A portable device and accompanying software provides users with a digital audio workstation with integrated tools take in raw audio data from physical inputs and map the raw data to various virtual outputs. Raw audio data for recording (recording data) is cast in real-time to a remote server. Monitoring data is simultaneously sent to collaborating devices via P2P protocols. The bitrate of the data for P2P transmissions can be adjusted to allow remote multiple users recording simultaneously to hear monitoring data in synchronicity, regardless of the speed of their respective internet connections. Face-to-face collaboration may be facilitated through the streaming of video data as the monitoring data. The low-latency of the P2P monitoring transmissions frees up bandwidth for the real-time streaming of the recording data. Audio recordings are uploaded to a central server, where they may be available to authorized collaborators for editing.

The present invention may be a method, a system, and/or a computer program product. An embodiment of the present invention provides a method for paraphrasing, on a client computer, text in a webpage, the method comprising the following: transferring a request for a webpage including a plurality of passages of text to a server; receiving the webpage from the server in response to the request; judging whether or not the received webpage has text which is a subject of paraphrase; in a case where the judgment is positive, paraphrasing the text; and displaying, on a display, the webpage including the paraphrased text. Another embodiment of the present invention provides a method for updating on a server, text in a webpage, the method comprising the following: receiving, from each of the devices, a set of URLs of a webpage, a location path of text which is a subject of paraphrase in the webpage, and paraphrased text; and replacing text in the webpage with text among the received text.

Methods and systems disclosed herein describe using machine learning to lock and unlock a device. Machine learning may be trained to recognize one or more features. Once the device has been trained to recognize one or more features, a user may define an unlock condition for the device using the one or more trained features. After defining the unlock condition, the device may be locked by verifying the one or more features that the user defined as the unlock condition using machine learning. When verification is successful, the device may be unlocked and the user allowed to access the device.

Non-limiting examples of the present disclosure describe creation and management of a contact associated with a document. A contact for a document in a first application may be created. The contact may be used to add content, from a second application, to the document. The contact may be stored. Contact data for the contact may be transmitted to one or more processing devices. An exemplary created contact may be used to transfer content from one or more applications to a document of another application. Other examples are also described.

A system for operating a sports gaming event using a graphical interface of a computing device application, the system including: a processor; and a memory coupled to the processor, wherein the memory stores instructions that, when executed by the processor, cause the processor to: receive sports data about a sporting event via a network; access a database of historical statistics relating to the sporting event and to players playing in the sporting event; select at least one betting scenario from a betting scenario database based on the sports data and the historical statistics; calculate an initial probability of the betting scenario occurring based on the sports data and the historical statistics; generate an initial price of the betting scenario based on the initial probability; display the betting scenario and the initial price on the graphical interface; and receive a response to the betting scenario from at least one user.

Example security systems for use between at least one upstream router and at least one downstream router, are described. A group or pool of security devices can be used to provide stateful security to bidirectional packet flows between upstream and downstream routers. The packets of the bidirectional flows are forwarded to particular security devices based on a consistent hash ring process. For a given flow, bidirectional state information is synchronized among some, but not all, of the security devices. The security devices among which such bidirectional flow state information is shared are determined using the same consistent hash ring process.

In some embodiments, a superconducting parametric amplification neural network (SPANN) includes neurons that operate in the analog domain, and a fanout network coupling the neurons that operates in the digital domain. Each neuron is provided one or more input currents having a resolution of several bits. The neuron weights the currents, sums the weighted currents with an optional bias or threshold current, then applies a nonlinear activation function to the result. The nonlinear function is implemented using a quantum flux parametron (QFP), thereby simultaneously amplifying and digitizing the output current signal. The digitized output of some or all neurons in each layer is provided to the next layer using a fanout network that operates to preserve the digital information held in the current.

Text is intelligently annotated by first creating a topic map summarizing topics of interest of the user. A data structure is created. The topic map is used to create two linked user dictionaries, a topic dictionary reflecting topic names and a traversal dictionary reflecting the knowledge structure of a topic. Actions may be linked with topic types. When the text to be annotated is being read, the topic data structure of the topics found in the text are automatically instantiated using the dictionaries and any actions previously linked to topic types. Instantiated topic data structures are automatically attached to the text being annotated. A user GUI may be created to allow the user to access and interact with the text annotations.

A method includes: partitioning learning data containing objective variables and explanatory variables into a plurality of subsets of data; executing regularization processing on first data in each of the partitioned subsets, and extracting a first element equal to zero; extracting, as a candidate, each model where an error ratio between first multiple regression and second multiple regression is equal to or more than a predetermined value, the first multiple regression being a result of multiple regression on second data which is test data in each of the partitioned subsets and is for use to calculate the error ratio of the learning data, the second multiple regression being a result of multiple regression on third data obtained by excluding the first element from the second data; and outputting a model where zero is substituted for an element that takes zero a predetermined or larger number of times in the candidate.

Implementations set forth herein relate to providing selectable autofill suggestions, which correspond to application actions that are at least partially fulfilled using server command data—prior to a user selecting a particular selectable autofill suggestion. Proactively fulfilling command data in this way mitigates latency between user selection of a suggestion and fulfillment of a particular action. Initially, a partial input can be processed to generate autofill suggestions, which can be communicated to a server device for further processing. The autofill suggestions can also be rendered for selection at a touch display interface, thereby allowing a user to select one of the autofill suggestions. As command fulfillment data is provided by the server, the command fulfillment data can be available to a corresponding application(s) in order that any corresponding actions can be at least partially fulfilled prior to user selection.