Abstract: Methods, systems and computer program products implementing hierarchical classification techniques are disclosed. A hierarchical classification system receives training data including labeled transaction records. The system determines tag sequences from the training data. The system clusters the tag sequences into clusters. The system determines a cluster-level classifier that is trained to predict a cluster for an input transaction record. The system determines a respective cluster-specific classifier for each cluster. The system trains the cluster-specific classifier to predict a label of entity of interest for an input transaction record, given a particular cluster. Upon receiving a test transaction record, the system first applies the cluster-level classifier to determine a particular cluster for the test transaction record, and then determines a label of entity of interest of the test transaction record by applying a cluster-specific classifier of that particular cluster.

Abstract: Methods, systems and computer program products generating diverse and representative set of samples from a large amount of transaction data are disclosed. A data sampling system receives transaction records. Each transaction record has multiple text segments. The system selects a subset of transaction records that contain least frequently appeared text segments. The system determines a respective vector representation for each selected transaction record. The system can measure similarity between transaction records based on the vector representations. The system assigns the selected transaction records to multiple clusters based on the vector representations and designated dimensions of importance. The system identifies one or more anchors that include transaction records on boundaries between clusters. The system filters the subset of transaction records by removing transaction records that are close to the anchors.

Abstract: Methods, systems and computer program products implementing hierarchical classification techniques are disclosed. A hierarchical classification system receives training data including labeled transaction records. The system determines tag sequences from the training data. The system clusters the tag sequences into clusters. The system determines a cluster-level classifier that is trained to predict a cluster for an input transaction record. The system determines a respective cluster-specific classifier for each cluster. The system trains the cluster-specific classifier to predict a label of entity of interest for an input transaction record, given a particular cluster. Upon receiving a test transaction record, the system first applies the cluster-level classifier to determine a particular cluster for the test transaction record, and then determines a label of entity of interest of the test transaction record by applying a cluster-specific classifier of that particular cluster.

Abstract: Methods, systems and computer program products generating diverse and representative set of samples from a large amount of transaction data are disclosed. A data sampling system receives transaction records. Each transaction record has multiple text segments. The system selects a subset of transaction records that contain least frequently appeared text segments. The system determines a respective vector representation for each selected transaction record. The system can measure similarity between transaction records based on the vector representations. The system assigns the selected transaction records to multiple clusters based on the vector representations and designated dimensions of importance. The system identifies one or more anchors that include transaction records on boundaries between clusters. The system filters the subset of transaction records by removing transaction records that are close to the anchors.

Abstract: Methods, systems and computer program products implementing data enrichment using global structure learning are disclosed. An information enrichment system predicts a likely canonical name from a transaction record in which names may be shortened, or extra token(s) inserted. In a training phase, the information enrichment system determines tag patterns based on labeled and unlabeled training transaction records. The tag patterns include co-occurrence probability and sequential order of co-occurrence of tags. In a testing phase, the information enrichment system receives a test transaction record. The information enrichment system predicts a likely tag sequence from the test transaction record based on the tag patterns. The information enrichment system predicts a canonical name based on likely tag values and token composition. The information enrichment system can then enrich the test transaction record with the predicted canonical name.

Abstract: Methods, systems and computer program products implementing hierarchical classification techniques are disclosed. A hierarchical classification system receives training data including labeled transaction records. The system determines tag sequences from the training data. The system clusters the tag sequences into clusters. The system determines a cluster-level classifier that is trained to predict a cluster for an input transaction record. The system determines a respective cluster-specific classifier for each cluster. The system trains the cluster-specific classifier to predict a label of entity of interest for an input transaction record, given a particular cluster. Upon receiving a test transaction record, the system first applies the cluster-level classifier to determine a particular cluster for the test transaction record, and then determines a label of entity of interest of the test transaction record by applying a cluster-specific classifier of that particular cluster.

Abstract: Methods and arrangements for effecting a cloud representation of audio content. An audio cloud is created and rendered, and user interaction with at least a clip portion of the audio cloud is afforded.

Abstract: Methods and arrangements for effecting a cloud representation of audio content. An audio cloud is created and rendered, and user interaction with at least a clip portion of the audio cloud is afforded.

Abstract: Embodiments of a system are disclosed for stress assessment of a call center agent while interacting with a customer. The system is for use with a communication network. The system includes a stress assessment device and an agent device that includes an imaging unit. The agent device is configured to capture video of a target region of exposed skin of the agent using the imaging unit, collect customer interaction data based on interaction with a customer device over the communication network, and communicate the captured video and the customer interaction data to the stress assessment device. The stress assessment device is configured to passively estimate agent stress-level based on the received video, and generate feedback to the agent based on correlation between the customer interaction data and the estimated stress-level over a predefined time interval.

