Fog computing architecture is used in various environments such as smart manufacturing, vehicular ad hoc networks. However, as an extension of cloud computing, inheriting security challenges of cloud computing is inevitable. Recently, Jia et al. proposed an authenticated key exchange scheme for fog-driven IoT health care systems. Unfortunately, we discovered their scheme is vulnerable to an ephemeral secret leakage attack. In this paper, we propose an authenticated key exchange scheme for fog computing. Compared with previous schemes, our proposed scheme performs better in terms of performance and security.

Portfolio management involves position sizing and resource allocation. Traditional and generic portfolio strategies require forecasting of future stock prices as model inputs, which is not a trivial task since those values are difficult to obtain in the real-world applications. To overcome the above limitations and provide a better solution for portfolio management, we developed a Portfolio Management System (PMS) using reinforcement learning with two neural networks (CNN and RNN). A novel reward function involving Sharpe ratios is also proposed to evaluate the performance of the developed systems. Experimental results indicate that the PMS with the Sharpe ratio reward function exhibits outstanding performance, increasing return by 39.0% and decreasing drawdown by 13.7% on average compared to the reward function of trading return. In addition, the proposed model is more suitable for the construction of a reinforcement learning portfolio, but has 1.98 times more drawdown risk than the . Among the conducted datasets, the PMS outperforms the benchmark strategies in TW50 and traditional stocks, but is inferior to a benchmark strategy in the financial dataset. The PMS is profitable, effective, and offers lower investment risk among almost all datasets. The novel reward function involving the Sharpe ratio enhances performance, and well supports resource-allocation for empirical stock trading.

In the production process from green beans to coffee bean packages, the defective bean removal (or in short, defect removal) is one of most labor-consuming stages, and many companies investigate the automation of this stage for minimizing human efforts. In this paper, we propose a deep-learning-based defective bean inspection scheme (DL-DBIS), together with a GAN (generative-adversarial network)-structured automated labeled data augmentation method (GALDAM) for enhancing the proposed scheme, so that the automation degree of bean removal with robotic arms can be further improved for coffee industries. The proposed scheme is aimed at providing an effective model to a deep-learning-based object detection module for accurately identifying defects among dense beans. The proposed GALDAM can be used to greatly reduce labor costs, since the data labeling is the most labor-intensive work in this sort of solutions. Our proposed scheme brings two main impacts to intelligent agriculture. First, our proposed scheme is can be easily adopted by industries as human effort in labeling coffee beans are minimized. The users can easily customize their own defective bean model without spending a great amount of time on labeling small and dense objects. Second, our scheme can inspect all classes of defective beans categorized by the SCAA (Specialty Coffee Association of America) at the same time and can be easily extended if more classes of defective beans are added. These two advantages increase the degree of automation in the coffee industry. The prototype of the proposed scheme was developed for studying integrated tests. Testing results of a case study reveal that the proposed scheme can efficiently and effectively generate models for identifying defective beans with accuracy and precision values up to $80\%$ .

The Earth’s climate change, colloquially known as global warming, is detrimental to life across the globe. The most significant contributor to the greenhouse gas (GHG) effect is carbon dioxide (CO₂) emission. In the United States (US) economy, the major benefactor of CO₂ emissions is the energy sector, with the top contribution coming from fossil fuels. The estimated 2020 CO₂ emission was 5981 million metric tons, despite a dramatic reduction in the trendline compared to the year 2019. An ultimatum for energy consumption rises from fiscal development, growing population, and technological advancements. Energy use and GHG emissions are inclined upward, provoking an unwholesome nation. This paper studies (i) the principal sources of energy use and CO₂ emission, (ii) the inclination of such sources, (iii) trends and drivers of GHG emissions, (iv) low carbon development and carbon footprint, and (v) the diverse US projects for reducing GHG emissions and the challenges in deploying them. We have forecasted the emissions from fossil fuels from 2025 to 2050 and compared the results using MAPE to calculate the mean percentage error. The forecasted results of 2050 show high accuracy, suggesting probable approaches to reduce further CO₂ emissions, measures to reduce emissions through carbon capture and sequestration, and help in the development of improved GHG mitigations for the nation.

To determine an appropriate trading time for buying or selling stocks is always a difficult task. The common way to deal with it is using trading strategies formed by technical or fundamental indictors. Lots of approaches have been presented on how to form trading strategies and how to set suitable parameters for those strategies. Furthermore, some approaches were also designed to optimize a trading strategy portfolio, which is a set of strategies where the return and risk of the portfolio can be maximized and minimized, respectively. To provide a more useful trading strategy portfolio, we first define a group trading strategy portfolio (GTSP). Then, an algorithm that utilizes the grouping genetic algorithm is designed for solving the GTSP optimization problem. In the chromosome representation, the grouping, strategy and weight parts are employed to encode a possible GTSP. The fitness value of a chromosome is calculated by the group balance, weight balance, portfolio return, and risk to assess the quality of every possible solution. Genetic operators, including crossover, mutation and inversion, are applied on the population to form new offspring. Evolution is continued until the stop conditions are reached. At last, experiments were conducted on two real datasets with different trends to show the advantages and effectiveness of the presented approach.

Money management is one of the most important issues in financial trading. Many skills of money managements are based on the Kelly criterion, which is a theoretical optimization of bidding an optimal fraction for position sizing. However, there is still a large gap between the theory and the real trading for money management. In this paper, we design an option trading strategy via Kelly criterion. While the price movements of options are highly volatile, various options’ portfolio can be formed by long or short at different strike prices to pre-lock the losses and profits; then we have a fixed profit and loss distribution via holding an option portfolio. Consequently, the Kelly criterion can be applied to the options’ trading for calculating the optimal bidding fraction. We propose a method for option trading, in finding the profitable option portfolio by bidding optimal fraction. Compared with prior works, our proposed model is a novel approach for options’ trading with the money management of position sizing. Experiments are conducted to show the feasibility and profitability of our method in practical scenarios. Future works are provided in the final section.