Because user prompts often lack professionalism in specific fields and the use of terminology， it is difficult for Large Language Models （LLM） to understand the intentions accurately and generate information that meets requirements of the field. Therefore， an Automated Preference Alignment Dual-Stage Prompt Tuning （APADPT） method was proposed to solve the preference alignment problem faced by LLM when applied in vertical fields. In APADPT， the refinement adjustments of input prompts were realized by constructing a supervised fine-tuning dataset containing human preferences and using LLM for semantic analysis and evaluation of pairwise replies. After dual-stage training， the prompt optimization rules in the general field were mastered by the model， and specialized adjustments based on characteristics of the vertical fields were performed by the model. Experimental results in the medical field show that APADPT improves the preference alignment consistency of API-based LLM and open-source LLM significantly， with the average winning rate increased by 9.5% to 20.5% under the condition of the same model parameter count. In addition， this method shows good robustness and generalization ability on all the open-source models with different parameter scales， providing a new optimization strategy for the application of LLM in vertical specialized fields， and contributing to improving model performance while maintaining generalization and adaptability of the model.

Addressing the issues of communication obstruction and unpredictable motion trajectories of Unmanned Aerial Vehicles （UAVs） under Denial of Service （DoS） attacks， research was conducted on the secure cluster control strategy for multi-UAV during DoS attacks within a framework that integrates Artificial Potential Field （APF） and Deep Deterministic Policy Gradient （DDPG） algorithm. Firstly， Hping3 was utilized to detect DoS attacks on all UAVs， thereby determining the network environment of the UAV cluster in real time. Secondly， when no attack was detected， the traditional APF was employed for cluster flight. After detecting attacks， the targeted UAVs were marked as dynamic obstacles while other UAV switched to control strategies generated by DDPG algorithm. Finally， with the proposed framework， the cooperation and advantage complementary of APF and DDPG were realized， and the effectiveness of the DDPG algorithm was validated through simulation in Gazebo. Simulation results indicate that Hping3 can detect the UAVs under attack in real time， and other normal UAVs can avoid obstacles stably after switching to DDPG algorithm， so as to ensure cluster security； the success rate of the switching obstacle avoidance strategy during DoS attacks is 72.50%， significantly higher than that of the traditional APF （31.25%）， and the switching strategy converges gradually， demonstrating a pretty stability； the trained DDPG obstacle avoidance strategy exhibits a degree of generalization， capable of completing tasks stably with 1 to 2 unknown obstacles appeared in the environment.

While advanced technologies such as Artificial Intelligence （AI）， big data， and cloud computing are developing rapidly， the difficulties to explain， certify and other issues of the technologies limit the practical application of them in various industries. Meanwhile， through monitoring the system state， RunTime Assurance （RTA） technology achieves the function switching， making “complex” into “simple”， thereby providing a preliminary solution to some complex system behaviors’ problems of difficulties to predict and explain， insecurity， unexplained results， with a broad prospect for development in the future. Therefore， a review was conducted on the current research status and development of RTA to offer researchers insights into the latest research trends and developmental directions in RTA technology. Firstly， the development history of RTA technology was reviewed， on the basis of describing the basic principle architecture and the switching logic of RTA， the current application research status of RTA in the fields of intelligent aviation， Unmanned Aerial Vehicle （UAV）， intelligent aerospace， and automated vehicle driving， as well as on Cyber-Physical System （CPS） and safe reinforcement learning were sorted out systematically. Finally， the development prospects of RTA technology were discussed.

KLEIN has experienced attacks such as truncated difference cryptanalysis and integral cryptanalysis since it was proposed. Its encryption structure has actual security， but the vulnerability of the key expansion algorithm leads to full-round key recovery attacks. Firstly， the key expansion algorithm was modified and an improved algorithm N-KLEIN was proposed. Secondly， an efficient quantum circuit was implemented on the S-box using the in-place method， which reduced the width and depth of the circuit and improved the implementation efficiency of the quantum circuit. Thirdly， the quantization of obfuscation operations was achieved using LUP decomposition technology. Then， an efficient quantum circuit was designed for N-KLEIN， and an efficient quantum circuit for all round N-KLEIN was proposed. Finally， the resource occupation for the quantum implementation of full-round N-KLEIN was evaluated and compared with the resources occupied by existing quantum implementations of lightweight block ciphers such as PRESENT and HIGHT. At the same time， an in-depth study was conducted on the cost of key search attacks based on Grover algorithm， and the cost of N-KLEIN-｛64，80，96｝ using Grover algorithm to search for keys under the Clifford+T model was given， and then the quantum security of N-KLEIN was evaluated. Comparative results indicate that the quantum implementation cost of N-KLEIN algorithm is significantly lower.

