International Business Machines Corporation

Kyndryl, Inc.

Other

Vijay Ekambaram

Sarbajit Kumar Rakshit

Vijay Kumar Ananthapur Bache

Saravanan Sadacharam

Padmanabha Venkatagiri Seshadri

Shinoj Zacharias

Ashok Pon Kumar Sree Prakash

Leigh Allen Williamson

Ashish Kumar Mathur

Nitendra Rajput

Smitkumar Narotambhai Marvaniya

Vivek Shyamlal Sharma

Vikas V Joshi

Shikhar Kwatra

Roger Snook

Ramasuri Narayanam

Gopal Kumar Bhageria

Prasenjit Dey

Malolan Chetlur

Vikrant Nandakumar

Giriprasad Sridhara

Jhilam Bera

Namit Kabra

Kuntal Dey

Sharad C Sundararajan

Swati Rallapalli

Seema Nagar

Hemant Kumar Sivaswamy

Ruhi Sharma Mittal

Srikanth K Murali

Yedendra Shrinivasan

Subhajit Bhuiya

Ravindranath Kokku

Pratyush Kumar

Gandhi Sivakumar

Nitin Kumar Gupta

Pankaj Satyanarayan Dayama

Ritesh Kumar Gupta

Bidhu Ranjan Sahoo

Sushain Pandit

Atri Mandal

Aaron K Baughman

Harish Bharti

Timo Kussmaul

Krishnasuri Narayanam

Uwe Karl Hansmann

Gargi Banerjee Dasgupta

Rajesh Phillips

Puneet Sharma

Arvind Kumar

Srikanth Govindaraj Tamilselvam

Sandeep Sukhija

Seng Chai Gan

Thomas Stober

Abhay Kumar Patra

Senthil Kumar Kumarasamy Mani

Jennifer L Szkatulski

Ashish Mittal

Samiulla Zakir Hussain Shaikh

Tarun Tater

Caleb Miles

Donald Andrew Padilla

Rakesh Shinde

Anirban Laha

Craig M Trim

Zachary A Silverstein

Christopher J Hardee

Manish Anand Bhide

Yannick Saillet

Shivkumar Kalyanaraman

Amith Singhee

Adam Lee Griffin

Tiberiu (Jim) Suto

Vinayaka D Pandit

Gray Franklin Cannon

L Venkata Subramaniam

Enara C Vijil

Brian Patrick Byrne

Howard Neil Anglin

Natalie Brooks Powell

Saurabh Sinha

Indervir Singh Banipal

Manikandan Padmanaban

Shivali Agarwal

Pralhad Dinesh Deshpande

Hima Prasad Karanam

Atul Mene

Luke Peter Macura

Harshit Kumar

Vikas Raykar

Adam Reudeau Holley

Vikram Yadav

Parag Jain

Ankur Tagra

Dipyaman Banerjee

Sambaran Bandyopadhyay

Pooja Malik

Ajay Chebbi

Satyam Dwivedi

Senthil K Mani

From a first model parameter, an autoencoder network is generated. A reconstruction error for the autoencoder network is measured, the reconstruction error comprising a difference between an input to the autoencoder network and a corresponding output from the autoencoder network, the input to the autoencoder network comprising a portion of an initial set of data. The reconstruction error and a confidence score corresponding to a complexity level of the autoencoder network are aggregated into a level of difficulty score of the autoencoder network. From the level of difficulty score and an initial data access policy level corresponding to the initial set of data, a derived data access policy level corresponding to the initial data access policy level is generated, the derived data access policy level enforcing access to a transformed set of data generated by applying a transformation to the initial set of data.

