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The problem of global warming is getting more serious, and vehicle emission is the main cause. In recent years, the number of locomotives in China has been increasing at a rate of more than 20% per year, and the problem of automobile pollution is becoming more serious. The transportation industry is the main source of fossil fuel combustion and environmental pollution. Therefore, in this paper, we propose a multi-view deep neural network (MVDNN) to analyze the key factors affecting the fuel consumption of automobiles. The experiments show that the introduction of human input improves the prediction accuracy and the root mean square error (RMSE) achieves 0.993. In addition, this paper also finds that for drivers, driving habits, driving frequency, and safety awareness are the most important factors affecting the fuel consumption of vehicles by combining Lasso regression with MVDNN. Finally, by comparing the prediction accuracy of different experiments, relevant policy suggestions are made. [ABSTRACT FROM AUTHOR]

The complex and varied urban road conditions have always been a difficult and a pivotal component in the study of driverless technology especially at intersections. In China, it is necessary for driverless cars to understand the gestures of traffic police. To identify the traffic police gesture at the intersection, the key step is to detect the traffic police at the intersection. At present, the research on traffic police detection is still in its infancy, there exists common problems such as slow detection speed and other real time problems in this method, and there is not a standardized traffic police data set ether. For the real time problems, this paper introduces the attention mechanism, and proposes a new real-time detection method of traffic police based on attention mechanism. The method proposed in this paper has strong robustness and can quickly complete the target detection task. For the data set problem, this paper analyzes and discloses similar data sets published through research. In the meanwhile, this paper collects 24,530 video data on the actual road, and it extracts and saves 12,000 pictures containing traffic police at frame rate of 30FPS as traffic police detection training and validated data set. [ABSTRACT FROM AUTHOR]

Mammography is an important screening criterion for breast cancer, one of the major diseases causing numerous deaths among female patients. Meanwhile, manual diagnosis of mammography is a time-consuming and labor-consuming job. Mammogram classification based on deep learning plays a vital role in computer-aided diagnosis (CAD) systems to mitigate the pressure on physicians. This paper proposes a learning-based multi-view mammogram classification model that captures long-distance dependence and extracts features of multiple receptive fields. Our model considers global and local features of mammography images using Transformer for global features and the proposed multiplex convolutions module for local features. We evaluate our proposed method on a dataset of mammography images obtained from a hospital in China. The proposed method achieves 90.57% accuracy and 94.86% AUC in benign or malignant classification tasks and outperforms other advanced methods for mammogram classification. It is worth noting that the proposed method only requires image-level labels and acts on the whole raw mammogram, which has clinical significance. [ABSTRACT FROM AUTHOR]

The mutant strains of COVID-19 caused a global explosion of infections, including many cities of China. In 2020, a hybrid AI model was proposed by Zheng et al., which accurately predicted the epidemic in Wuhan. As the main part of the hybrid AI model, ISI method makes two important assumptions to avoid over-fitting. However, the assumptions cannot be effectively applied to new mutant strains. In this paper, a more general method, named the multi-weight susceptible-infected model (MSI) is proposed to predict COVID-19 in Chinese Mainland. First, a Gaussian pre-processing method is proposed to solve the problem of data fluctuation based on the quantity consistency of cumulative infection number and the trend consistency of daily infection number. Then, we improve the model from two aspects: changing the grouped multi-parameter strategy to the multi-weight strategy, and removing the restriction of weight distribution of viral infectivity. Experiments on the outbreaks in many places in China from the end of 2021 to May 2022 show that, in China, an individual infected by Delta or Omicron strains of SARS-CoV-2 can infect others within 3–4 days after he/she got infected. Especially, the proposed method effectively predicts the trend of the epidemics in Xi'an, Tianjin, Henan, and Shanghai from December 2021 to May 2022. [ABSTRACT FROM AUTHOR]

