Abstract: Various embodiments are generally directed to techniques for training deep neural networks, such as with an iterative approach, for instance. Some embodiments are particularly directed to a deep neural network (DNN) training system that generates a hardened DNN by iteratively training DNNs with images that were misclassified by previous iterations of the DNN. One or more embodiments, for example, may include logic to generate an adversarial image that is misclassified by a first DNN that was previously trained with a set of sample images. In some embodiments, the logic may determine a second training set that includes the adversarial image that was misclassified by the first DNN and the first training set of one or more sample images. The second training set may be used to train a second DNN. In various embodiments, the above process may be repeated for a predetermined number of iterations to produce a hardened DNN.

Abstract: A neural network system comprises a plurality of neurons, comprising a layer of input neurons, one or more layers of hidden neurons, and a layer of output neurons. The system further comprises a plurality of arrays of weights, each array of weights being configured to receive a plurality of discrete data points from a first layer of neurons and to produce a corresponding discrete data point to a second layer of neurons during a feed forward operation, each array of weights comprising a plurality of resistive processing units (RPU) having respective settable resistances. The system includes a neuron control system configured to control an operation mode of each of the plurality of neurons, wherein the operation mode comprises: a feed forward mode, a back propagation mode, and a weight update mode.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting an action to be performed by a reinforcement learning agent. The method includes obtaining an observation characterizing a current state of an environment. For each layer parameter of each noisy layer of a neural network, a respective noise value is determined. For each layer parameter of each noisy layer, a noisy current value for the layer parameter is determined from a current value of the layer parameter, a current value of a corresponding noise parameter, and the noise value. A network input including the observation is processed using the neural network in accordance with the noisy current values to generate a network output for the network input. An action is selected from a set of possible actions to be performed by the agent in response to the observation using the network output.

Abstract: A machine learning system includes a coach machine learning system that uses machine learning to help a student machine learning system learn its system. By monitoring the student learning system, the coach machine learning system can learn (through machine learning techniques) “hyperparameters” for the student learning system that control the machine learning process for the student learning system. The machine learning coach could also determine structural modifications for the student learning system architecture. The learning coach can also control data flow to the student learning system.

Abstract: A method for encoding and storing text information using DNA as a storage medium, a decoding method therefor and an application thereof. The method for using DNA to store text information comprises: encoding characters into computer binary digits by means of encoding, and converting the binary digits into DNA sequences by means of transcoding; and artificially synthesizing the DNA sequences encoded with character information, positioning the characters by means of a designed ligation adapter, and assembling the DNA sequences encoded with the character information according to a pre-set order. The method for using DNA to store text information has the advantages of a small storage volume, a large storage capacity, a strong stability and low maintenance costs.

Abstract: A device may receive data associated with an event. The device may identify a context of the event based on receiving the data. The device may identify a similar event based on performing a comparison of the context of the event and a context of the similar event. The device may determine a set of pre-events associated with the event based on identifying a pre-event that occurred before the similar event. The set of pre-events may include at least one pre-event similar to the pre-event that occurred before the similar event. The device may determine a set of post-events associated with the event based on determining the set of pre-events and identifying a post-event that occurred after the similar event. The set of post-events may include at least one post-event similar to the post-event. The device may perform an action based on the set of post-events.

Abstract: In a system for enabling configuration of an ensemble of several solvers, such that the ensemble can efficiently solve a constraint problem, for each one of several candidate configurations, an array of scores is computed. The array corresponds to a statistical parameter related to a problem solution, and the computation is based on, at least in part, a set of features associated with the problem. One candidate configuration is assigned to a solver, and based on the array of scores associated with that candidate configuration the same or a different candidate configuration is assigned to a another solver. A system for dynamically reconfiguring an ensemble of solvers obtains runtime data from several solvers, and a new configuration is determined by applying a machine learning and/or heuristic analysis procedure to the runtime data. The configuration of a solver may be updated according to the new configuration while that solver is running.

Abstract: A computer-implemented method of analyzing text documents, includes identifying a relationship in a text document associated with an entity, building a predictive model from training data, in response to said identifying a relationship, wherein the predictive model includes a prediction error, and determining whether to store the identified relationship in memory, based on the prediction error.

