Abstract: An attention application, such as a web browser, includes a pipeline optimized for faster, more secure, and more private, viewing of hypermedia documents using a reader mode. The reader mode is “always on” in the sense that a classifier runs on every web page and every compatible page is rendered in the reader mode and not rendered in full, referred to as the bloat page. Significant time savings are gained by avoiding fetching and rendering the bloat page at all because the bloat page devours network bandwidth and computing resources. Avoiding loading the bloat page also avoids exposing the user to what are often abusive privacy infringements and security vulnerabilities from running executable code in the browser, while providing an uncluttered viewing experience of content that is actually of interest to the user.

Abstract: A decentralized and trust-minimizing computer architecture for computing rewards for users of an advertising system includes cryptographic black box accumulators (BBA), which is a cryptographic counter that only the issuer can update. An attention application requests initialization of a BBA from a guardian and subsequently requests updates to the BBA to track interactions between a user of the attention application and ads on the attention application. The guardian signs updates to the BBA to reach agreement on the state of ad interactions. The attention application may randomize the BBA and submit requests via an anonymous channel such that no participant can link two encounters with the BBA to each other or link the BBA to a specific attention application, thus improving user privacy. Reward redemption requests can be made based on a known policy and committed to a public blockchain for verification by observers that the protocol is operating correctly.

Abstract: A decentralized and trust-minimizing computer architecture for computing rewards for users of an advertising system includes cryptographic black box accumulators (BBA), which is a cryptographic counter that only the issuer can update. An attention application requests initialization of a BBA from a guardian and subsequently requests updates to the BBA to track interactions between a user of the attention application and ads on the attention application. The guardian signs updates to the BBA to reach agreement on the state of ad interactions. The attention application may randomize the BBA and submit requests via an anonymous channel such that no participant can link two encounters with the BBA to each other or link the BBA to a specific attention application, thus improving user privacy. Reward redemption requests can be made based on a known policy and committed to a public blockchain for verification by observers that the protocol is operating correctly.

Abstract: Classification of the user of an attention application is moved from the cloud, where the classification is performed by advertisers based on trackers that follow a user, to the attention application itself. A user of the attention application controls inputs to the classification model and can exclude sensitive privacy information from inclusion in the classification model. The classification model is applied locally at the attention application to a catalog of advertisements and without revealing to trackers and advertisers whether attention was paid to particular ads. An analytics provider may have increased access to attention applications and can form ad campaigns and provide performance data thereon to advertisers without infringing attention user privacy. The system directs value away from trackers and advertisers and to attention application users and publishers.

Abstract: Classification of the user of an attention application is moved from the cloud, where the classification is performed by advertisers based on trackers that follow a user, to the attention application itself. A user of the attention application controls inputs to the classification model and can exclude sensitive privacy information from inclusion in the classification model. The classification model is applied locally at the attention application to a catalog of advertisements and without revealing to trackers and advertisers whether attention was paid to particular ads. An analytics provider may have increased access to attention applications and can form ad campaigns and provide performance data thereon to advertisers without infringing attention user privacy. The system directs value away from trackers and advertisers and to attention application users and publishers.

Abstract: An attention application measures a user's attention focused on publisher content and advertisements to create an attention metric. Attention can be measured via hardware sensors or by user interactions with input/output hardware. A user attention metric profile can be used to modify content, content presentation, and/or match ads. Aggregate attention metrics can be used by publishers or third parties. Attention consumers may reward attention with a digital asset. A proof-of-attention can be made based on secure attention sensor hardware and/or a zero-knowledge proof.

Abstract: A decentralized and trust-minimizing computer architecture for computing rewards for users of an advertising system includes cryptographic black box accumulators (BBA), which is a cryptographic counter that only the issuer can update. An attention application requests initialization of a BBA from a guardian and subsequently requests updates to the BBA to track interactions between a user of the attention application and ads on the attention application. The guardian signs updates to the BBA to reach agreement on the state of ad interactions. The attention application may randomize the BBA and submit requests via an anonymous channel such that no participant can link two encounters with the BBA to each other or link the BBA to a specific attention application, thus improving user privacy. Reward redemption requests can be made based on a known policy and committed to a public blockchain for verification by observers that the protocol is operating correctly.

Abstract: An attention application, such as a web browser, includes a pipeline optimized for faster, more secure, and more private, viewing of hypermedia documents using a reader mode. The reader mode is “always on” in the sense that a classifier runs on every web page and every compatible page is rendered in the reader mode and not rendered in full, referred to as the bloat page. Significant time savings are gained by avoiding fetching and rendering the bloat page at all because the bloat page devours network bandwidth and computing resources. Avoiding loading the bloat page also avoids exposing the user to what are often abusive privacy infringements and security vulnerabilities from running executable code in the browser, while providing an uncluttered viewing experience of content that is actually of interest to the user.

Abstract: Various technologies described herein pertain to automation of crowd-sourced polling. At least one query can be received. The at least one query includes a request. A poll can be automatically generated based upon the at least one query, where the poll corresponds to the request. The poll can be submitted to a crowdsourcing backend, where instances of the poll are administered on the crowdsourcing backend. Moreover, crowd-sourced responses to the instances of the poll can be retrieved from the crowdsourcing backend. The crowd-sourced responses to the instances of the poll can respectively include crowd-sourced responses to the request. The crowd-sourced responses to the request can be converted to a random variable. An operation can be performed upon the random variable. The operation can include one or more of a statistical analysis (e.g., hypothesis testing), bias correction, an arithmetic operation, expected value computation, standard deviation computation, etc.