Accurate traffic flow prediction can effectively improve traffic efficiency and safety. This has become a trending topic in intelligent transportation systems. However, the occurrence of traffic accidents will cause great fluctuations of traffic flow in a short time, thus affecting the accuracy of flow prediction. This paper analyses the influence of traffic accident information on traffic flow, and proposes a grey convolutional neural network called G-CNN for traffic flow prediction. First, considering the small sample size of traffic accidents and the incomplete information description, the traffic flow and traffic speed change rate are defined to represent traffic accident grey information, and a grey fixed-weight clustering method for extracting the characteristics of traffic accident grey information is established. Second, the spatiotemporal characteristics of traffic flow are analysed, and a third-order tensor is developed to fuse them with the accident characteristics and serve as the input of the G-CNN. Then the G-CNN model is built to predict traffic flow in a traffic accident environment. The Canadian Whitemud Drive experiment on traffic flow without traffic accident information reveals that the developed G-CNN model has a better ability to predict future traffic flow with better accuracy and stability. Meanwhile, it is verified that the fluctuation of the traffic change rate will cause traffic accidents, and the probability of traffic accidents at a certain time point will be obtained through feature extraction of traffic accidents. Finally, the model is verified for the traffic flow of Hangzhou Viaduct in China, which demonstrates that the G-CNN model outperforms all of the compared models. [ABSTRACT FROM AUTHOR]

Recent years have witnessed the fast development of multimedia platforms in China, such as Youku, LeTV and Weibo. Images and videos are usually uploaded with textual descriptions, such as titles and introductions of these media. These texts are the key to multimedia content understanding, and this paper is dedicated to multimedia understanding with visual content entailment via recognizing semantic entailment in these texts. In fact, the natural language processing community has been manifesting increasing interest in semantic entailment recognition in English texts. Yet, so far not much attention has been paid to semantic entailment recognition in Chinese texts. Therefore, this paper investigates on multimedia semantic entailment with Chinese texts. Recognizing semantic entailment in Chinese texts can be cast as a classification problem. In this paper, a classification model is constructed based on support vector machine to detect high-level semantic entailment relations in Chinese text pair, including entailment and non-entailment for the Binary-Class and forward entailment, reverse entailment, bidirectional entailment, contradiction and independence for the Multi-Class. We explore different semantic feature combinations based on three kinds of Chinese textual features, including Chinese surface textual, Chinese lexical semantic and Chinese syntactic features, and utilize them to feed our classification model. The experiment results show that the accuracy of our classification model for semantic entailment recognition with the feature combination using all the three kinds of Chinese textual features achieves a much better performance than the baseline in Multi-Class and slightly better results than the baseline in the Binary-Class. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for solving the shallow-water transport equation using a physics-informed neural network (PINN) combined with characteristic-based split (CBS). Simulation of the tide in East Coast of China is performed to verify the applicability of the present method in a practical problem. Furthermore, we propose a boundary condition that accounts for the second-order partial derivative term, which is more appropriate for solving the diffusion equation with open domains than the commonly used assumption of zero boundary values. Our numerical results demonstrate that this boundary condition leads to improved convergence of the network. In addition, we introduce a parameter estimation method that requires information from the field at only two different times, yet yields accurate parameter estimates. We observe that excessive participation of variables in gradient backpropagation can lead to neural networks getting trapped in local optima. We use PINN combined with CBS method to solve 3-D incompressible flow. As the number of variables involved in gradient backpropagation increases, the accuracy of the solution decreases, which can partially support our viewpoint. The source codes for the numerical examples in this work are available at https://github.com/double110/PINN-cbs-.git. • We introduce a rapid version of PINN and solve 3-d incompressible flow. • We introduce a boundary condition in open domains to enhance accuracy and stability. • We develop a PINN-CBS method for parameter estimation. • Reduce the number of variables participating in backpropagation can improve accuracy. [ABSTRACT FROM AUTHOR]

