Binary code analysis allows analyzing binary code without having access to the corresponding source code. A binary, after disassembly, is expressed in an assembly language. This inspires us to approach binary analysis by leveraging ideas and techniques from Natural Language Processing (NLP), a fruitful area focused on processing text of various natural languages. We notice that binary code analysis and NLP share many analogical topics, such as semantics extraction, classification, and code/text comparison. This work thus borrows ideas from NLP to address two important code similarity comparison problems. (I) Given a pair of basic blocks of different instruction set architectures (ISAs), determining whether their semantics is similar; and (II) given a piece of code of interest, determining if it is contained in another piece of code of a different ISA. The solutions to these two problems have many applications, such as cross-architecture vulnerability discovery and code plagiarism detection.Despite the evident importance of Problem I, existing solutions are either inefficient or imprecise. Inspired by Neural Machine Translation (NMT), which is a new approach that tackles text across natural languages very well, we regard instructions as words and basic blocks as sentences, and propose a novel cross-(assembly)-lingual deep learning approach to solving Problem I, attaining high efficiency and precision. Many solutions have been proposed to determine whether two pieces of code, e.g., functions, are equivalent (called the equivalence problem), which is different from Problem II (called the containment problem). Resolving the cross-architecture code containment problem is a new and more challenging endeavor. Employing our technique for crossarchitecture basic-block comparison, we propose the first solution to Problem II. We implement a prototype system INNEREYE and perform a comprehensive evaluation. A comparison between our approach and existing approaches to Problem I shows that our system outperforms them in terms of accuracy, efficiency and scalability. The case studies applying the system demonstrate that our solution to Problem II is effective. Moreover, this research showcases how to apply ideas and techniques from NLP to largescale binary code analysis.

Solar energy is one of the most abundant renewable energy sources. For efficient utilization of solar energy, photovoltaic technology is regarded as the most important source. However, due to the intermittent and unstable characteristics of solar radiation, photoelectric conversion (PC) devices fail to meet the requirements of continuous power output. With the development of rechargeable electric energy storage systems (ESSs) (e.g., supercapacitors and batteries), the integration of a PC device and a rechargeable ESS has become a promising approach to solving this problem. The so‐called integrated photorechargeable ESSs which can directly store sunlight generated electricity in daylight and reversibly release it at night time, has a huge potential for future applications. This review summarizes the development of several types of mainstream integrated photorechargeable ESSs and introduces different working mechanisms for each photorechargeable ESS in detail. Several general perspectives on challenges and future development in the field are also provided.

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