A method of determining whether a user has fallen comprises detecting a potential fall using a motion sensing device, updating a probability of the potential fall being an actual fall based on an additional sensor, and updating the probability of the potential fall being an actual fall based on user context, the user context including an identified activity prior to the potential fall.

A memory for storing a directed acyclic graph (DAG) for access by an application being executed by one or more processors of a computing device is described. The DAG includes a plurality of nodes, wherein each node represents a data point within the DAG. The DAG further includes a plurality of directional edges. Each directional edge connects a pair of the nodes and represents a covering-covered relationship between two nodes. Each node comprises a subgraph consisting of the respective node and all other nodes reachable via a covering path that comprises a sequence of covering and covered nodes. Each node comprises a set of node parameters including at least an identifier and an address range. Each node and the legal address specify a cover path. Utilizing DAG Path Addressing with bindings the memory can be organized to store a generalization hierarchy of logical propositions.

A system for predicting degradation of a battery for use in an electric vehicle id presented. The system includes a computing device communicatively connected to at least a pack monitor unit, wherein the at least a pack monitor unit is configured to detect a battery pack datum of a plurality of battery modules incorporated in a battery pack. The computing device is further configured to receive the battery pack datum as a function of the at least a pack monitor unit, generate, as a function of the battery pack datum, a battery pack model associated with the battery pack of the electric vehicle, and determine a battery degradation prediction as a function of the battery pack datum and the battery pack model.

A system for predicting degradation of a battery for use in an electric vehicle id presented. The system includes a computing device communicatively connected to at least a pack monitor unit, wherein the at least a pack monitor unit is configured to detect a battery pack datum of a plurality of battery modules incorporated in a battery pack. The computing device is further configured to receive the battery pack datum as a function of the at least a pack monitor unit, generate, as a function of the battery pack datum, a battery pack model associated with the battery pack of the electric vehicle, and determine a battery degradation prediction as a function of the battery pack datum and the battery pack model.

Embodiments are directed to organizing conversation information. A tracker vocabulary may be provided to a universal model to predict a generalized vocabulary associated with the tracker vocabulary. A tracker model may be generated based on the portions of the universal model activated by the tracker vocabulary such that a remainder of the universal model may be excluded from the tracker model. Portions of a conversation stream may be provided to the tracker model. A match score may be generated based on the track model and the portions of the conversation stream such that the match score predicts if the portions of the conversation stream may be in the generalized vocabulary predicted for the tracker vocabulary. Tracker metrics may be collected based on the portions of the conversation and the match scores such that the tracker metrics may be included in reports or notifications.

A dynamic learning method for a robot includes a training and learning mode. The training and learning mode includes the following steps: dynamically annotating a belonging and use relationship between an object and a person in a three-dimensional environment to generate an annotation library; acquiring a rule library, and establishing a new rule and a new annotation by means of an interactive demonstration behavior based on the rule library and the annotation library; and updating the new rule to the rule library and updating the new annotation to the annotation library when it is determined that the established new rule is not in conflict with rules in the rule library and the new annotation is not in conflict with annotations in the annotation library.

A computer-implemented method for calculating Scalable Attributed Graph Embedding for Large-Scale Graph Analytics that includes computing a node embedding for a first node-attributed graph in a node embedded space. One or more random attributed graphs is generated in the node embedded space. A graph embedding operation is performed using a dissimilarity measure between one or more raw graphs and the one or more generated random graphs, and an edge-attributed graph into a second node-attributed graph using an adjoint graph.

Techniques are described with respect to facilitating client ergonomic support. An associated method includes receiving a plurality of posture datapoints associated with multiple clients and constructing a machine learning knowledge model based upon the plurality of posture datapoints in order to identify a plurality of predefined ergonomic support design elements. The method further includes receiving client-specific posture datapoints associated with a first client and analyzing, via the machine learning knowledge model, the client-specific posture datapoints in view of the plurality of posture datapoints in order to select an initial ergonomic support design element among the plurality of predefined ergonomic support design elements. The method further includes facilitate printing of the initial ergonomic support design element for a seat component associated with the first client. In an embodiment, the method further includes providing at least one ergonomic refinement to the first client based upon ergonomic sensor data.

Due to the high language use variability in real-life, manual construction of semantic resources to cover all synonyms is prohibitively expensive and may result in limited coverage. Described herein are systems and methods that automate the process of synonymy resource development, including both formal entities and noisy descriptions from end-users. Embodiments of a multi-task model with hierarchical task relationship are presented that learn more representative entity/term embeddings and apply them to synonym prediction. In model embodiments, a skip-gram word embedding model is extended by introducing an auxiliary task “neighboring word/term semantic type prediction” and hierarchically organize them based on the task complexity. In one or more embodiments, existing term-term synonymous knowledge is integrated into the word embedding learning framework. Embeddings trained from the multi-task model embodiments yield significant improvement for entity semantic relatedness evaluation, neighboring word/term semantic type prediction, and synonym prediction compared with baselines.

A method and apparatus for mining a competition relationship between POIs. An embodiment of the method includes: acquiring a graphlet mining result obtained by mining map retrieval data of users which encompasses attribute information of retrieved target POIs, the graphlet mining result encompassing occurrence frequencies of respective preset situations, and a preset situation comprising: conforming to attribute information of POIs represented by a corresponding preset graphlet and a preset association relationship between attribute information of at least two POIs; for a first and second POI, determining an occurrence frequency of a preset situation corresponding to a preset graphlet where attribute information of the first and second POI co-occur, and generating a relationship feature of the first and second POI; and inputting the relationship feature into a pre-trained relationship prediction model to obtain a competition relationship prediction result of the first POI and the second POI.