Strong eventual consistency of the collaborative editing framework WOOT

Commutative Replicated Data Types (CRDTs) are a promising new class of data structures for large-scale shared mutable content in applications that only require eventual consistency. The WithOut Operational Transforms (WOOT) framework is the first CRDT for collaborative text editing introduced by Oster et al. (In: Conference on Computer Supported Cooperative Work (CSCW). ACM, New York, pp 259–268, 2006a). Its eventual consistency property was verified only for a bounded model to date. While the consistency of many other previously published CRDTs had been shown immediately with their publication, the property for WOOT remained open for 14 years. We use a novel approach identifying a previously unknown sort-key based protocol that simulates the WOOT framework to show its consistency. We formalize the proof using the Isabelle/HOL proof assistant to machine-check its correctness.

SASLedger: A Secured, Accelerated Scalable Storage Solution for Distributed Ledger Systems

Blockchain technology provides a “tamper-proof distributed ledger” for its users. Typically, to ensure the integrity and immutability of the transaction data, each node in a blockchain network retains a full copy of the ledger; however, this characteristic imposes an increasing storage burden upon each node with the accumulation of data. In this paper, an off-chain solution is introduced to relieve the storage burden of blockchain nodes while ensuring the integrity of the off-chain data. In our solution, an off-chain remote DB server stores the fully replicated data while the nodes only store the commitments of the data to verify whether the off-chain data are tampered with. To minimize the influence on performance, the nodes will store data locally at first and transfer it to the remote DB server when otherwise idle. Our solution also supports accessing all historical data for newly joined nodes through a snapshot mechanism. The solution is implemented based on the Hyperledger Fabric (HLF). Experiments show that our solution reduces the block data for blockchain nodes by 93.3% compared to the original HLF and that our advanced solution enhances the TPS by 9.6% compared to our primary solution.

ECROs: building global scale systems from sequential code

To ease the development of geo-distributed applications, replicated data types (RDTs) offer a familiar programming interface while ensuring state convergence, low latency, and high availability. However, RDTs are still designed exclusively by experts using ad-hoc solutions that are error-prone and result in brittle systems. Recent works statically detect conflicting operations on existing data types and coordinate those at runtime to guarantee convergence and preserve application invariants. However, these approaches are too conservative, imposing coordination on a large number of operations. In this work, we propose a principled approach to design and implement efficient RDTs taking into account application invariants. Developers extend sequential data types with a distributed specification, which together form an RDT. We statically analyze the specification to detect conflicts and unravel their cause. This information is then used at runtime to serialize concurrent operations safely and efficiently. Our approach derives a correct RDT from any sequential data type without changes to the data type's implementation and with minimal coordination. We implement our approach in Scala and develop an extensive portfolio of RDTs. The evaluation shows that our approach provides performance similar to conflict-free replicated data types for commutative operations, and considerably improves the performance of non-commutative operations, compared to existing solutions.

An Improved Sentiment Extraction Model for Social Media Contents using spaCy Based Deep Neural Networks

Modern communication devices generate huge amount of data through the manifold usage of various social media applications. Among the entire generated data more than 40% are unstructured in nature. The industry also is reluctant to retain the data with the following characteristics: data containing asynchronous time stamp, replicated data, data which are broken while transmission and data that leads to misclassification. It is high time to drop-out the irrelevant data and considering the synchronous ones. In this article, a sentiment extraction model is proposed that governs the various social media contents. SpaCy is used as the preferred implementation language as it has many readily available libraries for the purpose of content classification. To avoid the over-fitting problem the actuators like “relu” and “sigmoid” are used. Even though many such classifiers are available for content classification, this article with the appropriate setting of Epoch counts, a categorical accuracy of 68% is obtained. The entire model is implemented in the TensorFlow based platform.

Definition and Testing of a Skills Framework to Evaluate the Effect of a Pedagogical Program in Entrepreneurship

Our work aims at contributing to research on entrepreneurship pedagogy by designing and testing a skills framework to evaluate the outcomes of pedagogical programs in entrepreneurship. A literature review was conducted to clarify the concept of skills. The first part of this article describes the components of the 17 main entrepreneurial skills identified in the literature (both transversal and specific to entrepreneurship). A post hoc experimental plan was chosen so that the results may be replicated. Data were collected online among business students and 444 completed forms were returned. The survey instrument was tested empirically by comparing the experimental group (265 participants of an entrepreneurship seminar) with the control group (179 non-participants). Results show the value of using a complete evaluation grid when evaluating a pedagogical program in entrepreneurship. Indeed, many transversal skills may evolve positively, not just the skills that are specific to entrepreneurship.