A non-transitory computer-readable storage medium storing a machine learning program that causes at least one computer to execute a process, the process includes classifying a plurality of entities included in a graph structure that indicates a relationship between the plurality of entities to generate a first group and a second group; specifying a first entity positioned in a connection portion of the graph structure between the first group and the second group; and training a machine learning model by inputting first training data that indicates a relationship between the first entity and a second entity of the plurality of entities into the machine learning model in priority to a plurality of pieces of training data that indicates the relationship between the plurality of entities other than the first training data.

Methods, apparatus, and processor-readable storage media for automatically adjusting storage system configurations in a storage-as-a-service environment using machine learning techniques are provided herein. An example computer-implemented method includes obtaining performance-related data for at least one storage system in a storage-as-a-service environment; processing at least a portion of the obtained performance-related data using one or more rule-based analyses; identifying, based at least in part on results of the processing, one or more storage system configurations, of the at least one storage system, requiring adjustment; determining, using at least one machine learning technique, one or more adjustment amounts for the one or more storage system configurations; and automatically adjusting the one or more storage system configurations, within the storage-as-a-service environment, in accordance with the one or more determined adjustment amounts.

A framework for assessing technical feasibility of additive manufacturing of an engineering design. This framework needs to be based on preliminary identification of key parameters that influence the decision making process. The parameters may also be customized for a particular application. Each of these parameters can be assigned weightage either relative or arrived at by paired comparison using a pre-determined minimum point method. Each of the attributes are then assigned scores which are then multiplied by the weightages assigned. The summation of all such scores on a weighted average basis indicates the potential for 3D printing of that part or assembly. It offers to select the right part to leverage the benefit of additive manufacturing. It narrows down on the ideal manufacturing process for the qualified parts and proposes to reduce subjectivity by using paired comparison of attributes. It also provides a faster assessment of technical aspects of the design.

Among other things, a developer of an interaction application for an enterprise can create items of content to be provided to an assistant platform for use in responses to requests of end-users. The developer can deploy the interaction application using defined items of content and an available general interaction model including intents and sample utterances having slots. The developer can deploy the interaction application without requiring the developer to formulate any of the intents, sample utterances, or slots of the general interaction model.

One or more computer processors create a binary cluster of events by bootstrapping a set of ground truths contained with a rule engine applied to a set of high-dimensional datapoints, wherein the binary cluster contains two clusters each containing a plurality of high-dimensional datapoints; determine one or more peer groups for a set of unknown high-dimensional datapoints utilizing a trained multiclass classifier, wherein the high-dimensional datapoints are assigned to one or more peer groups by the trained multiclass classifier using an incremental learning algorithm in order to reduce system resources; create an activity distribution for each unknown high-dimensional datapoint associated with a user in the set of unknown high-dimensional datapoints and each peer group; calculate a deviation percentage between the activity distribution of the user and each peer group associated with the user; and responsive to exceeding a deviation threshold, classify the user or associated high-dimensional datapoints as risky.

Provided is a method of identifying a candidate gene for a genetic disease includes obtaining a disease network, disease-gene association information, and a gene network, obtaining a single nucleotide polymorphism (SNP) network based on intra-relation data between a plurality of SNPs, and inter-relation data between genes and SNPs, creating a disease-gene-SNP multilayered network based on the disease network, the disease-gene association information, the gene network, the SNP network, and the interrelation data between genes and SNPs, and identifying a candidate gene for a genetic disease using the multilayered network.

The information processing apparatus 1 includes the input unit 101 that receives observation represented by a conjunction of atomic formulas, background knowledge represented by a set of logic formulas, and condition to be satisfied by a new rule represented by a logic formula; the rule candidate generation unit 102 that generates the new rule represented by a logic formula having a predicate, which is contained in the observation or the background knowledge, as an element; the abduction system unit 103 that executes abduction to derive best hypothesis by using the background knowledge, to which the new rule generated is added, and the observation as inputs; and the rule evaluation unit 104 that evaluates whether the new rule satisfies the condition to be satisfied by using the best hypothesis.

Actions between a customer and a merchant are guided by decision engine scores using a shared history from multiple merchants. In an example, an action request is received at a service provider. A prime merchant identifier is applied to a merchant database to obtain a policy stack. A customer identifier is applied to a transactions database to obtain a transaction history for the customer and different merchants. Rules of the policy stack are applied in sequence to action parameters and values of the customer transaction history at a decision engine of the service provider, the decision engine generating a first action score. A customer score from the prime merchant is applied to generate a final action score and the prime merchant is notified to conditionally execute the requested action based on the final action score.

Systems and methods for improving machine learning systems used to model topics on a plurality of calls are described herein. In an embodiment, a server computer receives plurality of digitally stored call transcripts that have been prepared from digitally recorded voice calls. The server computer uses a topic model of an artificial intelligence machine learning system, the topic model modeling words of a call as a function of one or more word distributions for each topic of a plurality of topics, to generate an output of the topic model which identifies the plurality of topics represented in the plurality of call transcripts. The server computer computes, for a particular topic of the plurality of topics a first value representing a vocabulary of the particular topic and a second value representing a consistency of the particular topic in two more call transcripts of the plurality of call transcripts which include the particular topic. Based, at least in part, on one or more of the first value or the second value, the server computer determines that the particular topic meets a particular criterion and, in response, updates the output of the topic model to remove the particular topic or distinguish the particular topic from other topics of the plurality of topics which do not meet the particular criterion.

An ontology mapping system 1 includes: a generation unit 21 that generates non-link training data 31 identifying non-link pairs other than link pairs, from among pairs each associating a node of a first ontology T1 with a node of a second ontology T2 associated by plural link pairs, each associating a node of a first ontology with a node of a second ontology, which is to be mapped to the node of the first ontology, and merges link training data 11 and the non-link training data 31 to generate training data 32; an estimation unit 25 that estimates an expression vector of each node by using a first neural network 33a and a second neural network 33b that have been trained with reference to the training data 32; and a mapping unit 26 that determines, based on a degree of difference between expression vectors of a node of the first ontology and a node of the second ontology, whether or not the nodes are mapped.