With the increased use of Internet, governments and large companies store and share massive amounts of personal data in such a way that leaves no space for transparency. When a user needs to achieve a simple task like applying for college or a driving license, he needs to visit a lot of institutions and organizations, thus leaving a lot of private data in many places. The same happens when using the Internet. These privacy issues raised by the centralized architectures along with the recent developments in the area of serverless applications demand a decentralized private data layer under user control.
Although many authors perceive choreographies as a mechanism to describe in a more formal way the contracts among several organizations @cite_7 , the academic research proposed the concept of executable choreographies @cite_8 @cite_1 @cite_13 . They suggest transforming the descriptions of the choreographies in code that is executed inside each organization participating in the choreography. As such, a choreography is not only a formal description of a contract among organizations but also a description of a workflow in an executable way. The same description (choreography) gets to run in several organizations in a decentralized manner (without the need for a centralized conductor) and therefore any need to translate the choreography into other programming languages disappears.
@cite_13 @cite_1 @cite_7 @cite_8
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Cross-organizational business processes gain more and more attention in the scientific community. For modeling these processes, choreographies are an adequate solution to describe the observable behavior for each business participant and the corresponding protocols between them. Nevertheless, current approaches mainly focus on modeling and conformance checking before runtime. In contrast, this contribution concentrates on the runtime validation of choreographies and will thus propose a monitoring infrastructure that supports this validation. We will sketch the requirements for a monitoring infrastructure as well as the solutions in order to meet these requirements. Without such a reliable monitoring framework, a runtime validation framework simply would not be able to correctly validate the interactions. A common task in dance, martial arts, animation, and many other movement genres is for the character to move in an innovative and yet stylistically consonant fashion. In this paper, we describe two mechanisms for automating this process and evaluate the results with a Turing Test. Our algorithms use the mathematics of chaos to achieve innovation and simple machine-learning techniques to enforce stylistic consonance. Because our goal is stylistic consonance, we used a Turing Test, rather than standard cross-validation-based approaches, to evaluate the results. This test indicated that the novel dance segments generated by these methods are nearing the quality of human-choreographed routines. The test-takers found the human-choreographed pieces to be more aesthetically pleasing than computer-choreographed pieces, but the computer-generated pieces were judged to be equally plausible and not significantly less graceful. Musical or choreographic variation—one of the most fundamental of compositional techniques—are based upon two phases of work: establishment of a grounding theme and a series of structured departures from that theme. Classic examples are Bach's Goldberg Variations for the keyboard or Balanchine's The Four Temperaments ballet. The properties of dynamical chaos provide unusual mechanisms for achieving these effects. The characteristic patterns of strange attractors can be used to capture the underlying theme of the sequence and chaos's sensitive dependence on initial conditions can be used to produce variations. This intriguing notion was proposed by Diana Dabby in the mid-1990s in the context of music and image (1, 2). Inspired by Dabby's work, we set out to apply some similar ideas to dance. The result was the pair of tools that are described in this paper: CHAOGRAPHER, which produces chaotic variations of keyframed movement sequences, and MOTIONMIND, which extracts the patterns from a corpus of movement sequences and uses those patterns to generates original and yet stylistically consonant movement. These two tools, working together, can produce novel, interesting movement. The core of CHAOGRAPHER's strategy for introducing novelty is to create a mapping that associates a sequence of body positions with a chaotic attractor, and then use that mapping to generate variations. The fixed attractor structure guarantees that the variations will resemble the original in a mathematically precise sense, while sensitive dependence on initial conditions guarantees We describe a method for introducing variations into predefined motion sequences using a chaotic symbol-sequence reordering technique. A progression of symbols representing the body positions in a dance piece, martial arts form, or other motion sequence is mapped onto a chaotic trajectory, establishing a symbolic dynamics that links the movement sequence and the attractor structure. A variation on the original piece is created by generating a trajectory with slightly different initial conditions, inverting the mapping, and using special corpus-based graph-theoretic interpolation schemes to smooth any abrupt transitions. Sensitive dependence guarantees that the variation is different from the original; the attractor structure and the symbolic dynamics guarantee that the two resemble one another in both aesthetic and mathematical senses. Enforcing in code privacy laws, internal company rules and principles like Privacy by Design is recognized as a challenge for the IT industry. In this paper we analyze the steps required and propose a guide towards this major goal. Our proposal is to emphasize the need to overcome the limits of service orchestration and create strong privacy and security enabling architectures based on two main ideas. The first idea is to use a semantic firewall that is capable to check privacy properties for the communication between applications and cloud and between cloud's sub-systems. The second idea is to improve current SOA architectures with architectures based on executable choreographies that can be formally verified. In this paper we identify three types of executable choreographies. New types of abstraction which machines can verify and humans can trust are enabled by executable choreographies that act like truly verifiable environments for cloud applications.