Predicting stock market trends is the basic daily routine task that investors should perform in the stock trading market. Traditional market trends prediction models are generally based on hand-crafted factors or features, which heavily rely on expensive expertise knowledge. Moreover, it is difficult to discover hidden features contained in the stock time series data, which are otherwise helpful for predicting stock market trends. In this paper, we propose a novel stock market trends prediction framework SMART with a self-supervised stock technical data sequence embedding model S3E. Specifically, the model encodes stock technical data sequences into embeddings, which are further trained with multiple self-supervised auxiliary tasks. With the learned sequence embeddings, we make stock market trends predictions based on an LSTM and a feed-forward neural network. We conduct extensive experiments on China A-Shares market and NASDAQ market to show that our model is highly effective for stock market trends prediction. We further deploy SMART in a leading financial service provider in China and the result demonstrates the effectiveness of the proposed method in real-world applications. [ABSTRACT FROM AUTHOR]

• CSI-SVQR is proposed for wind power probability density forecasting. • Cubic spline interpolation is used to reduce the noisy of wind power data. • The optimal CWC criterion is used to acquire the best PIs. • The SSE of mode criterion is used to acquire the best point forecasting results. • Wilcoxon test verifies that CSI improves the accuracy of forecasting methods. Accurate forecasting of wind power plays an important role in an effective and reliable power system. However, the fact of non-schedulability and fluctuation of wind power significantly increases the uncertainty of power systems. The output power of a wind farm is usually mixed with uncertainties, which reduce the effectiveness and accuracy of wind power forecasting. In order to handle the uncertainty of wind power, this paper first proposes to conduct outlier detection and reconstruct data before the prediction. Then, a wind power probability density forecasting method is proposed, based on cubic spline interpolation and support vector quantile regression (CSI-SVQR), which can better estimate the whole wind power probability density curve. However, the probability density prediction method can not acquire the optimal point prediction and interval prediction results at the same time. In order to analyze the uncertainty of wind power, the present study considers the prediction results from the perspective of probabilistic point prediction and interval prediction respectively. Three sets of real-world wind power data from Canada and China are used to validate the CSI-SVQR method. The results show that the proposed method not only efficiently eliminates the outliers of wind power but also provides the probability density function, offering a complete description of wind power generation fluctuation. Furthermore, more accurate point prediction and prediction interval (PI) can be obtained compared to existing methods. Wilcoxon signed rank test is used to verify that CSI can improve the performance of forecasting methods. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is an important component for self-driving. Traffic flow is closely related to population distribution, and the traffic flow is not only related to the absolute number of human population but also to their concerns and interests. Accurate spatio-temporal web traffic flow prediction is critical in many applications, such as bandwidth allocation, anomaly detection, congestion control and admission control. Most existing traffic flow prediction methods use models based on time-series analysis and remain inadequate for many real-world applications. Web traffic flow is found to be strongly associated with the spatio-temporal distribution of the population. Increasingly, it is critical to understand and make decisions based on the relationship between population patterns and web traffic flow patterns. It has been proven that different people have different responses to web events. Due to the complexity of spatial data structures and the huge volume of web traffic flow log data, it is difficult to routinely find the relationship between web events and population distributions without an appropriate processing framework. In this paper, we propose an innovative framework named GeoTrafficPredict to support the accurate spatio-temporal prediction of web traffic flow. GeoTrafficPredict provides a machine learning platform to learn the spatio-temporal pattern of traffic flow and use the pattern to predict the trend in both spatial and temporal dimension. Also, GeoTrafficPredict provide data aggregation portal and cloud-based computation function. GeoTrafficPredict deploys a series of computational images in a cloud computing environment, and the implementation on China's CSTNET illustrates the performance of our platform. [ABSTRACT FROM AUTHOR]

