Interstitial Lung Disease （ILD） segmentation labels are highly costly， leading to small sample sizes in existing datasets and resulting in poor performance of trained models. To address this issue， a segmentation algorithm for ILD based on multi-task learning was proposed. Firstly， a multi-task segmentation model was constructed based on U-Net. Then， the generated lung segmentation labels were used as auxiliary task labels for multi-task learning. Finally， a method of dynamically weighting the multi-task loss functions was used to balance the losses of the primary task and the secondary task. Experimental results on a self-built ILD dataset show that the Dice Similarity Coefficient （DSC） of the multi-task segmentation model reaches 82.61%， which is 2.26 percentage points higher than that of U-Net. The experimental results demonstrate that the proposed algorithm can improve the segmentation performance of ILD and can assist clinical doctors in ILD diagnosis.

For the shortcomings of poor interpretation ability and instability in neural network training， a Chambolle- Pock （CP） algorithm optimized denoising network based on Total Variational （TV） regularization， CPTV-Net， was proposed to solve the denoising problem of Low-Dose Computed Tomography （LDCT） images. Firstly， the TV constraint term was introduced into the L1 regularization term model to preserve the structural information of the image. Secondly， the CP algorithm was used to solve the denoising model and obtain specific iterative steps to ensure the convergence of the algorithm. Finally， the shallow CNN （Convolutional Neural Network） was used to learn the iterative formula of the primal dual variables of the linear operation. The neural network was used to calculate the solution of the model， and the network parameters were collected to optimize the combined data. The experimental results on simulated and real LDCT datasets show that compared with five advanced denoising methods such as REDCNN （Residual Encoder-Decoder Convolutional Neural Network） and TED-Net （Transformer Encoder-decoder Dilation Network）， CPTV-Net has the best Peak Signal-to-Noise Ratio （PSNR）， Structural SIMilarity （SSIM）， and Visual Information Fidelity （VIF） evaluation values， and can generate LDCT images with significant denoising effect and the most details preserved.

Aiming at the high computational complexity and large memory consumption of the existing super-resolution reconstruction networks， a lightweight image super-resolution reconstruction network based on Transformer-CNN was proposed， which made the super-resolution reconstruction network more suitable to be applied on embedded terminals such as mobile platforms. Firstly， a hybrid block based on Transformer-CNN was proposed， which enhanced the ability of the network to capture local-global depth features. Then， a modified inverted residual block， with special attention to the characteristics of the high-frequency region， was designed， so that the improvement of feature extraction ability and reduction of inference time were realized. Finally， after exploring the best options for activation function， the GELU （Gaussian Error Linear Unit） activation function was adopted to further improve the network performance. Experimental results show that the proposed network can achieve a good balance between image super-resolution performance and network complexity， and reaches inference speed of 91 frame/s on the benchmark dataset Urban100 with scale factor of 4， which is 11 times faster than the excellent network called SwinIR （Image Restoration using Swin transformer）， indicates that the proposed network can efficiently reconstruct the textures and details of the image and reduce a significant amount of inference time.

Federated Learning （FL） allows users to share and interact with multiple parties without directly uploading the original data， effectively reducing the risk of privacy leaks. However， existing research suggests that the adversary can still reconstruct raw data through shared gradient information. To further protect the privacy of federated learning， a deep shadow defense scheme of federated learning based on Generative Adversarial Network （GAN） was proposed. The original real data distribution features were learned by GAN and replaceable shadow data was generated. Then， the original model trained on real data was replaced by a shadow model trained on shadow data and was not directly accessible to the adversary. Finally， the real gradient was replaced by the shadow gradient generated by the shadow data in the shadow model and was not accessible to the adversary. Experiments were conducted on CIFAR10 and CIFAR100 datasets for comparison of the proposed scheme with the five defense schemes of adding noise， gradient clipping， gradient compression， representation perturbation and local regularization and sparsification. On CIFAR10 dataset， the Mean Square Error （MSE） and the Feature Mean Square Error （FMSE） of the proposed scheme were 1.18-5.34 and 4.46-1.03×10⁷ times， and the Peak Signal-to-Noise Ratio （PSNR） of the proposed scheme was 49.9%-90.8%. On CIFAR100 dataset， the MSE and the FMSE of the proposed scheme were 1.04-1.06 and 5.93-4.24×10³ times， and the PSNR of the proposed scheme was 96.0%-97.6%. Compared with the deep shadow defense method， the proposed scheme takes into account the actual attack capability of the adversary and the problems in shadow model training， and designs threat models and shadow model generation algorithms. It performs better in theory analysis and experiment result that of the comparsion schemes， and it can effectively reduce the risk of federated learning privacy leaks while ensuring accuracy.

