Abstract: The present disclosure generally relates to web page analysis, and more particularly to a classification system for web pages. The classification system may classify a web page as malicious based upon one or more signatures generated for the web page. For example, the classification system may compare one or more signatures generated for a first web page to one or more signatures generated for a second web page, where the first web page and the second web page are the same web page at different times or different web pages. Based upon a similarity of the signatures, the classification system may output whether the first web page is malicious. For another example, the classification system may include a classification model that is trained based upon one or more signatures for one or more classified web pages. The classification model may output whether the web page is malicious.

Abstract: A computing system is described that determines, based on user-initiated actions performed by a group of computing devices, an intent of a search using a particular search query received from a computing device. The computing system adjusts, based on the intent, at least a particular portion of search results obtained from the search using the search query by emphasizing information that satisfies the intent. The computing system sends, to the computing device, an indication of the adjusted search results.

Abstract: The present disclosure generally relates to web page analysis, and more particularly to detecting malicious behavior using an accomplice model. In certain embodiments, the accomplice model may determine that a URI is associated with malicious behavior based upon the URI being associated with an attribute determined to be related to malicious behavior. Examples of an attribute include a host system, a domain, or an element of a document used to render the web page. Examples of an element of a document used to render the web page may include an active/dynamic element (e.g., a function, a script, etc.) or an inactive/static element (e.g., a string, a number, a frame, a tracking username, a social networking username, etc.).

Abstract: The present disclosure generally relates to web page analysis, and more particularly to detecting malicious behavior using an accomplice model. In certain embodiments, the accomplice model may determine that a URI is associated with malicious behavior based upon the URI being associated with an attribute determined to be related to malicious behavior. Examples of an attribute include a host system, a domain, or an element of a document used to render the web page. Examples of an element of a document used to render the web page may include an active/dynamic element (e.g., a function, a script, etc.) or an inactive/static element (e.g., a string, a number, a frame, a tracking username, a social networking username, etc.).

Abstract: A computing system is described that determines, based on user-initiated actions performed by a group of computing devices, an intent of a search using a particular search query received from a computing device. The computing system adjusts, based on the intent, at least a particular portion of search results obtained from the search using the search query by emphasizing information that satisfies the intent. The computing system sends, to the computing device, an indication of the adjusted search results.

Abstract: A computing system is described that determines, based on user-initiated actions performed by a group of computing devices, an intent of a search using a particular search query received from a computing device. The computing system adjusts, based on the intent, at least a particular portion of search results obtained from the search using the search query by emphasizing information that satisfies the intent. The computing system sends, to the computing device, an indication of the adjusted search results.

Abstract: Embodiments are directed to using a hash signature of a rendered DOM object of a website to find similar content and behavior on other websites. Embodiments break a DOM into a large number of data portions (i.e., “shingles”), apply a hashing algorithm to the shingles, select a predetermined number of hashes from the hashed shingles according to a selection criteria to create a hash signature, and compare the hash signature to that of a reference page to determine similarity of website DOM object content. Embodiments can be used to identify phishing websites, defaced websites, spam websites, significant changes in the content of a webpage, copyright infringement, and any other suitable purposes related to the similarity between website DOM object content.

Abstract: The present disclosure generally relates to web page analysis, and more particularly to detecting malicious behavior using an accomplice model. In certain embodiments, the accomplice model may determine that a URI is associated with malicious behavior based upon the URI being associated with an attribute determined to be related to malicious behavior. Examples of an attribute include a host system, a domain, or an element of a document used to render the web page. Examples of an element of a document used to render the web page may include an active/dynamic element (e.g., a function, a script, etc.) or an inactive/static element (e.g., a string, a number, a frame, a tracking username, a social networking username, etc.).

Abstract: The present disclosure generally relates to web page analysis, and more particularly to detecting malicious behavior using an accomplice model. In certain embodiments, the accomplice model may determine that a URI is associated with malicious behavior based upon the URI being associated with an attribute determined to be related to malicious behavior. Examples of an attribute include a host system, a domain, or an element of a document used to render the web page. Examples of an element of a document used to render the web page may include an active/dynamic element (e.g., a function, a script, etc.) or an inactive/static element (e.g., a string, a number, a frame, a tracking username, a social networking username, etc.).

Abstract: Embodiments are directed to using a hash signature of a rendered DOM object of a website to find similar content and behavior on other websites. Embodiments break a DOM into a large number of data portions (i.e., “shingles”), apply a hashing algorithm to the shingles, select a predetermined number of hashes from the hashed shingles according to a selection criteria to create a hash signature, and compare the hash signature to that of a reference page to determine similarity of website DOM object content. Embodiments can be used to identify phishing websites, defaced websites, spam websites, significant changes in the content of a webpage, copyright infringement, and any other suitable purposes related to the similarity between website DOM object content.

Abstract: A computing system is described that determines, based on user-initiated actions performed by a group of computing devices, an intent of a search using a particular search query received from a computing device. The computing system adjusts, based on the intent, at least a particular portion of search results obtained from the search using the search query by emphasizing information that satisfies the intent. The computing system sends, to the computing device, an indication of the adjusted search results.

