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One of education's primary goals is to cultivate citizens who want to contribute to society. However, surprisingly little research has been conducted on how students' desire to contribute to society is related to crucial learning-related outcomes. The aim of this study was to examine how the desire to contribute to one's society, which we call societal motivation for shorthand, is associated with optimal learning-related outcomes such as self-regulated learning, deep learning, and achievement. The sample included 8,773 secondary school students from Hong Kong. The mean age of the students was 13.28 (SD = 1.09) years old. Students were asked to respond to self-reported surveys and answer achievement tests across two-time points, one year apart. Structural equation modeling was used to analyze the data. Results indicated that Time 1 societal motivation positively predicted Time 2 self-regulated learning and deep learning. These associations held despite controlling for auto-regressive effects and other relevant covariates such as Time 1 goals (mastery, performance, social, and extrinsic) and demographic variables. Our results showed that societal motivation is associated with optimal learning-related outcomes. This paper has theoretical implications for educational research by showing that societal motivation is an important yet neglected aspect of student motivation. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and deep learning techniques are playing an increasing role in astronomy to deal with the data avalanche. Here we describe an application for finding resolved planetary nebulae (PNe) in crowded, wide-field, narrow-band Hα survey imagery in the Galactic plane, to test and facilitate more objective, reproducible, efficient and reliable trawls for them. PNe are important for studying the late-stage stellar evolution of low-mass to intermediate-mass stars. However, the confirmed ∼3800 Galactic PNe fall far short of the numbers expected. Traditional visual searching for resolved PNe is time-consuming because of the large data size and areal coverage of modern astronomical surveys. The training and validation data set of our algorithm was built with the INT Photometric Hα Survey (IPHAS) and true PNe from the Hong Kong/AAO/Strasbourg Hα (HASH) data base. Our algorithm correctly identified 444 PNe in the validation set of 454 PNe, with only 16 explicable 'false' positives, achieving a precision rate of 96.5 per cent and a recall rate of 97.8 per cent. After transfer learning, it was then applied to the VST Photometric Hα Survey of the Southern Galactic plane and bulge (VPHAS+), examining 979 out of 2284 survey fields, each covering 1° × 1°. It returned ∼20 000 detections, including 2637 known PNe and other kinds of catalogued non-PNe. A total of 815 new high-quality PNe candidates were found, 31 of which were selected as top-quality targets for optical spectroscopic follow-up. We found that 74 per cent of them are true, likely, and possible PNe. Representative preliminary confirmatory spectroscopy results are presented here to demonstrate the effectiveness of our techniques, with full details to be given in our forthcoming paper. [ABSTRACT FROM AUTHOR]

This paper develops a cost-effective sequential route recommender system to provide real-time routing recommendations for vacant taxis searching for the next passenger. We propose a prediction-and-optimization framework to recommend the searching route that maximizes the expected profit of the next successful passenger pickup based on the dynamic taxi demand-supply distribution. Specifically, this system features a deep learning-based predictor that dynamically predicts the passenger pickup probability on a road segment and a recursive searching algorithm that recommends the optimal searching route. The predictor integrates a graph convolution network (GCN) to capture the spatial distribution and a long short-term memory (LSTM) to capture the temporal dynamics of taxi demand and supply. The GCN-LSTM model can accurately predict the pickup probability on a road segment with the consideration of potential taxi oversupply. Then, the dynamic distribution of pickup probability is fed into the route optimization algorithm to recommend the optimal searching routes sequentially as route inquiries emerge in the system. The recursion tree-based route optimization algorithm can significantly reduce the computational time and provide the optimal routes within seconds for real-time implementation. Finally, extensive experiments using Beijing Taxi GPS data demonstrate the effectiveness and efficiency of the proposed recommender system. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the Hong Kong Research Grants Council [Grants CityU 21500220, CityU 11504322] and the National Natural Science Foundation of China [Grant 72201222]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0112) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0112). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

