Abstract: A method for embedding an Internet of Things Universal Identifier (IoT UID) into one or more Greenfield devices is provided. The method includes manufacturing one or more Greenfield devices, and generating device property data based at least in part on the one or more Greenfield devices. The method further includes transmitting, to an Internet of Things (IoT) device registrar server, a registration request that includes the device property data. The method further includes interpreting one or more Internet of Things Universal Identifiers (IoT UIDs) generated in response to the transmitting of the registration request. The method further includes embedding the one or more IoT UIDs in the one or more Greenfield devices.

Abstract: Handshake protocol layer features are extracted from training data associated with encrypted network traffic of a plurality of classified devices. Record protocol layer features are extracted from the training data. One or more models are trained based on the extracted handshake protocol layer features and the extracted record protocol layer features. The one or more models are applied to an observed encrypted network traffic stream associated with a device to determine a predicted device classification of the device.

Abstract: Techniques for performing matrix multiplication in a data processing apparatus are disclosed, comprising apparatuses, matrix multiply instructions, methods of operating the apparatuses, and virtual machine implementations. Registers, each register for storing at least four data elements, are referenced by a matrix multiply instruction and in response to the matrix multiply instruction a matrix multiply operation is carried out. First and second matrices of data elements are extracted from first and second source registers, and plural dot product operations, acting on respective rows of the first matrix and respective columns of the second matrix are performed to generate a square matrix of result data elements, which is applied to a destination register. A higher computation density for a given number of register operands is achieved with respect to vector-by-element techniques.

Abstract: Described herein is a computer implemented method for managing a repository that includes one or more component packages and a repository configuration file listing repository dependencies. Each component package has a package configuration file listing package dependencies. The method including the steps of: parsing the repository configuration file; aliasing a package dependency listed in the repository configuration file with at least two repository dependency aliases; parsing a package configuration file; and linking each repository dependency alias to a package dependency listed in the package configuration file.

Abstract: An electric actuator for a marine steering system comprises a housing and an output shaft reciprocatingly received by the housing. There is a rotor disposed within the housing. The rotor is coupled to the output shaft of the electric actuator. Rotation of the rotor causing the output shaft of the electric actuator to reciprocate relative to the housing. There is a motor disposed within the housing. The motor has an output shaft coupled to the rotor. A longitudinal axis of the output shaft of the motor is parallel with a longitudinal axis of the output shaft of the electric actuator. There is also a drive mechanism disposed within the housing. The drive mechanism couples the output shaft of electric actuator to the rotor. The drive mechanism is on a plane radial to a longitudinal axis of the output shaft of the motor. There is an actuator position sensor disposed on the rotor for sensing a position of the rotor. The actuator position sensor senses an actual steering position based on a position of the rotor.

Abstract: An apparatus for data processing and a method of data processing are provided. Data processing operations are performed in response to instructions which reference architectural registers using physical registers to store data values when performing the data processing operations. Mappings between the architectural registers and the physical registers are stored, and when a data hazard condition is identified with respect to out-of-order program execution of an instruction, an architectural register specified in the instruction is remapped to an available physical register. A reorder buffer stores an entry for each destination architectural register specified by the instruction, entries being stored in program order, and an entry specifies a destination architectural register and an original physical register to which the destination architectural register was mapped before the architectural register remapped to an available physical register.

Abstract: Methods, systems and apparatus, including computer programs encoded on computer storage media, for training a policy neural network by repeatedly updating the policy neural network at each of a plurality of training iterations. One of the methods includes generating training data for the training iteration by controlling the agent in accordance with an improved policy that selects actions in response to input state representations. A best response computation is performed using (i) a candidate policy generated from respective policy neural networks as of one or more preceding iterations and (ii) a candidate value neural network. The candidate value neural network is configured to generate a value output that is an estimate of a value of the environment being in the state characterized by a state representation to complete a particular task. The policy neural network is updated by training the policy neural network on the training data.

