Running a large-scale Spiking Neural Network （SNN） on a distributed computing platform is one of the basic means to improve the level of brain-like computing intelligence. The difficulty lies in how to deploy the SNN to the corresponding number of computing nodes in order to make the overall system run with the best energy efficiency. To solve this problem， on the basis of NEural Simulation Tool-based （NEST-based） Workload Automatic Mapper for SNN （SWAM） proposed by others before， a workload automatic mapper for SNN， named SWAM2， based on precise communication modeling was proposed. In SWAM2， based on the NEST simulator， the communication part of the SNN workload was further accurately modeled； the quantization method of the parameters in the workload model was improved； the maximum network scale prediction method was designed. Experimental results on typical cases of SNN show that， the average prediction errors of SWAM2 were reduced by about 12.62 and 5.15 percentage points respectively compared with those of SWAM in workload communication and computing time prediction. When predicting the optimal mapping of the workload， the average accuracy of SWAM2 reached 97.55%， which was 13.13 percentage points higher than that of SWAM. SWAM2 avoids the process of manual trial and error by automatically predicting the optimal deployment/mapping of SNN workload on computing platform.

Aiming at the problem of category vocabulary noise and label noise in weakly-supervised text classification tasks， a weakly-supervised text classification model with label semantic enhancement was proposed. Firstly， the category vocabulary was denoised on the basis of the contextual semantic representation of the words in order to construct a highly accurate category vocabulary. Then， a word category prediction task based on MASK mechanism was constructed to fine-tune the pre-training model BERT （Bidirectional Encoder Representations from Transformers）， so as to learn the relationship between words and categories. Finally， a self-training module with label semantics introduced was used to make full use of all data information and reduce the impact of label noise in order to achieve word-level to sentence-level semantic conversion， thereby accurately predicting text sequence categories. Experimental results show that compared with the current state-of-the-art weakly-supervised text classification model LOTClass （Label-name-Only Text Classification）， the proposed method improves the classification accuracy by 5.29， 1.41 and 1.86 percentage points respectively on the public datasets THUCNews， AG News and IMDB.

Focused on the problems of low hiding capacity and poor quality of decrypted labeled images in the existing Reversible Data Hiding in Encrypted Image （RDHEI） methods， a new RDHEI method based on high-order bit-plane redundancy was proposed. Firstly， the original image was encrypted in blocks by Logistic mapping， and the redundancy of the high-order bit-plane of the pixels in the blocks was retained. Secondly， according to the rule of whether the numbers of high-order bits and low-order bits in the block were the same， the encrypted image blocks were divided into embeddable blocks and non-embeddedable blocks， and the low-order bit value of the pixel was replaced with the corresponding high-order bit value in the embeddable blocks， so that the high-order bit-plane redundancy was transferred to the low-order bit-plane. Finally， the confidential information was embedded in the embedding space vacated in the inner-block low-order bit-plane. After that， the operations of data extraction， image decryption and image lossless recovery were realized by the receiver with the key. In the simulation experiments on 6 images in the USC-SIPI standard image library， when the number of high-order bit-planes is equal to 3， the proposed method has the average embedding rate of the image of 1.73 bpp， and the average Peak Signal-to-Noise Ratio （PSNR） of the marked image after direct decryption reaches 47.20 dB. The experimental results show that the proposed method not only increases the information embedding capacity of the encrypted image， but also increases the PSNR value of the labeled image after direct decryption.

A static priority scheduling algorithm for periodic priority exchange was proposed to resolve the low-priority task latency problem in real-time multi-task system. In this method, a fixed period of timeslice was defined, and the two independent tasks of different priorities in the multi-task system exchanged their priority levels periodically. Under the precondition that the execution time of the task with higher priority could be guaranteed, the task with lower priority would have more opportunities to perform as soon as possible to shorten its execution delay time. The proposed method can effectively solve the bad real-time performance of low-priority task and improve the whole control capability of real-time multi-task system.