An estimation unit (136) retrieves a subtree that matches a query to be estimated, from subtrees included in a syntax tree created from a query inserted into a Web request. In addition, the estimation unit (136) presents information for specifying the type of damage of an attack and an attack target, the information being associated in advance with the subtree obtained by the retrieval of the retrieval unit.

An estimation unit retrieves a subtree that matches a query to be estimated, from subtrees included in a syntax tree created from a query inserted into a Web request. In addition, the estimation unit presents information for specifying the type of damage of an attack and an attack target, the information being associated in advance with the subtree obtained by the retrieval of the retrieval unit.

A document execution engine receives a training set of data including training documents that each include one or more passages associated with a passage type and a level of risk. The document execution engine trains a machine learned model based on the training set. The trained machine learned model, when applied to subsequently identified passages within documents in the document execution environment, can identify a passage with above threshold levels of risk (e.g., a high-risk passage) based on a passage type of the passage. The trained machine learned model can then provide for display the high-risk passage and a related passage of the same passage type from a second document within the document execution environment to the user via a document passage comparison interface. Differences between the passages can be highlighted, enabling a user to quickly compare and contrast the passages.

Techniques and systems for reducing data stored on a block processing storage system are described. A losslessly reduced representation of a data block can include references to one or more prime data element blocks, and optionally a description of a reconstitution program which, when applied to the one or more prime data element blocks, results in the data block.

Techniques to store graph information in a database are disclosed. In various embodiments, each node in a graph may be modeled as a micro b-tree. Node identity, attribute, edge, and edge attribute data may be stored in one or more pages modeled on page formats typically used to store index data for a relational database index. Data associated with a plurality of nodes and edges, each of said edges representing a relationship between two or more of said nodes, may be received. For each node, one or more pages of data may be created, each corresponding to a prescribed page size associated with a storage device in which said one or more pages are to be stored, and each page having a data structure that includes a variable-sized set of fixed length data slots and a variable-sized variable length data region.

Generating a universal learned model that appropriately controls a group of operating devices having the same configuration. Steps comprise subjecting a predetermined machine learning model to learning based on predetermined initial data to generate an initial learned model and an integration step of incorporating the initial learned model that controls a predetermined operating device into a plurality of operating devices, and integrating a plurality of individual learned models obtained by additional learning based on respective operation data obtained by operating the respective operating devices, thereby providing a universal learned model.

Methods, apparatus, systems and articles of manufacture to defend against adversarial machine learning are disclosed. An example apparatus includes a model trainer to train a classification model based on files with expected classifications; and a model modifier to select a convolution layer of the trained classification model based on an analysis of the convolution layers of the trained classification model; and replace the convolution layer with a tree-based structure to generate a modified classification model.

A method for (of) automatically detecting a security vulnerability in a source code using a machine learning model, characterized in that the method includes: obtaining the source code from a client codebase, wherein the client codebase is a complete or an incomplete body of the source code for a given software program or an application; and using a machine learning (ML) model to perform a ML based analysis on an abstract syntax tree (AST) for detecting a first security vulnerability over a static source code, the machine learning based analysis comprise (i) flattening the abstract syntax tree (AST) into a sequence of structured tokens, wherein the sequence of structured tokens includes a semantic structure and a syntactic structure of the source code, (ii) implementing a natural language processing technique on the sequence of structured tokens for mapping the sequence of structured tokens to one or more integers, (iii) pre-training the machine learning model using an unlabeled source code as an input to predict a subsequent sub-token in the sequence of structured tokens and (iv) training the machine learning model on a labeled source code to predict a presence or an absence of the first security vulnerability.

Techniques described herein relate to systems and methods of data storage, and more particularly to providing layering of file system functionality on an object interface. In certain embodiments, file system functionality may be layered on cloud object interfaces to provide cloud-based storage while allowing for functionality expected from a legacy applications. For instance, POSIX interfaces and semantics may be layered on cloud-based storage, while providing access to data in a manner consistent with file-based access with data organization in name hierarchies. Various embodiments also may provide for memory mapping of data so that memory map changes are reflected in persistent storage while ensuring consistency between memory map changes and writes. For example, by transforming a ZFS file system disk-based storage into ZFS cloud-based storage, the ZFS file system gains the elastic nature of cloud storage.

In one embodiment, a method includes receiving, at a network management system (NMS) from a client, a message having an object reference embedding a query, the query requesting an operation to be performed on data stored in a data tree maintained by the NMS. The method provides for generating, by the NMS, a result of the query by performing the operation on the data. In this embodiment, the method further provides for sending, by the NMS to the client, the result of the query. In some embodiments, the object reference may include a pathname.

A tree-based format may be implemented for data stored in a data store. A table may be maintained across one or multiple storage nodes in storage slabs. Storage slabs may be mapped to different nodes of a tree. Each node of the tree may be assigned a different range of distribution scheme values which identify what portions of the table are stored in the storage slab. Storage slabs mapped to child nodes in the tree may be assigned portions of the range of distribution scheme values assigned to a parent. Storage nodes may be added or removed for storing the table. Storage slabs may be moved from one storage node to another in order to accommodate the addition or removal of storage nodes.