Multimodal recommender systems have emerged as a pivotal paradigm for harnessing diverse data modalities to deliver personalized services. Contemporary research predominantly focuses on integrating heterogeneous modality information through graph learning. However, these approaches face two key challenges: (1) the inherent complexity of modalities, characterized by entangled redundant signals and noise; and (2) the challenge of effectively integrating multimodal representations, each of which may exert varying degrees of influence on users' preferences. To address these challenges, we propose a novel Collaboration-Guided Multimodal Disentanglement and Hierarchical Fusion for Recommendation (DHMRec), which simultaneously achieves intra-modal denoising disentanglement and inter-modal hierarchical fusion. Specifically, we introduce a collaboration-related modality disentanglement module to distinguish between modality-common and modality-specific features. Then, through multi-view graph learning to capture both item-item dependencies and user-item interaction patterns. Additionally, we implement hierarchical fusion between the disentangled multimodal features and ID embeddings using a positive-negative attention-aware fusion module and an interaction distribution-based alignment module. Extensive experiments on three benchmarks demonstrate that our DHMRec surpasses various state-of-the-art baselines, highlighting its effectiveness in intra-modal disentanglement and multimodal features fusion.

With the rapid development of scientific research, a large amount of literature materials (e.g., published papers) has brought a serious information overload problem to researchers. For those novices who have just stepped into a certain research field, the fact that they do not yet know their own research direction, coupled with the huge amount of literature materials and their varying quality, makes it even more difficult for them to retrieve high-quality papers. To this end, we propose a Cross-community academic Knowledge Graph based approach for machine learning paper Recommendation (CKGR). Considering the hierarchical structure of cross-community knowledge graphs, we utilize knowledge propagation in hyperbolic space for entity representation learning. To alleviate the data sparsity problem as well as to learn better entity representations, we further introduce a preference migration module and contrastive learning. Meanwhile, considering the semantic relationships among entities, we introduce textual information to enhance the connection among interacting nodes for better recommendation tasks.

In this work, we propose a privacy-preserving face recognition scheme based on secure similarity comparison on encrypted data. We innovatively split sensitive face embeddings into two shares and encrypt them to guarantee data privacy. We present a novel matrix blinding method to conduct face similarity computation on encrypted face embeddings. Furthermore, we design an effective re-encryption method to achieve non-local secure data update, which reduces the risk of data leakage. Simulation experiments demonstrate that the proposed scheme completes face recognition tasks securely and efficiently. With different size of datasets, scale of embeddings, and number of queries, our scheme accomplishes face recognition tasks at low costs without obvious accuracy penalty.

With the rapid development of artificial intelligence and big data technologies, gait recognition has become a key method for identity authentication. As a unique biometric characteristic, gait is difficult to counterfeit and supports long-distance, non-contact authentication, making it ideal for security, surveillance, and health monitoring. However, traditional gait authentication faces privacy and efficiency challenges. This paper presents a privacy-preserving gait authentication scheme based on partial Euclidean distance calculation, and the scheme encrypts gait features using block-diagonal orthogonal matrices and random perturbation vectors, enabling efficient encrypted computation on the cloud. Experimental results demonstrate that the proposed scheme improves processing efficiency and matching accuracy while protecting privacy. For instance, on the CASIA-B dataset, it reduces computation time by approximately 30% without compromising accuracy.

Motivated by applications in trajectory inference and particle tracking, we introduce **Smooth Schrödinger Bridges**. Our proposal generalizes prior work by allowing the reference process in the multi-marginal Schrödinger Bridge problem to be a smooth Gaussian process, leading to more regular and interpretable trajectories in applications. Though naïvely smoothing the reference process leads to a computationally intractable problem, we identify a class of processes (including the Matérn processes) for which the resulting Smooth Schrödinger Bridge problem can be *lifted* to a simpler problem on phase space, which can be solved in polynomial time. We develop a practical approximation of this algorithm that outperforms existing methods on numerous simulated and real single-cell RNAseq datasets.

In the field of machine learning, PCA based face recognition is widely applied in identity authentication and access control. Due to limited storage and computational capabilities, the client often needs to outsources face recognition tasks to cloud servers, which brings privacy leakage risks. Existing privacy-preserving schemes calculate the inner product of encrypted vectors to perform face matching, but they involve complex operations, making efficient recognition challenging and lacking in automatic verification of computational integrity. In this paper, we propose a blockchain-aided privacy-preserving PCA based face recognition scheme. Our approach uses sparse matrices to construct keys, reducing the number of non-zero elements in matrix operations. The scheme also introduces a blockchainaided verification and payment mechanism based on hash commitments to verify the integrity of computation tasks. This mechanism allows encrypted face images and face recognition results to be uploaded to the blockchain, effectively reducing the number of interactions between the client and the cloud server. Experimental results demonstrate that this scheme reduces the overall execution time by 43% without compromising recognition accuracy, achieving a recognition accuracy rate of 99.35%.

Transformers based on Self-Attention (SA) mechanism have demonstrated unrivaled superiority in numerous areas. Compared to RNN-based networks, Transformers can learn the temporal dependency representation of an entire sequence in parallel, while efficiently dealing with long-range dependencies. However, the $\mathcal {O}(L^{2})$O(L2) ($L$L denotes the length of the sequence) computational complexity of the SA mechanism and the high memory usage make the construction cost of the Transformer-based model prohibitively expensive. To address these challenges, we propose a Transformer-like model, HPformer: Low-Parameter Transformer with Temporal Dependency Hierarchical Propagation. HPformer first chunks the sequence into $K$K ($K = \left\lceil \log {L} \right\rceil + 1$K=logL+1, $\left\lceil \cdot \right\rceil$· denotes ceiling operation) sequence segments, then leverages the hierarchical propagation mechanism with $\mathcal {O}(L)$O(L) computational complexity to learn the temporal dependencies between the segments and within the segments, and ultimately generates $K$K vectors as $Key$Key matrices. This reduces the complexity of the SA mechanism from $\mathcal {O}(L^{2})$O(L2) to $\mathcal {O}(L\log {L})$O(LlogL). In addition, we employ a strategy of sharing $Key$Key and $Value$Value matrices between layers to build the HPformer, thus reducing memory usage. Extensive experiments based on public health informatics benchmark and Long-Range Arena (LRA) benchmark have demonstrated that HPformer has advantages over Transformer-based models in terms of memory usage and efficiency.