Automatically adjusting data access policies in data analytics

An approach is provided in which the approach stores, in a cached recycle bin, a set of sensor data that is sent from a sensor at a transmit frequency. The approach samples at least a portion of the set of sensor data from the cached recycle bin based on a sampling frequency and training a machine learning model using the sampled data. In response to detecting that a performance of the machine learning model falls below a threshold during the training, the approach adjusts the sampling frequency and re-sampling at least a portion of the sensor data based on the adjusted sampling frequency. The approach instructs the sensor to adjust the transmit frequency in response to determining that the performance of the machine learning model reaches the threshold using the re-sampled data.

Auto sampling in internet-of-things analytics system via cached recycle BINS753634

A method for testing and debugging interaction of collaborative mixed reality objects is disclosed. In one embodiment, such a method includes receiving inputs including a first mixed reality object expressed by a first set of attributes, a second mixed reality object expressed by a second set of attributes, a first individual test case associated with the first mixed reality object, and a second individual test case associated with the second mixed reality object. The method automatically generates, from the inputs, a collaborative mixed reality test case to evaluate interaction of the first mixed reality object with the second mixed reality object within a collaborative mixed reality environment. In certain embodiments, a generative-AI-based encoder-decoder architecture is used to generate the collaborative mixed reality test case from the inputs. A corresponding system and computer program product are also disclosed.

Testing of collaborative mixed reality objects

A system includes: a platform; a payload extending from a platform; an adjustable tether connecting the payload to the platform; and a sail extending from the payload. The system further includes a computer program that is executable to relocate the platform to the area of interest based on a threshold based decision which may include providing a tether and sail configuration.

Relocation based coverage area enhancement system

A processor may receive a route request from a user. The processor may receive data associated with predicted routes of one or more nearby vehicles. The processor may select a recipient vehicle from the one or more nearby vehicles, where the recipient vehicle is selected based on recipient selection criteria. The processor may determine one or more driving change requests for the recipient vehicle. The processor may send the one or more driving change requests and a proposed token to the recipient vehicle. The processor may provide a token to the recipient vehicle in response to the recipient vehicle implementing the driving change requested.

Cooperative operation of vehicles

Targeted acquisition of data for model training includes identifying attributes of classified samples of a collection of samples classified by a classification model, and generating at least one query based on the identified attributes, the at least one query tailored, based on the attributes, to retrieve additional training data for training the classification model to more accurately classify samples and avoid incorrect sample classification.

Targeted data acquisition for model training

Debris signature-based robotic cleaning device navigation includes operating the robotic cleaning device in a first mode as part of a vacuum cycle, the device including suction ports configurable for different suction power levels and each port having a suction path along which debris entering through the suction port is collected by the device. In the first operating mode the suction ports are operated at a first suction power level. The navigation also includes changing operation of the device to a second mode and in which the suction ports are operated at a greater suction power, measuring a respective amount of debris collected through each suction port, and selecting a direction in which to navigate the robotic cleaning device based on the debris measurements.

Debris signature-based robotic cleaning device navigation

Embodiments of the present disclosure provide systems and methods for synchronizing an unaligned audio stream and a corresponding unaligned video stream of real time streaming media. A non-limiting disclosed method comprises performing, using a video classifier, video reference point classification of a video stream based on an audio-video dataset; performing, using an audio classifier, audio reference point classification of the audio stream based on the audio-video dataset. The system correlates object related reference points in video segments of the video stream and in audio segments of the audio stream to identify a set of audio-video synchronization candidates. The system compares context of the set of audio-video synchronization candidates to identify an audio-video synchronization candidate to synchronize the audio stream and video stream based on reference point alignment.

Asynchronous content analysis for synchronizing audio and video streams

An embodiment includes retrofitting an existing control device with an automation panel that senses manual actuation of a control element of the control device, and automatically actuates the control element in response to a specified control signal. The embodiment collects state data indicative of an actuation state of the control element and context data of conditions at a time that the state data is collected, and generates a training dataset comprising collected state data and sensor data. It then uses this data to train a classification model to predict a control element actuation state based on sensor data. The embodiment deploys the trained classification model to process sensor data and upon detecting a mismatch between a predicted actuation state output from the trained classification model and an actual actuation state of the control element, the embodiment transmits the specified control signal to the automation panel to actuate the control element.

