The integration of space, air, and ground segments into unified Space-Air-Ground Integrated Networks (SAGINs) enables low-latency, ubiquitous, and scalable computing. However, such systems face critical challenges: ground terminals suffer from weak satellite links, UAV-based edge nodes have limited resources, and highly dynamic environments make it difficult to make efficient offloading and resource allocation decisions. Prior approaches often optimize either communication or computation in isolation and lack adaptability to real-time environmental feedback. This paper presents a novel perception-aware hybrid-action deep reinforcement learning (DRL) framework for joint optimization of task offloading, beamforming, and resource allocation in SAGINs. To improve tractability, the original non-convex problem is first decomposed using Block Coordinate Descent (BCD) and approximated with Successive Convex Approximation (SCA), generating a structured feasible action space. A Soft Actor-Critic (SAC) agent then learns policies over this space, informed by real-time UAV perception via mmWave radar and vision sensors that detect user density, link quality, and environmental blockages. The DRL agent operates over a hybrid action space, combining discrete offloading decisions with continuous controls such as beamforming weights, CPU frequency, and transmission power. We employ a constraint-aware action masking mechanism that prunes infeasible hybrid actions violating delay, power, or SNR limits, thereby accelerating learning while respecting SAGIN-specific constraints. Extensive simulations show that the proposed framework significantly outperforms greedy, no-perception DRL, and state-of-the-art DRL offloading algorithms in reducing latency and energy consumption, while improving offloading success and resource stability. These results highlight the effectiveness of combining analytical optimization structure with adaptive perception-driven learning for robust and scalable control in future SAGINs.

Abstract In the past, most strategies were mainly designed to focus on stocks or futures as the trading target. However, due to the enormous number of companies in the market, it is not easy to select a set of stocks or futures for investment. By investigating each company’s financial situation and the trend of the overall financial market, people can invest precisely in the market and choose to go long or short. Moreover, how to determine the position size of the transaction is also a problematic issue. In the past, many money management theories were based on the Kelly criterion. And they put a certain percentage of their total funds into the market for trading. Nonetheless, three massive problems cannot be overcome. First, futures are leveraged transactions, and extra funds must be deposited as margin. It causes that the position size is hard to be estimated by the Kelly criterion. The second point is that the trading strategy is difficult to determine the winning rate in the financial market and cannot be brought into the Kelly criterion to calculate the optimal fraction. Last, the financial data are always massive. A big data technique should be applied to resolve this issue and enhance the performance of the framework to reveal knowledge in the financial data. Therefore, in this paper, a concept of converting the original futures trading strategy into options trading is proposed. An LSTM (long short-term memory)-based framework is proposed to predict the profit probability of the original futures strategy and convert the corresponding daily take-profit and stop-loss points according to the delta value of the options. Finally, the proposed framework brings the results into the Kelly criterion to get the optimal fraction of options trading. The final research results show that options trading is closer to the optimal fraction calculated by the Kelly criterion than futures trading. If the original futures trading strategy can profit, the benefits after converting to options trading can be further superior.

Integrating diverse biomedical knowledge information is essential to enhance the accuracy and efficiency of medical diagnoses, facilitate personalized treatment plans, and ultimately improve patient outcomes. However, Biomedical Information Integration (BII) faces significant challenges due to variations in terminology and the complex structure of entity descriptions across different datasets. A critical step in BII is biomedical entity alignment, which involves accurately identifying and matching equivalent entities across diverse datasets to ensure seamless data integration. In recent years, Large Language Model (LLMs), such as Bidirectional Encoder Representations from Transformers (BERTs), have emerged as valuable tools for discerning heterogeneous biomedical data due to their deep contextual embeddings and bidirectionality. However, different LLMs capture various nuances and complexity levels within the biomedical data, and none of them can ensure their effectiveness in all heterogeneous entity matching tasks. To address this issue, we propose a novel Two-Stage LLM construction (TSLLM) framework to adaptively select and combine LLMs for Biomedical Information Integration (BII). First, a Multi-Objective Genetic Programming (MOGP) algorithm is proposed for generating versatile high-level LLMs, and then, a Single-Objective Genetic Algorithm (SOGA) employs a confidence-based strategy is presented to combine the built LLMs, which can further improve the discriminative power of distinguishing heterogeneous entities. The experiment utilizes OAEI's entity matching datasets, i.e., Benchmark and Conference, along with LargeBio, Disease and Phenotype datasets to test the performance of TSLLM. The experimental findings validate the efficiency of TSLLM in adaptively differentiating heterogeneous biomedical entities, which significantly outperforms the leading entity matching techniques.