Social insurance benefits qualification assessment is an important task to ensure that retirees enjoy their benefits according to the regulations. It also plays a key role in curbing social security frauds. In this paper, we report the deployment of the Intelligent Benefit Certification and Analysis (IBCA) platform, an AI-empowered platform for verifying the status of retirees to ensure proper dispursement of funds in Shandong province, China. Based on an improved Gated Recurrent Unit (GRU) neural network, IBCA aggregates missing value interpolation, temporal information, and global and local feature extraction to perform accurate retiree survival rate prediction. Based on the predicted results, a reliability assessment mechanism based on Variational Auto-Encoder (VAE) and Monte-Carlo Dropout (MC Dropout) is executed to perform reliability assessment. Deployed since November 2019, the IBCA platform has been adopted by 12 cities across the Shandong province, handling over 50 terabytes of data. It has empowered human resources and social services, civil affairs, and health care institutions to collaboratively provide high-quality public services. Under the IBCA platform, the efficiency of resources utilization as well as the accuracy of benefit qualification assessment have been significantly improved. It has helped Dareway Software Co. Ltd earn over RMB 50 million of revenue.

At present, the microservice architecture is widely used in modern software development for its flexibility and scalability. However, the huge data scale and complex invocation relationships between services make the root cause localization of faults in microservice systems extremely difficult. Some of the current root cause localization methods based on metrics data use history fault data to effectively improve the localization results, but there are challenges such as not representing history faults effectively and limiting the localization results to repetitive faults that have occurred in history. In this paper, we propose an automatic root cause localization framework MicroHFRCL based on the history fault library to address the above issues. MicroHFRCL constructs an instance causal graph based on metric data for causal analysis. The instance causal graph is weighted by encoding the anomalous subgraph and calculating the similarity of history faults. The PageRank algorithm is used to locate the root cause of faults. Among them, MicroHFRCL learns the structure and feature information of fault anomalous subgraphs through GCN and Transformer models, achieving effective representation of history faults and fast calculation of similarity in history fault codes, improving the efficiency of repetitive fault localization, and effectively solving the problem of the limitation of using history faults for root cause localization results. We implemented MicroHFRCL and tested it on the fault dataset collected by a benchmark microservice test system. Compared with the latest baseline models, MicroHFRCL has significantly improved the localization accuracy, and can also achieve good results in the case of small-scale history faults.

Approaches predicting the results of mutation testing by machine learning have been proposed to reduce the cost of mutation testing. The predictive approaches based on PIE theory and approaches based on natural language have been proposed. However, both PIE-based and natural language-based approaches have disadvantages, leading to a reduction in effectiveness at the test case level prediction. In order to predict at the test case level and improve the effectiveness of prediction, we propose Natural Language and PIE Predictive Mutation Testing (NLPIE-PMT), which combines advantages of PIE-based and natural language-based approaches and predict whether each test case kills each mutant in the cross-version scenario. The experimental results on subjects in Defects4J show that NLPIE-PMT can predict whether each test case kill each mutant with the average F1-score of 0.811, which is 0.135 and 0.046 higher than the PIE-based baseline and the natural language-based baseline respectively. NLPIE-PMT also performs better than the baselines in predicting mutation score.

An online service system may experience various performance faults during operation. Detecting and locating these faults after they occur can significantly impact the user experience and lead to significant losses. Therefore, it is necessary to predict faults before they occur. Existing methods for fault prediction typically only predict the possibility of fault, without providing more granular predictions, such as the type of fault. This can make troubleshooting more difficult for developers. In this paper, we propose a fine-grained fault prediction method called FSFP, which not only predicts the possibility of fault but also identifies the type of fault that may occur. The method initially collects performance monitoring metrics from the runtime system, including two types: normal operation and abnormal conditions. It then utilizes cross-attention to capture the interdependencies between these two types of monitoring metrics, followed by the construction of a multi-label classification model. We evaluated FSFP by injecting faults into a benchmark microservice system. In terms of predicting the possibility of fault, FSFP achieved a precision of 0.999, a recall of 0.998, and an F1 score of 0.999. In terms of predicting the type of fault, FSFP achieved an exact match ratio of 0.955 and a Hamming loss of 0.017. In terms of predicting six specific types of faults, FSFP achieved four optimal F1 scores.

The purpose of drug recommendation is to predict the effective and safe drug combinations required for the current visit based on the historical medical data of patients. How to better mine the hidden relationship in the medical data and effectively improve the accuracy of drug recommendation are research hotspots in the medical field. This paper proposes a Collaborative Cross-attention Drug Recommendation model (CCDR) based on patient and medical relationship representations, which mines medical data from two aspects to enhance the representation ability of the model. CCDR obtains patient representation vectors by modeling the patients’ historical sequence data using Bidirectional Gated Recurrent Unit. Meanwhile, CCDR designs a medical graph structure data learning method based on relationship division to better capture the complex association relationships among diagnoses, procedures, and drugs. Finally, the representation capability of the model is enhanced by introducing a collaborative cross-attention mechanism to fuse the information obtained from both medical sequence and graph structure data. The experimental results show that the CCDR model can effectively improve the performance of drug recommendation.

Federated learning is a new type of artificial intelligence technology. During the training process, the client transmits model parameter information instead of local data to ensure their privacy and security. But it also incurs higher communication costs. This article proposes a new federated learning pruning method, FedADP, with the aim of adaptively determining pruning ratios for each layer in each client model without infringing on client privacy, and achieving more accurate pruning effects. Our method not only reduces communication costs during the training process, but also maintains accuracy similar to the original model. We conducted experimental validation using classic models and datasets, and evaluated our scheme and traditional federated learning scheme in terms of model accuracy, communication cost, and computational cost.

Text-to-SQL aims to parse natural language problems into SQL queries, which can provide a simple interface to access large databases enabling SQL novices a quicker entry into databases. As the Text-to-SQL field is intensively studied, more and more models use GNNs to encode heterogeneous graph information in this task, and how to better obtain path information between nodes in database schema heterogeneous graphs and question-database schema heterogeneous graphs will greatly affect the effectiveness of the model parsing. Our work intends to explore the problem of solving the encoding of heterogeneous graph meta-paths in the Text-to-SQL task. Previous approaches usually use multi-layer GNNs to aggregate topological structure information between nodes. However, they ignored the structural information embedded at the edges and also failed to obtain nodes that are not directly connected but can provide contextual information through meta-paths. To solve the above problem, we propose Multi-Hop Relational Graph Attention Network based Text-to-SQL Parsing Model (MHRGATSQL) for learning topological information between nodes while obtaining semantic information embedded in the edge topology. We use multi-hop attention to modify the relational graph attention network to diffuse the attention scores throughout the network, thus increasing the “receptive field” of each layer of RGAT. Experimental results on the large-scale cross-domain Text-to-SQL dataset Spider show that our model obtains an absolute improvement of 1.7% compared to the baseline and alleviates the over-smoothing problem in the deep network model.

