With the increasing popularity of blockchain technologies in recent years, blockchain-based decentralized applications (DApps for short in this paper) have been rapidly developed and widely adopted in many areas, being a hot topic in both academia and industry. Despite of the importance of DApps, we still have quite little understanding of DApps along with its ecosystem. To bridge the knowledge gap, this paper presents the first comprehensive empirical study of blockchain-based DApps to date, based on an extensive dataset of 995 Ethereum DApps and 29,846,075 transaction logs over them. We make a descriptive analysis of the popularity of DApps, summarize the patterns of how DApps use smart contracts to access the underlying blockchain, and explore the worth-addressing issues of deploying and operating DApps. Based on the findings, we propose some implications for DApp users to select proper DApps, for DApp developers to improve the efficiency of DApps, and for blockchain vendors to enhance the support of DApps.Index Terms-decentralized applications, Ethereum, smart contract, empirical study • RQ1: How is the popularity of DApps distributed?We explore the popularity of DApps by the number of unique users, transactions, and transaction volumes, and compare categories of DApps. We also examine the change of popularity as time evolves. By answering this question, we can provide an overview of the DApp market for all stakeholders in the DApp ecosystem. • RQ2: Are there any common practices of developing DApps? We investigate whether DApps are open source and how smart contracts are organized in a DApp. By answering this question, we can reveal the development arXiv:1909.00939v1 [cs.SE]
Recently, several JavaScript-based deep learning frameworks have emerged, making it possible to perform deep learning tasks directly in browsers. However, little is known on what and how well we can do with these frameworks for deep learning in browsers. To bridge the knowledge gap, in this paper, we conduct the first empirical study of deep learning in browsers. We survey 7 most popular JavaScript-based deep learning frameworks, investigating to what extent deep learning tasks have been supported in browsers so far. Then we measure the performance of different frameworks when running different deep learning tasks. Finally, we dig out the performance gap between deep learning in browsers and on native platforms by comparing the performance of TensorFlow.js and TensorFlow in Python. Our findings could help application developers, deep-learning framework vendors and browser vendors to improve the efficiency of deep learning in browsers.
The abstractive method and extractive method are two main approaches for automatic document summarization. In this paper, to fully integrate the relatedness and advantages of both approaches, we propose a general unified framework for abstractive summarization which incorporates extractive summarization as an auxiliary task. In particular, our framework is composed of a shared hierarchical document encoder, a hierarchical attention mechanism-based decoder, and an extractor. We adopt multi-task learning method to train these two tasks jointly, which enables the shared encoder to better capture the semantics of the document. Moreover, as our main task is abstractive summarization, we constrain the attention learned in the abstractive task with the labels of the extractive task to strengthen the consistency between the two tasks. Experiments on the CNN/DailyMail dataset demonstrate that both the auxiliary task and the attention constraint contribute to improve the performance significantly, and our model is comparable to the state-of-the-art abstractive models. In addition, we cut half number of labels of the extractive task, pretrain the extractor, and jointly train the two tasks using the estimated sentence salience of the extractive task to constrain the attention of the abstractive task. The results do not decrease much compared with using full-labeled data of the auxiliary task.
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