With the impact of COVID-19 on education, online education is booming, enabling learners to access various courses. However, due to the overload of courses and redundant information, it is challenging for users to quickly locate courses they are interested in when faced with a massive number of courses. To solve this problem, we propose a deep course recommendation model with multimodal feature extraction based on the Long- and Short-Term Memory network (LSTM) and Attention mechanism. The model uses course video, audio, and title and introduction for multimodal fusion. To build a complete learner portrait, user demographic information, explicit and implicit feedback data were added. We conducted extensive and exhaustive experiments based on real datasets, and the results show that the AUC obtained a score of 79.89%, which is significantly higher than similar algorithms and can provide users with more accurate recommendation results in course recommendation scenarios.

The acknowledgment (ACK) compression makes the available bandwidth estimation in bottleneck bandwidth and round‐trip propagation time (BBR) inaccurate which results in the high queuing delay under low packet loss rate environments. The additional queuing delay affects the user experience of interactive applications like Skype. To solve the above‐mentioned issue, this Letter presents a novel method, named calibrating bandwidth estimation (CBE), to calibrate the bandwidth estimation. The CBE can detect the implausible ACK rates accurately and the extensive simulation prove the effectiveness of CBE.

Surface defect segmentation is the basis of industrial production, where pixel-level defect prediction plays a vital role in many applications. As a new trend, few-shot defect detection aims to recognize defects by combining information from a few defect samples. However, many traditional few-shot methods do not consider patch-level correlations between input images and memory banks. This work introduces a novel representation framework consisting hierarchical normal and defect memory banks for few-shot surface defect detection. We propose an adaptive feature bank updating scheme that dynamically integrates frequent features while discarding rare ones. We design a fine-grain feature replacement module rooted in a matching attention mechanism to highlight ambiguous regions for input images, and use fused features to identify overall defects. On benchmark data sets, our strategy can obtain in-depth insights from multiple scales, which showcase superiority in generalization for unidentified anomalies better than others in experiments. The experimental results for the 10-shot scenarios demonstrate that the proposed method achieves an average pixel AUC of 88.6%, 93.7%, and 98.7% on three benchmark data sets, respectively, and with more genuine and pseudo anomalous samples, the proposed approach can achieve 98.5% and 95.5% in image AUC and pixel F1 score respectively, outperforming the state-of-the-art with 1.2% and 24% in each metric.

It is crucial to ensure the privacy and authenticity of the owner’s information in car insurance claims. However, the current traditional car insurance claims scenario suffers from inefficiency, complex service, unreliable data, and data leakage. Therefore, considering the privacy and sensitivity of insurance information and car owner data, we can use blockchain, smart contracts, and zero-knowledge proof technology to improve the current problems. This paper proposes a novel car insurance claim scheme based on smart contracts, blockchain, and zero-knowledge proof. Our scheme focuses on preserving privacy in the car insurance authorization and claim process. We design a private smart contract for the creation and revocation of car insurance and public smart contract for the authorization and validation of car insurance. By using ZoKrates, generating zero-knowledge proofs off chain and verifying the proofs on chain reduces the amount of data storage and computation on chain and provides privacy protection for sensitive information. Experimental results confirm the efficacy of our scheme in terms of security and performance.