Abstract: Data mining large-scale corpora of scholarly publications, such as the full biomedical literature, which may consist of tens of millions of papers spanning decades of research. The present disclosure provides a Distributed Accelerated Semiring All-Pairs Shortest Path (DSNAPSHOT) algorithm for computing shortest paths of a knowledge graph using distributed-memory parallel computers accelerated by GPUs. DSNAPSHOT implementations can analyze connected input graphs with millions of vertices using a large number graphics processing units (e.g., the 24,576 GPUs of the Oak Ridge National Laboratory's Summit supercomputer system). DSNAPSHOT provides sustained performance of about 136*1015 floating-point operations per second (136 petaflop/s) at a parallel efficiency of about 90% under weak scaling and, in absolute speed, 70% of the performance given our computation (in the single-precision tropical semiring or “min-plus” algebra).

Abstract: A system and method design and optimize neural networks. The system and method include a data store that stores a plurality of gene vectors that represent diverse and distinct neural networks and an evaluation queue stored with the plurality of gene vectors. Secondary nodes construct, train, and evaluate the neural network and automatically render a plurality of fitness values asynchronously. A primary node executes a gene amplification on a select plurality of gene vectors, a crossing-over of the amplified gene vectors, and a mutation of the crossing-over gene vectors automatically and asynchronously, which are then transmitted to the evaluation queue. The process continuously repeats itself by processing the gene vectors inserted into the evaluation queue until a fitness level is reached, a network's accuracy level plateaus, a processing time period expires, or when some stopping condition or performance metric is met or exceeded.

Abstract: Data mining large-scale corpora of scholarly publications, such as the full biomedical literature, which may consist of tens of millions of papers spanning decades of research. The present disclosure provides a Distributed Accelerated Semiring All-Pairs Shortest Path (DSNAPSHOT) algorithm for computing shortest paths of a knowledge graph using distributed-memory parallel computers accelerated by GPUs. DSNAPSHOT implementations can analyze connected input graphs with millions of vertices using a large number graphics processing units (e.g., the 24,576 GPUs of the Oak Ridge National Laboratory's Summit supercomputer system). DSNAPSHOT provides sustained performance of about 136*1015 floating-point operations per second (136 petaflop/s) at a parallel efficiency of about 90% under weak scaling and, in absolute speed, 70% of the performance given our computation (in the single-precision tropical semiring or “min-plus” algebra).

Abstract: A system and method for document recommendation combines linguistic and term frequency approaches in order to improve overall performance and recommendation. Hypernyms are added to the source document set so that related documents can be recommended from the source document set. There are a variety of different parameters to configure that change operation of the document recommendation system.

Abstract: A system and method for document recommendation combines linguistic and term frequency approaches in order to improve overall performance and recommendation. Hypernyms are added to the source document set so that related documents can be recommended from the source document set. There are a variety of different parameters to configure that change operation of the document recommendation system.

Abstract: A computer implemented method detects intrusions using a computer by analyzing network traffic. The method includes a semi-supervised learning module connected to a network node. The learning module uses labeled and unlabeled data to train a semi-supervised machine learning sensor. The method records events that include a feature set made up of unauthorized intrusions and benign computer requests. The method identifies at least some of the benign computer requests that occur during the recording of the events while treating the remainder of the data as unlabeled. The method trains the semi-supervised learning module at the network node in-situ, such that the semi-supervised learning modules may identify malicious traffic without relying on specific rules, signatures, or anomaly detection.

Abstract: Disclosed is a method and system for discovering documents using a computer and providing a small set of the most relevant documents to the attention of a human observer. Using the method, the computer obtains a seed document from the user and generates a seed document vector using term frequency-inverse corpus frequency weighting. A keyword index for a plurality of source documents can be compared with the weighted terms of the seed document vector. The comparison is then filtered to reduce the number of documents, which define an initial subset of the source documents. Initial subset vectors are generated and compared to the seed document vector to obtain a similarity value for each comparison. Based on the similarity value, the method then recommends one or more of the source documents.

Abstract: An adaptive system processes social media streams in real time. The adaptive system included a data management engine that generates combined data sets by detecting and mining a plurality of text-based messages from a social networking service on the Internet. An analytics engine in communication with the data management engine monitors topics in the text-based messages and tracks topic evolution contained in the text-based messages. A visualization engine in communication with the analytics engine renders historical and current activity associated with the plurality of text-based messages.

Abstract: A computer implemented method detects intrusions using a computer by analysing network traffic. The method includes a semi-supervised learning module connected to a network node. The learning module uses labeled and unlabeled data to train a semi-supervised machine learning sensor. The method records events that include a feature set made up of unauthorized intrusions and benign computer requests. The method identifies at least some of the benign computer requests that occur during the recording of the events while treating the remainder of the data as unlabeled. The method trains the semi-supervised learning module at the network node in-situ, such that the semi-supervised learning modules may identify malicious traffic without relying on specific rules, signatures, or anomaly detection.