Abstract: Embodiments are directed to using a hash signature of a rendered DOM object of a website to find similar content and behavior on other websites. Embodiments break a DOM into a large number of data portions (i.e., “shingles”), apply a hashing algorithm to the shingles, select a predetermined number of hashes from the hashed shingles according to a selection criteria to create a hash signature, and compare the hash signature to that of a reference page to determine similarity of website DOM object content. Embodiments can be used to identify phishing websites, defaced websites, spam websites, significant changes in the content of a webpage, copyright infringement, and any other suitable purposes related to the similarity between website DOM object content.

Abstract: The present disclosure generally relates to web page analysis, and more particularly to a classification system for web pages. The classification system may classify a web page as malicious based upon one or more signatures generated for the web page. For example, the classification system may compare one or more signatures generated for a first web page to one or more signatures generated for a second web page, where the first web page and the second web page are the same web page at different times or different web pages. Based upon a similarity of the signatures, the classification system may output whether the first web page is malicious. For another example, the classification system may include a classification model that is trained based upon one or more signatures for one or more classified web pages. The classification model may output whether the web page is malicious.

Abstract: The present disclosure generally relates to web page analysis, and more particularly to detecting malicious behavior using an accomplice model. In certain embodiments, the accomplice model may determine that a URI is associated with malicious behavior based upon the URI being associated with an attribute determined to be related to malicious behavior. Examples of an attribute include a host system, a domain, or an element of a document used to render the web page. Examples of an element of a document used to render the web page may include an active/dynamic element (e.g., a function, a script, etc.) or an inactive/static element (e.g., a string, a number, a frame, a tracking username, a social networking username, etc.).

Abstract: A computing system is described that determines, based on survey information, a group of users of computing devices who have expressed interest in an event, wherein the group of users is included in a plurality of users, and wherein each of the plurality of users has an interest profile. The computing system also determines, based on the interest profiles for each of the users in the group of users, one or more interests indicating interest in the event, and identifies, based on the one or more interests indicating interest in the event, a user not included in the group of users that has an interest profile including the one or more interests indicating interest in the event. The computing system also transmits, to a computing device associated with the user, information about the event.

Abstract: Embodiments are directed to using a hash signature of a rendered DOM object of a website to find similar content and behavior on other websites. Embodiments break a DOM into a large number of data portions (i.e., “shingles”), apply a hashing algorithm to the shingles, select a predetermined number of hashes from the hashed shingles according to a selection criteria to create a hash signature, and compare the hash signature to that of a reference page to determine similarity of website DOM object content. Embodiments can be used to identify phishing websites, defaced websites, spam websites, significant changes in the content of a webpage, copyright infringement, and any other suitable purposes related to the similarity between website DOM object content.

Abstract: Embodiments are directed to using a hash signature of a rendered DOM object of a website to find similar content and behavior on other websites. Embodiments break a DOM into a large number of data portions (i.e., “shingles”), apply a hashing algorithm to the shingles, select a predetermined number of hashes from the hashed shingles according to a selection criteria to create a hash signature, and compare the hash signature to that of a reference page to determine similarity of website DOM object content. Embodiments can be used to identify phishing websites, defaced websites, spam websites, significant changes in the content of a webpage, copyright infringement, and any other suitable purposes related to the similarity between website DOM object content.

Abstract: Embodiments are directed to using a hash signature of a rendered DOM object of a website to find similar content and behavior on other websites. Embodiments break a DOM into a large number of data portions (i.e., “shingles”), apply a hashing algorithm to the shingles, select a predetermined number of hashes from the hashed shingles according to a selection criteria to create a hash signature, and compare the hash signature to that of a reference page to determine similarity of website DOM object content. Embodiments can be used to identify phishing websites, defaced websites, spam websites, significant changes in the content of a webpage, copyright infringement, and any other suitable purposes related to the similarity between website DOM object content.

Abstract: Embodiments are directed to using a hash signature of a rendered DOM object of a website to find similar content and behavior on other websites. Embodiments break a DOM into a large number of data portions (i.e., “shingles”), apply a hashing algorithm to the shingles, select a predetermined number of hashes from the hashed shingles according to a selection criteria to create a hash signature, and compare the hash signature to that of a reference page to determine similarity of website DOM object content. Embodiments can be used to identify phishing websites, defaced websites, spam websites, significant changes in the content of a webpage, copyright infringement, and any other suitable purposes related to the similarity between website DOM object content.

Abstract: There is disclosed a co-oxidation method and a reagent system of co-oxidation agents for removing from a contaminated zone below the ground surface a water-immiscible organic compound having a density greater than that of water from a contaminated zone below the ground surface, comprising the steps of (a) injecting into the contaminated zone a solution of at least one inorganic permanganate salt, (b) injecting into the zone a cosolvent compatible with the inorganic permanganate salt, (c) causing reaction of the permanganate salt with the water-immiscible organic compound, and (d) extracting co-oxidant from the contaminated zone, thereby diminishing the level of water-immiscible organic compound present in the contaminated zone.