Aiming at the problems of the leakage of model parameters， that the untrusted server may return wrong aggregation results， and the users participating in training may upload wrong or low-quality model parameters in the process of federated learning in edge computing scenarios， a privacy-preserving federated learning algorithm based on blockchain in edge computing was proposed. In the training process， firstly， the global model parameters were trained on the local dataset of each user by the users， and the model parameters obtained by training were uploaded to neighboring edge nodes through secret sharing， thereby protecting the local model parameters of the users. Secondly， the Euclidean distances between the shares of model parameters received by the edge nodes were computed， and the results of these calculations were uploaded to the blockchain. Finally， the Euclidean distances between model parameters were reconstructed by the blockchain， and then the global model parameter was aggregated after removing the poisoned updates. The security analysis proves the security of the proposed algorithm： even in the case of collusion of a part of edge nodes， the users’ local model parameter information will not be leaked. At the same time， the experimental results show the high accuracy of this algorithm： the accuracy of the proposed algorithm is 94.2% when the proportion of poisoned samples is 30%， which is close to the accuracy of the Federated Averaging （FedAvg） algorithm without poisoned samples （97.8%）， and the accuracy of FedAvg algorithm is decreased to 68.7% when the proportion of poisoned samples is 30%.

In order to solve the problem of most current research work only considering the factors affecting pedestrian interaction， based on State-Refinement Long Short-Term Memory （SR-LSTM） and attention mechanism， a Social-Interaction Generative Adversarial Network （SIGAN） for pedestrian trajectory prediction was proposed， namely SRA-SIGAN， where GAN was utilized to learn movement patterns of target pedestrians. Firstly， SR-LSTM was used as a location encoder to extract the information of motion intention. Secondly， the influence of pedestrians in the same scene was reasonably assigned by setting the velocity attention mechanism， thereby handling the pedestrian interaction better. Finally， the predicted future trajectory was generated by the decoder. Experimental results on several public datasets show that the performance of SRA-SIGAN model is good on the whole. Specifically on the Zara1 dataset， compared with SR-LSTM model，the Average Displacement Error （ADE）and Final Displacement Error （FDE）of SRA-SIGAN were reduced by 20.0% and 10.5%，respectively；compared with the SIGAN model，the ADE and FDE of SRA-SIGAN were decreased by 31.7% and 24.4%，respectively.

Aiming at the single link failure problem in the vehicle-road real-time query communication scenario of Software-Defined Internet of Vehicles （SDIV）， a fast link failure recovery method for SDIV was proposed， which considered link recovery delay and path transmission delay after link recovery. Firstly， the failure recovery delay was modeled， and the optimization goal of minimizing the delay was transformed into a 0-1 integer linear programming problem. Then， this problem was analyzed， two algorithms were proposed according to different situations， which tried to maximize the reuse of the existing calculation results. In specific， Path Recovery Algorithm based on Topology Partition （PRA-TP） was proposed when the flow table update delay was not able to be ignored compared with the path transmission delay， and Path Recovery Algorithm based on Single Link Search （PRA-SLS） was proposed when the flow table update delay was negligible because being farless than the path transmission delay. Experimental results show that compared with Dijkstra algorithm， PRA-TP can reduce the algorithm calculation delay by 25% and the path recovery delay by 40%， and PRA-SLS can reduce the algorithm calculation delay by 60%， realizing fast single link failure recovery at vehicle end.

