The Foundations of Statistics By Prof. Leonard J. Savage. (Wiley Publications in Statistics.) Pp. xv + 294. (New York; John Wiley and Sons, Inc.; London: Chapman and Hall, Ltd., 1954.) 48s. net.

Probability and Statistical Inference

In embodiments of the present invention improved capabilities are described for a virtualization environment adapted for development and deployment of at least one software workload, the virtualization environment having a metamodel framework that allows the association of a policy to the software workload upon development of the workload that is applied upon deployment of the software workload. This allows a developer to define a security zone and to apply at least one type of security policy with respect to the security zone including the type of security zone policy in the metamodel framework such that the type of security zone policy can be associated with the software workload upon development of the software workload, and if the type of security zone policy is associated with the software workload, automatically applying the security policy to the software workload when the software workload is deployed within the security zone.

System and method for a cloud computing abstraction layer with security zone facilities

Mobile phones have evolved from devices that are just used for voice and text communication to platforms that are able to capture and transmit a range of data types (image, audio, and location). The adoption of these increasingly capable devices by society has enabled a potentially pervasive sensing paradigm - participatory sensing. A coordinated participatory sensing system engages individuals carrying mobile phones to explore phenomena of interest using in situ data collection. For participatory sensing to succeed, several technical challenges need to be solved. In this paper, we discuss one particular issue: developing a recruitment framework to enable organizers to identify well-suited participants for data collections based on geographic and temporal availability as well as participation habits. This recruitment system is evaluated through a series of pilot data collections where volunteers explored sustainable processes on a university campus.

/pdf/recruitment-framework-for-participatory-sensing-data-1ynnk4ovqr.pdf

Recruitment framework for participatory sensing data collections

In one embodiment, a method comprises identifying, by an orchestrator executed by a physical machine, a plurality of virtualized network functions required for implementation of a virtualized network service for a customer, each virtualized network function having a corresponding and distinct virtualized container specifying attributes for defining execution of the corresponding virtualized network function within one or more physical machines; and setting by the orchestrator an interdependency indicator within each virtualized container based on association with the virtualized network service, enabling identification of each of the virtualized network functions as interdependent for coordinated execution of the virtualized network service.

Defining interdependent virtualized network functions for service level orchestration

In this paper we describe SANE, the first forwarding mechanism that combines the advantages of both social-aware and stateless approaches in pocket switched network routing. SANE is based on the observation“that we validate on real-world traces”that individuals with similar interests tend to meet more often. In our approach, individuals (network members) are characterized by their interest profile, a compact representation of their interests. Through extensive experiments, we show the superiority of social-aware, stateless forwarding over existing stateful, social-aware and stateless, social-oblivious forwarding. An important byproduct of our interest-based approach is that it easily enables innovative routing primitives, such as interest-casting. An interest-casting protocol is also described, and extensively evaluated through experiments based on both real-world and synthetic mobility traces.

/pdf/social-aware-stateless-forwarding-in-pocket-switched-4swmsmk5fs.pdf

Social-aware stateless forwarding in pocket switched networks

In particular embodiments, a method includes accessing sensor data from sensor nodes in a sensor network and aggregating the sensor data for communication to an indexer in the sensor network. The aggregation of the sensor data includes deduplicating the sensor data; validating the sensor data; formatting the sensor; generating metadata for the sensor data; and time-stamping the sensor data. The metadata identifies one or more pre-determined attributes of the sensor data. The method also includes communicating the aggregated sensor data to the indexer in the sensor network. The indexer is configured to index the aggregated sensor data according to a multi-dimensional array for querying of the aggregated sensor data along with other aggregated sensor data. One or more first ones of the dimensions of the multi-dimensional array include time and one or more second ones of the dimensions of the multi-dimensional include one or more of the pre-determined sensor-data attributes.

Aggregating Sensor Data

A method for summarizing capabilities in a hierarchically arranged data center includes receiving capabilities information, wherein the capabilities information is representative of capabilities of respective nodes at a first hierarchical level in the hierarchically arranged data center, clustering nodes based on groups of capabilities information, generating a histogram that represents individual node clusters, and sending the histogram to a next higher level in the hierarchically arranged data center. Relative rankings of capabilities may be used to order a sequence of clustering operations.

Data Center Capability Summarization

In particular embodiments, a system includes a sensor-data-collection network layer including multiple sensors. The sensor-data-collection network layer is a first logical layer of a sensor network. The system includes an aggregation network layer including one or more aggregators configured to access sensor data from the sensors and aggregate the sensor data. The aggregation network layer is a second logical layer residing logically above the first logical layer. The system includes an indexing network layer including one or more indexers that are configured to access the aggregated sensor data and generate an index of the aggregated sensor data according to a multi-dimensional array. The indexing network layer is a third logical layer residing logically above the second logical layer. The system includes a search network layer including one or more search engines. The search network layer is a fourth logical layer residing logically above the third logical layer.

A hierarchical network for collecting, aggregating, indexing, and searching sensor data

In the past few years, there has been significant developments in how machine learning can be used in various industries and research. This paper discusses the potential of utilizing machine learning technologies in healthcare and outlines various industry initiatives using machine learning initiatives in the healthcare sector.

A Study of Machine Learning in Healthcare

In particular embodiments, a method includes receiving a query for particular sensor data among multiple sensor data from multiple sensors. The plurality of sensor data has been indexed according to a multi-dimensional array. One or more first ones of the dimensions include time, and one or more second ones of the dimensions include one or more pre-determined sensor-data attributes. The method includes translating the query to correspond to the indexing of the plurality of sensor data. The translated query includes one or more values for one or more of the dimensions of the multi-dimensional array. The method includes communicating the translated query to search among the plurality of sensor data according to its indexing to identify the particular sensor data.

Debojyoti Dutta

Papers

Aggregating Sensor Data

Data Center Capability Summarization

A hierarchical network for collecting, aggregating, indexing, and searching sensor data

A Study of Machine Learning in Healthcare

Searching sensor data