In Hong Kong, merchandise trade statistics are compiled based on the commodity information given on the trade declarations submitted by traders. Due to the complexity of the standardised commodity classification system (i.e. Hong Kong Harmonized System, or HKHS in short), there are often reporting errors, especially in the commodity codes and quantities. With around 20 million declarations received annually, the availability of this big data source motivates us to adopt deep learning techniques to detect the reporting errors. This paper proposes a mechanism consisting of three deep learning models for checking the commodity code, quantity and value, which offers an end-to-end solution to data quality assurance for declarations. The results show that the proposed mechanism could enhance the accuracy of error detection, which is conducive to improving the quality of trade statistics. With the use of text analytics techniques, the mechanism could fully utilise free-text commodity descriptions declared by traders to check the accuracy of the declared information comprehensively. It also overcomes some limitations of the traditional rule-based models. The whole study demonstrates the potential of using deep learning approach in quality assurance of existing statistical systems for official statistics. [ABSTRACT FROM AUTHOR]

Mapping of highly dynamic changes in land use and land cover (LULC) can be hindered by various cloudy conditions with optical satellite images. These conditions result in discontinuities in high-temporal-density LULC mapping. In this paper, we developed an integrated time series mapping method to enhance the LULC mapping accuracy and frequency in cloud-prone areas by incorporating spectral-indices-fused deep models and time series reconstruction techniques. The proposed method first reconstructed cloud-contaminated pixels through time series filtering, during which the cloud masks initialized by a deep model were refined and updated during the reconstruction process. Then, the reconstructed time series images were fed into a spectral-indices-fused deep model trained on samples collected worldwide for classification. Finally, post-classification processing, including spatio-temporal majority filtering and time series refinement considering land–water interactions, was conducted to enhance the LULC mapping accuracy and consistency. We applied the proposed method to the cloud- and rain-prone Pearl River Delta (i.e., Guangdong–Hong Kong–Macao Greater Bay Area, GBA) and used time series Sentinel-2 images as the experimental data. The proposed method enabled seamless LULC mapping at a temporal frequency of 2–5 days, and the production of 10 m resolution annual LULC products in the GBA. The assessment yielded a mean overall accuracy of 87.01% for annual mapping in the four consecutive years of 2019–2022 and outperformed existing mainstream LULC products, including ESA WorldCover (83.98%), Esri Land Cover (85.26%), and Google Dynamic World (85.06%). Our assessment also reveals significant variations in LULC mapping accuracies with different cloud masks, thus underscoring their critical role in time series LULC mapping. The proposed method has the potential to generate seamless and near real-time maps for other regions in the world by using deep models trained on datasets collected globally. This method can provide high-quality LULC data sets at different time intervals for various land and water dynamics in cloud- and rain-prone regions. Notwithstanding the difficulties of obtaining high-quality LULC maps in cloud-prone areas, this paper provides a novel approach for the mapping of LULC dynamics and the provision of reliable annual LULC products. • Novel LULC mapping via indices-fused deep models and time series reconstruction. • High-temporal-density 10 m seamless LULC mapping in cloudy and rainy areas (PRD). • Overall mapping accuracy yielded up to 87.01%, outperforming existing LULC products. • Potential to generate seamless near real-time maps and high-quality LULC data sets. [ABSTRACT FROM AUTHOR]

