Computation offloading is a promising way to improve the performance as well as reducing the battery power consumption of a smartphone application by executing some parts of the application on a remote server. Supporting such capability is not easy for smartphone application developers due to (1) correctness: some code, e.g., that for GPS, gravity, and other sensors, can run only on the smartphone so that developers have to identify which parts of the application cannot be offloaded; (2) effectiveness: the reduced execution time must be greater than the network delay caused by computation offloading so that developers need to calculate which parts are worth offloading; (3) adaptability: smartphone applications often face changes of user requirements and runtime environments so that developers need to implement the adaptation on offloading. More importantly, considering the large number of today's smartphone applications, solutions applicable for legacy applications will be much more valuable. In this paper, we present a tool, named DPartner, that automatically refactors Android applications to be the ones with computation offloading capability. For a given Android application, DPartner first analyzes its bytecode for discovering the parts worth offloading, then rewrites the bytecode to implement a special program structure supporting ondemand offloading, and finally generates two artifacts to be deployed onto an Android phone and the server, respectively.

EOSIO has become one of the most popular blockchain platforms since its mainnet launch in June 2018. In contrast to the traditional PoW-based systems (e.g., Bitcoin and Ethereum), which are limited by low throughput, EOSIO is the first high throughput Delegated Proof of Stake system that has been widely adopted by many decentralized applications. Although EOSIO has millions of accounts and billions of transactions, little is known about its ecosystem, especially related to security and fraud. In this paper, we perform a large-scale measurement study of the EOSIO blockchain and its associated DApps. We gather a large-scale dataset of EOSIO and characterize activities including money transfers, account creation and contract invocation. Using our insights, we then develop techniques to automatically detect bots and fraudulent activity. We discover thousands of bot accounts (over 30% of the accounts in the platform) and a number of real-world attacks (301 attack accounts). By the time of our study, 80 attack accounts we identified have been confirmed by DApp teams, causing 828,824 EOS tokens losses (roughly \$2.6 million) in total.