Abstract: A gem imaging system includes a stage having a platform configured to hold an object. The system further includes a light source configured to illuminate the object. The system further includes a housing configured to at least partially enclose the object. The housing is configured to position a mobile device such that a camera lens of the mobile device is aligned with the object.

Abstract: A gem imaging system includes a stage having a platform configured to hold an object. The system further includes a light source configured to illuminate the object. The system further includes a housing configured to at least partially enclose the object. The housing is configured to position a mobile device such that a camera lens of the mobile device is aligned with the object.

Abstract: In an embodiment, a gem imaging system includes a stage having a platform configured to hold an object. In an embodiment, the system further includes a light source configured to illuminate the object. In an embodiment, the system further includes a housing configured to at least partially enclose the object, wherein the housing is configured to position a mobile device such that a camera lens of the mobile device is aligned with the object.

Abstract: A method, a computer program product, and a computer system for classifying bacteria. The method comprises extracting a morphology signature corresponding to one or more bacteria and extracting a motility signature corresponding to the one or more bacteria. The method further comprises merging the morphology signature and the motility signature into a merged vector signature and classifying the one or more bacteria based on the merged vector signature.

Abstract: An example operation may include one or more of scanning, by a mobile node, a physical object to generate a scan data, extracting, by the mobile node, a set of features from the scan data, generating, by the mobile node, a feature vector based on the set of the features, applying, by the mobile node, a cryptographic hash function to the feature vector to produce a hash value, encrypting, by the mobile node, the set of the features with the hash value, and executing a smart contract to store the encrypted set of the features on a blockchain.

Abstract: A first series of images of a first fluid is received. A first set of fluid characteristics of the first fluid is determined from the first series of images. A second series of images of a second fluid is received. A second set of fluid characteristics of the second fluid is determined from the second series of images. A match is determined to be found between the first set of fluid characteristics and the second set of fluid characteristics. The second fluid is identified based upon determining that the first set of fluid characteristics matches the second set of fluid characteristics.

Abstract: Facilitating code re-use includes receiving a first design document. The first user design document specifies a graphical layout of a first set of user interface elements of a user interface of a first application. First source code associated with the design document is received. The first source code is configured to implement the first set of user interface elements of the user interface. A processor extracts the first set of user interface elements from the first user interface design document. A first portion of the first source code corresponding to a first user interface element of the first set of user interface elements is identified. The first portion of the first source code is encapsulated into a module to enable re-use of the portion of the source code in a second application.

Abstract: A gemstone detection method is provided and includes using a camera to generate a set of training pictures illustrating three-dimensional features of a gemstone cut with a pattern. Each training picture in the set of training pictures includes facets of and inclusions within the gemstone visible along a point-of-view (POV) through the gemstone. The gemstone detection method further includes generating a trained neural network by training an untrained neural network using the set of training pictures and a set of training sketches of edges of the facets of the gemstone generated from the set of training pictures and using the trained neural network to iteratively generate machine-generated drawings from the set of training pictures. Each of the machine-generated drawings identifies edges of the facets of the gemstone. In addition, the gemstone detection method includes combining the set of machine-generated drawings into a three-dimensional model.

Abstract: A method, system and apparatus for source code auto-suggestion, including receiving and processing source code from a source code repository, extracting one or more features from the source code received from the source code repository, extracting one or more features from the source code within a development environment, comparing the one or more features from the source code received from the source code repository with the one or more features extracted from the source code within the development environment, and providing a segment of source code from the received and processed source code repository according to a similarity to the source code within the development environment.

Abstract: A method, a computer program product, and a computer system for classifying bacteria. The method comprises extracting a morphology signature corresponding to one or more bacteria and extracting a motility signature corresponding to the one or more bacteria. The method further comprises merging the morphology signature and the motility signature into a merged vector signature and classifying the one or more bacteria based on the merged vector signature.

