Catastrophic forgetting poses a significant challenge to Federated Class-Incremental Learning （FCIL）， leading to performance degradation of continuous tasks in FCIL. To address this issue， an FCIL method of Label Semantic Embedding （LSE） with Multi-Head Self-Attention （MHSA） — ATTLSE （ATTention Label Semantic Embedding） was proposed. Firstly， an LSE with MHSA was integrated with a generator. Secondly， during the stage of Data-Free Knowledge Distillation （DFKD）， the generator with MHSA was used to produce more meaningful data samples， which guided the training of client models and reduced the influence of catastrophic forgetting problem in FCIL. Experiments were carried out on the CIFAR-100 and Tiny_ImageNet datasets. The results demonstrate that the average accuracy of ATTLSE is improved by 0.06 to 6.45 percentage points compared to LANDER （Label Text Centered Data-Free Knowledge Transfer） method， so as to solve the catastrophic forgetting problem to certain extent of continuous tasks in FCIL.

As the core of steel production， hot rolling process has demands of strict production continuity and complex production technology. The random arrival of rush orders and urgent delivery requirements have adverse impacts on production continuity and quality stability. Aiming at those kind of dynamic events of rush order insertion， a hot rolling rescheduling optimization method was proposed. Firstly， the influence of order disturbance factor on the scheduling scheme was analyzed， and a mathematical model of hot rolling rescheduling was established with the optimization objective of minimizing the weighted sum of tardiness of orders and jump penalty of slabs. Then， an Estimation of Distribution Algorithm （EDA） for hot rolling rescheduling was designed. In this algorithm， aiming at the insertion processing of rush orders， an integer encoding scheme was proposed based on the insertion position， the probability model based on the characteristics of the model was designed， and the fitness function based on the penalty value was defined by considering the targets and constraints comprehensively. The feasibility and validity of the model and the algorithm were verified by the simulation experiment on the actual production data.

Accurate traffic flow prediction is very important in helping traffic management departments to take effective traffic control and guidance measures and travelers to plan routes reasonably. Aiming at the problem that the traditional deep learning models do not fully consider the spatial-temporal characteristics of traffic data， a CNN-LSTM prediction model based on attention mechanism， namely STCAL （Spatial-Temporal Convolutional Attention-LSTM network）， was established under the theoretical frameworks of Convolutional Neural Network （CNN） and Long Short-Term Memory （LSTM） unit and with the combination of the spatial-temporal characteristics of urban traffic flow. Firstly， the fine-grained grid division method was used to construct the spatial-temporal matrix of traffic flow. Secondly， CNN model was used as a spatial component to extract the spatial characteristics of urban traffic flow in different periods. Finally， the LSTM model based on attention mechanism was used as a dynamic time component to capture the temporal characteristics and trend variability of traffic flow， and the prediction of traffic flow was realized. Experimental results show that compared with Gated Recurrent Unit （GRU） and Spatio-Temporal Residual Network （ST-ResNet）， STCAL model has the Root Mean Square Error （RMSE） index reduced by 17.15% and 7.37% respectively， the Mean Absolute Error （MAE） index reduced by 22.75% and 9.14% respectively， and the coefficient of determination （R²） index increased by 11.27% and 2.37% respectively. At the same time， it is found that the proposed model has the prediction effect on weekdays with high regularity higher than that on weekends， and has the best prediction effect of morning peak on weekdays， showing that it can provide a basis for short-term urban regional traffic flow change monitoring.

Image visual saliency analysis algorithms have achieved satisfactory performance on existing datasets, but these datasets have two major problems. Firstly, most of the images contain only one salient object. Secondly, users' cognition of multiple salient objects in the same image was ignored during building salient objects' ground truth. The above problems result in that the performance of saliency analysis algorithms used in the real applications cannot be reflected by the evaluation on the existing datasets. So in this paper, a novel method of labeling the ground truth of salient objects was proposed. Firstly, a software to collect users' cognition of the important values of multiple salient objects in each image was designed and implemented. Then, according to the collected data from each user, the ground truth map represented as a gray scale image was created by manually labeling the regions covered by the salient objects. The pixel value of each region equals to the collected saliency in the first step. Based on the improved ground truth labeling method, a salient object dataset contains 1000 multi-object images was built. A ground truth map for each image was created to record users' cognition of the objects' saliencies. Then 10 state-of-the-art saliency analysis algorithms on existing datasets and the established dataset were compared. The experimental results show that these algorithms' performances are greatly reduced on the established dataset, such as the Area Under Curve of Receiver-Operating Characteristic (ROC-AUC) has a greatest decline of more than 0.5. The results prove the problems of existing datasets and the demand of building a new dataset, and point out the insufficiency of saliency analysis algorithms on complex images with multiple salient objects.

To solve the sensor node localization problem of Wireless Sensor and Actor Network (WSAN), a range-based localization algorithm with virtual force in WSAN was proposed in this paper, in which mobile actor nodes were used instead of Wireless Sensor Network (WSN) anchors for localization algorithm, and Time Of Arrival (TOA) was combined with virtual force. In this algorithm, the actor nodes were driven under the action of virtual force and made themself move close to the sensor node which sent location request, and node localization was completed by the calculation of the distance between nodes according to the signal transmission time. The simulation results show that the localization success rate of the proposed algorithm can be improved by 20% and the average localization time and cost are less than the traditional TOA algorithm. It can apply to real-time field with small number of actor nodes.

Because of the open nature of collaborative recommender systems and their reliance on user-specified judgments for building profiles, an attacker could affect the prediction by injecting a lot of biased data. In order to keep the authenticity of recommendations, the attack detection method based on Statistical Process Control (SPC) was proposed. The method constructed the Shewhart control chart by using the users' deviation from the average of rating numbers and detected attackers according to the warning rules of the chart, thus improving the robustness of collaborative recommender systems. The experiments demonstrate that the method is effective with high precision and high recall against a variety of attack models.

Recently, Lin, Tang and Wang proposed a multi-prime RSA based on a star architecture of key distribution and made use of it to construct a centralized group key management scheme. According to several main security requirements of group key management, from the perspective of cryptographic engineering practice and applying computational number theory, four kinds of attacks against this scheme were proposed: a ring idempotent attack, a chosen plaintext attack，an attack of extracting high order integer roots, and a collusion attack based on the elliptic curve factoring method and Chinese remainder theorem. The mathematical analysis and cryptanalysis indicate that under certain conditions these attacks can be realized efficiently, and it is the characteristic of "without rekeying the key server's encryption exponent" that causes such security risks.