The proliferation of Internet of Things (IoT) devices has led to the generation of massive amounts of data that require efficient aggregation for analysis and decision-making. However, multi-tier IoT systems, which involve multiple layers of devices and gateways, face more complex security challenges in data aggregation compared to ordinary IoT systems. In this paper, we propose an efficient privacy-preserving multi-functional data aggregation scheme for multi-tier IoT architecture. The scheme supports privacy-preserving calculation of mean, variance, and anomaly proportion. The scheme uses the Paillier cryptosystem and the BLS algorithm for encryption and signature, and uses blinding techniques to keep the size of the IoT system secret. In order to make the Paillier algorithm more suitable for the IoT scenario, we also improve its efficiency of encryption and decryption. The performance evaluation shows that the scheme improves encryption efficiency by 43.7% and decryption efficiency by 45% compared to the existing scheme.

As auxiliary collaborative signals, the entity connectivity and relation semanticity beneath knowledge graph (KG) triples can alleviate the data sparsity and cold-start issues of recommendation tasks. Thus many works consider obtaining user and item representations via information aggregation on graph-structured data within Euclidean space. However, the scale-free graphs (e.g., KGs) inherently exhibit non-Euclidean geometric topologies, such as tree-like and circle-like structures. The existing recommendation models built in a single type of embedding space do not have enough capacity to embrace various geometric patterns, consequently, resulting in suboptimal performance. To address this limitation, we propose a KG-aware recommendation model with mixed-curvature manifolds interaction learning, namely CurvRec. On the one hand, it aims to preserve various global geometric structures in KG with mixed-curvature manifold spaces as the backbone. On the other hand, we integrate Ricci curvature into graph convolutional networks (GCNs) to capture local geometric structural properties when aggregating neighbor nodes. Besides, to exploit the expressive spatial features in KG, we incorporate interaction learning to ensure the geometric message passing between curved manifolds. Specifically, we adopt curvature-aware geodesic distance metrics to maximize the mutual information between Euclidean space and non-Euclidean spaces. Through extensive experiments, we demonstrate that the proposed CurvRec outperforms state-of-the-art baselines.

Mobile devices (MDs) cannot fully run all computation/delay-sensitive tasks due to their limited computing resources. Mobile edge computing (MEC) meets the demand by providing massive resources for MDs and offloading task partitions to MEC servers. However, task offloading also brings communication delay and energy consumption. Therefore, it is challenging to associate resource-constrained MDs with appropriate MEC servers to minimize power consumption. To address this problem, a constrained mixed integer nonlinear program is formulated to optimize the total energy consumption of the system including MDs and MEC servers. To solve this problem, this work designs an improved meta-heuristic optimization algorithm called Self-adaptive and Multi-population Differential Annealed Optimization (SMDAO). Experimental results demonstrate that compared with its two state-of-the-art peers, the proposed SMDAO yields the best solution with the smallest total energy consumption in the least time.

Knowledge graph (KG) has been widely used in the field of recommender systems. There are some nodes in KG that guide the occurrence of interaction behaviors. We call them guided nodes. However, the current application doesn’t take into account the guided nodes in KG. We explore the utility of guided nodes in KG. It is applied in repository recommendations. In this paper, we propose an end-to-end framework, namely Guided Node Graph Convolutional Network (GNGCN), which effectively captures the connections between entities by mining the influence of related nodes. We extract samples of each entity in KG as their guided nodes and then combine the information and bias of the guided nodes when computing the representation of a given entity. The guided nodes can be extended to multiple hops. We evaluate our model on a real-world Github dataset named Github-SKG and music recommendation dataset, and the experimental results show that the method outperforms the recommendation baselines and our model is much lighter than others.

In recent years, there has been a lot of research on deep learning for survival prediction in EHR (Electronic Health Record). At present, EHR usually contains multivariate time series data with missing values. How to better predict mortality based on such data is what many studies are currently doing. Most of the mortality prediction methods based on deep learning only pay attention to missing value filling or adjusting the model structure to enhance the mortality prediction performance, but they do not combine these two aspects well. In this paper, we propose MTSSP (Multivariate Time Series for Survival Prediction), a new method that combines missing value filling and time series classification. It takes two representations of the loss pattern: the mask and the time interval, which are combined into the recurrent neural network for the interpolation of the missing value of patient characteristics. When the missing data values are interpolated, the model combines bidirectional RNN and one-dimensional CNN to jointly capture the patient's medical behavior from a global and local perspective to enhance the representation ability of data information, thereby improving the prediction accuracy of the model. In the end, we conducted our mortality prediction experiments on the real-world emergency MIMIC-III dataset and MIMIC-IV dataset. The experimental results demonstrate that the proposed approach has been shown to significantly outperform other approaches.

The explosive growth of machine learning (ML) methods is overloading users with choices for learning tasks. Method recommendation aims to alleviate this problem by selecting the most appropriate ML methods for given learning tasks. Recent research shows that the descriptive and structural information of the knowledge graphs (KGs) can significantly enhance the performance of ML method recommendation. However, existing studies have not fully explored the descriptive information in KGs, nor have they effectively exploited the descriptive and structural information to provide the necessary supervision. To address these limitations, we distinguish descriptive attributes from the traditional relationships in KGs with the rest as structural connections to expand the scope of KG descriptive information. Based on this insight, we propose the Cross-modal Knowledge Graph Contrastive learning (CKGC) approach, which regards information from descriptive attributes and structural connections as two modalities, learning informative node representations by maximizing the agreement between the descriptive view and the structural view. Through extensive experiments, we demonstrate that CKGC significantly outperforms the state-of-the-art baselines, achieving around 2% higher accurate click-through-rate (CTR) prediction, over 30% more accurate top-10 recommendation, and over 50% more accurate top-20 recommendation compared to the best performing existing approach.

Drug recommendation task based on the deep learning model has been widely studied and applied in the health care field in recent years. However, the accuracy of drug recommendation models still needs to be improved. In addition, the existing recommendation models either give only one recommendation (however, there may be a variety of drug combination options in practice) or can not provide the confidence level of the recommended result. To fill these gaps, a Drug Recommendation model based on Message Propagation neural network (denoted as DRMP) is proposed in this paper. Then, the Drug-Drug Interaction (DDI) knowledge is introduced into the proposed model to reduce the DDI rate in recommended drugs. Finally, the proposed model is extended to Bayesian Neural Network (BNN) to realize multiple recommendations and give the confidence of each recommendation result, so as to provide richer information to help doctors make decisions. Experimental results on public data sets show that the proposed model is superior to the best existing models.