Both the S-box （multiple outputs） in block ciphers and the feedback function in stream ciphers require special Boolean functions to ensure the security of the cipher algorithm. To solve the problems of excessive resource consumption of reconfigurable hardware operation units and low clock frequency caused by Non-Linear Boolean Function （NLBF） in the existing algorithms of stream cipher， a high-efficiency AIC（And-Inverter Cone）-based design scheme for NLBF reconfigurable operation units was proposed， namely RA-NLBF. Based on the theories of cryptography， after analyzing the NLBF characteristics of various stream cipher algorithms and extracting the function features of NLBF including the times of AND terms， the number of AND terms， and the number of input ports， an NLBF simplification method based on the dual-logic hybrid form of “Mixed Polarity Reed-Muller （MPRM）” and “Traditional Boolean function （TB）” was proposed， which reduced the number of NLBF AND terms by 29% and formed an NLBF expression suitable for the AIC. Based on the simplified expression characteristics， such as the distribution of the number of AND terms and the times of AND terms， reconfigurable AIC units and interconnection networks were designed to form the reconfigurable units that can satisfy the NLBF operation in the existing public stream cipher algorithms. The proposed RA-NLBF was verified by logic synthesis based on CMOS 180 nm technology， and the results show that the area of RA-NLBF is 12 949.67 μm2， and the clock frequency reaches 505 MHz， which is a 59.7% reduction in area and a 37.3% increase in clock frequency compared with Reconfigurable Logic Unit for Sequence Cryptographic （RSCLU）， an existing method with the same function.

Cyber-Physical System （CPS） is a distributed real-time feedback system that integrates computing， control， communication and physical elements， but the traditional modeling methods cannot meet the high requirements of CPS for spatiotemporal performance. To address this problem， a modeling method for Duration-Space Object Petri Net （DS-OPN） was proposed. Firstly， the object-oriented encapsulation technology and spatiotemporal elements were integrated into the Petri net， and the spatial and temporal description rules were designed to encapsulate scenario elements under the same object into the same object subnet system model. Secondly， the aggregation rules were defined to aggregate various subnet models to enable these models to describe the object change process in the CPS physical topology environment. Finally， taking the traffic CPS as an example， the dynamic behavior of the autonomous control overtaking system was modeled and simulated； at the same time， the coverability tree and the incidence matrix of the model were established to verify the model’s reachability and safety. Experimental results show that the model modeled by the proposed method has a clear representation of the logical structure of the system flow as well as accurate calculation of spatiotemporal factors， and meets the requirements of CPS in terms of real-time performance and security， which verifies the effectiveness and security of the modeling method.

The supply chain faces many challenges in the development process， including how to ensure the authenticity and reliability of information as well as the security of the traceability system in the process of product traceability， the security of products in the process of logistics， and the trust management in the financing process of small and medium enterprises. With characteristics of decentralization， immutability and traceability， blockchain provides efficient solutions to supply chain management， but there are some technical challenges in the actual implementation process. To study the applications of blockchain technology in the supply chain， some typical applications were discussed and analyzed. Firstly， the concept of supply chain and the current challenges were briefly introduced. Secondly， problems faced by blockchain in three different supply chain fields of information flow， logistics flow and capital flow were described， and a comparative analysis of related solutions was given. Finally， the technical challenges faced by blockchain in the practical applications of supply chain were summarized， and future applications were prospected.

