Temporal Knowledge Graph (TKG) representation learning embeds entities and relations into a low-dimensional space while preserving relational structures across time steps. Existing methods often neglect the critical role of timestamps in capturing evolving relational patterns. To bridge this gap, we propose MTT-TKG, a novel framework integrating three synergistic modules: (1) a Multi-Time Gate module modeling Knowledge Graph (KG) evolution across historical timestamps via multilayer gating; (2) a Time-Aware module capturing timestampspecific relational characteristics; (3) a Time-Guided module handling cross-graph temporal dependencies. An embeddingtime decoder completes the representation learning. Experiments on three real-world datasets demonstrate MTT-TKG’s superior performance in capturing temporal dynamics and relational structures.

The primary objective of interactive medical image segmentation systems is to achieve more precise segmentation outcomes with reduced human intervention. This endeavor holds significant clinical importance for both pre-diagnostic pathological assessments and prognostic recovery. Among the various interaction methods available, click-based interactions stand out as an intuitive and straightforward approach compared to alternatives such as graffiti, bounding boxes, and extreme points. To improve the model's ability to interpret click-based interactions, we propose a comprehensive interactive segmentation framework that leverages an iterative weighted loss function based on user clicks. To enhance the segmentation capabilities of the Plain-ViT backbone, we introduce a Residual Multi-Headed Self-Attention encoder with hierarchical inputs and residual connections, offering multiple perspectives on the data. This innovative architecture leads to a remarkable improvement in segmentation model performance. In this research paper, we assess the robustness of our proposed framework using a self-compiled T2-MRI image dataset of the prostate and three publicly available datasets containing images of other organs. Our experimental results convincingly demonstrate that our segmentation model surpasses existing state-of-the-art methods. Furthermore, the incorporation of an iterative loss function training strategy significantly accelerates the model's convergence rate during interactions. In the prostate dataset, we achieved an impressive Intersection over Union (IoU) score of 88.11% and Number of Clicks(NoC) at 80% are 7.03 clicks.

In the multiple unmanned aerial vehicle (UAV)-assisted downlink communication, it is challenging for UAV base stations (UAV BSs) to realize trajectory design and resource assignment in unknown environments. The cooperation and competition between UAV BSs in the communication network leads to a Markov game problem. Multi-agent reinforcement learning is a significant solution for the above decision-making. However, there are still many common issues, such as the instability of the strategy and low utilization of historical data, that limit its application. In this paper, a novel graph-attention multi-agent trust region (GA-MATR) reinforcement learning framework is proposed to solve the multi-UAV assisted communication problem. Graph recurrent network is introduced to process and analyze complex topology of the communication network, so as to extract useful information and patterns from observational information. The attention mechanism provides additional weighting for conveyed information, so that the critic network can accurately evaluate the value of behavior for UAV BSs. This provides more reliable feedback signals and helps the actor network update the strategy more effectively. Ablation simulations indicate that the proposed approach attains improved convergence over the baselines. UAV BSs learn the optimal communication strategies to achieve their maximum cumulative rewards. Additionally, the multi-agent trust region method with monotonic convergence provides an estimated Nash equilibrium for the multi-UAV assisted communication Markov game.

Although deep learning for Big Data analytics has achieved promising results in the field of optical coherence tomography (OCT) image denoising, the low recognition rate caused by complex noise distribution and a large number of redundant features is still a challenge faced by deep learning-based denoising methods. Moreover, the network with large depth will bring high computational complexity. To this end, we propose a progressive feature fusion attention dense network (PFFADN) for speckle noise removal in OCT images. We arrange densely connected dense blocks in the deep convolution network, and sequentially connect the shallow convolution feature map with the deep one extracted from each dense block to form a residual block. We add attention mechanism to the network to extract the key features and suppress the irrelevant ones. We fuse the output feature maps from all dense blocks and input them to the reconstruction output layer. We compare PFFADN with the state-of-the-art denoising algorithms on retinal OCT images. Experiments show that our method has better improvement in denoising performance.

