Efficient Decoding Algorithms for the Compute-and-Forward Strategy

We address in this paper decoding aspects of the Compute-and-Forward (CF) physical-layer network coding strategy. It is known that the original decoder for the CF is asymptotically optimal. However, its performance gap to optimal decoders in practical settings are still not known. In this work, we develop and assess the performance of novel decoding algorithms for the CF operating in the multiple access channel. For the fading channel, we analyze the ML decoder and develop a novel diophantine approximation-based decoding algorithm showed numerically to outperform the original CF decoder. For the Gaussian channel, we investigate the maximum a posteriori (MAP) decoder. We derive a novel MAP decoding metric and develop practical decoding algorithms proved numerically to outperform the original one

The Impact of IFRS Adoption on Value Relevance of Earnings and Book Value of Equity: The Case of Emerging Markets in African and Asian Regions

AbstractThe purpose of this study is to examine the effect of mandatory adoption of International Financial Reporting Standards on the value relevance of earnings and the book value of equity. The evidence derived from the study suggested that despite the strength in the overall explanatory power of the model during the two periods, the role of EPS became observable in the post-adoption period. By conducting further analysis, the results highlighted that the increase of the value level are positively influenced by a common law legal system, a high level of external economic openness, a strong investor protection, a full protection of minority shareholders and by a sophisticated capital market

Eco-Friendly Low Resource Security Surveillance Framework Toward Green AI Digital Twin

Most intelligent systems focused on how to improve performance including accuracy, processing speed with a massive number of data sets and those performance-biased intelligent systems, Red AI systems, have been applied to digital twin in smart cities. On the other hand, it is highly reasonable to consider Green AI features covering environmental, economic, social costs for advanced digital twin services. In this letter, we propose eco-friendly low resource security surveillance toward Green AI-enabled digital twin service, which provides eco-friendly security by the active participation of low resource devices. And, we formally define a problem whose objective is to maximize the participation of low source or reusable devices such that reusable surveillance borders are created within security district. Also, a dense sub-district with low resource devices priority completion scheme is proposed to resolve the problem. Then, the devised method is performed by expanded simulations and the achieved result is evaluated with demonstrated discussions

BeneWinD: An Adaptive Benefit Win–Win Platform with Distributed Virtual Emotion Foundation

In recent decades, online platforms that use Web 3.0 have tremendously expanded their goods, services, and values to numerous applications thanks to its inherent advantages of convenience, service speed, connectivity, etc. Although online commerce and other relevant platforms have clear merits, offline-based commerce and payments are indispensable and should be activated continuously, because offline systems have intrinsic value for people. With the theme of benefiting all humankind, we propose a new adaptive benefit platform, called BeneWinD, which is endowed with strengths of online and offline platforms. Furthermore, a new currency for integrated benefits, the win–win digital currency, is used in the proposed platform. Essentially, the proposed platform with a distributed virtual emotion foundation aims to provide a wide scope of benefits to both parties, the seller and consumer, in online and offline settings. We primarily introduce features, applicable scenarios, and services of the proposed platform. Different from previous systems and perspectives, BeneWinD can be combined with Web 3.0 because it deliberates based on the decentralized or distributed virtual emotion foundation, and the virtual emotion feature and the detected virtual emotion information with anonymity are open to everyone who wants to participate in the platform. It follows that the BeneWinD platform can be connected to the linked virtual emotion data block or win–win digital currency. Furthermore, crucial research challenges and issues are addressed in order to make great contributions to improve the development of the platform

Performance analysis to evaluate overtaking behavior on highways

Today, cities are facing new issues with the increase of the population and the massive urbanization. One of them is the mobility as the cities were not designed to support such increase. Improving mobility in smart cities, becomes an important challenge to avoid traffic jam and improving sustainability by reducing greenhouse effect. This contributes as well to having better life for citizen. Even with rich infrastructures, the vehicles behavior could decrease the traffic flow. This is why figure out how the mobility is done on the highways can give more details on how it can create traffic jams and then several solutions could be proposed to contribute to the improvements. This is the goal of this study. In this paper, a new stochastic model based on Markov chain is proposed, which represents the behaviors of overtaking on the highways. A full description of the model is given with numerical resolution to calculate several rewards such as delays and congestion. Intensive simulations were carried out to compare both simulations and analytical model results and to provide results for more complex configurations