At&t Intellectual Property I, L.p.

At+t Corp.

Google Inc.

University of California

Lucent Technologies Inc.

Harvard College

University of Southern California

New York University

At&t Labs, Inc.

Balachander Krishnamurthy

Divesh Srivastava

Oliver Spatscheck

Guy Jacobson

Craig Ellis Wills

Harsha Madhyastha

Albert Gordon Greenberg

Jia Wang

Nicholas G Duffield

Graham Cormode

Edward G Amoroso

Jacobus Erasmus Van Der Merwe

Jennifer Lynn Rexford

Edith Cohen

Adam Christopher Bender

Yaron Kanza

Adam Louis Buchsbaum

Glenn Stephen Fowler

Emily Dodwell

Ritwik Mitra

Patrick Guy Haffner

Dan Suciu

Walter Willinger

Rajat Malik

Anirudh Ramachandran

Karim M El Defrawy

Brice Augustin

Haakon Andreas Ringberg

Sivaramakrishnan Ramanathan

Raghvendra Savoor

Subhabrata Bapi Sen

Michael Rabinovich

Yin Sheng Zhang

Kermit Hal Purdy

Anees Abdul Shaikh

Ramon Caceres

Kiem-Phong Vo

Saurabh Kumar

Ganesh Krishnamurthi

Srinivasa Akella

Lakshminarayanan Subramanian

Vyas Sekar

Athina Markopoulou

Greg Minshall

Smriti Bhagat

Fabio Soldo

Pratap Ramamurthy

Yan Chen

Jelena Mirkovic

Minian Yu

Naser S Barghouti

A method performed by a processing system including at least one processor includes applying a contextual filter to mask a portion of at least one of: an input of a software application, an output of the software application, or an underlying dataset of the software application, where the contextual filter simulates a limitation of a user of the software application, executing the software application with the contextual filter applied to the at least one of: the input of the software application, the output of the software application, or the underlying dataset of the software application, collecting ambient data during the executing, and recommending, based on a result of the executing, a modification to the software application to improve at least one of: an accessibility of the software application or an inclusion of the software application.

Testing accessibility of geospatial applications using contextual filters

Aspects of the subject disclosure may include, for example, system and apparatus that enable operations that may include receiving, by a processing system, project data defining a proposed machine learning (ML) project of an entity and storing the project data in a project database with other project data for other projects. The operations may further include extracting extracted features of the proposed project and, based on the extracted features, determining a clustering assignment for the proposed project. Determining the clustering assignment may comprise comparing information about the proposed project including the extracted features with information about the other projects and assigning the proposed project to a cluster including one or more projects having similar bias characteristics as the proposed project. The operations may further include determining a risk of potential bias for the proposed project and, based on the risk of bias, recommending a corrective action to reduce the risk of bias. Machine learning models may be used for project clustering and bias score determination and may be readily updated as new ML projects are evaluated. Other embodiments are disclosed.

Bias scoring of machine learning project data

A processing system including at least one processor may obtain a time series of measurement values from a communication network and train a prediction model in accordance with the time series of measurement values to predict future instances of an event of interest, where the time series of measurement values is labeled with one or more indicators of instances of the event of interest. The processing system may then generate a deterministic finite automaton based upon the prediction model, convert the deterministic finite automaton into a rule set, and deploy the rule set to at least one network component of the communication network.

Prediction of network events via rule set representations of machine learning models

Aspects of the disclosure include, for example, obtaining input data. Further embodiments include a determination of a fast path prediction for a first time period according to the input data based on a fast path model. Embodiments include providing instructions to deliver information to a user device according to the fast path prediction. Additional embodiments include obtaining additional input data. Embodiments include a determination of a slow path prediction for the first time period according to the input data and the additional input data based on a slow path model, retraining the fast path model according to the input data and the fast path prediction, and training the slow path model according to the slow path prediction. Embodiments include a determination of a fast path negative impact metric and determination of a slow path negative impact metric. Other embodiments are disclosed.

Methods, systems, and devices for detecting and mitigating potential bias

Methods, systems, and products protect personally identifiable information. Many websites acquire the personally identifiable information without a user's knowledge or permission. Here, though, the user may control what personally identifiable information is shared with any website. For example, the personally identifiable information may be read from a header of a packet and compared to a requirement associated with a domain name.

Protection of personally identifiable information

In one embodiment, a method includes determining, by one or more processors, a weight of a link between a first node and a second node of a network, wherein the weight is proportional to a probability value of forwarding a probe packet from the first node to the second node of the network. The method also includes adjusting, by the processors, the weight of the link between the first node and the second node using binary exponential backoff. The method further includes determining, by the processors, to forward the probe packet to the second node of the network based on the adjusted weight of the link and one or more field values of the probe packet.

Software defined prober

A method and system for distributing content on a network through network-wide transactions is disclosed. The method and system monitors the network using triggered measurement of the performance of an element of the network, dynamically computing, based on the monitoring, the regions of the network with available performance capacity for the transaction to proceed at a given time, determining, based on the computing, a scheduled time for the transaction to proceed, and distributing the content according to a schedule related to the scheduled time.

Internet-wide scheduling of transactions

Tailored protection of personally identifiable information

A method and apparatus for enabling peer networks to reduce the exchange of unwanted traffic are disclosed. For example, the method receives at least one of: a source Internet Protocol (IP) address or a source IP address prefix that has been identified as a source of the unwanted traffic, by an originating peer network from a terminating peer network. The method then blocks the unwanted traffic destined to the terminating peer network by the originating peer network.

Method and apparatus for reducing unwanted traffic between peer networks

A system that incorporates teachings of the present disclosure may include, for example, a process that reduces a sampling size of a total population of on-line social network users based on a comparison of seed information to a population of on-line social network users. The reduced sampling of on-line social network users is compared to a social graph of the on-line social network users, wherein the social graph is obtained from an algorithm applied to the reduced sampling of the on-line social network users. An outlier is determined in the reduced sampling of on-line social network users based on a characterizing of a cluster of social network users. Additional embodiments are disclosed.

Method and apparatus to identify outliers in social networks

Described is a system and method for determining a classification of an application that includes initiating a stress test on the application, the stress test including a predetermined number of stress events, wherein the stress events are based on a network impairment. A response by the application to each stress event is identified and the application is classified as a function of the response into one of a first classification and a second classification, the first classification indicative of a normal application and the second classification indicative of an undesired application. If, the application is in the second classification, a network response procedure is executed.

System and method for inferring traffic legitimacy through selective impairment

A method includes determining, via a database interface system, a first number of database queries received from a query source during a time period. The method includes preventing, via the database interface system, access to requested information by the query source when the first number does not satisfy a first threshold. The method also includes preventing, via the database interface system, access to the requested information by the query source when no click-through commands associated with a second number of consecutive prior search results were received during the time period.

System and method of processing database queries

Growing community of inventors

New York, NY, United States of America

Balachander Krishnamurthy