Abstract: An illustrative embodiment of a computer-implemented process for non-leading computer aided detection of features of interest in a dataset, designates a particular formation using a computer recognizable gesture to identify a gestured location in an analyzed view of the dataset in response to a user identifying the particular formation in the analyzed view. The dataset is generated by a computer and representative of a portion of an object characterized by the dataset. Responsive to identifying the gestured location, the particular formation is displayed to the user, and a composition is revealed including additional structural imagery, functional imagery and findings resulting from machine learning and analysis. Responsive to revealing the composition to the user, the user is prompted to select performance of accept selection, reject selection or modify selection with regard to the particular formation displayed.

Abstract: The disclosed computer-implemented method for identifying subject-matter experts may include (i) collecting, by the computing device, a plurality of electronic messages transmitted within an organization, (ii) creating a message graph for the organization, (iii) extracting a plurality of topics from the plurality of electronic messages transmitted within the organization, (iv) annotating the message graph by correlating each topic within the plurality of topics with each edge of the message graph that represents an electronic message related to the topic, and (v) identifying, based on an analysis of the annotated message graph, at least one vertex that represents an expert on at least one topic from the plurality of topics. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Disclosed in some examples, are methods, systems, and machine readable mediums which provide an event processing system and indicator engine that provide indicators to one or more of the channel systems that pertain to a status of an issue experienced by a customer. The indicators are created in part based upon updates to the issue entered into a customer issue system and when displayed in a channel system, the indicators provide an indication that there is an update on the particular issue affecting the customer.

Abstract: A control system and method for controlling a system, which employs a data set representing a plurality of states and associated trajectories of an environment of the system; and which iteratively determines an estimate of an optimal control policy for the system. The iterative process performs the substeps, until convergence, of estimating a long term value for operation at a respective state of the environment over a series of predicted future environmental states; using a complex return of the data set to determine a bound to improve the estimated long term value; and producing an updated estimate of an optimal control policy dependent on the improved estimate of the long term value. The control system may produce an output signal to control the system directly, or output the optimized control policy. The system preferably is a reinforcement learning system which continually improves.

Abstract: Disclosed herein are system, method, and computer-readable device embodiments for automatically correcting erroneous license plate numbers generated by automatic license plate recognition. An embodiment operates by selecting a set of adjacent license plate recognition (LPR) stations comprising a first LPR station, a second LPR station, and a third LPR station, accessing an error pattern probability matrix for at least the third recognized license plate number accessed in relation to at least one of the first recognized license plate number or the second recognized license plate number differing from the third recognized license plate number, and determining a corrected license plate number based on the error pattern probability matrix. In some embodiments, LPR correction may be accurately realized even when an erroneous license plate numbers are missing characters, such as by visual obstruction from an LPR camera or sensor, or has no characters in common with the real license plate number.

Abstract: Systems and methods for automatic generating of a Bayes net content graph are disclosed herein. The system can include a memory including a mapping matrix. The system can include at least one server. The at least one server can generate a user matrix having n columns and p rows. In some aspects, each of the n columns is associated with a student and each of the p rows is associated with a content item. The at least one server can: store the user matrix in the memory; retrieve the mapping matrix from the memory; iteratively identify prerequisite relationships between the skills identified in the user matrix; generate edges between the skills in the user matrix based on the iteratively identified prerequisite relationships; and orient the edges between the skill.

Abstract: Multi-mode resonator is usually used to design broadband bandpass filters and the cross-shape resonator (CSR) is one of its typical types. The possibility of utilizing cross-shape multi-mode resonators is explored as a signal controller for superconducting qubits that are coplanar to the resonator. The multi-mode qubit-resonator coupling facilitates the design of future quantum information processor.

Abstract: Generating trial states for a variational quantum Eigenvalue solver (VQE) using a quantum computer is described. An example method includes selecting a number of samples S to capture from qubits for a particular trial state. The method further includes mapping a Hamiltonian to the qubits according the trial state. The method further includes setting up an entangler in the quantum computer, the entangler defining an entangling interaction between a subset of the qubits of the quantum computer. The method further includes reading out qubit states after post-rotations associated with Pauli terms in the target Hamiltonian, the reading out being performed for S samples. The method further includes computing an energy state using the S qubit states. The method further includes, in response to the estimated energy state not converging with an expected energy state, computing a new trial state for the VQE and iterating to compute the estimated energy using the new trial state.

