Microsoft Technology Licensing, LLC

Beijing Baidu Netcom Science and Technology Co., Ltd.

Ye Xu

Abhishek Gupta

Yang Zhang

Yong Zhu

Shakti Dhirendraji Sinha

Zhifan Feng

Viet Thuc Ha

Xianren Wu

Yan Yan

Satya Kanduri

Vijay K Dialani

Zhou Fang

Maria Puertas Calvo

Etan Micah Basseri

Shinesa Elaine Cambric

Ying Li

Shu Wang

Anmol Bhasin

Matthew Robert Scott

Hao Wei

Kun Jing

Chao Lu

Kexin Ren

Xiaohan Zhang

Jeffrey Thomas Sakowicz

Yaping Liu

Quan Yong Tang

Zang Li

Prithviraj Sanjeev Kanherkar

Sergio Romero Zambrano

Biying Tan

Caroline Katherine Templeton

Ahmet Bugdayci

Kaihua Yin

A trained machine learning model distinguishes between human-driven accounts and machine-driven accounts by performing anomaly detection based on sign-in data and optionally also based on directory data. This machine versus human distinction supports security improvements that apply security controls and other risk management tools and techniques which are specifically tailored to the kind of account being secured. Formulation heuristics can improve account classification accuracy by supplementing a machine learning model anomaly detection result, e.g., based on directory information, kind of IP address, kind of authentication, or various sign-in source characteristics. Machine-driven accounts masquerading as human-driven may be identified as machine-driven. Reviewed classifications may serve as feedback to improve the model's accuracy. A precursor machine learning model may generate training data for training a production account classification machine learning model.

Account classification using a trained model and sign-in data

Methods, systems, apparatuses, and computer-readable storage mediums described herein are configured to detect anomalous post-authentication behavior/state change(s) with respect to a workload identity. For example, audit logs that specify actions performed with respect to the workload identity of a platform-based identity service, a causing state change(s), while another identity is authenticated with the platform-based identity service, are analyzed. The audit log(s) are analyzed via a model for anomaly prediction based on actions. The model generates an anomaly score indicating a probability whether a particular sequence of the actions is indicative of anomalous behavior/state change(s). A determination is made that an anomalous behavior has occurred based on the anomaly score, and when anomalous behavior has occurred, a mitigation action may be performed that mitigates the anomalous behavior.

Detecting anomalous post-authentication behavior for a workload identity

Methods, systems, apparatuses, and computer-readable storage mediums described herein are configured to detect anomalous post-authentication behavior with respect to a user identity. For example, one or more audit logs that specify a plurality of actions performed with respect to the user identity of a platform-based identity service, while the user identity is authenticated with the platform-based identity service, are analyzed. The audit log(s) are analyzed via an anomaly prediction model that generates an anomaly score indicating a probability whether a particular sequence of actions of the plurality of actions is indicative of anomalous behavior. A determination is made that an anomalous behavior has occurred based on the anomaly score. In response to determining that anomalous behavior has occurred, a mitigation action may be performed that mitigates the anomalous behavior.

Systems and methods for detecting anomalous post-authentication behavior with respect to a user identity

Embodiments of the disclosure disclose a method, apparatus, server, and storage medium for incorporating a structured entity, wherein the method for incorporating a structured entity can comprise: selecting a candidate entity associated with a to-be-incorporated structured entity from a knowledge graph, determining the to-be-incorporated structured entity being an associated entity based on prior attribute information of a category of the candidate entity and a preset model, merging the associated entity and the candidate entity, and incorporating the associated entity into the knowledge graph. The embodiments can select a candidate entity, and then integrate a preset model using prior knowledge, which can effectively improve the efficiency and accuracy in associating entities, and reduce the amount of calculation, to enable the structured entity to be simply and efficiently incorporated into the knowledge graph.

Method, apparatus, server, and storage medium for incorporating structured entity

Embodiments of the present disclosure provide a method, apparatus, device and medium for determining text relevance. The method for determining text relevance may include: identifying, from a predefined knowledge base, a first set of knowledge elements associated with a first text and a second set of knowledge elements associated with a second text. The knowledge base includes a knowledge representation consist of knowledge elements. The method may further include: determining knowledge element relevance between the first set of knowledge elements and the second set of knowledge elements, and determining text relevance between the second text and the first text based at least on the knowledge element relevance.

Method, apparatus, device and medium for determining text relevance

Embodiments of the present provide a method and a device for processing an image, server and storage medium. The method includes: determining, based on an object type of an object in an image to be processed, a feature expression of the object in the image to be processed; and determining an entity associated with the object in the image to be processed based on the feature expression of the object in the image to be processed and a feature expression of an entity in a knowledge graph.

Image processing method and device for processing image, server and storage medium

In an example embodiment, one or more specified ideal candidates are used to perform a search in a database. One or more attributes are extracted from one or more ideal candidate member profiles. A search query is then generated based on the extracted one or more attributes. Then, a search is performed on member profiles in the social networking service using the generated search query, returning one or more result member profiles.

Query building for search by ideal candidates

In an example embodiment, one or more sample ideal candidate member profiles in a social networking service are obtained, as well as one or more sample search result member profiles in the social networking service. Then, for each unique pair of sample ideal candidate member profile and sample search result member profile, a label is generated using a score generated from log information of the social networking service, the log information including records of communications between a searcher and members of the social networking service, the score being higher if the searcher communicated with both the member corresponding sample ideal candidate member profile and the member corresponding to the sample search result member profile in a same search session. The generated labels are fed into a machine learning algorithm to train a combined ranking model used to output ranking scores for search result member profiles.

Generation of training data for ideal candidate search ranking model

In an example embodiment, a query for search results is received, the query including at least one value for one facet, a facet defining a categorical dimension for the search results. It is then determined that the facet in the query is exclusive. In response to the determination that the facet is exclusive: for each potential facet different from the facet in the query: for each potential value in the potential facet: conditional entropy gain of the value in the query and the potential value is determined. The potential value in the potential facet that has the highest conditional entropy gain is determined, as is the potential facet with the minimum maximum conditional entropy gain. Then the potential facet with the minimum maximum is input into a machine learning model, causing the machine learning model to output one or more suggested facets to add to the query.

Smart suggestions for query refinements

A system and method for temporal-based professional similarity are provided. In example embodiments, a request to identify, from among a plurality of member profiles of a social network service, a profile that is similar to a source profile, is received. Profile data of the source profile and a candidate profile are accessed from the social network service. Profile features are extracted from the profile data. The profile features include source features extracted from the profile data of the source profile and candidate features extracted from the profile data of the candidate profile. Respective profile features correspond to temporal data included in the profile data. Data structures are generated by structuring the profile features according to the temporal data. The data structures include a source data structure generated using the source features and a candidate data structure generated using the candidate features. A profile similarity score is determined by comparing the candidate data structure with the source data structure. The profile similarity score indicates the similarity between the candidate profile and the source profile.

Temporal-based professional similarity

Systems, methods, and computer-readable media for presenting video as at least a portion of the background of search engine home pages are provided. Upon receiving a request for presentation of a search engine home page, a static image is transmitted for presentation as at least a portion of the background for the search engine home page. Also transmitted is a streaming video file. The static image may or may not be associated with the streaming video file. After a predetermined amount of time, play of the streaming video file is initiated without user action, the streaming video file replacing the static image as at least a portion of the background of the search engine home page. In this way, the user receives a seamless transition from a static image to a video presentation and is provided a rich, immersive experience at the search engine home page.

Hover translation of search result captions

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Hanover, NH, United States of America

Ye Xu