Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support secure training of machine learning (ML) models that preserves privacy in untrusted environments using distributed executable file packages. The executable file packages may include files, libraries, scripts, and the like that enable a cloud service provider configured to provide ML model training based on non-encrypted data to also support homomorphic encryption of data and ML model training with one or more clients, particularly for a diagnosis prediction model trained using medical data. Because the training is based on encrypted client data, private client data such as patient medical data may be used to train the diagnosis prediction model without exposing the client data to the cloud service provider or others. Using homomorphic encryption enables training of the diagnosis prediction model using encrypted data without requiring decryption prior to training.

Abstract: Methods, systems, and computer-readable storage media for receiving, by an identity network and from a verifying entity, a query including an identifier that uniquely identifies an entity that is active in the mobility network and an attestation that is to be authenticated, resolving, by the identity network, the query to provide a resolved query, the resolved query including the attestation, resolving at least partly including identifying a data source of a plurality of data sources that is to be queried to authenticate the attestation, transmitting, by the identity network, the resolved query to the data source, receiving, by the identity network, a response to the resolved query, and transmitting, by the identity network, the response to the verifying entity.

Abstract: A device may receive purchase data identifying purchases by users of user devices and identifying non-temporal data associated with the users, and may preprocess the purchase data to generate sequences of multivariate and multimodal symbols. The device may process the sequences of multivariate and multimodal symbols, with a long short-term memory based encoder-decoder model, to generate sequence embeddings, and may process the non-temporal data associated with the users, with a knowledge graph, to determine knowledge graph embeddings capturing the non-temporal data. The device may process the sequence embeddings and the knowledge graph embeddings, with a knowledge graph embedding model, to generate modified sequence embeddings, and may process the modified sequence embeddings, with a clustering model, to determine clusters of the users in relation to products or services purchased by the users. The device may perform one or more actions based on the clusters of the users.

Abstract: Method, data processing system, and computer-readable storage media for responding to a user query. Receiving query from user, query pertaining to request for information. Based on query, generate prompts by masking sensitive information in query. Receive responses from foundation models in response to inputting prompts. Based on responses, generate common result set. By validating common result set with sensitive information, generate response. By supplementing response with sensitive information, generate user response. Providing user response in response to query to the user.

Abstract: Systems and methods for responsible AI compliance and governance management in AI Products are disclosed. The system receives a request to assess an enterprise product associated with a specific application. Further, the system may determine a plurality of datasets associated with the AI model of the enterprise product. Furthermore, the system generates a training dataset and a test dataset for the determined plurality of datasets associated with the AI model. The system generates a ranked list of recommended metrics for the enterprise product based on the generated training dataset and the test dataset. The system further determines a mitigation strategy for the enterprise product based on the generated ranked list of recommended metrics. Furthermore, the system creates a feedback loop for continuous training and tuning the AI model and the plurality of datasets based on the determined mitigation strategy.

Abstract: A system for issue prediction based on multidimensional data analysis includes a model generator that receives a resolved data item relating to a service issue. The resolved data item includes different attributes corresponding to multiple data dimensions and adjusts a population of attributes based on a statistical data model and a deep learning data model operating independent of each other. The statistical data model operates on the attributes for providing a predictive feature and the deep learning data model operates on the attributes for providing a predictive label based on performance metrics related to the data dimensions. The predictive feature and the predictive label collectively define training data. The model generator also trains a classification model based on the training data for predicting a potential issue related to an unresolved data item. The trained data model provides a trigger based on the potential issue being related to the performance metrics.

Abstract: Packaging devices such as bags, pouches, envelops, containers, and the like, can include an integrated heating system. The packaging devices described herein have multiple potential uses such as for the containment and heating of single-serving meals, drinks, massage oils, masks, body wax, anti-wrinkle eye creams, and the like, to be heated on the go. The packaging devices described herein can also be used for instant heating of food and/or beverages for backcountry use. In some embodiments, the packaging devices use fibrous natural materials (e.g., leaf skeletons and soft biomaterials like chitosan) along with silver nanowires to create completely biodegradable, and reusable self-heating packaging.

