Logistics management plays a crucial role in the execution and success of international trades. Current solutions for logistics management suffer from a number of issues. One of the major areas for improvement is inconsistent data and lack of trust and transparency between multiple participants. Distributed Ledger Technology (DLT) or blockchain has the inherent characteristics to address this issue, and is well suited to be applied to supply chain document and workflow management. This paper demonstrates how to apply DLT to achieve a higher level of efficiency through consistent data store, automated workflow process, and tamper-proof transaction history for provenance in the supply chain.

Community-based Question Answering (CQA) websites are attracting increasing numbers of users and contributors in recent years. However, duplicate questions frequently occur in CQA websites and are currently manually identified by the moderators. Automatic duplicate detection, on one hand, alleviates this laborious effort for moderators before taking close actions, and, on the other hand, helps question issuers quickly find answers. A number of studies have looked into related problems, but very limited works target Duplicate Detection in Programming CQA (PCQA), a branch of CQA that is dedicated to programmers. Existing works framed the task as a supervised learning problem on the question pairs and relied on only textual features. Moreover, the issue of selecting candidate duplicates from large volumes of historical questions is often un-addressed. To tackle these issues, we model duplicate detection as a two-stage “ranking-classification” problem over question pairs. In the first stage, we rank the historical questions according to their similarities to the newly issued question and select the top ranked ones as candidates to reduce the search space. In the second stage, we develop novel features that capture both textual similarity and latent semantics on question pairs, leveraging techniques in deep learning and information retrieval literature. Experiments on real-world questions about multiple programming languages demonstrate that our method works very well; in some cases, up to 25% improvement compared to the state-of-the-art benchmarks.

In this paper, we demonstrate a blockchain-based solution for transparently managing and analyzing data in a pay-as-you-go car insurance application. This application allows drivers who rarely use cars to only pay insurance premium for particular trips they would like to travel. One of the key challenges from database perspective is how to ensure all the data pertaining to the actual trip and premium payment made by the users are transparently recorded so that every party in the insurance contract including the driver, the insurance company, and the financial institution is confident that the data are tamper-proof and traceable.

Programming community-based question-answering (PCQA) websites such as Stack Overflow enable programmers to find working solutions to their questions. Despite detailed posting guidelines, duplicate questions that have been answered are frequently created. To tackle this problem, Stack Overflow provides a mechanism for reputable users to manually mark duplicate questions. This is a laborious effort, and leads to many duplicate questions remain undetected. Existing duplicate detection methodologies from traditional community based question-answering (CQA) websites are difficult to be adopted directly to PCQA, as PCQA posts often contain source code which is linguistically very different from natural languages. In this paper, we propose a methodology designed for the PCQA domain to detect duplicate questions. We model the detection as a classification problem over question pairs. To extract features for question pairs, our methodology leverages continuous word vectors from the deep learning literature, topic model features and phrases pairs that co-occur frequently in duplicate questions mined using machine translation systems. These features capture semantic similarities between questions and produce a strong performance for duplicate detection. Experiments on a range of real-world datasets demonstrate that our method works very well; in some cases over 30% improvement compared to state-of-the-art benchmarks. As a product of one of the proposed features, the association score feature, we have mined a set of associated phrases from duplicate questions on Stack Overflow and open the dataset to the public.

The feasibility of using adaptive object migration to enable the execution of heavy applications in pervasive environments, is determined by the computational efficiency of adaptation algorithms and the efficacy of their decisions. These two factors, which are largely predicated by the resource constraints of devices, are heavily influenced by the granularity at which adaptation decisions are performed. This paper proposes a new type of adaptation granularity which combines the efficiency of coarse level approaches with the efficacy of fine-grained adaptation. A novel approach for achieving this level of granularity through the dynamic decomposition of runtime class graphs is presented and empirically evaluated on a corpus of real world applications. It is shown that the approach improves the efficacy of adaptation decisions by reducing network overheads by a minimum of 17% to as much 99%, while maintaining comparable decision making efficiency to class level adaptation.

Adaptive offloading dynamically distributes portions of a computationally heavy application to remote devices to achieve context specific optimisations. However, since existing state-of-the-art approaches incur significant overhead from storing, updating and partitioning application graphs this paper proposes a novel distributed approach to alleviate much of this overhead. Specifically, each device maintains a graph consisting only of components in its own memory space, while maintaining abstraction elements for components in remote devices. This approach removes the need to store and update complete application graphs on each device and reduces the cost of partitioning an application during adaptation. An evaluation involving computationally heavy open-source applications adapting in a heterogeneous collaboration showed that the new approach reduced graph update network cost by 100%, collaboration-wide memory cost by between 37% and 50%, battery usage by between 63% and 93%, and adaptation time by between 19% and 98%, while improving efficacy of adaptation by 12% and 34% for two of the considered applications.