Images derived from an individual sensor fail to simultaneously satisfy the demands of high spatial, spectral, and temporal resolutions. Multisource remote sensing image (RSI) fusion provides efficient access to high-spatial-resolution multispectral (HRMS) images [spatial-spectral fusion (SSF)] and high temporal- and spatial-resolution images [spatiotemporal fusion (STF)]. While existing deep learning (DL)-based models can mainly implement either SSF or STF, there is an urgent need for models that can simultaneously implement both SSF and STF. A unified generative adversarial network with convolution and Transformer (CTUGAN) for SSF and STF is proposed. CTUGAN contains a adaptive convolutional Transformer generator (ACTG) and multiresolution convolutional Transformer discriminator (MCTD), both with the convolution and Transformer. First, a bidirectional local-global feature encoder is devised in the ACTG to extract local-global features via a high-to-low resolution and a low-to-high resolution. Then, a multihead cross-attention fusion decoder (MCAFD) is devised to aggregate and fuse complementary local-global features of various levels and resolutions hierarchically to restore valuable information. Moreover, MCTDs adversely learn multiresolution local-global features to identify the relative reality of products, and a generalized loss function is built to accomplish full supervision. Finally, numerous experiments on the SSF data (Gaofen-2 (GF-2) and QuikBird) and STF data [Coleambally Irrigation Area (CIA) and lower Gwydir catchment (LGC)] demonstrate that the proposed CTUGAN model outperforms both subjective and objective evaluations.

Chest imaging plays an essential role in diagnosing and predicting patients with COVID-19 with evidence of worsening respiratory status. Many deep learning-based approaches for pneumonia recognition have been developed to enable computer-aided diagnosis. However, the long training and inference time makes them inflexible, and the lack of interpretability reduces their credibility in clinical medical practice. This paper aims to develop a pneumonia recognition framework with interpretability, which can understand the complex relationship between lung features and related diseases in chest X-ray (CXR) images to provide high-speed analytics support for medical practice. To reduce the computational complexity to accelerate the recognition process, a novel multi-level self-attention mechanism within Transformer has been proposed to accelerate convergence and emphasize the task-related feature regions. Moreover, a practical CXR image data augmentation has been adopted to address the scarcity of medical image data problems to boost the model's performance. The effectiveness of the proposed method has been demonstrated on the classic COVID-19 recognition task using the widespread pneumonia CXR image dataset. In addition, abundant ablation experiments validate the effectiveness and necessity of all of the components of the proposed method.

Unmanned aerial vehicle (UAV)-assisted communication is a significant technology in 6G communication. In order to cope with the dynamic trajectory optimization problem of the air-ground network, the interaction between entities is modeled as a Markov game firstly. Then, the model-free multi-agent reinforcement learning (MARL) is adopted to optimize individual decision-making. This enables agents to learn the mobile patterns of others, so as to optimize their own mobile strategy. However, there are some common issues when executing the benchmark MARL algorithms, such as biased estimation and local optimum. To solve these problems, an enhanced multi-agent proximal policy optimization algorithm is proposed with policy clipping and average evaluation to guarantee the fast convergence and accurate estimation. Simulations demonstrate that this method produces superior convergence than the benchmark algorithms. It allows the UAV base station, ground users and the aerial jammer to adopt the optimal mobile strategies to achieve their respective maximum cumulative rewards. In addition, the stable strategies of agents constitute the approximate Nash equilibrium for the UAV-assisted communication Markov Game.

As one of the most relevant data preprocessing techniques, discretization has played an important role in data mining, which is widely applied in industrial control. It can transform continuous features to discrete ones, thus improving the efficiency of data processing and adapting to learning algorithms that require discrete data as inputs. However, traditional discretization methods have shortcomings, such as highly complex programs, excessive numbers of intervals obtained, and significant loss of necessary information in the preprocessing of high-resolution remote sensing big data. Moreover, the large number of mixed pixels in the image is a primary reason for the uncertainty of remote sensing information systems, and current discretization methods are based on the assumption that one pixel only corresponds to the spectral information of a single object, without considering the influence of the uncertainty caused by a mixed spectrum, which causes the classification accuracy to drop after discretization. We propose a discretization method for high-resolution remote sensing big data. We determine the membership degree of each pixel in training samples through linear decomposition and establish the individual fitness function based on a fuzzy rough model. An adaptive genetic algorithm selects discrete breakpoints, and a MapReduce framework calculates the individual fitness of the population in parallel to obtain the optimal discretization scheme in the minimum time. Our method is compared to the best state-of-the-art discretization algorithms on the authentic remote sensing datasets. Experiments verified the effectiveness of the proposed method, which provides strong support for the subsequent processing of images.