Accurate prediction of patients’ ICU transfer events is of great significance for improving ICU treatment efficiency. ICU transition prediction task based on Electronic Health Records (EHR) is a temporal mining task like many other health informatics mining tasks. In the EHR-based temporal mining task, existing approaches are usually unable to mine and exploit patterns used to improve model performance. This article proposes a network based on Interval Pattern-Aware, Multi-Scale Interval Pattern-Aware (MSIPA) network. MSIPA mines different interval patterns in temporal EHR data according to the short, medium, and long intervals. MSIPA utilizes the Scaled Dot-Product Attention mechanism to query the contexts corresponding to the three scale patterns. Furthermore, Transformer will use all three types of contextual information simultaneously for ICU transfer prediction. Extensive experiments on real-world data demonstrate that an MSIPA network outperforms state-of-the-art methods.

The huge number of machine learning (ML) methods has resulted in significant information overload. Faced with an overwhelming number of ML methods, it is challenging to select appropriate ones for the given dataset and task. In general, the names of ML methods or datasets are rather condensed, thus lacking specific explanations, while the rich latent relationships between ML entities are not fully explored. In this paper, we propose a description-enhanced machine learning knowledge graph-based approach - DEKR - to help recommend appropriate ML methods for given ML datasets. The proposed knowledge graph (KG) not only includes the connections between entities but also contains the descriptions of the dataset and method entities. DEKR fuses the structural information with the description information of entities in the knowledge graph. It is a deep hybrid recommendation framework, which incorporates the knowledge graph-based and text-based methods, overcoming the limitations of previous knowledge graph-based recommendation systems that ignore the description information. There are two key components of DEKR: 1) a graph neural network aggregating information from multi-order neighbors with attention to enrich the seed (i.e. dataset or method) node's own representation, and 2) a deep collaborative filtering network based on the description text to obtain the linear and nonlinear interactions of description features. Through extensive experiments, we demonstrated the efficiency of DEKR, which outperforms the current state-of-the-art baselines by a large margin.

In China, with the continuous development of national health insurance policies, more and more people have joined the health insurance. How to accurately predict patients future medical treatment behavior becomes a hotspot issue. The biggest challenge in this issue is how to improve the prediction performance by modeling health insurance data with high-dimensional time characteristics. At present, most of the research is to solve this issue by using Recurrent Neural Networks (RNNs) to construct an overall prediction model for the medical visit sequences. However, RNNs can not effectively solve the long-term dependence, and RNNs ignores the importance of time interval of the medical visit sequence. Additionally, the global model may lose some important content to different groups. In order to solve these problems, we propose a Grouping and Global Attention based Time-aware Bidirectional Long Short-Term Memory (GGATB-LSTM) model to achieve medical treatment behavior prediction. The model first constructs a heterogeneous information network based on health insurance data, and uses a tensor CANDECOMP/PARAFAC decomposition method to achieve similarity grouping. In terms of group prediction, a global attention and time factor are introduced to extend the bidirectional LSTM. Finally, the proposed model is evaluated by using real dataset, and conclude that GGATB-LSTM is better than other methods.

MapReduce is a programming model, which is widely-used in parallel processing of big data. Its' performance is significantly affected by configuration parameters. However, the huge parameter space and interact of parameters make it impossible to explore all the parameter combinations manually. In this paper, we propose RJHCT, a novel approach to automatically tune the configuration parameters for MapReduce applications. We use random forest regression to predict the execution time of single task, and then we predict the job execution time by packing algorithm. Leveraging the prediction model, we use genetic algorithm to search the optimal configuration parameters automatically for a given MapReduce application. Experimental results demonstrated that RJHCT improves the performance of MapReduce applications by factors of 0.28x compared with the default configuration.

When using the microservice architecture to build a software development knowledge base, due to the differences in the user's knowledge level, different users have different needs for knowledge, which causes the problem of unpredictable system load. This paper proposes a load prediction model based on User Knowledge Level Evolution (UKLE). We use user historical access data and user private data to build user portraits, learn the evolution law of user knowledge levels, and thus describe the diverse knowledge growth paths of users. Finally, we predict the future knowledge access trends of users. And we use a hybrid prediction model based on linear and non-linear methods to perform load prediction for service load. The final load prediction model combines the characteristics of user knowledge level evolution and a hybrid prediction model based on service historical load, which improves the model's prediction accuracy and robustness.

Electricity information tracking systems are increasingly being adopted across China. Such systems can collect real-time power consumption data from users, and provide opportunities for artificial intelligence (AI) to help power companies and authorities make optimal demand-side management decisions. In this paper, we discuss power utilization improvement in Shandong Province, China with a deployed AI application - the Power Intelligent Decision Support (PIDS) platform. Based on improved short-term power consumption gap prediction, PIDS uses an optimal power adjustment plan which enables fine-grained Demand Response (DR) and Orderly Power Utilization (OPU) recommendations to ensure stable operation while minimizing power disruptions and improving fair treatment of participating companies. Deployed in August 2018, the platform is helping over 400 companies optimize their power consumption through DR while dynamically managing the OPU process for around 10,000 companies. Compared to the previous system, power outage under PIDS through planned shutdown has been reduced from 16% to 0.56%, resulting in significant gains in economic activities.

Mining Electronic Health Records (EHRs) is of great significance to improve the efficiency and quality of medical services. In recent years, researchers have used deep recurrent neural networks to predict patients' next-period prescriptions. The main challenges of predicting next-period prescriptions are as follows: i) The latent interdependence information which can be utilized to enhance the representation of the data between heterogeneous features is dynamic. However, most existing approaches do not consider capturing and utilizing this information. ii) Conventional recurrent neural networks cannot store historical interdependent information between heterogeneous features and take advantage of it. To address these challenges, We propose a novel attention mechanism named Cross Attention (CA) that can capture the interdependence between two sequences. We further propose three types of recurrent neural networks that can capture and utilize current and historical latent interdependence between the two sequences. Extensive experiments on real-world data demonstrate that DSCA networks can capture the interdependence information between sequences and outperform state-of-the-art methods.

