Abstract: This disclosure provides methods, components, devices, and systems for providing task offloading services from a device onto a device charger in a secure device ecosystem. Some aspects, more specifically, relate to a context-aware device charger communicating with a trusted device within the secure device ecosystem where the device can offload tasks and computations to the device charger. In some aspects, the trusted device transmits a context state during operation across the secure device ecosystem, which can be received by the device charger, where the context state includes a task associated with an activity on the device. The device charger may perform services associated with the task, such as performing computations associated with the task, and may transmit information associated with the computations back to the trusted device.

Abstract: A method comprising retrieving one or more requirements of knowledge to be extracted; generating a prompt corresponding to the one or more requirements; validating the prompt by executing a large language model using the prompt and evaluating the response predicted by the large language model; fine-tuning the large language model using validation data generated as a result of validating the prompt; and executing the fine-tuned large language model using a text corpus to analyze one or more item reviews and generate a pair of at least one entity and a respective relationship sentiment value for the entity.

Abstract: This disclosure provides methods, components, devices, and systems for providing task offloading services from a device onto a device charger in a secure device ecosystem. Some aspects, more specifically, relate to a context-aware device charger communicating with a trusted device within the secure device ecosystem where the device can offload tasks and computations to the device charger. In some aspects, the trusted device transmits a context state during operation across the secure device ecosystem, which can be received by the device charger, where the context state includes a task associated with an activity on the device. The device charger may perform services associated with the task, such as performing computations associated with the task, and may transmit information associated with the computations back to the trusted device.

Abstract: This disclosure provides systems, methods, and devices for memory systems that support automatic backup of data upon detection of a potential damage condition. In a first aspect, a method includes detecting, by at least one processor of an apparatus, a potential damage condition for the apparatus and, in response to detection of the potential damage condition for the apparatus, performing, by the at least one processor of the apparatus, a data backup of data stored in at least one memory of the apparatus. Other aspects and features are also claimed and described.

Abstract: This disclosure provides systems, methods, and devices for memory systems that support automatic backup of data upon detection of a potential damage condition. In a first aspect, a method includes detecting, by at least one processor of an apparatus, a potential damage condition for the apparatus and, in response to detection of the potential damage condition for the apparatus, performing, by the at least one processor of the apparatus, a data backup of data stored in at least one memory of the apparatus. Other aspects and features are also claimed and described.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to detect deepfake content. An example apparatus to determine whether input media is authentic includes a classifier to generate a first probability based on a first output of a local binary model manager, a second probability based on a second output of a filter model manager, and a third probability based on a third output of an image quality assessor, a score analyzer to obtain the first, second, and third probabilities from the classifier, and in response to obtaining a first result and a second result, generate a score indicative of whether the input media is authentic based on the first result, the second result, the first probability, the second probability, and the third probability.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to detect deepfake content. An example apparatus to determine whether input media is authentic includes a classifier to generate a first probability based on a first output of a local binary model manager, a second probability based on a second output of a filter model manager, and a third probability based on a third output of an image quality assessor, a score analyzer to obtain the first, second, and third probabilities from the classifier, and in response to obtaining a first result and a second result, generate a score indicative of whether the input media is authentic based on the first result, the second result, the first probability, the second probability, and the third probability.

Abstract: A method for providing visual contents to user for monitoring processes in an industrial system. The method comprises receiving plurality of variables associated with processes in industrial system. Further, the method comprises determining presence of one or more critical variables based on one or more parameters, using machine learning model. Furthermore, the method comprises identifying one or more first visual contents by associating the one or more critical variables with plurality of visual contents. Each of the plurality of visual contents represents one or more processes from the plurality of processes and corresponding variables. Moreover, the method comprises identifying one or more second visual contents based on availability of behaviour data of user, using the machine learning model. Thereafter, the method comprises providing the one or more first visual contents and the one or more second visual contents to the user.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to detect deepfake content. An example apparatus to determine whether input media is authentic includes a classifier to generate a first probability based on a first output of a local binary model manager, a second probability based on a second output of a filter model manager, and a third probability based on a third output of an image quality assessor, a score analyzer to obtain the first, second, and third probabilities from the classifier, and in response to obtaining a first result and a second result, generate a score indicative of whether the input media is authentic based on the first result, the second result, the first probability, the second probability, and the third probability.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to detect deepfake content. An example apparatus to determine whether input media is authentic includes a classifier to generate a first probability based on a first output of a local binary model manager, a second probability based on a second output of a filter model manager, and a third probability based on a third output of an image quality assessor, a score analyzer to obtain the first, second, and third probabilities from the classifier, and in response to obtaining a first result and a second result, generate a score indicative of whether the input media is authentic based on the first result, the second result, the first probability, the second probability, and the third probability.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to detect deepfake content. An example apparatus to determine whether input media is authentic includes a classifier to generate a first probability based on a first output of a local binary model manager, a second probability based on a second output of a filter model manager, and a third probability based on a third output of an image quality assessor, a score analyzer to obtain the first, second, and third probabilities from the classifier, and in response to obtaining a first result and a second result, generate a score indicative of whether the input media is authentic based on the first result, the second result, the first probability, the second probability, and the third probability.