Abstract: What is disclosed is a system and method for determining whether a patient has an acute respiratory infection. In one embodiment, the present method involves using a handheld device to acquire an audio signal of a sound made by a patient coughing. The audio signal is then communicated, by the handheld device, to a remote computing device. Upon receiving the audio signals, signal are repeatedly retrieved from a database of signals associated with different severities of various acute respiratory conditions. A comparison is made between the received audio signal and the retrieved signals. As a result of the comparison, a determination is made whether the patient has an acute respiratory infection. An audio playback device may be employed for playing the audio signal so that a medical professional can listen to that audio signal and facilitate the determination. Various embodiments are disclosed.

Abstract: Methods and arrangements for prioritizing customer service restoration, in the event of service failure or compromise such that any adverse effect of the service disruption on the customer is minimized, the perceived drop in quality of service, if any, is minimized and timely and efficient resource reallocation for service restoration is achieved. Input data relating to customer service protocols is assimilated. A service restoration order is implemented responsive to a service disruption and based on the assimilated input data. This implementing includes determining bufferable and non-bufferable services, postponing restoration of the bufferable services, and determining an order of priority of the non-bufferable services.

Abstract: Methods and arrangements for effecting a cloud representation of audio content. An audio cloud is created and rendered, and user interaction with at least a clip portion of the audio cloud is afforded.

Abstract: Methods and arrangements for effecting a cloud representation of audio content. An audio cloud is created and rendered, and user interaction with at least a clip portion of the audio cloud is afforded.

Abstract: Described herein are methods, systems, apparatuses and products for reconstruction of a smooth speech signal from a stuttered speech signal. One aspect provides for accessing a stored speech signal having stuttering; identifying at least one stuttered region in the stored speech signal; modifying the at least one stuttered region in the stored speech signal; and responsive to modifying the at least one stuttered region, reconstructing a smooth speech signal corresponding to the stored speech signal. Other embodiments are disclosed.

Abstract: Methods and arrangements for employing descriptors for agent-customer interactions are disclosed. Filtering the pooled records based on one or more predetermined criteria is done such that analyzing the filtered records and comparing one interaction between an agent and a customer with another interaction between an agent and a customer may occur.

Abstract: Described herein are methods, systems, apparatuses and products for reconstruction of a smooth speech signal from a stuttered speech signal. One aspect provides for accessing a stored speech signal having stuttering; identifying at least one stuttered region in the stored speech signal; modifying the at least one stuttered region in the stored speech signal; and responsive to modifying the at least one stuttered region, reconstructing a smooth speech signal corresponding to the stored speech signal. Other embodiments are disclosed.

Abstract: Described herein are methods, systems, apparatuses and products for reconstruction of a smooth speech signal from a stuttered speech signal. One aspect provides for accessing a stored speech signal having stuttering; identifying at least one stuttered region in the stored speech signal; modifying the at least one stuttered region in the stored speech signal; and responsive to modifying the at least one stuttered region, reconstructing a smooth speech signal corresponding to the stored speech signal. Other embodiments are disclosed.

Abstract: Described herein are methods, systems, apparatuses and products for reconstruction of a smooth speech signal from a stuttered speech signal. One aspect provides for accessing a stored speech signal having stuttering; identifying at least one stuttered region in the stored speech signal; modifying the at least one stuttered region in the stored speech signal; and responsive to modifying the at least one stuttered region, reconstructing a smooth speech signal corresponding to the stored speech signal. Other embodiments are disclosed.

Abstract: Methods and arrangements for prioritizing customer service restoration, in the event of service failure or compromise such that any adverse effect of the service disruption on the customer is minimized, the perceived drop in quality of service, if any, is minimized and timely and efficient resource reallocation for service restoration is achieved. Input data relating to customer service protocols is assimilated. A service restoration order is implemented responsive to a service disruption and based on the assimilated input data. This implementing includes determining bufferable and non-bufferable services, postponing restoration of the bufferable services, and determining an order of priority of the non-bufferable services.

Abstract: Methods and arrangements for employing descriptors for agent-customer interactions are disclosed. Filtering the pooled records based on one or more predetermined criteria is done such that analyzing the filtered records and comparing one interaction between an agent and a customer with another interaction between an agent and a customer may occur.