A two‐stream remote sensing image fusion network (RCAMTFNet) based on the residual channel attention mechanism is proposed by introducing the residual channel attention mechanism (RCAM) in this paper. In the RCAMTFNet, the spatial features of PAN and the spectral features of MS are extracted, respectively, by a two‐channel feature extraction layer. Multiresidual connections allow the network to adapt to a deeper network structure without the degradation. The residual channel attention mechanism is introduced to learn the interdependence between channels, and then the correlation features among channels are adapted on the basis of the dependency. In this way, image spatial information and spectral information are extracted exclusively. What is more, pansharpening images are reconstructed across the board. Experiments are conducted on two satellite datasets, GaoFen‐2 and WorldView‐2. The experimental results show that the proposed algorithm is superior to the algorithms to some existing literature in the comparison of the values of reference evaluation indicators and nonreference evaluation indicators.

Intelligent reflecting surface (IRS) is of low-cost and energy-efficiency and will be a promising technology for the future wireless communications like sixth generation. To address the problem of conventional directional modulation (DM) that Alice only transmits single confidential bit stream (CBS) to Bob with multiple antennas in a line-of-sight channel, IRS is proposed to create friendly multipaths for DM such that two CBSs can be transmitted from Alice to Bob. This will significantly enhance the secrecy rate (SR) of DM. To maximize the SR (Max-SR), a general non-convex optimization problem is formulated with the unit-modulus constraint of IRS phase-shift matrix (PSM), and the general alternating iterative (GAI) algorithm is proposed to jointly obtain the transmit beamforming vectors (TBVs) and PSM by alternately optimizing one and fixing another. To reduce its high complexity, a low-complexity iterative algorithm for Max-SR is proposed by placing the constraint of null-space (NS) on the TBVs, called NS projection (NSP). Here, each CBS is transmitted separately in the NSs of other CBS and AN channels. Simulation results show that the SRs of the proposed GAI and NSP can approximately double that of IRS-based DM with single CBS for massive IRS in the high signal-to-noise ratio region.

Feature discretization is one of the most relevant techniques for data preprocessing in remote sensing research area. Its main goal is to transform the continuous features of images into discrete ones to improve the efficiency of intelligent image processing algorithms, thus helping experts to more easily understand and use the acquired remote sensing data. In this article, we focus on feature discretization for classification of high-resolution remote sensing images in coastal areas. In these images, interactions among multiple bands exist, noises interfere, and maritime domain-specific prior knowledge is difficult to get. To address these challenges, we propose a hybrid metric method, based on information entropy and chi-square test, to calculate the stability of the discrete interval and the similarity of adjacent intervals. In addition, we use the degree of dependence among knowledge from the rough set theory as the evaluation criterion for discretization schemes and then scan each band in turn with the strategy of first splitting then merging, to obtain the optimal set of discrete features. Our method has been compared with the best state-of-the-art discretization algorithms on the GF-2 and Landsat 8 satellite datasets. Experiments show that the proposed method achieves better classification accuracy for high-resolution remote sensing images in coastal areas. It can not only effectively mine the correlation between features but also filter the outliers in bands, thus producing as few discrete intervals as possible while ensuring data consistency.