Abstract: An apparatus for an Internet of Things device registry display includes a user input processing circuit structured to interpret one or more user input command values, an Internet of Things Universal Identification (IoT UID) identification circuit structured to determine one or more IoT UIDs, based at least in part on the one or more user input command values, a device lookup circuit structured to: generate a query that includes the one or more IoT UIDs, and retrieve device property data corresponding to the one or more IoT UIDs, a query provisioning circuit structured to transmit the query to an IoT device registrar server, a device property processing circuit structured to interpret the device property data generated by the IoT device registrar server in response to the query, and a display circuit structured to display the device property data with the corresponding one or more IoT UIDs.

Abstract: An apparatus and method of operating a data processing apparatus are disclosed. The apparatus comprises data processing circuitry to perform data processing operations in response to a sequence of instructions, wherein the data processing circuitry is capable of performing speculative execution of at least some of the sequence of instructions. A cache structure comprising entries stores temporary copies of data items which are subjected to the data processing operations and speculative execution tracking circuitry monitors correctness of the speculative execution and responsive to indication of incorrect speculative execution to cause entries in the cache structure allocated by the incorrect speculative execution to be evicted from the cache structure.

Abstract: A technique for controlling access to one or more attractions is achieved using a number of access keys, each being issued to one or more users. An electronic queue management part manages a virtual queue in respect of each attraction and receives electronic requests for attraction access, each request relating to an access key and being for the users associated with it to access a particular attraction. Receipt of each request causes the respective users to be added to a corresponding virtual queue. A time at which each group of users reaches the front of the virtual queue and can access the attraction is determined. The users access the attractions by presenting an access key to an access control part, in communication with the electronic queue management part. Only a user presenting an access key at the correct time for accessing the attraction is allowed access to the attraction.

Abstract: Examples of the present disclosure relate to an apparatus comprising execution circuitry to execute instructions defining data processing operations on data items. The apparatus comprises cache storage to store temporary copies of the data items. The apparatus comprises prefetching circuitry to a) predict that a data item will be subject to the data processing operations by the execution circuitry by determining that the data item is consistent with an extrapolation of previous data item retrieval by the execution circuitry, and identifying that at least one control flow element of the instructions indicates that the data item will be subject to the data processing operations by the execution circuitry; and b) prefetch the data item into the cache storage.

Abstract: Core-shell microcapsules comprising a hydrophobic core surrounded by a shell comprising a thermosetting resin comprising moieties derived from polyisocyanates, substantially unprotonated chitosan and another amine different from chitosan, characterized in that the substantially unprotonated chitosan is delivered in the solid, powder form to the locus of the encapsulation reaction. The core-shell microcapsules are provided in the form of a slurry comprising 10 to 50 wt % of microcapsules, based on the total weight of the slurry, and the shell of the microcapsules comprises from 0.1 to 20 wt % of moieties derived from chitosan, based on the total weight of the shell.

Abstract: Portable fitness monitoring methods are disclosed. In an embodiment, a portable fitness monitoring method includes a method for providing audible output to a user during an athletic activity using a portable fitness monitoring device. The method includes the steps of receiving an audio feedback file package that includes a first audio feedback file, updating the audio feedback file package, and processing the updated audio feedback file package to provide audible output to the user through an audio output device during the athletic activity.

Abstract: Described is an encapsulated composition comprising at least one core-shell microcapsule. The at least one core-shell microcapsule comprises a core comprising at least one benefit agent and a shell surrounding the core. The shell comprises a polymeric stabilizer that is formed by combination of a polymeric surfactant with at least one aminosilane. Disclosed are also a method of preparing an encapsulated composition and a use of such an encapsulated composition to enhance the performance of perfume and/or cosmetic ingredients in consumer goods.

