Abstract: Methods, systems, and devices supporting managing a data processing flow are described. A device (e.g., an application server) may host a cloud-based collaboration application, such as an interactive document application. The device may receive an instance of a data processing flow for a flow application based on a first user input to the cloud-based collaboration application. The device may receive the instance of the data processing flow from a source device hosting the flow application. The device may embed the flow application in the cloud-based collaboration application. The device may then receive user inputs to the data processing flow from multiple users collaborating on the same flow in the cloud-based collaboration application. Based on the user inputs, the device may modify the instance of the data processing flow and transmit the modified instance back to the source device to synchronize the data processing flow in the flow application.

Abstract: Methods, systems, and devices supporting managing a data processing flow are described. A device (e.g., an application server) may host a cloud-based collaboration application, such as an interactive document application. The device may receive an instance of a data processing flow for a flow application based on a first user input to the cloud-based collaboration application. The device may receive the instance of the data processing flow from a source device hosting the flow application. The device may embed the flow application in the cloud-based collaboration application. The device may then receive user inputs to the data processing flow from multiple users collaborating on the same flow in the cloud-based collaboration application. Based on the user inputs, the device may modify the instance of the data processing flow and transmit the modified instance back to the source device to synchronize the data processing flow in the flow application.

Abstract: The invention relates to a system for filtering messages—the system includes a seed filter having associated therewith a false positive rate and a false negative rate. A new filter is also provided for filtering the messages, the new filter is evaluated according to the false positive rate and the false negative rate of the seed filter, the data used to determine the false positive rate and the false negative rate of the seed filter are utilized to determine a new false positive rate and a new false negative rate of the new filter as a function of threshold. The new filter is employed in lieu of the seed filter if a threshold exists for the new filter such that the new false positive rate and new false negative rate are together considered better than the false positive and the false negative rate of the seed filter.

Abstract: The present invention involves a system and method that facilitate identifying human interaction by utilizing HIPs such as order-based HIPs and determining a difficulty rating of any type of HIPs in an automated fashion. Order-based HIPs require a user to identify elements in the sequence as well as to identify a correct order of the elements in the sequence. The invention involves presenting a user with at least two HIPs such that the HIP can be of known and/or unknown difficulty. A user that correctly answers the HIP of known difficulty gains access to the HIP-controlled resource, action or application. The user's response to the HIP of unknown difficulty can then be examined and employed to determine whether that HIP is too difficult for humans to solve. Alternatively, at least one HIP can be presented. Difficulty of individual HIP parameters can also be determined.

Abstract: The subject invention provides for a feedback loop system and method that facilitate classifying items in connection with spam prevention in server and/or client-based architectures. The invention makes uses of a machine-learning approach as applied to spam filters, and in particular, randomly samples incoming email messages so that examples of both legitimate and junk/spam mail are obtained to generate sets of training data. Users which are identified as spam-fighters are asked to vote on whether a selection of their incoming email messages is individually either legitimate mail or junk mail. A database stores the properties for each mail and voting transaction such as user information, message properties and content summary, and polling results for each message to generate training data for machine learning systems. The machine learning systems facilitate creating improved spam filter(s) that are trained to recognize both legitimate mail and spam mail and to distinguish between them.

Abstract: Described is a technology by which phishing-related data sources are processed into aggregated data and a given site evaluated the aggregated data using a predictive model to automatically determine whether the given site is likely to be a phishing site. The predictive model may be built using machine learning based on training data, e.g., including known phishing sites and/or known non-phishing sites. To determine whether an object corresponding to a site is likely a phishing-related object are described, various criteria are evaluated, including one or more features of the object when evaluated. The determination is output in some way, e.g., made available to a reputation service, used to block access to a site or warn a user before allowing access, and/or used to assist a hand grader in being more efficient in evaluating sites.

Abstract: The present invention involves a system and method that facilitate extracting data from messages for spam filtering. The extracted data can be in the form of features, which can be employed in connection with machine learning systems to build improved filters. Data associated with origination information as well as other information embedded in the body of the message that allows a recipient of the message to contact and/or respond to the sender of the message can be extracted as features. The features, or a subset thereof, can be normalized and/or deobfuscated prior to being employed as features of the machine learning systems. The (deobfuscated) features can be employed to populate a plurality of feature lists that facilitate spam detection and prevention. Exemplary features include an email address, an IP address, a URL, an embedded image pointing to a URL, and/or portions thereof.

Abstract: Extraction analysis techniques biased, in part, by query frequency information from a query log file and/or search engine cache are employed along with machine learning processes to determine candidate keywords and/or phrases of web documents. Web oriented features associated with the candidate keywords and/or phrases are also utilized to analyze the web documents. A keyword and/or phrase extraction mechanism can be utilized to score keywords and/or phrases in a web document and estimate a likelihood that the keywords and/or phrases are relevant, for example, in an advertising system and the like.

Abstract: Embodiment of proofs to filter spam are presented herein.

Abstract: While interacting with a messaging interface, keywords or other search criteria are automatically identified and used to perform a search. Search results and sponsored links (or advertisements) are displayed to the user within the messaging interface. To alleviate privacy concerns, this process will not be performed unless the user has explicitly opted-in to the search feature. In another embodiment, the user can highlight keywords in an outbound or inbound message to trigger a search without leaving the messaging interface. In another embodiment, the user can input a search keyword or phrase to trigger a search without leaving the messaging interface.