As the development of artificial intelligence， deep neural network has become an essential tool in various pattern recognition tasks. Deploying deep Convolutional Neural Networks （CNN） on edge computing equipment is challenging due to storage space and computing resource constraints. Therefore， deep network compression has become an important research topic in recent years. Low-rank decomposition and vector quantization are the most popular network compression techniques， which both try to find a compact representation of the original network， thereby reducing the redundancy of network parameters. By establishing a joint compression framework， a deep network compression method based on low-rank decomposition and vector decomposition — Quantized Tensor Decomposition （QTD） was proposed to obtain higher compression ratio by performing further quantization based on the low-rank structure of network. Experimental results of classical ResNet and the proposed method on CIFAR-10 dataset show that the volume can be compressed to 1% by QTD with a slight accuracy drop of 1.71 percentage points. Moreover， the proposed method was compared with the quantization-based method PQF （Permute， Quantize， and Fine-tune）， the low-rank decomposition-based method TDNR （Tucker Decomposition with Nonlinear Response）， and the pruning-based method CLIP-Q （Compression Learning by In-parallel Pruning-Quantization） on large dataset ImageNet. Experimental results show that QTD can maintain better classification accuracy with same compression range.

Focusing on the issue that only one instruction substitution with 5 operators and 13 substitution schemes is supported in Obfuscator Low Level Virtual Machine （OLLVM） at the instruction obfuscation level， an improved instruction obfuscation framework InsObf was proposed. InsObf， including junk code insertion and instruction substitution， was able to enhance the obfuscation effect at the instruction level based on OLLVM. For junk code insertion， firstly， the dependency of the instruction inside the basic block was analyzed， and then two kinds of junk code， multiple jump and bogus loop， were inserted to disrupt the structure of the basic block. For instruction substitution， based on OLLVM， it was expanded to 13 operators， with 52 instruction substitution schemes. The framework prototype was implemented on Low Level Virtual Machine （LLVM）. Experimental results show that compared to OLLVM， InsObf has the cyclomatic complexity and resilience increased by almost four times， with a time cost of about 10 percentage points and a space cost of about 20 percentage points higher. Moreover， InsObf can provide higher code complexity compared to Armariris and Hikari， which are also improved on the basis of OLLVM， at the same order of magnitude of time and space costs. Therefore， InsObf can provide effective protection at the instruction level.

The use of Unmanned Aerial Vehicle （UAV） to continuously monitor designated areas can play a role in deterring invasion and damage as well as discovering abnormalities in time， but the fixed monitoring rules are easy to be discovered by the invaders. Therefore， it is necessary to design a random algorithm for UAV flight path. In view of the above problem， a UAV persistent monitoring path planning algorithm based on Value Function Iteration （VFI） was proposed. Firstly， the state of the monitoring target point was selected reasonably， and the remaining time of each monitoring node was analyzed. Secondly， the value function of the corresponding state of this monitoring target point was constructed by combining the reward/penalty benefit and the path security constraint. In the process of the VFI algorithm， the next node was selected randomly based on ε principle and roulette selection. Finally， with the goal that the growth of the value function of all states tends to be saturated， the UAV persistent monitoring path was solved. Simulation results show that the proposed algorithm has the obtained information entropy of 0.905 0， and the VFI running time of 0.363 7 s. Compared with the traditional Ant Colony Optimization （ACO）， the proposed algorithm has the information entropy increased by 216%， and the running time decreased by 59%，both randomness and rapidity have been improved. It is verified that random UAV flight path is of great significance to improve the efficiency of persistent monitoring.

In the Service Oriented Architecture （SOA）， an improved Krill Herd algorithm PRKH with adaptive crossover and random perturbation operator was proposed to solve the problem of easily falling into local optimum and high time cost in the process of service composition optimization. Firstly， a service composition optimization model was established based on Quality of Service （QoS）， and the QoS calculation formulas and normalization methods under different structures were given. Then， based on the Krill Herd （KH） algorithm， the adaptive crossover probability and the random disturbance based on the actual offset were added to achieve a good balance between the global search ability and the local search ability of krill herd. Finally， through simulation， the proposed algorithm was compared with KH algorithm， Particle Swarm Optimization （PSO） algorithm， Artificial Bee Colony （ABC） algorithm and Flower Pollination Algorithm （FPA）. Experimental results show that the PRKH algorithm can find better QoS composite services faster.

