Abstract: Techniques to dynamically customize a menu system presented to a user by a voice interaction system are provided. Audio data from a user that includes the speech of a user can be received. Features can be extracted from the received audio data, including a vocabulary of the speech of the user. The extracted features can be compared to features associated with a plurality of user group models. A user group model to assign to the user from the plurality of user group models can be determined based on the comparison. The user group models can cluster users together based on estimated characteristics of the users and can specify customized menu systems for each different user group. Audio data can then be generated and provided to the user in response to the received audio data based on the determined user group model assigned to the user.

Abstract: Various embodiments are generally directed to techniques detect transaction card skimmers based on emitted and detected light patterns. Embodiments may also include techniques to detect transaction card skimmers based on detected ambient light levels.

Abstract: Exemplary embodiments relate to the secure storage of security questions through an immutable log, such as a blockchain. The security questions may be stored in a centralized location, accessible from an application or browser tab running on the user's device. When a security question is required, such as to perform a password reset on a website, the website may interact with the application or browser tab, which retrieves the question(s) from the blockchain. The user may enter their answers to the question(s), which may be hashed by the application or tab. The hashed answers may be entered into the original requesting website, which may verify with the blockchain that the correct answers have been provided. Thus, the requesting website sees neither the questions nor the answers. Additional security features may include logging requests for questions, so that a user can determine if a security question may have been compromised.

Abstract: Various embodiments are generally directed to techniques of adjusting the combination of the samples in a training batch or training set. Embodiments include techniques to determine an accuracy for each class of a classification model, for example. Based on the determined accuracies, the combination of the samples in the training batch may be adjusted or modified to improve the training of the classification model.

Abstract: Techniques to enhance employee performance using machine learning are described. In some embodiments, these techniques are directed to rendering recommendations to employee reviews by processing, via an input device, an employee review between a manager and an employee, the employee review comprising employee-related remarks by the manager, using the machine learning model to identify at least one employee-related remark of the employee review to have a negative impact on employee performance, the negative impact being attributed to a reviewer type of the manager or a personality type of the employee, and displaying, on an output device, an annotated employee review wherein the annotated employee review comprises the employee review and data indicating that the at least one employee-related remark is likely to result in the negative impact. Other embodiments are described and claimed.

Abstract: Techniques for providing a real-time service that protects personal data of clients from customer service agents are provided. Customer data that includes personal data indicative of sensitive information of a customer can be received from the customer. The personal data within the received customer data can be detected and a token that does not include the sensitive information of the customer can be generated. The personal data and the generated token can be stored along with data indicating a relationship between the token and the personal data. The personal data in the received customer data can be replaced by the token to form modified customer data. The modified customer data can be provided to a customer service representative. The token within the modified customer data can later be detected and associated with the personal data without revealing the personal data to the customer service representative.

Abstract: Techniques to improve operation of an augmented reality device and/or system utilizing fiducial markers and/or colorspace conversions are provided. In various embodiments, one or more fiducial markers in an environment associated with the augmented reality device are updated and optimized in relation to environmental changes utilizing one or more color-space conversion techniques. Other embodiments are described and claimed.

Abstract: Disclosed are examples for providing functions to receive a media file to be stored in a media repository. In the examples, a location in the media repository may be assigned to the media file. A media file address in a blockchain platform may be assigned to the media file. Metadata including the assigned location in the media repository and the assigned media file address in the blockchain platform may be added to the media file. A media file hash value may be generated by applying a hash function to the media file including the metadata. The media file hash value may be included in a message and uploaded to the assigned media file address in the blockchain platform as a transaction in the blockchain. An indication that the media file is uploaded to the media repository may be delivered to a subscriber device from which the media file was received.

Abstract: An apparatus may include a memory to store instructions; and processing circuitry, coupled with the memory, operable to execute the instructions. When executed, the instructions may cause the processing circuitry to identify a matrix code; read a first layer of the matrix code, the first layer comprising a first account identifier associated with an account, wherein the first layer corresponds to a first color channel; and read a second layer of the matrix code, the second layer comprising a set of account data, associated with the account, wherein the second layer corresponds to a second color channel, different than the first color channel.

Abstract: Techniques for routing to content within a mesh network based on a blockchain are provided. An electronic device can receive data from a mesh network to establish the device as a node on the mesh network. The established node can generate a cryptocurrency wallet that can be loaded with an amount of cryptocurrency. A blockchain can store searchable transactions related to content stored within the mesh network. The established node can search the blockchain to locate content and can transmit a request for certain content. The established node may issue a payment to a node hosting the requested content and/or to any intermediate nodes facilitating the established node's access to the content.

Abstract: Logic may detect fraudulent transactions. Logic may determine, by a neural network based on the data about a transaction, a deviation of the transaction from a range of purchases predicted for the customer, wherein the neural network is pretrained to predict purchases by the customer based on a purchase history of the customer. Logic may compare the deviation of the transaction from purchases predicted by the customer against a deviation threshold to determine whether the transaction is within the range of purchases predicted by the neural network. Logic may generate a notification in response to a determination that the deviation of the transaction from the range of purchases predicted exceeds a deviation threshold, the notification to identify the transaction as a potentially fraudulent transaction. Logic may train the neural network based on the transaction in response to a determination that transaction is not a fraudulent transaction.