Quantitative trading, relying on diverse parameter combinations, is becoming increasingly the norm for trading strategies in financial investments. The performance of these strategies is intricately linked to these parameters. However, the performance on the training set after backtesting does not ensure success on a test set and may lead to overfitting. This study emphasizes enhancing stability and robustness in trading-strategy parameters by introducing a ’parameter plateau.’ Traditional brute-force methods for exploring high-dimensional parameter spaces can be intricate and time-consuming. To address this challenge, we present an efficient alternative that identifies stable and robust parameters by configuring parameter plateaus to mitigate overfitting risks. A step-by-step search algorithm is proposed to determine the optimal parameters, leveraging the power of particle-swarm optimization. In continuous, multi-dimensional solution spaces, particle-swarm optimization is invaluable for the swift and effective discovery of the desired solutions. Experiments underscore the substantial influence of the parameter plateau concept on parameter selection, highlighting the pivotal role of particle-swarm optimization in efficiently navigating complex solution spaces and thereby enabling the discovery of stable and profitable trading strategies.

Guided depth map super-resolution (GDSR) is one of the mainstream methods in depth map super-resolution, as high-resolution color images can guide the reconstruction of the depth maps and are often easy to obtain. However, how to make full use of extracted guidance information of the color image to improve the depth map reconstruction remains a challenging problem. In this paper, we first design a multi-scale feedback module (MF) that extracts multi-scale features and alleviates the information loss in network propagation. We further propose a novel multi-scale feedback network (MSF-Net) for guided depth map super-resolution, which can better extract and refine the features by sequentially joining MF blocks. Specifically, our MF block uses parallel sampling layers and feedback links between multiple time steps to better learn information at different scales. Moreover, an inter-scale attention module (IA) is proposed to adaptively select and fuse important features at different scales. Meanwhile, depth features and corresponding color features are interacted using cross-domain attention conciliation module (CAC) after each MF block. We evaluate the performance of our proposed method on both synthetic and real captured datasets. Extensive experimental results validate that the proposed method achieves state-of-the-art performance in both objective and subjective quality.

When the concentration focuses on data mining, frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are commonly addressed and researched. Many related algorithms are proposed to reveal the general relationship between utility, frequency, and items in transaction databases. Although these algorithms can mine FIMs or HUIMs quickly, these algorithms merely take into account frequency or utility as a unilateral criterion for itemsets but the other factors (e.g., distance, price) could be also valuable for decision-making. A new skyline framework has been presented to mine frequent high utility patterns (SFUPs) to better support user decision-making. Several new algorithms have been proposed one after another. However, the Internet of Things (IoT), mobile Internet, and traditional Internet are generating massive amounts of data every day, and these cutting-edge standalone algorithms can not satisfy the new challenge of finding interesting patterns from this data. Big Data uses a distributed architecture in the form of cloud computing to filter and process this data to extract useful information. This paper proposes a novel parallel algorithm on Hadoop as a three-stage iterative algorithm based on MapReduce. MapReduce is used to divide the mining tasks of the whole large data set into multiple independent sub-tasks to find frequent and high utility patterns in parallel. Numerous experiments were done in this paper, and from the results, the algorithm can handle large datasets and show good performance on Hadoop clusters.

Trading strategies can be divided into two categories, i.e., those with momentum characteristic and those that appear contrarian. The characteristics of trading strategies have been widely studied; however, there has been relatively little work on the characteristics of stocks. Furthermore, there is no standard approach to the classification of stocks in terms of momentum and contrarian. This article presents a fuzzy momentum contrarian uncertain characteristic system for the classification and quantification of stock characteristics. Random trading, stop-loss, and take-profit mechanisms are first used to identify characteristics, and then, a novel profitability index with a type-2 fuzzy set module is used to quantify them. In the experiments, 41 stocks on the Taiwan 50 index were deemed suitable for momentum strategies, whereas nine stocks were deemed suitable for contrarian strategies. An uphill relationship between profitability index and trading performance is observed, which produced correlation coefficients of 0.148–0.539 and a classification accuracy of 52.0–60.0%. However, the proposed system greatly improved classification performance, resulting in correlation coefficients of 0.572–0.722 with an accuracy of 63.6–84.5%. In the real-world application, the proposed system outperforms the benchmark among all datasets and increases the profitability by 1.5 times on the Taiwan 50 dataset. These results clearly demonstrate the efficiency of the proposed system in the quantification and classification of stocks suited to momentum- and contrarian-type trading strategies and also in the real-world applications.

Abstract 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.

