Measures for the Improvement of Feasibility Studies and Investment Reviews: Identification and Verification of Major Project Sectors Considering Balanced Regional Development

This study identified the balanced development indicators that affected the results of the 2019 central investment review of local financial investment projects in South Korea. Factors with positive B values and categorized under the sectors of safety, health, and social welfare were given greater weight during the investment review. Based on the empirical analysis results and verification of the findings using sector-specific weights, this study proposed measures to improve investment reviews of local financial projects considering balanced regional development. We believe that our study makes a significant contribution to the literature because there is a lack of empirical studies on the topic, especially those using sector-specific weights based on investment review criteria. Further, we believe that this paper will be of interest to the readership of your journal because it addresses balanced regional development, which is considered a prerequisite for sustainable economic growth

Fully private and secure coded matrix multiplication with colluding workers

In this paper, we propose a new coded computation scheme that can alleviate straggler effects in distributed computing. We consider data security and master’s privacy for matrix multiplication tasks. The proposed scheme, called fully private and secure coded matrix multiplication (FPSCMM), ensures data security and master’s privacy on two data matrices for multiplication tasks from colluding workers. We also show that the storage overhead at workers can be reduced by FPSCMM, since it is enough for workers to store the encoded matrices with sub-blocks. Lastly, we compare FPSCMM with the existing master’s privacy-preserving coded matrix multiplication schemes

Action-Bounding for Reinforcement Learning in Energy Harvesting Communication Systems

In this paper, we consider a power allocation problem for energy harvesting communication systems, where a transmitter wants to send the desired messages to the receiver with the harvested energy in its rechargeable battery. We propose a new power allocation strategy based on deep reinforcement learning technique to maximize the expected total transmitted data for a given random energy arrival and random channel process. The key idea of our scheme is to lead the transmitter, rather than learning the undesirable power allocation policies, by an action-bounding technique using only causal knowledge of the energy and channel processes. This technique helps traditional reinforcement learning algorithms to work more accurately in the systems, and increases the performance of the learning algorithms. Moreover, we show that the proposed scheme achieves better performance with respect to the expected total transmitted data compared to existing power allocation strategies.N

Repair Rates for Multiple Descriptions on Distributed Storage

In a traditional distributed storage system, a source can be restored perfectly when a certain subset of servers is contacted. The coding is independent of the contents of the source. This paper considers instead a lossy source coding version of this problem where the more servers that are contacted, the higher the quality of the restored source. An example could be video stored on distributed storage. In information theory, this is called the multiple description problem, where the distortion depends on the number of descriptions received. The problem considered in this paper is how to restore the system operation when one of the servers fail and a new server replaces it, that is, repair. The requirement is that the distortions in the restored system should be no more than in the original system. The question is how many extra bits are needed for repair. We find an achievable rate and show that this is optimal in certain cases. One conclusion is that it is necessary to design the multiple description codes with repair in mind; just using an existing multiple description code results in unnecessary high repair rates

Rate Maximization with Reinforcement Learning for Time-Varying Energy Harvesting Broadcast Channels

In this paper, we consider a power allocation optimization technique for a time-varying fading broadcast channel in energy harvesting communication systems, in which a transmitter with a rechargeable battery transmits messages to receivers using the harvested energy. We first prove that the optimal online power allocation policy for the sum rate maximization of the transmitter is an increasing function of harvested energy, remaining battery, and each user's channel gain. We then construct an appropriate neural network by relying on increasing behavior of the optimal policy. This two-step approach, by using an effective function approximation as well as providing a fundamental guideline for neural network design, can prevent us from wasting the representational capacity of neural networks. On the basis of the neural network, we apply the policy gradient method to solve the power allocation problem. To validate the performance of our approach, we compare it with the closed-form the optimal policy in a partially observable Markov problem. Through further experiments, it is observed that our online solution achieves a performance close to the theoretical upper bound of the performance in a time-varying fading broadcast channel.N