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Smart contracts vulnerability auditing is vitally critical to ensure transaction execution in normal on blockchain. The current data-driven approaches normally tokenize smart contracts into a series of sequences according to only one tokenization standard for vulnerability detection purpose, resulting some of the semantic contexts could not be reflected within restricted sequence length. To address this limitation, we generate sequences from smart contracts in three tokenization standards for which we utilize n-gram language model to capture semantic contexts respectively, and finally exploiting our effective combination strategy of Intersection or Union to integrate the audited results from multiple semantic contexts. In order to evaluate the proposed approach, we applied it on over 7200 Ethereum smart contract samples. Experimental result shows our proposed method is capable of detecting vulnerabilities and competitive with the baseline in test sets, with improved precision of over 44% when Intersection is applied in their results, as well as improved Recall measure up by over 300% and F-measure up by 220% when Union is applied. Our proposed method for smart contract vulnerability detection, an important tool for developing quality decentralized software applications, is able to analyze multiple semantic contexts and successfully detects more true vulnerabilities with high precision, outperforming that of the baseline approaches.

Due to the limited battery capacity of mobile devices, various CPU power governors and dynamic frequency adjustment schemes have been proposed to reduce CPU energy consumption. However, most such schemes are app-oblivious, ignoring an important fact that real-world applications often exhibit multiple execution phases that perform different functionality and may request different amounts of hardware resources. Having a unified app-level frequency setting for different phases of an application may not be energy efficient enough and may even violate the desirable latency performance required by certain phases. Motivated by this observation, in this paper, we present PIFA, which is an intelligent Phase Identification and Frequency Adjustment framework for energy-efficient and time-sensitive mobile computing. PIFA addresses two major challenges of fully automatically identifying different execution phases of an application and efficiently integrating the phase identification results for runtime frequency adjustment. We have fully implemented PIFA on the Android platform. An extensive set of experiments using real-world Android applications from multiple app categories demonstrate that PIFA achieves closely better performance than the desired latency requirement specified for each phase, while dramatically reducing energy consumption (e.g., >30% energy reduction for most apps) and incurring rather small runtime overhead (e.g.

This paper presents parameters self-turning fuzzy control of magnetic levitation system of controllable excitation linear synchronous motor(CELSM) magnetic suspension feeding platform. Magnetic suspension feeding platform has the characteristics of direct drive and magnetic suspension, which meet requirements of high speed, high positioning precision. Magnetic levitation system is an important subsystem of feeding platform, which is highly nonlinear, inherently unstable, time-varying and highly dependant on initial condition. In view of the above phenomena, the main pole field directional control mode is used to minimize the impact of magnetic coupling phenomenon of CELSM on the suspension system. In the normal force, the electromagnetic force generated by the interaction between the field excitation and the mover core is taken as controllable force, and the other parts are treated as disturbances of the suspension system. A parameters self-turning fuzzy controller is designed to control maglev system. Simulation results reveal that the proposed control scheme meets the performance requirements.

Biological and bioinspired swimmers move by shape actuation. Great progress has been made in understanding the direct control of shape variables for locomotory purposes. However, indirect control and soft actuation of the shape variables via actuation of the fluid medium is less well explored. Here, we examine the nonlinear coupling between shape variables and locomotion in a model system that consists of a two-link deformable body in oscillating flow. That is, the actuation is applied to the fluid medium and not the swimmer. We determine effective conditions that lead to symmetric swimming in terms of four dimensionless parameters: the mass and spring stiffness of the swimmer and the amplitude of oscillations and viscosity of the background flow. We find optimal parameters that maximize swimming. We then examine the stability of these swimming motions and observe stable to unstable swimming. These results suggest that one can tune the background flow properties to control the swimmer motion, and thus, they may have profound implications on design and employment of man-made swimmers in oscillatory flows.