Abstract: Methods and arrangements for managing data collection relating to equipment in an asset network. A model is trained based on historical data relative to equipment in the asset network, wherein the model is employed for recommending at least one action for further data collection from the equipment. The model is adapted based on inputs comprising two or more of: historical sensor data, from one or more sensors obtaining data relative to the equipment; equipment maintenance and/or replacement data; a current system state; and a geographical position of one or more individuals who report data. The adapted model is employed to recommend at least one subsequent best action for collecting data relative to the equipment. Other variants and embodiments are broadly contemplated herein.

Abstract: Methods, systems, and computer program products for categorizing log records at run-time are provided herein. A computer-implemented method includes generating one or more template signatures to be associated with each of multiple templates, wherein each of the multiple templates comprises a concatenation of one or more words; processing each of multiple log records derived from a data stream to determine a composition of each of the multiple log records; matching one or more of the generated template signatures to each of the multiple log records based on the determined composition of each of the multiple log records; and outputting an identification of (i) each of the multiple log records and (ii) the one or more generated template signatures matched thereto.

Abstract: A system and method for constructing training dictionaries with multichannel information. An exemplary method takes into account the effect of the acoustic path while training multichannel acoustic data. A method that uses different time-frequency resolutions in machine learning training is also presented.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a machine learning model that has been trained through reinforcement learning to select a content item. One of the methods includes receiving first data characterizing a first context in which a first content item may be presented to a first user in a presentation environment; and providing the first data as input to a long-term engagement machine learning model, the model having been trained through reinforcement learning to: receive a plurality of inputs, and process each of the plurality of inputs to generate a respective engagement score for each input that represents a predicted, time-adjusted total number of selections by the respective user of future content items presented to the respective user in the presentation environment if the respective content item is presented in the respective context.

Abstract: Techniques for decoupling cognitive model training from execution of the cognitive model are provided. In one example, a computer program product is provided that determines cognitive data based on context data and a model of interpreting the context data. The cognitive data can comprise prediction data that represents a prediction relating to a state of an environment. The context data and the cognitive data can be transmitted to a server, and an updated model can be received in response.

Abstract: Techniques for generating a warning filter to filter the warnings output from a static program analysis tool are provided. In one example, a computer-implemented method comprises determining feature vector data for a set of warnings, wherein the set of warnings is generated in response to static analysis of a computer program, and wherein the feature vector data comprises a feature vector indicative of an attribute of a warning of the set of warnings. The computer-implemented method also comprises determining a warning filter that identifies a first subset of the set of warnings as representing true positives based on the feature vector data and classified warning data, and wherein the classified warning data represents a second subset of the set of warnings that have been classified to indicate whether respective members of the second subset are indicative of true positives.

Abstract: For a visit of a user to a web page where the user's identity on an online system is not presently known to the online system, the online system uses a machine learning model to make a prediction of the user's identity. The online system obtains visit data about the visit of the user to the web page. The online system identifies candidate user IDs that may represent the user, based on the visit data and data known about previous visits of the candidate user IDs. The online system derives visit features for each candidate user ID based on a relationship between the current visit data and previous visit data for the candidate user ID. The online system provides the visit features for each candidate user ID to a prediction model that determines whether, or how likely, the candidate user ID accurately identifies the visiting user, and based on the determinations selects one of the candidate user IDs as the most likely user ID for the visiting user.

Abstract: A method and/or system for heterogeneous predictive models generation based on sampling of big data is disclosed. The method involves receiving a dataset and a target column associated with the dataset at a data processing engine from a distributed data warehouse. One or more columns associated with the dataset are classified at the data processing engine as a categorical column or a continuous column. One or more parameters in the dataset are identified to extract a sample data from the dataset. The sample data from the dataset is extracted based on the identified one or more parameters. One or more rank ordered machine learning algorithms are recommended to one or more users, to generate one or more predictive models from the sample data. One or more heterogeneous predictive models are generated based on the rank ordered algorithm through one or more iterations.