Abstract: Classification of the user of an attention application is moved from the cloud, where the classification is performed by advertisers based on trackers that follow a user, to the attention application itself. A user of the attention application controls inputs to the classification model and can exclude sensitive privacy information from inclusion in the classification model. The classification model is applied locally at the attention application to a catalog of advertisements and without revealing to trackers and advertisers whether attention was paid to particular ads. An analytics provider may have increased access to attention applications and can form ad campaigns and provide performance data thereon to advertisers without infringing attention user privacy. The system directs value away from trackers and advertisers and to attention application users and publishers.

Abstract: An attention application measures a user's attention focused on publisher content and advertisements to create an attention metric. Attention can be measured via hardware sensors or by user interactions with input/output hardware. A user attention metric profile can be used to modify content, content presentation, and/or match ads. Aggregate attention metrics can be used by publishers or third parties. Attention consumers may reward attention with a digital asset. A proof-of-attention can be made based on secure attention sensor hardware and/or a zero-knowledge proof.

Abstract: A “Poll Optimizer” provides automated techniques for performing various combinations of both static and runtime optimizations for crowd-sourced queries including, but not limited to, crowd-sourced opinion-based polls. These optimizations have been observed to improve poll performance by reducing factors such as completion times, monetary costs, and error rates of polls. In various implementations, the Poll Optimizer receives an input query representing a crowd-sourced poll that is formatted as a multi-layer structure (e.g., LINQ-based queries natively supported by .NET languages, JQL-based queries supported by JAVA, etc.). The Poll optimizer then iteratively reduces the multi-layer structure of the input query to construct a reformulated query. This reformulated query is then matched to an optimized execution process selected from a plurality of predefined execution processes.

Abstract: An exemplary method includes providing an application that includes client-side code and server-side code, instrumenting the client-side code and the server-side code to generate timestamps, distributing the instrumented client-side code and the instrumented server-side code and monitoring timestamps generated during execution of the application. In such a method, where timestamps generated by the client-side code and timestamps generated by the server-side code occur along a common timeline, a developer can monitor performance of the distributed application. Other exemplary methods, systems, etc., are also disclosed.

Abstract: Disclosed herein are systems and methods for detecting script code malware and generating signatures. A plurality of script code samples are received and transformed into a plurality of tokenized samples. The tokenized samples are based on syntactical elements of the plurality of script code samples. One or more clusters of samples are determined based on similarities in different ones of the plurality of tokenized samples, and known malicious code having a threshold similarity to a representative sample of the cluster of samples is identified. Based on the identifying, the cluster of samples is identified as malicious. Based at least on respective ones of the plurality of tokenized samples associated with the cluster of samples, a generalized code signature usable to identify the script code samples in the cluster of samples is generated.

Abstract: The claimed subject matter includes techniques for processing gestures. An example method includes receiving a gesture from an application. The gesture includes one or more primitives from a language that is domain-specific to gestures. The method also further includes receiving skeletal data from a motion detection system. The method also includes comparing the skeletal data with the gesture from the application in a runtime module. The method also further includes sending a gesture event to the application.

Abstract: A processing system for distributed multi-tier applications is provided. The system includes a server component that executes a replica of a client-side application, where a client component executes the client-side application. The client component captures events from the client-side application and transmits the events to the replica to validate the computational integrity security of the application.

Abstract: An automatic context-sensitive sanitization technique detects errors due to the mismatch of a sanitizer sequence with a browser parsing context. A pre-deployment analyzer automatically detects violating paths that contain a sanitizer sequence that is inconsistent with a browsing context associated with outputting an untrusted input. The pre-deployment analyzer determines a correct sanitizer sequence which is stored in a sanitization cache. During the runtime execution of the web application, a path detector tracks execution of the web application in relation to the violating paths. The correct sanitizer sequence can be applied when the runtime execution follows a violating path.

Abstract: The subject disclosure is directed towards crowd-based approach to boosting the correctness of a computer program. Results from candidate programs obtained from a first crowd and which may be blended with one another into synthesized programs are sent to a second crowd for evaluation. Based upon the results, a training set evolves and programs are filtered based upon fitness. The process of blending and fitness evaluation with an evolved training set may be iteratively repeated to find a most fit program.

Abstract: A security engine may be selected from a plurality of security engines to apply one or more security mechanisms to a section of source code of an application. In some cases, the section of source code may be identified by one or more security mechanism identifiers included in the source code. The security engine may generate machine-readable code that corresponds to the section of source code for which the one or more security mechanisms are to be applied. The machine-readable code may be executed on a plurality of computing devices. In one implementation, applying the security mechanisms to the section of source code may include producing zero-knowledge proofs of knowledge for the section of source code.

Abstract: An “AR Privacy API” provides an API that allows applications and web browsers to use various content rendering abstractions to protect user privacy in a wide range of web-based immersive augmented reality (AR) scenarios. The AR Privacy API extends the traditional concept of “web pages” to immersive “web rooms” wherein any desired combination of existing or new 2D and 3D content is rendered within a user's room or other space. Advantageously, the AR Privacy API and associated rendering abstractions are useable by a wide variety of applications and web content for enhancing the user's room or other space with web-based immersive AR content. Further, the AR Privacy API is implemented using any existing or new web page coding platform, including, but not limited to HTML, XML, CSS, JavaScript, etc., thereby enabling existing web content and coding techniques to be smoothly integrated into a wide range of web room AR scenarios.