International Business Machines Corporation

Richard B Segal

Ashish Jagmohan

Krishna Chaitanya Ratakonda

Mark N Wegman

Roman Vaculin

Elham Khabiri

Ying Li

Vadakkedathu Thomas Rajan

Ta-Hsin Li

Pu Huang

Kaan Kudsi Katircioglu

Axel Martens

Jeffrey Owen Kephart

Yi-Min Chee

Rakesh Mohan

Gerald James Tesauro

Barry Leiba

Joel P Ossher

Pamela N Luna

T K Balachandran

Piyawadee Sukaviriya

Lisa A Smith

Steve Richard White

Richard Thomas Goodwin

Jason L Crawford

Petar Ristoski

Alina Beygelzimer

Janusz Marecki

Robin Lougee

Jing Fu

Kimberly C Lang

Christian Harris

Tenzin Yeshi

Shlomo Hershkop

A method, system, and computer program product for representational learning of product formulas are provided. The method accesses a set of product formulas. Each product formula includes a set of ingredient tuples. A directed graph is generated from the set of product formulas. The directed graph including a node for each ingredient of the sets of ingredient tuples of the set of formulas. The method generates a weighted graph from the directed graph. The weighted graph has a weight assigned to each edge in the directed graph. The method generates an embedding model based on the directed graph. A set of embeddings is determined for the weighted graph where each node is represented with low-dimensional numerical vectors.

Method, system, and computer program product for representational machine learning for product formulation

Techniques for outside-in mapping for corpus pairs are provided. In one example, a computer-implemented method comprises: inputting first keywords associated with a first domain corpus; extracting a first keyword of the first keywords; inputting second keywords associated with a second domain corpus; generating an embedded representation of the first keyword via a trained model and generating an embedded representation of the second keywords via the trained model; and scoring a joint embedding affinity associated with a joint embedding. The scoring the joint embedding affinity comprises: transforming the embedded representation of the first keyword and the embedded representation of the second keywords via the trained model; determining an affinity value based on comparing the first keyword to the second keywords; and based on the affinity value, aggregating the joint embedding of the embedded representation of the first keyword and the embedded representation of the second keywords within the second domain corpus.

Joint embedding of corpus pairs for domain mapping

According to embodiments, methods, systems, and computer program products are provided for receiving one or more input compositions comprising one or more materials, assigning a material vector to each material, learning, for each of the input compositions, a composition vector based on the material vectors of the materials that form each composition, assigning predicted rating values having a confidence level to each of the composition vectors, selecting a composition to be rated based on the confidence levels, presenting the selected composition to be rated to a user, receiving a user rating for the composition to be rated; adjusting the predicted rating values and confidence levels of the composition vectors that have not been rated by the user, and generating a predictive model to predict a user's ratings for compositions when confidence levels of each composition vector is above a predetermined threshold value.

System and method for identifying composition preferences

A service engagement map may be generated based on data collected associated with the service transition and delivery processes. The service engagement map may be refined iteratively by discovering additional data associated with the service transition and delivery processes and updating the service engagement map according to the additional data. Engagement metrics may be computed based on the service engagement map and presented. The service engagement map may also be presented visually.

Metrics management and monitoring system for service transition and delivery management

Assignment scheduling for service projects, in one aspect, may comprise preparing input parameter data for servicing a client service request; generating a schedule for servicing the client service request by executing an optimization algorithm with the input parameter data; determining whether the schedule is acceptable by the client; and repeating automatically the preparing, the generating, the transmitting and the determining until it is determined that the schedule is acceptable by the client, wherein each iteration automatically prepares different input parameter data for inputting to the optimization algorithm and generates a different schedule based on the different input parameter data.

Scheduling for service projects via negotiation

A method, system and computer program product for choosing actions in a state of a planning problem. The system simulates one or more sequences of actions, state transitions and rewards starting from the current state of the planning problem. During the simulation of performing a given action in a given state, a data record is maintained of observed contextual state information, and observed cumulative reward resulting from the action. The system performs a regression fit on the data records, enabling estimation of expected reward as a function of contextual state. The estimations of expected rewards are used to guide the choice of actions during the simulations. Upon completion of all simulations, the top-level action which obtained highest mean reward during the simulations is recommended to be executed in the current state of the planning problem.

Monte-Carlo planning using contextual information

A system, method and computer program product for planning actions in a repeated Stackelberg Game, played for a fixed number of rounds, where the payoffs or preferences of the follower are initially unknown to the leader, and a prior probability distribution over follower types is available. In repeated Bayesian Stackelberg games, the objective is to maximize the leader's cumulative expected payoff over the rounds of the game. The optimal plans in such games make intelligent tradeoffs between actions that reveal information regarding the unknown follower preferences, and actions that aim for high immediate payoff. The method solves for such optimal plans according to a Monte Carlo Tree Search method wherein simulation trials draw instances of followers from said prior probability distribution. Some embodiments additionally implement a method for pruning dominated leader strategies.

Optimal policy determination using repeated stackelberg games with unknown player preferences

A system includes at least one router for routing email messages from a sender node to a destination node; a system memory; a network interface; a database; and a processor configured for: extracting the delivery path information from the email message; determining a network path for the email message using delivery path information; comparing the delivery path information with the plurality of prior delivery paths; determining a measure of similarity between the network path of the received email message and one or more of the plurality of prior email paths; and determining a spam score for the email received, based on the measure of similarity.

Recognizing spam email

A method for detecting undesirable emails combines input from two or more spam classifiers to provide improved classification effectiveness and robustness. The method includes obtaining a score from each of a plurality of constituent spam classifiers by applying them to a given input email. The method further includes obtaining a combined spam score from a combined spam classifier that takes as input the plurality of constituent spam classifier scores, the combined spam classifier being computed automatically in accordance with a specified false-positive vs. false-negative tradeoff. The method further includes identifying the given input email as an undesirable email if the combined spam score indicates that the input e-mail is undesirable.

Detecting spam email using multiple spam classifiers

A method includes steps of receiving an email message comprising a plurality of packets and delivery-path information; determining a path for the email using the delivery-path information; comparing the path with a plurality of prior email paths; determining a measure of similarity between the path of the email received and one or more of the plurality of prior email paths; and determining a spam score for the email received, based on the measure of similarity. Other embodiments include a computer readable medium comprising computer code for performing the above function and an information processing system including a processor configured (i.e., hard-wired or programmed) to perform the method.

Method for recognizing spam email

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Chappaqua, NY, United States of America

Richard B Segal