Session-based recommendation aims to recommend the next item a user might be interested in given limited session information, e.g., only clicks are available. Existing researches usually use the RNN-based method or combine user long-term and current session interests to learn certain user preferences. However, due to the uncertainty of user behaviors and the limited behavior information, these methods may lose information of the relevant behavioral features and introduce some noise of unrelated behaviors. For online platforms, such as knowledge base platforms, these items are not isolated but connected with each other. We can use the correlation between items to capture the potential long-distance interests of the user. Therefore, in this paper, we propose a co-attention model with sequential behaviors and side information to obtain a complete representation of user's preferences. For obtaining relevant side information outside the session, we aggregate the corresponding entities and relations of each item to get the representation of the neighborhood information. Finally, extensive experiments are carried out on two real datasets, and the experimental results demonstrate the validity of our model. In particular, the proposed model performs well in cold start scenarios and is well interpreted for the recommended results.

With the development of the internet technology, a large amount of softwares have emerged to meet users' increasing needs. At the mean time, software systems have been faced with a problem that they must adapt to the dynamic network environment. It is obvious that a variety of software development models have been proposed in the past few decades. However, the majority of these methods are gradually unadaptable to new circumstances. In this paper, we proposed a new software development model integrating collective intelligence, service and ecosystem. On the one hand, we have introduced the model in detail. On the other hand, We took a practical example to demonstrate the effectiveness of the proposed model.

At present, software development is an important way to make our life more convenient and intelligent. With the development of software programming, we have accumulated a lot of expert experience and common sense of domain knowledge. How to effectively organize and reuse these high-quality knowledge has become an urgent problem because reusing high-quality expert knowledge can greatly improve the efficiency of solving problems, especially for the novices of programming. When we encounter the programming problems, the usual solutions to get the answers is to query the search engines or consult the senior developers. However, these solutions have the following limitations: 1) the information in the field of software development is relatively scattered, for example, the demanded information is distributed on different websites. The developers need to query the search engine several times to get the information that they want, which is unfriendly, especially for the novices; 2) there is no such a unified organization form for the information we retrieve, and we need to process it further to get the answers, which is inefficient. To address the above limitations, we propose to build a software development knowledge graph (SoftKG) through Wikipedia taxonomy. Specifically, we propose a framework to build SoftKG based on open source knowledge communities.

Nowadays, prediction for medical treatment migration has become one of the interesting issues in the field of health informatics. This is because the medical treatment migration behavior is closely related to the evaluation of regional medical level, the rational use of medical resources, and the distribution of medical insurance. Therefore, a prediction model for medical treatment migration based on medical insurance data is introduced in this paper. First, a medical treatment graph is constructed based on medical insurance data. The medical treatment graph is a heterogeneous graph, which contains entities such as patients, diseases, hospitals, medicines, hospitalization events, and the relations between these entities. However, existing graph neural networks are unable to capture the time-series relationships between event-type entities. To this end, a prediction model based on Graph Convolutional Network (GCN) is proposed in this paper, namely, Event-involved GCN (EGCN). The proposed model aggregates conventional entities based on attention mechanism, and aggregates event-type entities based on a gating mechanism similar to LSTM. In addition, jumping connection is deployed to obtain the final node representation. In order to obtain embedded representations of medicines based on external information (medicine descriptions), an automatic encoder capable of embedding medicine descriptions is deployed in the proposed model. Finally, extensive experiments are conducted on a real medical insurance data set. Experimental results show that our model's predictive ability is better than the best models available.

This article studies the core maintenance problem for dynamic graphs which requires to update each vertex's core number with the insertion/deletion of vertices/edges. Previous algorithms can either process one edge associated with a vertex in each iteration or can only process one superior edge associated with the vertex (an edge 〈u; v〉 is a superior edge of vertex u if v' core number is no less than u's core number) in each iteration. Thus for high superior-degree vertices (the vertices associated with many superior edges) insertions/deletions, previous algorithms become very inefficient. In this article, we discovered a new structure called joint edge set whose insertions/deletions make each vertex's core number change at most one. The joint edge set mainly contains all the superior edges associated with the high superior-degree vertices as long as these vertices are 3+-hop independent. Based on this discovery, faster parallel algorithms are devised to solve the core maintenance problems. In our algorithms, we can process all edges in the joint edge set in one iteration and thus can greatly increase the parallelism and reduce the processing time. The results of extensive experiments conducted on various types of real-world, temporal, and synthetic graphs illustrate that the proposed algorithms achieve good efficiency, stability and scalability. Specifically, the new algorithms can outperform the single-edge processing algorithms by up to four orders of magnitude. Compared with the matching based algorithm and the superior edge based algorithm, our algorithms show a significant speedup up to 60× in the processing time.

Survival prediction based on health insurance data is a promising research direction, since through health insurance data, we may be able to uncover the implicit information reflecting the health status of patients. Deep learning models, especially the Recurrent Neural Networks (RNNs), are powerful tools. However, at this stage, it is often difficult to explain the semantics of the features and evolution laws learned by RNNs. Therefore, this paper proposes a survival prediction model based on graph pattern mining. First, each patient's health insurance data are built as a Heterogeneous Information Network (HIN). Then, frequent patterns are mined from these HINs and each frequent pattern is regarded as a feature, called “pattern feature”. At last, the predictive survival time is given by an improved random forest, which is able to take into account the censored data. We conducted experiments on a real health insurance data set. The experimental results show that the model has better predictive ability than the traditional survival prediction models.

With the development of health insurance, the consequent problem of health insurance fraud has become increasingly serious. Outlier detection is a common method for health insurance anomaly detection. Researchers use some prior knowledge to assume patterns and indicators of interest. However, fraud pattern is concealed and changeable. The method of mining anomalies from fixed patterns is difficult to meet the current needs. In order to overcome this limitation, this paper combines the rich expression ability of heterogeneous information networks to model the complex relationships between entities, and establish a health insurance business representation model. The paper explores all possible business patterns, interrelated business portfolio patterns and related indicators in the field of health insurance. Considering the dynamic nature of a network, anomaly mining is carried out from both horizontal and vertical perspectives. Among them, the horizontal comparison adopts a fixed time period. The vertical comparison dynamically adjusts the time period according to the frequency of occurrence of the health insurance pattern instance, and then performs indicator calculation and outlier detection. Finally, the experimental results on the real data set show that our approach can narrow the scope of professional review and find more records of possible fraud than traditional methods.

