Abstract:
A data processing method receives a set of time-series user data and also receives a privacy requirement of the time-series user data. Next, the time-series user data is transformed using the privacy requirement such that the transforming satisfies differential privacy.
Abstract:
An inpainting method includes retrieving image information at an electronic device, where the image information identifies an area within an image. The method also includes retrieving, using the electronic device, semantic information including a plurality of semantic classes and a semantic class distribution for each semantic class of the plurality of semantic classes. The method further includes generating semantic codes associated with different portions of the image based on the image information and the semantic information. In addition, the method includes constructing the area within the image by generating image content based on the semantic information.
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:
Intent determination based on one or more multi-model structures can include generating an output from each of a plurality of domain-specific models in response to a received input. The domain-specific models can comprise simultaneously trained machine learning models that are trained using a corresponding local loss metric for each domain-specific model and a global loss metric for the plurality of domain-specific models. The presence or absence of an intent corresponding to one or more domain-specific models can be determined by classifying the output of each domain-specific model.
Abstract:
A recommendation method includes determining one or more aspects of a first item based on at least one descriptive text of the first item. The recommendation method also includes updating a knowledge graph containing nodes that represent multiple items, multiple users, and multiple aspects. Updating the knowledge graph includes linking one or more nodes representing the one or more aspects of the first item to a node representing the first item with one or more first edges. Each of the one or more first edges identifies weights associated with (i) user sentiment about the associated aspect of the first item and (ii) an importance of the associated aspect to the first item. In addition, the recommendation method includes recommending a second item for a user with an explanation based on at least one aspect linked to the second item in the knowledge graph.
Abstract:
One embodiment provides a method comprising, in a training phase, receiving one or more malware samples, extracting multi-aspect features of malicious behaviors triggered by the malware samples, determining evolution patterns of the malware samples based on the multi-aspect features, and predicting mutations of the malware samples based on the evolution patterns. Another embodiment provides a method comprising, in a testing phase, receiving a new mobile application, extracting a first set of multi-aspect features for the new mobile application using a learned feature model, and determining whether the new mobile application is a mutation of a malicious application using a learned classification model and the first set of multi-aspect features.
Abstract:
A computing system includes a control unit configured to determine a usage context including a capability of a device, a usage time and a device location associated with the device, and a user context of one or more users with access to the device; analyze a privacy risk level of a resource based on a resource content included in the resource, a metadata concerning the resource, a collective input regarding the resource, and the usage context; and generate one or more options for sharing the resource with the device based on the privacy risk level and the usage context.
Abstract:
A modulation method referred to as Time Shift Keying (TSK) is used to transmit messages between two devices in a highly energy efficient manner. A message represented by an inaudible audio signal is modulated on a transmitting device. The audio signal is comprised of an array of non-zero amplitude delimiter signals with time periods of zero-amplitude transmission between delimiters. The time duration of the zero-amplitude transmission periods is mapped to a symbol, multiple symbols are then assembled into a message. On the transmitting device, the audio signal is broken into pieces or sequences of bits which are mapped to symbols. On the receiving device, the time durations of zero-amplitude transmission are translated to the symbols which are assembled to the message. The delimiter signals have gradually increasing and decreasing amplitudes and have a length such that make them detectable by the receiving device.
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:
A computing system includes: an initialization module configured to generate initial sharing options; a rating analysis module, coupled to the initialization module, configured to generate a privacy score and a benefit score with a control unit for one or more of the initial sharing options; a mapping module, coupled to the rating analysis module, configured to generate a map based on the initial sharing options, the privacy score, and the benefit score; and a tuning module, coupled to the mapping module, configured to: analyze an initial distribution of the map, and generate the tuned sharing options based on the initial distribution for displaying on a device.