Abstract: A method for recommending those interventions most likely to achieve a desired state involves predicting the efficacy and engagement of interventions based on the experience of prior users who undertook the interventions. Physiological and personal parameters of the user are acquired. The user's initial state and desired state are determined. The engagement and efficacy levels of each intervention are predicted and used to determine the likelihood that the transition achieved by each intervention achieves its predicted end state. The likelihood that a second transition achieves the desired state is also determined based on efficacy and engagement for the second transition whose starting state is the end state of the first transition. The first and second interventions are identified whose associated transitions have the greatest combined likelihood of achieving the desired state compared to all other intervention combinations. The user is then prompted to engage in the first and second interventions.

Abstract: A system for triggering mental healthcare services based on prediction of critical events receives both structured data and unstructured data about mental healthcare patients. Feature extraction is performed, thereby generating records of structured data, and strings of vectors of unstructured data. The system makes a quality assessment about the structured data, and a quality assessment about the unstructured data. In a model selection step, the system selects one model out of a plurality of selectable models, where the selection is made based on the quality assessments made. The selected model is trained with records of structured data and with strings of vectors of unstructured data. New real-time data is supplied to the trained model so that the trained model predicts whether a crisis event is likely to occur. If the system predicts that a crisis event is likely to occur, then the system outputs an alert.

Abstract: A system for determining a mental health state of a user and for adjusting output content accordingly includes a monitoring unit that monitors parameters of the user and acquires corresponding data, an analysis unit that extracts features from data acquired by the monitoring unit, a classification unit that detects change in the mental health state of the user based on the features extracted by the analysis unit and classifies the change in mental state of the user, a control unit that adapts the content to be output to the user by the output unit based on the detected change in the mental health state of the user classified by the classification unit, and an output unit that outputs content to the user based on the detected change in the mental health state of the user. A method determines the mental health state of the user and adjusts output content accordingly.

Abstract: A method for efficiently extracting user data to improve the user's well-being involves acquiring user data through the user's interactions with an application running on the user's smartphone. Based on the user data, a graphical model is generated with nodes whose magnitudes correspond to values of the user data. A recommended range of values of user data is determined based on state vectors and diffusion metrics derived from the graphical model. The user data is a list of pairs of values of current state personal constructs X and desired state opposite personal constructs Y. The graphical model includes a bipartite graph indicative of how frequently specific pairs of personal and opposite personal constructs (X,Y) appear in the list. The recommended range of values ranks activities that the application recommends to the user to improve the user's well-being by enabling the user to reach the desired state from the current state.

Abstract: We disclose a cloud-computing system configurable to allocate cloud resources to application functions based on a performance model generated for some or all of such functions by monitoring the performance of an instance pool employed for their execution. In an example embodiment, a corresponding performance model is generated by iteratively forcing the instance pool, during a learning phase, to operate in a manner that enables a control entity of the cloud-computing system to adequately sample different sub-ranges of an operational range, thereby providing a sufficient set of performance data points to a model-building module thereof. The model-building module operates to generate the performance model using a sufficient set of performance data points and then provides the model parameters to the control entity, wherein the model parameters can be used, e.g., to optimally configure and allocate the cloud resources to the application functions during subsequent operation.

Abstract: An approach is provided to analyze data center performance. The approach includes analyzing the data centers, with each of the data centers are represented by a plurality of data center records. Available capacity at each of the data centers is identified based on the selected data center's set of data center records. In addition, a current workload currently being performed by each of the data centers is identified. The approach compares the identified current workload of each data center with the identified available capacity at the other data centers. Data centers are added to a consolidation consideration list in response to the comparisons. The consolidation consideration list is further analyzed to select data centers for consolidation or elimination.

Abstract: An approach is provided to visually depict cohort data to a user. The approach includes receiving a cohort display request (e.g., from a user, etc.). In response to the request, the approach retrieves a plurality of cohort data records, with each of the cohort data records corresponding to a cohort selected from a group of cohorts. In this approach, the group of cohorts represent similar computer systems. The approach displays a set of graphic user interface (GUI) controls on a display device, with each of the GUI controls corresponds to one of the retrieved cohort data records. A user selection is received that corresponds to a selected one of the GUI controls. The system responds by executing a cohort action on the cohort corresponding to the selected GUI control.