With the rapid development of e-commerce and the popularity of the Internet， it is more convenient to exchange and return goods. Therefore， the customers’ demands for goods show the characteristics of timeliness， variety， small batch， exchanging and returning. Aiming at Location-Routing Problem with Simultaneous Pickup and Delivery （LRPSPD） with capacity and considering the characteristics of customers’ diversified demands， a mathematical model of LRPSPD & Time Window （LRPSPDTW） was established. Improved FireWorks Algorithm （IFWA） was used to solve the model， and the corresponding neighborhood operations were carried out for the fireworks explosion and mutation. The performance of the fireworks algorithm was evaluated with some benchmark LRPSPD examples. The correctness and effectiveness of the proposed model and algorithm were verified by a large number of numerical experiments. Experimental results show that compared with Branch and Cut algorithm （B&C）， the average error between the result of IFWA and the standard solution is reduced by 0.33 percentage points. The proposed algorithm shortens the time to find the optimal solution， and provides a new way of thinking for solving location-routing problems.

In certain fixed industrial application scenarios， the tolerance of the target detection algorithms to miss detection is very low. However， while increasing the recall， some non-overlapping virtual frames are likely to be regularly generated around the target. The traditional Non-Maximum Suppression （NMS） strategy has the main function to suppress multiple repeated detection frames of the same target， and cannot solve the above problem. To this end， an anisotropic NMS method was designed by adopting different suppression strategies for different directions around the target， and was able to effectively eliminate the regular virtual frames. The target shape and the regular virtual frame in a fixed industrial scene often have a certain relevance. In order to promote the accurate execution of anisotropic NMS in different directions， a ratio Intersection over Union （IoU） loss function was designed to guide the model to fit the shape of the target. In addition， an automatic labeling dataset augmentation method was used for the regular target， which reduced the workload of manual labeling and enlarged the scale of the dataset. Experimental results show that the proposed method has significant effects on the roll groove detection dataset， and when it is applied to the YOLO （You Only Look Once） series of algorithms， the detection precision is improved without reducing the speed. At present， the algorithm has been successfully applied to the production line of a cold rolling mill that automatically grabs rolls.

Heuristic and machine learning based code smell detection methods have been proved to have limitations， and most of these methods focus on the common code smells. In order to solve these problems， a deep learning based method was proposed to detect three relatively rare code smells which are related to coupling， those are Intensive Coupling， Dispersed Coupling and Shotgun Surgery. First， the metrics of three code smells were extracted， and the obtained data were processed. Second， a deep learning model combining Convolutional Neural Network （CNN） and attention mechanism was constructed， and the introduced attention mechanism was able to assign weights to the metric features. The datasets were extracted from 21 open source projects， and the detection methods were validated in 10 open source projects and compared with CNN model. Experimental results show that the proposed model achieves the better performance with the code smell precisions of 93.61% and 99.76% for Intensive Coupling and Dispersed Coupling respectively， and the CNN model achieves the better results with the code smell precision of 98.59% for Shotgun Surgery.

A multimodal sequential recommendation algorithm based on contrastive learning technology was proposed to improve the accuracy of sequential recommendation algorithm by using multimodal information of commodities. Firstly， to obtain the visual representations such as the color and shape of the product， the visual modal information of the product was extracted by utilizing the contrastive learning framework， where the data enhancement was performed by changing the color and intercepting the center area of the product. Secondly， the textual information of each commodity was embedded into a low-dimensional space， so that the complete multimodal representation of each commodity could be obtained. Finally， a Recurrent Neural Network （RNN） was used for modeling the sequential interactions of multimodal information according to the time sequence of the product， then the preference representation of user was obtained and used for commodity recommendation. The proposed algorithm was tested on two public datasets and compared with the existing sequential recommendation algorithm LESSR. Experimental results prove that the ranking performance of the proposed algorithm is improved， and the recommendation performance remains basically unchanged after the feature dimension value reaches 50.

