Abstract: The description relates to cameras, such as security cameras, and providing guidance for positioning cameras to achieve desired goals. One example can receive an image of a scene overlaid with transparent indicators that reflect accuracy of object detection in individual regions of the image. The example can correlate input received from the user on the display relative to the regions. The example can analyze subsequent images of the scene with rules derived from the input from the user.

Abstract: A computer system is provided, including a first data storage with a first retrieval latency, a second data storage with a second retrieval latency that is higher than the first retrieval latency, and a processor coupled to a memory that stores instructions, which cause the processor to store a series of recovery points of a data collection in the first data storage. For a current recovery point of the series of recovery points, the processor is further configured to compute a difference between an incremental changed block value of one or more prior recovery points and a number of memory blocks inherited from the one or more prior recovery points. The processor generates and outputs a storage transfer recommendation to store a subset of the one or more of the prior recovery points in the second data storage rather than the first data storage, based on the computed difference.

Abstract: Methods, systems, and computer products are herein provided for lazy evaluation of input data by a machine learning (ML) framework. An ML pipeline receives input data and compiles a chain of operators into a chain of dataviews configured for lazy evaluation of the input data. Each dataview in the chain represents a computation over data as a non-materialized view of the data. The ML pipeline receives a request for column data and selects a chain of delegates comprising one or more delegates for one or more dataviews in the chain to fulfill the request. The ML pipeline processes the input data with the selected chain of delegates. The ML pipeline performs delegate chaining on a dataview. A feature value for a feature column of the dataview is determined based on the delegate chaining and provided to an ML algorithm to predict column data.

Abstract: High surface area coatings are applied to solid substrates to increase the surface area available for solid-phase synthesis of polymers. The high surface area coatings use three-dimensional space to provide more area for functional groups to bind polymers than an untreated solid substrate. The polymers may be oligonucleotides, polypeptides, or another type of polymer. The solid substrate is a rigid supportive layer made from a material such as glass, a silicon material, a metal material, and plastic. The coating may be thin films, hydrogels, microparticles. The coating may be made from a metal oxide, a high-? dielectric, a low-? dielectric, an etched metal, a carbon material, or an organic polymer. The functional groups may be hydroxyl groups, amine groups, thiolate groups, alkenes, n-alkenes, alkalines, N-Hydroxysuccinimide (NHS)-activated esters, polyaniline, aminosilane groups, silanized oxides, oligothiophenes, and diazonium compounds.

Abstract: Techniques for annotating electronic content items include receiving over a communication network a first request to associate a first tag from among a plurality different of tags with first electronic content, wherein each tag from among the plurality of tags is: configured to label electronic content, and associated with a respective task from among a plurality of tasks; recording, in response to receiving the first request, a first association between the first electronic content and the first tag; and transmitting over the communication network to a first recipient client: the first electronic content, and an instruction to the first recipient client to cause the first recipient client to execute, in response to receiving the first electronic content, the respective task associated with the first tag on the first electronic content.

Abstract: Techniques of streaming data processing for hybrid online meetings are disclosed herein. In one example, a method includes receiving, at the remote server, a video stream captured by a camera in the conference room. The video stream captures images of multiple local participants of an online meeting. The method also includes determining identities of the captured images of the multiple local participants in the received video stream using meeting information of the online meeting and generating a set of individual video streams each corresponding to one of the multiple local participants. The set of individual video streams can then be transmitted to the second computing device corresponding to a remote participant of the online meeting as if the multiple local participants are virtually joining the online meeting.

Abstract: In non-limiting examples of the present disclosure, systems, methods and devices for predicting hardware failure events are presented. A time series comprising event log data for a plurality of events and a plurality of event types that occurred on a server computing device may be received. The time series may be filtered for a subset of the plurality of event types. The filtered time series may be processed with a recurrent neural network that has been trained to predict hardware failure events from time series data comprising the subset of the plurality of event types. A prediction may be made that a hardware failure event will occur on the server computing device within a threshold duration of time. A prophylactic follow-up action corresponding to the predicted hardware failure event may be performed.

Abstract: An example inline frame monitor is disclosed. The inline frame monitor injects monitoring logic into a document object model to monitor an activity within a dynamically loaded inline frame of a web page. Data regarding the activity within the dynamically loaded inline frame is received. A policy is applied to validate or invalidate the activity within the dynamically loaded inline frame.

Abstract: Examples of the present disclosure describe systems and methods of dynamically modifying execution environment for varying data or executables. The data or executables may be a multimedia file and the execution environment may be multimedia playback settings. In aspects, content comprising various media items may be received by a client device. The media items may be evaluated to determine a set of characteristics for the media items. The set of characteristics may be compared to a user's preferred configuration settings or previously-played media items. Based on the comparison, the set of characteristics may be altered to be consistent with the preferred configuration settings (e.g., a modified set of characteristics may be estimated). The modified media items may then be played under known playback settings. In aspects, the media item under consideration may be played as is using an estimated playback configuration to enable a user-preferable playback experience.

Abstract: A computing system identifies mitigation actions in response to failures within a computer network. A service level objective is obtained by the computing system for client-resource data flows traversing the computer network between client-side and resource-side nodes. Indication of a failure event at a network location of the computer network is obtained. For each mitigation action of a set of candidate mitigation actions, an estimated impact to a distribution of the service level objective is determined for the mitigation action by applying simulated client-resource data flows to a network topology model of the computer network in combination with the mitigation action and the failure event. One or more target mitigation actions are identified by the computing system from the set of candidate mitigation actions based on a comparison of the estimated impacts of the set of candidate mitigation actions.

