Embodiments are provided for protecting boot block space in a memory device. Such a memory device may include a memory array having a protected portion and a serial interface controller. The memory device may have a register that enables or disables access to the portion when data indicating whether to enable or disable access to the portion is written into the register via a serial data in (SI) input.

Techniques disclosed herein are directed towards streaming keyphrase detection which can be customized to detect one or more particular keyphrases, without requiring retraining of any model(s) for those particular keyphrase(s). Many implementations include processing audio data using a speaker separation model to generate separated audio data which isolates an utterance spoken by a human speaker from one or more additional sounds not spoken by the human speaker, and processing the separated audio data using a text independent speaker identification model to determine whether a verified and/or registered user spoke a spoken utterance captured in the audio data. Various implementations include processing the audio data and/or the separated audio data using an automatic speech recognition model to generate a text representation of the utterance. Additionally or alternatively, the text representation of the utterance can be processed to determine whether at least a portion of the text representation of the utterance captures a particular keyphrase. When the system determines the registered and/or verified user spoke the utterance and the system determines the text representation of the utterance captures the particular keyphrase, the system can cause a computing device to perform one or more actions corresponding to the particular keyphrase.

A user having a computer hardware device can perform a secure transaction by entering on the device user data comprising unique knowledge of the user (such as a password) or biometric information of the user or both, generating with the device processor a pseudo random number, and generating a seed for a public/private key pair by combining the user data and the pseudo random number. The key pair is generated with the seed and transmitted to the server. Also a digital signature is created with the private key and the user data and also transmitted to the digital signature. The digital signature is verified using the public key and if the user data matches previously stored user data, the transaction is allowed to proceed.

An information processing system includes a device, a first apparatus that is connected to the device via a first network, and a second apparatus that is connected to the device via a second network and is unable to receive a request from the first apparatus. The device includes an input part that inputs image data and a transmission part. In a case where the second apparatus corresponds to an output destination of the image data, the transmission part transmits the image data to the second apparatus via the second network. In a case where an apparatus that is capable of receiving data from the first apparatus corresponds to the output destination of the image data, the transmission part transmits to the first apparatus via the first network a transmission request to have the image data transmitted to the output destination.

The present disclosure describes systems and methods of using iris data for authentication. A biometric encoder may translate an image of the iris into a rectangular representation of the iris. The rectangular representation may include a plurality of rows corresponding to a plurality of annular portions of the iris. The biometric encoder may extract an intensity profile from at least one of the plurality of rows, the intensity profile modeled as a stochastic process. The biometric encoder may obtain a stationary stochastic component of the intensity profile by removing a non-stationary stochastic component from the intensity profile. The biometric encoder may remove at least a noise component from the stationary component using auto-regressive based modeling, to produce at least a non-linear background signal, and may combine the non-stationary component and the at least the non-linear background signal, to produce a biometric template for authenticating the person.

A device locator mode or find my device (FMD) mode can allow a lost, stolen, or misplaced mobile device to be located. The FMD mode can be enabled or disabled on a mobile device. Sometimes thieves or unauthorized parties attempt to disable the FMD mode. To prevent this, the mobile device can transmit a unique device identifier to a remote server. The remote server can link the device identifier to a cloud-based user account associated with the user of the mobile device. Before an FMD mode is disabled on the mobile device, before the content or settings can be erased, and/or before a mobile device is permitted to be activated/reactivated, a user of the mobile device can be challenged for the credentials of the cloud-based user account. If valid credentials are provided, FMD can be disabled, the content or settings can be reset, and/or the device can be activated/reactivated.

Disclosed is an operation display device, including: a display unit; a touch panel to simultaneously detect touch operations carried out with fingers to a display surface of the display unit; a finger shape detecting unit to detect a shape of a contact part of the finger, which contacts to the display surface in each of the touch operations; and a control unit to control display contents of the display unit, wherein when the touch operations are simultaneously detected by the touch panel, the control unit judges whether the touch operations are an operation carried out by one operator or are operations carried out by a plurality of operators in accordance with the shape of the contact part of the finger relating to each touch operation, and changes the display contents of the display unit in accordance with a result of judging the touch operations.

A system for and method of storing data comprising: encoding a file into a plurality of fragments; retrieving storage configuration data from a data management store including data associated with a plurality of remote storage volumes, the storage configuration data comprising an indication of a predefined data transmission size corresponding to each remote storage volume; using the storage configuration data to identify a storage strategy associating each fragment with a remote storage volume, wherein using the storage configuration data includes using the indications of the pre defined data transmission sizes; packaging one or more fragments each associated with a common identified remote storage volume as identified by the storage strategy to form a data bundle; communicating the data bundle to the respective common identified remote storage volume associated with the fragments in the data bundle; and storing the fragments at that identified remote storage volume.

A computer-implemented method is described. A first portion and a second portion of a message are received from a user via a user interface of a computer device. Each portion includes one or more letters. The first portion includes initials of a person. The method includes generating a virtual cryptic note. The virtual cryptic note includes the first portion arranged in a first orientation, and the second portion arranged in a second orientation that is rotated relative to the first orientation with the one or more letters of the second portion overlapping the initials of the first portion. The virtual cryptic note can be modified by changing an order of the first portion relative to the second portion or changing the orientation of at least one of the first portion and the second portion. The first portion and the second portion can be encrypted.

The technology disclosed relates to authenticating users using a plurality of non-deterministic registration biometric inputs. During registration, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate sets of feature vectors. The non-deterministic biometric inputs can include a plurality of face images and a plurality of voice samples of a user. A characteristic identity vector for the user can be determined by averaging feature vectors. During authentication, a plurality of non-deterministic biometric inputs are given as input to a trained machine learning model to generate a set of authentication feature vectors. The sets of feature vectors are projected onto a surface of a hyper-sphere. The system can authenticate the user when a cosine distance between the authentication feature vector and a characteristic identity vector for the user is less than a pre-determined threshold.