Traditional multi-label classification algorithms are based on binary label prediction. However， the binary labels can only indicate whether the data has relevant categories， so that they contain less semantic information and cannot fully represent the label semantic information. In order to fully mine the semantic information of label space， a new Multi-Label classification algorithm based on Non-negative matrix factorization and Sparse representation （MLNS） was proposed. In the proposed algorithm， the non-negative matrix factorization and sparse representation technologies were combined to transform the binary labels of data into the real labels， thereby enriching the label semantic information and improving the classification effect. Firstly， the label latent semantic space was obtained by the non-negative matrix factorization of label space， and the label latent semantic space was combined with the original feature space to form a new feature space. Then， the global similarity relation between samples was obtained by the sparse coding of the obtained feature space. Finally， the binary label vectors were reconstructed by using the obtained similarity relation to realize the transformation between binary labels and real labels. The proposed algorithm was compared with the algorithms such as Multi-Label classification Based on Gravitational Model （MLBGM）， Multi-Label Manifold Learning （ML²）， multi-Label learning with label-specific FeaTures （LIFT） and Multi-Label classification based on the Random Walk graph and the K-Nearest Neighbor algorithm （MLRWKNN） on 5 standard multi-label datasets and 5 evaluation metrics. Experimental results show that， the proposed MLNS is better than the compared multi-label classification algorithms in multi-label classification， the proposed MLNS ranks top1 in 50% cases， top 2 in 76% cases and top 3 in all cases.

Aiming at the problem that the improved federated average algorithm based on analytic hierarchy process was affected by subjective factors when calculating its data quality， an improved federated weighted average algorithm was proposed to process multi-source data from the perspective of data quality. Firstly， the training samples were divided into pre-training samples and pre-testing samples. Then， the accuracy of the initial global model on the pre-training data was used as the quality weight of the data source. Finally， the quality weight was introduced into the federated average algorithm to reupdate the weights in the global model. The simulation results show that the model trained by the improved federal weighted average algorithm get the higher accuracy compared with the model trained by the traditional federal average algorithm， which is improved by 1.59% and 1.24% respectively on equally divided and unequally divided datasets. At the same time， compared with the traditional multi-party data retraining method， although the accuracy of the proposed model is slightly reduced， the security of data and model is improved.

Because the traditional developer recommendation methods focus on analyzing the developers’ professional abilities and the interaction information with the tasks， without considering the problem of collaboration between the developers， a developer recommendation method based on Environment-Class， Agent， Role， Group， and Object （E-CARGO） model was proposed. Firstly， the developer collaborative development process was described as a role-based collaboration problem and modeled by E-CARGO model combining the characteristics of collaborative development. Then， a fuzzy judgment matrix was established by Fuzzy Analytic Hierarchy Process （FAHP） method to obtain the developer ability index weights and weighted sum of them， thereby obtaining the set of historical comprehensive ability evaluation of the developers. Finally， in view of the uncertainty and dynamic characteristics of the developers’ comprehensive ability evaluation， the cloud model theory was used to analyze the set of historical comprehensive ability evaluation of the developers to obtain the developers’ competence for each task， and the cplex optimization package was used to solve the developer recommendation problem. Experimental results show that the proposed method can obtain the best developer recommendation results within an acceptable time range， which verifies the effectiveness of the proposed method.

In order to solve the problems that the iterative efficiencies of the existing privacy protection k-means clustering schemes are low， the server in the centralized differential privacy preserving k-means clustering scheme may be attacked， and the server in the localized differential privacy protection k-means clustering scheme may return wrong clustering results， a Multi-party Privacy Protection k-means Clustering Scheme based on Blockchain （M-PPkCS/B） was proposed. Taking advantages of localized differential privacy technology and the characteristics of the blockchain such as being open， transparent， and non-tamperable， firstly， a Multi-party k-means Clustering Center Initialization Algorithm （M-kCCIA） was designed to improve the iterative efficiency of clustering while protecting user privacy， and ensure the correctness of initial clustering centers jointly generated by the users. Then， a Blockchain-based Privacy Protection k-means Clustering Algorithm （Bc-PPkCA） was designed， and a smart contract of clustering center updating algorithm was constructed. The clustering center was updated iteratively by the above smart contract on the blockchain to ensure that each user was able to obtain the correct clustering results. Through experiments on the datasets HTRU2 and Abalone， the results show that while ensuring that each user obtains the correct clustering results， the accuracy can reach 97.53% and 96.19% respectively， the average iteration times of M-kCCIA is 5.68 times and 2.75 times less than that of the algorithm of randomly generating initial cluster center called Random Selection （RS）.

