Aiming at the problem that the imbalance of datasets in credit risk assessment affects the prediction effect of the model， a credit risk prediction model based on Borderline Adaptive Synthetic Minority Oversampling TEchnique （BA-SMOTE） and Focal Loss-Light Gradient Boosting Machine （FLLightGBM） was proposed. Firstly， on the basis of Borderline Synthetic Minority Oversampling TEchnique （Borderline-SMOTE）， the adaptive idea and new interpolation method were introduced， so that different numbers of new samples were generated for each minority sample at the border， and the positions of the new samples were closer to the original minority sample， thereby balancing the dataset. Secondly， the Focal Loss function was used to improve the loss function of LightGBM （Light Gradient Boosting Machine） algorithm， and the improved algorithm was used to train a new dataset to obtain the final BA-SMOTE-FLLightGBM model constructed by BA-SMOTE method and FLLightGBM algorithm. Finally， on Lending Club dataset， the credit risk prediction was performed. Experimental results show that compared with other imbalanced classification algorithms RUSBoost （Random Under-Sampling with adaBoost）， CUSBoost （Cluster-based Under-Sampling with adaBoost）， KSMOTE-AdaBoost （K-means clustering SMOTE with AdaBoost）， and AK-SMOTE-Catboost （AllKnn-SMOTE-Catboost）， the constructed model has a significant improvement on two evaluation indicators G-mean and AUC （Area Under Curve） with 9.0%-31.3% and 5.0%-14.1% respectively. The above results verify that the proposed model has a better default prediction effect in credit risk assessment.

This paper proposed a Low-Power oriented cache Design (LPD) of Level 2 (L2) cache for multi-core processors. LPD considered three different ways to reduce the power consumption while promising the best performance: Low Power oriented Hybrid cache Partition algorithm (LPHP), Cache Reconfiguration Algorithm (CRA), and Way-Prediction based on L2 cache Partition algorithm (WPP-L2). LPHP and CRA closed the columns that were not in use dynamically. WPP-L2 predicted one appropriate way before cache accesses, which could save the access time, so as to save power. These three methods of LPD saved power consumption by 20.5%, 17% and 64.6% on average over the traditional Least Recently Used (LRU) strategy with improvement of the throughput and little influence on the runtime of programs. The experimental results show that this method can reduce the power of multi-core processors significantly and maintain the system performance.

A new security localization algorithm based on trust mechanism was proposed to recognize the malicious beacon nodes timely in UnderWater Sensor Network (UWSN). According to the location information offered by the beacon nodes and combining cluster structure with trust mechanism, this algorithm used Beta distribution to form the initial trust value and the trust update weight could be set as required. In order to reduce the influence of the instability of underwater acoustic channel on the trust evaluation process, meanwhile, recognize the trust cheating of malicious beacon nodes, this algorithm proposed a mechanism named TFM (Trust Filter Mechanism), which calculated and quantized the trust value, and let the cluster head node decide whether each beacon node was credible or not. The results of simulation prove that the proposed algorithm is suitable for UWSNs and it can recognize malicious beacon nodes timely, and the accuracy and security of localization system are greatly improved.