•The latest applications of deep learning in stock market prediction are presented.•The literature is reviewed with a general workflow for stock market prediction.•The often-ignored implementation ...and reproducibility in other surveys are examined.•The future directions along with the research frontiers are pointed out.
Stock market prediction has been a classical yet challenging problem, with the attention from both economists and computer scientists. With the purpose of building an effective prediction model, both linear and machine learning tools have been explored for the past couple of decades. Lately, deep learning models have been introduced as new frontiers for this topic and the rapid development is too fast to catch up. Hence, our motivation for this survey is to give a latest review of recent works on deep learning models for stock market prediction. We not only category the different data sources, various neural network structures, and common used evaluation metrics, but also the implementation and reproducibility. Our goal is to help the interested researchers to synchronize with the latest progress and also help them to easily reproduce the previous studies as baselines. Based on the summary, we also highlight some future research directions in this topic.
In recent years, satellite networks have been proposed as an essential part of next-generation mobile communication systems. Software defined networking techniques are introduced in satellite ...networks to handle the growing challenges induced by time-varying topology, intermittent inter-satellite link and dramatically increased satellite constellation size. This survey covers the latest progress of software defined satellite networks, including key techniques, existing solutions, challenges, opportunities, and simulation tools. To the best of our knowledge, this paper is the most comprehensive survey that covers the latest progress of software defined satellite networks. An open GitHub repository is further created where the latest papers on this topic will be tracked and updated periodically. Compared with these existing surveys, this survey contributes from three aspects: (1) an up-to-date SDN-oriented review for the latest progress of key techniques and solutions in software defined satellite networks; (2) an inspiring summary of existing challenges, new research opportunities and publicly available simulation tools for follow-up studies; (3) an effort of building a public repository to track new results.
Loan default prediction has been a significant problem in the financial domain because overdue loans may incur significant losses. Machine learning methods have been introduced to solve this problem, ...but there are still many challenges including feature multicollinearity, imbalanced labels, and small data sample problems. To replicate the success of deep learning in many areas, an effective regularization technique named muddling label regularization is introduced in this letter, and an ensemble of feed-forward neural networks is proposed, which outperforms machine learning and deep learning baselines in a real-world dataset.
Ship detection and angle estimation in SAR images play an important role in marine surveillance. Previous works have detected ships first and estimated their orientations second. This is ...time-consuming and tedious. In order to solve the problems above, we attempt to combine these two tasks using a convolutional neural network so that ships may be detected and their orientations estimated simultaneously. The proposed method is based on the original SSD (Single Shot Detector), but using a rotatable bounding box. This method can learn and predict the class, location, and angle information of ships using only one forward computation. The generated oriented bounding box is much tighter than the traditional bounding box and is robust to background disturbances. We develop a semantic aggregation method which fuses features in a top-down way. This method can provide abundant location and semantic information, which is helpful for classification and location. We adopt the attention module for the six prediction layers. It can adaptively select meaningful features and neglect weak ones. This is helpful for detecting small ships. Multi-orientation anchors are designed with different sizes, aspect ratios, and orientations. These can consider both speed and accuracy. Angular regression is embedded into the existing bounding box regression module, and thus the angle prediction is output with the position and score, without requiring too many extra computations. The loss function with angular regression is used for optimizing the model. AAP (average angle precision) is used for evaluating the performance. The experiments on the dataset demonstrate the effectiveness of our method.
•Robotic total stations (RTSs) are applied to subway tunnel displacement monitoring.•An automatic subway tunnel displacement monitoring system is developed.•The mathematical models for both short and ...long monitoring zone are derived and tested.
Real-time displacement monitoring is important for ensuring the safety of subway tunnel structures when construction activities are carried out near them. Owing to the special environmental characteristics of tunnels, many displacement monitoring techniques are either inapplicable to subway tunnel structures or have limitations in terms of the accuracy and automation of monitoring. Therefore, this article focuses on the automatic monitoring of subway tunnel displacement using robotic total stations. Using prisms as reflectors, the robotic total stations can perform highly accurate displacement monitoring by measuring angles and distances in a non-contact manner. A mathematical model for displacement monitoring with one total station in a short monitoring zone is developed, and a model for monitoring a long tunnel zone with multiple total stations is derived to guarantee the accuracy of monitoring. An automatic displacement monitoring system with robotic total stations is built, where the data acquisition and data management of the system are detailed through its hardware composition and software functions, respectively. An experiment on a tunnel showed the available monitoring range of a single robotic total station, and the accuracy of monitoring the long zone with multiple robotic total stations was verified. The application of the monitoring system to the Guangzhou Metro Line 2 confirmed its robustness.
Communication networks are important infrastructures in contemporary society. There are still many challenges that are not fully solved and new solutions are proposed continuously in this active ...research area. In recent years, to model the network topology, graph-based deep learning has achieved the state-of-the-art performance in a series of problems in communication networks. In this survey, we review the rapidly growing body of research using different graph-based deep learning models, e.g. graph convolutional and graph attention networks, in various problems from different types of communication networks, e.g. wireless networks, wired networks, and software defined networks. We also present a well-organized list of the problem and solution for each study and identify future research directions. To the best of our knowledge, this paper is the first survey that focuses on the application of graph-based deep learning methods in communication networks involving both wired and wireless scenarios. To track the follow-up research, a public GitHub repository is created, where the relevant papers will be updated continuously.
Abstract
This study introduces four rock–soil characteristics factors, that is, Lithology, Rock Structure, Rock Infiltration, and Rock Weathering, which based on the properties of rock formations, to ...predict Landslide Susceptibility Mapping (LSM) in Three Gorges Reservoir Area from Zigui to Badong. Logistic regression, artificial neural network, support vector machine is used in LSM modeling. The study consists of three main steps. In the first step, these four factors are combined with the 11 basic factors to form different factor combinations. The second step randomly selects training (70% of the total) and validation (30%) datasets out of grid cells corresponding to landslide and non-landslide locations in the study area. The final step constructs the LSM models to obtain different landslide susceptibility index maps and landslide susceptibility zoning maps. The specific category precision, receiver operating characteristic curve, and 5 other statistical evaluation methods are used for quantitative evaluations. The evaluation results show that, in most cases, the result based on Rock Structure are better than the result obtained by traditional method based on Lithology, have the best performance. To further study the influence of rock–soil characteristic factors on the LSM, these four factors are divided into “Intrinsic attribute factors” and “External participation factors” in accordance with the participation of external factors, to generate the LSMs. The evaluation results show that the result based on Intrinsic attribute factors are better than the result based on External participation factors, indicating the significance of Intrinsic attribute factors in LSM. The method proposed in this study can effectively improve the scientificity, accuracy, and validity of LSM.
Handwritten numeral recognition is a classical and important task in the computer vision area. We propose two novel deep learning models for this task, which combine the edge extraction method and ...Siamese/Triple network structures. We evaluate the models on seven handwritten numeral datasets and the results demonstrate both the simplicity and effectiveness of our models, comparing to baseline methods.
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