Query is the main workload of a database system， and its efficiency determines the performance of the database system. There are multiple execution plans for a query， and the existing query optimizers can only statically select a better execution plan for a query according to the configuration parameters of the database system. There are complex and variable resource contentions between concurrent queries， and such contentions are difficult to be reflected accurately through configuration parameters； besides， the efficiency of the same execution plan is not consistent in different scenarios. The selection of the execution plans for concurrent queries needs to consider the influence between queries — query interaction. Based on the above， a metric for measuring the influence of query interaction on the query under concurrent query called QIs （Query Interactions） was proposed. For the selection of query execution plan under concurrent query， a method called TRating （Time Rating） was proposed to dynamically select the execution plan for the query. In the method， the influence of query interaction on the queries executed with different plans in the query combination was measured， and the plan with small influence of query interaction was selected as the better execution plan for the query. Experimental results show that TRating can select a better execution plan for the query with an accuracy of 61%， which is 25% higher than that of the query optimizer； and the accuracy of the proposed method is as high as 69% when selecting suboptimal execution plan for the query.

To get a path for a continuum robot in the environment like the aircraft fuel tank, a path planning algorithm based on regional-advance strategy was proposed. By combining with the mechanical constraints of the robot, the method could ensure that arbitrary points can be reached in the single cabin. With the flexibility of movement, but the hyper-redundant freedom degree of the continuum robot brings about both the multiple path solutions in three-dimensional space and high time complexity. The approach based on dimension reduction, which is transforming the planning in three-dimensional space into that in two-dimensional plane, was presented to reduce the computing complexity. The single cabin of the aircraft fuel tank was divided to two regions, and the planning strategy was determined by the regional location of the target point. Finally, the Matlab simulation experiments were carried out, and the practicability and effectiveness of the proposed method were verified.

It is difficult for the existing methods to get overall sentiment orientation of the comment text. To solve this problem, the method of multi-document sentiment summarization based on Latent Dirichlet Allocation (LDA) model was proposed. In this method, all the subjective sentences were extracted by sentiment analysis and described by LDA model, then a summary was generated based on the weight of sentences which combined the importance of words and the characteristics of sentences. The experimental results show that this method can effectively identify key sentiment sentences, and achieve good results in precision, recall and F-measure.

To solve the problem of transmission in wireless networks, a transmission and scheduling scheme based on W-learning algorithm in wireless networks was proposed in this paper. Building the system model based on Markov Decision Progress (MDP), with the help of W-learning algorithm, the goal of using this scheme was to transmit intelligently, namely, the package loss under the premise of energy saving by choosing which one to transmit and the transmit mode legitimately was reduced. The curse of dimensionality was overcome by state aggregate method, and the number of actions was reduced by action set reduction scheme. The storage space compression ratio of successive approximation was 41%; the storage space compression ratio of W-learning algorithm was 43%. Finally, the simulation results were given to evaluate the performances of the scheme, which showed that the proposed scheme can transport data as much as possible on the basis of energy saving, the state aggregation method and the action set reduction scheme can simplify the calculation with little influence on the performance of algorithms.

The major procedures of text classification such as uniform text format expression, training, testing and classifying based on Naive Bayesian text classification algorithm were implemented using MapReduce programming mode. The experiments were given in Hadoop cloud computing environment. The experimental results indicate basically linear speedup with an increasing number of node computers. A recall rate of 86% was achieved when classifying Chinese Web pages.