Abstract: A device may receive tracking data identifying trajectories of objects, and may annotate the tracking data to identify object categories for the tracking data. The device may identify objects based on the object categories, and may determine object trajectories for the objects. The device may transform the object trajectories to overhead planar trajectories, and may generate simplified object trajectories. The device may convert a continuous space associated with the simplified object trajectories into a discrete space, and may convert the discrete space to a lower dimensional space. The device may transform the object trajectories to binary feature vectors, and may process the binary feature vectors, with a clustering model, to determine trajectory clusters. The device may train a classification model with the binary feature vectors and the trajectory clusters, and may cause the trained classification model to be implemented.

Abstract: Methods, systems, and apparatus are provided for generating an image. A personalized text prompt is generated by processing an input embedding using a transformer model followed by a first fully connected neural network. The input embedding comprises a multi-dimensional embedding vector associated with a user profile and a plurality of user items. A scored label set is generated identifying a user's preferences by processing a set of attributes for the plurality of user items using a second fully connected neural network. The image is generated by processing the personalized text prompt and the scored label set using a diffusion model.

Abstract: Methods, systems, and apparatus for a bi-directional quantum annealing approach to Markov random field networks for machine learning in image analysis. In one aspect, a method includes obtaining training data comprising features extracted from a first set of images; training a deep quantum restricted Boltzmann machine (QRBM) comprising multiple layers using the training data, the training comprising layer-wise training of the multiple layers, wherein training each layer of the multiple layers comprises evaluating a restricted Boltzmann machine (RBM) probability distribution using bi-directional quantum annealing; and validating the trained deep QRBM using test data comprising features extracted from a second set of images.

Abstract: An Artificial Intelligence (AI) based filter apparatus includes an input filter and an output filter protecting a generative AI model and preventing restricted content from being transmitted to user devices. When a user query is received, the input filter determines if the user query can be transmitted to the generative AI model by generating an input risk score for the received user query. If the user query is transmitted and a model query response is received from the generative AI model, the output filter determines an output risk score based on which the model query response may be transmitted to the user. The input filter and the output filter each include a pre-trained language model as a base with additional layers trained to estimate the corresponding risk scores.

Abstract: Implementations are directed to receiving a set of tuples, each tuple including an entity and a product from a set of products, for each tuple: generating, by an embedding module, a total latent vector as input to a recommender network, the total latent vector generated based on a structural vector, a textual vector, and a categorical vector, each generated based on a product profile of a respective product and an entity profile of the entity, generating, by a context integration module, a latent context vector based on a context vector representative of a context of the entity, and inputting the total latent vector and the latent context vector to the recommender network, the recommender network being trained by few-shot learning using a multi-task loss function, and generating, by the recommender network, a prediction including a set of recommendations specific to the entity.

Abstract: The present disclosure relates to a system, a method, and a product for using deep learning models to quantify and/or improve trust in conversations. The system includes a non-transitory memory storing instructions executable to construct a deep-learning network to quantify trust scores; and a processor in communication with the non-transitory memory. The processor executes the instructions to cause the system to: obtain a trust score for each voice sample in a plurality of audio samples, generate a predicated trust score by the deep-learning network based on each voice sample in the plurality of audio samples, wherein the deep-learning network comprises a plurality of branches and an aggregation network configured to aggregate results from the plurality of branches, and train the deep-learning network based on the predicated trust score and the trust score for each voice sample to obtain a training result.