Abstract: Methods and systems for selecting a selected-sub-set of features from a plurality of features for training a machine learning module, the training of the machine learning module to enable classification of an electronic document to a target label, the plurality of features associated with the electronic document. In one embodiment, the method comprises analyzing a given training document to extract the plurality of features, and for a given not-yet-selected feature of the plurality of features: generating a set of relevance parameters iteratively, generating a set of redundancy parameters iteratively and determining a feature significance score based on the set of relevance parameters and the set of redundancy parameters. The method further comprises selecting a feature associated with a highest value of the feature significance score and adding the selected feature to the selected-sub-set of features.

Abstract: A method includes using a computational network to learn and predict time-series data. The computational network is configured to receive the time-series data and perform transformation-invariant encoding of the time-series data. The computational network includes one or more encoding layers. The method also includes feeding back future predictions of the time-series data through inertial adjustments of transformations. The inertial adjustments preserve the invariants in the computational network. The computational network could further include one or more pooling layers each configured to reduce dimensionality of data, where the one or more pooling layers provide the transformation invariance for the encoding.

Abstract: A control device includes a machine learning device that learns a state of a spindle during normal machining without a collision of the spindle, and the machine learning device includes a state observation unit that observes spindle estimated load torque data indicating an estimated load torque value for the spindle and spindle acceleration data indicating an acceleration value of the spindle as state variables representing a current state of an environment and a learning unit that learns a correlation between the estimated load torque values for the spindle and the acceleration values of the spindle during the normal machining with use of the state variables.

Abstract: Systems, methods, and computer readable media are disclosed for generating, modifying, and using machine learning models to predict and evaluate differences between groups. Methods disclosed herein may include identifying variables that characterize members of a first group, generating shift indicators using the identified variables, generating a machine learning model using the shift indicators and the first group, using the machine learning model and the group to predict shifts between the first group and a predicted second group, determining an aggregate population shift and an aggregate performance shift between the first group and an actual second group, and identifying an impact of one or more of the shift indicators on the aggregate population shift or performance shift. Systems and methods disclosed herein may be configured to receive requests to predict and evaluate differences between group, and to return such predictions and evaluations to one or more users.

Abstract: Methods and apparatuses for identifying and executing one or more interactive condition evaluation tests and collecting and analyzing user behavior data to generate an output are provided. In some examples, user information may be received and one or more interactive condition evaluation tests may be identified. An instruction may be transmitted to a computing device of a user and executed on the computing device to enable functionality of one or more sensors that may be used in the identified tests. Upon initiating a test, data may be collected from the one or more sensors. The collected sensor data may be transmitted to the system and processed using one or more machine learning datasets. Additionally, user behavior data may be collected and processed using one or more machine learning datasets. The sensor data, the user behavior data, and other data may be used together to generate an output.

Abstract: Described processes include: obtaining, with a distributed computer system, a utilization graph, the utilization graph indicating one or more sequences in which one or more content-consumers accesses content items; obtaining content-consumer input scores indicative of assessments of the content-distribution platform by content-consumers; determining, based on the utilization graph, a measure of network centrality for each node in a set of nodes; determining, for each node in the set of nodes, a network-effect score based on both the measure of network centrality of the node and at least some of the content-consumer input scores; and determining, for each of at least some of the different content-contributors, an aggregate network-effect score for the content-contributor.

Abstract: An automated method, media and system for data unification that transforms disparate data from a plurality of sources into an integrated database. In the integrated database data are stored in at least one table in accordance with a common model or schema and a common metadata standard.

Abstract: An apparatus includes a processor and a memory. The memory stores instructions that when executed by the processor cause the processor to: identify a keyword from machine-readable text; identify a contact center resource based on the identified keyword; update a first group of keywords associated with the contact center resource based on the identified keyword; invoke an action based on analysis of the keyword associated with the contact center resource; monitor the action and report results in response; and update a second group of keywords according to analysis of the results.

Abstract: A method for productivity view management may include receiving, by a processor, a plurality of collaborative artifacts for a user and organizing the plurality of collaborative artifacts in a list based on a predetermined order. The method may also include analyzing, by the processor, the list of collaborative artifacts using a predetermined criterion and determining a view management fatigue factor threshold for the user. The method may further include automatically inserting, by the processor, a downtime artifact within the list of collaborative artifacts based on analyzing the list of collaborative artifacts to prevent the user from exceeding the view management fatigue factor threshold.