In view of the complex Container Loading Problem (CLP), the optimal loading plan with heuristic information and the ant colony algorithm was proposed. Firstly, a mathematical model was generated. Considering the strong search ability, potential parallelism and scalability of ant colony algorithm, the proposed algorithm was combined with the triple-tree structure to split the layout of space in turn. Then, the three-dimensional rectangular objects of different sizes were placed to the layout space under the constraints. An ant colony algorithm based on spatial partition was designed to solve the optimal procedure. Finally, a design example that 700 pieces of goods were loaded into a 40-foot (12.025m) high cubic was calculated. The experimental results show that the proposed method can enhance the utilization of the container and it has a strong practicality.

A task allocation and scheduling algorithm called Dynamic Dual-Directional Priority (DDDP) was presented. This algorithm considered the priority of real-time task and the sub synthetically, and constructed a dynamic dual-directional priority task allocation model, realized the task allocation and scheduling of the master/sub model in data transmission. In the simulation experiments with some typical data of various parameters under normal workload and overload situation, the DDDP algorithm has improved the performance of scheduling obviously compared with a classical Earliest Deadline First (EDF) algorithm which only considers the deadlines of real-time tasks.

The state of subjects/objects and the current context have the decisive effect on the authorization result in pervasive computing environment. Focusing on the problem that current authorization models use simple states of subjects/objects and the context information leads to failure in pervasive computing environment, a novel dynamic access control model based on the state of subjects/objects and the current context was proposed. The main elements, the architecture and the authorization algorithm of the model were described. Compared to the existing authorization models, the unified scheme of the context information proposed to affect the authorization result enhances the expression ability of the model, and makes the model simple as well. The model is more suitable for pervasive computing environment.

In this paper, an improved algorithm on fast multi-resolution motion estimation was proposed, which made use of the multi-resolution property and wavelet matching error characteristic. Based on Partial Distortion Elimination (PDE) algorithm, we improved the speed of motion estimation by improving searching order, matching order and comparison interval. Experimental results show that the proposed algorithm can achieve the same estimate accuracy as Full Search Algorithm (FSA), while the computation complexity is reduced extremely.

Image splicing is a technique commonly used in image tampering. To implement image splicing blind detection，a new splicing detection scheme was proposed. Image splicing detection could be regarded as a two-class pattern recognition problem and the model was established based on moment features and some Image Quality Metrics (IQMs) extracted from the given test image. This model could measure statistical differences between original image and spliced image. Kernel-based Support Vector Machine (SVM) was chosen as a classifier to train and test the given images. Experimental results demonstrate that this new splicing detection scheme has some advantages of high-accuracy and wide-application.

Airline seat inventory optimization is a very profitable tool for airline. Current researches are focused on network seat inventory optimization, which has high complication of combination of the ODF (Origin, Destination, Fare) and seat number. Due to the large number of decision variables, traditional optimization models are hard to compute. Although some LP approximation methods of traditional models improve their practical applicability, they still take long time to compute and have high complexity when network is large. We used ant colony algorithm to solve network seat inventory optimization in this paper. It is shown that ant colony algorithm can solve problem quickly and gain good results, and it is easy to implement.

Under a mobile computing environment, it can reduce both the amounts of data transmission and energy consumption of mobile units to select a suitable join query processing scheme based on accurate cost estimate of operations. A cost estimate method for join query processing was presented after a new asymmetrical feature of energy consumption at a mobile unit was exploited. The costs of join query processing schemes were evaluated from data transmission and energy consumption, and the performances of them were comparatively analyzed. Then four practical criteria were achieved to guide us to select the suitable processing scheme and minimize the cost of join operation. The experiments fully demonstrate that the estimate method and the criteria are reliable, and more applicable compared with the existing similar models and results.