Towards an Intelligent Workflow Designer based on the Reuse of Workflow Patterns

In order to perform process-aware information systems we need sophisticated methods and concepts for designing and modeling processes. Recently, research on workflow patterns has emerged in order to increase the reuse of recurring workflow structures. However, current workflow modeling tools do not provide functionalities that enable users to define, query, and reuse workflow patterns properly. In this paper we gather a suite for both process modeling and normalization based on workflow patterns reuse. This suite must be used in the extension of some workflow design tool. The suite comprises components for the design of processes from both legacy systems and process modeling

Privacy-preserving model learning on a blockchain network-of-networks.

ObjectiveTo facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks.Materials and methodsWe propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology.ResultsHierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level.DiscussionHierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns.ConclusionWe demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction

Percolation scheduling with resource constraints

This paper presents a new approach to resource-constrained compiler extraction of fine-grain parallelism, targeted towards VLIW supercomputers, and in particular, the IBM VLIW (Very Large Instruction Word) processor. The algorithms described integrate resource limitations into Percolation Scheduling—a global parallelization technique—to deal with resource constraints, without sacrificing the generality and completeness of Percolation Scheduling in the process. This is in sharp contrast with previous approaches which either applied only to conditional-free code, or drastically limited the parallelization process by imposing relatively local heuristic resource constraints early in the scheduling process

ICT-DCU question answering task at NTCIR-6

This paper describes details of our participation in the NTCIR-6 Chinese-to-Chinese Question Answering task. We use the “retrieval plus extraction approach” to get answers for questions. We first split the documents into short passages, and then retrieve potentially relevant passages for a question, and finally extract named entity answers from the most relevant passages. For question type identification, we use simple heuristic rules which cover most questions. The Lemur toolkit was used with the okapi model for document retrieval. Results of our task submission are given and some preliminary conclusions drawn

Computer-aided proofs for multiparty computation with active security

Secure multi-party computation (MPC) is a general cryptographic technique that allows distrusting parties to compute a function of their individual inputs, while only revealing the output of the function. It has found applications in areas such as auctioning, email filtering, and secure teleconference. Given its importance, it is crucial that the protocols are specified and implemented correctly. In the programming language community it has become good practice to use computer proof assistants to verify correctness proofs. In the field of cryptography, EasyCrypt is the state of the art proof assistant. It provides an embedded language for probabilistic programming, together with a specialized logic, embedded into an ambient general purpose higher-order logic. It allows us to conveniently express cryptographic properties. EasyCrypt has been used successfully on many applications, including public-key encryption, signatures, garbled circuits and differential privacy. Here we show for the first time that it can also be used to prove security of MPC against a malicious adversary. We formalize additive and replicated secret sharing schemes and apply them to Maurer's MPC protocol for secure addition and multiplication. Our method extends to general polynomial functions. We follow the insights from EasyCrypt that security proofs can be often be reduced to proofs about program equivalence, a topic that is well understood in the verification of programming languages. In particular, we show that in the passive case the non-interference-based definition is equivalent to a standard game-based security definition. For the active case we provide a new NI definition, which we call input independence