As a successful business model, "in-app purchase" has been adopted by massive applications (Apps) gradually. Users can purchase various virtual goods in different kinds of Apps, such as the license to download movies or songs. In-app purchase helps App operators gain huge income, and meanwhile provides users with flexibility in using Apps. Recently, iOS Apps have suffered the attack of fraudulent purchase. Attackers leverage the vulnerabilities in iOS payment system to purchase virtual goods at zero or low cost. More seriously, unscrupulous attackers solicit customers publicly and provide purchasing services, which has caused huge financial loss to business entities. It becomes of great importance to detect the fraudulent in-app purchases in iOS Apps, and then take measures such as confiscating goods to minimize profit loss. In this paper, we propose a system called Payment-Guard to achieve this objective, which designs various features to characterize a purchase from four perspectives including App account behavior, device behavior, IP behavior and union behavior of (App account, device, IP), then conducts detection based on the features. We perform comprehensive experiments based on data collected from "Honor of Kings" App, which is one of the most famous MOBA games in China and allows players to recharge App accounts for virtual currency. Experimental results demonstrated that Payment-Guard can detect 92.2% malicious in-app purchases and with only 2% false positive rate. [ABSTRACT FROM AUTHOR]

This paper makes a prediction of Chinese stock index (CSI) future prices using fuzzy sets and multivariate fuzzy time series method. We select Chinese CSI 300 index futures as the research object. The fuzzy time series model combines the fuzzy theory and the time series theory, thus this model can solve the fuzzy data in stock index futures prices. This paper establishes a multivariate model and improves the accuracy of computation. By combing traditional fuzzy time series models and rough set method, we use fuzzy c-mean algorithm to make the data into discrete. Further more, we deal with the rules in mature modules of the rough set and then refine the rules using data mining algorithms. Finally, we use the CSI 300 index futures to test our model and make a prediction of the prices. [ABSTRACT FROM AUTHOR]

Modeling interpretable artificial intelligence (AI) for flood forecasting represents a serious challenge: both accuracy and interpretability are indispensable. Because of the uncertainty and nonlinearity of flood, existing hydrological solutions always achieve low prediction robustness while machine learning (ML) approaches neglect the physical interpretability of models. In this paper, we focus on the need for flood forecasting and propose an interpretable Spatio-Temporal Attention Long Short Term Memory model (STA-LSTM) based on LSTM and attention mechanism. We use dynamic attention mechanism and LSTM to build model, Max-Min method to normalize data, variable control method to select hyperparameters, and Adam algorithm to train the model. Emphasis is placed on the visualization and interpretation of attention weights. Experiment results on three small and medium basins in China suggest that the proposed STA-LSTM model outperforms Historical Average (HA), Fully Connected Network (FCN), Convolutional Neural Networks (CNN), Graph Convolutional Networks (GCN), original LSTM (LSTM), spatial attention LSTM (SA-LSTM), and temporal attention LSTM (TA-LSTM) in most cases. Visualization and interpretation of spatial and temporal attention weights reflect the reasonability of the proposed attention-based model. [ABSTRACT FROM AUTHOR]

• Introduce the state-of-art temporal fusion transformers (TFT) into economic system forecasting based on the high compatibility between deep learning and the nonlinear characteristics of socio-economic systems. • Propose a scientific economic system forecasting framework on the foundation of the extended gross final product (GFP). • Build a four-dimensional evaluation system including numerical error, direction accuracy, timing error and turning point accuracy. • Empirically forecast the monthly output indicators of the Chinese macroeconomic system. • TFT results have obvious advantages over benchmark models including ARIMA, BP, LSTM, TCN and N-BEATS through comparison analysis. Although helpful in reducing the uncertainty associated with economic activities, economic forecasting often suffers from low accuracy. Recognizing the high compatibility between deep learning and the nonlinear characteristics of socioeconomic systems, in this paper, we introduce state-of-the-art temporal fusion transformers (TFTs) into the field of economic system forecasting and predict the performance of the Chinese macroeconomic system. Based on an extended analysis of gross final product (GFP) and the intertemporal dynamic relationship between demand-side indicators and output indicators, we establish a scientific economic forecasting framework. To summarize the forecasting characteristics of the TFT algorithm, we compare its one-step and three-step modeling effects in forecasting output indicators with a series of representative benchmark models. According to our proposed four-dimensional evaluation system, the forecasts for China's macroeconomic system provided by the TFT model have obvious advantages in terms of overall stability, forecasting efficiency, reduction of numerical and timing errors, direction accuracy, and turning point accuracy. The forecast results show that China's economy faces a risk of slowing growth in the post-pandemic period. [ABSTRACT FROM AUTHOR]