The gridless one‐bit direction of arrival (DOA) estimator is proposed to estimate electromagnetic (EM) sources on a nested cross‐dipole array, and the multiple measurement vectors (MMV) mode is introduced to improve the reliability of parameter estimation. The gridless method is based on atomic norm minimization, solved by alternating direction multiplier method (ADMM). With gridless method used, sign inconsistency caused by one‐bit measurements and basis mismatches by traditional grid‐based algorithms can be avoided. Furthermore, the reconstructed denoising measurements with fast convergence and stable recovery accuracy are obtained by ADMM. Finally, spatial smoothing root multiple signal classification (SSRMUSIC) and dual polynomial (DP) methods are used, respectively, to estimate the DOAs on the reconstructed denoising measurements. Numerical results show that our method one‐bit ADMM‐SSRMUSIC has a better performance than that of one‐bit SSRMUSIC used directly. At low signal to noise ratio (SNR) and low snapshot, the one‐bit ADMM‐DP has an excellent performance which is even better than that of unquantized MUSIC. In addition, the proposed methods are also suitable for both completely polarized (CP) signals and partially polarized (PP) signals.

Plant play a very important role in the protection of the ecological balance. Compared to manual identification of plants, automated plant identification enable experts to process significantly greater numbers of plants with higher efficiencies in shorter periods of time. In this study, we propose an effective deep Convolutional Neural Network (CNN)-based model that is capable of automatically identifying and classifying plant species in Hainan by studying the details of their leaves. We apply transfer learning based on CNN to fine-tune the pre-trained models. Further, the optimal values of associated hyperparameters that maximize the accuracy of the proposed method are determined. Finally, experiments are carried out on two available botanical datasets: the Flavia dataset with 32 classes and the HNPlant dataset with 10 classes. The results demonstrate that the highest classification accuracies exhibited by the proposed CNN-based model on the Flavia and HNPlant datasets are 89% and 95%, respectively, thus establishing their effectiveness.

Pre‐segmentation is known as a crucial step in medical image analysis. Many approaches have been proposed to make improvement to both the quality and efficiency of segmentation. However, existing methods are lacking in robustness to the variation in the edges and textures of the target. In order to address these drawbacks, a novel attention Gabor network (AGnet) based on deep learning for medical image segmentation that is capable of automatically paying more attention to the edge and consistently for improvement to the segmentation performance is proposed. The proposed model consists of two components. The first one is to determine the approximate location of the organs of interest in the image using convolution filters, and the other one is to highlight salient edge features intended for a specific segmentation task using Gabor filters. In order to facilitate collaboration in between the two parts, a region attention mechanism based on Gabor maps is suggested. The mechanism improved performance by learning to focus on the salient regions of the image that are useful for the authors' tasks. As indicated by the experimental results, the AGnet is capable of enhancing the prediction performance while maintaining the computational efficiency, which makes it comparable with other state‐of‐the‐art approaches.

In mobile data acquisition, mobile robots usually face challenging tasks when collecting information in an undetermined environment with energy limitation and time-sensitive requirements. We formulate the task of data acquisition as a multiobjective optimization problem under energy and time constraints. In our investigation, three objectives for data acquisition are considered, including collecting the largest amount of information, moving along a path with the smallest probability of encountering obstacles, and traveling with shortest possible overall distance. To resolve the formulated problem which yields the best path for a mobile robot, we propose a mixed cognition particle swarm optimization (MCPSO) algorithm, which adopts the min-max normalization to calculate the fitness, and we transform the multiobjective optimization problem into a single-objective optimization problem by summation after normalization. The efficiency of the MCPSO algorithm is evaluated for mobile data acquisition in several well-known benchmarks by simulation. The simulation results demonstrate that the proposed MCPSO algorithm can achieve higher accuracy and faster convergence compared with other particle swarm optimization algorithms.