Environmental protection is an important issue in recent decades, and renewable energy is an ideal solution for eco-friendly power generation. Solar-power generation is a popular renewable energy with low cost and small environmental footprint, which leads to exponential growth and high industrial investment. A mature solar business model has been established, but some uncertainties hinder the development, especially when focusing on the lack of solar-radiation. To address these issues, in this article we propose a hedging system to hedge the low-radiation risk for solar-investors through the designed IoT-based data, edge-based models for predicting solar-radiation as well as hedging options. Our experimental results show that the edge-based predictive models can obtain an R-squared value of 0.841 and a correlation coefficient of 0.917. For binary options designed in the hedging system, the broker can obtain stable payoffs with the highest Sharpe ratio of 3.354, and the investors can obtain large payoffs during low-radiation. Our simulation results show the effectiveness of the proposed hedging system for investors (buyer-side), simultaneously, present the motivation of the broker (seller-side) to join the designed hedging system utilized in solar-power generation.

Opening range breakout (ORB) is a well-known intraday trading strategy via technical analysis. ORB lacks robustness against market uncertainties (e.g., information from contradictory sources), and does not consider all relevant market characteristics. Furthermore, the closing strategies in generic ORB are not well defined. In this study, we developed an evolutionary ORB-based model, which utilized historical data to optimize thresholds in order to enhance profitability, and developed protective closing strategies aimed at to prevent unacceptable losses. Selecting appropriate thresholds and parameters for ORB is a non-trivial task, due to the fact that the search space exceeds sixty-five thousand options. We used evolutionary computation to derive rational strategies and parameters for ORB. The proposed framework based on a genetic algorithm optimizes the parameters related to threshold selection and protective closing strategies. In experiments, this resulted in annual returns of 9.3% (representing a 2.8% improvement over the original strategy) and Sharpe ratio of 2.5 (an improvement of 1.0), while reducing the maximum drawdown by half. The proposed scheme also reduced computational overhead by 89% compared to a grid search.

Opening range breakout (ORB) is a well-known intraday trading strategy that generates trading signals through technical analysis; however, ORB does not make full use of market characteristics and does not define closing strategy. These problems make the ORB strategy not stable or robust enough. In this paper, we adjust thresholds through historical data to enhance profitability, and design protective closing strategy to prevent unacceptable losses. However, there are numerous parameters combinations, and the solution space is approximately 214. Therefore, we implement genetic algorithm to improve the efficiency and rationality of parameter selection. We found that the performance of the GAORB_Sharpe with stop-loss mechanism is outstanding. The strategy we proposed can generate 9.303% annual return and 15.716% Sharpe ratio, which is 2.5% and 5% more than the original strategy, and cut the maximum drawdown by half. Further, it can save 90% of the computation by genetic algorithm. In summary, we recommend adjusting the threshold and implementing stop-loss mechanism to the ORB strategy, and selecting parameters through genetic algorithms to improve overall performance.

Portfolio management is a critical issue which should be skilled by position sizing and resource allocation. Traditional and generic portfolio strategies require to forecast the future stocks prices as the model inputs, which is not a trivial task in the real-world applications. To solve the above limitations and provide a better solution for the portfolio management to the inventors, we then develop a portfolio management system (PMS) with equity market neutral strategy in reinforcement learning. A novel reward function involving Sharpe ratio is also designed to evaluate the performance of the developed systems. Experimental results indicate that the PMS with Sharpe ratio reward function has the outstanding performance, and increase the return 39.0% and decrease the drawdown of 13.7% on average than that with reward function of trading return. In addition, the developed PMS_CNN model is more suitable and profitable to construct RL portfolio, but has a 1.98 times more drawdown risk than the PMS_RNN. Overall, the proposed PMS outperforms the benchmark strategies in the measurements of total return and Sharpe ratio. The PMS is profitable and effective with lower investment risk, and the novel reward function by involving Sharpe ratio really enhances the performance, and well support the resource-allocation in the empirical stock trading.

Portfolio management and money management have always been important issues for investors and researchers in the financial field. The Kelly criterion is a theoretical approach of money management, and is a mathematical method for optimizing long-term expected return. Kelly criterion requires the future outcomes distribution as input, which can be predicted through the techniques of machine learning (ML). With the revolutionary growth of the amount of information, big data is the key to boost ML prediction, therefore, we introduce a general Kelly framework, including the strength of Kelly, ML, and big data. In addition, we propose the Kelly stationary index (KSI) to quantify the stationarity of the stock's outcomes distribution, which will affect the trading period and forecasting frequency. We calculate the KSI of each constituent stock of Taiwan's 50, and apply the Kelly criterion strategy to verify the effectiveness of KSI. The experimental results show that there is a moderate downhill relationship between the strategy performance and KSI with the -0.591 of correlation coefficient. It also indicates that the closer the estimated distribution is to the actual distribution, the higher the expected profit. In the future, we will use KSI for money management, strategy development, and apply KSI into the general Kelly framework.