Abstract: The present disclosure provides a system architecture for designing and monitoring privacy-aware services and improving privacy regulation compliance. A privacy-preserving knowledge graph (PPKG) system provides functionality for modelling and analyzing processes that use, share, or request sensitive data from users and the outcomes of such functionality may be utilized to modify the design of the processes (e.g., to improve security of the process, regulatory compliance of the process, and the like). The PPKG system may also be used to modify the process, such as to write code that may be compiled into executable form and deployed to a run-time environment. A privacy-preserving posture (PPP) system monitors the run-time environment and analyzes where processes obtain, store, and share sensitive data. The PPP system may identify run-time vulnerabilities that may pose risks with respect to the sensitive data, as well as areas where modifications could be made to improve regulatory compliance.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support cross-server containerized application allocation, multi-tenant namespace management, and data layer deployment in an edge environment. To illustrate, containers associated with an application are deployed to edge servers based on a geographic characteristic of the edge server with respect to an edge device receiving services from the application. A common data layer is provided across the edge environment to manage communications between the different containers, and between the application and other applications of the edge environment. Managing the communication is based on namespaces and/or a modality (e.g., private or public) associated with the application. Authentication configuration of the application is used to determine edge resource access for the containers of the application.

Abstract: A DevSecOps integration system that can analyze a source repository and provide recommendations on security services, along with supported security tools, that can be included as part of the DevOps pipeline. The DevSecOps integration system can automatically fit the selected security tools into the appropriate stage of the pipeline to optimize security protection. In some embodiments, the DevSecOps integration system automatically generates various and multiple CI/CD-specific integrations, scripts, and security code. In one example, the security tools are selected in response to user selection(s), such as an initial input file and code parameters, and automatically identifies where the scripts should be inserted in the development process.

Abstract: Aspects of the present disclosure provide an environmental probe configured to be at least partially inserted into the ground at a location and to provide defensible space monitoring and maintenance for a home or other structure. The environmental probe may include one or more environmental sensors configured to perform various environmental measurements to generate environmental measurement data. The environmental probe (e.g., a processor of the environmental probe) may compare the environmental measurement data to one or more thresholds to determine an alert state associated with the location. The environmental probe may include one or more wireless interfaces configured to enable communication with a remote device, such as a smart hub device or other environmental probes. The environmental probe may transmit an indicator of the alert state to the remote device to enable performance of one or more operations based on the alert state.

Abstract: Implementations include a computer-implemented method for mitigating cyber security risk of an enterprise network, the method comprising: receiving an analytical attack graph (AAG) representing paths within the enterprise network with respect to at least one target asset, the AAG defining a digital twin of the enterprise network and comprising a set of rule nodes, each rule node representing an attack tactic that can be used to move along a path of the AAG; integrating the AAG with a knowledge graph comprising a set of asset nodes, each asset node representing a digital asset that can be affected by one or more of the attack tactics; determining, based on integrating the AAG with the knowledge graph, a plurality of security controls, each security control having an assigned priority value; and selectively implementing the security controls in the enterprise network based on the assigned priority values of the security controls.

Abstract: In some examples, temporal impact analysis of cascading events on metaverse-based organization avatar entities may include determining a temporal impact of a metaverse event on a specified organization avatar entity. With respect to the specified organization avatar entity, a similarity of the metaverse event may be determined in a current temporal context to past events. A reaction plan of a plurality of reaction plans may be selected from an event database and based on the determined similarity. Based on an analysis of the temporal impact with respect to the selected reaction plan, instructions may be generated to execute the selected reaction plan by a metaverse operating environment.

Abstract: Aspects of the present disclosure provide systems, methods, and computer-readable storage media that support cooperative training of machine learning (ML) models that preserves privacy in untrusted environments. For example, a server (or cloud-based computing device(s)) may be configured to “split” an initial ML model into various partial ML models, some of which are provided to client devices for training based on client-specific data. Output data generated during the training at the client devices may be provided to the server for use in training corresponding server-side partial ML models. After training of the partial ML models is complete, the server may aggregate the trained partial ML models to construct an aggregate ML model for deployment to the client devices. Because the client data is not shared with other entities, privacy is maintained, and the splitting of the ML models enables offloading of computing resource-intensive training from client devices to the server.