Abstract: The present disclosure relates to a system and method for vulnerability localization based on deep learning, which comprises, at a minimum, a processor configured to: analyze a code file under detection to obtain a first abstract syntax tree devoid of semantic information; build upon the first abstract syntax tree by incorporating data-flow edges and/or control-flow edges, thereby forming a second abstract syntax tree with semantic-flow enhancement; split the second abstract syntax tree to obtain a plurality of second abstract syntax sub-trees; and input these second abstract syntax sub-trees into a pre-established vulnerability detection and localization model.

Abstract: This specification provide example Java bytecode injection methods, apparatuses and machine-readable storage media, which can be applied to a Javaagent framework deployed in a Java virtual machine. In an example, a to-be-injected Java bytecode submitted by a target service module in at least one service module is obtained, and a target implementation class corresponding to the to-be-injected Java bytecode is determined. A target injection point type corresponding to the target implementation class is determined, and the target injection point type is respectively matched with mapping relationships maintained in the Java virtual machine to determine a target injection mode corresponding to the target injection point type. A globally unique class transformer is invoked so that the class transformer injects, based on the target injection mode, the Java bytecode into a class file that corresponds to the target service module and that is to be loaded by the Java virtual machine.

Abstract: This specification provide example Java bytecode injection methods, apparatuses and machine-readable storage media, which can be applied to a Javaagent framework deployed in a Java virtual machine. In an example, a to-be-injected Java bytecode submitted by a target service module in at least one service module is obtained, and a target implementation class corresponding to the to-be-injected Java bytecode is determined. A target injection point type corresponding to the target implementation class is determined, and the target injection point type is respectively matched with mapping relationships maintained in the Java virtual machine to determine a target injection mode corresponding to the target injection point type. A globally unique class transformer is invoked so that the class transformer injects, based on the target injection mode, the Java bytecode into a class file that corresponds to the target service module and that is to be loaded by the Java virtual machine.

Abstract: A system and a method for recommending a set of fashion items. The method includes: providing fashion categories, fashion graphs each corresponding to one fashion category, and fitting formulas for each fashion graph; receiving a query fashion item having a query fashion attribute; selecting a fashion category, where the query fashion attribute in the selected fashion category has a highest number of fashion items; performing a similarity search between image of the query fashion item and images of fashion items in the fitting formulas; mapping the searched fitting formula to the fashion graph of the selected fashion category to obtain nodes matching the fashion items in the searched fitting formula; and recommending the set of fashion items respectively selected from fashion items in the matched nodes. The fitting formulas are generated based on co-appearance of fashion items from any two nodes of the corresponding fashion graph.