Power demand response aims to clip the peak and fill the valley of power load by interruptible load management. Its closely related to the safety and economic benefit of the power system. At present, various provinces in China have carried out the pilot work of interruptible load management. The power companies signed contracts with power users to stipulate power users to adjust the power load consumption during peak hours or in emergency situations. The singed contracts should be fair for all power users. At the same time, the specific information about the signed contracts should be protected for privacy preservation, which is a typical federated learning scenario. A fair and privacy-preserving incentive pricing model should be proposed to stimulate power uses to participate in thee interruptible load management. To this end, a power demand response incentive pricing model is proposed. Based on the multi-attribute sealed auction game, the bidding model between the power company and the power users is established. In the single bidding model, the risk of the power user's demand response is evaluated firstly. The power users could be classified into different group according to their power load characteristics. Then a user classification selection algorithm' is proposed, which enables both the power company's revenue and power user risk to be considered, and enables the selected users to achieve balanced peak clipping. A fair mechanism based on integrals is proposed to assure the fairness between all power users. The case simulations demonstrate that effectiveness of the proposed incentive pricing model.

The increasing momentum of service-oriented architecture has led to the emergence of divergent delivered services, where service selection is meritedly required to obtain the target service fulfilling the requirements from both users and service providers. Despite many existing works have extensively handled the issue of service selection, it remains an open question in the case where requests from multiple users are performed simultaneously by a certain set of shared candidate services. Meanwhile, there exist some constraints enforced on the context of service selection, e.g. service placement location and contracts between users and service providers. In this paper, we focus on the QoS-aware service selection with constraints from a fairness aspect, with the objective of achieving max-min fairness across multiple service requests sharing candidate service sets. To be more specific, we formulate this problem as a lexicographical maximization problem, which is far from trivial to deal with practically due to its inherently multi-objective and discrete nature. A fairness-aware algorithm for concurrent service selection (FASS) is proposed, whose basic idea is to iteratively solve the single-objective subproblems by transforming them into linear programming problems. Experimental results based on real-world datasets also validate the effectiveness and practicality of our proposed approach.

With the development and adoption of the electricity information tracking system in China, real-time electricity consumption big data have become available to enable artificial intelligence (AI) to help power companies and the urban management departments to make demand side management decisions. We demonstrate the Power Intelligent Decision Support (PIDS) platform, which can generate Orderly Power Utilization (OPU) decision recommendations and perform Demand Response (DR) implementation management based on a short-term load forecasting model. It can also provide different users with query and application functions to facilitate explainable decision support.

In this paper, we developed an agent-based virtual interview training system which can help college students with high shyness level to improve interview skills and reduce their anxiety by themselves before they take a real interview. The system includes three main contents: three virtual agents with different types of personality, three kinds of interview training contents and a multidimensional evaluation method, so that it is able to meet common demands of preparing for interviews. User study indicates the system can help shy college students cope with interview anxiety and improve their interview training performance effectively.

This paper presents a toolkit called Rotbav for easily constructing mixed reality (MR) apps that can be experienced in real time in large indoor physical space via HoloLens. It resolves the problem that existing MR devices, e.g. HoloLens, are unable to scan and model an entire large scene with several rooms at once. We introduce a custom data structure called VorPa, based on the Voronoi diagram, to implement path editing, accelerated rendering and location effectively. Our experiments and applications show that the toolkit is convenient and easy to use for constructing MR apps targeting large indoor physical spaces, in which users can roam in real time.

While standard product recommendation systems have proven to be useful for e-commerce, they mainly rely on some prior information about users and products, such as ratings and intrinsic properties of products as well as profile attributes of users. In e-commerce settings, however, a more complete understanding of the demands of customers and the enterprise network constructed by suppliers and manufacturers can be utilized to improve the quality of product recommendations. Moreover, user ratings may be very sparse in some domains. Standard approaches suffer from such data sparsity and neglect to account for important additional dependencies that can be taken into consideration. This motivates us to design a new recommendation model, which incorporates information of network into rating prediction. In this paper, we propose a network-constrained tensor factorization approach, which imposes network constraints as regularization terms on tensor non-negative factorization to improve the accuracy of prediction. To solve the network-constrained regularization problem in our model, we use the Alternating Direction Method of Multipliers (ADMM) method. Experiment results on real-world dataset demonstrate that our approach outperforms other state-of-the-art baselines.

Performance analysis is important for service clouds serving composite service application jobs containing parallelizable tasks, for optimizing the degree of parallelism (DOP) and resource allocation schemes could improve performance obviously. In this paper, we describe a novel tandem queuing network with a parallel multi-station multi-server system as an analytical model for service clouds serving composite service application jobs. We design a partition method (termed the 'pleasing partition') to help us propose an analytical model for parallelizable service which is the vital fraction of composite service. After that, we could obtain a complete probability distribution of response time, waiting time and other important performance metrics calculated by our proposed analytical model. Thus, to use this model, cloud operators could determine proper job configurations and resource allocation schemes, for achieving specific QoS (Quality of Service). Extensive simulations are conducted to validate that our analytical model has high accuracy in predicting performance metrics of composite service application jobs.

Hashing technique has attracted considerable attention for large-scale multimedia retrieval due to its low storage cost and fast query speed. Moreover, many hashing models have been proposed for cross-modal retrieval task. However, there are still some problems that need to be further considered. For example, a majority of them directly use linear projection matrix to project heterogeneous data into a common space, which may lead to large error as there are some heterogeneous data with semantic similarity hard to be close in latent space when linear projection is used. Besides, most existing cross-modal hashing methods use a simple pairwise similarity matrix for preserving the label information when learning. This kind of pairwise similarity cannot fully utilize the discriminative property of label information. Furthermore, most existing supervised ones try to solve a relaxed continuous optimization problem by dropping the discrete constraints, which may lead to large quantization error. To overcome these limitations, in this paper, we propose a novel cross-modal hashing method, called Dictionary Learning based Supervised Discrete Hashing (DLSDH). Specifically, it learns dictionaries and generates sparse representation for every instance, which is more suitable to be projected to a latent space. To make full use of label information, it uses cosine similarity to construct a new pairwise similarity matrix which can contain more information. Moreover, it directly learns the discrete hash codes instead of relaxing the discrete constraints. Extensive experiments are conducted on three benchmark datasets and the results demonstrate that it outperforms several state-of-the-art methods for cross-modal retrieval task.

Smart traffic prediction system provides significant benefits in solving the city traffic congestion. However, existing smart transportation system needs a lot of real-time traffic data and accurate location information to display the traffic condition. We hope that we can use the data which is easy to be obtained, and then predict a reliable congestion time. To address this problem, this paper studied a smart traffic forecasting system based on SWARIMA model. The system includes three steps: 1) use the sliding windows to calculate and process real-time data stream; 2) establish the SWARIMA model and make regression analysis; and 3) from a statistical point of view, calculate the elastic interval and predict the congestion trend. Our system is capable of accepting the real-time traffic data stream for the congestion prediction, in addition, we reduce the actual running parameters to three attributes: 1) speed; 2) time; and 3) location information. When faced with the challenges of real-time traffic congestion, the system can timely and effectively calculate the congestion trends and provide three reliable elastic intervals: 1) warning; 2) congestion; and 3) mitigation, which has significance to improve traffic condition and alleviate urban road congestion.