Abstract: Task handling in a master data management (MDM) system. A task pattern associated with a task completed is identified, in a task handling system. If the identified task pattern matches an existing task pattern in a task patterns database, then a pattern count value of the matching existing task pattern is incremented, and the task patterns database is searched for task patterns that are similar to the matching existing task pattern. If similar task patterns are identified, for each of the similar task patterns, a duplicate pattern count is specified that is based on the number of identified similar task patterns. If the duplicate pattern count for any one of the similar task patterns exceeds a specified threshold, a new task is created comprising data on completion actions for the matching existing task pattern and each of the one or more similar task patterns.

Abstract: Efficiency improvements for electronic task managers and an improved user experience are realized when more relevant and fewer irrelevant tasks are presented to users and users are given greater control in manipulating those task items. By heuristically determining times, locations, and semantics associated with task relevance and integrating the management of tasks into more applications, the functionality of the systems providing for electronic task management is improved, as computer resources are spent with greater utility to the users and the user experience is improved for the users.

Abstract: A method for project status management is provided that includes creating an issue identification data structure in a non-transitory memory device using a processor. Generating a notification to an analyst using the processor to process the issue identification data structure. Creating one or more sub-component dependencies in the issue identification data structure using the processor. Receiving issue resolution data associated with the issue identification data structure at the processor. Creating a new business intelligence rule using the processor. Associating the new business intelligence rule with the issue identification data structure using the processor.

Abstract: A measure factory for generating analytic measures includes data sets representing business activities arranged as columnar arrays with each column being associated with a distinct source rule that applies to the column when it is used as a data source. The measure factory includes factory rules that govern which operations on available data sources may be executed under what conditions in the measure factory, such as by taking into account the source rules and other applicable factory rules. A factory rule execution hierarchy governs the execution of ready factory rules that lack dependency on other factory rules before executing ready factory rules that have dependency on other factory rules. A script generation facility generates a script to process the plurality of factory rules according to the factory rule execution hierarchy.

Abstract: A method of risk monitoring in online business is disclosed. The method captures parameters of a user request and business data of processing the request by a business processing system; packs the parameters and the business data into an event object; analyzes the event object according to risk monitoring rules; and generates a risk record if a risk is determined to exist in an operation event that produces the business data. The method further determines a risk processing mode according to the risk record and a risk processing configuration, and processes the risk of the operation event in the risk processing mode. Also disclosed is a system for risk monitoring in online business.

Abstract: A specification of the process model is received. The process model includes a plurality of process components. A relationship between a first process component and another process component of the plurality of process components is determined using a predictive model. A process rule for the first process component is determined. The process rule specified a second process component to be executed. The process rule includes the relationship determined using the predictive model or a heuristic rule. The second process component is executed according to the process rule.

Abstract: A computer system comprises a display system and a portfolio builder in communication with the display system. The portfolio builder identifies activities of an employee. In response to identifying the activities, the portfolio builder identifies achievements of the employee from the activities. Further, the portfolio builder identifies recommended achievements from the achievements based on the application of one or more rules in a policy. Still further, the portfolio builder associates the recommended achievements with an achievement portfolio of the employee, enabling performing an operation for the organization based on the skills for the people in the organization.

Abstract: Methods and apparatuses allow an insurance agency to track their current performance in managing existing customers and bringing in new customers. A tool may obtain information from a user regarding characteristics of the user's agency and/or agency goals. The tool may use this information to determine an agency's business efficiency. The tool may also provide advice regarding marketing spending and/or additional human resources needed. The tool may benchmark an insurance agency against other agencies in the market. The tool may also display how generated leads are converted to new business and/or income for an agency.

Abstract: An exemplary embodiment of the present invention will provide a method using a computer, and a computer system, for providing image-customizing labels that are adapted for receiving computer-based, generic, value-bearing item indicia such as, for example, computer-based, generic, postage indicia. The exemplary embodiment of the present invention will comprise generating a display of a user interface screen that is adapted for receiving an order from a user for at least one image-customized label adapted for bearing a computer-based, generic, value-bearing item.

Abstract: A goal tracking system including a goal retrieval unit that retrieves a listing of goals, with each goal having at least one prerequisite, and retrieves a listing of completed goals related to a user, a goal analysis unit that compares the completed goal of the user with the prerequisite of the goal and determines if the user is qualified to achieve at least one goal from the listing of goals based on the completed goals of the user.

