Abstract: Techniques for implementing artificial crowd intelligence via networking of recommendation engines are disclosed. In some example embodiments, a base recommendation engine performs a computer-implemented method comprising: receiving a recommendation request; generating a first plurality of recommendations based on the recommendation request using a base recommendation model; obtaining one or more other recommendations from one or more other recommendation engines, each one of the other recommendation engines being configured to generate the other recommendations based on the recommendation request using a corresponding recommendation model; obtaining a corresponding weight for the other recommendation engine(s); selecting at least one recommendation from the first plurality of recommendations and the other recommendation(s) using the corresponding weight(s); and causing the selected recommendation(s) to be displayed.

Abstract: Techniques for implementing artificial crowd intelligence via networking of recommendation engines are disclosed. In some example embodiments, a base recommendation engine performs a computer-implemented method comprising: receiving a recommendation request; generating a first plurality of recommendations based on the recommendation request using a base recommendation model; obtaining one or more other recommendations from one or more other recommendation engines, each one of the other recommendation engines being configured to generate the other recommendations based on the recommendation request using a corresponding recommendation model; obtaining a corresponding weight for the other recommendation engine(s); selecting at least one recommendation from the first plurality of recommendations and the other recommendation(s) using the corresponding weight(s); and causing the selected recommendation(s) to be displayed.

Abstract: Techniques for implementing artificial crowd intelligence via networking of recommendation engines are disclosed. In some example embodiments, a base recommendation engine performs a computer-implemented method comprising: receiving a recommendation request; generating a first plurality of recommendations based on the recommendation request using a base recommendation model; obtaining one or more other recommendations from one or more other recommendation engines, each one of the other recommendation engines being configured to generate the other recommendations based on the recommendation request using a corresponding recommendation model; obtaining a corresponding weight for the other recommendation engine(s); selecting at least one recommendation from the first plurality of recommendations and the other recommendation(s) using the corresponding weight(s); and causing the selected recommendation(s) to be displayed.

Abstract: Techniques for implementing artificial crowd intelligence via networking of recommendation engines are disclosed. In some example embodiments, a base recommendation engine performs a computer-implemented method comprising: receiving a recommendation request; generating a first plurality of recommendations based on the recommendation request using a base recommendation model; obtaining one or more other recommendations from one or more other recommendation engines, each one of the other recommendation engines being configured to generate the other recommendations based on the recommendation request using a corresponding recommendation model; obtaining a corresponding weight for the other recommendation engine(s); selecting at least one recommendation from the first plurality of recommendations and the other recommendation(s) using the corresponding weight(s); and causing the selected recommendation(s) to be displayed.

Abstract: Methods, systems, and computer-readable storage media for receiving, from an integrated development environment (IDE), authored code, the authored code having been input by a developer to the IDE, receiving developer data representative of the developer, and context data and domain data associated with the authored code, determining, at least partially based on processing the authored code, the developer data, the context data, and the domain data through one or more machine learning (ML) models, that one or more code recommendations are to be displayed to the developer in the IDS, defining a sub-set of code recommendations at least partially based on the authored code and the context data, the sub-set of code recommendations comprising at least one code snippet, and displaying graphical representations of code recommendations in the sub-set of code recommendations to the developer within the IDE.

Abstract: Input data is received, by a server, for a user from one or more data sources. A set of user language features associated with the input data are determined. The set of user language features is compared to multiple sets of known language features. Each set of known language features includes language features associated with the use of a non-native language by a speaker of a respective native language. A native language of the user is determined based on the comparing. A personalized recommendation is generated based on the determined native language of the user.

Abstract: Methods, systems, and computer-readable storage media for receiving, from an integrated development environment (IDE), authored code, the authored code having been input by a developer to the IDE, receiving developer data representative of the developer, and context data and domain data associated with the authored code, determining, at least partially based on processing the authored code, the developer data, the context data, and the domain data through one or more machine learning (ML) models, that one or more code recommendations are to be displayed to the developer in the IDS, defining a sub-set of code recommendations at least partially based on the authored code and the context data, the sub-set of code recommendations comprising at least one code snippet, and displaying graphical representations of code recommendations in the sub-set of code recommendations to the developer within the IDE.

