Many Cloud services provide generic (e.g., Amazon S3 or Dropbox) or data-specific Cloud storage (e.g., Google Picasa or SoundCloud). Although both Cloud storage service types have the data storage in common, they present heterogeneous characteristics: different interfaces, accounting and charging schemes, privacy and security levels, functionality and, among the data-specific Cloud storage services, different data type restrictions. This paper proposes PiCsMu (Platform-independent Cloud Storage System for Multiple Usage), a novel approach exploiting heterogeneous data storage of different Cloud services by building a Cloud storage overlay, which aggregates multiple Cloud storage services, provides enhanced privacy, and offers a distributed file sharing system. As opposed to P2P file sharing, where data and indices are stored on peers, PiCsMu uses Cloud storage systems for data storage, while maintaining a distributed index. The main contribution of this work is to show the feasibility to store arbitrary data in different Cloud services for private use and/or for file sharing. Furthermore, the evaluation of the prototype confirms the scalability with respect to different file sizes and also shows that a moderate overhead in terms of storage and processing time is required. Abstract-Many Cloud services provide generic (e.g., Amazon S3 or Dropbox) or data-specific Cloud storage (e.g., Google Picasa or SoundCloud). Although both Cloud storage service types have the data storage in common, they present heterogeneous characteristics: different interfaces, accounting and charging schemes, privacy and security levels, functionality and, among the data-specific Cloud storage services, different data type restrictions. This paper proposes PiCsMu (Platform-independent Cloud Storage System for Multiple Usage), a novel approach exploiting heterogeneous data storage of different Cloud services by building a Cloud storage overlay, which aggregates multiple Cloud storage services, provides enhanced privacy, and offers a distributed file sharing system. As opposed to P2P file sharing, where data and indices are stored on peers, PiCsMu uses Cloud storage systems for data storage, while maintaining a distributed index. The main contribution of this work is to show the feasibility to store arbitrary data in different Cloud services for private use and/or for file sharing. Furthermore, the evaluation of the prototype confirms the scalability with respect to different file sizes and also shows that a moderate overhead in terms of storage and processing time is required.

Interfacing robots with real biological systems is a potential approach to realizing truly adaptive machines, which is a long-standing engineering challenge. Although plants are widely spread and versatile, little attention has been given to creating cybernetic systems incorporating plants. Producing such systems requires two main steps: the acquisition and interpretation of biological signals, and issuing the appropriate stimulation signals for controlling the physiological response of the biological part. We investigate an automated physiological recovery of young avocado plants by realizing a closed interaction loop between the avocado plant and a water-control device. The study considers the two aforementioned steps by reading out postural cues (leaf inclination) and electrophysiological (biopotential) signals from the plant, and controlling the water resource adaptive to the drought condition of an avocado plant. Analysis of the two signals reveals time-frequency patterns of increased power and global synchronization in the narrow bands when water is available, and local synchronization in the broad bands for water shortage. The results indicate the feasibility of interface technologies between plants and machines, and provide preliminary support for achieving adaptive plant-based 'machines' based on plants' large and robust physiological spectrum and machines' control scheme diversity. We further discuss fundamental impediments hindering the use of living organisms like plants for artificial systems.