Abstract: A cloud computing platform includes first device having a graph or tree structure with a node which receives data. The data is processed by the node or communicated to a child node for processing. A first node in the graph or tree structure determines the reconfiguration of a portion of the graph or tree structure on a second device. The reconfiguration may include moving a second node and some or all of its descendant nodes. The second and descendant nodes may be copied to the second device.

Abstract: Disclosed is a method and system for discovering documents using a computer and providing a small set of the most relevant documents to the attention of a human observer. Using the method, the computer obtains a seed document from the user and generates a seed document vector using term frequency-inverse corpus frequency weighting. A keyword index for a plurality of source documents can be compared with the weighted terms of the seed document vector. The comparison is then filtered to reduce the number of documents, which define an initial subset of the source documents. Initial subset vectors are generated and compared to the seed document vector to obtain a similarity value for each comparison. Based on the similarity value, the method then recommends one or more of the source documents.

Abstract: Medical reports are converted to document vectors in computing apparatus and sampled by applying a maximum variation sampling function including a fitness function to the document vectors to reduce a number of medical records being processed and to increase the diversity of the medical records being processed. Linguistic phrases are extracted from the medical records and converted to s-grams. A Haar wavelet function is applied to the s-grams over the preselected time interval; and the coefficient results of the Haar wavelet function are examined for patterns representing the likelihood of health abnormalities. This confirms certain s-grams as precursors of the health abnormality and a parameter can be calculated in relation to the occurrence of such a health abnormality.

Abstract: A cloud computing platform includes first device having a graph or tree structure with a node which receives data. The data is processed by the node or communicated to a child node for processing. A first node in the graph or tree structure determines the reconfiguration of a portion of the graph or tree structure on a second device. The reconfiguration may include moving a second node and some or all of its descendant nodes. The second and descendant nodes may be copied to the second device.

Abstract: Medical reports are converted to document vectors in computing apparatus and sampled by applying a maximum variation sampling function including a fitness function to the document vectors to reduce a number of medical records being processed and to increase the diversity of the medical records being processed. Linguistic phrases are extracted from the medical records and converted to s-grams. A Haar wavelet function is applied to the s-grams over the preselected time interval; and the coefficient results of the Haar wavelet function are examined for patterns representing the likelihood of health abnormalities. This confirms certain s-grams as precursors of the health abnormality and a parameter can be calculated in relation to the occurrence of such a health abnormality.

Abstract: The method and system of the invention involves processing each new document (20) coming into the system into a document vector (16), and creating a document vector with reduced dimensionality (17) for comparison with the data model (15) without recomputing the data model (15). These operations are carried out by a first computer (11) while a second computer (12) updates the data model (18), which can be comprised of an initial large group of documents (19) and is premised on the computing an initial data model (13, 14, 15) to provide a reference point for determining document vectors from documents processed from the data stream (20).

Abstract: A computer method and system for storing, retrieving and displaying information has a multiplexing agent (20) that calculates a new document vector (25) for a new document (21) to be added to the system and transmits the new document vector (25) to master cluster agents (22) and cluster agents (23) for evaluation. These agents (22, 23) perform the evaluation and return values upstream to the multiplexing agent (20) based on the similarity of the document to documents stored under their control. The multiplexing agent (20) then sends the document (21) and the document vector (25) to the master cluster agent (22), which then forwards it to a cluster agent (23) or creates a new cluster agent (23) to manage the document (21). The system also searches for stored documents according to a search query having at least one term and identifying the documents found in the search, and displays the documents in a clustering display (80) of similarity so as to indicate similarity of the documents to each other.

Abstract: A computer method of gathering and summarizing large amounts of information comprises collecting information from a plurality of information sources (14, 51) according to respective maps (52) of the information sources (14), converting the collected information from a storage format to XML-language documents (26, 53) and storing the XML-language documents in a storage medium, searching for documents (55) according to a search query (13) having at least one term and identifying the documents (26) found in the search, and displaying the documents as nodes (33) of a tree structure (32) having links (34) and nodes (33) so as to indicate similarity of the documents to each other.

Abstract: The method and system of the invention involves processing each new document (20) coming into the system into a document vector (16), and creating a document vector with reduced dimensionality (17) for comparison with the data model (15) without recomputing the data model (15). These operations are carried out by a first computer (11) while a second computer (12) updates the data model (18), which can be comprised of an initial large group of documents (19) and is premised on the computing an initial data model (13, 14, 15) to provide a reference point for determining document vectors from documents processed from the data stream (20).

Abstract: A method of analyzing computer intrusion detection information that looks beyond known attacks and abnormal access patterns to the critical information that an intruder may want to access. Unique target identifiers and type of work performed by the networked targets is added to audit log records. Analysis using vector space modeling, dissimilarity matrix comparison, and clustering of the event records is then performed.

Abstract: A computer method of gathering and summarizing large amounts of information comprises collecting information from a plurality of information sources (14, 51) according to respective maps (52) of the information sources (14), converting the collected information from a storage format to XML-language documents (26, 53) and storing the XML-language documents in a storage medium, searching for documents (55) according to a search query (13) having at least one term and identifying the documents (26) found in the search, and displaying the documents as nodes (33) of a tree structure (32) having links (34) and nodes (33) so as to indicate similarity of the documents to each other.