International Business Machines Corporation

Su Liu

Eric Rozner

Inseok Hwang

Cheng Xu

Chin Ngai Sze

Yu Gu

Yaoguang Wei

Debbie A Anglin

Shunguo Yan

Jun Su

Kai Liu

Manjunath Ravi

Boyi Tzen

Si Bin Fan

Fan Yang

Zhichao Li

Yang Liang

Hamid Majdabadi

Peng Hui Jiang

Chungkuk Yoo

Fang Wang

Yin Xia

Jeffrey Alan Calcaterra

John David Wilson

Sushain Pandit

Jinho Lee

Denise Marie Genty

Wu Song Fang

Fang Lu

Romelia H Flores

Guang Han Sui

Howard Neil Anglin

Qin Qiong Zhang

Anita Govindjee

Itzhack Goldberg

Zachary A Silverstein

Nadiya Kochura

Janani Janakiraman

Shi Kun Li

Kanak Behari Agarwal

Yu Zhu

Wei Shan Dong

Li Li

Jeremy R Fox

Martin Oberhofer

Neil Sondhi

George Francis Ramsay, Iii

Rui Li Xu

Yu Liu

Jian Li

Vishal Chittranjan Aslot

Barton Wayne Emanuel

Zhi Li Guan

Jennifer D Mulsow

Ivan Matthew Milman

Wei T Huang

Yuan Wang

Charles Daniel Wolfson

Dan Li

Xing Xing Wang

Xue Yong Zhang

Paul Llamas Virgen

Susan Marie Cox

Logan Bailey

Wei Jun Zheng

Robert W Thompson

Xiao Xia Mao

Rhonda L Childress

David B Kumhyr

Jun Zhou

Zhuo Li

Ting Chen

Johnny Menh-Han Shieh

Harigovind Venkatraj Ramasamy

Wen Jun Yin

Tushar Agrawal

Andrew Jason Lavery

Yi Liu

Yan Sun

Miao Liu

Yu Huang

Shi Lei Zhang

Steven Edward Atkin

Tiberiu (Jim) Suto

Saritha Arunkumar

Chih-Wen Su

Michael Baessler

Jeffrey Alan Schmidt

Ping Xiao

Meng Wan

Rui Yang

Ying Nan Li

Riaz Yousuf Hussain

He Fang Zhang

Aruna Yedavilli

Hisham Emad Elshishiny

Fei Shen

Jill S Dhillon

Yuan Jin

Yin Qian

Provided are a computer program product, system, and method for data analysis of interaction information for interacting participants to determine a connection intention. An audio and/or a visual feed collected at an event is processed to determine whether interacting participants at the event are engaged in an interaction. A duration of the interaction, attributes of the interaction, and personal profile information of the interacting participants are determined. The duration of the interaction, the attributes of the interaction, and the personal profile information are processed to determine a connection intention indicating whether the interacting participants should connect or should not connect. Requests for the interacting participants to connect are transmitted in response to determining the connection intention indicates the interacting participants should connect.

Data analysis of interaction information for interacting participants to determine a connection intention

Provided are techniques for providing security with a Web Extension Control Bot (WEC-Bot). A request is transmitted for analysis of a web page that is associated with an extension and with a Uniform Resource Locator (URL). A sensitive sub web page with sensitive content and a non-sensitive sub web page with non-sensitive content for the web page are received. The non-sensitive sub web page is sent to the extension. Output is received from the extension for the non-sensitive sub web page. The sensitive sub web page is modified to hide the sensitive content. The web page is displayed with the output from the extension for the non-sensitive sub web page and with the modified sensitive sub web page.

Providing security with a web extension control bot

Various embodiments are provided herein for implementing multiple thread scenes in a computing environment virtualized metaverse. A multi-layered thread to be instanced in the virtualized metaverse is developed. A main thread scene, and a virtualized avatar that is projected in the main thread scene, is developed. The creation and merger of the main thread scene with other thread scenes featuring the virtualized avatar throughout the multi-layered thread according to a predetermined methodology or overall theme, is enabled.

Implementing multiple thread scenes in virtualized metaverse

A method and system for registering images as non-fungible token images include a non-fungible token verifier verifying that an image has not been registered as a registered non-fungible token. Once the image is verified, an object recognizer recognizes objects of the image that are classified into a object queue. A characteristic extractor then extracts one or more characteristics of the queued objects. A non-fungible token denormalizer then denormalizes the image into one or more denormalized images each having denormalization attributes. A uniqueness purifier then inspects objects of each of the denormalized images for characteristics extracted from objects in the queue, where matching characteristics between the objects of the denormalized images and objects of the image define unique parameters. Non-fungible token register then registers the image as a unique non-fungible token image. A non-fungible token verification method is carried out along with, or independent of, the non-fungible token registration method.

Non-fungible token registration and verification methods

An approach for enhancing retrieval of drill through data is provided. A determination is made as to whether a source report generated from query results of a query by a user contains a data item that is associated with drill through data. In response to a positive determination, a data drill MetadataBot (DDMB) is initiated. In response to activation, the DDMB searches metadata associated with the query results to identify a set of DDMB parameters for the data item. These DDMB parameters can include the cell that contains the data item and a target report containing the drill through data for the cell. Based on this search, the DDMB generates an augmented report that contains a visual identifier for the drill through data-associated cell, allowing the user to retrieve the target report by interacting with the visual identifier.