Rock classification provides vital information to geosciences and geological engineering practices. Reaping the benefits of the advances of computer vision-based deep learning artificial intelligence (AI) technology, this study aims to develop a next-generation convolutional neural network (CNN) to perform automatic rock classification. Two major challenging issues have been particularly addressed. First, most of the previous rock classifications are simply transfer learning of CNNs that are trained by life-like scenarios. Second, classifying rock types with similar textures leads to severe overfitting of CNNs. In this study, a novel CNN called HKUDES_Net is proposed and implemented to classify seven common Hong Kong rock types, namely fine-grained granite, medium-grained granite, coarse-grained granite, coarse ash tuff, fine ash tuff, feldsparphyric rhyolite, and granodiorite. With the aid of dynamic expansion, squeeze and excitation, and other strategies, HKUDES_Net can classify rock types with similar texture patterns/colors but different grain sizes. As compared with the other ten landmark CNNs and seven feature-based algorithms, HKUDES_Net has the best performance in precision (90.9%), recall (90.4%), and f1-score (90.5%). By implementing the alerting level, which restricts the training loss hovering above a small constant and prevents the validation loss from rising, the overfitting has been efficiently eliminated. The proposal and implementation of HKUDES_Net highlight the value of interdisciplinary research and will continuously pave the way for better coupling AI and geosciences. Highlights: A next-generation convolutional neural network (CNN) called HKUDES_Net is developed to perform automatic rock classification. HKUDES_Net can classify rock types with similar texture patterns/colors but different grain sizes. By implementing the alerting level in HKUDES_Net, the overfitting has been efficiently eliminated. Plain Language Summary: Rock classification provides vital information to geosciences and geological engineering practices. However, the traditional way of rock classification is to a certain degree experience-based. Computer vision-based artificial intelligence (AI), especially convolutional neural network (CNN), a sub-branch of AI, may help people automatically classify rock types with the hope to train AI models (CNNs), which can instantly and precisely perceive rock images similar to how human beings observe rocks. In other words, by putting a rock image into a CNN, the CNN will read the information from the image and tell people what rock type in the image is. This study develops and implements a next-generation CNN called HKUDES_Net to classify seven different rock types. Adopting different computational strategies, HKUDES_Net can classify rock types of different grain sizes with similar texture patterns/colors, which is challenging for other CNNs. As compared with the other landmark CNNs and machine learning algorithms, HKUDES_Net has much a better prediction accuracy performance. The proposal and implementation of HKUDES_Net exemplify an interdisciplinary research study, and we are confident that this paper will be instrumental to pave the way for further coupling AI and geosciences. [ABSTRACT FROM AUTHOR]

Travel time estimation plays an essential role in the high-granular traffic control and management of urban roads with distinct lane-changing conditions among lanes. However, little attention has been given to the estimation of distributions of travel times among different lanes and different vehicle types in addition to their expected values. This paper proposes a new method for estimating lane-based travel time distributions with consideration of different vehicle types through matching low-resolution vehicle video images taken by conventional traffic surveillance cameras. The vehicle type classification is based on vehicle sizes and deep learning features extracted by densely connected convolutional neural networks, and the vehicle re-identification is conducted through a lane-based bipartite graph matching technique. A case study is carried out on a congested urban road in Hong Kong. Results show that the proposed method performs well in estimating the lane-level travel time distributions by vehicle type which can be very helpful for various lane-based and vehicle type-specific traffic management schemes. [ABSTRACT FROM AUTHOR]

Short-term origin–destination (OD) flow prediction is vital for operations planning, control, and management in urban railway systems. While the entry and exit passenger demand prediction problem has been studied in various studies, the OD passenger flow prediction problem receives much less attention. One key challenge for short-term OD flow prediction is the partial observability of the OD flow information due to trips having not been completed at a certain time interval. This paper develops a novel deep learning architecture for the OD flow prediction in urban railway systems and examines various mechanisms for data representation and for dealing with partial information. The deep learning framework consists of three main components, including multiple LSTM networks with an attention mechanism capturing short/long-term temporal dependencies, a temporally shifted graph matrix for spatiotemporal correlations, and a reconstruction mechanism for partial OD flow observations. The model is validated using smart card data from Hong Kong's Mass Transit Railway (MTR) system and compared with state-of-the-art prediction models. Experiments are designed to examine the characteristics of the proposed approach and its various components. The results show the superior performance (accuracy and robustness) of the proposed model and also the importance of partial observations of OD flow information in improving prediction performance. In terms of data representation, predicting the deviation of OD flows performs consistently better than predicting OD flows directly. [ABSTRACT FROM AUTHOR]