Abstract: First resource utilization information for a first customer of a cloud platform and second resource utilization information for a second customer of the cloud platform are accessed. A first prediction regarding future resource utilization by the first customer and a second prediction regarding future resource utilization by the second customer are determined. A resource reallocation recommendation that recommends reallocating one or more resources between the first customer and the second customer is determined, based on the first prediction and the second prediction. The resource reallocation recommendation is provided.

Abstract: Techniques for implementing artificial crowd intelligence via networking of recommendation engines are disclosed. In some example embodiments, a base recommendation engine performs a computer-implemented method comprising: receiving a recommendation request; generating a first plurality of recommendations based on the recommendation request using a base recommendation model; obtaining one or more other recommendations from one or more other recommendation engines, each one of the other recommendation engines being configured to generate the other recommendations based on the recommendation request using a corresponding recommendation model; obtaining a corresponding weight for the other recommendation engine(s); selecting at least one recommendation from the first plurality of recommendations and the other recommendation(s) using the corresponding weight(s); and causing the selected recommendation(s) to be displayed.

Abstract: First resource utilization information for a first customer of a cloud platform and second resource utilization information for a second customer of the cloud platform are accessed. A first prediction regarding future resource utilization by the first customer and a second prediction regarding future resource utilization by the second customer are determined. A resource reallocation recommendation that recommends reallocating one or more resources between the first customer and the second customer is determined, based on the first prediction and the second prediction. The resource reallocation recommendation is provided.

Abstract: Input data is received, by a server, for a user from one or more data sources. A set of user language features associated with the input data are determined. The set of user language features is compared to multiple sets of known language features. Each set of known language features includes language features associated with the use of a non-native language by a speaker of a respective native language. A native language of the user is determined based on the comparing. A personalized recommendation is generated based on the determined native language of the user.

Abstract: Techniques are disclosed for use in authentication. In one embodiment, a method is disclosed. The method comprises receiving an electronic request to access a computerized resource. The electronic request comprises a user identifier identifying a user associated with the electronic request and a first access token. The method comprises retrieving, in response to receiving the electronic request, a time-varying token seed associated with the generation of access tokens during a time period. The time-varying token seed is different if retrieved after the expiration of the time period. The method comprises generating a second access token based on the time-varying token seed and the user identifier identifying the user. The method comprises comparing the first and second access tokens to determine a similarity between the respective access tokens. The method comprises determining whether to allow the user access to the computerized resource based on the similarity between the access tokens.

Abstract: A technique controls access to a resource. The technique includes deriving, by processing circuitry, a password based on a phrase/thought provided by a user. The technique further includes confirming with the user that the password is to control access to the resource. The technique further includes, after confirming with the user that the password is to control access to the resource, imposing a requirement that the user provide the password before obtaining access to the resource. Such a password may be formed by concatenating multiple words (e.g., four words) that may be unrelated to each other. Such a password may be relatively strong since the resulting concatenation would not be found in any dictionary, and since it would be an extremely difficult and time consuming endeavor to predict such a password by attempting to combine words from a dictionary to form the concatenations.

Abstract: Techniques are disclosed for verifying caller identification in voice communications. In one embodiment, there is disclosed a method comprising receiving a request to establish a voice call between communications devices. The request comprising encrypted information and an identifier identifying a caller associated with the request to establish the voice call. The method further comprises retrieving, in response to receiving the request, a decryption key from an authentication source by requesting from the authentication source the decryption key associated with the identifier. The method further comprises utilizing the decryption key to decrypt the encrypted information and produce decrypted information as well as comparing the decrypted information and the identifier to determine a similarity therebetween. The method further comprises providing, based on the comparison, an indication of whether or not the request is deemed associated with fraud.