NP-Logic Systems and Model-Equivalence Reductions

In this paper we investigate the existence of model-equivalence reduction between NP-logic systems which are logic systems with model existence problem in NP. It is shown that among all NP-systems with model checking problem in NP, the existentially quantified propositional logic (\exists PF) is maximal with respect to poly-time model-equivalent reduction. However, \exists PF seems not a maximal NP-system in general because there exits a NP-system with model checking problem D^P-complete

Digital forensics challenges to big data in the cloud

As a new research area, Digital Forensics is a subject in a rapid development society. Cyber security for Big Data in the Cloud is getting attention more than ever. Computing breach requires digital forensics to seize the digital evidence to locate who done it and what has been done maliciously and possible risk/damage assessing what loss could leads to. In particular, for Big Data attack cases, Digital Forensics has been facing even more challenge than original digital breach investigations. Nowadays, Big Data due to its characteristics of three “V”s (Volume, Velocity, and Variety), they are either synchronized with Cloud (Such as smart phone) or stored on the Cloud, in order to sort out the storage capacity etc. problems, which made Digital Forensics investigation even more difficult. The Big Data-Digital Forensics issue for Cloud is difficult due to some issues. One of them is physically identify specific wanted device. Data are distributed in the cloud, customer or the digital forensics practitioner cannot have a fully access control like the traditional investigation does. The Smart City technique is making use of ICT (information communications technology) to collecting, detecting, analysing and integrating the key information data of core systems in running the cities. Meantime, the control is making intelligent responses to different requirements that include daily livelihood, PII (Personally identifiable information) security, environmental protection, public safety, industrial and commercial activities and city services. The Smart City data are Big Data, collected and gathered by the IoT (Internet of Things). This paper has summerised our review on the trends of Digital Forensics served for Big Data. The evidence acquisition challenge is discussed. A case study of a Smart City project with the IoT collected services Big data which are stored at the cloud computing environment is represented. The techniques can be generalised to other Big Data in the Cloud environment

Active RIS-Assisted mmWave Indoor Signal Enhancement Based on Transparent RIS

Due to the serious path loss of millimeter-wave (mmWave), the signal sent by the base station is seriously attenuated when it reaches the indoors. Recent studies have proposed a glass-based metasurface that can enhance mmWave indoor signals. The transparent reconfigurable intelligent surface (RIS) focuses on the mmWave signal to a specific location indoors. In this paper, a novel RIS-assisted mmWave indoor enhancement scheme is proposed, in which a transparent RIS is deployed on the glass to enhance mmWave indoor signals, and three assisted transmission scenarios, namely passive RIS (PRIS), active RIS (ARIS), and a novel hybrid RIS (HRIS) are proposed. This paper aims to maximize the signal-to-noise ratio (SNR) of the received signal for the three assisted transmission scenarios. The closed-form solution to the maximum SNR is presented in the PRIS and the ARIS-assisted transmission scenarios. Meanwhile, the closed-form solution to the maximum SNR for the HRIS-assisted transmission scenario is presented for given active unit cells. In addition, the performance of the proposed scheme is analyzed under three assisted transmission scenarios. The results indicate that under a specific RIS power budget, the ARIS-assisted transmission scenario achieves the highest data rate and energy efficiency. Also, it requires very few unit cells, thus dramatically reducing the size of the metasurface

The Importance of Director External and Internal Social Networks to Stock Price Crash Risk

Prior research documents that information transmitted via director networks affects firms’ policies and real economic activities. We explore whether information flow through director networks influences managers’ ability to hoard bad news. We find that the extent of external connections of the board of directors is negatively associated with future stock price crash risk. Additional analysis implies that this evidence is driven by firms with more powerful executives, with weaker auditor monitoring, or subject to strong investor protection, and by directors with greater monitoring incentives or responsibilities, with less firm-specific knowledge, and with more valuable reputations to protect. We further find that director external network size is negatively associated with a variety of bad new hoarding signals. Collectively, our research lends empirical support for the monitoring view under which better informed directors narrow the scope for bad news hoarding evident in stock price crash risk. In another series of tests, we fail to find evidence consistent with the information leakage view under which directors pass sensitive firm-specific information to connections who trade on the information before its public release. Other analysis helps dispel the concern that the endogenous match between directors and companies is spuriously responsible for our core results. In contrast to our strong, robust evidence on the role that director external networks play, we only find some results implying that CEO-director internal networks shape stock price crash risk.

Delay Sensitive Communications over Cognitive Radio Networks

Supporting the quality of service of unlicensed users in cognitive radio networks is very challenging, mainly due to dynamic resource availability because of the licensed users' activities. In this paper, we study the optimal admission control and channel allocation decisions in cognitive overlay networks in order to support delay sensitive communications of unlicensed users. We formulate it as a Markov decision process problem, and solve it by transforming the original formulation into a stochastic shortest path problem. We then propose a simple heuristic control policy, which includes a threshold-based admission control scheme and and a largest-delay-first channel allocation scheme, and prove the optimality of the largest-delay-first channel allocation scheme. We further propose an improved policy using the rollout algorithm. By comparing the performance of both proposed policies with the upper-bound of the maximum revenue, we show that our policies achieve close-to-optimal performance with low complexities.Comment: 11 pages, 8 figure