silvano Garnerone, formerly a postdoctoral researcher at USC and now of the University of Waterloo, to see whether quantum computing could be used to run the Google algorithm faster.
As opposed to traditional computer bits, which can encode distinctly either a one or a zero, quantum computers use quantum bits or "qubits," which can encode a one and a zero at the same time. This property, called superposition, some day will allow quantum computers to perform certain calculations much faster than traditional computers.
Currently, there isn't a quantum computer in the world anywhere near large enough to run Google's page ranking algorithm for the entire web. To simulate how a quantum computer might perform, the researchers generated models of the web that simulated a few thousand web pages.
The simulation showed that a quantum computer could, in principle, return the ranking of the most important pages in the web faster than traditional computers, and that this quantum speedup would improve the more pages needed to be ranked. Further, the researchers showed that to simply determine whether the web's page rankings should be updated, a quantum computer would be able to spit out a yes-or-no answer exponentially faster than a traditional computer.
This research was funded by number of sources, including the National Science Foundation, the NASA Ames Research Center, the Lockheed Martin Corporation University Research Initiative program, and a Google faculty research award to Lidar.
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Story Source:
The above story is reprinted from materials provided by University of Southern California.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Journal Reference:
Silvano Garnerone, Paolo Zanardi, Daniel Lidar. Adiabatic Quantum Algorithm for Search Engine Ranking. Physical Review Letters, 2012; 108 (23) DOI: 10.1103/PhysRevLett.108.230506Note: If no author is given, the source is cited instead.
Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.
Saturday, June 2, 2012
Google goes cancer: Search engine algorithm finds cancer biomarkers
The researcher's own version of the Google algorithm has been used in this study to find new cancer biomarkers, which are molecules produced by cancer cells. Biomarkers can help to detect cancer earlier in body fluids or directly in the cancer tissue obtained in an operation or biopsy. Finding these biomarkers is often difficult and time consuming. Another problem is that markers found in different studies for the same types of cancer almost never overlap.
This problem has been circumvented using the Google strategy, which takes into account the content of a web page and also how these pages are connected via hyperlinks. With this strategy as the model, the authors made use of the fact that proteins in a cell are connected through a network of physical and regulatory interactions; the 'protein Facebook' so to speak.
"Once we added the network information in our analysis, our biomarkers became more reproducible," said Christof Winter, the paper's first author. Using this network information and the Google Algorithm, a significant overlap was found with an earlier study from the University of North Carolina. There, a connection was made with a protein which can assess aggressiveness in pancreatic cancer.
Although the new biomarkers seem to mark an improvement over currently used diagnostic tools, they are far from perfect and still need to be validated in a larger follow-up study before they can be used in clinical practice. It remains an open problem to turn these insights into novel drugs which slow down cancer progression. A first step in this direction is the group's cooperation with the Dresden-based biotech company RESprotect, who are running a clinical trial on a pancreas cancer drug.
TU Dresden is a leading German university, whose Center for Regenerative Therapies was awarded excellence status in the national excellence initiative. The work was a cooperation between the bioinformatics group of Prof. Dr. Michael Schroeder and the medical groups of Dr. Christian Pilarsky and Prof. Robert Grützmann.
Share this story on Facebook, Twitter, and Google:Other social bookmarking and sharing tools:
Story Source:
The above story is reprinted from materials provided by Public Library of Science.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Journal Reference:
Christof Winter, Glen Kristiansen, Stephan Kersting, Janine Roy, Daniela Aust, Thomas Knösel, Petra Rümmele, Beatrix Jahnke, Vera Hentrich, Felix Rückert, Marco Niedergethmann, Wilko Weichert, Marcus Bahra, Hans J. Schlitt, Utz Settmacher, Helmut Friess, Markus Büchler, Hans-Detlev Saeger, Michael Schroeder, Christian Pilarsky, Robert Grützmann. Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes. PLoS Computational Biology, 2012; 8 (5): e1002511 DOI: 10.1371/journal.pcbi.1002511Note: If no author is given, the source is cited instead.
Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.