International Business Machines Corporation

Business Machines Corporation

Yu-Siang Chen

Ching-Chun Liu

Ryan Young

Ting-Chieh Yu

Joey H Y Tseng

June-Ray Lin

Ying-Chen Yu

Cheng-Fang Lin

Ci-Hao Wu

Pao-Chuan Liao

Ci-Wei Lan

Tzu-Chen Chao

Tao-Hung Jung

Chih-Chiang Hung

Amanda P L Yang

Amanda Pl Yang

A method, computer system, and a computer program product for processing digital content is provided. The present invention may include building a sensitive sentence classification model. The present invention may include receiving a digital content, wherein the digital content is intended for one or more groups of content consumers. The present invention may include processing the digital content using the sensitive sentence classification model. The present invention may include generating a consumer specific digital content for each of the one or more groups of content consumers.

Automatic processing of meetings for confidential content

A malware family identification engine constructs a graph data structure of direct relationships between malware instances and malware families, direct relationships between malware instances and detected tags, and indirect relationships between detected tags and malware families. The engine builds a dictionary data structure comprising detected tag entries linking each detected tag to one or more malware family nodes based on the graph data structure. The engine identifies significant indirect entities (SIEs) within the detected tag entries of the dictionary data structure and selects a SIE with a highest number of out-going links (OGLs) as a root node in a family tree data structure, recursively connects SIEs with a number of OGLs less than the highest number of OGLs to the root node in the family tree data structure, and converts each SIE name in the family tree data structure to a chained family entity name in the family tree data structure.

Automated identification of malware families based on shared evidences

A computer-implemented method according to one embodiment includes running an initial network on a plurality of images to detect actors pictured therein and body joints of the detected actors. The method further includes running fully-connected networks in parallel, one fully-connected network for each of the detected actors, to reconstruct complete three-dimensional poses of the actors. Sequential model fitting is performed on the plurality of images. The sequential model fitting is based on results of running the initial network and the fully-connected networks. The method further includes determining, based on the sequential model fitting, a locational position for a camera in which the camera has a view of a possible point of collision of two or more of the actors. The camera is instructed to be positioned in the locational position.

Determination of a locational position for a camera to capture a collision of two or more actors

A live stream, that includes a video stream and an audio stream, of a presenter is monitored. The live stream is attended by an audience that includes one or more audience members. One or more stream content features of the live stream at a first window of time is transmitted to a multimodal machine learning model. One or more audience content features of the audience at the first window of time is transferred to the multimodal model. One or more feature results, based on the stream content features and based on the audience content features, of the first window of time is obtained from the multimodal model. The feature results are sent to an auditory machine learning model. A first audio signal from the auditory machine learning model is received. An augmented stream of the first window of time is generated based on the first audio signal.

Audio customization in streaming environment

Computer technology for sending an image a device to be authenticated. The image is designed to be classified to a first category by an image classifier, and the first category is different from a nature category of the image. A response message can be received from the device. The response message indicates a second category of the image determined by the device. Then, the device is determined to be an authorized device in response to the second category being consistent with the first category.

Authentication based on image classification

In an approach to detecting the transmission of messages, analyzing said messages, calculating a message risk score and transmitting a warning notification, one or more computer processors detect transmission of a message from a user to a selected recipient. The one or more computer processors extract message information from the detected message. The one or more computer processors retrieve one or more historical conversations between the user and the selected recipient of the detected message. The one or more computer processors determine a risk score corresponding to sending the detected message to the selected recipient based on applying the extracted message information and the retrieved historical conversations to a cognitive model.

Mistaken message prevention based on multiple classification layers

A computer implemented method for matching services to potential receivers of the services that includes registering at least one of service providers and service receivers as members to a service matching system. The service matching system collects data from the members. Permission to collect data from the members is revocable at any time by the members. The method further includes taking an order from a first service receiver to receive goods from a service provider; and calculating a route of the first service receiver to obtain the goods from the service provider. The method further includes matching the route of the first service receiver with a potential delivery route to a second service receiver having an order with the service provider; and offering a promotion to the first service receiver to deliver the order by the second service receiver with the service provider over the potential delivery route.

Growing community of inventors

Minxiong, Taiwan

Yu-Siang Chen