Morphodynamic modeling and causes of closure of My A inlet

Morphodynamics and sediment transport of the My A inlet in the low flow season are modeled using Delft3D. The simulation model takes into account the forcing of waves, tides and river flows. Model outputs of sediment transport and morphological changes of allow analysing the mechanism and cause of inlet closure. The analysis shows that longshore sediment is accreted on the northern side of the inlet both on the ebb tidal delta and along the north coast, but onshore sediment transport by wave reworking is the main process to close the inlet

An experiment in integrating sentiment features for tech stock prediction in Twitter

Economic analysis indicates a relationship between consumer sentiment and stock price movements. In this study we harness features from Twitter messages to capture public mood related to four Tech companies for predicting the daily up and down price movements of these companies’ NASDAQ stocks. We propose a novel model combining features namely positive and negative sentiment, consumer confidence in the product with respect to ‘bullish’ or ‘bearish’ lexicon and three previous stock market movement days. The features are employed in a Decision Tree classifier using cross-fold validation to yield accuracies of 82.93%,80.49%, 75.61% and 75.00% in predicting the daily up and down changes of Apple (AAPL), Google (GOOG), Microsoft (MSFT) and Amazon (AMZN) stocks respectively in a 41 market day sample

VEGAS: a variable length-based genetic algorithm for ensemble selection in deep ensemble learning.

In this study, we introduce an ensemble selection method for deep ensemble systems called VEGAS. The deep ensemble models include multiple layers of the ensemble of classifiers (EoC). At each layer, we train the EoC and generates training data for the next layer by concatenating the predictions for training observations and the original training data. The predictions of the classifiers in the last layer are combined by a combining method to obtain the final collaborated prediction. We further improve the prediction accuracy of a deep ensemble model by searching for its optimal configuration, i.e., the optimal set of classifiers in each layer. The optimal configuration is obtained using the Variable-Length Genetic Algorithm (VLGA) to maximize the prediction accuracy of the deep ensemble model on the validation set. We developed three operators of VLGA: roulette wheel selection for breeding, a chunk-based crossover based on the number of classifiers to generate new offsprings, and multiple random points-based mutation on each offspring. The experiments on 20 datasets show that VEGAS outperforms selected benchmark algorithms, including two well-known ensemble methods (Random Forest and XgBoost) and three deep learning methods (Multiple Layer Perceptron, gcForest, and MULES)

An efficient approach to measure the difficulty degree of practical programming exercises based on student performances

oai:ojs.www.rev-jec.org:article/282This study examines the generality of easy to hard practice questions in programming subjects. One of the most important contributions is to propose four new formulas for determining the difficulty degree of questions. These formulas aim to describe different aspects of difficulty degree from the learner's perspective instead of the instructor's subjective opinions. Then, we used clustering technique to group the questions into three easy, medium and difficult degrees. The results will be the baseline to consider the generality of the exercise sets according to each topic. The proposed solution is then tested on the data set that includes the results of the two subjects: Programming Fundamentals, Data Structures and Algorithms from Ho Chi Minh City University of Technology. The most important result is to suggest the instructors complete various degrees according to each topic for better evaluating student's performance

EVALUATION OF SOLAR RADIATION ESTIMATED FROM HIMAWARI-8 SATELLITE OVER VIETNAM REGION

The development of Solar energy system is growing rapidly in Vietnam in recent years by encouragement of the Government in renewable energy. Requirement for accurate knowledge of the solar radiation reaching the surface is increasingly important in the successful deployment of Solar photovoltaic plants. However, measurements of different components of solar resources including direct normal irradiance (DNI) and global horizontal irradiance (GHI) are limited to few stations over whole country. Satellite imagery provides an ability to monitor the surface radiation over large areas at high spatial and temporal resolution as alternatives at low cost. Observations from the new Japanese geostationary satellite Himawari-8 produce imagery covering Asia-Pacific region, permitting estimation of GHI and DNI over Vietnam at 10-minute temporal resolution. However, accurate comparisons with ground observations are essential to assess their uncertainty. In this study, we evaluated the Himawari-8 radiation product AMATERASS provided by JST/CREST TEEDDA using observations recorded at 5 stations in different regions of Vietnam. The result shows good agreement between satellite estimation and observed data with high correlation of range 0.92-0.94, but better in clear-sky episodes.Because of AMATERASS outperform, we used it for validating ERA-Interim reanalysis in the spatial scale. The comparison was made dividedly for 7 climate zones and 4 seasons. The conclusion is that ERA-Interim is also well associated with satellite-based estimates in seasonal trend for all season, but in average the reanalysis has negative bias towards satellite estimates. This underestimation is more pronounced in the months of JJA and SON periods and in the north part of Vietnam because of unpredicted cloud in the ERA reanalysis

Heterogeneous ensemble selection for evolving data streams.

Ensemble learning has been widely applied to both batch data classification and streaming data classification. For the latter setting, most existing ensemble systems are homogenous, which means they are generated from only one type of learning model. In contrast, by combining several types of different learning models, a heterogeneous ensemble system can achieve greater diversity among its members, which helps to improve its performance. Although heterogeneous ensemble systems have achieved many successes in the batch classification setting, it is not trivial to extend them directly to the data stream setting. In this study, we propose a novel HEterogeneous Ensemble Selection (HEES) method, which dynamically selects an appropriate subset of base classifiers to predict data under the stream setting. We are inspired by the observation that a well-chosen subset of good base classifiers may outperform the whole ensemble system. Here, we define a good candidate as one that expresses not only high predictive performance but also high confidence in its prediction. Our selection process is thus divided into two sub-processes: accurate-candidate selection and confident-candidate selection. We define an accurate candidate in the stream context as a base classifier with high accuracy over the current concept, while a confident candidate as one with a confidence score higher than a certain threshold. In the first sub-process, we employ the prequential accuracy to estimate the performance of a base classifier at a specific time, while in the latter sub-process, we propose a new measure to quantify the predictive confidence and provide a method to learn the threshold incrementally. The final ensemble is formed by taking the intersection of the sets of confident classifiers and accurate classifiers. Experiments on a wide range of data streams show that the proposed method achieves competitive performance with lower running time in comparison to the state-of-the-art online ensemble methods

DEFEG: deep ensemble with weighted feature generation.