Because portfolio management is a problem of optimization, many approaches have been presented to address it. In this paper, we propose an enhanced approach to obtain a diverse group stock portfolio (DGSP) from a large number of stocks using the island-based grouping genetic algorithm with the divide-and-conquer strategy. The main concept is that the proposed approach divides the given stocks into several subsets, and for every island, it will select a random subset to form its own initial population. During the evolution process, the stock synchronization mechanism is designed to adjust stocks in an island after chromosome migration. At last, experimental results on a real dataset were made to show the effectiveness and efficiency of the proposed approach.

Opening Range Breakout (ORB) is a fairly intraday trading strategy. We set the resistance and the support levels by the price in opening interval to follow the trend in the futures market. However, such kind of strategies is not profitable for most commodities in recent years in the changing market. In this paper, we attempt to improve the original ORB strategy by considering the effect of trends continuity on the event. We adjust the predetermined threshold for upper bound and lower bound. This strategy is called Threshold Adjusting ORB or TA_ORB. We implement this modified ORB strategy on the Taiwan Index Futures from 2008 to 2012. Compared with the original ORB strategy, we got 145.98% return in 2008 (bear market), 81.86% return in 2009 (bull market) and 32.25% annual return in 2008-2012 (five-year period) which are 4.0 times, 1.4 times, and 2.6 times more than original ORB, respectively. TA_ORB performs outstanding in large fluctuation, especially in the bear market. Performance can verify that the observations of TA_ORB improve the stability of the breakthrough signal, enhance the return, and reduce strategic risk. Further, we plan to use neural network to make more precise predictions and implement these strategies in different commodities.

In this paper, we propose an algorithm for obtaining a multi-period group stock portfolio based on the grouping genetic algorithm. It encodes a multi-period group stock portfolio into a chromosome by the belonging, grouping, group availability and weight parts. Every chromosome is then evaluated by three factors: the accumulated return, the accumulated safety, and the investment style factors. A front pool which is a set of non-dominated solutions is also maintained to enhance the diversity of the proposed approach. Experiments were conducted on the financial dataset to show the merits of the proposed approach.

Prediction of stocks is complicated by the dynamic, complex, and chaotic environment of the stock market. Many studies predict stock price movements using deep learning models. Although the attention mechanism has gained popularity recently in neural machine translation, little focus has been devoted to attention-based deep learning models for stock prediction. This paper proposes an attention-based long short-term memory model to predict stock price movement and make trading strategies.

Due to the variety of financial markers, to determine an appropriate timing for buying or selling stocks is always a difficult task, the common way to handle it is using trading strategies formed by technical or fundamental indicators. To deal with the problem, an approach was proposed for optimizing a group trading strategy portfolio in the previous approach. To avoid unpredictable loss, the stop-loss and take-profit points are commonly used by investors to handle it. However, they are not easy determined by users when different trading strategies are employed. In this paper, attempting to provide a more useful group trading strategy portfolio, we propose an algorithm for obtaining a group trading strategy portfolio and its stop-loss and take-profit points using the grouping genetic algorithm. Experiments were conducted on a real dataset to reveal the effectiveness of the proposed approach.

Broadcast is an efficient communication channel on wireless sensor networks. Through authentic broadcast, deployed sensors can perform legitimate actions issued by a base station. According to previous literature, a complete solution for authentic broadcast is digital signature based on asymmetric cryptography. However, asymmetric cryptography utilizes expensive operations, which result in computational bottlenecks. Among these cryptosystems, Elliptic Curve Cryptography (ECC) seems to be the most efficient and the most popular choice. Unfortunately, signature verification in ECC is not efficient enough. In this article, we propose an authentic broadcast scheme based on RSA. Unlike conventional approaches, the proposed scheme adopts short moduli to enhance performance. Meanwhile, the weakness of short moduli can be fixed with rekeying strategies. To minimize the rekeying overhead, a Multi-Modulus RSA generation algorithm, which can reduce communication overhead by 50%, is proposed. We implemented the proposed scheme on MICAz. On 512-bit moduli, each verification spends at most 0.077 seconds, which is highly competitive with other public-key cryptosystems.

We present new variants of an RSA whose key generation algorithms output two distinct RSA key pairs having the same public and private exponents. This family of variants, called dual RSA, can be used in scenarios that require two instances of RSA with the advantage of reducing the storage requirements for the keys. Two applications for dual RSA, blind signatures and authentication/secrecy, are proposed. In addition, we also provide the security analysis of dual RSA. Compared to normal RSA, the security boundary should be raised when applying dual RSA to the types of small-d, small-e, and rebalanced-RSA.