The effective extraction of continuous features in ocean optical remote sensing image is the key to achieve the automatic detection and identification for marine vessel targets. Since many of the existing data mining algorithms can only deal with discrete attributes, it is necessary to transform the continuous features into discrete ones for adapting to these intelligent algorithms. However, most of the current discretization methods do not consider the mutual exclusion within the attribute set when selecting breakpoints, and cannot guarantee that the indiscernible relationship of information system is not destroyed. Obviously, they are not suitable for processing ocean optical remote sensing data with multiple features. Aiming at this problem, a multivariable optical remote sensing image feature discretization method applied to marine vessel targets recognition is presented in this paper. Firstly, the information equivalent model of remote sensing image is established based on the theories of information entropy and rough set. Secondly, the change extent of indiscernible relationship in the model before and after discretization is evaluated. Thirdly, multiple scans are executed for each band until the termination condition is satisfied for generating the optimal number of intervals. Finally, we carry out the simulation analysis of the high-resolution remote sensing image data collected near the coast of South China Sea. In addition, we also compare the proposed method with the current mainstream discretization algorithms. Experiments validate that the proposed method has better comprehensive performance in terms of interval number, data consistency, running time, prediction accuracy and recognition rate.

In this paper, a sparse recovery algorithm based on a double-pulse FDA-MIMO radar is proposed to jointly extract the angle and range estimates of targets. Firstly, the angle estimates of targets are calculated by transmitting a pulse with a zero frequency increment and employing the improved l 1 -SVD method. Subsequently, the range estimates of targets are achieved by utilizing a pulse with a nonzero frequency increment. Specifically, after obtaining the angle estimates of targets, we perform dimensionality reduction processing on the overcomplete dictionary to achieve the automatically paired range and angle in range estimation. Grid partition will bring a heavy computational burden. Therefore, we adopt an iterative grid refinement method to alleviate the above limitation on parameter estimation and propose a new iteration criterion to improve the error between real parameters and their estimates to get a trade-off between the high-precision grid and the atomic correlation. Finally, the proposed algorithm is evaluated by providing the results of the Cramér-Rao lower bound (CRLB) and numerical root mean square error (RMSE).

Medical data is amplified in terms of speed and capacity in a very fast way, which creates obstacles for users to quickly access valid information.We present a DIK-based Question-Answering Architecture for Medical Self-Service.In addition, we propose a model based on the attention mechanism to extract high-quality medical entity concepts from the Chinese Electronic Medical Records (EMR).Then we modeled the medical data based on the DIK architecture (Data graph, Information graph, and Knowledge graph), construct a Question-Answering model (DIK-QA) for medical self-service that meets the needs of users to quickly and accurately find the medical information they need in massive medical data.Finally, we have realized this approach and applied it to real-world systems.The experimental results on our medical dataset show that the DIK-QA can effectively handle 4W (who/what/why/how) questions, which can help users find the information they need accurately.

To keep pace with the developments in medical informatics, health medical data is being collected continually. But, owing to the diversity of its categories and sources, medical data has become so complicated in many hospitals that it now needs a clinical decision support (CDS) system for its management. To effectively utilize the accumulating health data, we propose a CDS framework that can integrate heterogeneous health data from different sources such as laboratory test results, basic information of patients, and health records into a consolidated representation of features of all patients. Using the electronic health medical data so created, multilabel classification was employed to recommend a list of diseases and thus assist physicians in diagnosing or treating their patients' health issues more efficiently. Once the physician diagnoses the disease of a patient, the next step is to consider the likely complications of that disease, which can lead to more diseases. Previous studies reveal that correlations do exist among some diseases. Considering these correlations, a k-nearest neighbors algorithm is improved for multilabel learning by using correlations among labels (CML-kNN). The CML- kNN algorithm first exploits the dependence between every two labels to update the origin label matrix and then performs multilabel learning to estimate the probabilities of labels by using the integrated features. Finally, it recommends the top N diseases to the physicians. Experimental results on real health medical data establish the effectiveness and practicability of the proposed CDS framework.

Nowadays, the collaborative filtering techniques have demonstrated an excellent performance in the top-N recommendation. However conventional methods in similarity measurement are insufficient when the condition of data sparsity and cold start occur, which leads to a poor accuracy in prediction. In order to concur the limitation, a collaborative filtering algorithm of calculating similarity based on item rating and attributes is proposed. Firstly, we calculate the similarity of item attributes, then calculate the similarity of the project according to the user rating of the project. Meanwhile, a weighted control coefficient is proposed to combine the similarity between item attributes and rating of items, which contribute to obtain nearest neighbors. Experiments have shown that our algorithm has major potential in solving the problem of cold start, therefore improving the precision of the recommendation system.

