Summary Prior knowledge plays a crucial role in image completion. Although almost all of the existing image completion methods use prior knowledge to complete the image to be repaired from different perspectives, the learning and modeling of the prior knowledge is still a challenging problem. In order to address this issue, we propose a novel prior knowledge distillation framework (PKD‐Net) which could distill prior knowledge of structure and style from multiple semantic space and generates not only plausible content but also consistent style with surrounding image area. Our PKD‐Net replaces the skip connection in the vanilla U‐Net with a semantic shift attention module. The semantic shift attention module takes features from encoder layer and those from decoder layer as input pairs to output shifted features which take into account the long‐range dependency of encoder layer features and corresponding decoder layer features from the perspective of local structure and style. Semantic shift attention module models the global interdependencies in local spatial structures (patches centered at each position) and style (appearance texture) dimensions respectively, which could implement distillation of prior knowledge from two aspects: structure and style. Experiments on multiple datasets including faces (CelebA, CelebA‐HQ) and natural images (ImageNet, Places2, Paris Street View) demonstrate that our proposed approach generates higher quality completion results than existing ones.

The energy consumption problem is a hot topic in Android communities. The high energy cost caused by improper development brings lots of complaints from users. An effective and efficient energy consumption analysis technique can guide the developers to improve the energy efficiency of their apps. Existing researches on this problem focus on either system entity level that gives the energy consumption of the hardware, or source line level that calculates the energy cost of source codes. With the consideration of accuracy and cost of analysis, this paper proposes a lightweight and automatic approach to estimate the method-level energy consumption for Android apps. We construct a statistical model from a set of energy values obtained by Dalvik bytecode based instrumentation and software-based measurement, to predict the energy consumption of execution sequences of methods. The experiments on several real-world apps show that the proposed techniques have low overhead while persisting acceptable accuracy.

Existing deadlock detectors are either not scalable or may report false positives when suggesting cycles as potential deadlocks. Additionally, they may not effectively trigger deadlocks and handle false positives. We propose a technique called ConLock+, which firstly analyzes each cycle and its corresponding execution to identify a set of scheduling constraints that are necessary conditions to trigger the corresponding deadlock. The ConLock+technique then performs a second run to enforce the set of constraints, which will trigger a deadlock if the cycle is a real one. Or if not, ConLock+reports a steering failure for that cycle and also identifies other similar cycles which would also produce steering failures. For each confirmed deadlock, ConLock+performs a static analysis to identify conflicting memory access that would also contribute to the occurrence of the deadlock. This analysis is helpful to enable developers to understand and fix deadlocks. ConLock+has been validated on a suite of real-world programs with 16 real deadlocks. The results show that across all 811 cycles, ConLock+confirmed all of the 16 deadlocks with a probability of ≥80 percent. For the remaining cycles, ConLock+reported steering failures and also identified that five deadlocks also involved conflicting memory accesses.