Highlights • A succinct and efficient Opposition-Based Learning strategy is inherited in the algorithm. • A novel efficient strategy called Global Grid Ranking is proposed. • A charming multi-objective optimization algorithm called MOWOA is demonstrated. Abstract Nature-inspired computing has attracted a lot of research effort especially for addressing real-world multi-objective optimization problem (MOP). This paper proposes a new nature-inspired optimization algorithm which is named opposition-based multi-objective whale optimization algorithm with global grid ranking (MOWOA). The proposed approach utilizes several parts to enhance the performance in optimization. First, the efficient evolution process is inherited from the single objective whale optimization algorithm(WOA). Second, opposition-based learning(OBL) is applied into the algorithm. Meanwhile, a novel mechanism called global grid ranking(GGR) which is inspired by grid mechanism has been incorporated into the proposed algorithm. To show the significance of the proposed algorithm, MOWOA is tested on a diverse set of benchmark with a series of well-known evolutionary algorithms and the influence of each individual strategy is also verified through 14 benchmarks. Moreover, the new proposed algorithm is also applied to the simple data clustering problem and a real-world water optimization problem in China. The results demonstrate that MOWOA is not only an algorithm with well performance for bench-mark problems but also expected to have a more wide application in real-world engineering problems. [ABSTRACT FROM AUTHOR]

With rapid development of financial services and products, credit risk assessment has recently gained considerable attention in the field of financial risk management. In this paper, an improved credit risk assessment approach is presented. Based on the credit data from China Banking Regulatory Commission (CBRC), a multi-dimensional and multi-level credit risk indicator system is constructed. In particular, we present an improved sequential minimal optimization (SMO) learning algorithm, named four-variable SMO (FV-SMO), for credit risk classification model. At each iteration, it jointly selects four variables into the working set and an theorem is proposed to guarantee the analytical solution of sub-problem. The assessment is made on China credit dataset and two benchmark credit datasets from UCI database and CD-ROM database. Experimental results demonstrate FV-SMO is competitive in saving the computational cost and outperforms other five state-of-the-art classification methods in credit risk assessment accuracy. [ABSTRACT FROM AUTHOR]

Wechat has become a popular way in China for users to connect and share information with their friends. In particular, Wechat Friends Circle is a popular platform for users to express themselves. It is useful in many real applications, such as personalized recommendations, to calculate the relationship strength between Friends Circle users. However, most existed researches do not apply to the Wechat Friends Circle due to the uniqueness that users can only see a common friend's comments or point praise, which is quite different from the general social network. To meet this need, in this paper we propose a general framework to measure the relationship strengths between different users in Wechat Friends Circle, considering not only the similarity of users’ profile information, but also the interaction between users. We conduct a set of experiments on Friends Circle dataset, from which we learn that the proposed framework is efficient and promising on improving the performances of relationship strength calculation. [ABSTRACT FROM AUTHOR]

The world׳s urban population growth and economic development have led to the reshaping of metropolitan space layouts among residential, employment and shopping locations, generating growing mismatch between travel demand and transport services. A reliable method to accurately analyze mobility demand and underlying transport network systems and to identify areas with serious mismatch problems is important for the design of effective policy measures. In this paper, we make use of the wide deployment of GPS devices in vehicles in many cities today, to develop such a method. This approach is developed using GPS data collected from all taxis operating in the Chinese city of Harbin between July and September in 2013. It consists of four major steps. First, city-wide mobility patterns are modeled based on GPS trajectories. This model captures a set of key traffic characteristics for each pair of regions in the entire urban network, including travel demand, travel speed and route directness of travel paths. From this model, a set of indicators is then built to measure the road transport performance between the regions, and the areas with serious mismatch problems are subsequently pinpointed. Finally, the identified problematic regions are further examined and specific transport problems are analyzed. By applying the proposed method to the city of Harbin, the potential and effectiveness of this method are demonstrated. Moreover, with more and more urban vehicles being equipped with GPS devices, the designed method can be easily transferred to other cities, thus paving a way for the adoption of the presented approach for an up-to-date and spatial-temporal sensitive road network analysis approach that supports the establishment of a more sustainable urban transport system. [ABSTRACT FROM AUTHOR]