Collaborative filtering algorithm is one of the most widely used algorithms in recommender systems and has demonstrated promising results. But it relies too much on similarity to find the nearest neighbors. Whatever, the trust between users is also an import factor needed to be considered. This paper proposed a collaborative filtering algorithm that combined the user similarity and trust to obtain a more appropriate nearest neighbors set. Users not only have same interests as their nearest neighbors, but also have higher level of acceptance in the items recom-mended by their nearest neighbors. Extensive experiments based on Film Trust and MovieLens datasets have shown that the approach has major potential in improving the accuracy of recommended item.

When the traditional collaborative filtering algorithm faces high sparse data, its precision and quality of recommendation become unsatisfied. With the development of social networks, it is possible to selectively fill the missing value in the user-item matrix by using the friendship or trust relationship information of social networks. According to the memory-based collaborative filtering algorithm, in the paper, the two steps which are similarity calculation and user rating prediction are taken into account. Besides, this paper has filled appropriately the missing value and improved memory-based collaborative filtering recommendation algorithms to integrate the social relations. The experiment on the Epinions dataset shows that the improved algorithm can effectively alleviate the sparsity problem of user rating data and perform better than other classic algorithms in RMSE and MAP evaluation metrics.

The merchandise recommendation is an important part of electronic commerce. In view of the difficulty in obtaining user private information and modeling user interest, this paper is based on the relationship between goods for commodity recommendation. We use fuzzy clustering learning to construct quasi-classes space. Through the intersection of quasi-class and the collection of goods that are being ordered by users, we can know the customers appetites for merchandise, and then recommend the goods. In the construction of quasi-classes space, the value of the threshold Λ must be appropriate, because the threshold Λ determines the size of the quasi-class. It will affect the recommendation of the goods that the size of the quasi-class is too large or too small. The influence of threshold Λ on commodity recommendation is discussed by numerical example, and we finally find the best value of Λ in this paper.

Health services delivery relating to chronic diseases in elders are becoming a hot spot with the progressively severity in population aging in China. To provide a better service system for the elders, researchers have used active rule based techniques including Event-Condition-Active (ECA). In this paper, we enhanced ECA to ECA-Sequence (ECA-S) rules to detect abnormal condition in physiological indices. Our method extracts accurate health information from historical sequence data. The ECA-S is used to differentiate burst variations in physiological indices from morbid cases. Our further experiment in giving early warning for diabetic patients shows that ECA-S can deal with the burst abnormal physiological index problem. Finally, an evaluation is made among existing ECA based solutions including prime ECA, ECA-Parameters, ECA-Post-condition and proved that our proposed ECA-S method performs better in health diagnosis quality as compared to others.

As an exchange foundation of cloud software services, the cloud marketplace plays an important role to promote the capability and creativity of cloud services. However, existing cloud marketplaces are usually based on online stores operated by some particular companies. Users are usually required to get involved in their particular ecosystems, seriously restricting their creativity and competition. Moreover, these marketplaces lack efficient and flexible service publishing and discovery mechanisms. Therefore, this makes them difficult to handle a huge number of dynamic cloud service exchanges efficiently. To address this issue, we propose in this paper an open cloud marketplace middleware system for cloud software services based on a service publishing and subscription model. This model extends traditional content-based distributed publish/subscribe paradigm. By introducing the matching mechanism of multidimensional contents such as service functionality, service behavior, and quality of service etc, a new distributed publish/subscribe paradigm for service publishing and subscription is proposed. This new paradigm allows service providers to publish their services to and allows service consumers to subscribe services from the open marketplace based on service descriptions and requirements. We illustrate the design of the open cloud marketplace and present our preliminary results.