This paper reports a new knowledge-driven engineered feature learning approach in response to the Global AI Challenge for Building E&M Facilities held by the Electrical and Mechanical Service Department (EMSD) of the Hong Kong SAR. The results were awarded with a Grand Prize by the competition organizer. A dynamically engineered multi-modal feature learning (DEMMFL) method is proposed for predicting the cooling load of two office buildings. The DEMMFL model is estimated with the Lasso-ridge regression and compared with other well-known methods such as the Lasso. The novel approach applies control system knowledge to engineer useful features and explore load patterns for multi-mode modeling. Deep learning methods including LSTM, GRU, and AutoGluon are implemented for automated machine learning and tested in parallel to compare the performance of the proposed model with existing methods. The proposed model is demonstrated to predict long-term cooling load most accurately using engineered features from weather information only. • A new dynamic feature engineering method for office building cooling load prediction. • Slow and fast dynamic features engineered from available weather data only. • Control-knowledge based feature to learn on-hours dynamic responses. • Multi-modal approach with eight day-type features to learn occupancy loads. • Deep recurrent learning and AutoML methods compared to the proposed method. [ABSTRACT FROM AUTHOR]

The dissemination of fake news on social media platforms is an issue of considerable interest, as it can be used to misinform people or lead them astray, which is particularly concerning when it comes to political events. The recent event of Hong Kong protests triggered an outburst of fake news posts that were identified on Twitter, which were then promptly removed and compiled into datasets to promote research. These datasets focusing on linguistic content were used in previous work to classify between tweets spreading fake and real news using traditional machine learning algorithms (Zervopoulos et al., in: IFIP international conference on artificial intelligence applications and innovations, Springer, Berlin, 2020). In this paper, the experimentation process on the previously constructed dataset is extended using deep learning algorithms along with a diverse set of input features, ranging from raw text to handcrafted features. Experiments showed that the deep learning algorithms outperformed the traditional approaches, reaching scores as high as 99.3% F1 Score, with the multilingual state-of-the-art model XLM-RoBERTa outperforming other algorithms using raw untranslated text. The combination of both traditional and deep learning algorithms allows for increased performance through the latter, while also gaining insight regarding tweet structure from the interpretability of the former. [ABSTRACT FROM AUTHOR]

Numerous news or event pictures are taken and shared on the internet every day that have abundant information worth being mined, but only a small fraction of them are geotagged. The visual content of the news image hints at clues of the geographical location because they are usually taken at the site of the incident, which provides a prerequisite for geo-localization. This paper proposes an automated pipeline based on deep learning for the geo-localization of news pictures in a large-scale urban environment using geotagged street view images as a reference dataset. The approach obtains location information by constructing an attention-based feature extraction network. Then, the image features are aggregated, and the candidate street view image results are retrieved by the selective matching kernel function. Finally, the coordinates of the news images are estimated by the kernel density prediction method. The pipeline is tested in the news pictures in Hong Kong. In the comparison experiments, the proposed pipeline shows stable performance and generalizability in the large-scale urban environment. In addition, the performance analysis of components in the pipeline shows the ability to recognize localization features of partial areas in pictures and the effectiveness of the proposed solution in news picture geo-localization. [ABSTRACT FROM AUTHOR]

Purpose: Our purpose was to develop a deep learning-based computed tomography (CT) perfusion mapping (DL-CTPM) method that synthesizes lung perfusion images from CT images.Methods and Materials: This paper presents a retrospective analysis of the pulmonary technetium-99m-labeled macroaggregated albumin single-photon emission CT (SPECT)/CT scans obtained from 73 patients at Queen Mary Hospital in Hong Kong in 2019. The left and right lung scans were separated to double the size of the data set to 146. A 3-dimensional attention residual neural network was constructed to extract textural features from the CT images and reconstruct corresponding functional images. Eighty-four samples were randomly selected for training and cross-validation, and the remaining 62 were used for model testing in terms of voxel-wise agreement and function-wise concordance. To assess the voxel-wise agreement, the Spearman's correlation coefficient (R) and structural similarity index measure between the images predicted by the DL-CTPM and the corresponding SPECT perfusion images were computed to assess the statistical and perceptual image similarities, respectively. To assess the function-wise concordance, the Dice similarity coefficient (DSC) was computed to determine the similarity of the low/high functional lung volumes.Results: The evaluation of the voxel-wise agreement showed a moderate-to-high voxel value correlation (0.6733 ± 0.1728) and high structural similarity (0.7635 ± 0.0697) between the SPECT and DL-CTPM predicted perfusions. The evaluation of the function-wise concordance obtained an average DSC value of 0.8183 ± 0.0752 for high-functional lungs (range, 0.5819-0.9255) and 0.6501 ± 0.1061 for low-functional lungs (range, 0.2405-0.8212). Ninety-four percent of the test cases demonstrated high concordance (DSC >0.7) between the high-functional volumes contoured from the predicted and ground-truth perfusions.Conclusions: We developed a novel DL-CTPM method for estimating perfusion-based lung functional images from the CT domain using a 3-dimensional attention residual neural network, which yielded moderate-to-high voxel-wise approximations of lung perfusion. To further contextualize these results toward future clinical application, a multi-institutional large-cohort study is warranted. [ABSTRACT FROM AUTHOR]

