Abstract: An approach for a fast and accurate word embedding model, “desc2vec,” for out-of-dictionary (OOD) words with a model learning from the dictionary descriptions of the word is disclosed. The approach includes determining that a target text element is not in a set of reference text elements, information describing the target text element is obtained. The information comprises a set of descriptive text elements. A set of vectorized representations for the set of descriptive text elements is determined. A target vectorized representation for the target text element is determined based on the set of vectorized representations using a machine learning model. The machine learning model is trained to represent a predetermined association between the set of vectorized representations for the set of descriptive text elements describing the target text element and the target vectorized representation.

Abstract: An approach for a fast and accurate word embedding model, “desc2vec,” for out-of-dictionary (OOD) words with a model learning from the dictionary descriptions of the word is disclosed. The approach includes determining that a target text element is not in a set of reference text elements, information describing the target text element is obtained. The information comprises a set of descriptive text elements. A set of vectorized representations for the set of descriptive text elements is determined. A target vectorized representation for the target text element is determined based on the set of vectorized representations using a machine learning model. The machine learning model is trained to represent a predetermined association between the set of vectorized representations for the set of descriptive text elements describing the target text element and the target vectorized representation.

Abstract: The present disclosure provides a method for disease control of plants, comprising predicting the probability of a disease occurrence and suggesting a suitable and effective control measure for the identified pathogen and/or host. The present disclosure also provides an advisory service with recommended management actions and other alerts and notifications.

Abstract: Provided are systems, methods, and media for multiphase graph partitioning for malware entity detection. An example method includes receiving an input string associated with the malware entity. A determination is made as to whether the input string includes a symbolic word, a non-symbolic word, a symbolic phrase, or a non-symbolic phrase. A branching graph is formed based on a combination of the input string and a plurality of stored strings that are each associated with the malware entity to determine whether the input string is a valid detection name of the malware entity, in which the branching graph is formed by at least performing a first graph partitioning stage and a second graph partitioning stage. The input string is then labeled based on the formed branching graph and then outputted to a malware detection engine.

Abstract: A method, computer program product, and computing system device for receiving, on a computing device, a plurality of webpages. At least one webpage may be filtered from the plurality of webpages into at least one set of webpages using a decision tree algorithm. At least one remaining webpage may be filtered from the plurality of webpages into the at least one set of webpages using a supported vector machine (SVM) algorithm.

Abstract: A method, computer program product, and computing system device for receiving, on a computing device, a plurality of webpages. At least one webpage may be filtered from the plurality of webpages into at least one set of webpages using a decision tree algorithm. At least one remaining webpage may be filtered from the plurality of webpages into the at least one set of webpages using a supported vector machine (SVM) algorithm.

Abstract: Provided are systems, methods, and media for multiphase graph partitioning for malware entity detection. An example method includes receiving an input string associated with the malware entity. A determination is made as to whether the input string includes a symbolic word, a non-symbolic word, a symbolic phrase, or a non-symbolic phrase. A branching graph is formed based on a combination of the input string and a plurality of stored strings that are each associated with the malware entity to determine whether the input string is a valid detection name of the malware entity, in which the branching graph is formed by at least performing a first graph partitioning stage and a second graph partitioning stage. The input string is then labeled based on the formed branching graph and then outputted to a malware detection engine.