Abstract: Logic may assign a customer identification to a model to associate a first customer with the model to detect fraudulent transactions. Logic may determine one or more clusters to associate with the first customer based on characteristics associated with the first customer. Logic may associate one or more cluster identifications with the first customer. Each cluster identification may identify one cluster of the one or more clusters. Each cluster may identify a group of customers based on characteristics associated with the group of customers. Logic may cause the model to transmit to a customer device associated with the first customer. Logic may receive transaction data for a transaction for one customer of the group of customers associated with a first cluster. And logic may communicate modified transaction data to customer devices of more than one customer of the group of customers associated with the first cluster.

Abstract: Logic may detect fraudulent transactions. Logic may determine, by a neural network based on the data about a transaction, a deviation of the transaction from a range of purchases predicted for the customer, wherein the neural network is pretrained to predict purchases by the customer based on a purchase history of the customer. Logic may compare the deviation of the transaction from purchases predicted by the customer against a deviation threshold to determine whether the transaction is within the range of purchases predicted by the neural network. Logic may generate a notification in response to a determination that the deviation of the transaction from the range of purchases predicted exceeds a deviation threshold, the notification to identify the transaction as a potentially fraudulent transaction. Logic may train the neural network based on the transaction in response to a determination that transaction is not a fraudulent transaction.

Abstract: Techniques to detect unauthorized access and/or use of a user's account provided by an online web service are provided. A user's activity on the online web service can be monitored to determine a model of the user flow through the online web service. The model of user flow can be unique to the user or can be a model assigned to a cluster of users having similar behaviors or mannerisms when navigating the online web service. The model can vary based on the type of device used by the user to access the online web service. Once a model is determined, subsequent activity on the online web service by the user can be monitored and compared to the model of user flow. Unauthorized access and/or use of the user's online account can be detected if the subsequent activity fails to fit the model of the user flow.

Abstract: Logic may create new images by adding more than one synthetic image on a template. Logic may provide a template with a background for a new image. Logic may provide a set of models, each model to comprise a generative adversarial network (GAN), the GANs pretrained independently to generate different synthetic images. Logic may select two or more models from the set of models. Logic may generate, by the two or more models, two or more of the different synthetic images. Logic may combine the two or more of the different synthetic images with the template to create the new image. And logic may train a set of GANs independently, to generate one of two or more different synthetic images on a blank image background, the different synthetic images to comprise a subset of the multiple features of a new image.

Abstract: Techniques to improve detection and security of images, including formation and detection of matrix-based images. Some techniques include logic to process image data, generate one or more colorspaces associated with that data, and perform colorspace conversions based on the generated colorspace. The logic may be further configured to generate an image based on the colorspace conversions, including but not limited to a matrix bar code. The logic may be further configured to apply one or both of an ultraviolet layer and an infrared layer to the image, e.g. matrix barcode, generated from the colorspace conversion(s). Other embodiments are described and claimed.

Abstract: Various embodiments are generally directed to techniques of adjusting the combination of the samples in a training batch or training set. Embodiments include techniques to determine an accuracy for each class of a classification model, for example. Based on the determined accuracies, the combination of the samples in the training batch may be adjusted or modified to improve the training of the classification model.

Abstract: Techniques to improve detection and security of a displayed image on a screen of a computer device, such as a device, and in response to environmental changes, including formation and detection of matrix-based images that may be dynamically altered and optimized as a result of environmental changes. Some techniques include logic to process image data, generate one or more colorspaces associated with that data, and perform colorspace conversions based on the generated colorspace. The logic may be further configured to generate an image based on the colorspace conversions, including but not limited to a matrix bar code. The logic may further be configured to alter the displayed image based on environmental changes and optimize the newly generated image utilizing one or more colorspace conversions in relation to a new environment generated as a result of the environmental changes. Other embodiments are described and claimed.

Abstract: Techniques to transmit encoded data along a transmission medium and decode the transmitted data along the transmission medium are provided. Some techniques include logic to encode data transmitted along a transmission medium, such as a fiberoptic line or cable, where the encoding is pursuant to a conversion between a first and second colorspace. The logic may further be configured to decode the data once it is received at a node along the fiberoptic line, where the colorspace conversion provides the basis, key, or cipher for preforming the decoding operation. The logic may be further configured to alter the encryption and decryption basis, key, or cypher by altering the colorspace scheme defining the encoding (and by extension the decoding) during transmission, including a transmission that takes place after a previous transmission governed by the previously defined (and subsequently altered) colorspace conversion scheme. Other embodiments are described and claimed.

Abstract: A system and method are disclosed in which a user engaging in an online or in-person transaction with a merchant may create automatic expiring transactions for a credit card. The user provides the credit card information to an online interface or a point-of-sale device but specifies an expiration date that is on or before the actual expiration date of the credit card, known as an artificial expiration date. A token for the merchant is generated with the artificial expiration date. The issuing bank will honor the transaction with the artificial expiration date using the merchant-specific token, and the token may continue to be used with the online merchant until the artificial expiration date.