Abstract: The user interfaces, methods and systems described herein facilitate user interaction with an ad space by conveying additional advertising content via a preview pane and facilitate automatically generating the content of the preview pane. By way of example, an electronic advertisement is conveyed to a user in an ad space provided by a third party, and a secondary advertisement generating component automatically generates at least part of the content of a secondary advertisement. The secondary advertisement provides content associated with the electronic advertisement and occurs upon receiving a user indication. A context acquiring component also may provide context information to the secondary advertisement generating component to automatically generate at least part of the content of the secondary advertisement. By way of another example, a user is provided with one or more ads from a plurality of different advertisers in a first ad space maintained by an ad space supplier.

Abstract: The subject invention provides for systems and methods that facilitate optimizing one or mores sets of training data by utilizing an Exponential distribution as the prior on one or more parameters in connection with a maximum entropy (maxent) model to mitigate overfitting. Maxent is also known as logistic regression. More specifically, the systems and methods can facilitate optimizing probabilities that are assigned to the training data for later use in machine learning processes, for example. In practice, training data can be assigned their respective weights and then a probability distribution can be assigned to those weights.

Abstract: Decision trees populated with classifier models are leveraged to provide enhanced spam detection utilizing separate email classifiers for each feature of an email. This provides a higher probability of spam detection through tailoring of each classifier model to facilitate in more accurately determining spam on a feature-by-feature basis. Classifiers can be constructed based on linear models such as, for example, logistic-regression models and/or support vector machines (SVM) and the like. The classifiers can also be constructed based on decision trees. “Compound features” based on internal and/or external nodes of a decision tree can be utilized to provide linear classifier models as well. Smoothing of the spam detection results can be achieved by utilizing classifier models from other nodes within the decision tree if training data is sparse. This forms a base model for branches of a decision tree that may not have received substantial training data.

Abstract: Phishing detection, prevention, and notification is described. In an embodiment, a messaging application facilitates communication via a messaging user interface, and receives a communication, such as an email message, from a domain. A phishing detection module detects a phishing attack in the communication by determining that the domain is similar to a known phishing domain, or by detecting suspicious network properties of the domain. In another embodiment, a Web browsing application receives content, such as data for a Web page, from a network-based resource, such as a Web site or domain. The Web browsing application initiates a display of the content, and a phishing detection module detects a phishing attack in the content by determining that a domain of the network-based resource is similar to a known phishing domain, or that an address of the network-based resource from which the content is received has suspicious network properties.

Abstract: Phishing detection, prevention, and notification is described. In an embodiment, a messaging application facilitates communication via a messaging user interface, and receives a communication, such as an email message, from a domain. A phishing detection module detects a phishing attack in the communication by determining that the domain is similar to a known phishing domain, or by detecting suspicious network properties of the domain. In another embodiment, a Web browsing application receives content, such as data for a Web page, from a network-based resource, such as a Web site or domain. The Web browsing application initiates a display of the content, and a phishing detection module detects a phishing attack in the content by determining that a domain of the network-based resource is similar to a known phishing domain, or that an address of the network-based resource from which the content is received has suspicious network properties.

Abstract: A system and method facilitating training machine learning systems utilizing sequential conditional generalized iterative scaling is provided. The invention includes an expected value update component that modifies an expected value based, at least in part, upon a feature function of an input vector and an output value, a sum of lambda variable and a normalization variable. The invention further includes an error calculator that calculates an error based, at least in part, upon the expected value and an observed value. The invention also includes a parameter update component that modifies a trainable parameter based, at least in part, upon the error. A variable update component that updates at least one of the sum of lambda variable and the normalization variable based, at least in part, upon the error is also provided.

Abstract: An advertisement sales system comprises a receiver component that receives a request to purchase impressions on at least one of web pages and application programs based at least in part on one of an exact and approximate keyword match. A sales component sells a percentage of all such impressions to an initiator of the request. For instance, an approximate keyword match can be a match of one of a prefix and a suffix of a phrase.

Abstract: A computer-implemented system that facilitates sale of advertising space on a plurality of web pages by way of a posted-price market comprises an interface component that receives pricing information relating to a plurality of spaces on the plurality of search pages. A posting component communicatively coupled to the interface component posts the pricing information so that the pricing information is accessible to a prospective buyer. For example, the pricing information can be based at least in part on one or more of size of a space on a search page, location of a space on a search page, duration that a space will be utilized for advertising, and demand associated with a space on a search page. In accordance with another aspect of the subject invention, the pricing information can relate to a percentage of page views that will display advertisements relating to a purchaser on at least one of the plurality of spaces.

Abstract: A computer-implemented system that facilitates sale of advertising space on a plurality of web pages by way of a posted-price market comprises an interface component that receives pricing information relating to a plurality of spaces on the plurality of search pages. A posting component communicatively coupled to the interface component posts the pricing information so that the pricing information is accessible to a prospective buyer. For example, the pricing information can be based at least in part on one or more of size of a space on a search page, location of a space on a search page, duration that a space will be utilized for advertising, and demand associated with a space on a search page. In accordance with another aspect of the subject invention, the pricing information can relate to a percentage of page views that will display advertisements relating to a purchaser on at least one of the plurality of spaces.

Abstract: The subject invention provides a unique system and method that facilitates displaying content-targeted advertisements within applications running on an end-user or client computer. To mitigate privacy concerns, one or more advertisements can be stored on a client computer. At least one advertisement can be displayed on the client based at least in part on the context relating to the user's interaction with the client. By doing so, the user's private data or content is not passed to the server—meanwhile content-targeted advertising processing can be run on the user's content to determine which advertisements to display. Different forms of advertisements can be displayed to the user when the client is offline or online to facilitate optimizing use interaction with the advertisements and billing capabilities. To ensure that advertisements are displayed when content is displayed on a client, various encryption and decryption techniques can be employed to mitigate tampering of advertisement display code.