Cloud computing is a promising distributed computing platform for big data applications, e.g., scientific applications, since excessive resources can be obtained from cloud services for processing and storing both existing and generated application datasets. However, when tasks process big data stored in distributed data centers, the inevitable data movements will cause huge bandwidth cost and execution delay. In this paper, we construct a tripartite graph based model to formulate the data replica placement problem and propose a genetic algorithm based data replica placement strategy for scientific applications to reduce data transmissions in cloud. Our approach can reduce 1) the size of moved data, 2) the time of data movement and 3) the number of movements. We conduct experiments to compare the proposed strategy with the random placement strategy used in Hadoop Distributed Files System (HDFS), which demonstrates that our strategy has better performance for scientific applications in clouds.

Performance analysis is crucial to the successful development of cloud computing paradigm. And it is especially important for a cloud computing center serving parallelizable application jobs, for determining a proper degree of parallelism could reduce the mean service response time and thus improve the performance of cloud computing obviously. In this paper, taking the cloud based rendering service platform as an example application, we propose an approximate analytical model for cloud computing centers serving parallelizable jobs using M/M/c/r queuing systems, by modeling the rendering service platform as a multi-station multi-server system. We solve the proposed analytical model to obtain a complete probability distribution of response time, blocking probability and other important performance metrics for given cloud system settings. Thus this model can guide cloud operators to determine a proper setting, such as the number of servers, the buffer size and the degree of parallelism, for achieving specific performance levels. Through extensive simulations based on both synthetic data and real-world workload traces, we show that our proposed analytical model can provide approximate performance prediction results for cloud computing centers serving parallelizable jobs, even those job arrivals follow different distributions.

Recently, hashing based approximate nearest neighbor search has attracted much attention in large scale data search task. Moreover, some cross-modal hashing methods have also been proposed to perform efficient search of different modalities. However, there are still some problems to be further considered. For example, some of them cannot make use of label information, which contains helpful information to generate hash codes; some of them firstly relax binary constraints during optimization, then threshold continuous outputs to binary, which could generate large quantization error. To consider these problems, in this paper, we propose a supervised cross-modal hashing without relaxation (SCMH-WR). It can not only make use of label information, but also generate the final binary codes directly, i.e., without relaxing binary constraints. Specifically, it maps different modalities into a common low-dimension subspace with preserving the similarity of labels; at the same time, it learns a rotation matrix to minimize the quantization error and gets the final binary codes. In addition, an iterative algorithm is proposed to tackle the optimization problem. SCMH-WR is tested on three benchmark data sets. Experimental results demonstrate that SCMH-WR outperforms state-of-the-art hashing methods for cross-modal search task.

Soft resource allocation is an important factor of system configuration which plays a critical role in guaranteeing the performance of multi-tier web service systems. There is a tradeoff between real-time performance and resource consumption, and thus the real-time adjustment of soft resource allocation in response to dynamic workload is quite challenging. In this paper, we propose a real-time soft resource allocation method that integrates both model-based analysis and real-time optimization. Specifically, a multi-tier web service system is firstly formulated by a queueing network model, and theoretical analyses are provided. Then, an optimization approach for real-time soft resource allocation is designed by applying sliding window techniques, in order to cope with dynamic workloads and performance demands. Based on the RUBiS benchmark system, model parameters are obtained by measurements and the efficacy of our approach is finally validated.

Software Defined Networking (SDN) is emerging as a new paradigm in which the control plane is decoupled from the data plane. SDN architectures enable the abstraction of network elements from the chaos of infrastructures to be service resources. The deployment of applications and network services can be largely simplified by taking advantage of the standardized open Application Program Interface (API). In the open literature, many efforts have been taken to evaluate the forwarding performance of the data plane. Jackson model is frequently employed to characterize the feature of such architectures. Further, network traffic frequently exhibits self-similar characteristic that has got a universal recognition. Analytical models without taking the traffic self-similarity into account may lead to unexpected results. To this end, this paper has proposed an analytical model to evaluate the Quality of Service (QoS) of SDN data plane with self-similar input traffic. Priority service is employed in data plane to decrease the sojourn time of the packets not matched. This is because packets traveled across the control plane are more sensitive to stringent delay bound. The QoS of the model can be evaluated by a decomposition approach. Extensive experimental results suggest that our model has a great accuracy and applicability.

Recently, hashing techniques have witnessed an increase in popularity due to their low storage cost and high query speed for large scale data retrieval task, e.g., image retrieval. Many methods have been proposed; however, most existing hashing techniques focus on single view data. In many scenarios, there are multiple views in data samples. Thus, those methods working on single view can not make full use of rich information contained in multi-view data. Although some methods have been proposed for multi-view data; they usually relax binary constraints or separate the process of learning hash functions and binary codes into two independent stages to bypass the obstacle of handling the discrete constraints on binary codes for optimization, which may generate large quantization error. To consider these problems, in this paper, we propose a novel hashing method, i.e., Discrete Multi-view Hashing (DMVH), which can work on multi-view data directly and make full use of rich information in multi-view data. Moreover, in DMVH, we optimize discrete codes directly instead of relaxing the binary constraints so that we could obtain high-quality hash codes. Simultaneously, we present a novel approach to construct similarity matrix, which can not only preserve local similarity structure, but also keep semantic similarity between data points. To solve the optimization problem in DMVH, we further propose an alternate algorithm. We test the proposed model on three large scale data sets. Experimental results show that it outperforms or is comparable to several state-of-the-arts.

As many countries in the world start to experience population aging, there are an increasing number of people relying on medical insurance to access healthcare resources. Medical insurance frauds are causing billions of dollars in losses for public healthcare funds. The detection of medical insurance frauds is an important and difficult challenge for the artificial intelligence (AI) research community. This paper outlines HFDA, a hybrid AI approach to effectively and efficiently identify fraudulent medical insurance claims which has been tested in an online medical insurance claim system in China.

Soft resources, which are system software components that use hardware or synchronize the use of hardware, are playing a critical role in the performance of multi-tier web systems, and thus it is quite important to tune the soft resource allocation for using the limited hardware resources to obtain maximum effectiveness. In this paper, we integrate both theoretical and experimental studies to the soft resource allocation problem. Specifically, we apply the queueing network model for formulating multi-tier web systems, and conduct experimental measurements based on the RUBiS benchmark system to obtain precise model parameters. Quantitative analysis is carried out, based on which an optimization model as well as an algorithm are put forward for soft resource allocation. The efficacy of our approach is validated by both theoretical analyses and experimental results.

