Company Financing, Capital Structure, and Ownership: A Survey, and Implications for Developing Economies

This paper critically surveys the key literature on corporate financing policy, capital structure and firm ownership in order to identify the leading theoretical and empirical issues in this area. The theoretical component of the survey attempts to reconcile competing theories of capital structure and appraises recent models which use agency theory and asymmetric information to explore the impact of managerial shareholdings, corporate strategy and taxation on the firm’s capital structure. The empirical component focuses on univariate analyses as well as multivariate models of capital structure, and makes a comparison between theoretical predictions and empirical results. Implications are identified in terms of promising research ideas (PRIs) for further research. The bulk of the empirical research that we survey is concerned with the experience of a few western industrial countries, and the implications of this research are assessed accordingly. However, we also aim to draw out implications for new research in developing and newly industrialised countries with an expanding corporate sector.

Self-Similarity Breeds Resilience

Self-similarity is the property of a system being similar to a part of itself. We posit that a special class of behaviourally self-similar systems exhibits a degree of resilience to adversarial behaviour. We formalise the notions of system, adversary and resilience in operational terms, based on transition systems and observations. While the general problem of proving systems to be behaviourally self-similar is undecidable, we show, by casting them in the framework of well-structured transition systems, that there is an interesting class of systems for which the problem is decidable. We illustrate our prescriptive framework for resilience with some small examples, e.g., systems robust to failures in a fail-stop model, and those avoiding side-channel attacks

Lightweight Classification of IoT Malware Based on Image Recognition

The Internet of Things (IoT) is an extension of the traditional Internet, which allows a very large number of smart devices, such as home appliances, network cameras, sensors and controllers to connect to one another to share information and improve user experiences. Current IoT devices are typically micro-computers for domain-specific computations rather than traditional functionspecific embedded devices. Therefore, many existing attacks, targeted at traditional computers connected to the Internet, may also be directed at IoT devices. For example, DDoS attacks have become very common in IoT environments, as these environments currently lack basic security monitoring and protection mechanisms, as shown by the recent Mirai and Brickerbot IoT botnets. In this paper, we propose a novel light-weight approach for detecting DDos malware in IoT environments.We firstly extract one-channel gray-scale images converted from binaries, and then utilize a lightweight convolutional neural network for classifying IoT malware families. The experimental results show that the proposed system can achieve 94.0% accuracy for the classification of goodware and DDoS malware, and 81.8% accuracy for the classification of goodware and two main malware families

Program Execution on Reconfigurable Multicore Architectures

Based on the two observations that diverse applications perform better on different multicore architectures, and that different phases of an application may have vastly different resource requirements, Pal et al. proposed a novel reconfigurable hardware approach for executing multithreaded programs. Instead of mapping a concurrent program to a fixed architecture, the architecture adaptively reconfigures itself to meet the application's concurrency and communication requirements, yielding significant improvements in performance. Based on our earlier abstract operational framework for multicore execution with hierarchical memory structures, we describe execution of multithreaded programs on reconfigurable architectures that support a variety of clustered configurations. Such reconfiguration may not preserve the semantics of programs due to the possible introduction of race conditions arising from concurrent accesses to shared memory by threads running on the different cores. We present an intuitive partial ordering notion on the cluster configurations, and show that the semantics of multithreaded programs is always preserved for reconfigurations "upward" in that ordering, whereas semantics preservation for arbitrary reconfigurations can be guaranteed for well-synchronised programs. We further show that a simple approximate notion of efficiency of execution on the different configurations can be obtained using the notion of amortised bisimulations, and extend it to dynamic reconfiguration