Aiming at the shortcomings of datasets sparsity and cold start in the traditional Item-based collaborative filtering recommendation algorithm, to improve the calculating accuracy of similarity and recommendation quality, taking attribute theory as theoretical basis, a collaborative filtering recommendation algorithm based on item attributes is proposed. Through analyzing the items, the attributes are listed and attribute weights are calculated, the similarity between items is calculated by taking advantage of attribute barycenter coordinate model and item attribute weights, and then produce recommendations forecasts. Finally, the experimental results show that the compared with traditional algorithm the proposed algorithm can effectively alleviate the user rating data sparsity problem and improve the quality of recommendation system.

We identify that to improve the reusability of implementation in service engineering, we need to collect and modulate reusable strategy, knowledge and experience crossing business modeling, knowledge management and economic analysis simultaneously from an interdisciplinary perspective. Strategically to ease the complexity, we adopt the existing experience from design patterns by forming a service design pattern called service value broker(SVB). SVB can efficiently integrate the three domains with relieved complexity, enhanced reusability and efficiency, etc, catering different abstraction levels. In this paper, we focus on modeling the foundational aspects of SVB presentation and value transaction, etc.

E-Service related technologies have a profound and revolutionary impact on many traditional areas. We try to outline its influence on economics which we call E-service Economics at fundamental level. We model the new characteristics of this trend along two themes: value driven decision making which covers both reuse based value added analysis and service value broker (SVB) pattern which embodies the integration of information technology and business wisdoms, and E-contract based realization which equally supports collaborative and competitive business activities. An initial case based on E-contract to show the attained flexibility, dynamic and consistency of automation is demonstrated.

The service value broker(SVB) pattern integrates business modeling, knowledge management and economic analysis with relieved complexity, enhanced reusability and efficiency, etc. The study of SVB is an emerging interdisciplinary subject which will help to promote the reuse of knowledge, strategy and experience in service based designs and solutions. In this paper, we focus on enumerating collected SVBs empirically with initial analysis on their composition manners. The results from this paper will play a dominating role in fueling a coming E-service Economics era.

When medical images transmitted and stored in hospitals, it require strict security, confidentiality and integrity. However, the transmission of wireless and wired networks has made the medical information vulnerable to attacks like tampering, hacking etc. And the ROI of medical image is unable to tolerate significant changes. In order to dealing these problems, we have proposed an algorithm that introducing the digital watermarking technology to increase the security of medical images. The scheme uses a part of sign sequence of DCT coefficients as the feature vector of images. It can avoid the sophisticated process of finding the Region of Interest (ROI) of medical images. At the same time, the watermarking image is encrypted by Logistic Map to enhance its confidentiality. The experimental results show that the scheme has strong robustness against common attacks and geometric attacks. Moreover, compared with the existing medical watermarking techniques, it can embed much more data, less complexity and make embed multi watermarks realized.

By analyzing the basic principle of Otsu method and its application in image segmentation, and according to the distribution characteristics of the target and background, an improved threshold image segmentation algorithm based on the Otsu method is developed. By narrowing the selection range of threshold and searching the minimum variance ratio, the improved algorithm selects the optimal threshold. Through the compared with the Otsu method and other methods, the results show that the new improved algorithm has these advantages such as high segmentation precision and fast computation speed.

Based on the comparison of advantage and disadvantage between Google and digital library, a method for service chain of digital library based on service-oriented architecture (SOA) is presented. Then an integrated framework for service chain of digital library is put forward, and the information exchange approach and software system architecture layers based on SOA are discussed. Finally, based on the theory of grid and its application, a content service grid for digital library alliance (DLA) is constructed, and the components and their functions of every grid layer are analyzed.

Service chain management is a hot topic in the field of service industry and service science currently. Aim at digital library industry, An organizational framework of service chain for digital library based on SOA is proposed, a service chain for digital library is composed of digital library alliances cooperating with content service providers expediently and quickly provide content services for customers, and meet the customers' individual requirement. Then, a cooperative service framework for digital library alliances (DLAs) based on grid is presented, and the components and their functions of every grid layer are analyzed. Finally, a sample of a service executing process in service chain for digital library is proposed. A service chain for digital library is blessed with the advantages of search engines and library, and thus can provide well-suited, high efficient and integrated information and knowledge services for customers.