Abstract: An example storage chest is provided. The example storage chest may include a box portion and a lid. The box portion may include a front wall, a rear wall, and a floor attached to the front and rear walls to form a primary compartment for storage of items. The lid may include a front panel, a rear panel, and a top panel attached to the front a rear panels. The rear panel may be pivotably coupled to the rear wall of the box portion. The front panel, the rear panel, and the top panel may define a secondary compartment within the lid for storage of items. The secondary compartment may include a lid storage compartment comprising a hingedly affixed door.

Abstract: Systems and methods for implementing an artificially intelligent virtual assistant includes collecting a user query; using a competency classification machine learning model to generate a competency label for the user query; using a slot identification machine learning model to segment the text of the query and label each of the slots of the query; generating a slot value for each of the slots of the query; generating a handler for each of the slot values; and using the slot values to: identify an external data source relevant to the user query, fetch user data from the external data source, and apply one or more operations to the query to generate response data; and using the response data, to generate a response to the user query.

Abstract: Systems and methods of intelligent formation and acquisition of machine learning training data for implementing an artificially intelligent dialogue system includes constructing a corpora of machine learning test corpus that comprise a plurality of historical queries and commands sampled from production logs of a deployed dialogue system; configuring training data sourcing parameters to source a corpora of raw machine learning training data from remote sources of machine learning training data; calculating efficacy metrics of the corpora of raw machine learning training data, wherein calculating the efficacy metrics includes calculating one or more of a coverage metric value and a diversity metric value of the corpora of raw machine learning training data; using the corpora of raw machine learning training data to train the at least one machine learning classifier if the calculated coverage metric value of the corpora of machine learning training data satisfies a minimum coverage metric threshold.

Abstract: Systems and methods for intelligently training a machine learning model includes: configuring a machine learning (ML) training data request for a pre-existing machine learning classification model; transmitting the machine learning training data request to each of a plurality of external training data sources, wherein each of the plurality of external training data sources is different; collecting and storing the machine learning training data from each of the plurality of external training data sources; processing the collected machine learning training data using a predefined training data processing algorithm; and in response to processing the collected machine learning training data, deploying a subset of the collected machine learning training data into a live machine learning model.

Abstract: Systems and methods for mapping applications onto system resource of a computing platform are discussed. The computing platform may receive, using control circuitry, a request to run a plurality of applications on a computing platform having a plurality of system resources. The computing platform may determine a plurality of mapping configurations for the plurality of applications onto the plurality of system resources. The computing platform may execute the plurality of applications with each of the plurality of mapping configurations. The computing platform may determine at least one performance metric based on the executed plurality of applications for each of the plurality of mapping configurations. The computing platform may select a selected mapping configuration among the plurality of mapping configurations based on at least one determined performance metric.

Abstract: Systems and methods of intelligent formation and acquisition of machine learning training data for implementing an artificially intelligent dialogue system includes constructing a corpora of machine learning test corpus that comprise a plurality of historical queries and commands sampled from production logs of a deployed dialogue system; configuring training data sourcing parameters to source a corpora of raw machine learning training data from remote sources of machine learning training data; calculating efficacy metrics of the corpora of raw machine learning training data, wherein calculating the efficacy metrics includes calculating one or more of a coverage metric value and a diversity metric value of the corpora of raw machine learning training data; using the corpora of raw machine learning training data to train the at least one machine learning classifier if the calculated coverage metric value of the corpora of machine learning training data satisfies a minimum coverage metric threshold.

Abstract: Systems and methods for implementing an artificially intelligent virtual assistant includes collecting a user query; using a competency classification machine learning model to generate a competency label for the user query; using a slot identification machine learning model to segment the text of the query and label each of the slots of the query; generating a slot value for each of the slots of the query; generating a handler for each of the slot values; and using the slot values to: identify an external data source relevant to the user query, fetch user data from the external data source, and apply one or more operations to the query to generate response data; and using the response data, to generate a response to the user query.

Abstract: Systems and methods for intelligently training a machine learning model includes: configuring a machine learning (ML) training data request for a pre-existing machine learning classification model; transmitting the machine learning training data request to each of a plurality of external training data sources, wherein each of the plurality of external training data sources is different; collecting and storing the machine learning training data from each of the plurality of external training data sources; processing the collected machine learning training data using a predefined training data processing algorithm; and in response to processing the collected machine learning training data, deploying a subset of the collected machine learning training data into a live machine learning model.