Abstract: To evaluate performance of organizational units using Human Capital Value (HCV), the input data spread across an enterprise may be received from a user. The input data includes employee data, project related data, and organizational unit data for performance evaluation. The input data is analyzed for generating HCV variables. The HCV variables are stored in a repository (108). Further, the HCV variables may be parsed to determine an optimal set of variables. Based on the parsing, an efficiency of each organizational unit is computed. The computing is based on a Multi Criteria Decision Analysis (MCDA) technique. Based on the computing, the organizational units are ranked in a decreasing order of efficiency.

Abstract: A system and method for generating an agent behavioral analytics including extracting text-based features, sentiment-based features, and prosody-based features from tagged calls; training a machine learning behavioral model, based on the text-based features, the sentiment-based features, and the prosody-based features extracted from the tagged calls and an at least one score associated with an at least one behavioral metric of the tagged calls, to produce a trained machine learning behavioral model; extracting text-based features, sentiment-based features, and prosody-based features from an incoming call; and using the trained machine learning behavioral model to produce an at least one behavioral label for the agent in the incoming call for the at least one behavioral metric, based on the text-based features of the incoming call, the sentiment-based features of the incoming call and the prosody-based features of the incoming call.

Abstract: A method for unmanned delivery of an item to a desired delivery location includes receiving, at an unmanned vehicle, first data representative of an approximate geographic location of the desired delivery location, receiving, at the unmanned vehicle, second data representative of a fiducial expected to be detectable at the desired delivery location, using the first data to operate the unmanned vehicle to travel to the approximate geographic location of the desired delivery location, upon arriving at the approximate geographic location of the desired delivery location, using the second data to operate the unmanned vehicle to detect the fiducial; and upon detecting the fiducial, using the fiducial to operate the unmanned vehicle to travel to the delivery location and deliver the item.

Abstract: An apparatus, termed a destination validation device (DVD), comprises a sensor configured to sense physical presence of a parcel and to obtain metadata from said parcel; at least one data processor connected with at least one memory storing software instructions that when executed causes the DVD to at least determine from reading the obtained metadata whether a signature is required for delivery of the parcel, and prepare a destination delivery acknowledgement indicating whether said signature is required; and a transmitter configured to communicate the prepared destination delivery acknowledgement external of the DVD. In this manner a delivery service can verify that the parcel is at the physical delivery location. Further details provide for also obtaining the (digital) signature when the metadata indicates it is required for delivery of the parcel. Other embodiments include a method and a computer program product comprised of software instructions on a computer-readable medium.

Abstract: A predictive engine on a computer environment comprising a shared pool of configurable computing resources is executed to perform a predictive analysis on data pipelined into the computer environment, the data received from a plurality of sources and in a plurality of different formats, the predictive engine generating a network level cost information based on the predictive analysis on a dynamic and continuous basis. Asynchronous communication comprising the network level cost information from the predictive engine is received and a set of candidate nodes for order fulfillment is generated based on the network level cost information and a defined distance between the set of candidate nodes and a target destination. An optimization engine on the computer environment is invoked that filters the set of candidate nodes. A number of fulfillment nodes that meet one or more of a constraint and preconfigured rule is output.

Abstract: Methods and systems are described for sharing shipment condition information in a wireless node network having a plurality of network devices and a server. In one method, a first node detects an advertising signal broadcast from a second node, where the first node is a first of the network devices and related to a first package, and the second node is a second of the network devices and related to a second package. The method continues by associating the first node and the second node. The first node then accesses the shipment condition information from a memory in the first node, and transmits or otherwise shares the shipment condition information with the second node if the first node is authorized to share the shipment condition information with the second node.

Abstract: A node-enabled autonomous vehicle conducting a logistics transaction for a package includes an autonomous vehicle and a mobile master node on the vehicle. The vehicle moves, pursuant to control input, from a first location to a transaction location related to the logistics transaction. The mobile master node has a short range communication interface to communicate with other nodes in a wireless node network and a longer range communication interface to communicate directly with a server in the network. The vehicle's mobile master node is operative to receive shipment information from the server that identifies the package and an identification of a node associated with the package; provide a control signal to a control input of the vehicle causing the vehicle to move to the transaction location; and automatically conduct the logistics transaction related to the package if the mobile master node successfully associates with a node associated with the package.