With the significant breakthrough of Deep Neural Networks in recent years, multi-layer architecture has influenced other sub-fields of machine learning including ensemble learning. In 2017, Zhou and Feng introduced a deep random forest called gcForest that involves several layers of Random Forest-based classifiers. Although gcForest has outperformed several benchmark algorithms on specific datasets in terms of classification accuracy and model complexity, its input features do not ensure better performance when going deeply through layer-by-layer architecture. We address this limitation by introducing a deep ensemble model with a novel feature generation module. Unlike gcForest where the original features are concatenated to the outputs of classifiers to generate the input features for the subsequent layer, we integrate weights on the classifiers’ outputs as augmented features to grow the deep model. The usage of weights in the feature generation process can adjust the input data of each layer, leading the better results for the deep model. We encode the weights using variable-length encoding and develop a variable-length Particle Swarm Optimisation method to search for the optimal values of the weights by maximizing the classification accuracy on the validation data. Experiments on a number of UCI datasets confirm the benefit of the proposed method compared to some well-known benchmark algorithms

MỘT SỐ KẾT QUẢ NGHIÊN CỨU BƯỚC ĐẦU VỀ ĐỘNG LỰC TRẦM TÍCH LƠ LỬNG TRONG MÙA LŨ TẠI VÙNG BIỂN VEN BỜ CỬA SÔNG HẬU

Land-ocean interactions in the coastal zone are severely influenced by tidal processces. In regions of high sediment discharge like the coast of Hau river estuary, these processes are even more significant when we analyse data in flood season (September) - which belongs to Agreement of Cooperation in Science and Technology between Vietnam and US (2013-2015) and project VAST-DLT.06/15-16 (2015-2016). Our goal is to investigate the sedimentation  processes. Additionally, we investigated the influence of the tidal currents in relation to the suspended sediment. Salinity (PSU - Practical salinity unit), suspended sediment concentration (NTU - Nephelometric Turbidity Units) were measured by Compac-CTD (Depth temperature conductivity chlorophyll turbidity) and OBS-3A (Turbidity and Temperature Monitoring System) instruments. The results show that the suspended sediment concentration (SSC) correlate with tidal current velocities. The tidal current velocities are up to 60 cm/s near the surface and 40 cm/s near the bottom, increasing SSC in the water column at bottom layer (24 NTU) and decreasing SSC at surface layer (8 NTU). Processes of sediment transport and deposition in flood tide in flood season are influenced by tidal currents more significantly than those in dry season. This leads to an asymmetry of the tidal ellipses and the different deposition between seasons. The analytical results imply the influence of tide and tidal currents on SSC in the coast of Hau river estuary, hence, the spread, sea water and fresh water mixing processes have difference during tidal phases and seasons.Các mối tương tác biển - đất liền trong vùng ven bờ bị chi phối chủ yếu bởi các quá trình thủy động lực như sóng, dòng chảy, lưu lượng nước sông, thủy triều trong đó thủy triều đóng vai trò quan trọng. Tại các khu vực có lưu lượng phù sa lớn như vùng ven biển sông Hậu, các quá trình này càng trở nên rõ rệt khi phân tích từ số liệu khảo sát trong thời kỳ mùa lũ (tháng 9) thuộc chương trình hợp tác khoa học và công nghệ giữa Việt Nam và Hoa Kỳ (2013 - 2015) và đề tài độc lập mã số VAST-ĐLT.06/15-16 (2015 - 2016). Trong chuyến khảo sát này, mục đích là điều tra sự lắng đọng và phân bố theo không gian, thời gian của hàm lượng trầm tích lơ lửng. Ngoài ra, chúng tôi còn khảo sát ảnh hưởng của dòng triều trong mối tương quan với hàm lượng trầm tích lơ lửng. Độ muối (đơn vị PSU - Practical Salinity Unit), hàm lượng trầm tích lơ lửng (đơn vị đo NTU - Nephelometric Turbidity Units) được đo bằng thiết bị Compac-CTD (Depth temperature conductivity chlorophyll turbidity), và thiết bị đo độ đục OBS-3A (Turbidity and Temperature Monitoring System). Kết quả nghiên cứu cho thấy hàm lượng trầm tích lơ lửng tương quan với vận tốc dòng chảy. Tốc độ dòng chảy khi triều lên đến 60 cm/s ở lớp mặt và 40 cm/s ở đáy tạo nên sự tăng nồng độ trầm tích lơ lửng trong cột nước ở tầng đáy 24 NTU và 8 NTU tại tầng mặt. Trong pha triều lên, quá trình vận chuyển và lắng đọng trầm tích lơ lửng bị chi phối bởi dòng triều dài hơn so với mùa khô. Điều này cho thấy sự bất đối xứng của elip thủy triều và dẫn đến sự lắng đọng trầm tích lơ lửng trong các mùa là khác nhau. Từ kết quả phân tích có thể thấy vùng biển ven bờ cửa sông Hậu, hàm lượng trầm tích lơ lửng chịu sự chi phối bởi dòng chảy triều và thủy triều là rất lớn, do đó quá trình lan truyền, xáo trộn nước sông và biển có sự khác biệt đáng kể trong các điều kiện triều và điều kiện mùa