• A real-time flood simulation method based on cross-scale correlation has been proposed. • Super-resolution deep learning models can downscale flow depth and velocity. • The deep learning model can be trained with limited assumed rainfall scenarios. • The proposed method simulates pluvial flooding process under arbitrary rainstorms. • The analysis is 2690 times faster, with errors of 0.082 m for depth and 0.088 m/s for velocity. Real-time information on flooding extent, severity, and duration is necessary for effective metropolitan flood emergency management. Existing pluvial flood analysis methods are unable to simulate real-time regional flooding processes under spatiotemporally varying rainstorms. This paper presents a deep learning-enabled super-resolution hydrodynamic flood analysis method to simulate the real-time pluvial flooding process over a large area under spatiotemporally varying rainstorms. Compared with existing flood downscaling techniques, which are limited to flow depth, the proposed method produces high-resolution flow depth and velocity predictions, providing more comprehensive information for flood emergency management. The proposed method adopts a coarse-grid hydrodynamic model to generate a low-resolution flood map time series, which is subsequently converted to high-resolution flood maps by a deep learning model. The deep learning model can be trained using a limited number of assumed rainfall scenarios, which greatly reduces data preparation effort. The proposed method is applied to a complex terrain of 352 km2 in Hong Kong that covers both mountainous and urban areas. Results show that the proposed method simulates the spatiotemporal variations of flood depth and velocity with root mean square errors as low as 0.082 m and 0.088 m/s, respectively, and correlation coefficients of 0.962 and 0.921, respectively. The computation time for a 48-h rainfall event in the study area is less than 30 s, which is 2690 times faster than the direct fine-grid hydrodynamic analysis. The deep learning-enabled super-resolution hydrodynamic flood analysis method provides a promising computational tool for emergency flood risk management. [ABSTRACT FROM AUTHOR]

Public open space (POS) is essential to urban areas. Assessing them usually requires tedious approaches such as fieldwork and manual processes. Street View Imagery (SVI) and Computer Vision (CV) have been adopted in some urban environment research, bringing fine granularity and human perspective. However, limited aspects have been subject in these studies, and SVI and CV have not been used for holistic POS assessment. This research introduces a novel approach of employing them in conjunction with traditionally used geospatial and remote sensing data for automating POS assessment and doing so extensively. Indicators from both subjective and objective perspectives are developed, and CV algorithms are adopted for retrieving visual features. In a case study spanning 800 POS in Hong Kong and Singapore, a method is designed to predict both subjective and objective scores. The results demonstrate the perceptual models achieved acceptable to high accuracy scores, and suggest that SVI reflects different aspects of POS compared to previous approaches. The paper concludes that SVI can be adopted in POS assessment as a new instrument, extending their research scope to rarely considered off-road areas, and contributing with a new approach for the design and allocation of POS in urban planning. • Comprehensive assessment using street view imagery and computer vision • Uncovering the usability of rarely used street-level images beyond streetscapes • Developing and comparing objective and subjective perspectives for assessment • Combination of multi-sourced indicators reduces the bias of single data source. • Imagery adds unparalleled insights to traditionally used GIS or remote sensing data. [ABSTRACT FROM AUTHOR]