In cloud computing services, according to the customized privacy protection policy by the tenant and the sub chunk-confusion based on privacy protection technology, we can partition the tenant’s data into many chunks and confuse the relationships among chunks, which makes the attacker cannot infer tenant’s information by simply combining attributes. But it still has security issues. For example, with the amount of data growing, there may be a few hidden association rules among some attributes of the data chunks. Through these rules, it is possible to get some of the privacy information of the tenant. To address this issue, the paper proposes a privacy protection mechanism based on chunk-confusion privacy protection technology for association rules. The mechanism can detect unidimensional and multidimensional attributes association rules, hide them by adding fake data, re-chunking and re-grouping, and then ensure the privacy of tenant’s data. In addition, this mechanism also provides evaluation formulas. They filter detected association rules, remove the invalid and improve system performance. They also evaluate the effect of privacy protection. The experimental evaluation proves that the mechanism proposed in this paper can better protect the data privacy of tenant and has feasibility and practicality in real world applications.

In Software as a Service model, i.e. SaaS, tenants' sensitive data are stored and processed at the platform of untrusted service providers. Data privacy has become the biggest challenge hindering wider adoption of software as a service. Data combination privacy has been proposed to protect privacy of data combination through sensitive association hidden. However, this approach doesn't consider the scenario where tenants' requirements changed and customization happened. When tenants customize data schema or privacy requirements, there is a possibility that underlying physical data chunk schema collides with the privacy requirements of tenants. This paper proposed the data combination privacy preservation adjusting mechanism for data privacy leakage caused by on demand customization of software as a service. Three principles of privacy preservation adjusting mechanism are proposed. Based on the adjusting mechanism, there would no more privacy leakage than before customization during the adjusting process to the customized schema. Analysis and experiments demonstrate the corrective and effective of the data privacy preservation adjusting mechanism for software as a service.

SaaS is a novel software model that data and applications of service are outsourced to service provider. Although SaaS model offers many benefits for small and medium enterprises, data privacy issue is the most challenge for the development of SaaS. In this paper we propose a new hybrid fragmentation approach which is different from traditional data encryption to protect data. We define three kinds of privacy constraints to support finger-grained privacy customization. We also give a heuristic hybrid fragmentation algorithm which considers query efficiency to produce a hybrid fragmentation. We make some experiments to analyze our approach in the paper.

In PaaS, the issue of resource provisioning becomes more challenging because a great number of applications share and compete for resources simultaneously. PaaS platform should be able to maximize the resources utilization, while satisfying the performance requirements of all applications. However on-line request workload can be fluctuated during the run time of applications and static resource allocation may cause either under provisioning or over provisioning problem. In this paper, we propose an adaptive resource provisioning approach to dynamically allocate resources for applications according to the workload variation. In case of performance violation, our approach can make an efficient plan to keep application performance within a valid range.

To enforce reliability of composite Web Services at run-time, this paper proposes a transaction management model based on Compensation Planning Graph for Web Service composition. First, a web service composition model with transactional properties is introduced. Second, in order to handle transaction when exceptions are occurred, compensation dependency relationships and Compensation Planning Graph is introduced, and an algorithm of automatic generation of compensation dependency relationships and transaction handling based on CPG is proposed. Finally, evaluation methods of QoS of web services and user satisfaction of transaction handling are given. During the execution of composite web services, this approach can guarantee compensation achieving through forward- or backward-compensation. In addition, in this model, a novel concept named Transfer Service is proposed to solve the problem that there are no compensation services or unsuccessful retriable services after many retry times. This model and method can improve self-adjustability and stability of composite services in the course of deployment and execution. Simulations prove that this approach can efficiently guarantee the reliability of composite services at run-time.

QoS has become an important measure for web service selection. In this paper, we present an approach which can provide the approximate QoS value for users, and support finding the optimal web service. Firstly, it clusters the users based on location and network condition, then according to the QoS historical statistics of users in the same cluster, uses the linear regression algorithm to predict the QoS value based on invocation time and workload.

Web service has already been an important paradigm for web applications. Growing number of services need efficiently locating the desired web services. The similarity metric of web services plays important role in service search and classification. The very small text fragments in WSDL of web services are unsuitable for applying the traditional IR techniques. We describe our approach which supports the similarity search and classification of service operations. The approach firstly employs the external knowledge to compute the semantic distance of terms from two compared services. The similarity of services is measured upon these distances. Previous researches treat terms within the same WSDL documents as the isolated words and neglect the semantic association among them, hence lower down the accuracy of the similarity metric. We provide our method which tries to reflect the underlying semantics of web services by utilizing the terms within WSDL fully. The experiments show that our method works well on both service classification and query.

Key-word based researches of service discovery focus on direct match of userpsilas requirements and often neglect relations between services. While techniques based on conventional semantic offer many kinds of relations, considerable time is spent on reasoning. In this paper, we utilizes E-FCM (Element Fuzzy Cognitive Map) to describe services for the reason that E-FCM can keep the semantic information as much as possible and E-FCMs can be automatically created for web services. Furthermore, instead of reasoning, the semantic relations among E-FCM are built based on computation, therefore semantic relations among services can be found out quickly. We focus on the associated semantic relations among services because complex applications always comprise of services with associated functions. The associated link network (ALN) is constructed upon associated relations to generate associated web service flows, which can be used to create complex applications, thus to facilitate discovery efficiency and improve utilization of services.

Currently technologies of Web services have greatly developed and provided a new application platform for CSCW. For a business process across enterprises, cooperation of multiple Web services is usually required to achieve the final goal. However, most current Web services choreography proposals, such as BPEL4WS or WSCI, only provide a fixed execution flow, lacking of flexibility and adaptability. In this paper, we propose a flexible workflow model TDWF, which can specify the execution process of Web services according to task dependency information, so that the Web services can be composed dynamically. We also put forward an algorithm to verify the correctness of the composition process. Besides, the proposed compensation mechanism of workflow could properly satisfy the requirements of end users

The compatibility analysis is absolutely necessary for guaranteeing the correct composition of Web services, no matter what styles the composition takes, statically or dynamically. In this paper, we provide a formalization of Web services behavior using the approach of automata. With this understanding, we propose a definition of role among Web services interactions. As a result, we can check whether two or more Web services are compatible in collaboration or not.