High dropout rate of MOOC is criticized while a dramatically increasing number of learners are appealed to these online learning platforms. Various works have been done on analysis and prediction of dropout. Machine learning techniques are widely applied to this field. However, a single classifier may not always perform reliable for predictions. In this work, we study dropout prediction for MOOC. A technique is proposed to predict dropouts using learning activity information of learners. We applied a two-layer cascading classifier with a combination of three different machine learning classifiers - Random Forest (RF), Support Vector Machine (SVM), and MultiNomial Logistic Regression (MLR) for prediction. Experimental results indicate that the technique is promising in predicting dropouts with achieving 97% precision.

The object detection algorithm is a key component for the autonomous operation of unmanned surface vehicles (USVs). However, owing to complex marine conditions, it is difficult to obtain large-scale, fully labeled surface object datasets. Shipborne sensors are often susceptible to external interference and have unsatisfying performance, compromising the results of traditional object detection tasks. In this paper, a few-shot surface object detection method is proposed based on multimodal sensor systems for USVs. The multi-modal sensors were used for three-dimensional object detection, and the ability of USVs to detect moving objects was enhanced, realizing metric learning-based few-shot object detection for USVs. Compared with conventional methods, the proposed method enhanced the classification results of few-shot tasks. The proposed approach achieves relatively better performance in three sampled sets of well-known datasets, i.e., 2%, 10%, 5% on average precision (AP) and 28%, 24%, 24% on average orientation similarity (AOS). Therefore, this study can be potentially used for various applications where the number of labeled data is not enough to acquire a compromising result.

The appearance of cloud computing has provided an efficient and economical way for users to receive computing resources with a pay-as-you-go model from cloud service provider. Cloud computing has attracted wide attention in both academia and industrial application. However, with the widely deploying of large-scale data center, high energy consumption becomes one of the key challenges and difficulties that restricting the development of cloud computing. Due to the limitations and dynamics of resources, how to map the cloud tasks to the resources aiming at decreasing the energy consumption has remained an essential issue. In this paper, we have made some improvements to the DeepJS and present DeepEnergyJS to minimize energy consumption for data center that receives enormous numbers of tasks arrive dynamically. We firstly introduce a power model to estimate the power of data center of the moment, then design a reward function that reflects the goal of optimizing power consumption and implement the simulation experiment on independent workloads to verity its availability of reducing the energy consumption. Finally, DeepJS considered only the independent workloads, we model inter-task dependencies in jobs and then conduct experiments in a similar way with independent workloads to prove that DeepEnergyJS can be extended for workloads that with dependencies which make it more functional. What's more, our experiments are based on different configured servers while the severs used in DeepJS are all the same, which make our study closer to the real cloud computing environment. The experiments results show that DeepEnergyJS can significantly outperforms three baselines in both independent workloads and workloads with dependencies.

In this work, we studied time management behavior, performance of students, and their association in online learning. We propose three novel time-related features, i.e., video-watching frequency, video-watching interval and learning efficiency. Leveraging these features, we train classifiers for identifying study habits of students and assessing the impact of behavior on performance. Experimental results on dataset from our online learning platform demonstrate that the proposed features are effective for learning behavior description. Comprehensive experiments show that our method can get promising performance.

Digital Ocean Construction has become a national strategy, the development of which is restricted by traditional IPv4 network. In this paper, IPv6 transition technology is researched to solve the current digital ocean network problems, and an IPv6 transition mechanism is proposed. The work of collecting data with IPv6 sensor network in the digital ocean by deploying border gateway devices which support 6LoWPAN and IPv6 network has been done. The border gateway devices can communicate with digital ocean IPv6 datacenters over an IPv4 network by using 6rd tunnel technology. With dual stack equipment, the digital ocean IPv6 datacenters can be accessed by both IPv4 and IPv6 networks. This transition mechanism satisfies the network demands for research and development of digital ocean.

In view of the massive data transmission and storage of various types of ocean data, and the limitations of IPv4 transmission protocols on marine projects, we propose a digital ocean cloud platform architecture based on IPv6 intelligent gateway, which utilizes an unlimited number of address resource pools of IPv6. The characteristics of efficient and secure data dissemination combined with cloud computing service technology, based on distributed storage technology, provide a safe and reliable storage environment for marine sensor data, ocean images and text, and ocean data through cloud computing technology and ocean data can be aggregated, classified and processed and through cloud computing technology can deepen the value of ocean data.