Abstract: A gemstone detection method is provided and includes using a camera to generate a set of training pictures illustrating three-dimensional features of a gemstone cut with a pattern. Each training picture in the set of training pictures includes facets of and inclusions within the gemstone visible along a point-of-view (POV) through the gemstone. The gemstone detection method further includes generating a trained neural network by training an untrained neural network using the set of training pictures and a set of training sketches of edges of the facets of the gemstone generated from the set of training pictures and using the trained neural network to iteratively generate machine-generated drawings from the set of training pictures. Each of the machine-generated drawings identifies edges of the facets of the gemstone. In addition, the gemstone detection method includes combining the set of machine-generated drawings into a three-dimensional model.

Abstract: In an embodiment, a method of estimating the cost of a software project comprising receiving natural language software descriptions and electronic source code files for respective completed software projects; storing, in a computer memory, an input set of functional labels and size data extracted from each of the source code files, the functional labels corresponding to labels in a software development library; training a natural language processing model to output a project set of functional labels for one of the source code files; training a regression analysis model to output a project size for the one of the source code files; predicting, using the natural language understanding model, a proposal set of functional labels; predicting, using the regression analysis model, a proposal size; and using the proposal size to calculate a proposal cost; and preparing a software development proposal that includes a natural language proposed-software description and the proposal cost.

Abstract: In an embodiment, a gem imaging system includes a stage having a platform configured to hold an object. In an embodiment, the system further includes a light source configured to illuminate the object. In an embodiment, the system further includes a housing configured to at least partially enclose the object, wherein the housing is configured to position a mobile device such that a camera lens of the mobile device is aligned with the object.

Abstract: An example operation may include one or more of scanning, by a mobile node, a physical object to generate a scan data, extracting, by the mobile node, a set of features from the scan data, generating, by the mobile node, a feature vector based on the set of the features, applying, by the mobile node, a cryptographic hash function to the feature vector to produce a hash value, encrypting, by the mobile node, the set of the features with the hash value, and executing a smart contract to store the encrypted set of the features on a blockchain.

Abstract: Facilitating code re-use includes receiving a first design document. The first user design document specifies a graphical layout of a first set of user interface elements of a user interface of a first application. First source code associated with the design document is received. The first source code is configured to implement the first set of user interface elements of the user interface. A processor extracts the first set of user interface elements from the first user interface design document. A first portion of the first source code corresponding to a first user interface element of the first set of user interface elements is identified. The first portion of the first source code is encapsulated into a module to enable re-use of the portion of the source code in a second application.

Abstract: A presentation document specifying a graphical layout of a user interface is received. A processor extracts a first user interface graphical feature from the presentation document. The extracted first user interface graphical feature is matched to one or more first candidate user interface graphical features to determine a first selected candidate user interface graphical feature. A user interface code representation of the user interface is synthesized based upon the first selected candidate user interface graphical feature.

Abstract: A first series of images of a first fluid is received. A first set of fluid characteristics of the first fluid is determined from the first series of images. A second series of images of a second fluid is received. A second set of fluid characteristics of the second fluid is determined from the second series of images. A match is determined to be found between the first set of fluid characteristics and the second set of fluid characteristics. The second fluid is identified based upon determining that the first set of fluid characteristics matches the second set of fluid characteristics.

Abstract: A first series of images of a first fluid is received. A first set of fluid characteristics of the first fluid is determined from the first series of images. A second series of images of a second fluid is received. A second set of fluid characteristics of the second fluid is determined from the second series of images. A match is determined to be found between the first set of fluid characteristics and the second set of fluid characteristics. The second fluid is identified based upon determining that the first set of fluid characteristics matches the second set of fluid characteristics.

Abstract: A first series of images of a first fluid is received. A first set of fluid characteristics of the first fluid is determined from the first series of images. A second series of images of a second fluid is received. A second set of fluid characteristics of the second fluid is determined from the second series of images. A match is determined to be found between the first set of fluid characteristics and the second set of fluid characteristics. The second fluid is identified based upon determining that the first set of fluid characteristics matches the second set of fluid characteristics.