Abstract:
A method includes that for each model from multiple models, evaluating a model prediction accuracy based on a dataset of a user over a first time duration. The dataset includes a sequence of actions with corresponding contexts based on electronic device interactions. Each model is trained to predict a next action at a time point within the first time duration, based on a first behavior sequence over a first time period from the dataset before the time point, a second behavior sequence over a second time period from the dataset before the time point, and context at the time point. A model is selected from the multiple models based on its model prediction accuracy for the user based on a domain. An action to be initiated at a later time using an electronic device of the user is recommended using the selected model during a second time duration.
Abstract:
A method includes deploying a neural network (NN) model on an electronic device. The NN model being generated by training a first NN architecture on a first dataset. A first function defines a first layer of the first NN architecture. The first function is constructed based on approximating a second function applied by a second layer of a second NN architecture. Retraining of the NN model is enabled on the electronic device using a second data set.
Abstract:
An apparatus, method, and computer readable medium for management of infinite data streams. The apparatus includes a memory that stores streaming data with a data set and a processor operably connected to the memory. The processor transforms the data set to a second data set. To transform the data set, the processor determines whether a difference level exceeds a threshold, and transforms the data set by adding a noise when the difference level exceeds the threshold. When the difference level does not exceed the threshold, the processor determines whether a retroactive count is greater than a threshold, transforms the data set by adding a second noise when the retroactive count is greater than the threshold, and transforms the data set by adding a third noise when the retroactive count is not greater than the threshold. The processor transmits the second data set to a data processing system for further processing.
Abstract:
An electronic system includes: a control unit configured to operate on a user interface; and the user interface, coupled to the control unit, configure to: present an application coupled to an access configuration to customize a permission level for a service type, and receive an input for changing the permission level of the service type for accessing a resource type for customizing an operation of the application on a device.
Abstract:
An electronic system includes: a control unit configured to: calculating a risk score based on a permission requested by an application, generating a summary presentation based on the risk score for presenting a risk visualization of a privacy risk posed by an application, generating a subcategory presentation based on the risk score for presenting the risk visualization of the privacy risk posed to a device feature by the application, and a user interface, coupled to the control unit, configure to present a risk presentation including the summary presentation, the subcategory presentation, or a combination thereof for displaying on a device.
Abstract:
A computing system includes: a communication unit configured to access a target account including a feature; a control unit, coupled to the communication unit, configured to: calculate a comparison result based on the feature, determine an anonymity threshold for conforming the target account with a comparison account, and determine the feature for the target account based on the comparison result and the anonymity threshold for displaying on a device.
Abstract:
A system for equality testing, the system comprising a first client device including a first private data unit, a second client device including a second private data unit, and a server. The server receives a first obfuscated data unit corresponding to the first private data unit from the first client device, and a second obfuscated data unit corresponding to the second private data unit from the second client device. The server performs a vector calculation based on the first and second obfuscated data units to generate a combination of the first and second obfuscated data units. The server sends the combination to the first client device. The first client device is configured to determine whether the first private data unit is equal to the second private data unit based on the combination.
Abstract:
In one embodiment, a method includes accessing at least a portion of a training dataset for a trained neural network that includes multiple layers, where each layer includes a number of parameters, and where the training dataset includes multiple training samples that each include an input and a ground-truth output used to train the trained neural network. The method further includes training a hypernetwork to generate a layer-specific compression mask for each of one or more of the multiple layers of the trained neural network. The method further includes generating, by the trained hypernetwork, a final layer-specific compression mask for the trained neural network and compressing the trained neural network by reducing, for each of the one or more layers of the neural network, the number of parameters of that layer according to the final layer-specific compression mask.
Abstract:
A method includes obtaining a batch of training data including multiple paired image-text pairs and multiple unpaired image-text pairs, where each paired image-text pair and each unpaired image-text pair includes an image and a text. The method also includes training a machine learning model using the training data based on an optimization of a combination of losses. The losses include, for each paired image-text pair, (i) a first multi-modal representation loss based on the paired image-text pair and (ii) a second multi-modal representation loss based on two or more unpaired image-text pairs, selected from among the multiple unpaired image-text pairs, wherein each of the two or more unpaired image-text pairs includes either the image or the text of the paired image-text pair.
Abstract:
An inpainting method includes obtaining an image including an object having a delicate shape and identifying a target region within the image, where the target region is adjacent to the object. The method also includes using a first mask to separate the image into a number of semantic categories and aggregating neighboring contexts for the target region based on the semantic categories. The method further includes restoring, based on the aggregated contexts, textures in the target region without affecting the delicate shape of the object. In addition, the method includes displaying a refined image including the restored textures in the target region and the object.