Artificial intelligence based bot-enhanced retrieval of drill through data

A computer implemented method performs a task for a vehicle. A number of processor units detects a presence of the vehicle on a road. The number of processor units divides the task to be performed for the vehicle into subtasks. The number of processor units assigns the subtasks to Internet of Things nodes on possible paths that the vehicle can travel on the road. The number of processor units processes the subtasks using a set of the Internet of Things nodes on a selected path in the possible paths in real time as the vehicle moves on the selected path to form subtask results. The number of processor units combines the subtask results from the processed subtasks to form a task result for the task.

IoT node coordination

Provided are a computer program product, system, and method for processing participant input in a virtual meeting to determine effects to render in the virtual meeting. A natural language processor (NLP) processes a sentence to determine a structural representation of the sentence comprising a subject and object, which is processed to determine whether the object of the sentence comprises an object of recognition and the subject of the sentence is a participant in the virtual meeting. The structural representation of the sentence is processed to determine an attention level in response to determining that the structural representation of the sentence includes the object of recognition and the subject comprises a participant in the virtual meeting. In response to the attention level exceeding a threshold, a recognition attention effect is rendered in the virtual meeting associated with the attention level, observable by the participants of the virtual meeting.

Processing participant input in a virtual meeting to determine effects to render in the virtual meeting

A method for automatically generating, correlating, and presenting visual content along with an audio stream associated with an audiobook is provided. The method may further include, in response to receiving an audio stream, automatically segmenting the audio stream into audio segments. The method may also include automatically identifying keywords, topics, and entities associated with each audio segment. The method may further include automatically identifying and retrieving visual content related to the identified keywords, topics, and entities of each audio segment from a visual content repository. The method may also include automatically integrating the identified and retrieved visual content with an audio segment. The method may further include automatically generating and displaying the visual content on the display at the designated time during play of the audio stream.

Audiobook visual aid assistance

According to one embodiment, a method, computer system, and computer program product for item management is provided. The present invention may include identifying all items within sensor feeds of a sensor set within a venue; creating a list comprising entries for the identified items, the entries comprising a name, location and presence of the items; based on continuously monitoring the sensor feeds, dynamically updating the list to track the location and/or presence of the items in real time; responsive to receiving a query from a user requesting a lost item, selecting an item in the list that matches the name of the lost item; and guiding the user to the location of the selected item.

Method of household item inventory management

A computer-implemented method to automatically adjust a view of a video stream to focus on a relevant item. The method includes monitoring activity in video feed of a video conference. The method further includes identifying, in the video feed, an item that a user is interacting with during the video conference. The method also includes determining a relevance score for to the item, where the relevance score represents a likelihood the item is a focus of the video conference. The method includes determining the relevance score is above a relevance threshold for the item. The method also includes adjusting, in response to the relevance score being above the relevance threshold, a focus the video feed, where the adjusting is configured to focus on the item.

Content based focus adjustment

A broker capability augmenting method, system, and computer program product for increasing a capability of a Message Queuing Telemetry Transfer (MQTT) broker in an Internet-of-Things (IoT) network including obtaining data from an IoT neighborhood to detect an IoT network topology over time, collecting data from IoT devices in the IoT neighborhood, defining an input parameter to a machine learning model, and training the machine learning model on the collected data and the input parameter.

Augmenting broker capabilities on massive IoT scenarios

In an approach to improve the privacy of chat groups within a virtual world, in response to identifying a private chat group has been created between two or more users, embodiments collect, by an internet of things (IoT) sensor set, a voice for each user in the private chat group. Further, embodiments analyze, by an analysis module, the collected voice, an intonation associated with the collected voice and an original pronunciation associated with the collected voice, and generate a de-content pronunciation based on the analysis of the collected voice, the intonation, and the original pronunciation. Additionally, embodiments output, by the IoT sensor set, the intonation and the original pronunciation to the two or more users in the private chat group and output, by the IoT sensor set and a client computer, the intonation and the de-content pronunciation to one or more users outside the private chat group.

Computer-based privacy protection for chat groups in a virtual environment

Techniques are described with respect to a system, method, and computer program product for representing Internet-Of-Things (IOT) devices in a virtual environment. An associated method includes detecting a plurality of IOT devices associated with a physical space; extracting a plurality of IOT capabilities associated with the plurality of IOT devices; rendering a virtual IOT device based on the plurality of IOT capabilities; and visualizing the virtual IOT device within the virtual environment associated with the physical space.

Virtualizing internet-of-things devices in virtual environments

An embodiment for detecting the danger of an object in close proximity of a subject is provided. The embodiment may include receiving real-time data from one or more IoT devices in a surrounding environment. The embodiment may also include detecting and classifying one or more subjects and one or more objects in an image from the one or more IoT devices. The embodiment may further include identifying one or more risk factors associated with each object. The embodiment may also include correlating the one or more risk factors associated with each object with the one or more subjects in the image. The embodiment may further include identifying relative positions of the one or more subjects and the one or more objects in the image. The embodiment may also include in response to determining a current position of at least one subject is dangerous, notifying a user of the danger.

Proximity danger detection based on subject and object distance analysis

A computer implemented method compiles a source code. A computer system loads the source code into a first memory. The computer system loads a rendered source code into a second memory, wherein the rendered source code is a rendered version of the source code. The computer system determines whether a difference is present between the source code in the first memory and the rendered source code in the second memory. The computer system performs a set of actions in compiling the source code in response to determining that the difference between the source code and the rendered source code is present.

Compiler suppression of invisible trojan source code

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Austin, TX, United States of America

Su Liu