In this paper, a semantic description method based on improved WM algorithm is proposed to characterize the facial features of larger Chinese ethnic groups. We firstly utilize the face landmarking technique to extract facial feature points automatically. Geometric features are defined with these detected landmarks, including distances, perimeters and areas. Then the WM method is improved to generate linguistic rule from facial geometric feature data, which implements semantic description for multi-ethnic facial characteristics. Finally, a case study of learning ethnicity from face with proposed method is investigated in CEFD database. The experiment results indicate that the linguistic rule base obtained by method is competitive in ethnicity recognition compared with method Naive Bayes, C4.5, Decision Table, Random Forest, Adaboost and Logistic regression in terms of accuracy and interpretability. [ABSTRACT FROM AUTHOR]

In order to effectively manage the use of existing infrastructures and prevent the emergency caused by the large gathered crowd, the short-term passenger flow forecasting technology becomes more and more significant in the field of intelligent transportation system. However, there are few studies discussing how to predict different kinds of passenger flows in the subway system. In this paper, a novel hybrid model Wavelet-SVM is proposed, and it combines the complementary advantages of Wavelet and SVM models, and meanwhile overcomes their shortcomings respectively. The Wavelet-SVM forecasting approach consists of three important stages. The first stage decomposes the passenger flow data into different high frequency and low frequency series by wavelet. During the prediction stage, the SVM method is applied to learn and predict the corresponding high frequency and low frequency series. In the last stage, the diverse predicted sequences are reconstructed by wavelet. The experimental results show that the approach not only has the best forecasting performance compared with the state-of-the-art techniques but also appears to be the most promising and robust based on the historical passenger flow data in Beijing subway system and several standard evaluation measures. [ABSTRACT FROM AUTHOR]

In recent years, multi-source clustering has received a significant amount of attention. Several multi-source clustering methods have been developed from different perspectives. In this paper, aiming at addressing the problem of Chinese–Tibetan bilingual document clustering, a novel bilingual clustering scheme is proposed, which can well capture both the intralingua document structures and interlingua document relations. The proposed scheme consists of three major phases. Firstly, to properly combine the feature structures of documents in different languages, a bilingual graph is constructed. In the second phase, two bilingual similarity matrices are computed based on the random walk performed in the bilingual graph. Finally, the similarity based clustering methods are performed on the two bilingual similarity matrices so as to generate cluster structures for documents in each language respectively, which lead to the corresponding bilingual clustering methods. Extensive experiments conducted on two Chinese–Tibetan bilingual document sets have confirmed the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Abstract: The recent years have witnessed a growing interest in two advanced strategies to cope with the data clustering problem, namely, clustering ensembles and multi-objective clustering. In this paper, we present a genetic programming based approach that can be considered as a hybrid of these strategies, thereby allowing that different hierarchical clustering ensembles be simultaneously evolved taking into account complementary validity indices. Results of computational experiments conducted with artificial and real datasets indicate that, in most of the cases, at least one of the Pareto optimal partitions returned by the proposed approach compares favorably or go in par with the consensual partitions yielded by two well-known clustering ensemble methods in terms of clustering quality, as gauged by the corrected Rand index. [ABSTRACT FROM AUTHOR]

Abstract: This paper compares the predictive performance of ARIMA, artificial neural network and the linear combination models for forecasting wheat price in Chinese market. Empirical results show that the combined model can improve the forecasting performance significantly in contrast with its counterparts in terms of the error evaluation measurements. However, as far as turning points and profit criterions are concerned, the ANN model is best as well as at capturing a significant number of turning points. The results are conflicting when implementing dissimilar forecasting criteria (the quantitative and the turning points measurements) to evaluate the performance of three models. The ANN model is overall the best model, and can be used as an alternative method to model Chinese future food grain price. [Copyright &y& Elsevier]