Cognitive retrofit for legacy control devices

Inadvertent data swaps can be prevented by measuring volume of transactions in distributed computing environment to determine locations for potential data swaps; and managing a correlation between a thread identification (ID) and transaction header (ID) for transactions in the distributed computing environment. In some embodiments, the prevention of data swaps can further include performing a data transmission interruption to avoid data swaps at the locations for potential data swaps. When the thread identification (ID) and transaction header (ID) do not match the potential for data swaps can be high.

Data swap prevention in distributed computing environments based on mapping among global user identification, correlation identification and thread identification

Approaches presented herein enable maneuvering collaborative robots to rescue persons in a hydrological disaster. A plurality of robots are dispersed in a body of water to spread out and seek victims using cooperative foraging techniques within resource constraints. A location of victims located by a robot using sensing techniques is communicated to other robots. A situational assessment is performed using victim location information to determine a number of robots to deploy to the location. The deployed robots are directed to perform coordinated maneuvers to create a connected floatation unit to support floatation of victims for rescue.

Maneuvering collaborative devices for a rescue mission

Mitigating mistranscriptions resolves errors in a transcription of the audio portion of a video based on a semantic matching with contextualized data electronically garnered from one or more sources other than the audio portion of the video. A mistranscription is identified using a pretrained word embedding model that maps words to an embedding space derived from the contextualizing data. A similarity value for each vocabulary word of a multi-word vocabulary of the pretrained word embedding model is determined in relation to the mistranscription. Candidate words are selected based on the similarity values, each indicating a closeness of a corresponding vocabulary word to the mistranscription. The textual rendering is modified by replacing the mistranscription with a candidate word that, based on average semantic similarity values, is more similar to the mistranscription than is each other candidate word.

Audio mistranscription mitigation

A system and method are disclosed that utilizes an artificial intelligence based virtual proxy node. The virtual proxy node includes an intent resolution model and communicates between a smart audio device and at least one secondary device, wherein the at least one secondary device is configured to be controlled by a smart audio device or smart hub. The virtual proxy node tracks interactions between the smart audio device and the at least one secondary device to derive historical and context data from the tracking interactions. The virtual proxy node uses the historical and context data to predict which secondary device will be successful in responding to the user input command and broadcasts the input command to the virtual proxy node associated with one of the at least one secondary device. The virtual proxy node includes an intent resolution model trained by historical and context data.

AI-based virtual proxy nodes for intent resolution in smart audio devices

Methods, systems, and computer program products for facilitating user selection using trend-based joint embeddings are provided herein. A method includes obtaining a selection of an item in an online catalog; determining a compatible item of the plurality of items at least in part by providing the selected at least one item and at least one previously selected item corresponding to the user to a trend-based machine learning model, wherein the trend-based machine learning model is trained on historical data associated with the item in the online catalog and fine-tuned based on current trend data from multiple data sources; receiving feedback in response to outputting the at least one compatible item; identifying one or more attributes related to the at least one compatible item based on the feedback; and using the trend-based machine learning model to determine at least one additional compatible item based on the one or more attributes.

Facilitating user selection using trend-based joint embeddings

Targeted acquisition of data for model training includes automatically generating metadata describing samples, of an initial dataset, in neighborhoods of an embedding space in which the samples are embedded. The samples described by the automatically generated metadata are classified by a classification model, and include both correctly classified samples in the neighborhoods and incorrectly classified samples in the neighborhoods. Additionally, attributes of one or more correctly classified samples of the collection of samples and one or more incorrectly classified samples of the collection of samples are identified, and queries are generated based on the identified attributes, the queries tailored, based on the attributes, to retrieve additional training data for training the classification model to more accurately classify samples and avoid incorrect sample classification.

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Bengaluru, India

Vijay Ekambaram