Aiming at the problem that traditional confidence threshold setting methods for positive and negative association rules are difficult to limit the number of low-reliability rules and easy to miss some interesting association rules, a new two-level confidence threshold setting method combined with the rule's itemset correlation was proposed, called PNMC-TWO. Firstly, taking into account the consistency, validity and interestingness of rules, under the framework of correlation-support-confidence, on the basis of the computation relationship between rule confidence and itemset support of the rule, the law of confidence of rule changing with support of itemsets of the rule was analyzed systematically. And then, combined with the user's requirement of high confidence and interesting rules in actual mining, a new confidence threshold setting model was proposed to avoid the blindness and randomness of the traditional methods when setting the threshold. Finally, the proposed method was compared with the original two-threshold method in terms of the quantity and quality of the rule. The experimental results show that the new two-level threshold method not only can ensure that the extracted association rules are more effective and interesting, but also can reduce the number of low-reliability rules significantly.

Many existing scheduling approaches in cloud data centers try to consolidate Virtual Machines (VMs) by VM live migration technique to minimize the number of Physical Machines (PMs) and hence minimize the energy consumption, however, it introduces high migration overhead; furthermore, the cost factor that leads to high payment cost for cloud users is usually not taken into account. Aiming at energy reduction for cloud providers and payment saving for cloud users, as well as guaranteeing the deadline of user tasks, a heuristic task scheduling algorithm called Energy and Deadline-Aware with Non-Migration Scheduling (EDA-NMS) was proposed. The execution of the tasks that have loose deadlines was postponed to avoid waking up new PMs and migration overhead, thus reducing the energy consumption. The results of extensive experiments show that compared with Proactive and Reactive Scheduling (PRS) algorithm, by selecting a smart VM combination scheme, EDA-NMS can reduce the static energy consumption and ensure the lowest payment with meeting the deadline requirement for key user tasks.

A Byzantine Fault Tolerance (BFT) schema was proposed to solve the problem that most Byzantine fault tolerance protocols were not suitable for service-oriented computing and other emerging computing models because of the assumption that the services were passive and independent. Service replicas were created on both sides of service requester and service provider. State machine replication algorithm was used to reach agreement on the ID and the content of the request after three rounds of communications among service replicas. After receiving a request, replicas submitted the request to upper application logic. After receiving the reply, replicas on service requester reached agreement on the ID and the content of the reply after three rounds of communications among services replicas and then accepted the reply. To deal with the problem of only having simple correctness reasoning and lacking of formal verification, an I/O automaton was used to model the protocol and simulation relation method was used as a tool to prove the correctness of the protocol more formally and rigorously. A highly abstract simple I/O automata S was constructed, which meeted safety and liveness. The parties of the protocol were broken down into several simple member I/O automata including front-end automaton, back-end automaton and multicast channel automaton. It is proved that the system composed of member I/O automata realizes the automata S. I/O automaton can accurately describe the protocol, which makes the correctness proof more standard than inductive reasoning.

Focusing on the fading and shadowing effect in satellite channel, a Hybrid Satellite-Terrestrial Cooperative System (HSTCS) was presented, and the closed-form expression of the outage probability was evaluated using the Land Mobile Satellite (LMS) channel. A selective Decode-and-Forward (DF) scheme was implemented between a source node (the satellite) and a destination node (a terrestrial station), and signals from the satellite and terrestrial relay were combined at destination. The analytical expression of the outage probability was verified with the Matlab simulation. The results show that the system can improve the outage performance through the diversity gain, compared with the direct transmission.

In order to real-time adjust the humanoid robot gait, a humanoid robot gait generation method was proposed. The robot motion was simplified to the inverted pendulum motion of the three-dimensional linear model, and passed the pre-planned Zero Moment Point (ZMP) trajectory. According to the Center of Mass (CoM) and ZMP relations, the CoM trajectory was obtained. The front side gait and lateral gait were simplified into seven-link and five-link structures, the triangle theorem was used to calculate the angle of each joint, and the ZMP equation was introduced to discuss the stability of the walking process. The system simulation was done according to given conditions and combined with the actual system and its operation conditions were analyzed to verify the validity of the proposed planning method.

Services selection is important in Grid workflow. To deal with the inefficient resources utilization brought by greedy competition for QoS in current Web services composition methods, an "on-demand" Grid services selection method based on commodity market was put forward. Based on global evaluation model for Grid workflow, services selection was modeled as a 0-1 multi-dimensional knapsack problem by transforming the service requester's requirements into constraints. Furthermore, the selection model was improved with economic "price-demand relationship". Finally, experiments show that the proposed services selection method can bring load balance, which is helpful to the utilization of Grid resources.

The E-commerce leads to the moreintensive competition among the entities, and personalization recommendation would be the breakthrough to increase the customer's loyalty and promote the profits. The dynamic mining algorithm was proposed and improved to realize the personalization recommendation based on customers purchase sequences. The definition of time contraint was put forward considering the impact of the length of the times compartmentalization and the selflearning function of the system was realized. The result of an imitating experiment indicates that the recommendation method proposed is feasible and effctive.