Abstract: According to examples, an apparatus may include a processor that may identify activities of an entity on resources over a predetermined period of time, in which the entity is to use permissions assigned to the entity over the resources to perform the identified activities. The processor may also identify which of a plurality of groups of permissions includes the permissions the entity used to perform the identified activities and may determine permutations of the identified plurality of groups of permissions. The processor may further calculate respective scores for each of the determined permutations to identify permutations of the groups of permissions having the lowest scores and may output information pertaining to the determined permutations having the lowest scores.

Abstract: Described are examples for providing a system for managing configuration and policies for a virtualized wide area network (vWAN) support on a wide area network (WAN). The vWAN includes a plurality of virtual network entities associated with geographic locations including the physical computing resources of the WAN and virtual connections between the virtual network entities. The system includes a network safety component for managing configurations and policies of the vWAN on the WAN. The network safety component receives a change to a policy or configuration of the vWAN from an operator of a network connected to the vWAN. The network safety component evaluates a set of safety rules for the operator based on the change and a network state of a physical WAN underlying the vWAN. The network safety component generates an error message in response to at least one of the set of safety rules failing the evaluation.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system performs processing related to a first set of features for a first entity using a first series of embedding layers, wherein the processing includes applying each embedding layer in the first series of embedding layers to a concatenation of all outputs of one or more layers preceding the embedding layer. Next, the system obtains a first embedding as an output of a first final layer in the first series of embedding layers. The system then outputs the first embedding for use by a machine learning model.

Abstract: Expanding search engine functionality using AI models. A method includes, as part of a search session, receiving user input at a search engine. One or more searches on a set of data using the user input. Search results are provided from the one or more searches to a user. Based on a history of the search session, suggestions are provided in a user interface of AI models that could be applied to expand potential search results for the search session. User input is received at the user interface selecting one or more of the suggested AI model. The one or more selected AI models are applied to expand the set of data. Search results to the user based on searching the expanded set of data.

Abstract: Embodiments of the present disclosure include systems and methods for training neural networks. In one embodiment, neural network may receive input data and produce output results in response to the input data and weights of the neural network. An error is determined at an output of the neural network based on the output results. The error is propagated in a reverse direction through the neural network from the output and one or more intermediate outputs to adjust the weights.

Abstract: Embodiments relate to improving user experiences when executing binary code that has been translated from other binary code. Binary code (instructions) for a source instruction set architecture (ISA) cannot natively execute on a processor that implements a target ISA. The instructions in the source ISA are binary-translated to instructions in the target ISA and are executed on the processor. The overhead of performing binary translation and/or the overhead of executing binary-translated code are compensated for by increasing the speed at which the translated code is executed, relative to non-translated code. Translated code may be executed on hardware that has one or more power-performance parameters of the processor set to increase the performance of the processor with respect to the translated code. The increase in power-performance for translated code may be proportional to the degree of translation overhead.

Abstract: An optical communications network comprises optical data links interconnected by add-drop nodes, the optical data links comprising data channels. The data channels are allocated into equal-sized bins. In response to a first data channel request between a given source-destination pair, one of the equal-sized bins is assigned to the data channel request. In response to requests for additional bandwidth for the same source-destination data channel request, unused channels within the assigned equal-sized bin are allocated to the data channel request. In response to subsequent data channel requests between different source-destination pairs, additional unallocated equal-sized bins are assigned to the subsequent data channel requests. In response to subsequent data channel requests when resource sharing for one equal-sized bin, data channels in the last equal-sized bin are assigned using the reverse channel assignment process. Reverse channel assignment can also be used for other bins as an option.

Abstract: Apparatuses, computer readable medium, and methods are disclosed for verifying skills of members of an online connection network. The apparatus, computer readable medium, and methods may include a method including responding to a first member of the online connection network indicating a skill possessed by the first member by selecting a skill verification user interface (UI) to present to a second member of the online connection network where the first member and the second member are connected via the online connection network. The method may further include presenting the skill verification UI to the second member, where the skill verification UI presents an indication of the first member, an indication of the skill, and a query regarding a competence level of the skill possessed by the first member. The method may further include receiving a response to the query and determining a skill validation value of the skill for the first member based on the response and a machine learning model.

Abstract: In an example embodiment, a system is provided whereby a machine learning model is trained to predict a standardization for a given raw title. A neural network may be trained whose input is a raw title (such as a query string) and a list of candidate titles (either title identifications in a taxonomy, or English strings), which produces a probability that the raw title and each candidate belong to the same title. The model is able to standardize titles in any language included in the training data without first having to perform language identification or normalization of the title. Additionally, the model is able to benefit from the existence of “loan words” (words adopted from a foreign language with little or no modification) and relations between languages.

Abstract: Techniques for determining a global state for multiple related calls are disclosed. A first call between a first device and a second device is established, where the first call is assigned a user call identifier. A second call between the first device and a third device is established. A shared call operation is established by assigning the user call identifier to the second call. The user call identifier is assigned to the second call in addition to already being assigned to the first call. A first status of the first call and a second status of the second call are determined. The first status and the second status are used to determine a global state of the shared call operation. The global state of the shared call operation is updated in response to one or both of the first status or the second status changing.