Abstract: Systems and methods for implementing an artificially intelligent virtual assistant includes collecting a user query; using a competency classification machine learning model to generate a competency label for the user query; using a slot identification machine learning model to segment the text of the query and label each of the slots of the query; generating a slot value for each of the slots of the query; generating a handler for each of the slot values; and using the slot values to: identify an external data source relevant to the user query, fetch user data from the external data source, and apply one or more operations to the query to generate response data; and using the response data, to generate a response to the user query.

Abstract: A system is provided for monitoring, regenerating and replacing the code of running applications with semantically equivalent, specialized code versions that reflect the demands of the execution environment. The system includes a co-designed compiler and runtime system that virtualizes a selected set of edges in a host program, where these edges provide hooks through which the runtime system may redirect execution into an intermediate representation utilized to optimize introspective and extrospective processes.

Abstract: A system is provided for monitoring, regenerating and replacing the code of running applications with semantically equivalent, specialized code versions that reflect the demands of the execution environment. The system includes a co-designed compiler and runtime system that virtualizes a selected set of edges in a host program, where these edges provide hooks through which the runtime system may redirect execution into an intermediate representation utilized to optimize introspective and extrospective processes.

Abstract: A system is provided for monitoring, regenerating and replacing the code of running applications with semantically equivalent, specialized code versions that reflect the demands of the execution environment. The system includes a co-designed compiler and runtime system that virtualizes a selected set of edges in a host program, where these edges provide hooks through which the runtime system may redirect execution into an intermediate representation utilized to optimize introspective and extrospective processes.

Abstract: Aspects of the invention may be used to allocate tasks among computing machines in large scale computing systems. In one aspect, the method includes executing a first task in the plurality of tasks on a first computing machine and determining a performance degradation threshold for the first task. The method further includes calculating a predicted performance degradation of the first task when a second task is executed on the first computing machine, wherein the predicted performance degradation is determined by comparing a performance interference score of the second task with a performance sensitivity curve of the first task. The method further includes executing the second task on the first computing machine when the predicted performance degradation of the first task is below the performance degradation threshold.

Abstract: Systems and methods for mapping applications onto system resource of a computing platform are discussed. The computing platform may receive, using control circuitry, a request to run a plurality of applications on a computing platform having a plurality of system resources. The computing platform may determine a plurality of mapping configurations for the plurality of applications onto the plurality of system resources. The computing platform may execute the plurality of applications with each of the plurality of mapping configurations. The computing platform may determine at least one performance metric based on the executed plurality of applications for each of the plurality of mapping configurations. The computing platform may select a selected mapping configuration among the plurality of mapping configurations based on at least one determined performance metric.

Abstract: A system is provided for monitoring, regenerating and replacing the code of running applications with semantically equivalent, specialized code versions that reflect the demands of the execution environment. The system includes a co-designed compiler and runtime system that virtualizes a selected set of edges in a host program, where these edges provide hooks through which the runtime system may redirect execution into an intermediate representation utilized to optimize introspective and extrospective processes.

Abstract: A first indicator of a first number of cache misses to a cache memory of a multicore processor for a first application over a first time period is received. The first application executes on a first core of the processor and a second application simultaneously executes on a second core of the processor during the first time period. The first and second cores share the cache memory. A second indicator of a second number of cache misses to the cache memory for the first application over a second time period is received. During the second time period, the first application executes on the first core and the second application does not execute on the second core. A degree of contention among the first and second applications is determined based on the first and second indicators, and execution of the second application is adjusted based on the determined degree of contention.

Abstract: Techniques and systems for scenario based optimization can include generating multiple different versions of a program segment based on different respective execution scenarios associated with an execution of a program, the program operable to use the program segment versions. In another aspect, techniques and systems can include executing a program executable associated with multiple different versions of a program segment, analyzing the execution for an indication of at least one of the execution scenarios to select one of the program segment versions based on the indication, and causing the execution to use the selected program segment version during at least a portion of the execution.

Abstract: An integrated projector comprising a body with a projector circuit internally and a projection lens externally, a card reader installed on the body to read the data inside the memory card, a video interface installed on the body to receive video signals, and a video player that is a DVD player built-in inside the body. Users can read the data in the memory card, watch TV and watch DVD with a single projector without extra wiring.

Abstract: An integrated projector comprising a body with a projector circuit internally and a projection lens externally, a card reader installed on the body to read the data inside the memory card, a video interface installed on the body to receive video signals, and a video player that is a DVD player built-in inside the body. Users can read the data in the memory card, watch TV and watch DVD with a single projector without extra wiring.