Edge detection is a boundary-based segmentation method to extract important information from an image, and it is a research hotspot in the fields of computer vision and image analysis. Especially feature extraction is also the basis of image segmentation, target detection, and recognition. In recent years, in order to solve the problems of edge detection refinement and low detection accuracy, the industry has proposed multiscale fusion wavelet edge, spectral clustering, network reconstruction, and other edge detection algorithms based on deep learning. In order to enable researchers to understand the current research status of edge detection, this paper first introduces the classic algorithm of traditional edge detection, compare with advantages and disadvantages of different edge detection algorithms. Then, it summarizes the main edge detection methods based on deep learning in recent years and classifies and compares them according to the implementation technology. Finally, it shows the development direction of edge detection algorithm research.

Recently, machine learning has become popular in various fields like healthcare, smart transportation, network, and big data. However, the labelled training dataset, which is one of the most core of machine learning, cannot meet the requirements of quantity, quality, and diversity due to the limitation of data sources. Crowdsourcing systems based on mobile computing seem to address the bottlenecks faced by machine learning due to their unique advantages; i.e., crowdsourcing can make professional and nonprofessional participate in the collection and annotation process, which can greatly improve the quantity of the training dataset. Additionally, distributed blockchain technology can be embedded into crowdsourcing systems to make it transparent, secure, traceable, and decentralized. Moreover, truth discovery algorithm can improve the accuracy of annotation. Reasonable incentive mechanism will attract many workers to provide plenty of dataset. In this paper, we review studies applying mobile crowdsourcing to training dataset collection and annotation. In addition, after reviewing researches on blockchain or incentive mechanism, we propose a new possible combination of machine learning and crowdsourcing systems.

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Applying Artificial Intelligence to Chinese language translation in CL (Computational Linguistics) is of practical significance for economic boosts and cultural exchanges. In the present work, the Bi-directional Long Short-Term Memory (BiLSTM) network is employed to extract Chinese text features regarding the overlapping semantic roles in Chinese language translation and hard-to-converge training of high-dimensional text word vectors in text classification during translation. Besides, the AlexNet is optimized to extract the local features of the text and meanwhile update and learn network parameters in the deep network. Then, the attention mechanism is introduced to build a forecasting algorithm of Chinese language translation based on BiLSTM and improved AlexNet. At last, the forecasting algorithm is simulated to validate its performance. Some state-of-art algorithms are selected for a comparative experiment, including Long Short-Term Memory, Regions with Convolutional Neural Networks features, AlexNet, and Support Vector Machine. Results demonstrate that the forecasting algorithm proposed here can achieve a feature identification accuracy of 90.55%, at least an improvement of 4.24% over other algorithms. Besides, it provides an Area Under the Curve of above 90%, a training duration of about 54.21s, and a test duration of about 19.07s. Regarding the performance of Chinese language translation, the algorithm proposed here provides a Bilingual Evaluation Understudy (BLEU) value of 28.21 on the training set, with a Performance Gain Ratio (PGR) reaching 111.55%; on the test set, its BLEU reaches 40.45, with a PGR of 129.80%. Hence, this forecasting algorithm is notably superior to other algorithms, which can enhance the machine translation performance. Through experiments, the Chinese language translation algorithm constructed here improves translation performance while ensuring a high correct identification rate, providing experimental references for the later intelligent development of Chinese language translation in CL.