Abstract: A behavioral biometrics deep learning (BBDL) pipeline is provided, comprising a plurality of stages of machine learning computer models that operate to provide a behavioral biometric based authenticator operating based on spatiotemporal input data. The BBDL pipeline receives spatiotemporal input data over a plurality of time intervals, each time interval having a corresponding subset of the spatiotemporal input data. For each time interval, machine learning computer model(s) of a corresponding stage process a subset of the spatiotemporal input data corresponding to the time interval to generate an output vector having values indicative of an internal representation of spatiotemporal traits of the entity. Output vectors are accumulated across the plurality of stages of the BBDL pipeline to generate a final output vector indicative of the spatiotemporal traits of the entity represented in the spatiotemporal input data. The entity is authenticated based on the final output vector.

Abstract: Mechanisms are provided for obfuscating a trained configuration of a trained machine learning model. A trained machine learning model processes input data to generate an initial output vector having classification values for each of the plurality of predefined classes. A perturbation insertion engine determines a subset of classification values in the initial output vector into which to insert perturbations. A perturbation insertion engine modifies classification values in the subset of classification values by inserting a perturbation in a function associated with generating the output vector for the classification values in the subset of classification values, to thereby generate a modified output vector. The trained machine learning model outputs the modified output vector. The perturbation modifies the subset of classification values to obfuscate the trained configuration of the trained machine learning model while maintaining accuracy of classification of the input data.

Abstract: Adaptive verifiable training enables the creation of machine learning models robust with respect to multiple robustness criteria. In general, such training exploits inherent inter-class similarities within input data and enforces multiple robustness criteria based on this information. In particular, the approach exploits pairwise class similarity and improves the performance of a robust model by relaxing robustness constraints for similar classes and increasing robustness constraints for dissimilar classes. Between similar classes, looser robustness criteria (i.e., smaller ?) are enforced so as to minimize possible overlap when estimating the robustness region during verification. Between dissimilar classes, stricter robustness regions (i.e., larger ?) are enforced. If pairwise class relationships are not available initially, preferably they are generated by receiving a pre-trained classifier and then applying a clustering algorithm (e.g., agglomerative clustering) to generate them.

Abstract: A neural network is augmented to enhance robustness against adversarial attack. In this approach, a fully-connected additional layer is associated with a last layer of the neural network. The additional layer has a lower dimensionality than at least one or more intermediate layers. After sizing the additional layer appropriately, a vector bit encoding is applied. The encoding comprises an encoding vector for each output class. Preferably, the encoding is an n-hot encoding, wherein n represents a hyperparameter. The resulting neural network is then trained to encourage the network to associated features with each of the hot positions. In this manner, the network learns a reduced feature set representing those features that contain a high amount of information with respect to each output class, and/or to learn constraints between those features and the output classes. The trained neural network is used to perform a classification that is robust against adversarial examples.

Abstract: An apparatus and method are provided for controlling allocation of instructions into an instruction cache storage. The apparatus comprises processing circuitry to execute instructions, fetch circuitry to fetch instructions from memory for execution by the processing circuitry, and an instruction cache storage to store instructions fetched from the memory by the fetch circuitry. Cache control circuitry is responsive to the fetch circuitry fetching a target instruction from a memory address determined as a target address of an instruction flow changing instruction, at least when the memory address is within a specific address range, to prevent allocation of the fetched target instruction into the instruction cache storage unless the fetched target instruction is at least one specific type of instruction. It has been found that such an approach can inhibit the performance of speculation-based caching timing side-channel attacks.

Abstract: A method, apparatus and computer program product to protect a deep neural network (DNN) having a plurality of layers including one or more intermediate layers. In this approach, a training data set is received. During training of the DNN using the received training data set, a representation of activations associated with an intermediate layer is recorded. For at least one or more of the representations, a separate classifier (model) is trained. The classifiers, collectively, are used to train an outlier detection model. Following training, the outliner detection model is used to detect an adversarial input on the deep neural network. The outlier detection model generates a prediction, and an indicator whether a given input is the adversarial input. According to a further aspect, an action is taken to protect a deployed system associated with the DNN in response to detection of the adversary input.